code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ....utils import _LazyModule
A__ = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
A__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 82 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if isinstance(snake_case , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class __lowerCAmelCase :
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
pass
def snake_case ( self ):
"""simple docstring"""
pass
def snake_case ( self ):
"""simple docstring"""
pass
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase = VisionTextDualEncoderConfig.from_vision_text_configs(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], config.projection_dim) )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_snake_case , text_model=_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = {"""vision_model""": vision_model, """text_model""": text_model}
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_snake_case , text_model=_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
_lowerCAmelCase = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
_lowerCAmelCase = after_output[0].numpy()
_lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_snake_case , 1e-5 )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_snake_case , text_model=_snake_case )
_lowerCAmelCase = model(
input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case , output_attentions=_snake_case )
_lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(_snake_case ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
_lowerCAmelCase = to_atuple(vision_model.config.image_size )
_lowerCAmelCase = to_atuple(vision_model.config.patch_size )
_lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
_lowerCAmelCase = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
_lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(_snake_case ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def snake_case ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = np.abs((a - b) ).max()
self.assertLessEqual(_snake_case , _snake_case , F'Difference between torch and flax is {diff} (>= {tol}).' )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_save_load(**_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**_snake_case )
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_pretrained_model_and_inputs()
_lowerCAmelCase = model_a(**_snake_case )
_lowerCAmelCase = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(_snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(_snake_case )
_lowerCAmelCase = model_a(**_snake_case )
_lowerCAmelCase = after_outputs[0].numpy()
_lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_snake_case , 1e-5 )
@require_tf
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-random-bert""" )
_lowerCAmelCase = 13
_lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_lowerCAmelCase = random_attention_mask([batch_size, 4] )
_lowerCAmelCase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = TFViTModel(_snake_case , name="""vision_model""" )
_lowerCAmelCase = TFBertModel(_snake_case , name="""text_model""" )
return vision_model, text_model
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFViTModelTester(self )
_lowerCAmelCase = TFBertModelTester(self )
_lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
_lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = vision_config_and_inputs
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-deit-tf""" , """hf-internal-testing/tiny-random-roberta""" )
_lowerCAmelCase = 13
_lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_lowerCAmelCase = random_attention_mask([batch_size, 4] )
_lowerCAmelCase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_snake_case , text_model=_snake_case )
_lowerCAmelCase = model(
input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case , output_attentions=_snake_case )
_lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(_snake_case ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
_lowerCAmelCase = to_atuple(vision_model.config.image_size )
_lowerCAmelCase = to_atuple(vision_model.config.patch_size )
_lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
_lowerCAmelCase = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
_lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(_snake_case ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = TFDeiTModel(_snake_case , name="""vision_model""" )
_lowerCAmelCase = TFRobertaModel(_snake_case , name="""text_model""" )
return vision_model, text_model
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFDeiTModelTester(self )
_lowerCAmelCase = TFRobertaModelTester(self )
_lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
_lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = vision_config_and_inputs
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-clip-tf""" , """hf-internal-testing/tiny-random-bert""" )
_lowerCAmelCase = 13
_lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_lowerCAmelCase = random_attention_mask([batch_size, 4] )
_lowerCAmelCase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = TFCLIPVisionModel(_snake_case , name="""vision_model""" )
_lowerCAmelCase = TFBertModel(_snake_case , name="""text_model""" )
return vision_model, text_model
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFCLIPVisionModelTester(self )
_lowerCAmelCase = TFBertModelTester(self )
_lowerCAmelCase = clip_model_tester.prepare_config_and_inputs()
_lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase = vision_config_and_inputs
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(
"""clip-italian/clip-italian""" , logit_scale_init_value=1.0 , from_pt=_snake_case )
_lowerCAmelCase = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
_lowerCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
_lowerCAmelCase = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=_snake_case , padding=_snake_case , return_tensors="""np""" )
_lowerCAmelCase = model(**_snake_case )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
_lowerCAmelCase = np.array([[1.228_4727, 0.310_4122]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , _snake_case , atol=1e-3 ) )
| 82 | 1 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
A__ = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
A__ = 25_00_04
A__ = 25_00_20
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase = MBartTokenizer
__lowerCamelCase = MBartTokenizerFast
__lowerCamelCase = True
__lowerCamelCase = True
def snake_case ( self ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
_lowerCAmelCase = MBartTokenizer(_snake_case , keep_accents=_snake_case )
tokenizer.save_pretrained(self.tmpdirname )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = MBartTokenizer(_snake_case , keep_accents=_snake_case )
_lowerCAmelCase = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(_snake_case , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_snake_case ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
_lowerCAmelCase = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
_snake_case , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
_lowerCAmelCase = tokenizer.convert_tokens_to_ids(_snake_case )
self.assertListEqual(
_snake_case , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
_lowerCAmelCase = tokenizer.convert_ids_to_tokens(_snake_case )
self.assertListEqual(
_snake_case , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
def snake_case ( self ):
"""simple docstring"""
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
_lowerCAmelCase = (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart""", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
_lowerCAmelCase = self.rust_tokenizer_class.from_pretrained(_snake_case , **_snake_case )
_lowerCAmelCase = self.tokenizer_class.from_pretrained(_snake_case , **_snake_case )
_lowerCAmelCase = tempfile.mkdtemp()
_lowerCAmelCase = tokenizer_r.save_pretrained(_snake_case )
_lowerCAmelCase = tokenizer_p.save_pretrained(_snake_case )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
_lowerCAmelCase = tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f )
self.assertSequenceEqual(_snake_case , _snake_case )
# Checks everything loads correctly in the same way
_lowerCAmelCase = tokenizer_r.from_pretrained(_snake_case )
_lowerCAmelCase = tokenizer_p.from_pretrained(_snake_case )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(_snake_case , _snake_case ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(_snake_case )
# Save tokenizer rust, legacy_format=True
_lowerCAmelCase = tempfile.mkdtemp()
_lowerCAmelCase = tokenizer_r.save_pretrained(_snake_case , legacy_format=_snake_case )
_lowerCAmelCase = tokenizer_p.save_pretrained(_snake_case )
# Checks it save with the same files
self.assertSequenceEqual(_snake_case , _snake_case )
# Checks everything loads correctly in the same way
_lowerCAmelCase = tokenizer_r.from_pretrained(_snake_case )
_lowerCAmelCase = tokenizer_p.from_pretrained(_snake_case )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(_snake_case , _snake_case ) )
shutil.rmtree(_snake_case )
# Save tokenizer rust, legacy_format=False
_lowerCAmelCase = tempfile.mkdtemp()
_lowerCAmelCase = tokenizer_r.save_pretrained(_snake_case , legacy_format=_snake_case )
_lowerCAmelCase = tokenizer_p.save_pretrained(_snake_case )
# Checks it saved the tokenizer.json file
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
_lowerCAmelCase = tokenizer_r.from_pretrained(_snake_case )
_lowerCAmelCase = tokenizer_p.from_pretrained(_snake_case )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(_snake_case , _snake_case ) )
shutil.rmtree(_snake_case )
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( unittest.TestCase ):
__lowerCamelCase = '''facebook/mbart-large-en-ro'''
__lowerCamelCase = [
''' UN Chief Says There Is No Military Solution in Syria''',
''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
]
__lowerCamelCase = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
'''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
]
__lowerCamelCase = [8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2, EN_CODE]
@classmethod
def snake_case ( cls ):
"""simple docstring"""
_lowerCAmelCase = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="""en_XX""" , tgt_lang="""ro_RO""" )
_lowerCAmelCase = 1
return cls
def snake_case ( self ):
"""simple docstring"""
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""] , 250001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""] , 250004 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""] , 250020 )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , _snake_case )
def snake_case ( self ):
"""simple docstring"""
self.assertIn(_snake_case , self.tokenizer.all_special_ids )
_lowerCAmelCase = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
_lowerCAmelCase = self.tokenizer.decode(_snake_case , skip_special_tokens=_snake_case )
_lowerCAmelCase = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=_snake_case )
self.assertEqual(_snake_case , _snake_case )
self.assertNotIn(self.tokenizer.eos_token , _snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = ["""this is gunna be a long sentence """ * 20]
assert isinstance(src_text[0] , _snake_case )
_lowerCAmelCase = 10
_lowerCAmelCase = self.tokenizer(_snake_case , max_length=_snake_case , truncation=_snake_case ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , _snake_case )
self.assertEqual(len(_snake_case ) , _snake_case )
def snake_case ( self ):
"""simple docstring"""
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""] ) , [250026, 250001] )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = tempfile.mkdtemp()
_lowerCAmelCase = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(_snake_case )
_lowerCAmelCase = MBartTokenizer.from_pretrained(_snake_case )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , _snake_case )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=_snake_case , return_tensors="""pt""" )
_lowerCAmelCase = shift_tokens_right(batch["""labels"""] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=_snake_case , truncation=_snake_case , max_length=len(self.expected_src_tokens ) , return_tensors="""pt""" , )
_lowerCAmelCase = shift_tokens_right(batch["""labels"""] , self.tokenizer.pad_token_id )
self.assertIsInstance(_snake_case , _snake_case )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
_lowerCAmelCase = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , _snake_case )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE] )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.tokenizer(self.src_text , padding=_snake_case , truncation=_snake_case , max_length=3 , return_tensors="""pt""" )
_lowerCAmelCase = self.tokenizer(
text_target=self.tgt_text , padding=_snake_case , truncation=_snake_case , max_length=10 , return_tensors="""pt""" )
_lowerCAmelCase = targets["""input_ids"""]
_lowerCAmelCase = shift_tokens_right(_snake_case , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.tokenizer._build_translation_inputs(
"""A test""" , return_tensors="""pt""" , src_lang="""en_XX""" , tgt_lang="""ar_AR""" )
self.assertEqual(
nested_simplify(_snake_case ) , {
# A, test, EOS, en_XX
"""input_ids""": [[62, 3034, 2, 250004]],
"""attention_mask""": [[1, 1, 1, 1]],
# ar_AR
"""forced_bos_token_id""": 250001,
} , )
| 82 |
def _UpperCAmelCase ( snake_case = 50 ):
"""simple docstring"""
_lowerCAmelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(f"{solution() = }")
| 82 | 1 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
"""facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''encodec'''
def __init__( self , _snake_case=[1.5, 3.0, 6.0, 12.0, 24.0] , _snake_case=24000 , _snake_case=1 , _snake_case=False , _snake_case=None , _snake_case=None , _snake_case=128 , _snake_case=32 , _snake_case=1 , _snake_case=[8, 5, 4, 2] , _snake_case="weight_norm" , _snake_case=7 , _snake_case=7 , _snake_case=3 , _snake_case=2 , _snake_case=True , _snake_case="reflect" , _snake_case=2 , _snake_case=2 , _snake_case=1.0 , _snake_case=1024 , _snake_case=None , _snake_case=True , **_snake_case , ):
"""simple docstring"""
_lowerCAmelCase = target_bandwidths
_lowerCAmelCase = sampling_rate
_lowerCAmelCase = audio_channels
_lowerCAmelCase = normalize
_lowerCAmelCase = chunk_length_s
_lowerCAmelCase = overlap
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_filters
_lowerCAmelCase = num_residual_layers
_lowerCAmelCase = upsampling_ratios
_lowerCAmelCase = norm_type
_lowerCAmelCase = kernel_size
_lowerCAmelCase = last_kernel_size
_lowerCAmelCase = residual_kernel_size
_lowerCAmelCase = dilation_growth_rate
_lowerCAmelCase = use_causal_conv
_lowerCAmelCase = pad_mode
_lowerCAmelCase = compress
_lowerCAmelCase = num_lstm_layers
_lowerCAmelCase = trim_right_ratio
_lowerCAmelCase = codebook_size
_lowerCAmelCase = codebook_dim if codebook_dim is not None else hidden_size
_lowerCAmelCase = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
F'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}' )
super().__init__(**_snake_case )
@property
def snake_case ( self ):
"""simple docstring"""
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def snake_case ( self ):
"""simple docstring"""
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def snake_case ( self ):
"""simple docstring"""
return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 82 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class __lowerCAmelCase ( unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = {
"""task_specific_params""": {
"""summarization""": {"""length_penalty""": 1.0, """max_length""": 128, """min_length""": 12, """num_beams""": 4},
"""summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 142, """min_length""": 56, """num_beams""": 4},
"""summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
}
}
_lowerCAmelCase = {
"""task_specific_params.summarization.length_penalty""": 1.0,
"""task_specific_params.summarization.max_length""": 128,
"""task_specific_params.summarization.min_length""": 12,
"""task_specific_params.summarization.num_beams""": 4,
"""task_specific_params.summarization_cnn.length_penalty""": 2.0,
"""task_specific_params.summarization_cnn.max_length""": 142,
"""task_specific_params.summarization_cnn.min_length""": 56,
"""task_specific_params.summarization_cnn.num_beams""": 4,
"""task_specific_params.summarization_xsum.length_penalty""": 1.0,
"""task_specific_params.summarization_xsum.max_length""": 62,
"""task_specific_params.summarization_xsum.min_length""": 11,
"""task_specific_params.summarization_xsum.num_beams""": 6,
}
self.assertEqual(flatten_dict(_snake_case ) , _snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(_snake_case ) , x.transpose() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case ) , transpose(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , transpose(_snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case ) , transpose(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , transpose(_snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case ) , np.asarray(transpose(_snake_case ) ) ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , np.asarray(transpose(_snake_case , axes=(1, 2, 0) ) ) ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , np.reshape(_snake_case , (4, 3) ) ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , np.reshape(_snake_case , (12, 5) ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , reshape(_snake_case , (4, 3) ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , reshape(_snake_case , (12, 5) ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , reshape(_snake_case , (4, 3) ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , reshape(_snake_case , (12, 5) ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , np.asarray(reshape(_snake_case , (4, 3) ) ) ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , np.asarray(reshape(_snake_case , (12, 5) ) ) ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(_snake_case ) , np.squeeze(_snake_case ) ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , np.squeeze(_snake_case , axis=2 ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case ) , squeeze(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , squeeze(_snake_case , axis=2 ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case ) , squeeze(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , squeeze(_snake_case , axis=2 ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case ) , np.asarray(squeeze(_snake_case ) ) ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , np.asarray(squeeze(_snake_case , axis=2 ) ) ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , np.expand_dims(_snake_case , axis=1 ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , expand_dims(_snake_case , axis=1 ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , expand_dims(_snake_case , axis=1 ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , np.asarray(expand_dims(_snake_case , axis=1 ) ) ) )
| 82 | 1 |
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __lowerCAmelCase :
def __init__( self , _snake_case , _snake_case=13 , _snake_case=32 , _snake_case=3 , _snake_case=4 , _snake_case=[10, 20, 30, 40] , _snake_case=[2, 2, 3, 2] , _snake_case=True , _snake_case=True , _snake_case=37 , _snake_case="gelu" , _snake_case=10 , _snake_case=0.02 , _snake_case=["stage2", "stage3", "stage4"] , _snake_case=[2, 3, 4] , _snake_case=None , ):
"""simple docstring"""
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = image_size
_lowerCAmelCase = num_channels
_lowerCAmelCase = num_stages
_lowerCAmelCase = hidden_sizes
_lowerCAmelCase = depths
_lowerCAmelCase = is_training
_lowerCAmelCase = use_labels
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_act
_lowerCAmelCase = num_labels
_lowerCAmelCase = initializer_range
_lowerCAmelCase = out_features
_lowerCAmelCase = out_indices
_lowerCAmelCase = scope
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowerCAmelCase = None
if self.use_labels:
_lowerCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
_lowerCAmelCase = self.get_config()
return config, pixel_values, labels
def snake_case ( self ):
"""simple docstring"""
return ConvNextConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=_snake_case , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def snake_case ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = ConvNextModel(config=_snake_case )
model.to(_snake_case )
model.eval()
_lowerCAmelCase = model(_snake_case )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def snake_case ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = ConvNextForImageClassification(_snake_case )
model.to(_snake_case )
model.eval()
_lowerCAmelCase = model(_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = ConvNextBackbone(config=_snake_case )
model.to(_snake_case )
model.eval()
_lowerCAmelCase = model(_snake_case )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
_lowerCAmelCase = None
_lowerCAmelCase = ConvNextBackbone(config=_snake_case )
model.to(_snake_case )
model.eval()
_lowerCAmelCase = model(_snake_case )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = config_and_inputs
_lowerCAmelCase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase = (
(
ConvNextModel,
ConvNextForImageClassification,
ConvNextBackbone,
)
if is_torch_available()
else ()
)
__lowerCamelCase = (
{'''feature-extraction''': ConvNextModel, '''image-classification''': ConvNextForImageClassification}
if is_torch_available()
else {}
)
__lowerCamelCase = True
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = ConvNextModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=_snake_case , has_text_modality=_snake_case , hidden_size=37 )
def snake_case ( self ):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def snake_case ( self ):
"""simple docstring"""
return
@unittest.skip(reason="""ConvNext does not use inputs_embeds""" )
def snake_case ( self ):
"""simple docstring"""
pass
@unittest.skip(reason="""ConvNext does not support input and output embeddings""" )
def snake_case ( self ):
"""simple docstring"""
pass
@unittest.skip(reason="""ConvNext does not use feedforward chunking""" )
def snake_case ( self ):
"""simple docstring"""
pass
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase = model_class(_snake_case )
_lowerCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCAmelCase = [*signature.parameters.keys()]
_lowerCAmelCase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_snake_case )
def snake_case ( self ):
"""simple docstring"""
def check_hidden_states_output(_snake_case , _snake_case , _snake_case ):
_lowerCAmelCase = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
_lowerCAmelCase = model(**self._prepare_for_class(_snake_case , _snake_case ) )
_lowerCAmelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_lowerCAmelCase = self.model_tester.num_stages
self.assertEqual(len(_snake_case ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase = True
check_hidden_states_output(_snake_case , _snake_case , _snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCAmelCase = True
check_hidden_states_output(_snake_case , _snake_case , _snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_snake_case )
@slow
def snake_case ( self ):
"""simple docstring"""
for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase = ConvNextModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def snake_case ( self ):
"""simple docstring"""
return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224""" ) if is_vision_available() else None
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224""" ).to(_snake_case )
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(images=_snake_case , return_tensors="""pt""" ).to(_snake_case )
# forward pass
with torch.no_grad():
_lowerCAmelCase = model(**_snake_case )
# verify the logits
_lowerCAmelCase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _snake_case )
_lowerCAmelCase = torch.tensor([-0.0260, -0.4739, 0.1911] ).to(_snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _snake_case , atol=1e-4 ) )
@require_torch
class __lowerCAmelCase ( unittest.TestCase , lowerCamelCase__ ):
__lowerCamelCase = (ConvNextBackbone,) if is_torch_available() else ()
__lowerCamelCase = ConvNextConfig
__lowerCamelCase = False
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = ConvNextModelTester(self )
| 82 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class __lowerCAmelCase ( lowerCamelCase__ ):
@staticmethod
def snake_case ( _snake_case ):
"""simple docstring"""
_lowerCAmelCase = parser.add_parser("""download""" )
download_parser.add_argument(
"""--cache-dir""" , type=_snake_case , default=_snake_case , help="""Path to location to store the models""" )
download_parser.add_argument(
"""--force""" , action="""store_true""" , help="""Force the model to be download even if already in cache-dir""" )
download_parser.add_argument(
"""--trust-remote-code""" , action="""store_true""" , help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine""" , )
download_parser.add_argument("""model""" , type=_snake_case , help="""Name of the model to download""" )
download_parser.set_defaults(func=_snake_case )
def __init__( self , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = model
_lowerCAmelCase = cache
_lowerCAmelCase = force
_lowerCAmelCase = trust_remote_code
def snake_case ( self ):
"""simple docstring"""
from ..models.auto import AutoModel, AutoTokenizer
AutoModel.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
AutoTokenizer.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
| 82 | 1 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFCvtForImageClassification, TFCvtModel
from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __lowerCAmelCase ( lowerCamelCase__ ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(_snake_case , """embed_dim""" ) )
self.parent.assertTrue(hasattr(_snake_case , """num_heads""" ) )
class __lowerCAmelCase :
def __init__( self , _snake_case , _snake_case=13 , _snake_case=64 , _snake_case=3 , _snake_case=[16, 48, 96] , _snake_case=[1, 3, 6] , _snake_case=[1, 2, 10] , _snake_case=[7, 3, 3] , _snake_case=[4, 2, 2] , _snake_case=[2, 1, 1] , _snake_case=[2, 2, 2] , _snake_case=[False, False, True] , _snake_case=[0.0, 0.0, 0.0] , _snake_case=0.02 , _snake_case=1e-12 , _snake_case=True , _snake_case=True , _snake_case=2 , ):
"""simple docstring"""
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = image_size
_lowerCAmelCase = patch_sizes
_lowerCAmelCase = patch_stride
_lowerCAmelCase = patch_padding
_lowerCAmelCase = is_training
_lowerCAmelCase = use_labels
_lowerCAmelCase = num_labels
_lowerCAmelCase = num_channels
_lowerCAmelCase = embed_dim
_lowerCAmelCase = num_heads
_lowerCAmelCase = stride_kv
_lowerCAmelCase = depth
_lowerCAmelCase = cls_token
_lowerCAmelCase = attention_drop_rate
_lowerCAmelCase = initializer_range
_lowerCAmelCase = layer_norm_eps
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowerCAmelCase = None
if self.use_labels:
# create a random int32 tensor of given shape
_lowerCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
_lowerCAmelCase = self.get_config()
return config, pixel_values, labels
def snake_case ( self ):
"""simple docstring"""
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def snake_case ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = TFCvtModel(config=_snake_case )
_lowerCAmelCase = model(_snake_case , training=_snake_case )
_lowerCAmelCase = (self.image_size, self.image_size)
_lowerCAmelCase , _lowerCAmelCase = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
_lowerCAmelCase = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
_lowerCAmelCase = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def snake_case ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = TFCvtForImageClassification(_snake_case )
_lowerCAmelCase = model(_snake_case , labels=_snake_case , training=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = config_and_inputs
_lowerCAmelCase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase = (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
__lowerCamelCase = (
{'''feature-extraction''': TFCvtModel, '''image-classification''': TFCvtForImageClassification}
if is_tf_available()
else {}
)
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFCvtModelTester(self )
_lowerCAmelCase = TFCvtConfigTester(self , config_class=_snake_case , has_text_modality=_snake_case , hidden_size=37 )
def snake_case ( self ):
"""simple docstring"""
self.config_tester.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
@unittest.skip(reason="""Cvt does not output attentions""" )
def snake_case ( self ):
"""simple docstring"""
pass
@unittest.skip(reason="""Cvt does not use inputs_embeds""" )
def snake_case ( self ):
"""simple docstring"""
pass
@unittest.skip(reason="""Cvt does not support input and output embeddings""" )
def snake_case ( self ):
"""simple docstring"""
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("""GPU""" ) ) == 0 , reason="""TF does not support backprop for grouped convolutions on CPU.""" , )
def snake_case ( self ):
"""simple docstring"""
super().test_dataset_conversion()
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("""GPU""" ) ) == 0 , reason="""TF does not support backprop for grouped convolutions on CPU.""" , )
@slow
def snake_case ( self ):
"""simple docstring"""
super().test_keras_fit()
@unittest.skip(reason="""Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8""" )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = tf.keras.mixed_precision.Policy("""mixed_float16""" )
tf.keras.mixed_precision.set_global_policy(_snake_case )
super().test_keras_fit()
tf.keras.mixed_precision.set_global_policy("""float32""" )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase = model_class(_snake_case )
_lowerCAmelCase = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCAmelCase = [*signature.parameters.keys()]
_lowerCAmelCase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _snake_case )
def snake_case ( self ):
"""simple docstring"""
def check_hidden_states_output(_snake_case , _snake_case , _snake_case ):
_lowerCAmelCase = model_class(_snake_case )
_lowerCAmelCase = model(**self._prepare_for_class(_snake_case , _snake_case ) )
_lowerCAmelCase = outputs.hidden_states
_lowerCAmelCase = len(self.model_tester.depth )
self.assertEqual(len(_snake_case ) , _snake_case )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase = True
check_hidden_states_output(_snake_case , _snake_case , _snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCAmelCase = True
check_hidden_states_output(_snake_case , _snake_case , _snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_snake_case )
@slow
def snake_case ( self ):
"""simple docstring"""
for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase = TFCvtModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def snake_case ( self ):
"""simple docstring"""
return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(images=_snake_case , return_tensors="""tf""" )
# forward pass
_lowerCAmelCase = model(**_snake_case )
# verify the logits
_lowerCAmelCase = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , _snake_case )
_lowerCAmelCase = tf.constant([0.9285, 0.9015, -0.3150] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , _snake_case , atol=1e-4 ) )
| 82 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = XCLIPTextConfig()
# derive patch size from model name
_lowerCAmelCase = model_name.find("""patch""" )
_lowerCAmelCase = int(model_name[start_idx + len("""patch""" ) : start_idx + len("""patch""" ) + 2] )
_lowerCAmelCase = XCLIPVisionConfig(patch_size=snake_case , num_frames=snake_case )
if "large" in model_name:
_lowerCAmelCase = 7_68
_lowerCAmelCase = 30_72
_lowerCAmelCase = 12
_lowerCAmelCase = 10_24
_lowerCAmelCase = 40_96
_lowerCAmelCase = 16
_lowerCAmelCase = 24
_lowerCAmelCase = 7_68
_lowerCAmelCase = 30_72
if model_name == "xclip-large-patch14-16-frames":
_lowerCAmelCase = 3_36
_lowerCAmelCase = XCLIPConfig.from_text_vision_configs(snake_case , snake_case )
if "large" in model_name:
_lowerCAmelCase = 7_68
return config
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if name == "token_embedding.weight":
_lowerCAmelCase = name.replace("""token_embedding.weight""" , """text_model.embeddings.token_embedding.weight""" )
if name == "positional_embedding":
_lowerCAmelCase = name.replace("""positional_embedding""" , """text_model.embeddings.position_embedding.weight""" )
if "ln_1" in name:
_lowerCAmelCase = name.replace("""ln_1""" , """layer_norm1""" )
if "ln_2" in name:
_lowerCAmelCase = name.replace("""ln_2""" , """layer_norm2""" )
if "c_fc" in name:
_lowerCAmelCase = name.replace("""c_fc""" , """fc1""" )
if "c_proj" in name:
_lowerCAmelCase = name.replace("""c_proj""" , """fc2""" )
if name.startswith("""transformer.resblocks""" ):
_lowerCAmelCase = name.replace("""transformer.resblocks""" , """text_model.encoder.layers""" )
if "attn.out_proj" in name and "message" not in name:
_lowerCAmelCase = name.replace("""attn.out_proj""" , """self_attn.out_proj""" )
if "ln_final" in name:
_lowerCAmelCase = name.replace("""ln_final""" , """text_model.final_layer_norm""" )
# visual encoder
if name == "visual.class_embedding":
_lowerCAmelCase = name.replace("""visual.class_embedding""" , """vision_model.embeddings.class_embedding""" )
if name == "visual.positional_embedding":
_lowerCAmelCase = name.replace("""visual.positional_embedding""" , """vision_model.embeddings.position_embedding.weight""" )
if name.startswith("""visual.transformer.resblocks""" ):
_lowerCAmelCase = name.replace("""visual.transformer.resblocks""" , """vision_model.encoder.layers""" )
if "visual.conv1" in name:
_lowerCAmelCase = name.replace("""visual.conv1""" , """vision_model.embeddings.patch_embedding""" )
if "visual.ln_pre" in name:
_lowerCAmelCase = name.replace("""visual.ln_pre""" , """vision_model.pre_layernorm""" )
if "visual.ln_post" in name:
_lowerCAmelCase = name.replace("""visual.ln_post""" , """vision_model.post_layernorm""" )
if "visual.proj" in name:
_lowerCAmelCase = name.replace("""visual.proj""" , """visual_projection.weight""" )
if "text_projection" in name:
_lowerCAmelCase = name.replace("""text_projection""" , """text_projection.weight""" )
# things on top
if "prompts_visual_proj" in name:
_lowerCAmelCase = name.replace("""prompts_visual_proj""" , """prompts_visual_projection""" )
if "prompts_visual_ln" in name:
_lowerCAmelCase = name.replace("""prompts_visual_ln""" , """prompts_visual_layernorm""" )
# mit
if name == "mit.positional_embedding":
_lowerCAmelCase = name.replace("""positional""" , """position""" )
if name.startswith("""mit.resblocks""" ):
_lowerCAmelCase = name.replace("""mit.resblocks""" , """mit.encoder.layers""" )
# prompts generator
if name.startswith("""prompts_generator.norm""" ):
_lowerCAmelCase = name.replace("""prompts_generator.norm""" , """prompts_generator.layernorm""" )
return name
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_lowerCAmelCase = orig_state_dict.pop(snake_case )
if "attn.in_proj" in key:
_lowerCAmelCase = key.split(""".""" )
if key.startswith("""visual""" ):
_lowerCAmelCase = key_split[3]
_lowerCAmelCase = config.vision_config.hidden_size
if "message_attn" in key:
if "weight" in key:
_lowerCAmelCase = val[
:dim, :
]
_lowerCAmelCase = val[
dim : dim * 2, :
]
_lowerCAmelCase = val[
-dim:, :
]
else:
_lowerCAmelCase = val[
:dim
]
_lowerCAmelCase = val[
dim : dim * 2
]
_lowerCAmelCase = val[
-dim:
]
else:
if "weight" in key:
_lowerCAmelCase = val[
:dim, :
]
_lowerCAmelCase = val[
dim : dim * 2, :
]
_lowerCAmelCase = val[
-dim:, :
]
else:
_lowerCAmelCase = val[:dim]
_lowerCAmelCase = val[
dim : dim * 2
]
_lowerCAmelCase = val[-dim:]
elif key.startswith("""mit""" ):
_lowerCAmelCase = key_split[2]
_lowerCAmelCase = config.vision_config.mit_hidden_size
if "weight" in key:
_lowerCAmelCase = val[:dim, :]
_lowerCAmelCase = val[dim : dim * 2, :]
_lowerCAmelCase = val[-dim:, :]
else:
_lowerCAmelCase = val[:dim]
_lowerCAmelCase = val[dim : dim * 2]
_lowerCAmelCase = val[-dim:]
else:
_lowerCAmelCase = key_split[2]
_lowerCAmelCase = config.text_config.hidden_size
if "weight" in key:
_lowerCAmelCase = val[:dim, :]
_lowerCAmelCase = val[
dim : dim * 2, :
]
_lowerCAmelCase = val[-dim:, :]
else:
_lowerCAmelCase = val[:dim]
_lowerCAmelCase = val[
dim : dim * 2
]
_lowerCAmelCase = val[-dim:]
else:
_lowerCAmelCase = rename_key(snake_case )
if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
_lowerCAmelCase = val.T
_lowerCAmelCase = val
return orig_state_dict
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if num_frames == 8:
_lowerCAmelCase = """eating_spaghetti_8_frames.npy"""
elif num_frames == 16:
_lowerCAmelCase = """eating_spaghetti.npy"""
elif num_frames == 32:
_lowerCAmelCase = """eating_spaghetti_32_frames.npy"""
_lowerCAmelCase = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename=snake_case , repo_type="""dataset""" , )
_lowerCAmelCase = np.load(snake_case )
return list(snake_case )
def _UpperCAmelCase ( snake_case , snake_case=None , snake_case=False ):
"""simple docstring"""
_lowerCAmelCase = {
# fully supervised kinetics-400 checkpoints
"""xclip-base-patch32""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth""",
"""xclip-base-patch32-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"""
),
"""xclip-base-patch16""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth""",
"""xclip-base-patch16-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"""
),
"""xclip-large-patch14""": """https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&export=download&confirm=t&uuid=b26caedc-88e2-473e-830a-9d158b653cdb""",
"""xclip-large-patch14-16-frames""": """https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&export=download&confirm=t&uuid=538fa810-e671-4050-b385-9a623f89804f""",
# fully supervised kinetics-600 checkpoints
"""xclip-base-patch16-kinetics-600""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"""
),
"""xclip-base-patch16-kinetics-600-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"""
),
"""xclip-large-patch14-kinetics-600""": """https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&export=download&confirm=t&uuid=141d4977-4a65-44ae-864f-4b0c19f838be""",
# few shot
"""xclip-base-patch16-hmdb-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"""
),
"""xclip-base-patch16-hmdb-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"""
),
"""xclip-base-patch16-hmdb-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"""
),
"""xclip-base-patch16-hmdb-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"""
),
"""xclip-base-patch16-ucf-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"""
),
"""xclip-base-patch16-ucf-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"""
),
"""xclip-base-patch16-ucf-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"""
),
"""xclip-base-patch16-ucf-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"""
),
# zero shot
"""xclip-base-patch16-zero-shot""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth""",
}
_lowerCAmelCase = model_to_url[model_name]
_lowerCAmelCase = 8
if "16-frames" in model_name:
_lowerCAmelCase = 16
elif "shot" in model_name:
_lowerCAmelCase = 32
_lowerCAmelCase = get_xclip_config(snake_case , snake_case )
_lowerCAmelCase = XCLIPModel(snake_case )
model.eval()
if "drive" in checkpoint_url:
_lowerCAmelCase = """pytorch_model.bin"""
gdown.cached_download(snake_case , snake_case , quiet=snake_case )
_lowerCAmelCase = torch.load(snake_case , map_location="""cpu""" )["""model"""]
else:
_lowerCAmelCase = torch.hub.load_state_dict_from_url(snake_case )["""model"""]
_lowerCAmelCase = convert_state_dict(snake_case , snake_case )
_lowerCAmelCase = XCLIPModel(snake_case )
_lowerCAmelCase , _lowerCAmelCase = model.load_state_dict(snake_case , strict=snake_case )
assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
model.eval()
_lowerCAmelCase = 3_36 if model_name == """xclip-large-patch14-16-frames""" else 2_24
_lowerCAmelCase = VideoMAEImageProcessor(size=snake_case )
_lowerCAmelCase = CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32""" )
_lowerCAmelCase = CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32""" )
_lowerCAmelCase = XCLIPProcessor(image_processor=snake_case , tokenizer=snake_case )
_lowerCAmelCase = prepare_video(snake_case )
_lowerCAmelCase = processor(
text=["""playing sports""", """eating spaghetti""", """go shopping"""] , videos=snake_case , return_tensors="""pt""" , padding=snake_case )
print("""Shape of pixel values:""" , inputs.pixel_values.shape )
with torch.no_grad():
_lowerCAmelCase = model(**snake_case )
# Verify outputs
_lowerCAmelCase = outputs.logits_per_video
_lowerCAmelCase = logits_per_video.softmax(dim=1 )
print("""Probs:""" , snake_case )
# kinetics-400
if model_name == "xclip-base-patch32":
_lowerCAmelCase = torch.tensor([[0.0_019, 0.9_951, 0.0_030]] )
elif model_name == "xclip-base-patch32-16-frames":
_lowerCAmelCase = torch.tensor([[7.09_99E-04, 9.98_83E-01, 4.55_80E-04]] )
elif model_name == "xclip-base-patch16":
_lowerCAmelCase = torch.tensor([[0.0_083, 0.9_681, 0.0_236]] )
elif model_name == "xclip-base-patch16-16-frames":
_lowerCAmelCase = torch.tensor([[7.69_37E-04, 9.97_28E-01, 1.94_73E-03]] )
elif model_name == "xclip-large-patch14":
_lowerCAmelCase = torch.tensor([[0.0_062, 0.9_864, 0.0_075]] )
elif model_name == "xclip-large-patch14-16-frames":
_lowerCAmelCase = torch.tensor([[3.38_77E-04, 9.99_37E-01, 2.88_88E-04]] )
# kinetics-600
elif model_name == "xclip-base-patch16-kinetics-600":
_lowerCAmelCase = torch.tensor([[0.0_555, 0.8_914, 0.0_531]] )
elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
_lowerCAmelCase = torch.tensor([[3.85_54E-04, 9.99_29E-01, 3.27_54E-04]] )
elif model_name == "xclip-large-patch14-kinetics-600":
_lowerCAmelCase = torch.tensor([[0.0_036, 0.9_920, 0.0_045]] )
# few shot
elif model_name == "xclip-base-patch16-hmdb-2-shot":
_lowerCAmelCase = torch.tensor([[7.18_90E-06, 9.99_94E-01, 5.65_59E-05]] )
elif model_name == "xclip-base-patch16-hmdb-4-shot":
_lowerCAmelCase = torch.tensor([[1.03_20E-05, 9.99_93E-01, 6.24_35E-05]] )
elif model_name == "xclip-base-patch16-hmdb-8-shot":
_lowerCAmelCase = torch.tensor([[4.13_77E-06, 9.99_90E-01, 9.83_86E-05]] )
elif model_name == "xclip-base-patch16-hmdb-16-shot":
_lowerCAmelCase = torch.tensor([[4.13_47E-05, 9.99_62E-01, 3.34_11E-04]] )
elif model_name == "xclip-base-patch16-ucf-2-shot":
_lowerCAmelCase = torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]] )
elif model_name == "xclip-base-patch16-ucf-4-shot":
_lowerCAmelCase = torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]] )
elif model_name == "xclip-base-patch16-ucf-8-shot":
_lowerCAmelCase = torch.tensor([[0.0_027, 0.9_904, 0.0_070]] )
elif model_name == "xclip-base-patch16-ucf-16-shot":
_lowerCAmelCase = torch.tensor([[9.82_19E-04, 9.95_93E-01, 3.08_63E-03]] )
# zero shot
elif model_name == "xclip-base-patch16-zero-shot":
_lowerCAmelCase = torch.tensor([[3.50_82E-04, 9.97_85E-01, 1.79_66E-03]] )
else:
raise ValueError(F'Model name {model_name} not supported' )
assert torch.allclose(snake_case , snake_case , atol=1E-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(snake_case )
if push_to_hub:
print("""Pushing model, processor and slow tokenizer files to the hub...""" )
model.push_to_hub(snake_case , organization="""nielsr""" )
processor.push_to_hub(snake_case , organization="""nielsr""" )
slow_tokenizer.push_to_hub(snake_case , organization="""nielsr""" )
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""xclip-base-patch32""",
type=str,
help="""Name of the model.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
A__ = parser.parse_args()
convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 82 | 1 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
A__ = logging.get_logger(__name__)
A__ = {"""vocab_file""": """spiece.model"""}
A__ = {
"""vocab_file""": {
"""TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
}
}
class __lowerCAmelCase ( lowerCamelCase__ ):
def __init__( self , _snake_case , _snake_case=False , _snake_case=True , _snake_case=False , _snake_case="<s>" , _snake_case="</s>" , _snake_case="<unk>" , _snake_case="<sep>" , _snake_case="<pad>" , _snake_case="<cls>" , _snake_case="<mask>" , _snake_case=["<eop>", "<eod>"] , _snake_case = None , **_snake_case , ):
"""simple docstring"""
_lowerCAmelCase = AddedToken(_snake_case , lstrip=_snake_case , rstrip=_snake_case ) if isinstance(_snake_case , _snake_case ) else mask_token
_lowerCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_snake_case , remove_space=_snake_case , keep_accents=_snake_case , bos_token=_snake_case , eos_token=_snake_case , unk_token=_snake_case , sep_token=_snake_case , pad_token=_snake_case , cls_token=_snake_case , mask_token=_snake_case , additional_special_tokens=_snake_case , sp_model_kwargs=self.sp_model_kwargs , **_snake_case , )
_lowerCAmelCase = 3
_lowerCAmelCase = do_lower_case
_lowerCAmelCase = remove_space
_lowerCAmelCase = keep_accents
_lowerCAmelCase = vocab_file
_lowerCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_snake_case )
try:
import jieba
except ModuleNotFoundError as error:
raise error.__class__(
"""You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
"""See https://pypi.org/project/jieba/ for installation.""" )
_lowerCAmelCase = jieba
_lowerCAmelCase = str.maketrans(""" \n""" , """\u2582\u2583""" )
@property
# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
def snake_case ( self ):
"""simple docstring"""
return len(self.sp_model )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = {self.convert_ids_to_tokens(_snake_case ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
"""simple docstring"""
_lowerCAmelCase = self.__dict__.copy()
_lowerCAmelCase = None
return state
def __setstate__( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
_lowerCAmelCase = {}
_lowerCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def snake_case ( self , _snake_case ):
"""simple docstring"""
if self.remove_space:
_lowerCAmelCase = """ """.join(inputs.strip().split() )
else:
_lowerCAmelCase = inputs
_lowerCAmelCase = outputs.replace("""``""" , """\"""" ).replace("""''""" , """\"""" )
if not self.keep_accents:
_lowerCAmelCase = unicodedata.normalize("""NFKD""" , _snake_case )
_lowerCAmelCase = """""".join([c for c in outputs if not unicodedata.combining(_snake_case )] )
if self.do_lower_case:
_lowerCAmelCase = outputs.lower()
return outputs
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.preprocess_text(_snake_case )
_lowerCAmelCase = self.sp_model.encode(_snake_case , out_type=_snake_case )
_lowerCAmelCase = []
for piece in pieces:
if len(_snake_case ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit():
_lowerCAmelCase = self.sp_model.EncodeAsPieces(piece[:-1].replace(_snake_case , """""" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
_lowerCAmelCase = cur_pieces[1:]
else:
_lowerCAmelCase = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(_snake_case )
else:
new_pieces.append(_snake_case )
return new_pieces
def snake_case ( self , _snake_case ):
"""simple docstring"""
return self.sp_model.PieceToId(_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
return self.sp_model.IdToPiece(_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = """""".join(_snake_case ).replace(_snake_case , """ """ ).strip()
return out_string
def snake_case ( self , _snake_case , _snake_case = None ):
"""simple docstring"""
_lowerCAmelCase = [self.sep_token_id]
_lowerCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def snake_case ( self , _snake_case , _snake_case = None , _snake_case = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_snake_case , token_ids_a=_snake_case , already_has_special_tokens=_snake_case )
if token_ids_a is not None:
return ([0] * len(_snake_case )) + [1] + ([0] * len(_snake_case )) + [1, 1]
return ([0] * len(_snake_case )) + [1, 1]
def snake_case ( self , _snake_case , _snake_case = None ):
"""simple docstring"""
_lowerCAmelCase = [self.sep_token_id]
_lowerCAmelCase = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def snake_case ( self , _snake_case , _snake_case = None ):
"""simple docstring"""
if not os.path.isdir(_snake_case ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
_lowerCAmelCase = os.path.join(
_snake_case , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_snake_case ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _snake_case )
elif not os.path.isfile(self.vocab_file ):
with open(_snake_case , """wb""" ) as fi:
_lowerCAmelCase = self.sp_model.serialized_model_proto()
fi.write(_snake_case )
return (out_vocab_file,)
def snake_case ( self , *_snake_case , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase = super()._decode(*_snake_case , **_snake_case )
_lowerCAmelCase = text.replace(""" """ , """""" ).replace("""\u2582""" , """ """ ).replace("""\u2583""" , """\n""" )
return text
| 82 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@register_to_config
def __init__( self , _snake_case = 768 , ):
"""simple docstring"""
super().__init__()
_lowerCAmelCase = nn.Parameter(torch.zeros(1 , _snake_case ) )
_lowerCAmelCase = nn.Parameter(torch.ones(1 , _snake_case ) )
def snake_case ( self , _snake_case = None , _snake_case = None , ):
"""simple docstring"""
_lowerCAmelCase = nn.Parameter(self.mean.to(_snake_case ).to(_snake_case ) )
_lowerCAmelCase = nn.Parameter(self.std.to(_snake_case ).to(_snake_case ) )
return self
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = (embeds - self.mean) * 1.0 / self.std
return embeds
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = (embeds * self.std) + self.mean
return embeds
| 82 | 1 |
def _UpperCAmelCase ( snake_case = 60_08_51_47_51_43 ):
"""simple docstring"""
try:
_lowerCAmelCase = int(snake_case )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" )
if n <= 0:
raise ValueError("""Parameter n must be greater than or equal to one.""" )
_lowerCAmelCase = 2
_lowerCAmelCase = 0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
_lowerCAmelCase = i
while n % i == 0:
_lowerCAmelCase = n // i
i += 1
return int(snake_case )
if __name__ == "__main__":
print(f"{solution() = }")
| 82 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase = AudioLDMPipeline
__lowerCamelCase = TEXT_TO_AUDIO_PARAMS
__lowerCamelCase = TEXT_TO_AUDIO_BATCH_PARAMS
__lowerCamelCase = frozenset(
[
'''num_inference_steps''',
'''num_waveforms_per_prompt''',
'''generator''',
'''latents''',
'''output_type''',
'''return_dict''',
'''callback''',
'''callback_steps''',
] )
def snake_case ( self ):
"""simple docstring"""
torch.manual_seed(0 )
_lowerCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=(32, 64) , class_embed_type="""simple_projection""" , projection_class_embeddings_input_dim=32 , class_embeddings_concat=_snake_case , )
_lowerCAmelCase = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_snake_case , set_alpha_to_one=_snake_case , )
torch.manual_seed(0 )
_lowerCAmelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
_lowerCAmelCase = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , projection_dim=32 , )
_lowerCAmelCase = ClapTextModelWithProjection(_snake_case )
_lowerCAmelCase = RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta""" , model_max_length=77 )
_lowerCAmelCase = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=16000 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=_snake_case , )
_lowerCAmelCase = SpeechTaHifiGan(_snake_case )
_lowerCAmelCase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""vocoder""": vocoder,
}
return components
def snake_case ( self , _snake_case , _snake_case=0 ):
"""simple docstring"""
if str(_snake_case ).startswith("""mps""" ):
_lowerCAmelCase = torch.manual_seed(_snake_case )
else:
_lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
_lowerCAmelCase = {
"""prompt""": """A hammer hitting a wooden surface""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
}
return inputs
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(_snake_case ) == 256
_lowerCAmelCase = audio[:10]
_lowerCAmelCase = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = 3 * [inputs["""prompt"""]]
# forward
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = 3 * [inputs.pop("""prompt""" )]
_lowerCAmelCase = audioldm_pipe.tokenizer(
_snake_case , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=_snake_case , return_tensors="""pt""" , )
_lowerCAmelCase = text_inputs["""input_ids"""].to(_snake_case )
_lowerCAmelCase = audioldm_pipe.text_encoder(
_snake_case , )
_lowerCAmelCase = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
_lowerCAmelCase = F.normalize(_snake_case , dim=-1 )
_lowerCAmelCase = prompt_embeds
# forward
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = 3 * ["""this is a negative prompt"""]
_lowerCAmelCase = negative_prompt
_lowerCAmelCase = 3 * [inputs["""prompt"""]]
# forward
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = 3 * [inputs.pop("""prompt""" )]
_lowerCAmelCase = []
for p in [prompt, negative_prompt]:
_lowerCAmelCase = audioldm_pipe.tokenizer(
_snake_case , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=_snake_case , return_tensors="""pt""" , )
_lowerCAmelCase = text_inputs["""input_ids"""].to(_snake_case )
_lowerCAmelCase = audioldm_pipe.text_encoder(
_snake_case , )
_lowerCAmelCase = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
_lowerCAmelCase = F.normalize(_snake_case , dim=-1 )
embeds.append(_snake_case )
_lowerCAmelCase , _lowerCAmelCase = embeds
# forward
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = PNDMScheduler(skip_prk_steps=_snake_case )
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = """egg cracking"""
_lowerCAmelCase = audioldm_pipe(**_snake_case , negative_prompt=_snake_case )
_lowerCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(_snake_case ) == 256
_lowerCAmelCase = audio[:10]
_lowerCAmelCase = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = PNDMScheduler(skip_prk_steps=_snake_case )
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = """A hammer hitting a wooden surface"""
# test num_waveforms_per_prompt=1 (default)
_lowerCAmelCase = audioldm_pipe(_snake_case , num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
_lowerCAmelCase = 2
_lowerCAmelCase = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
_lowerCAmelCase = 2
_lowerCAmelCase = audioldm_pipe(_snake_case , num_inference_steps=2 , num_waveforms_per_prompt=_snake_case ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
_lowerCAmelCase = 2
_lowerCAmelCase = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=_snake_case ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = audioldm_pipe.vocoder.config.sampling_rate
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = audioldm_pipe(audio_length_in_s=0.016 , **_snake_case )
_lowerCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(_snake_case ) / vocoder_sampling_rate == 0.016
_lowerCAmelCase = audioldm_pipe(audio_length_in_s=0.032 , **_snake_case )
_lowerCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(_snake_case ) / vocoder_sampling_rate == 0.032
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = ["""hey"""]
_lowerCAmelCase = audioldm_pipe(_snake_case , num_inference_steps=1 )
_lowerCAmelCase = output.audios.shape
assert audio_shape == (1, 256)
_lowerCAmelCase = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
_lowerCAmelCase = SpeechTaHifiGan(_snake_case ).to(_snake_case )
_lowerCAmelCase = audioldm_pipe(_snake_case , num_inference_steps=1 )
_lowerCAmelCase = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def snake_case ( self ):
"""simple docstring"""
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._test_inference_batch_single_identical(test_mean_pixel_difference=_snake_case )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def snake_case ( self ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_snake_case )
@slow
class __lowerCAmelCase ( unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case ( self , _snake_case , _snake_case="cpu" , _snake_case=torch.floataa , _snake_case=0 ):
"""simple docstring"""
_lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
_lowerCAmelCase = np.random.RandomState(_snake_case ).standard_normal((1, 8, 128, 16) )
_lowerCAmelCase = torch.from_numpy(_snake_case ).to(device=_snake_case , dtype=_snake_case )
_lowerCAmelCase = {
"""prompt""": """A hammer hitting a wooden surface""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 2.5,
}
return inputs
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_inputs(_snake_case )
_lowerCAmelCase = 25
_lowerCAmelCase = audioldm_pipe(**_snake_case ).audios[0]
assert audio.ndim == 1
assert len(_snake_case ) == 81920
_lowerCAmelCase = audio[77230:77240]
_lowerCAmelCase = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] )
_lowerCAmelCase = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
_lowerCAmelCase = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_inputs(_snake_case )
_lowerCAmelCase = audioldm_pipe(**_snake_case ).audios[0]
assert audio.ndim == 1
assert len(_snake_case ) == 81920
_lowerCAmelCase = audio[27780:27790]
_lowerCAmelCase = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] )
_lowerCAmelCase = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3e-2
| 82 | 1 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader
from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
ROUGE_KEYS,
LegacySeqaSeqDataset,
SeqaSeqDataset,
assert_all_frozen,
calculate_bleu,
calculate_rouge,
check_output_dir,
flatten_list,
freeze_embeds,
freeze_params,
get_git_info,
label_smoothed_nll_loss,
lmap,
pickle_save,
save_git_info,
save_json,
use_task_specific_params,
)
# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train # noqa
A__ = logging.getLogger(__name__)
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''summarization'''
__lowerCamelCase = ['''loss''']
__lowerCamelCase = ROUGE_KEYS
__lowerCamelCase = '''rouge2'''
def __init__( self , _snake_case , **_snake_case ):
"""simple docstring"""
if hparams.sortish_sampler and hparams.gpus > 1:
_lowerCAmelCase = False
elif hparams.max_tokens_per_batch is not None:
if hparams.gpus > 1:
raise NotImplementedError("""Dynamic Batch size does not work for multi-gpu training""" )
if hparams.sortish_sampler:
raise ValueError("""--sortish_sampler and --max_tokens_per_batch may not be used simultaneously""" )
super().__init__(_snake_case , num_labels=_snake_case , mode=self.mode , **_snake_case )
use_task_specific_params(self.model , """summarization""" )
save_git_info(self.hparams.output_dir )
_lowerCAmelCase = Path(self.output_dir ) / """metrics.json"""
_lowerCAmelCase = Path(self.output_dir ) / """hparams.pkl"""
pickle_save(self.hparams , self.hparams_save_path )
_lowerCAmelCase = 0
_lowerCAmelCase = defaultdict(_snake_case )
_lowerCAmelCase = self.config.model_type
_lowerCAmelCase = self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size
_lowerCAmelCase = {
"data_dir": self.hparams.data_dir,
"max_source_length": self.hparams.max_source_length,
"prefix": self.model.config.prefix or "",
}
_lowerCAmelCase = {
"""train""": self.hparams.n_train,
"""val""": self.hparams.n_val,
"""test""": self.hparams.n_test,
}
_lowerCAmelCase = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
_lowerCAmelCase = {
"""train""": self.hparams.max_target_length,
"""val""": self.hparams.val_max_target_length,
"""test""": self.hparams.test_max_target_length,
}
assert self.target_lens["train"] <= self.target_lens["val"], F'target_lens: {self.target_lens}'
assert self.target_lens["train"] <= self.target_lens["test"], F'target_lens: {self.target_lens}'
if self.hparams.freeze_embeds:
freeze_embeds(self.model )
if self.hparams.freeze_encoder:
freeze_params(self.model.get_encoder() )
assert_all_frozen(self.model.get_encoder() )
_lowerCAmelCase = get_git_info()["""repo_sha"""]
_lowerCAmelCase = hparams.num_workers
_lowerCAmelCase = None # default to config
if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer , _snake_case ):
_lowerCAmelCase = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
_lowerCAmelCase = self.decoder_start_token_id
_lowerCAmelCase = (
SeqaSeqDataset if hasattr(self.tokenizer , """prepare_seq2seq_batch""" ) else LegacySeqaSeqDataset
)
_lowerCAmelCase = False
_lowerCAmelCase = self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
if self.hparams.eval_max_gen_length is not None:
_lowerCAmelCase = self.hparams.eval_max_gen_length
else:
_lowerCAmelCase = self.model.config.max_length
_lowerCAmelCase = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = {
k: self.tokenizer.batch_decode(v.tolist() ) if """mask""" not in k else v.shape for k, v in batch.items()
}
save_json(_snake_case , Path(self.output_dir ) / """text_batch.json""" )
save_json({k: v.tolist() for k, v in batch.items()} , Path(self.output_dir ) / """tok_batch.json""" )
_lowerCAmelCase = True
return readable_batch
def snake_case ( self , _snake_case , **_snake_case ):
"""simple docstring"""
return self.model(_snake_case , **_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.tokenizer.batch_decode(
_snake_case , skip_special_tokens=_snake_case , clean_up_tokenization_spaces=_snake_case )
return lmap(str.strip , _snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.tokenizer.pad_token_id
_lowerCAmelCase , _lowerCAmelCase = batch["""input_ids"""], batch["""attention_mask"""]
_lowerCAmelCase = batch["""labels"""]
if isinstance(self.model , _snake_case ):
_lowerCAmelCase = self.model._shift_right(_snake_case )
else:
_lowerCAmelCase = shift_tokens_right(_snake_case , _snake_case )
if not self.already_saved_batch: # This would be slightly better if it only happened on rank zero
_lowerCAmelCase = decoder_input_ids
self.save_readable_batch(_snake_case )
_lowerCAmelCase = self(_snake_case , attention_mask=_snake_case , decoder_input_ids=_snake_case , use_cache=_snake_case )
_lowerCAmelCase = outputs["""logits"""]
if self.hparams.label_smoothing == 0:
# Same behavior as modeling_bart.py, besides ignoring pad_token_id
_lowerCAmelCase = nn.CrossEntropyLoss(ignore_index=_snake_case )
assert lm_logits.shape[-1] == self.vocab_size
_lowerCAmelCase = ce_loss_fct(lm_logits.view(-1 , lm_logits.shape[-1] ) , tgt_ids.view(-1 ) )
else:
_lowerCAmelCase = nn.functional.log_softmax(_snake_case , dim=-1 )
_lowerCAmelCase , _lowerCAmelCase = label_smoothed_nll_loss(
_snake_case , _snake_case , self.hparams.label_smoothing , ignore_index=_snake_case )
return (loss,)
@property
def snake_case ( self ):
"""simple docstring"""
return self.tokenizer.pad_token_id
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self._step(_snake_case )
_lowerCAmelCase = dict(zip(self.loss_names , _snake_case ) )
# tokens per batch
_lowerCAmelCase = batch["""input_ids"""].ne(self.pad ).sum() + batch["""labels"""].ne(self.pad ).sum()
_lowerCAmelCase = batch["""input_ids"""].shape[0]
_lowerCAmelCase = batch["""input_ids"""].eq(self.pad ).sum()
_lowerCAmelCase = batch["""input_ids"""].eq(self.pad ).float().mean()
# TODO(SS): make a wandb summary metric for this
return {"loss": loss_tensors[0], "log": logs}
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
return self._generative_step(_snake_case )
def snake_case ( self , _snake_case , _snake_case="val" ):
"""simple docstring"""
self.step_count += 1
_lowerCAmelCase = {k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
_lowerCAmelCase = losses["""loss"""]
_lowerCAmelCase = {
k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
}
_lowerCAmelCase = (
generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
)
_lowerCAmelCase = torch.tensor(_snake_case ).type_as(_snake_case )
generative_metrics.update({k: v.item() for k, v in losses.items()} )
losses.update(_snake_case )
_lowerCAmelCase = {F'{prefix}_avg_{k}': x for k, x in losses.items()}
_lowerCAmelCase = self.step_count
self.metrics[prefix].append(_snake_case ) # callback writes this to self.metrics_save_path
_lowerCAmelCase = flatten_list([x["""preds"""] for x in outputs] )
return {
"log": all_metrics,
"preds": preds,
F'{prefix}_loss': loss,
F'{prefix}_{self.val_metric}': metric_tensor,
}
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
return calculate_rouge(_snake_case , _snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = time.time()
# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
_lowerCAmelCase = self.model.generate(
batch["""input_ids"""] , attention_mask=batch["""attention_mask"""] , use_cache=_snake_case , decoder_start_token_id=self.decoder_start_token_id , num_beams=self.eval_beams , max_length=self.eval_max_length , )
_lowerCAmelCase = (time.time() - ta) / batch["""input_ids"""].shape[0]
_lowerCAmelCase = self.ids_to_clean_text(_snake_case )
_lowerCAmelCase = self.ids_to_clean_text(batch["""labels"""] )
_lowerCAmelCase = self._step(_snake_case )
_lowerCAmelCase = dict(zip(self.loss_names , _snake_case ) )
_lowerCAmelCase = self.calc_generative_metrics(_snake_case , _snake_case )
_lowerCAmelCase = np.mean(lmap(_snake_case , _snake_case ) )
base_metrics.update(gen_time=_snake_case , gen_len=_snake_case , preds=_snake_case , target=_snake_case , **_snake_case )
return base_metrics
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
return self._generative_step(_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
return self.validation_epoch_end(_snake_case , prefix="""test""" )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.n_obs[type_path]
_lowerCAmelCase = self.target_lens[type_path]
_lowerCAmelCase = self.dataset_class(
self.tokenizer , type_path=_snake_case , n_obs=_snake_case , max_target_length=_snake_case , **self.dataset_kwargs , )
return dataset
def snake_case ( self , _snake_case , _snake_case , _snake_case = False ):
"""simple docstring"""
_lowerCAmelCase = self.get_dataset(_snake_case )
if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
_lowerCAmelCase = dataset.make_sortish_sampler(_snake_case , distributed=self.hparams.gpus > 1 )
return DataLoader(
_snake_case , batch_size=_snake_case , collate_fn=dataset.collate_fn , shuffle=_snake_case , num_workers=self.num_workers , sampler=_snake_case , )
elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
_lowerCAmelCase = dataset.make_dynamic_sampler(
self.hparams.max_tokens_per_batch , distributed=self.hparams.gpus > 1 )
return DataLoader(
_snake_case , batch_sampler=_snake_case , collate_fn=dataset.collate_fn , num_workers=self.num_workers , )
else:
return DataLoader(
_snake_case , batch_size=_snake_case , collate_fn=dataset.collate_fn , shuffle=_snake_case , num_workers=self.num_workers , sampler=_snake_case , )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_dataloader("""train""" , batch_size=self.hparams.train_batch_size , shuffle=_snake_case )
return dataloader
def snake_case ( self ):
"""simple docstring"""
return self.get_dataloader("""val""" , batch_size=self.hparams.eval_batch_size )
def snake_case ( self ):
"""simple docstring"""
return self.get_dataloader("""test""" , batch_size=self.hparams.eval_batch_size )
@staticmethod
def snake_case ( _snake_case , _snake_case ):
"""simple docstring"""
BaseTransformer.add_model_specific_args(_snake_case , _snake_case )
add_generic_args(_snake_case , _snake_case )
parser.add_argument(
"""--max_source_length""" , default=1024 , type=_snake_case , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--max_target_length""" , default=56 , type=_snake_case , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--val_max_target_length""" , default=142 , type=_snake_case , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--test_max_target_length""" , default=142 , type=_snake_case , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument("""--freeze_encoder""" , action="""store_true""" )
parser.add_argument("""--freeze_embeds""" , action="""store_true""" )
parser.add_argument("""--sortish_sampler""" , action="""store_true""" , default=_snake_case )
parser.add_argument("""--overwrite_output_dir""" , action="""store_true""" , default=_snake_case )
parser.add_argument("""--max_tokens_per_batch""" , type=_snake_case , default=_snake_case )
parser.add_argument("""--logger_name""" , type=_snake_case , choices=["""default""", """wandb""", """wandb_shared"""] , default="""default""" )
parser.add_argument("""--n_train""" , type=_snake_case , default=-1 , required=_snake_case , help="""# examples. -1 means use all.""" )
parser.add_argument("""--n_val""" , type=_snake_case , default=500 , required=_snake_case , help="""# examples. -1 means use all.""" )
parser.add_argument("""--n_test""" , type=_snake_case , default=-1 , required=_snake_case , help="""# examples. -1 means use all.""" )
parser.add_argument(
"""--task""" , type=_snake_case , default="""summarization""" , required=_snake_case , help="""# examples. -1 means use all.""" )
parser.add_argument("""--label_smoothing""" , type=_snake_case , default=0.0 , required=_snake_case )
parser.add_argument("""--src_lang""" , type=_snake_case , default="""""" , required=_snake_case )
parser.add_argument("""--tgt_lang""" , type=_snake_case , default="""""" , required=_snake_case )
parser.add_argument("""--eval_beams""" , type=_snake_case , default=_snake_case , required=_snake_case )
parser.add_argument(
"""--val_metric""" , type=_snake_case , default=_snake_case , required=_snake_case , choices=["""bleu""", """rouge2""", """loss""", None] )
parser.add_argument("""--eval_max_gen_length""" , type=_snake_case , default=_snake_case , help="""never generate more than n tokens""" )
parser.add_argument("""--save_top_k""" , type=_snake_case , default=1 , required=_snake_case , help="""How many checkpoints to save""" )
parser.add_argument(
"""--early_stopping_patience""" , type=_snake_case , default=-1 , required=_snake_case , help=(
"""-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"""
""" val_check_interval will effect it."""
) , )
return parser
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''translation'''
__lowerCamelCase = ['''loss''']
__lowerCamelCase = ['''bleu''']
__lowerCamelCase = '''bleu'''
def __init__( self , _snake_case , **_snake_case ):
"""simple docstring"""
super().__init__(_snake_case , **_snake_case )
_lowerCAmelCase = hparams.src_lang
_lowerCAmelCase = hparams.tgt_lang
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
return calculate_bleu(_snake_case , _snake_case )
def _UpperCAmelCase ( snake_case , snake_case=None ):
"""simple docstring"""
Path(args.output_dir ).mkdir(exist_ok=snake_case )
check_output_dir(snake_case , expected_items=3 )
if model is None:
if "summarization" in args.task:
_lowerCAmelCase = SummarizationModule(snake_case )
else:
_lowerCAmelCase = TranslationModule(snake_case )
_lowerCAmelCase = Path(args.data_dir ).name
if (
args.logger_name == "default"
or args.fast_dev_run
or str(args.output_dir ).startswith("""/tmp""" )
or str(args.output_dir ).startswith("""/var""" )
):
_lowerCAmelCase = True # don't pollute wandb logs unnecessarily
elif args.logger_name == "wandb":
from pytorch_lightning.loggers import WandbLogger
_lowerCAmelCase = os.environ.get("""WANDB_PROJECT""" , snake_case )
_lowerCAmelCase = WandbLogger(name=model.output_dir.name , project=snake_case )
elif args.logger_name == "wandb_shared":
from pytorch_lightning.loggers import WandbLogger
_lowerCAmelCase = WandbLogger(name=model.output_dir.name , project=F'hf_{dataset}' )
if args.early_stopping_patience >= 0:
_lowerCAmelCase = get_early_stopping_callback(model.val_metric , args.early_stopping_patience )
else:
_lowerCAmelCase = False
_lowerCAmelCase = args.val_metric == """loss"""
_lowerCAmelCase = generic_train(
snake_case , snake_case , logging_callback=SeqaSeqLoggingCallback() , checkpoint_callback=get_checkpoint_callback(
args.output_dir , model.val_metric , args.save_top_k , snake_case ) , early_stopping_callback=snake_case , logger=snake_case , )
pickle_save(model.hparams , model.output_dir / """hparams.pkl""" )
if not args.do_predict:
return model
_lowerCAmelCase = """"""
_lowerCAmelCase = sorted(glob.glob(os.path.join(args.output_dir , """*.ckpt""" ) , recursive=snake_case ) )
if checkpoints:
_lowerCAmelCase = checkpoints[-1]
_lowerCAmelCase = checkpoints[-1]
trainer.logger.log_hyperparams(model.hparams )
# test() without a model tests using the best checkpoint automatically
trainer.test()
return model
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
A__ = pl.Trainer.add_argparse_args(parser)
A__ = SummarizationModule.add_model_specific_args(parser, os.getcwd())
A__ = parser.parse_args()
main(args)
| 82 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __lowerCAmelCase ( lowerCamelCase__ ):
# to overwrite at feature extractactor specific tests
__lowerCamelCase = None
__lowerCamelCase = None
@property
def snake_case ( self ):
"""simple docstring"""
return self.feat_extract_tester.prepare_feat_extract_dict()
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(_snake_case , """feature_size""" ) )
self.assertTrue(hasattr(_snake_case , """sampling_rate""" ) )
self.assertTrue(hasattr(_snake_case , """padding_value""" ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(_snake_case ) == len(_snake_case ) for x, y in zip(_snake_case , processed_features[input_name] ) ) )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_snake_case )
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="""np""" )
_lowerCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_lowerCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_snake_case )
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="""pt""" )
_lowerCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_lowerCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_snake_case )
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="""tf""" )
_lowerCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_lowerCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def snake_case ( self , _snake_case=False ):
"""simple docstring"""
def _inputs_have_equal_length(_snake_case ):
_lowerCAmelCase = len(input[0] )
for input_slice in input[1:]:
if len(_snake_case ) != length:
return False
return True
def _inputs_are_equal(_snake_case , _snake_case ):
if len(_snake_case ) != len(_snake_case ):
return False
for input_slice_a, input_slice_a in zip(_snake_case , _snake_case ):
if not np.allclose(np.asarray(_snake_case ) , np.asarray(_snake_case ) , atol=1e-3 ):
return False
return True
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_snake_case )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = self.feat_extract_tester.seq_length_diff
_lowerCAmelCase = self.feat_extract_tester.max_seq_length + pad_diff
_lowerCAmelCase = self.feat_extract_tester.min_seq_length
_lowerCAmelCase = self.feat_extract_tester.batch_size
_lowerCAmelCase = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
_lowerCAmelCase = feat_extract.pad(_snake_case , padding=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""max_length""" , max_length=len(speech_inputs[-1] ) )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""max_length""" )[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=_snake_case , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_are_equal(_snake_case , _snake_case ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
_lowerCAmelCase = feat_extract.pad(_snake_case , pad_to_multiple_of=10 )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , pad_to_multiple_of=10 )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , pad_to_multiple_of=10 , max_length=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , pad_to_multiple_of=10 , max_length=_snake_case , return_tensors="""np""" , )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(all(len(_snake_case ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(_snake_case , _snake_case ) )
_lowerCAmelCase = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(_snake_case ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
_lowerCAmelCase = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1e-3 )
def snake_case ( self , _snake_case=False ):
"""simple docstring"""
def _inputs_have_equal_length(_snake_case ):
_lowerCAmelCase = len(input[0] )
for input_slice in input[1:]:
if len(_snake_case ) != length:
return False
return True
def _inputs_are_equal(_snake_case , _snake_case ):
if len(_snake_case ) != len(_snake_case ):
return False
for input_slice_a, input_slice_a in zip(_snake_case , _snake_case ):
if not np.allclose(np.asarray(_snake_case ) , np.asarray(_snake_case ) , atol=1e-3 ):
return False
return True
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_snake_case )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , truncation=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
# truncate to smallest with np
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" , truncation=_snake_case , )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
# truncate to middle
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=_snake_case , return_tensors="""np""" , )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[1] ) , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_are_equal(_snake_case , _snake_case ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , truncation=_snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""longest""" , truncation=_snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""longest""" , truncation=_snake_case )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""max_length""" , truncation=_snake_case )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
_lowerCAmelCase = 12
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_snake_case , truncation=_snake_case , )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_snake_case , )
_lowerCAmelCase = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
_lowerCAmelCase = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
_lowerCAmelCase = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
def snake_case ( self ):
"""simple docstring"""
self._check_padding(numpify=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._check_padding(numpify=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._check_truncation(numpify=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._check_truncation(numpify=_snake_case )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""pt""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""tf""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_dict
_lowerCAmelCase = True
_lowerCAmelCase = self.feature_extraction_class(**_snake_case )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = [len(_snake_case ) for x in speech_inputs]
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )
self.assertIn("""attention_mask""" , _snake_case )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , _snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_dict
_lowerCAmelCase = True
_lowerCAmelCase = self.feature_extraction_class(**_snake_case )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = [len(_snake_case ) for x in speech_inputs]
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = min(_snake_case )
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=_snake_case , truncation=_snake_case , return_tensors="""np""" )
self.assertIn("""attention_mask""" , _snake_case )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
| 82 | 1 |
import math
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
assert isinstance(snake_case , snake_case ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Negatives, 0, 1 and all even numbers are not primes
return False
_lowerCAmelCase = range(3 , int(math.sqrt(snake_case ) + 1 ) , 2 )
return not any(not number % i for i in odd_numbers )
def _UpperCAmelCase ( snake_case , snake_case=1 , **snake_case ):
"""simple docstring"""
_lowerCAmelCase = factor * value
_lowerCAmelCase = value
while not is_prime(snake_case ):
value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1 , **snake_case )
return value
| 82 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''poolformer'''
def __init__( self , _snake_case=3 , _snake_case=16 , _snake_case=16 , _snake_case=3 , _snake_case=4.0 , _snake_case=[2, 2, 6, 2] , _snake_case=[64, 128, 320, 512] , _snake_case=[7, 3, 3, 3] , _snake_case=[4, 2, 2, 2] , _snake_case=[2, 1, 1, 1] , _snake_case=4 , _snake_case=0.0 , _snake_case="gelu" , _snake_case=True , _snake_case=1e-5 , _snake_case=0.02 , **_snake_case , ):
"""simple docstring"""
_lowerCAmelCase = num_channels
_lowerCAmelCase = patch_size
_lowerCAmelCase = stride
_lowerCAmelCase = padding
_lowerCAmelCase = pool_size
_lowerCAmelCase = hidden_sizes
_lowerCAmelCase = mlp_ratio
_lowerCAmelCase = depths
_lowerCAmelCase = patch_sizes
_lowerCAmelCase = strides
_lowerCAmelCase = num_encoder_blocks
_lowerCAmelCase = drop_path_rate
_lowerCAmelCase = hidden_act
_lowerCAmelCase = use_layer_scale
_lowerCAmelCase = layer_scale_init_value
_lowerCAmelCase = initializer_range
super().__init__(**_snake_case )
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = version.parse('''1.11''' )
@property
def snake_case ( self ):
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def snake_case ( self ):
"""simple docstring"""
return 2e-3
| 82 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase = DiTPipeline
__lowerCamelCase = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
__lowerCamelCase = PipelineTesterMixin.required_optional_params - {
'''latents''',
'''num_images_per_prompt''',
'''callback''',
'''callback_steps''',
}
__lowerCamelCase = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
__lowerCamelCase = False
def snake_case ( self ):
"""simple docstring"""
torch.manual_seed(0 )
_lowerCAmelCase = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=_snake_case , activation_fn="""gelu-approximate""" , num_embeds_ada_norm=1000 , norm_type="""ada_norm_zero""" , norm_elementwise_affine=_snake_case , )
_lowerCAmelCase = AutoencoderKL()
_lowerCAmelCase = DDIMScheduler()
_lowerCAmelCase = {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
return components
def snake_case ( self , _snake_case , _snake_case=0 ):
"""simple docstring"""
if str(_snake_case ).startswith("""mps""" ):
_lowerCAmelCase = torch.manual_seed(_snake_case )
else:
_lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
_lowerCAmelCase = {
"""class_labels""": [1],
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = self.pipeline_class(**_snake_case )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = pipe(**_snake_case ).images
_lowerCAmelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_lowerCAmelCase = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
_lowerCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_snake_case , 1e-3 )
def snake_case ( self ):
"""simple docstring"""
self._test_inference_batch_single_identical(relax_max_difference=_snake_case , expected_max_diff=1e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def snake_case ( self ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@require_torch_gpu
@slow
class __lowerCAmelCase ( unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256""" )
pipe.to("""cuda""" )
_lowerCAmelCase = ["""vase""", """umbrella""", """white shark""", """white wolf"""]
_lowerCAmelCase = pipe.get_label_ids(_snake_case )
_lowerCAmelCase = pipe(_snake_case , generator=_snake_case , num_inference_steps=40 , output_type="""np""" ).images
for word, image in zip(_snake_case , _snake_case ):
_lowerCAmelCase = load_numpy(
F'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy' )
assert np.abs((expected_image - image).max() ) < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512""" )
_lowerCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("""cuda""" )
_lowerCAmelCase = ["""vase""", """umbrella"""]
_lowerCAmelCase = pipe.get_label_ids(_snake_case )
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = pipe(_snake_case , generator=_snake_case , num_inference_steps=25 , output_type="""np""" ).images
for word, image in zip(_snake_case , _snake_case ):
_lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
F'/dit/{word}_512.npy' )
assert np.abs((expected_image - image).max() ) < 1e-1
| 82 |
def _UpperCAmelCase ( snake_case = 10_00 ):
"""simple docstring"""
_lowerCAmelCase = -1
_lowerCAmelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
_lowerCAmelCase = (n * n - 2 * a * n) // (2 * n - 2 * a)
_lowerCAmelCase = n - a - b
if c * c == (a * a + b * b):
_lowerCAmelCase = a * b * c
if candidate >= product:
_lowerCAmelCase = candidate
return product
if __name__ == "__main__":
print(f"{solution() = }")
| 82 | 1 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = args.log_outputs
_lowerCAmelCase = """_""".join(args.dataset.split("""/""" ) + [args.config, args.split] )
# load metric
_lowerCAmelCase = load_metric("""wer""" )
_lowerCAmelCase = load_metric("""cer""" )
# compute metrics
_lowerCAmelCase = wer.compute(references=result["""target"""] , predictions=result["""prediction"""] )
_lowerCAmelCase = cer.compute(references=result["""target"""] , predictions=result["""prediction"""] )
# print & log results
_lowerCAmelCase = F'WER: {wer_result}\nCER: {cer_result}'
print(snake_case )
with open(F'{dataset_id}_eval_results.txt' , """w""" ) as f:
f.write(snake_case )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
_lowerCAmelCase = F'log_{dataset_id}_predictions.txt'
_lowerCAmelCase = F'log_{dataset_id}_targets.txt'
with open(snake_case , """w""" ) as p, open(snake_case , """w""" ) as t:
# mapping function to write output
def write_to_file(snake_case , snake_case ):
p.write(F'{i}' + """\n""" )
p.write(batch["""prediction"""] + """\n""" )
t.write(F'{i}' + """\n""" )
t.write(batch["""target"""] + """\n""" )
result.map(snake_case , with_indices=snake_case )
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = """[,?.!\-\;\:\"“%‘”�—’…–]""" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
_lowerCAmelCase = re.sub(snake_case , """""" , text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
_lowerCAmelCase = ["""\n\n""", """\n""", """ """, """ """]
for t in token_sequences_to_ignore:
_lowerCAmelCase = """ """.join(text.split(snake_case ) )
return text
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = load_dataset(args.dataset , args.config , split=args.split , use_auth_token=snake_case )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
_lowerCAmelCase = AutoFeatureExtractor.from_pretrained(args.model_id )
_lowerCAmelCase = feature_extractor.sampling_rate
# resample audio
_lowerCAmelCase = dataset.cast_column("""audio""" , Audio(sampling_rate=snake_case ) )
# load eval pipeline
if args.device is None:
_lowerCAmelCase = 0 if torch.cuda.is_available() else -1
_lowerCAmelCase = pipeline("""automatic-speech-recognition""" , model=args.model_id , device=args.device )
# map function to decode audio
def map_to_pred(snake_case ):
_lowerCAmelCase = asr(
batch["""audio"""]["""array"""] , chunk_length_s=args.chunk_length_s , stride_length_s=args.stride_length_s )
_lowerCAmelCase = prediction["""text"""]
_lowerCAmelCase = normalize_text(batch["""sentence"""] )
return batch
# run inference on all examples
_lowerCAmelCase = dataset.map(snake_case , remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(snake_case , snake_case )
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
parser.add_argument(
"""--model_id""", type=str, required=True, help="""Model identifier. Should be loadable with 🤗 Transformers"""
)
parser.add_argument(
"""--dataset""",
type=str,
required=True,
help="""Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets""",
)
parser.add_argument(
"""--config""", type=str, required=True, help="""Config of the dataset. *E.g.* `'en'` for Common Voice"""
)
parser.add_argument("""--split""", type=str, required=True, help="""Split of the dataset. *E.g.* `'test'`""")
parser.add_argument(
"""--chunk_length_s""", type=float, default=None, help="""Chunk length in seconds. Defaults to 5 seconds."""
)
parser.add_argument(
"""--stride_length_s""", type=float, default=None, help="""Stride of the audio chunks. Defaults to 1 second."""
)
parser.add_argument(
"""--log_outputs""", action="""store_true""", help="""If defined, write outputs to log file for analysis."""
)
parser.add_argument(
"""--device""",
type=int,
default=None,
help="""The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.""",
)
A__ = parser.parse_args()
main(args)
| 82 |
from __future__ import annotations
import math
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(snake_case ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = str(snake_case )
_lowerCAmelCase = [n]
for i in range(1 , len(snake_case ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if len(str(snake_case ) ) > 3:
if not is_prime(int(str(snake_case )[-3:] ) ) or not is_prime(int(str(snake_case )[:3] ) ):
return False
return True
def _UpperCAmelCase ( snake_case = 11 ):
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = 13
while len(snake_case ) != count:
if validate(snake_case ):
_lowerCAmelCase = list_truncated_nums(snake_case )
if all(is_prime(snake_case ) for i in list_nums ):
list_truncated_primes.append(snake_case )
num += 2
return list_truncated_primes
def _UpperCAmelCase ( ):
"""simple docstring"""
return sum(compute_truncated_primes(11 ) )
if __name__ == "__main__":
print(f"{sum(compute_truncated_primes(11)) = }")
| 82 | 1 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
A__ = logging.get_logger(__name__)
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = ['''pixel_values''']
def __init__( self , _snake_case = True , _snake_case = None , _snake_case = PILImageResampling.BILINEAR , _snake_case = True , _snake_case = None , _snake_case = True , _snake_case = 1 / 255 , _snake_case = True , _snake_case = None , _snake_case = None , **_snake_case , ):
"""simple docstring"""
super().__init__(**_snake_case )
_lowerCAmelCase = size if size is not None else {"""shortest_edge""": 256}
_lowerCAmelCase = get_size_dict(_snake_case , default_to_square=_snake_case )
_lowerCAmelCase = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
_lowerCAmelCase = get_size_dict(_snake_case , param_name="""crop_size""" )
_lowerCAmelCase = do_resize
_lowerCAmelCase = size
_lowerCAmelCase = resample
_lowerCAmelCase = do_center_crop
_lowerCAmelCase = crop_size
_lowerCAmelCase = do_rescale
_lowerCAmelCase = rescale_factor
_lowerCAmelCase = do_normalize
_lowerCAmelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_lowerCAmelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def snake_case ( self , _snake_case , _snake_case , _snake_case = PILImageResampling.BICUBIC , _snake_case = None , **_snake_case , ):
"""simple docstring"""
_lowerCAmelCase = get_size_dict(_snake_case , default_to_square=_snake_case )
if "shortest_edge" not in size:
raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
_lowerCAmelCase = get_resize_output_image_size(_snake_case , size=size["""shortest_edge"""] , default_to_square=_snake_case )
return resize(_snake_case , size=_snake_case , resample=_snake_case , data_format=_snake_case , **_snake_case )
def snake_case ( self , _snake_case , _snake_case , _snake_case = None , **_snake_case , ):
"""simple docstring"""
_lowerCAmelCase = get_size_dict(_snake_case )
if "height" not in size or "width" not in size:
raise ValueError(F'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}' )
return center_crop(_snake_case , size=(size["""height"""], size["""width"""]) , data_format=_snake_case , **_snake_case )
def snake_case ( self , _snake_case , _snake_case , _snake_case = None , **_snake_case ):
"""simple docstring"""
return rescale(_snake_case , scale=_snake_case , data_format=_snake_case , **_snake_case )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case = None , **_snake_case , ):
"""simple docstring"""
return normalize(_snake_case , mean=_snake_case , std=_snake_case , data_format=_snake_case , **_snake_case )
def snake_case ( self , _snake_case , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = ChannelDimension.FIRST , **_snake_case , ):
"""simple docstring"""
_lowerCAmelCase = do_resize if do_resize is not None else self.do_resize
_lowerCAmelCase = size if size is not None else self.size
_lowerCAmelCase = get_size_dict(_snake_case , default_to_square=_snake_case )
_lowerCAmelCase = resample if resample is not None else self.resample
_lowerCAmelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
_lowerCAmelCase = crop_size if crop_size is not None else self.crop_size
_lowerCAmelCase = get_size_dict(_snake_case , param_name="""crop_size""" )
_lowerCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
_lowerCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowerCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
_lowerCAmelCase = image_mean if image_mean is not None else self.image_mean
_lowerCAmelCase = image_std if image_std is not None else self.image_std
_lowerCAmelCase = make_list_of_images(_snake_case )
if not valid_images(_snake_case ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
_lowerCAmelCase = [to_numpy_array(_snake_case ) for image in images]
if do_resize:
_lowerCAmelCase = [self.resize(image=_snake_case , size=_snake_case , resample=_snake_case ) for image in images]
if do_center_crop:
_lowerCAmelCase = [self.center_crop(image=_snake_case , size=_snake_case ) for image in images]
if do_rescale:
_lowerCAmelCase = [self.rescale(image=_snake_case , scale=_snake_case ) for image in images]
if do_normalize:
_lowerCAmelCase = [self.normalize(image=_snake_case , mean=_snake_case , std=_snake_case ) for image in images]
_lowerCAmelCase = [to_channel_dimension_format(_snake_case , _snake_case ) for image in images]
_lowerCAmelCase = {"""pixel_values""": images}
return BatchFeature(data=_snake_case , tensor_type=_snake_case )
def snake_case ( self , _snake_case , _snake_case = None ):
"""simple docstring"""
_lowerCAmelCase = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_snake_case ) != len(_snake_case ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(_snake_case ):
_lowerCAmelCase = target_sizes.numpy()
_lowerCAmelCase = []
for idx in range(len(_snake_case ) ):
_lowerCAmelCase = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=_snake_case )
_lowerCAmelCase = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_snake_case )
else:
_lowerCAmelCase = logits.argmax(dim=1 )
_lowerCAmelCase = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 82 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
A__ = logging.get_logger(__name__)
class __lowerCAmelCase ( lowerCamelCase__ ):
def __init__( self , **_snake_case ):
"""simple docstring"""
requires_backends(self , ["""bs4"""] )
super().__init__(**_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = element if element.name else element.parent
for parent in child.parents: # type: bs4.element.Tag
_lowerCAmelCase = parent.find_all(child.name , recursive=_snake_case )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(_snake_case ) else next(i for i, s in enumerate(_snake_case , 1 ) if s is child ) )
_lowerCAmelCase = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = BeautifulSoup(_snake_case , """html.parser""" )
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = []
for element in html_code.descendants:
if type(_snake_case ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
_lowerCAmelCase = html.unescape(_snake_case ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(_snake_case )
_lowerCAmelCase , _lowerCAmelCase = self.xpath_soup(_snake_case )
stringaxtag_seq.append(_snake_case )
stringaxsubs_seq.append(_snake_case )
if len(_snake_case ) != len(_snake_case ):
raise ValueError("""Number of doc strings and xtags does not correspond""" )
if len(_snake_case ) != len(_snake_case ):
raise ValueError("""Number of doc strings and xsubs does not correspond""" )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = """"""
for tagname, subs in zip(_snake_case , _snake_case ):
xpath += F'/{tagname}'
if subs != 0:
xpath += F'[{subs}]'
return xpath
def __call__( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = False
# Check that strings has a valid type
if isinstance(_snake_case , _snake_case ):
_lowerCAmelCase = True
elif isinstance(_snake_case , (list, tuple) ):
if len(_snake_case ) == 0 or isinstance(html_strings[0] , _snake_case ):
_lowerCAmelCase = True
if not valid_strings:
raise ValueError(
"""HTML strings must of type `str`, `List[str]` (batch of examples), """
F'but is of type {type(_snake_case )}.' )
_lowerCAmelCase = bool(isinstance(_snake_case , (list, tuple) ) and (isinstance(html_strings[0] , _snake_case )) )
if not is_batched:
_lowerCAmelCase = [html_strings]
# Get nodes + xpaths
_lowerCAmelCase = []
_lowerCAmelCase = []
for html_string in html_strings:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.get_three_from_single(_snake_case )
nodes.append(_snake_case )
_lowerCAmelCase = []
for node, tag_list, sub_list in zip(_snake_case , _snake_case , _snake_case ):
_lowerCAmelCase = self.construct_xpath(_snake_case , _snake_case )
xpath_strings.append(_snake_case )
xpaths.append(_snake_case )
# return as Dict
_lowerCAmelCase = {"""nodes""": nodes, """xpaths""": xpaths}
_lowerCAmelCase = BatchFeature(data=_snake_case , tensor_type=_snake_case )
return encoded_inputs
| 82 | 1 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class __lowerCAmelCase :
def __init__( self , _snake_case , _snake_case=14 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=False , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=4 , _snake_case=4 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=512 , _snake_case=0.02 , ):
"""simple docstring"""
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = seq_length
_lowerCAmelCase = is_training
_lowerCAmelCase = use_input_mask
_lowerCAmelCase = use_token_type_ids
_lowerCAmelCase = use_labels
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = rotary_dim
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_act
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = initializer_range
_lowerCAmelCase = None
_lowerCAmelCase = vocab_size - 1
_lowerCAmelCase = vocab_size - 1
_lowerCAmelCase = vocab_size - 1
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase = None
if self.use_input_mask:
_lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCAmelCase = GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=_snake_case , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = config_and_inputs
_lowerCAmelCase = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = 20
_lowerCAmelCase = model_class_name(_snake_case )
_lowerCAmelCase = model.init_cache(input_ids.shape[0] , _snake_case )
_lowerCAmelCase = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype="""i4""" )
_lowerCAmelCase = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
_lowerCAmelCase = model(
input_ids[:, :-1] , attention_mask=_snake_case , past_key_values=_snake_case , position_ids=_snake_case , )
_lowerCAmelCase = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype="""i4""" )
_lowerCAmelCase = model(
input_ids[:, -1:] , attention_mask=_snake_case , past_key_values=outputs_cache.past_key_values , position_ids=_snake_case , )
_lowerCAmelCase = model(_snake_case )
_lowerCAmelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=F'Max diff is {diff}' )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = 20
_lowerCAmelCase = model_class_name(_snake_case )
_lowerCAmelCase = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
_lowerCAmelCase = model.init_cache(input_ids.shape[0] , _snake_case )
_lowerCAmelCase = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
_lowerCAmelCase = model(
input_ids[:, :-1] , attention_mask=_snake_case , past_key_values=_snake_case , position_ids=_snake_case , )
_lowerCAmelCase = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype="""i4""" )
_lowerCAmelCase = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=_snake_case , position_ids=_snake_case , )
_lowerCAmelCase = model(_snake_case , attention_mask=_snake_case )
_lowerCAmelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=F'Max diff is {diff}' )
@require_flax
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
__lowerCamelCase = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = FlaxGPTJModelTester(self )
def snake_case ( self ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(_snake_case , _snake_case , _snake_case , _snake_case )
def snake_case ( self ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
_snake_case , _snake_case , _snake_case , _snake_case )
@tooslow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = GPTaTokenizer.from_pretrained("""gpt2""" , pad_token="""<|endoftext|>""" , padding_side="""left""" )
_lowerCAmelCase = tokenizer(["""Hello this is a long string""", """Hey"""] , return_tensors="""np""" , padding=_snake_case , truncation=_snake_case )
_lowerCAmelCase = FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B""" )
_lowerCAmelCase = False
_lowerCAmelCase = model.config.eos_token_id
_lowerCAmelCase = jax.jit(model.generate )
_lowerCAmelCase = jit_generate(
inputs["""input_ids"""] , attention_mask=inputs["""attention_mask"""] , pad_token_id=tokenizer.pad_token_id ).sequences
_lowerCAmelCase = tokenizer.batch_decode(_snake_case , skip_special_tokens=_snake_case )
_lowerCAmelCase = [
"""Hello this is a long string of text.\n\nI'm trying to get the text of the""",
"""Hey, I'm a little late to the party. I'm going to""",
]
self.assertListEqual(_snake_case , _snake_case )
@is_pt_flax_cross_test
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
_lowerCAmelCase = self._prepare_for_class(_snake_case , _snake_case )
_lowerCAmelCase = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
_lowerCAmelCase = model_class.__name__[4:] # Skip the "Flax" at the beginning
_lowerCAmelCase = getattr(_snake_case , _snake_case )
_lowerCAmelCase , _lowerCAmelCase = pt_inputs["""input_ids"""].shape
_lowerCAmelCase = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(_snake_case ):
_lowerCAmelCase = 0
_lowerCAmelCase = 1
_lowerCAmelCase = 0
_lowerCAmelCase = 1
_lowerCAmelCase = pt_model_class(_snake_case ).eval()
_lowerCAmelCase = model_class(_snake_case , dtype=jnp.floataa )
_lowerCAmelCase = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , _snake_case )
_lowerCAmelCase = fx_state
with torch.no_grad():
_lowerCAmelCase = pt_model(**_snake_case ).to_tuple()
_lowerCAmelCase = fx_model(**_snake_case ).to_tuple()
self.assertEqual(len(_snake_case ) , len(_snake_case ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(_snake_case , _snake_case ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(_snake_case )
_lowerCAmelCase = model_class.from_pretrained(_snake_case , from_pt=_snake_case )
_lowerCAmelCase = fx_model_loaded(**_snake_case ).to_tuple()
self.assertEqual(
len(_snake_case ) , len(_snake_case ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output_loaded, pt_output in zip(_snake_case , _snake_case ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@is_pt_flax_cross_test
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
_lowerCAmelCase = self._prepare_for_class(_snake_case , _snake_case )
_lowerCAmelCase = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
_lowerCAmelCase = model_class.__name__[4:] # Skip the "Flax" at the beginning
_lowerCAmelCase = getattr(_snake_case , _snake_case )
_lowerCAmelCase = pt_model_class(_snake_case ).eval()
_lowerCAmelCase = model_class(_snake_case , dtype=jnp.floataa )
_lowerCAmelCase = load_flax_weights_in_pytorch_model(_snake_case , fx_model.params )
_lowerCAmelCase , _lowerCAmelCase = pt_inputs["""input_ids"""].shape
_lowerCAmelCase = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(_snake_case ):
_lowerCAmelCase = 0
_lowerCAmelCase = 1
_lowerCAmelCase = 0
_lowerCAmelCase = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
_lowerCAmelCase = pt_model(**_snake_case ).to_tuple()
_lowerCAmelCase = fx_model(**_snake_case ).to_tuple()
self.assertEqual(len(_snake_case ) , len(_snake_case ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(_snake_case , _snake_case ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(_snake_case )
_lowerCAmelCase = pt_model_class.from_pretrained(_snake_case , from_flax=_snake_case )
with torch.no_grad():
_lowerCAmelCase = pt_model_loaded(**_snake_case ).to_tuple()
self.assertEqual(
len(_snake_case ) , len(_snake_case ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(_snake_case , _snake_case ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@tooslow
def snake_case ( self ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
_lowerCAmelCase = model_class_name.from_pretrained("""EleutherAI/gpt-j-6B""" )
_lowerCAmelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(_snake_case )
| 82 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
A__ = TypeVar("""T""")
A__ = TypeVar("""U""")
class __lowerCAmelCase ( Generic[T, U] ):
def __init__( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = key
_lowerCAmelCase = val
_lowerCAmelCase = None
_lowerCAmelCase = None
def __repr__( self ):
"""simple docstring"""
return (
F'Node: key: {self.key}, val: {self.val}, '
F'has next: {bool(self.next )}, has prev: {bool(self.prev )}'
)
class __lowerCAmelCase ( Generic[T, U] ):
def __init__( self ):
"""simple docstring"""
_lowerCAmelCase = DoubleLinkedListNode(_snake_case , _snake_case )
_lowerCAmelCase = DoubleLinkedListNode(_snake_case , _snake_case )
_lowerCAmelCase , _lowerCAmelCase = self.rear, self.head
def __repr__( self ):
"""simple docstring"""
_lowerCAmelCase = ["""DoubleLinkedList"""]
_lowerCAmelCase = self.head
while node.next is not None:
rep.append(str(_snake_case ) )
_lowerCAmelCase = node.next
rep.append(str(self.rear ) )
return ",\n ".join(_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
_lowerCAmelCase = node
_lowerCAmelCase = previous
_lowerCAmelCase = node
_lowerCAmelCase = self.rear
def snake_case ( self , _snake_case ):
"""simple docstring"""
if node.prev is None or node.next is None:
return None
_lowerCAmelCase = node.next
_lowerCAmelCase = node.prev
_lowerCAmelCase = None
_lowerCAmelCase = None
return node
class __lowerCAmelCase ( Generic[T, U] ):
__lowerCamelCase = {}
def __init__( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = DoubleLinkedList()
_lowerCAmelCase = capacity
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = {}
def __repr__( self ):
"""simple docstring"""
return (
F'CacheInfo(hits={self.hits}, misses={self.miss}, '
F'capacity={self.capacity}, current size={self.num_keys})'
)
def __contains__( self , _snake_case ):
"""simple docstring"""
return key in self.cache
def snake_case ( self , _snake_case ):
"""simple docstring"""
if key in self.cache:
self.hits += 1
_lowerCAmelCase = self.cache[key]
_lowerCAmelCase = self.list.remove(self.cache[key] )
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(_snake_case )
return node.val
self.miss += 1
return None
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
_lowerCAmelCase = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(_snake_case ) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
_lowerCAmelCase = DoubleLinkedListNode(_snake_case , _snake_case )
self.list.add(self.cache[key] )
self.num_keys += 1
else:
# bump node to the end of the list, update value
_lowerCAmelCase = self.list.remove(self.cache[key] )
assert node is not None # node guaranteed to be in list
_lowerCAmelCase = value
self.list.add(_snake_case )
@classmethod
def snake_case ( cls , _snake_case = 128 ):
"""simple docstring"""
def cache_decorator_inner(_snake_case ) -> Callable[..., U]:
def cache_decorator_wrapper(*_snake_case ) -> U:
if func not in cls.decorator_function_to_instance_map:
_lowerCAmelCase = LRUCache(_snake_case )
_lowerCAmelCase = cls.decorator_function_to_instance_map[func].get(args[0] )
if result is None:
_lowerCAmelCase = func(*_snake_case )
cls.decorator_function_to_instance_map[func].put(args[0] , _snake_case )
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(_snake_case , """cache_info""" , _snake_case ) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| 82 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
A__ = R"""
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.
Args:
title_sep (`str`, *optional*, defaults to `\" / \"`):
Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
doc_sep (`str`, *optional*, defaults to `\" // \"`):
Separator inserted between the text of the retrieved document and the original input when calling
[`RagRetriever`].
n_docs (`int`, *optional*, defaults to 5):
Number of documents to retrieve.
max_combined_length (`int`, *optional*, defaults to 300):
Max length of contextualized input returned by [`~RagRetriever.__call__`].
retrieval_vector_size (`int`, *optional*, defaults to 768):
Dimensionality of the document embeddings indexed by [`RagRetriever`].
retrieval_batch_size (`int`, *optional*, defaults to 8):
Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
[`RagRetriever`].
dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):
A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
using `datasets.list_datasets()`).
dataset_split (`str`, *optional*, defaults to `\"train\"`)
Which split of the `dataset` to load.
index_name (`str`, *optional*, defaults to `\"compressed\"`)
The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and
`\"compressed\"`.
index_path (`str`, *optional*)
The path to the serialized faiss index on disk.
passages_path (`str`, *optional*):
A path to text passages compatible with the faiss index. Required if using
[`~models.rag.retrieval_rag.LegacyIndex`]
use_dummy_dataset (`bool`, *optional*, defaults to `False`)
Whether to load a \"dummy\" variant of the dataset specified by `dataset`.
label_smoothing (`float`, *optional*, defaults to 0.0):
Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
in the loss calculation. If set to 0, no label smoothing is performed.
do_marginalize (`bool`, *optional*, defaults to `False`):
If `True`, the logits are marginalized over all documents by making use of
`torch.nn.functional.log_softmax`.
reduce_loss (`bool`, *optional*, defaults to `False`):
Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
do_deduplication (`bool`, *optional*, defaults to `True`):
Whether or not to deduplicate the generations from different context documents for a given input. Has to be
set to `False` if used while training with distributed backend.
exclude_bos_score (`bool`, *optional*, defaults to `False`):
Whether or not to disregard the BOS token when computing the loss.
output_retrieved(`bool`, *optional*, defaults to `False`):
If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
`context_attention_mask` are returned. See returned tensors for more detail.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
forced_eos_token_id (`int`, *optional*):
The id of the token to force as the last generated token when `max_length` is reached. Usually set to
`eos_token_id`.
"""
@add_start_docstrings(lowerCamelCase__ )
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''rag'''
__lowerCamelCase = True
def __init__( self , _snake_case=None , _snake_case=True , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=" / " , _snake_case=" // " , _snake_case=5 , _snake_case=300 , _snake_case=768 , _snake_case=8 , _snake_case="wiki_dpr" , _snake_case="train" , _snake_case="compressed" , _snake_case=None , _snake_case=None , _snake_case=False , _snake_case=False , _snake_case=0.0 , _snake_case=True , _snake_case=False , _snake_case=False , _snake_case=False , _snake_case=True , _snake_case=None , **_snake_case , ):
"""simple docstring"""
super().__init__(
bos_token_id=_snake_case , pad_token_id=_snake_case , eos_token_id=_snake_case , decoder_start_token_id=_snake_case , forced_eos_token_id=_snake_case , is_encoder_decoder=_snake_case , prefix=_snake_case , vocab_size=_snake_case , **_snake_case , )
assert (
"question_encoder" in kwargs and "generator" in kwargs
), "Config has to be initialized with question_encoder and generator config"
_lowerCAmelCase = kwargs.pop("""question_encoder""" )
_lowerCAmelCase = question_encoder_config.pop("""model_type""" )
_lowerCAmelCase = kwargs.pop("""generator""" )
_lowerCAmelCase = decoder_config.pop("""model_type""" )
from ..auto.configuration_auto import AutoConfig
_lowerCAmelCase = AutoConfig.for_model(_snake_case , **_snake_case )
_lowerCAmelCase = AutoConfig.for_model(_snake_case , **_snake_case )
_lowerCAmelCase = reduce_loss
_lowerCAmelCase = label_smoothing
_lowerCAmelCase = exclude_bos_score
_lowerCAmelCase = do_marginalize
_lowerCAmelCase = title_sep
_lowerCAmelCase = doc_sep
_lowerCAmelCase = n_docs
_lowerCAmelCase = max_combined_length
_lowerCAmelCase = dataset
_lowerCAmelCase = dataset_split
_lowerCAmelCase = index_name
_lowerCAmelCase = retrieval_vector_size
_lowerCAmelCase = retrieval_batch_size
_lowerCAmelCase = passages_path
_lowerCAmelCase = index_path
_lowerCAmelCase = use_dummy_dataset
_lowerCAmelCase = output_retrieved
_lowerCAmelCase = do_deduplication
_lowerCAmelCase = use_cache
if self.forced_eos_token_id is None:
_lowerCAmelCase = getattr(self.generator , """forced_eos_token_id""" , _snake_case )
@classmethod
def snake_case ( cls , _snake_case , _snake_case , **_snake_case ):
"""simple docstring"""
return cls(question_encoder=question_encoder_config.to_dict() , generator=generator_config.to_dict() , **_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = copy.deepcopy(self.__dict__ )
_lowerCAmelCase = self.question_encoder.to_dict()
_lowerCAmelCase = self.generator.to_dict()
_lowerCAmelCase = self.__class__.model_type
return output
| 82 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A__ = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""MvpTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = ["""MvpTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = [
"""MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MvpForCausalLM""",
"""MvpForConditionalGeneration""",
"""MvpForQuestionAnswering""",
"""MvpForSequenceClassification""",
"""MvpModel""",
"""MvpPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
A__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 82 | 1 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = ArgumentParser(
description=(
"""PyTorch TPU distributed training launch """
"""helper utility that will spawn up """
"""multiple distributed processes"""
) )
# Optional arguments for the launch helper
parser.add_argument("""--num_cores""" , type=snake_case , default=1 , help="""Number of TPU cores to use (1 or 8).""" )
# positional
parser.add_argument(
"""training_script""" , type=snake_case , help=(
"""The full path to the single TPU training """
"""program/script to be launched in parallel, """
"""followed by all the arguments for the """
"""training script"""
) , )
# rest from the training program
parser.add_argument("""training_script_args""" , nargs=snake_case )
return parser.parse_args()
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = parse_args()
# Import training_script as a module.
_lowerCAmelCase = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
_lowerCAmelCase = script_fpath.stem
_lowerCAmelCase = importlib.import_module(snake_case )
# Patch sys.argv
_lowerCAmelCase = [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 82 |
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 0
while number > 0:
_lowerCAmelCase = number % 10
sum_of_digits += last_digit
_lowerCAmelCase = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def _UpperCAmelCase ( snake_case = 1_00 ):
"""simple docstring"""
_lowerCAmelCase = factorial(snake_case )
_lowerCAmelCase = split_and_add(snake_case )
return result
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).strip())))
| 82 | 1 |
def _UpperCAmelCase ( snake_case = 10_00 ):
"""simple docstring"""
_lowerCAmelCase = -1
_lowerCAmelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
_lowerCAmelCase = (n * n - 2 * a * n) // (2 * n - 2 * a)
_lowerCAmelCase = n - a - b
if c * c == (a * a + b * b):
_lowerCAmelCase = a * b * c
if candidate >= product:
_lowerCAmelCase = candidate
return product
if __name__ == "__main__":
print(f"{solution() = }")
| 82 |
A__ = [0, 2, 4, 6, 8]
A__ = [1, 3, 5, 7, 9]
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 10
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
_lowerCAmelCase = 0
for digit in range(10 ):
_lowerCAmelCase = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 10 , snake_case , snake_case )
return result
_lowerCAmelCase = 0
for digita in range(10 ):
_lowerCAmelCase = digita
if (remainder + digita) % 2 == 0:
_lowerCAmelCase = ODD_DIGITS
else:
_lowerCAmelCase = EVEN_DIGITS
for digita in other_parity_digits:
_lowerCAmelCase = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 10 , snake_case , snake_case , )
return result
def _UpperCAmelCase ( snake_case = 9 ):
"""simple docstring"""
_lowerCAmelCase = 0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(snake_case , 0 , [0] * length , snake_case )
return result
if __name__ == "__main__":
print(f"{solution() = }")
| 82 | 1 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase = FlaxAutoencoderKL
@property
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = 4
_lowerCAmelCase = 3
_lowerCAmelCase = (32, 32)
_lowerCAmelCase = jax.random.PRNGKey(0 )
_lowerCAmelCase = jax.random.uniform(_snake_case , ((batch_size, num_channels) + sizes) )
return {"sample": image, "prng_key": prng_key}
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = {
"""block_out_channels""": [32, 64],
"""in_channels""": 3,
"""out_channels""": 3,
"""down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
"""up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
"""latent_channels""": 4,
}
_lowerCAmelCase = self.dummy_input
return init_dict, inputs_dict
| 82 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
A__ = logging.get_logger(__name__)
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = OrderedDict()
for key, value in state_dict.items():
if key.startswith("""module.encoder""" ):
_lowerCAmelCase = key.replace("""module.encoder""" , """glpn.encoder""" )
if key.startswith("""module.decoder""" ):
_lowerCAmelCase = key.replace("""module.decoder""" , """decoder.stages""" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
_lowerCAmelCase = key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
_lowerCAmelCase = key.replace(F'patch_embed{idx}' , F'patch_embeddings.{int(snake_case )-1}' )
if "norm" in key:
_lowerCAmelCase = key.replace("""norm""" , """layer_norm""" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
_lowerCAmelCase = key[key.find("""glpn.encoder.layer_norm""" ) + len("""glpn.encoder.layer_norm""" )]
_lowerCAmelCase = key.replace(F'layer_norm{idx}' , F'layer_norm.{int(snake_case )-1}' )
if "layer_norm1" in key:
_lowerCAmelCase = key.replace("""layer_norm1""" , """layer_norm_1""" )
if "layer_norm2" in key:
_lowerCAmelCase = key.replace("""layer_norm2""" , """layer_norm_2""" )
if "block" in key:
# replace for example block1 by block.0
_lowerCAmelCase = key[key.find("""block""" ) + len("""block""" )]
_lowerCAmelCase = key.replace(F'block{idx}' , F'block.{int(snake_case )-1}' )
if "attn.q" in key:
_lowerCAmelCase = key.replace("""attn.q""" , """attention.self.query""" )
if "attn.proj" in key:
_lowerCAmelCase = key.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in key:
_lowerCAmelCase = key.replace("""attn""" , """attention.self""" )
if "fc1" in key:
_lowerCAmelCase = key.replace("""fc1""" , """dense1""" )
if "fc2" in key:
_lowerCAmelCase = key.replace("""fc2""" , """dense2""" )
if "linear_pred" in key:
_lowerCAmelCase = key.replace("""linear_pred""" , """classifier""" )
if "linear_fuse" in key:
_lowerCAmelCase = key.replace("""linear_fuse.conv""" , """linear_fuse""" )
_lowerCAmelCase = key.replace("""linear_fuse.bn""" , """batch_norm""" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
_lowerCAmelCase = key[key.find("""linear_c""" ) + len("""linear_c""" )]
_lowerCAmelCase = key.replace(F'linear_c{idx}' , F'linear_c.{int(snake_case )-1}' )
if "bot_conv" in key:
_lowerCAmelCase = key.replace("""bot_conv""" , """0.convolution""" )
if "skip_conv1" in key:
_lowerCAmelCase = key.replace("""skip_conv1""" , """1.convolution""" )
if "skip_conv2" in key:
_lowerCAmelCase = key.replace("""skip_conv2""" , """2.convolution""" )
if "fusion1" in key:
_lowerCAmelCase = key.replace("""fusion1""" , """1.fusion""" )
if "fusion2" in key:
_lowerCAmelCase = key.replace("""fusion2""" , """2.fusion""" )
if "fusion3" in key:
_lowerCAmelCase = key.replace("""fusion3""" , """3.fusion""" )
if "fusion" in key and "conv" in key:
_lowerCAmelCase = key.replace("""conv""" , """convolutional_layer""" )
if key.startswith("""module.last_layer_depth""" ):
_lowerCAmelCase = key.replace("""module.last_layer_depth""" , """head.head""" )
_lowerCAmelCase = value
return new_state_dict
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
_lowerCAmelCase = state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight' )
_lowerCAmelCase = state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.bias' )
# next, add keys and values (in that order) to the state dict
_lowerCAmelCase = kv_weight[
: config.hidden_sizes[i], :
]
_lowerCAmelCase = kv_bias[: config.hidden_sizes[i]]
_lowerCAmelCase = kv_weight[
config.hidden_sizes[i] :, :
]
_lowerCAmelCase = kv_bias[config.hidden_sizes[i] :]
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_lowerCAmelCase = Image.open(requests.get(snake_case , stream=snake_case ).raw )
return image
@torch.no_grad()
def _UpperCAmelCase ( snake_case , snake_case , snake_case=False , snake_case=None ):
"""simple docstring"""
_lowerCAmelCase = GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12] , decoder_hidden_size=64 , depths=[3, 8, 27, 3] )
# load image processor (only resize + rescale)
_lowerCAmelCase = GLPNImageProcessor()
# prepare image
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(images=snake_case , return_tensors="""pt""" ).pixel_values
logger.info("""Converting model...""" )
# load original state dict
_lowerCAmelCase = torch.load(snake_case , map_location=torch.device("""cpu""" ) )
# rename keys
_lowerCAmelCase = rename_keys(snake_case )
# key and value matrices need special treatment
read_in_k_v(snake_case , snake_case )
# create HuggingFace model and load state dict
_lowerCAmelCase = GLPNForDepthEstimation(snake_case )
model.load_state_dict(snake_case )
model.eval()
# forward pass
_lowerCAmelCase = model(snake_case )
_lowerCAmelCase = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
_lowerCAmelCase = torch.tensor(
[[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]] )
elif "kitti" in model_name:
_lowerCAmelCase = torch.tensor(
[[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]] )
else:
raise ValueError(F'Unknown model name: {model_name}' )
_lowerCAmelCase = torch.Size([1, 4_80, 6_40] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3] , snake_case , atol=1E-4 )
print("""Looks ok!""" )
# finally, push to hub if required
if push_to_hub:
logger.info("""Pushing model and image processor to the hub...""" )
model.push_to_hub(
repo_path_or_name=Path(snake_case , snake_case ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=snake_case , )
image_processor.push_to_hub(
repo_path_or_name=Path(snake_case , snake_case ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=snake_case , )
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""",
default=None,
type=str,
help="""Path to the original PyTorch checkpoint (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
)
parser.add_argument(
"""--model_name""",
default="""glpn-kitti""",
type=str,
help="""Name of the model in case you're pushing to the hub.""",
)
A__ = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 82 | 1 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewTokensCriteria,
MaxTimeCriteria,
StoppingCriteriaList,
validate_stopping_criteria,
)
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = 3
_lowerCAmelCase = 250
_lowerCAmelCase = ids_tensor((batch_size, length) , _snake_case )
_lowerCAmelCase = torch.ones((batch_size, length) , device=_snake_case , dtype=torch.float ) / length
return input_ids, scores
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self._get_tensors(5 )
_lowerCAmelCase = StoppingCriteriaList(
[
MaxLengthCriteria(max_length=10 ),
MaxTimeCriteria(max_time=0.1 ),
] )
self.assertFalse(criteria(_snake_case , _snake_case ) )
_lowerCAmelCase , _lowerCAmelCase = self._get_tensors(9 )
self.assertFalse(criteria(_snake_case , _snake_case ) )
_lowerCAmelCase , _lowerCAmelCase = self._get_tensors(10 )
self.assertTrue(criteria(_snake_case , _snake_case ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = MaxLengthCriteria(max_length=10 )
_lowerCAmelCase , _lowerCAmelCase = self._get_tensors(5 )
self.assertFalse(criteria(_snake_case , _snake_case ) )
_lowerCAmelCase , _lowerCAmelCase = self._get_tensors(9 )
self.assertFalse(criteria(_snake_case , _snake_case ) )
_lowerCAmelCase , _lowerCAmelCase = self._get_tensors(10 )
self.assertTrue(criteria(_snake_case , _snake_case ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = MaxNewTokensCriteria(start_length=5 , max_new_tokens=5 )
_lowerCAmelCase , _lowerCAmelCase = self._get_tensors(5 )
self.assertFalse(criteria(_snake_case , _snake_case ) )
_lowerCAmelCase , _lowerCAmelCase = self._get_tensors(9 )
self.assertFalse(criteria(_snake_case , _snake_case ) )
_lowerCAmelCase , _lowerCAmelCase = self._get_tensors(10 )
self.assertTrue(criteria(_snake_case , _snake_case ) )
_lowerCAmelCase = StoppingCriteriaList([criteria] )
self.assertEqual(criteria_list.max_length , 10 )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self._get_tensors(5 )
_lowerCAmelCase = MaxTimeCriteria(max_time=0.1 )
self.assertFalse(criteria(_snake_case , _snake_case ) )
_lowerCAmelCase = MaxTimeCriteria(max_time=0.1 , initial_timestamp=time.time() - 0.2 )
self.assertTrue(criteria(_snake_case , _snake_case ) )
def snake_case ( self ):
"""simple docstring"""
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] ) , 10 )
with self.assertWarns(_snake_case ):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] ) , 11 )
_lowerCAmelCase = validate_stopping_criteria(StoppingCriteriaList() , 11 )
self.assertEqual(len(_snake_case ) , 1 )
| 82 |
from math import isqrt, loga
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , snake_case , snake_case ):
_lowerCAmelCase = False
return [i for i in range(2 , snake_case ) if is_prime[i]]
def _UpperCAmelCase ( snake_case = 80_08_00 , snake_case = 80_08_00 ):
"""simple docstring"""
_lowerCAmelCase = degree * loga(snake_case )
_lowerCAmelCase = int(snake_case )
_lowerCAmelCase = calculate_prime_numbers(snake_case )
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = len(snake_case ) - 1
while left < right:
while (
prime_numbers[right] * loga(prime_numbers[left] )
+ prime_numbers[left] * loga(prime_numbers[right] )
> upper_bound
):
right -= 1
hybrid_integers_count += right - left
left += 1
return hybrid_integers_count
if __name__ == "__main__":
print(f"{solution() = }")
| 82 | 1 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from transformers import (
BertTokenizer,
BlipConfig,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
)
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = """https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
_lowerCAmelCase = Image.open(requests.get(snake_case , stream=snake_case ).raw ).convert("""RGB""" )
_lowerCAmelCase = transforms.Compose(
[
transforms.Resize((image_size, image_size) , interpolation=InterpolationMode.BICUBIC ),
transforms.ToTensor(),
transforms.Normalize((0.48_145_466, 0.4_578_275, 0.40_821_073) , (0.26_862_954, 0.26_130_258, 0.27_577_711) ),
] )
_lowerCAmelCase = transform(snake_case ).unsqueeze(0 ).to(snake_case )
return image
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if "visual_encoder" in key:
_lowerCAmelCase = re.sub("""visual_encoder*""" , """vision_model.encoder""" , snake_case )
if "blocks" in key:
_lowerCAmelCase = re.sub(R"""blocks""" , """layers""" , snake_case )
if "attn" in key:
_lowerCAmelCase = re.sub(R"""attn""" , """self_attn""" , snake_case )
if "norm1" in key:
_lowerCAmelCase = re.sub(R"""norm1""" , """layer_norm1""" , snake_case )
if "norm2" in key:
_lowerCAmelCase = re.sub(R"""norm2""" , """layer_norm2""" , snake_case )
if "encoder.norm" in key:
_lowerCAmelCase = re.sub(R"""encoder.norm""" , """post_layernorm""" , snake_case )
if "encoder.patch_embed.proj" in key:
_lowerCAmelCase = re.sub(R"""encoder.patch_embed.proj""" , """embeddings.patch_embedding""" , snake_case )
if "encoder.pos_embed" in key:
_lowerCAmelCase = re.sub(R"""encoder.pos_embed""" , """embeddings.position_embedding""" , snake_case )
if "encoder.cls_token" in key:
_lowerCAmelCase = re.sub(R"""encoder.cls_token""" , """embeddings.class_embedding""" , snake_case )
if "self_attn" in key:
_lowerCAmelCase = re.sub(R"""self_attn.proj""" , """self_attn.projection""" , snake_case )
return key
@torch.no_grad()
def _UpperCAmelCase ( snake_case , snake_case=None ):
"""simple docstring"""
if config_path is not None:
_lowerCAmelCase = BlipConfig.from_pretrained(snake_case )
else:
_lowerCAmelCase = BlipConfig(projection_dim=5_12 , text_config={} , vision_config={} )
_lowerCAmelCase = BlipForConditionalGeneration(snake_case ).eval()
_lowerCAmelCase = """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"""
_lowerCAmelCase = blip_decoder(pretrained=snake_case , image_size=3_84 , vit="""base""" )
_lowerCAmelCase = pt_model.eval()
_lowerCAmelCase = pt_model.state_dict()
for key in modified_state_dict.copy():
_lowerCAmelCase = modified_state_dict.pop(snake_case )
_lowerCAmelCase = rename_key(snake_case )
_lowerCAmelCase = value
hf_model.load_state_dict(snake_case )
_lowerCAmelCase = 3_84
_lowerCAmelCase = load_demo_image(image_size=snake_case , device="""cpu""" )
_lowerCAmelCase = BertTokenizer.from_pretrained("""bert-base-uncased""" )
_lowerCAmelCase = tokenizer(["""a picture of"""] ).input_ids
_lowerCAmelCase = hf_model.generate(snake_case , snake_case )
assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]
_lowerCAmelCase = hf_model.generate(snake_case )
assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]
if pytorch_dump_folder_path is not None:
hf_model.save_pretrained(snake_case )
# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
_lowerCAmelCase = (
"""https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"""
)
_lowerCAmelCase = blip_vqa(pretrained=snake_case , image_size=snake_case , vit="""base""" )
vqa_model.eval()
_lowerCAmelCase = vqa_model.state_dict()
for key in modified_state_dict.copy():
_lowerCAmelCase = modified_state_dict.pop(snake_case )
_lowerCAmelCase = rename_key(snake_case )
_lowerCAmelCase = value
_lowerCAmelCase = BlipForQuestionAnswering(snake_case )
hf_vqa_model.load_state_dict(snake_case )
_lowerCAmelCase = ["""How many dogs are in this image?"""]
_lowerCAmelCase = tokenizer(snake_case , return_tensors="""pt""" ).input_ids
_lowerCAmelCase = hf_vqa_model.generate(snake_case , snake_case )
print(tokenizer.decode(answer[0] ) )
assert tokenizer.decode(answer[0] ) == "[UNK] 1 [SEP]"
if pytorch_dump_folder_path is not None:
hf_vqa_model.save_pretrained(pytorch_dump_folder_path + """_vqa""" )
_lowerCAmelCase = """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"""
_lowerCAmelCase = blip_itm(pretrained=snake_case , image_size=snake_case , vit="""base""" )
itm_model.eval()
_lowerCAmelCase = itm_model.state_dict()
for key in modified_state_dict.copy():
_lowerCAmelCase = modified_state_dict.pop(snake_case )
_lowerCAmelCase = rename_key(snake_case )
_lowerCAmelCase = value
_lowerCAmelCase = BlipForImageTextRetrieval(snake_case )
_lowerCAmelCase = ["""A picture of a woman with a dog sitting in a beach"""]
_lowerCAmelCase = tokenizer(
snake_case , return_tensors="""pt""" , padding="""max_length""" , truncation=snake_case , max_length=35 , ).input_ids
hf_itm_model.load_state_dict(snake_case )
hf_itm_model.eval()
_lowerCAmelCase = hf_itm_model(snake_case , snake_case , use_itm_head=snake_case )
_lowerCAmelCase = hf_itm_model(snake_case , snake_case , use_itm_head=snake_case )
assert out[0].item() == 0.2_110_687_494_277_954
assert torch.nn.functional.softmax(out_itm[0] , dim=1 )[:, 1].item() == 0.45_698_845_386_505_127
if pytorch_dump_folder_path is not None:
hf_itm_model.save_pretrained(pytorch_dump_folder_path + """_itm""" )
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
A__ = parser.parse_args()
convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 82 |
from __future__ import annotations
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = str(snake_case )
return n == n[::-1]
def _UpperCAmelCase ( snake_case = 1_00_00_00 ):
"""simple docstring"""
_lowerCAmelCase = 0
for i in range(1 , snake_case ):
if is_palindrome(snake_case ) and is_palindrome(bin(snake_case ).split("""b""" )[1] ):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| 82 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFast,
BlipImageProcessor,
GPTaTokenizer,
InstructBlipProcessor,
PreTrainedTokenizerFast,
)
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = tempfile.mkdtemp()
_lowerCAmelCase = BlipImageProcessor()
_lowerCAmelCase = GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model""" )
_lowerCAmelCase = BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
_lowerCAmelCase = InstructBlipProcessor(_snake_case , _snake_case , _snake_case )
processor.save_pretrained(self.tmpdirname )
def snake_case ( self , **_snake_case ):
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **_snake_case ).tokenizer
def snake_case ( self , **_snake_case ):
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **_snake_case ).image_processor
def snake_case ( self , **_snake_case ):
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **_snake_case ).qformer_tokenizer
def snake_case ( self ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
_lowerCAmelCase = [Image.fromarray(np.moveaxis(_snake_case , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = InstructBlipProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() , qformer_tokenizer=self.get_qformer_tokenizer() , )
processor.save_pretrained(self.tmpdirname )
_lowerCAmelCase = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
_lowerCAmelCase = self.get_image_processor(do_normalize=_snake_case , padding_value=1.0 )
_lowerCAmelCase = InstructBlipProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_snake_case , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _snake_case )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _snake_case )
self.assertIsInstance(processor.qformer_tokenizer , _snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_image_processor()
_lowerCAmelCase = self.get_tokenizer()
_lowerCAmelCase = self.get_qformer_tokenizer()
_lowerCAmelCase = InstructBlipProcessor(
tokenizer=_snake_case , image_processor=_snake_case , qformer_tokenizer=_snake_case )
_lowerCAmelCase = self.prepare_image_inputs()
_lowerCAmelCase = image_processor(_snake_case , return_tensors="""np""" )
_lowerCAmelCase = processor(images=_snake_case , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_image_processor()
_lowerCAmelCase = self.get_tokenizer()
_lowerCAmelCase = self.get_qformer_tokenizer()
_lowerCAmelCase = InstructBlipProcessor(
tokenizer=_snake_case , image_processor=_snake_case , qformer_tokenizer=_snake_case )
_lowerCAmelCase = """lower newer"""
_lowerCAmelCase = processor(text=_snake_case )
_lowerCAmelCase = tokenizer(_snake_case , return_token_type_ids=_snake_case )
_lowerCAmelCase = qformer_tokenizer(_snake_case , return_token_type_ids=_snake_case )
for key in encoded_tokens.keys():
self.assertListEqual(encoded_tokens[key] , encoded_processor[key] )
for key in encoded_tokens_qformer.keys():
self.assertListEqual(encoded_tokens_qformer[key] , encoded_processor["""qformer_""" + key] )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_image_processor()
_lowerCAmelCase = self.get_tokenizer()
_lowerCAmelCase = self.get_qformer_tokenizer()
_lowerCAmelCase = InstructBlipProcessor(
tokenizer=_snake_case , image_processor=_snake_case , qformer_tokenizer=_snake_case )
_lowerCAmelCase = """lower newer"""
_lowerCAmelCase = self.prepare_image_inputs()
_lowerCAmelCase = processor(text=_snake_case , images=_snake_case )
self.assertListEqual(
list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""] , )
# test if it raises when no input is passed
with pytest.raises(_snake_case ):
processor()
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_image_processor()
_lowerCAmelCase = self.get_tokenizer()
_lowerCAmelCase = self.get_qformer_tokenizer()
_lowerCAmelCase = InstructBlipProcessor(
tokenizer=_snake_case , image_processor=_snake_case , qformer_tokenizer=_snake_case )
_lowerCAmelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_lowerCAmelCase = processor.batch_decode(_snake_case )
_lowerCAmelCase = tokenizer.batch_decode(_snake_case )
self.assertListEqual(_snake_case , _snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_image_processor()
_lowerCAmelCase = self.get_tokenizer()
_lowerCAmelCase = self.get_qformer_tokenizer()
_lowerCAmelCase = InstructBlipProcessor(
tokenizer=_snake_case , image_processor=_snake_case , qformer_tokenizer=_snake_case )
_lowerCAmelCase = """lower newer"""
_lowerCAmelCase = self.prepare_image_inputs()
_lowerCAmelCase = processor(text=_snake_case , images=_snake_case )
self.assertListEqual(
list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""] , )
| 82 |
from collections.abc import Iterable
from typing import Generic, TypeVar
A__ = TypeVar("""_T""")
class __lowerCAmelCase ( Generic[_T] ):
def __init__( self , _snake_case = None ):
"""simple docstring"""
_lowerCAmelCase = list(iterable or [] )
_lowerCAmelCase = []
def __len__( self ):
"""simple docstring"""
return len(self._stacka ) + len(self._stacka )
def __repr__( self ):
"""simple docstring"""
return F'Queue({tuple(self._stacka[::-1] + self._stacka )})'
def snake_case ( self , _snake_case ):
"""simple docstring"""
self._stacka.append(_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self._stacka.pop
_lowerCAmelCase = self._stacka.append
if not self._stacka:
while self._stacka:
stacka_append(stacka_pop() )
if not self._stacka:
raise IndexError("""Queue is empty""" )
return self._stacka.pop()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 82 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A__ = {
"""configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
"""tokenization_biogpt""": ["""BioGptTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = [
"""BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BioGptForCausalLM""",
"""BioGptForTokenClassification""",
"""BioGptForSequenceClassification""",
"""BioGptModel""",
"""BioGptPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
from .tokenization_biogpt import BioGptTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_biogpt import (
BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptPreTrainedModel,
)
else:
import sys
A__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 82 |
A__ = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A__ = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = True
_lowerCAmelCase = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(snake_case , snake_case , snake_case )
order.append(snake_case )
return order
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = True
_lowerCAmelCase = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(snake_case , snake_case , snake_case )
return component
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = len(snake_case ) * [False]
_lowerCAmelCase = {vert: [] for vert in range(len(snake_case ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(snake_case )
_lowerCAmelCase = []
for i, was_visited in enumerate(snake_case ):
if not was_visited:
order += topology_sort(snake_case , snake_case , snake_case )
_lowerCAmelCase = []
_lowerCAmelCase = len(snake_case ) * [False]
for i in range(len(snake_case ) ):
_lowerCAmelCase = order[len(snake_case ) - i - 1]
if not visited[vert]:
_lowerCAmelCase = find_components(snake_case , snake_case , snake_case )
components_list.append(snake_case )
return components_list
| 82 | 1 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
A__ = logging.get_logger(__name__)
class __lowerCAmelCase ( lowerCamelCase__ ):
def __init__( self , *_snake_case , **_snake_case ):
"""simple docstring"""
warnings.warn(
"""The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use OwlViTImageProcessor instead.""" , _snake_case , )
super().__init__(*_snake_case , **_snake_case )
| 82 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader
from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
ROUGE_KEYS,
LegacySeqaSeqDataset,
SeqaSeqDataset,
assert_all_frozen,
calculate_bleu,
calculate_rouge,
check_output_dir,
flatten_list,
freeze_embeds,
freeze_params,
get_git_info,
label_smoothed_nll_loss,
lmap,
pickle_save,
save_git_info,
save_json,
use_task_specific_params,
)
# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train # noqa
A__ = logging.getLogger(__name__)
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''summarization'''
__lowerCamelCase = ['''loss''']
__lowerCamelCase = ROUGE_KEYS
__lowerCamelCase = '''rouge2'''
def __init__( self , _snake_case , **_snake_case ):
"""simple docstring"""
if hparams.sortish_sampler and hparams.gpus > 1:
_lowerCAmelCase = False
elif hparams.max_tokens_per_batch is not None:
if hparams.gpus > 1:
raise NotImplementedError("""Dynamic Batch size does not work for multi-gpu training""" )
if hparams.sortish_sampler:
raise ValueError("""--sortish_sampler and --max_tokens_per_batch may not be used simultaneously""" )
super().__init__(_snake_case , num_labels=_snake_case , mode=self.mode , **_snake_case )
use_task_specific_params(self.model , """summarization""" )
save_git_info(self.hparams.output_dir )
_lowerCAmelCase = Path(self.output_dir ) / """metrics.json"""
_lowerCAmelCase = Path(self.output_dir ) / """hparams.pkl"""
pickle_save(self.hparams , self.hparams_save_path )
_lowerCAmelCase = 0
_lowerCAmelCase = defaultdict(_snake_case )
_lowerCAmelCase = self.config.model_type
_lowerCAmelCase = self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size
_lowerCAmelCase = {
"data_dir": self.hparams.data_dir,
"max_source_length": self.hparams.max_source_length,
"prefix": self.model.config.prefix or "",
}
_lowerCAmelCase = {
"""train""": self.hparams.n_train,
"""val""": self.hparams.n_val,
"""test""": self.hparams.n_test,
}
_lowerCAmelCase = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
_lowerCAmelCase = {
"""train""": self.hparams.max_target_length,
"""val""": self.hparams.val_max_target_length,
"""test""": self.hparams.test_max_target_length,
}
assert self.target_lens["train"] <= self.target_lens["val"], F'target_lens: {self.target_lens}'
assert self.target_lens["train"] <= self.target_lens["test"], F'target_lens: {self.target_lens}'
if self.hparams.freeze_embeds:
freeze_embeds(self.model )
if self.hparams.freeze_encoder:
freeze_params(self.model.get_encoder() )
assert_all_frozen(self.model.get_encoder() )
_lowerCAmelCase = get_git_info()["""repo_sha"""]
_lowerCAmelCase = hparams.num_workers
_lowerCAmelCase = None # default to config
if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer , _snake_case ):
_lowerCAmelCase = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
_lowerCAmelCase = self.decoder_start_token_id
_lowerCAmelCase = (
SeqaSeqDataset if hasattr(self.tokenizer , """prepare_seq2seq_batch""" ) else LegacySeqaSeqDataset
)
_lowerCAmelCase = False
_lowerCAmelCase = self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
if self.hparams.eval_max_gen_length is not None:
_lowerCAmelCase = self.hparams.eval_max_gen_length
else:
_lowerCAmelCase = self.model.config.max_length
_lowerCAmelCase = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = {
k: self.tokenizer.batch_decode(v.tolist() ) if """mask""" not in k else v.shape for k, v in batch.items()
}
save_json(_snake_case , Path(self.output_dir ) / """text_batch.json""" )
save_json({k: v.tolist() for k, v in batch.items()} , Path(self.output_dir ) / """tok_batch.json""" )
_lowerCAmelCase = True
return readable_batch
def snake_case ( self , _snake_case , **_snake_case ):
"""simple docstring"""
return self.model(_snake_case , **_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.tokenizer.batch_decode(
_snake_case , skip_special_tokens=_snake_case , clean_up_tokenization_spaces=_snake_case )
return lmap(str.strip , _snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.tokenizer.pad_token_id
_lowerCAmelCase , _lowerCAmelCase = batch["""input_ids"""], batch["""attention_mask"""]
_lowerCAmelCase = batch["""labels"""]
if isinstance(self.model , _snake_case ):
_lowerCAmelCase = self.model._shift_right(_snake_case )
else:
_lowerCAmelCase = shift_tokens_right(_snake_case , _snake_case )
if not self.already_saved_batch: # This would be slightly better if it only happened on rank zero
_lowerCAmelCase = decoder_input_ids
self.save_readable_batch(_snake_case )
_lowerCAmelCase = self(_snake_case , attention_mask=_snake_case , decoder_input_ids=_snake_case , use_cache=_snake_case )
_lowerCAmelCase = outputs["""logits"""]
if self.hparams.label_smoothing == 0:
# Same behavior as modeling_bart.py, besides ignoring pad_token_id
_lowerCAmelCase = nn.CrossEntropyLoss(ignore_index=_snake_case )
assert lm_logits.shape[-1] == self.vocab_size
_lowerCAmelCase = ce_loss_fct(lm_logits.view(-1 , lm_logits.shape[-1] ) , tgt_ids.view(-1 ) )
else:
_lowerCAmelCase = nn.functional.log_softmax(_snake_case , dim=-1 )
_lowerCAmelCase , _lowerCAmelCase = label_smoothed_nll_loss(
_snake_case , _snake_case , self.hparams.label_smoothing , ignore_index=_snake_case )
return (loss,)
@property
def snake_case ( self ):
"""simple docstring"""
return self.tokenizer.pad_token_id
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self._step(_snake_case )
_lowerCAmelCase = dict(zip(self.loss_names , _snake_case ) )
# tokens per batch
_lowerCAmelCase = batch["""input_ids"""].ne(self.pad ).sum() + batch["""labels"""].ne(self.pad ).sum()
_lowerCAmelCase = batch["""input_ids"""].shape[0]
_lowerCAmelCase = batch["""input_ids"""].eq(self.pad ).sum()
_lowerCAmelCase = batch["""input_ids"""].eq(self.pad ).float().mean()
# TODO(SS): make a wandb summary metric for this
return {"loss": loss_tensors[0], "log": logs}
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
return self._generative_step(_snake_case )
def snake_case ( self , _snake_case , _snake_case="val" ):
"""simple docstring"""
self.step_count += 1
_lowerCAmelCase = {k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
_lowerCAmelCase = losses["""loss"""]
_lowerCAmelCase = {
k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
}
_lowerCAmelCase = (
generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
)
_lowerCAmelCase = torch.tensor(_snake_case ).type_as(_snake_case )
generative_metrics.update({k: v.item() for k, v in losses.items()} )
losses.update(_snake_case )
_lowerCAmelCase = {F'{prefix}_avg_{k}': x for k, x in losses.items()}
_lowerCAmelCase = self.step_count
self.metrics[prefix].append(_snake_case ) # callback writes this to self.metrics_save_path
_lowerCAmelCase = flatten_list([x["""preds"""] for x in outputs] )
return {
"log": all_metrics,
"preds": preds,
F'{prefix}_loss': loss,
F'{prefix}_{self.val_metric}': metric_tensor,
}
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
return calculate_rouge(_snake_case , _snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = time.time()
# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
_lowerCAmelCase = self.model.generate(
batch["""input_ids"""] , attention_mask=batch["""attention_mask"""] , use_cache=_snake_case , decoder_start_token_id=self.decoder_start_token_id , num_beams=self.eval_beams , max_length=self.eval_max_length , )
_lowerCAmelCase = (time.time() - ta) / batch["""input_ids"""].shape[0]
_lowerCAmelCase = self.ids_to_clean_text(_snake_case )
_lowerCAmelCase = self.ids_to_clean_text(batch["""labels"""] )
_lowerCAmelCase = self._step(_snake_case )
_lowerCAmelCase = dict(zip(self.loss_names , _snake_case ) )
_lowerCAmelCase = self.calc_generative_metrics(_snake_case , _snake_case )
_lowerCAmelCase = np.mean(lmap(_snake_case , _snake_case ) )
base_metrics.update(gen_time=_snake_case , gen_len=_snake_case , preds=_snake_case , target=_snake_case , **_snake_case )
return base_metrics
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
return self._generative_step(_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
return self.validation_epoch_end(_snake_case , prefix="""test""" )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.n_obs[type_path]
_lowerCAmelCase = self.target_lens[type_path]
_lowerCAmelCase = self.dataset_class(
self.tokenizer , type_path=_snake_case , n_obs=_snake_case , max_target_length=_snake_case , **self.dataset_kwargs , )
return dataset
def snake_case ( self , _snake_case , _snake_case , _snake_case = False ):
"""simple docstring"""
_lowerCAmelCase = self.get_dataset(_snake_case )
if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
_lowerCAmelCase = dataset.make_sortish_sampler(_snake_case , distributed=self.hparams.gpus > 1 )
return DataLoader(
_snake_case , batch_size=_snake_case , collate_fn=dataset.collate_fn , shuffle=_snake_case , num_workers=self.num_workers , sampler=_snake_case , )
elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
_lowerCAmelCase = dataset.make_dynamic_sampler(
self.hparams.max_tokens_per_batch , distributed=self.hparams.gpus > 1 )
return DataLoader(
_snake_case , batch_sampler=_snake_case , collate_fn=dataset.collate_fn , num_workers=self.num_workers , )
else:
return DataLoader(
_snake_case , batch_size=_snake_case , collate_fn=dataset.collate_fn , shuffle=_snake_case , num_workers=self.num_workers , sampler=_snake_case , )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_dataloader("""train""" , batch_size=self.hparams.train_batch_size , shuffle=_snake_case )
return dataloader
def snake_case ( self ):
"""simple docstring"""
return self.get_dataloader("""val""" , batch_size=self.hparams.eval_batch_size )
def snake_case ( self ):
"""simple docstring"""
return self.get_dataloader("""test""" , batch_size=self.hparams.eval_batch_size )
@staticmethod
def snake_case ( _snake_case , _snake_case ):
"""simple docstring"""
BaseTransformer.add_model_specific_args(_snake_case , _snake_case )
add_generic_args(_snake_case , _snake_case )
parser.add_argument(
"""--max_source_length""" , default=1024 , type=_snake_case , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--max_target_length""" , default=56 , type=_snake_case , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--val_max_target_length""" , default=142 , type=_snake_case , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--test_max_target_length""" , default=142 , type=_snake_case , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument("""--freeze_encoder""" , action="""store_true""" )
parser.add_argument("""--freeze_embeds""" , action="""store_true""" )
parser.add_argument("""--sortish_sampler""" , action="""store_true""" , default=_snake_case )
parser.add_argument("""--overwrite_output_dir""" , action="""store_true""" , default=_snake_case )
parser.add_argument("""--max_tokens_per_batch""" , type=_snake_case , default=_snake_case )
parser.add_argument("""--logger_name""" , type=_snake_case , choices=["""default""", """wandb""", """wandb_shared"""] , default="""default""" )
parser.add_argument("""--n_train""" , type=_snake_case , default=-1 , required=_snake_case , help="""# examples. -1 means use all.""" )
parser.add_argument("""--n_val""" , type=_snake_case , default=500 , required=_snake_case , help="""# examples. -1 means use all.""" )
parser.add_argument("""--n_test""" , type=_snake_case , default=-1 , required=_snake_case , help="""# examples. -1 means use all.""" )
parser.add_argument(
"""--task""" , type=_snake_case , default="""summarization""" , required=_snake_case , help="""# examples. -1 means use all.""" )
parser.add_argument("""--label_smoothing""" , type=_snake_case , default=0.0 , required=_snake_case )
parser.add_argument("""--src_lang""" , type=_snake_case , default="""""" , required=_snake_case )
parser.add_argument("""--tgt_lang""" , type=_snake_case , default="""""" , required=_snake_case )
parser.add_argument("""--eval_beams""" , type=_snake_case , default=_snake_case , required=_snake_case )
parser.add_argument(
"""--val_metric""" , type=_snake_case , default=_snake_case , required=_snake_case , choices=["""bleu""", """rouge2""", """loss""", None] )
parser.add_argument("""--eval_max_gen_length""" , type=_snake_case , default=_snake_case , help="""never generate more than n tokens""" )
parser.add_argument("""--save_top_k""" , type=_snake_case , default=1 , required=_snake_case , help="""How many checkpoints to save""" )
parser.add_argument(
"""--early_stopping_patience""" , type=_snake_case , default=-1 , required=_snake_case , help=(
"""-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"""
""" val_check_interval will effect it."""
) , )
return parser
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''translation'''
__lowerCamelCase = ['''loss''']
__lowerCamelCase = ['''bleu''']
__lowerCamelCase = '''bleu'''
def __init__( self , _snake_case , **_snake_case ):
"""simple docstring"""
super().__init__(_snake_case , **_snake_case )
_lowerCAmelCase = hparams.src_lang
_lowerCAmelCase = hparams.tgt_lang
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
return calculate_bleu(_snake_case , _snake_case )
def _UpperCAmelCase ( snake_case , snake_case=None ):
"""simple docstring"""
Path(args.output_dir ).mkdir(exist_ok=snake_case )
check_output_dir(snake_case , expected_items=3 )
if model is None:
if "summarization" in args.task:
_lowerCAmelCase = SummarizationModule(snake_case )
else:
_lowerCAmelCase = TranslationModule(snake_case )
_lowerCAmelCase = Path(args.data_dir ).name
if (
args.logger_name == "default"
or args.fast_dev_run
or str(args.output_dir ).startswith("""/tmp""" )
or str(args.output_dir ).startswith("""/var""" )
):
_lowerCAmelCase = True # don't pollute wandb logs unnecessarily
elif args.logger_name == "wandb":
from pytorch_lightning.loggers import WandbLogger
_lowerCAmelCase = os.environ.get("""WANDB_PROJECT""" , snake_case )
_lowerCAmelCase = WandbLogger(name=model.output_dir.name , project=snake_case )
elif args.logger_name == "wandb_shared":
from pytorch_lightning.loggers import WandbLogger
_lowerCAmelCase = WandbLogger(name=model.output_dir.name , project=F'hf_{dataset}' )
if args.early_stopping_patience >= 0:
_lowerCAmelCase = get_early_stopping_callback(model.val_metric , args.early_stopping_patience )
else:
_lowerCAmelCase = False
_lowerCAmelCase = args.val_metric == """loss"""
_lowerCAmelCase = generic_train(
snake_case , snake_case , logging_callback=SeqaSeqLoggingCallback() , checkpoint_callback=get_checkpoint_callback(
args.output_dir , model.val_metric , args.save_top_k , snake_case ) , early_stopping_callback=snake_case , logger=snake_case , )
pickle_save(model.hparams , model.output_dir / """hparams.pkl""" )
if not args.do_predict:
return model
_lowerCAmelCase = """"""
_lowerCAmelCase = sorted(glob.glob(os.path.join(args.output_dir , """*.ckpt""" ) , recursive=snake_case ) )
if checkpoints:
_lowerCAmelCase = checkpoints[-1]
_lowerCAmelCase = checkpoints[-1]
trainer.logger.log_hyperparams(model.hparams )
# test() without a model tests using the best checkpoint automatically
trainer.test()
return model
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
A__ = pl.Trainer.add_argparse_args(parser)
A__ = SummarizationModule.add_model_specific_args(parser, os.getcwd())
A__ = parser.parse_args()
main(args)
| 82 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@register_to_config
def __init__( self , _snake_case = 768 , ):
"""simple docstring"""
super().__init__()
_lowerCAmelCase = nn.Parameter(torch.zeros(1 , _snake_case ) )
_lowerCAmelCase = nn.Parameter(torch.ones(1 , _snake_case ) )
def snake_case ( self , _snake_case = None , _snake_case = None , ):
"""simple docstring"""
_lowerCAmelCase = nn.Parameter(self.mean.to(_snake_case ).to(_snake_case ) )
_lowerCAmelCase = nn.Parameter(self.std.to(_snake_case ).to(_snake_case ) )
return self
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = (embeds - self.mean) * 1.0 / self.std
return embeds
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = (embeds * self.std) + self.mean
return embeds
| 82 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if isinstance(snake_case , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class __lowerCAmelCase :
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
pass
def snake_case ( self ):
"""simple docstring"""
pass
def snake_case ( self ):
"""simple docstring"""
pass
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase = VisionTextDualEncoderConfig.from_vision_text_configs(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], config.projection_dim) )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_snake_case , text_model=_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = {"""vision_model""": vision_model, """text_model""": text_model}
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_snake_case , text_model=_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
_lowerCAmelCase = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
_lowerCAmelCase = after_output[0].numpy()
_lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_snake_case , 1e-5 )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_snake_case , text_model=_snake_case )
_lowerCAmelCase = model(
input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case , output_attentions=_snake_case )
_lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(_snake_case ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
_lowerCAmelCase = to_atuple(vision_model.config.image_size )
_lowerCAmelCase = to_atuple(vision_model.config.patch_size )
_lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
_lowerCAmelCase = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
_lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(_snake_case ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def snake_case ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = np.abs((a - b) ).max()
self.assertLessEqual(_snake_case , _snake_case , F'Difference between torch and flax is {diff} (>= {tol}).' )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_save_load(**_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**_snake_case )
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_pretrained_model_and_inputs()
_lowerCAmelCase = model_a(**_snake_case )
_lowerCAmelCase = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(_snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(_snake_case )
_lowerCAmelCase = model_a(**_snake_case )
_lowerCAmelCase = after_outputs[0].numpy()
_lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_snake_case , 1e-5 )
@require_tf
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-random-bert""" )
_lowerCAmelCase = 13
_lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_lowerCAmelCase = random_attention_mask([batch_size, 4] )
_lowerCAmelCase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = TFViTModel(_snake_case , name="""vision_model""" )
_lowerCAmelCase = TFBertModel(_snake_case , name="""text_model""" )
return vision_model, text_model
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFViTModelTester(self )
_lowerCAmelCase = TFBertModelTester(self )
_lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
_lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = vision_config_and_inputs
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-deit-tf""" , """hf-internal-testing/tiny-random-roberta""" )
_lowerCAmelCase = 13
_lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_lowerCAmelCase = random_attention_mask([batch_size, 4] )
_lowerCAmelCase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_snake_case , text_model=_snake_case )
_lowerCAmelCase = model(
input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case , output_attentions=_snake_case )
_lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(_snake_case ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
_lowerCAmelCase = to_atuple(vision_model.config.image_size )
_lowerCAmelCase = to_atuple(vision_model.config.patch_size )
_lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
_lowerCAmelCase = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
_lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(_snake_case ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = TFDeiTModel(_snake_case , name="""vision_model""" )
_lowerCAmelCase = TFRobertaModel(_snake_case , name="""text_model""" )
return vision_model, text_model
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFDeiTModelTester(self )
_lowerCAmelCase = TFRobertaModelTester(self )
_lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
_lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = vision_config_and_inputs
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-clip-tf""" , """hf-internal-testing/tiny-random-bert""" )
_lowerCAmelCase = 13
_lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_lowerCAmelCase = random_attention_mask([batch_size, 4] )
_lowerCAmelCase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = TFCLIPVisionModel(_snake_case , name="""vision_model""" )
_lowerCAmelCase = TFBertModel(_snake_case , name="""text_model""" )
return vision_model, text_model
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFCLIPVisionModelTester(self )
_lowerCAmelCase = TFBertModelTester(self )
_lowerCAmelCase = clip_model_tester.prepare_config_and_inputs()
_lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase = vision_config_and_inputs
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(
"""clip-italian/clip-italian""" , logit_scale_init_value=1.0 , from_pt=_snake_case )
_lowerCAmelCase = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
_lowerCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
_lowerCAmelCase = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=_snake_case , padding=_snake_case , return_tensors="""np""" )
_lowerCAmelCase = model(**_snake_case )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
_lowerCAmelCase = np.array([[1.228_4727, 0.310_4122]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , _snake_case , atol=1e-3 ) )
| 82 | 1 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class __lowerCAmelCase :
def __init__( self , _snake_case , _snake_case=sys.maxsize ):
"""simple docstring"""
_lowerCAmelCase = """bilinear"""
_lowerCAmelCase = max_size
_lowerCAmelCase = short_edge_length
def __call__( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = []
for img in imgs:
_lowerCAmelCase , _lowerCAmelCase = img.shape[:2]
# later: provide list and randomly choose index for resize
_lowerCAmelCase = np.random.randint(self.short_edge_length[0] , self.short_edge_length[1] + 1 )
if size == 0:
return img
_lowerCAmelCase = size * 1.0 / min(_snake_case , _snake_case )
if h < w:
_lowerCAmelCase , _lowerCAmelCase = size, scale * w
else:
_lowerCAmelCase , _lowerCAmelCase = scale * h, size
if max(_snake_case , _snake_case ) > self.max_size:
_lowerCAmelCase = self.max_size * 1.0 / max(_snake_case , _snake_case )
_lowerCAmelCase = newh * scale
_lowerCAmelCase = neww * scale
_lowerCAmelCase = int(neww + 0.5 )
_lowerCAmelCase = int(newh + 0.5 )
if img.dtype == np.uinta:
_lowerCAmelCase = Image.fromarray(_snake_case )
_lowerCAmelCase = pil_image.resize((neww, newh) , PILImageResampling.BILINEAR )
_lowerCAmelCase = np.asarray(_snake_case )
else:
_lowerCAmelCase = img.permute(2 , 0 , 1 ).unsqueeze(0 ) # 3, 0, 1) # hw(c) -> nchw
_lowerCAmelCase = nn.functional.interpolate(
_snake_case , (newh, neww) , mode=self.interp_method , align_corners=_snake_case ).squeeze(0 )
img_augs.append(_snake_case )
return img_augs
class __lowerCAmelCase :
def __init__( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] , cfg.INPUT.MAX_SIZE_TEST )
_lowerCAmelCase = cfg.INPUT.FORMAT
_lowerCAmelCase = cfg.SIZE_DIVISIBILITY
_lowerCAmelCase = cfg.PAD_VALUE
_lowerCAmelCase = cfg.INPUT.MAX_SIZE_TEST
_lowerCAmelCase = cfg.MODEL.DEVICE
_lowerCAmelCase = torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
_lowerCAmelCase = torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
_lowerCAmelCase = lambda _snake_case : (x - self.pixel_mean) / self.pixel_std
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = tuple(max(_snake_case ) for s in zip(*[img.shape for img in images] ) )
_lowerCAmelCase = [im.shape[-2:] for im in images]
_lowerCAmelCase = [
nn.functional.pad(
_snake_case , [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] , value=self.pad_value , )
for size, im in zip(_snake_case , _snake_case )
]
return torch.stack(_snake_case ), torch.tensor(_snake_case )
def __call__( self , _snake_case , _snake_case=False ):
"""simple docstring"""
with torch.no_grad():
if not isinstance(_snake_case , _snake_case ):
_lowerCAmelCase = [images]
if single_image:
assert len(_snake_case ) == 1
for i in range(len(_snake_case ) ):
if isinstance(images[i] , torch.Tensor ):
images.insert(_snake_case , images.pop(_snake_case ).to(self.device ).float() )
elif not isinstance(images[i] , torch.Tensor ):
images.insert(
_snake_case , torch.as_tensor(img_tensorize(images.pop(_snake_case ) , input_format=self.input_format ) )
.to(self.device )
.float() , )
# resize smallest edge
_lowerCAmelCase = torch.tensor([im.shape[:2] for im in images] )
_lowerCAmelCase = self.aug(_snake_case )
# transpose images and convert to torch tensors
# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
# now normalize before pad to avoid useless arithmetic
_lowerCAmelCase = [self.normalizer(_snake_case ) for x in images]
# now pad them to do the following operations
_lowerCAmelCase , _lowerCAmelCase = self.pad(_snake_case )
# Normalize
if self.size_divisibility > 0:
raise NotImplementedError()
# pad
_lowerCAmelCase = torch.true_divide(_snake_case , _snake_case )
if single_image:
return images[0], sizes[0], scales_yx[0]
else:
return images, sizes, scales_yx
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
boxes[:, 0::2] *= scale_yx[:, 1]
boxes[:, 1::2] *= scale_yx[:, 0]
return boxes
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
assert torch.isfinite(snake_case ).all(), "Box tensor contains infinite or NaN!"
_lowerCAmelCase , _lowerCAmelCase = box_size
tensor[:, 0].clamp_(min=0 , max=snake_case )
tensor[:, 1].clamp_(min=0 , max=snake_case )
tensor[:, 2].clamp_(min=0 , max=snake_case )
tensor[:, 3].clamp_(min=0 , max=snake_case )
| 82 |
def _UpperCAmelCase ( snake_case = 50 ):
"""simple docstring"""
_lowerCAmelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(f"{solution() = }")
| 82 | 1 |
from __future__ import annotations
class __lowerCAmelCase :
def __init__( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = data
_lowerCAmelCase = None
_lowerCAmelCase = None
def _UpperCAmelCase ( snake_case ): # In Order traversal of the tree
"""simple docstring"""
if tree:
display(tree.left )
print(tree.data )
display(tree.right )
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
return 1 + max(depth_of_tree(tree.left ) , depth_of_tree(tree.right ) ) if tree else 0
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
else:
return not tree.left and not tree.right
def _UpperCAmelCase ( ): # Main function for testing.
"""simple docstring"""
_lowerCAmelCase = Node(1 )
_lowerCAmelCase = Node(2 )
_lowerCAmelCase = Node(3 )
_lowerCAmelCase = Node(4 )
_lowerCAmelCase = Node(5 )
_lowerCAmelCase = Node(6 )
_lowerCAmelCase = Node(7 )
_lowerCAmelCase = Node(8 )
_lowerCAmelCase = Node(9 )
print(is_full_binary_tree(snake_case ) )
print(depth_of_tree(snake_case ) )
print("""Tree is: """ )
display(snake_case )
if __name__ == "__main__":
main()
| 82 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class __lowerCAmelCase ( unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = {
"""task_specific_params""": {
"""summarization""": {"""length_penalty""": 1.0, """max_length""": 128, """min_length""": 12, """num_beams""": 4},
"""summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 142, """min_length""": 56, """num_beams""": 4},
"""summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
}
}
_lowerCAmelCase = {
"""task_specific_params.summarization.length_penalty""": 1.0,
"""task_specific_params.summarization.max_length""": 128,
"""task_specific_params.summarization.min_length""": 12,
"""task_specific_params.summarization.num_beams""": 4,
"""task_specific_params.summarization_cnn.length_penalty""": 2.0,
"""task_specific_params.summarization_cnn.max_length""": 142,
"""task_specific_params.summarization_cnn.min_length""": 56,
"""task_specific_params.summarization_cnn.num_beams""": 4,
"""task_specific_params.summarization_xsum.length_penalty""": 1.0,
"""task_specific_params.summarization_xsum.max_length""": 62,
"""task_specific_params.summarization_xsum.min_length""": 11,
"""task_specific_params.summarization_xsum.num_beams""": 6,
}
self.assertEqual(flatten_dict(_snake_case ) , _snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(_snake_case ) , x.transpose() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case ) , transpose(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , transpose(_snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case ) , transpose(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , transpose(_snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case ) , np.asarray(transpose(_snake_case ) ) ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , np.asarray(transpose(_snake_case , axes=(1, 2, 0) ) ) ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , np.reshape(_snake_case , (4, 3) ) ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , np.reshape(_snake_case , (12, 5) ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , reshape(_snake_case , (4, 3) ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , reshape(_snake_case , (12, 5) ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , reshape(_snake_case , (4, 3) ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , reshape(_snake_case , (12, 5) ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , np.asarray(reshape(_snake_case , (4, 3) ) ) ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , np.asarray(reshape(_snake_case , (12, 5) ) ) ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(_snake_case ) , np.squeeze(_snake_case ) ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , np.squeeze(_snake_case , axis=2 ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case ) , squeeze(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , squeeze(_snake_case , axis=2 ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case ) , squeeze(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , squeeze(_snake_case , axis=2 ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case ) , np.asarray(squeeze(_snake_case ) ) ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , np.asarray(squeeze(_snake_case , axis=2 ) ) ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , np.expand_dims(_snake_case , axis=1 ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , expand_dims(_snake_case , axis=1 ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , expand_dims(_snake_case , axis=1 ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , np.asarray(expand_dims(_snake_case , axis=1 ) ) ) )
| 82 | 1 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
A__ = logging.get_logger(__name__)
class __lowerCAmelCase ( lowerCamelCase__ ):
def __init__( self , **_snake_case ):
"""simple docstring"""
requires_backends(self , ["""bs4"""] )
super().__init__(**_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = element if element.name else element.parent
for parent in child.parents: # type: bs4.element.Tag
_lowerCAmelCase = parent.find_all(child.name , recursive=_snake_case )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(_snake_case ) else next(i for i, s in enumerate(_snake_case , 1 ) if s is child ) )
_lowerCAmelCase = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = BeautifulSoup(_snake_case , """html.parser""" )
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = []
for element in html_code.descendants:
if type(_snake_case ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
_lowerCAmelCase = html.unescape(_snake_case ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(_snake_case )
_lowerCAmelCase , _lowerCAmelCase = self.xpath_soup(_snake_case )
stringaxtag_seq.append(_snake_case )
stringaxsubs_seq.append(_snake_case )
if len(_snake_case ) != len(_snake_case ):
raise ValueError("""Number of doc strings and xtags does not correspond""" )
if len(_snake_case ) != len(_snake_case ):
raise ValueError("""Number of doc strings and xsubs does not correspond""" )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = """"""
for tagname, subs in zip(_snake_case , _snake_case ):
xpath += F'/{tagname}'
if subs != 0:
xpath += F'[{subs}]'
return xpath
def __call__( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = False
# Check that strings has a valid type
if isinstance(_snake_case , _snake_case ):
_lowerCAmelCase = True
elif isinstance(_snake_case , (list, tuple) ):
if len(_snake_case ) == 0 or isinstance(html_strings[0] , _snake_case ):
_lowerCAmelCase = True
if not valid_strings:
raise ValueError(
"""HTML strings must of type `str`, `List[str]` (batch of examples), """
F'but is of type {type(_snake_case )}.' )
_lowerCAmelCase = bool(isinstance(_snake_case , (list, tuple) ) and (isinstance(html_strings[0] , _snake_case )) )
if not is_batched:
_lowerCAmelCase = [html_strings]
# Get nodes + xpaths
_lowerCAmelCase = []
_lowerCAmelCase = []
for html_string in html_strings:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.get_three_from_single(_snake_case )
nodes.append(_snake_case )
_lowerCAmelCase = []
for node, tag_list, sub_list in zip(_snake_case , _snake_case , _snake_case ):
_lowerCAmelCase = self.construct_xpath(_snake_case , _snake_case )
xpath_strings.append(_snake_case )
xpaths.append(_snake_case )
# return as Dict
_lowerCAmelCase = {"""nodes""": nodes, """xpaths""": xpaths}
_lowerCAmelCase = BatchFeature(data=_snake_case , tensor_type=_snake_case )
return encoded_inputs
| 82 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class __lowerCAmelCase ( lowerCamelCase__ ):
@staticmethod
def snake_case ( _snake_case ):
"""simple docstring"""
_lowerCAmelCase = parser.add_parser("""download""" )
download_parser.add_argument(
"""--cache-dir""" , type=_snake_case , default=_snake_case , help="""Path to location to store the models""" )
download_parser.add_argument(
"""--force""" , action="""store_true""" , help="""Force the model to be download even if already in cache-dir""" )
download_parser.add_argument(
"""--trust-remote-code""" , action="""store_true""" , help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine""" , )
download_parser.add_argument("""model""" , type=_snake_case , help="""Name of the model to download""" )
download_parser.set_defaults(func=_snake_case )
def __init__( self , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = model
_lowerCAmelCase = cache
_lowerCAmelCase = force
_lowerCAmelCase = trust_remote_code
def snake_case ( self ):
"""simple docstring"""
from ..models.auto import AutoModel, AutoTokenizer
AutoModel.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
AutoTokenizer.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
| 82 | 1 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
A__ = {"""UserAgent""": UserAgent().random}
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = script.contents[0]
_lowerCAmelCase = json.loads(data[data.find("""{\"config\"""" ) : -1] )
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class __lowerCAmelCase :
def __init__( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = F'https://www.instagram.com/{username}/'
_lowerCAmelCase = self.get_json()
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = requests.get(self.url , headers=_snake_case ).text
_lowerCAmelCase = BeautifulSoup(_snake_case , """html.parser""" ).find_all("""script""" )
try:
return extract_user_profile(scripts[4] )
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3] )
def __repr__( self ):
"""simple docstring"""
return F'{self.__class__.__name__}(\'{self.username}\')'
def __str__( self ):
"""simple docstring"""
return F'{self.fullname} ({self.username}) is {self.biography}'
@property
def snake_case ( self ):
"""simple docstring"""
return self.user_data["username"]
@property
def snake_case ( self ):
"""simple docstring"""
return self.user_data["full_name"]
@property
def snake_case ( self ):
"""simple docstring"""
return self.user_data["biography"]
@property
def snake_case ( self ):
"""simple docstring"""
return self.user_data["business_email"]
@property
def snake_case ( self ):
"""simple docstring"""
return self.user_data["external_url"]
@property
def snake_case ( self ):
"""simple docstring"""
return self.user_data["edge_followed_by"]["count"]
@property
def snake_case ( self ):
"""simple docstring"""
return self.user_data["edge_follow"]["count"]
@property
def snake_case ( self ):
"""simple docstring"""
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def snake_case ( self ):
"""simple docstring"""
return self.user_data["profile_pic_url_hd"]
@property
def snake_case ( self ):
"""simple docstring"""
return self.user_data["is_verified"]
@property
def snake_case ( self ):
"""simple docstring"""
return self.user_data["is_private"]
def _UpperCAmelCase ( snake_case = "github" ):
"""simple docstring"""
import os
if os.environ.get("""CI""" ):
return # test failing on GitHub Actions
_lowerCAmelCase = InstagramUser(snake_case )
assert instagram_user.user_data
assert isinstance(instagram_user.user_data , snake_case )
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 1_50
assert instagram_user.number_of_followers > 12_00_00
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "support@github.com"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith("""https://instagram.""" )
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
A__ = InstagramUser("""github""")
print(instagram_user)
print(f"{instagram_user.number_of_posts = }")
print(f"{instagram_user.number_of_followers = }")
print(f"{instagram_user.number_of_followings = }")
print(f"{instagram_user.email = }")
print(f"{instagram_user.website = }")
print(f"{instagram_user.profile_picture_url = }")
print(f"{instagram_user.is_verified = }")
print(f"{instagram_user.is_private = }")
| 82 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = XCLIPTextConfig()
# derive patch size from model name
_lowerCAmelCase = model_name.find("""patch""" )
_lowerCAmelCase = int(model_name[start_idx + len("""patch""" ) : start_idx + len("""patch""" ) + 2] )
_lowerCAmelCase = XCLIPVisionConfig(patch_size=snake_case , num_frames=snake_case )
if "large" in model_name:
_lowerCAmelCase = 7_68
_lowerCAmelCase = 30_72
_lowerCAmelCase = 12
_lowerCAmelCase = 10_24
_lowerCAmelCase = 40_96
_lowerCAmelCase = 16
_lowerCAmelCase = 24
_lowerCAmelCase = 7_68
_lowerCAmelCase = 30_72
if model_name == "xclip-large-patch14-16-frames":
_lowerCAmelCase = 3_36
_lowerCAmelCase = XCLIPConfig.from_text_vision_configs(snake_case , snake_case )
if "large" in model_name:
_lowerCAmelCase = 7_68
return config
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if name == "token_embedding.weight":
_lowerCAmelCase = name.replace("""token_embedding.weight""" , """text_model.embeddings.token_embedding.weight""" )
if name == "positional_embedding":
_lowerCAmelCase = name.replace("""positional_embedding""" , """text_model.embeddings.position_embedding.weight""" )
if "ln_1" in name:
_lowerCAmelCase = name.replace("""ln_1""" , """layer_norm1""" )
if "ln_2" in name:
_lowerCAmelCase = name.replace("""ln_2""" , """layer_norm2""" )
if "c_fc" in name:
_lowerCAmelCase = name.replace("""c_fc""" , """fc1""" )
if "c_proj" in name:
_lowerCAmelCase = name.replace("""c_proj""" , """fc2""" )
if name.startswith("""transformer.resblocks""" ):
_lowerCAmelCase = name.replace("""transformer.resblocks""" , """text_model.encoder.layers""" )
if "attn.out_proj" in name and "message" not in name:
_lowerCAmelCase = name.replace("""attn.out_proj""" , """self_attn.out_proj""" )
if "ln_final" in name:
_lowerCAmelCase = name.replace("""ln_final""" , """text_model.final_layer_norm""" )
# visual encoder
if name == "visual.class_embedding":
_lowerCAmelCase = name.replace("""visual.class_embedding""" , """vision_model.embeddings.class_embedding""" )
if name == "visual.positional_embedding":
_lowerCAmelCase = name.replace("""visual.positional_embedding""" , """vision_model.embeddings.position_embedding.weight""" )
if name.startswith("""visual.transformer.resblocks""" ):
_lowerCAmelCase = name.replace("""visual.transformer.resblocks""" , """vision_model.encoder.layers""" )
if "visual.conv1" in name:
_lowerCAmelCase = name.replace("""visual.conv1""" , """vision_model.embeddings.patch_embedding""" )
if "visual.ln_pre" in name:
_lowerCAmelCase = name.replace("""visual.ln_pre""" , """vision_model.pre_layernorm""" )
if "visual.ln_post" in name:
_lowerCAmelCase = name.replace("""visual.ln_post""" , """vision_model.post_layernorm""" )
if "visual.proj" in name:
_lowerCAmelCase = name.replace("""visual.proj""" , """visual_projection.weight""" )
if "text_projection" in name:
_lowerCAmelCase = name.replace("""text_projection""" , """text_projection.weight""" )
# things on top
if "prompts_visual_proj" in name:
_lowerCAmelCase = name.replace("""prompts_visual_proj""" , """prompts_visual_projection""" )
if "prompts_visual_ln" in name:
_lowerCAmelCase = name.replace("""prompts_visual_ln""" , """prompts_visual_layernorm""" )
# mit
if name == "mit.positional_embedding":
_lowerCAmelCase = name.replace("""positional""" , """position""" )
if name.startswith("""mit.resblocks""" ):
_lowerCAmelCase = name.replace("""mit.resblocks""" , """mit.encoder.layers""" )
# prompts generator
if name.startswith("""prompts_generator.norm""" ):
_lowerCAmelCase = name.replace("""prompts_generator.norm""" , """prompts_generator.layernorm""" )
return name
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_lowerCAmelCase = orig_state_dict.pop(snake_case )
if "attn.in_proj" in key:
_lowerCAmelCase = key.split(""".""" )
if key.startswith("""visual""" ):
_lowerCAmelCase = key_split[3]
_lowerCAmelCase = config.vision_config.hidden_size
if "message_attn" in key:
if "weight" in key:
_lowerCAmelCase = val[
:dim, :
]
_lowerCAmelCase = val[
dim : dim * 2, :
]
_lowerCAmelCase = val[
-dim:, :
]
else:
_lowerCAmelCase = val[
:dim
]
_lowerCAmelCase = val[
dim : dim * 2
]
_lowerCAmelCase = val[
-dim:
]
else:
if "weight" in key:
_lowerCAmelCase = val[
:dim, :
]
_lowerCAmelCase = val[
dim : dim * 2, :
]
_lowerCAmelCase = val[
-dim:, :
]
else:
_lowerCAmelCase = val[:dim]
_lowerCAmelCase = val[
dim : dim * 2
]
_lowerCAmelCase = val[-dim:]
elif key.startswith("""mit""" ):
_lowerCAmelCase = key_split[2]
_lowerCAmelCase = config.vision_config.mit_hidden_size
if "weight" in key:
_lowerCAmelCase = val[:dim, :]
_lowerCAmelCase = val[dim : dim * 2, :]
_lowerCAmelCase = val[-dim:, :]
else:
_lowerCAmelCase = val[:dim]
_lowerCAmelCase = val[dim : dim * 2]
_lowerCAmelCase = val[-dim:]
else:
_lowerCAmelCase = key_split[2]
_lowerCAmelCase = config.text_config.hidden_size
if "weight" in key:
_lowerCAmelCase = val[:dim, :]
_lowerCAmelCase = val[
dim : dim * 2, :
]
_lowerCAmelCase = val[-dim:, :]
else:
_lowerCAmelCase = val[:dim]
_lowerCAmelCase = val[
dim : dim * 2
]
_lowerCAmelCase = val[-dim:]
else:
_lowerCAmelCase = rename_key(snake_case )
if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
_lowerCAmelCase = val.T
_lowerCAmelCase = val
return orig_state_dict
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if num_frames == 8:
_lowerCAmelCase = """eating_spaghetti_8_frames.npy"""
elif num_frames == 16:
_lowerCAmelCase = """eating_spaghetti.npy"""
elif num_frames == 32:
_lowerCAmelCase = """eating_spaghetti_32_frames.npy"""
_lowerCAmelCase = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename=snake_case , repo_type="""dataset""" , )
_lowerCAmelCase = np.load(snake_case )
return list(snake_case )
def _UpperCAmelCase ( snake_case , snake_case=None , snake_case=False ):
"""simple docstring"""
_lowerCAmelCase = {
# fully supervised kinetics-400 checkpoints
"""xclip-base-patch32""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth""",
"""xclip-base-patch32-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"""
),
"""xclip-base-patch16""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth""",
"""xclip-base-patch16-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"""
),
"""xclip-large-patch14""": """https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&export=download&confirm=t&uuid=b26caedc-88e2-473e-830a-9d158b653cdb""",
"""xclip-large-patch14-16-frames""": """https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&export=download&confirm=t&uuid=538fa810-e671-4050-b385-9a623f89804f""",
# fully supervised kinetics-600 checkpoints
"""xclip-base-patch16-kinetics-600""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"""
),
"""xclip-base-patch16-kinetics-600-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"""
),
"""xclip-large-patch14-kinetics-600""": """https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&export=download&confirm=t&uuid=141d4977-4a65-44ae-864f-4b0c19f838be""",
# few shot
"""xclip-base-patch16-hmdb-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"""
),
"""xclip-base-patch16-hmdb-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"""
),
"""xclip-base-patch16-hmdb-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"""
),
"""xclip-base-patch16-hmdb-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"""
),
"""xclip-base-patch16-ucf-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"""
),
"""xclip-base-patch16-ucf-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"""
),
"""xclip-base-patch16-ucf-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"""
),
"""xclip-base-patch16-ucf-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"""
),
# zero shot
"""xclip-base-patch16-zero-shot""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth""",
}
_lowerCAmelCase = model_to_url[model_name]
_lowerCAmelCase = 8
if "16-frames" in model_name:
_lowerCAmelCase = 16
elif "shot" in model_name:
_lowerCAmelCase = 32
_lowerCAmelCase = get_xclip_config(snake_case , snake_case )
_lowerCAmelCase = XCLIPModel(snake_case )
model.eval()
if "drive" in checkpoint_url:
_lowerCAmelCase = """pytorch_model.bin"""
gdown.cached_download(snake_case , snake_case , quiet=snake_case )
_lowerCAmelCase = torch.load(snake_case , map_location="""cpu""" )["""model"""]
else:
_lowerCAmelCase = torch.hub.load_state_dict_from_url(snake_case )["""model"""]
_lowerCAmelCase = convert_state_dict(snake_case , snake_case )
_lowerCAmelCase = XCLIPModel(snake_case )
_lowerCAmelCase , _lowerCAmelCase = model.load_state_dict(snake_case , strict=snake_case )
assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
model.eval()
_lowerCAmelCase = 3_36 if model_name == """xclip-large-patch14-16-frames""" else 2_24
_lowerCAmelCase = VideoMAEImageProcessor(size=snake_case )
_lowerCAmelCase = CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32""" )
_lowerCAmelCase = CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32""" )
_lowerCAmelCase = XCLIPProcessor(image_processor=snake_case , tokenizer=snake_case )
_lowerCAmelCase = prepare_video(snake_case )
_lowerCAmelCase = processor(
text=["""playing sports""", """eating spaghetti""", """go shopping"""] , videos=snake_case , return_tensors="""pt""" , padding=snake_case )
print("""Shape of pixel values:""" , inputs.pixel_values.shape )
with torch.no_grad():
_lowerCAmelCase = model(**snake_case )
# Verify outputs
_lowerCAmelCase = outputs.logits_per_video
_lowerCAmelCase = logits_per_video.softmax(dim=1 )
print("""Probs:""" , snake_case )
# kinetics-400
if model_name == "xclip-base-patch32":
_lowerCAmelCase = torch.tensor([[0.0_019, 0.9_951, 0.0_030]] )
elif model_name == "xclip-base-patch32-16-frames":
_lowerCAmelCase = torch.tensor([[7.09_99E-04, 9.98_83E-01, 4.55_80E-04]] )
elif model_name == "xclip-base-patch16":
_lowerCAmelCase = torch.tensor([[0.0_083, 0.9_681, 0.0_236]] )
elif model_name == "xclip-base-patch16-16-frames":
_lowerCAmelCase = torch.tensor([[7.69_37E-04, 9.97_28E-01, 1.94_73E-03]] )
elif model_name == "xclip-large-patch14":
_lowerCAmelCase = torch.tensor([[0.0_062, 0.9_864, 0.0_075]] )
elif model_name == "xclip-large-patch14-16-frames":
_lowerCAmelCase = torch.tensor([[3.38_77E-04, 9.99_37E-01, 2.88_88E-04]] )
# kinetics-600
elif model_name == "xclip-base-patch16-kinetics-600":
_lowerCAmelCase = torch.tensor([[0.0_555, 0.8_914, 0.0_531]] )
elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
_lowerCAmelCase = torch.tensor([[3.85_54E-04, 9.99_29E-01, 3.27_54E-04]] )
elif model_name == "xclip-large-patch14-kinetics-600":
_lowerCAmelCase = torch.tensor([[0.0_036, 0.9_920, 0.0_045]] )
# few shot
elif model_name == "xclip-base-patch16-hmdb-2-shot":
_lowerCAmelCase = torch.tensor([[7.18_90E-06, 9.99_94E-01, 5.65_59E-05]] )
elif model_name == "xclip-base-patch16-hmdb-4-shot":
_lowerCAmelCase = torch.tensor([[1.03_20E-05, 9.99_93E-01, 6.24_35E-05]] )
elif model_name == "xclip-base-patch16-hmdb-8-shot":
_lowerCAmelCase = torch.tensor([[4.13_77E-06, 9.99_90E-01, 9.83_86E-05]] )
elif model_name == "xclip-base-patch16-hmdb-16-shot":
_lowerCAmelCase = torch.tensor([[4.13_47E-05, 9.99_62E-01, 3.34_11E-04]] )
elif model_name == "xclip-base-patch16-ucf-2-shot":
_lowerCAmelCase = torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]] )
elif model_name == "xclip-base-patch16-ucf-4-shot":
_lowerCAmelCase = torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]] )
elif model_name == "xclip-base-patch16-ucf-8-shot":
_lowerCAmelCase = torch.tensor([[0.0_027, 0.9_904, 0.0_070]] )
elif model_name == "xclip-base-patch16-ucf-16-shot":
_lowerCAmelCase = torch.tensor([[9.82_19E-04, 9.95_93E-01, 3.08_63E-03]] )
# zero shot
elif model_name == "xclip-base-patch16-zero-shot":
_lowerCAmelCase = torch.tensor([[3.50_82E-04, 9.97_85E-01, 1.79_66E-03]] )
else:
raise ValueError(F'Model name {model_name} not supported' )
assert torch.allclose(snake_case , snake_case , atol=1E-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(snake_case )
if push_to_hub:
print("""Pushing model, processor and slow tokenizer files to the hub...""" )
model.push_to_hub(snake_case , organization="""nielsr""" )
processor.push_to_hub(snake_case , organization="""nielsr""" )
slow_tokenizer.push_to_hub(snake_case , organization="""nielsr""" )
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""xclip-base-patch32""",
type=str,
help="""Name of the model.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
A__ = parser.parse_args()
convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 82 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBirdPegasusConfig""",
"""BigBirdPegasusOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = [
"""BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BigBirdPegasusForCausalLM""",
"""BigBirdPegasusForConditionalGeneration""",
"""BigBirdPegasusForQuestionAnswering""",
"""BigBirdPegasusForSequenceClassification""",
"""BigBirdPegasusModel""",
"""BigBirdPegasusPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
BigBirdPegasusConfig,
BigBirdPegasusOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
BigBirdPegasusForCausalLM,
BigBirdPegasusForConditionalGeneration,
BigBirdPegasusForQuestionAnswering,
BigBirdPegasusForSequenceClassification,
BigBirdPegasusModel,
BigBirdPegasusPreTrainedModel,
)
else:
import sys
A__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 82 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@register_to_config
def __init__( self , _snake_case = 768 , ):
"""simple docstring"""
super().__init__()
_lowerCAmelCase = nn.Parameter(torch.zeros(1 , _snake_case ) )
_lowerCAmelCase = nn.Parameter(torch.ones(1 , _snake_case ) )
def snake_case ( self , _snake_case = None , _snake_case = None , ):
"""simple docstring"""
_lowerCAmelCase = nn.Parameter(self.mean.to(_snake_case ).to(_snake_case ) )
_lowerCAmelCase = nn.Parameter(self.std.to(_snake_case ).to(_snake_case ) )
return self
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = (embeds - self.mean) * 1.0 / self.std
return embeds
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = (embeds * self.std) + self.mean
return embeds
| 82 | 1 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = (DPMSolverSDEScheduler,)
__lowerCamelCase = 10
def snake_case ( self , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase = {
"""num_train_timesteps""": 1100,
"""beta_start""": 0.0001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""noise_sampler_seed""": 0,
}
config.update(**_snake_case )
return config
def snake_case ( self ):
"""simple docstring"""
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=_snake_case )
def snake_case ( self ):
"""simple docstring"""
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=_snake_case , beta_end=_snake_case )
def snake_case ( self ):
"""simple docstring"""
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=_snake_case )
def snake_case ( self ):
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_snake_case )
scheduler.set_timesteps(self.num_inference_steps )
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
_lowerCAmelCase = sample.to(_snake_case )
for i, t in enumerate(scheduler.timesteps ):
_lowerCAmelCase = scheduler.scale_model_input(_snake_case , _snake_case )
_lowerCAmelCase = model(_snake_case , _snake_case )
_lowerCAmelCase = scheduler.step(_snake_case , _snake_case , _snake_case )
_lowerCAmelCase = output.prev_sample
_lowerCAmelCase = torch.sum(torch.abs(_snake_case ) )
_lowerCAmelCase = torch.mean(torch.abs(_snake_case ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.47_8210_4492_1875 ) < 1e-2
assert abs(result_mean.item() - 0.2178_7059_6456_5277 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_3521_1181_6406 ) < 1e-2
assert abs(result_mean.item() - 0.2_2342_9068_9229_9652 ) < 1e-3
else:
assert abs(result_sum.item() - 162.52_3834_2285_1562 ) < 1e-2
assert abs(result_mean.item() - 0.211_6195_7085_1326 ) < 1e-3
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config(prediction_type="""v_prediction""" )
_lowerCAmelCase = scheduler_class(**_snake_case )
scheduler.set_timesteps(self.num_inference_steps )
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
_lowerCAmelCase = sample.to(_snake_case )
for i, t in enumerate(scheduler.timesteps ):
_lowerCAmelCase = scheduler.scale_model_input(_snake_case , _snake_case )
_lowerCAmelCase = model(_snake_case , _snake_case )
_lowerCAmelCase = scheduler.step(_snake_case , _snake_case , _snake_case )
_lowerCAmelCase = output.prev_sample
_lowerCAmelCase = torch.sum(torch.abs(_snake_case ) )
_lowerCAmelCase = torch.mean(torch.abs(_snake_case ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 124.77_1492_0043_9453 ) < 1e-2
assert abs(result_mean.item() - 0.1_6226_2890_1481_6284 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 128.1_6633_6059_5703 ) < 1e-2
assert abs(result_mean.item() - 0.1_6688_3260_0116_7297 ) < 1e-3
else:
assert abs(result_sum.item() - 119.8_4875_4882_8125 ) < 1e-2
assert abs(result_mean.item() - 0.1560_5306_6253_6621 ) < 1e-3
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_snake_case )
scheduler.set_timesteps(self.num_inference_steps , device=_snake_case )
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter.to(_snake_case ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
_lowerCAmelCase = scheduler.scale_model_input(_snake_case , _snake_case )
_lowerCAmelCase = model(_snake_case , _snake_case )
_lowerCAmelCase = scheduler.step(_snake_case , _snake_case , _snake_case )
_lowerCAmelCase = output.prev_sample
_lowerCAmelCase = torch.sum(torch.abs(_snake_case ) )
_lowerCAmelCase = torch.mean(torch.abs(_snake_case ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.46_9573_9746_0938 ) < 1e-2
assert abs(result_mean.item() - 0.2_1805_9346_0798_2635 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_3536_3769_5312 ) < 1e-2
assert abs(result_mean.item() - 0.2_2342_9083_8241_5771 ) < 1e-3
else:
assert abs(result_sum.item() - 162.52_3834_2285_1562 ) < 1e-2
assert abs(result_mean.item() - 0.211_6195_7085_1326 ) < 1e-3
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_snake_case , use_karras_sigmas=_snake_case )
scheduler.set_timesteps(self.num_inference_steps , device=_snake_case )
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter.to(_snake_case ) * scheduler.init_noise_sigma
_lowerCAmelCase = sample.to(_snake_case )
for t in scheduler.timesteps:
_lowerCAmelCase = scheduler.scale_model_input(_snake_case , _snake_case )
_lowerCAmelCase = model(_snake_case , _snake_case )
_lowerCAmelCase = scheduler.step(_snake_case , _snake_case , _snake_case )
_lowerCAmelCase = output.prev_sample
_lowerCAmelCase = torch.sum(torch.abs(_snake_case ) )
_lowerCAmelCase = torch.mean(torch.abs(_snake_case ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 176.66_9741_3574_2188 ) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1e-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 177.63_6535_6445_3125 ) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1e-2
else:
assert abs(result_sum.item() - 170.3_1352_2338_8672 ) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1e-2
| 82 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase = AudioLDMPipeline
__lowerCamelCase = TEXT_TO_AUDIO_PARAMS
__lowerCamelCase = TEXT_TO_AUDIO_BATCH_PARAMS
__lowerCamelCase = frozenset(
[
'''num_inference_steps''',
'''num_waveforms_per_prompt''',
'''generator''',
'''latents''',
'''output_type''',
'''return_dict''',
'''callback''',
'''callback_steps''',
] )
def snake_case ( self ):
"""simple docstring"""
torch.manual_seed(0 )
_lowerCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=(32, 64) , class_embed_type="""simple_projection""" , projection_class_embeddings_input_dim=32 , class_embeddings_concat=_snake_case , )
_lowerCAmelCase = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_snake_case , set_alpha_to_one=_snake_case , )
torch.manual_seed(0 )
_lowerCAmelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
_lowerCAmelCase = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , projection_dim=32 , )
_lowerCAmelCase = ClapTextModelWithProjection(_snake_case )
_lowerCAmelCase = RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta""" , model_max_length=77 )
_lowerCAmelCase = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=16000 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=_snake_case , )
_lowerCAmelCase = SpeechTaHifiGan(_snake_case )
_lowerCAmelCase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""vocoder""": vocoder,
}
return components
def snake_case ( self , _snake_case , _snake_case=0 ):
"""simple docstring"""
if str(_snake_case ).startswith("""mps""" ):
_lowerCAmelCase = torch.manual_seed(_snake_case )
else:
_lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
_lowerCAmelCase = {
"""prompt""": """A hammer hitting a wooden surface""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
}
return inputs
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(_snake_case ) == 256
_lowerCAmelCase = audio[:10]
_lowerCAmelCase = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = 3 * [inputs["""prompt"""]]
# forward
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = 3 * [inputs.pop("""prompt""" )]
_lowerCAmelCase = audioldm_pipe.tokenizer(
_snake_case , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=_snake_case , return_tensors="""pt""" , )
_lowerCAmelCase = text_inputs["""input_ids"""].to(_snake_case )
_lowerCAmelCase = audioldm_pipe.text_encoder(
_snake_case , )
_lowerCAmelCase = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
_lowerCAmelCase = F.normalize(_snake_case , dim=-1 )
_lowerCAmelCase = prompt_embeds
# forward
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = 3 * ["""this is a negative prompt"""]
_lowerCAmelCase = negative_prompt
_lowerCAmelCase = 3 * [inputs["""prompt"""]]
# forward
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = 3 * [inputs.pop("""prompt""" )]
_lowerCAmelCase = []
for p in [prompt, negative_prompt]:
_lowerCAmelCase = audioldm_pipe.tokenizer(
_snake_case , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=_snake_case , return_tensors="""pt""" , )
_lowerCAmelCase = text_inputs["""input_ids"""].to(_snake_case )
_lowerCAmelCase = audioldm_pipe.text_encoder(
_snake_case , )
_lowerCAmelCase = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
_lowerCAmelCase = F.normalize(_snake_case , dim=-1 )
embeds.append(_snake_case )
_lowerCAmelCase , _lowerCAmelCase = embeds
# forward
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = PNDMScheduler(skip_prk_steps=_snake_case )
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = """egg cracking"""
_lowerCAmelCase = audioldm_pipe(**_snake_case , negative_prompt=_snake_case )
_lowerCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(_snake_case ) == 256
_lowerCAmelCase = audio[:10]
_lowerCAmelCase = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = PNDMScheduler(skip_prk_steps=_snake_case )
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = """A hammer hitting a wooden surface"""
# test num_waveforms_per_prompt=1 (default)
_lowerCAmelCase = audioldm_pipe(_snake_case , num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
_lowerCAmelCase = 2
_lowerCAmelCase = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
_lowerCAmelCase = 2
_lowerCAmelCase = audioldm_pipe(_snake_case , num_inference_steps=2 , num_waveforms_per_prompt=_snake_case ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
_lowerCAmelCase = 2
_lowerCAmelCase = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=_snake_case ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = audioldm_pipe.vocoder.config.sampling_rate
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = audioldm_pipe(audio_length_in_s=0.016 , **_snake_case )
_lowerCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(_snake_case ) / vocoder_sampling_rate == 0.016
_lowerCAmelCase = audioldm_pipe(audio_length_in_s=0.032 , **_snake_case )
_lowerCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(_snake_case ) / vocoder_sampling_rate == 0.032
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = ["""hey"""]
_lowerCAmelCase = audioldm_pipe(_snake_case , num_inference_steps=1 )
_lowerCAmelCase = output.audios.shape
assert audio_shape == (1, 256)
_lowerCAmelCase = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
_lowerCAmelCase = SpeechTaHifiGan(_snake_case ).to(_snake_case )
_lowerCAmelCase = audioldm_pipe(_snake_case , num_inference_steps=1 )
_lowerCAmelCase = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def snake_case ( self ):
"""simple docstring"""
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._test_inference_batch_single_identical(test_mean_pixel_difference=_snake_case )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def snake_case ( self ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_snake_case )
@slow
class __lowerCAmelCase ( unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case ( self , _snake_case , _snake_case="cpu" , _snake_case=torch.floataa , _snake_case=0 ):
"""simple docstring"""
_lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
_lowerCAmelCase = np.random.RandomState(_snake_case ).standard_normal((1, 8, 128, 16) )
_lowerCAmelCase = torch.from_numpy(_snake_case ).to(device=_snake_case , dtype=_snake_case )
_lowerCAmelCase = {
"""prompt""": """A hammer hitting a wooden surface""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 2.5,
}
return inputs
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_inputs(_snake_case )
_lowerCAmelCase = 25
_lowerCAmelCase = audioldm_pipe(**_snake_case ).audios[0]
assert audio.ndim == 1
assert len(_snake_case ) == 81920
_lowerCAmelCase = audio[77230:77240]
_lowerCAmelCase = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] )
_lowerCAmelCase = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
_lowerCAmelCase = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_inputs(_snake_case )
_lowerCAmelCase = audioldm_pipe(**_snake_case ).audios[0]
assert audio.ndim == 1
assert len(_snake_case ) == 81920
_lowerCAmelCase = audio[27780:27790]
_lowerCAmelCase = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] )
_lowerCAmelCase = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3e-2
| 82 | 1 |
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def _UpperCAmelCase ( ):
"""simple docstring"""
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
assert nand_gate(1 , 1 ) == 0
if __name__ == "__main__":
print(nand_gate(0, 0))
print(nand_gate(0, 1))
print(nand_gate(1, 0))
print(nand_gate(1, 1))
| 82 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __lowerCAmelCase ( lowerCamelCase__ ):
# to overwrite at feature extractactor specific tests
__lowerCamelCase = None
__lowerCamelCase = None
@property
def snake_case ( self ):
"""simple docstring"""
return self.feat_extract_tester.prepare_feat_extract_dict()
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(_snake_case , """feature_size""" ) )
self.assertTrue(hasattr(_snake_case , """sampling_rate""" ) )
self.assertTrue(hasattr(_snake_case , """padding_value""" ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(_snake_case ) == len(_snake_case ) for x, y in zip(_snake_case , processed_features[input_name] ) ) )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_snake_case )
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="""np""" )
_lowerCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_lowerCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_snake_case )
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="""pt""" )
_lowerCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_lowerCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_snake_case )
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="""tf""" )
_lowerCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_lowerCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def snake_case ( self , _snake_case=False ):
"""simple docstring"""
def _inputs_have_equal_length(_snake_case ):
_lowerCAmelCase = len(input[0] )
for input_slice in input[1:]:
if len(_snake_case ) != length:
return False
return True
def _inputs_are_equal(_snake_case , _snake_case ):
if len(_snake_case ) != len(_snake_case ):
return False
for input_slice_a, input_slice_a in zip(_snake_case , _snake_case ):
if not np.allclose(np.asarray(_snake_case ) , np.asarray(_snake_case ) , atol=1e-3 ):
return False
return True
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_snake_case )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = self.feat_extract_tester.seq_length_diff
_lowerCAmelCase = self.feat_extract_tester.max_seq_length + pad_diff
_lowerCAmelCase = self.feat_extract_tester.min_seq_length
_lowerCAmelCase = self.feat_extract_tester.batch_size
_lowerCAmelCase = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
_lowerCAmelCase = feat_extract.pad(_snake_case , padding=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""max_length""" , max_length=len(speech_inputs[-1] ) )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""max_length""" )[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=_snake_case , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_are_equal(_snake_case , _snake_case ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
_lowerCAmelCase = feat_extract.pad(_snake_case , pad_to_multiple_of=10 )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , pad_to_multiple_of=10 )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , pad_to_multiple_of=10 , max_length=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , pad_to_multiple_of=10 , max_length=_snake_case , return_tensors="""np""" , )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(all(len(_snake_case ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(_snake_case , _snake_case ) )
_lowerCAmelCase = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(_snake_case ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
_lowerCAmelCase = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1e-3 )
def snake_case ( self , _snake_case=False ):
"""simple docstring"""
def _inputs_have_equal_length(_snake_case ):
_lowerCAmelCase = len(input[0] )
for input_slice in input[1:]:
if len(_snake_case ) != length:
return False
return True
def _inputs_are_equal(_snake_case , _snake_case ):
if len(_snake_case ) != len(_snake_case ):
return False
for input_slice_a, input_slice_a in zip(_snake_case , _snake_case ):
if not np.allclose(np.asarray(_snake_case ) , np.asarray(_snake_case ) , atol=1e-3 ):
return False
return True
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_snake_case )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , truncation=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
# truncate to smallest with np
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" , truncation=_snake_case , )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
# truncate to middle
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=_snake_case , return_tensors="""np""" , )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[1] ) , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_are_equal(_snake_case , _snake_case ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , truncation=_snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""longest""" , truncation=_snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""longest""" , truncation=_snake_case )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""max_length""" , truncation=_snake_case )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
_lowerCAmelCase = 12
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_snake_case , truncation=_snake_case , )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_snake_case , )
_lowerCAmelCase = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
_lowerCAmelCase = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
_lowerCAmelCase = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
def snake_case ( self ):
"""simple docstring"""
self._check_padding(numpify=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._check_padding(numpify=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._check_truncation(numpify=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._check_truncation(numpify=_snake_case )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""pt""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""tf""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_dict
_lowerCAmelCase = True
_lowerCAmelCase = self.feature_extraction_class(**_snake_case )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = [len(_snake_case ) for x in speech_inputs]
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )
self.assertIn("""attention_mask""" , _snake_case )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , _snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_dict
_lowerCAmelCase = True
_lowerCAmelCase = self.feature_extraction_class(**_snake_case )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = [len(_snake_case ) for x in speech_inputs]
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = min(_snake_case )
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=_snake_case , truncation=_snake_case , return_tensors="""np""" )
self.assertIn("""attention_mask""" , _snake_case )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
| 82 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
A__ = logging.get_logger(__name__)
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = OrderedDict()
for key, value in state_dict.items():
if key.startswith("""module.encoder""" ):
_lowerCAmelCase = key.replace("""module.encoder""" , """glpn.encoder""" )
if key.startswith("""module.decoder""" ):
_lowerCAmelCase = key.replace("""module.decoder""" , """decoder.stages""" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
_lowerCAmelCase = key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
_lowerCAmelCase = key.replace(F'patch_embed{idx}' , F'patch_embeddings.{int(snake_case )-1}' )
if "norm" in key:
_lowerCAmelCase = key.replace("""norm""" , """layer_norm""" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
_lowerCAmelCase = key[key.find("""glpn.encoder.layer_norm""" ) + len("""glpn.encoder.layer_norm""" )]
_lowerCAmelCase = key.replace(F'layer_norm{idx}' , F'layer_norm.{int(snake_case )-1}' )
if "layer_norm1" in key:
_lowerCAmelCase = key.replace("""layer_norm1""" , """layer_norm_1""" )
if "layer_norm2" in key:
_lowerCAmelCase = key.replace("""layer_norm2""" , """layer_norm_2""" )
if "block" in key:
# replace for example block1 by block.0
_lowerCAmelCase = key[key.find("""block""" ) + len("""block""" )]
_lowerCAmelCase = key.replace(F'block{idx}' , F'block.{int(snake_case )-1}' )
if "attn.q" in key:
_lowerCAmelCase = key.replace("""attn.q""" , """attention.self.query""" )
if "attn.proj" in key:
_lowerCAmelCase = key.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in key:
_lowerCAmelCase = key.replace("""attn""" , """attention.self""" )
if "fc1" in key:
_lowerCAmelCase = key.replace("""fc1""" , """dense1""" )
if "fc2" in key:
_lowerCAmelCase = key.replace("""fc2""" , """dense2""" )
if "linear_pred" in key:
_lowerCAmelCase = key.replace("""linear_pred""" , """classifier""" )
if "linear_fuse" in key:
_lowerCAmelCase = key.replace("""linear_fuse.conv""" , """linear_fuse""" )
_lowerCAmelCase = key.replace("""linear_fuse.bn""" , """batch_norm""" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
_lowerCAmelCase = key[key.find("""linear_c""" ) + len("""linear_c""" )]
_lowerCAmelCase = key.replace(F'linear_c{idx}' , F'linear_c.{int(snake_case )-1}' )
if "bot_conv" in key:
_lowerCAmelCase = key.replace("""bot_conv""" , """0.convolution""" )
if "skip_conv1" in key:
_lowerCAmelCase = key.replace("""skip_conv1""" , """1.convolution""" )
if "skip_conv2" in key:
_lowerCAmelCase = key.replace("""skip_conv2""" , """2.convolution""" )
if "fusion1" in key:
_lowerCAmelCase = key.replace("""fusion1""" , """1.fusion""" )
if "fusion2" in key:
_lowerCAmelCase = key.replace("""fusion2""" , """2.fusion""" )
if "fusion3" in key:
_lowerCAmelCase = key.replace("""fusion3""" , """3.fusion""" )
if "fusion" in key and "conv" in key:
_lowerCAmelCase = key.replace("""conv""" , """convolutional_layer""" )
if key.startswith("""module.last_layer_depth""" ):
_lowerCAmelCase = key.replace("""module.last_layer_depth""" , """head.head""" )
_lowerCAmelCase = value
return new_state_dict
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
_lowerCAmelCase = state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight' )
_lowerCAmelCase = state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.bias' )
# next, add keys and values (in that order) to the state dict
_lowerCAmelCase = kv_weight[
: config.hidden_sizes[i], :
]
_lowerCAmelCase = kv_bias[: config.hidden_sizes[i]]
_lowerCAmelCase = kv_weight[
config.hidden_sizes[i] :, :
]
_lowerCAmelCase = kv_bias[config.hidden_sizes[i] :]
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_lowerCAmelCase = Image.open(requests.get(snake_case , stream=snake_case ).raw )
return image
@torch.no_grad()
def _UpperCAmelCase ( snake_case , snake_case , snake_case=False , snake_case=None ):
"""simple docstring"""
_lowerCAmelCase = GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12] , decoder_hidden_size=64 , depths=[3, 8, 27, 3] )
# load image processor (only resize + rescale)
_lowerCAmelCase = GLPNImageProcessor()
# prepare image
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(images=snake_case , return_tensors="""pt""" ).pixel_values
logger.info("""Converting model...""" )
# load original state dict
_lowerCAmelCase = torch.load(snake_case , map_location=torch.device("""cpu""" ) )
# rename keys
_lowerCAmelCase = rename_keys(snake_case )
# key and value matrices need special treatment
read_in_k_v(snake_case , snake_case )
# create HuggingFace model and load state dict
_lowerCAmelCase = GLPNForDepthEstimation(snake_case )
model.load_state_dict(snake_case )
model.eval()
# forward pass
_lowerCAmelCase = model(snake_case )
_lowerCAmelCase = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
_lowerCAmelCase = torch.tensor(
[[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]] )
elif "kitti" in model_name:
_lowerCAmelCase = torch.tensor(
[[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]] )
else:
raise ValueError(F'Unknown model name: {model_name}' )
_lowerCAmelCase = torch.Size([1, 4_80, 6_40] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3] , snake_case , atol=1E-4 )
print("""Looks ok!""" )
# finally, push to hub if required
if push_to_hub:
logger.info("""Pushing model and image processor to the hub...""" )
model.push_to_hub(
repo_path_or_name=Path(snake_case , snake_case ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=snake_case , )
image_processor.push_to_hub(
repo_path_or_name=Path(snake_case , snake_case ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=snake_case , )
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""",
default=None,
type=str,
help="""Path to the original PyTorch checkpoint (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
)
parser.add_argument(
"""--model_name""",
default="""glpn-kitti""",
type=str,
help="""Name of the model in case you're pushing to the hub.""",
)
A__ = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 82 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''poolformer'''
def __init__( self , _snake_case=3 , _snake_case=16 , _snake_case=16 , _snake_case=3 , _snake_case=4.0 , _snake_case=[2, 2, 6, 2] , _snake_case=[64, 128, 320, 512] , _snake_case=[7, 3, 3, 3] , _snake_case=[4, 2, 2, 2] , _snake_case=[2, 1, 1, 1] , _snake_case=4 , _snake_case=0.0 , _snake_case="gelu" , _snake_case=True , _snake_case=1e-5 , _snake_case=0.02 , **_snake_case , ):
"""simple docstring"""
_lowerCAmelCase = num_channels
_lowerCAmelCase = patch_size
_lowerCAmelCase = stride
_lowerCAmelCase = padding
_lowerCAmelCase = pool_size
_lowerCAmelCase = hidden_sizes
_lowerCAmelCase = mlp_ratio
_lowerCAmelCase = depths
_lowerCAmelCase = patch_sizes
_lowerCAmelCase = strides
_lowerCAmelCase = num_encoder_blocks
_lowerCAmelCase = drop_path_rate
_lowerCAmelCase = hidden_act
_lowerCAmelCase = use_layer_scale
_lowerCAmelCase = layer_scale_init_value
_lowerCAmelCase = initializer_range
super().__init__(**_snake_case )
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = version.parse('''1.11''' )
@property
def snake_case ( self ):
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def snake_case ( self ):
"""simple docstring"""
return 2e-3
| 82 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class __lowerCAmelCase ( unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = {
"""task_specific_params""": {
"""summarization""": {"""length_penalty""": 1.0, """max_length""": 128, """min_length""": 12, """num_beams""": 4},
"""summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 142, """min_length""": 56, """num_beams""": 4},
"""summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
}
}
_lowerCAmelCase = {
"""task_specific_params.summarization.length_penalty""": 1.0,
"""task_specific_params.summarization.max_length""": 128,
"""task_specific_params.summarization.min_length""": 12,
"""task_specific_params.summarization.num_beams""": 4,
"""task_specific_params.summarization_cnn.length_penalty""": 2.0,
"""task_specific_params.summarization_cnn.max_length""": 142,
"""task_specific_params.summarization_cnn.min_length""": 56,
"""task_specific_params.summarization_cnn.num_beams""": 4,
"""task_specific_params.summarization_xsum.length_penalty""": 1.0,
"""task_specific_params.summarization_xsum.max_length""": 62,
"""task_specific_params.summarization_xsum.min_length""": 11,
"""task_specific_params.summarization_xsum.num_beams""": 6,
}
self.assertEqual(flatten_dict(_snake_case ) , _snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(_snake_case ) , x.transpose() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case ) , transpose(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , transpose(_snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case ) , transpose(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , transpose(_snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case ) , np.asarray(transpose(_snake_case ) ) ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , np.asarray(transpose(_snake_case , axes=(1, 2, 0) ) ) ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , np.reshape(_snake_case , (4, 3) ) ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , np.reshape(_snake_case , (12, 5) ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , reshape(_snake_case , (4, 3) ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , reshape(_snake_case , (12, 5) ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , reshape(_snake_case , (4, 3) ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , reshape(_snake_case , (12, 5) ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , np.asarray(reshape(_snake_case , (4, 3) ) ) ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , np.asarray(reshape(_snake_case , (12, 5) ) ) ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(_snake_case ) , np.squeeze(_snake_case ) ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , np.squeeze(_snake_case , axis=2 ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case ) , squeeze(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , squeeze(_snake_case , axis=2 ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case ) , squeeze(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , squeeze(_snake_case , axis=2 ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case ) , np.asarray(squeeze(_snake_case ) ) ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , np.asarray(squeeze(_snake_case , axis=2 ) ) ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , np.expand_dims(_snake_case , axis=1 ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , expand_dims(_snake_case , axis=1 ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , expand_dims(_snake_case , axis=1 ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , np.asarray(expand_dims(_snake_case , axis=1 ) ) ) )
| 82 |
def _UpperCAmelCase ( snake_case = 10_00 ):
"""simple docstring"""
_lowerCAmelCase = -1
_lowerCAmelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
_lowerCAmelCase = (n * n - 2 * a * n) // (2 * n - 2 * a)
_lowerCAmelCase = n - a - b
if c * c == (a * a + b * b):
_lowerCAmelCase = a * b * c
if candidate >= product:
_lowerCAmelCase = candidate
return product
if __name__ == "__main__":
print(f"{solution() = }")
| 82 | 1 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class __lowerCAmelCase :
def __init__( self , _snake_case , _snake_case=13 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=False , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=5 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=512 , _snake_case=16 , _snake_case=2 , _snake_case=0.02 , _snake_case=3 , _snake_case=4 , _snake_case=None , ):
"""simple docstring"""
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = seq_length
_lowerCAmelCase = is_training
_lowerCAmelCase = use_input_mask
_lowerCAmelCase = use_token_type_ids
_lowerCAmelCase = use_labels
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_act
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = type_vocab_size
_lowerCAmelCase = type_sequence_label_size
_lowerCAmelCase = initializer_range
_lowerCAmelCase = num_labels
_lowerCAmelCase = num_choices
_lowerCAmelCase = scope
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase = None
if self.use_input_mask:
_lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCAmelCase = None
if self.use_token_type_ids:
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
if self.use_labels:
_lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_lowerCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
_lowerCAmelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case ( self ):
"""simple docstring"""
return OpenLlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_snake_case , initializer_range=self.initializer_range , use_stable_embedding=_snake_case , )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = OpenLlamaModel(config=_snake_case )
model.to(_snake_case )
model.eval()
_lowerCAmelCase = model(_snake_case , attention_mask=_snake_case )
_lowerCAmelCase = model(_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , ):
"""simple docstring"""
_lowerCAmelCase = True
_lowerCAmelCase = OpenLlamaModel(_snake_case )
model.to(_snake_case )
model.eval()
_lowerCAmelCase = model(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , )
_lowerCAmelCase = model(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , )
_lowerCAmelCase = model(_snake_case , attention_mask=_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , ):
"""simple docstring"""
_lowerCAmelCase = OpenLlamaForCausalLM(config=_snake_case )
model.to(_snake_case )
model.eval()
_lowerCAmelCase = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , ):
"""simple docstring"""
_lowerCAmelCase = True
_lowerCAmelCase = True
_lowerCAmelCase = OpenLlamaForCausalLM(config=_snake_case )
model.to(_snake_case )
model.eval()
# first forward pass
_lowerCAmelCase = model(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , use_cache=_snake_case , )
_lowerCAmelCase = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
_lowerCAmelCase = ids_tensor((self.batch_size, 3) , config.vocab_size )
_lowerCAmelCase = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
_lowerCAmelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
_lowerCAmelCase = torch.cat([input_mask, next_mask] , dim=-1 )
_lowerCAmelCase = model(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , output_hidden_states=_snake_case , )["""hidden_states"""][0]
_lowerCAmelCase = model(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , past_key_values=_snake_case , output_hidden_states=_snake_case , )["""hidden_states"""][0]
# select random slice
_lowerCAmelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_lowerCAmelCase = output_from_no_past[:, -3:, random_slice_idx].detach()
_lowerCAmelCase = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_snake_case , _snake_case , atol=1e-3 ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = config_and_inputs
_lowerCAmelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
__lowerCamelCase = (OpenLlamaForCausalLM,) if is_torch_available() else ()
__lowerCamelCase = (
{
'''feature-extraction''': OpenLlamaModel,
'''text-classification''': OpenLlamaForSequenceClassification,
'''text-generation''': OpenLlamaForCausalLM,
'''zero-shot''': OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
__lowerCamelCase = False
__lowerCamelCase = False
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = OpenLlamaModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=_snake_case , hidden_size=37 )
def snake_case ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_lowerCAmelCase = type
self.model_tester.create_and_check_model(*_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCAmelCase = 3
_lowerCAmelCase = input_dict["""input_ids"""]
_lowerCAmelCase = input_ids.ne(1 ).to(_snake_case )
_lowerCAmelCase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_lowerCAmelCase = OpenLlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
_lowerCAmelCase = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCAmelCase = 3
_lowerCAmelCase = """single_label_classification"""
_lowerCAmelCase = input_dict["""input_ids"""]
_lowerCAmelCase = input_ids.ne(1 ).to(_snake_case )
_lowerCAmelCase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_lowerCAmelCase = OpenLlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
_lowerCAmelCase = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCAmelCase = 3
_lowerCAmelCase = """multi_label_classification"""
_lowerCAmelCase = input_dict["""input_ids"""]
_lowerCAmelCase = input_ids.ne(1 ).to(_snake_case )
_lowerCAmelCase = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
_lowerCAmelCase = OpenLlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
_lowerCAmelCase = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip("""Open-Llama buffers include complex numbers, which breaks this test""" )
def snake_case ( self ):
"""simple docstring"""
pass
@parameterized.expand([("""linear""",), ("""dynamic""",)] )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCAmelCase = ids_tensor([1, 10] , config.vocab_size )
_lowerCAmelCase = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
_lowerCAmelCase = OpenLlamaModel(_snake_case )
original_model.to(_snake_case )
original_model.eval()
_lowerCAmelCase = original_model(_snake_case ).last_hidden_state
_lowerCAmelCase = original_model(_snake_case ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
_lowerCAmelCase = {"""type""": scaling_type, """factor""": 10.0}
_lowerCAmelCase = OpenLlamaModel(_snake_case )
scaled_model.to(_snake_case )
scaled_model.eval()
_lowerCAmelCase = scaled_model(_snake_case ).last_hidden_state
_lowerCAmelCase = scaled_model(_snake_case ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(_snake_case , _snake_case , atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(_snake_case , _snake_case , atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(_snake_case , _snake_case , atol=1e-5 ) )
| 82 |
from __future__ import annotations
import math
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(snake_case ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = str(snake_case )
_lowerCAmelCase = [n]
for i in range(1 , len(snake_case ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if len(str(snake_case ) ) > 3:
if not is_prime(int(str(snake_case )[-3:] ) ) or not is_prime(int(str(snake_case )[:3] ) ):
return False
return True
def _UpperCAmelCase ( snake_case = 11 ):
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = 13
while len(snake_case ) != count:
if validate(snake_case ):
_lowerCAmelCase = list_truncated_nums(snake_case )
if all(is_prime(snake_case ) for i in list_nums ):
list_truncated_primes.append(snake_case )
num += 2
return list_truncated_primes
def _UpperCAmelCase ( ):
"""simple docstring"""
return sum(compute_truncated_primes(11 ) )
if __name__ == "__main__":
print(f"{sum(compute_truncated_primes(11)) = }")
| 82 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A__ = {
"""configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FunnelConfig"""],
"""convert_funnel_original_tf_checkpoint_to_pytorch""": [],
"""tokenization_funnel""": ["""FunnelTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = ["""FunnelTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = [
"""FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FunnelBaseModel""",
"""FunnelForMaskedLM""",
"""FunnelForMultipleChoice""",
"""FunnelForPreTraining""",
"""FunnelForQuestionAnswering""",
"""FunnelForSequenceClassification""",
"""FunnelForTokenClassification""",
"""FunnelModel""",
"""FunnelPreTrainedModel""",
"""load_tf_weights_in_funnel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = [
"""TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFFunnelBaseModel""",
"""TFFunnelForMaskedLM""",
"""TFFunnelForMultipleChoice""",
"""TFFunnelForPreTraining""",
"""TFFunnelForQuestionAnswering""",
"""TFFunnelForSequenceClassification""",
"""TFFunnelForTokenClassification""",
"""TFFunnelModel""",
"""TFFunnelPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
from .tokenization_funnel import FunnelTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_funnel_fast import FunnelTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_funnel import (
FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
FunnelBaseModel,
FunnelForMaskedLM,
FunnelForMultipleChoice,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForSequenceClassification,
FunnelForTokenClassification,
FunnelModel,
FunnelPreTrainedModel,
load_tf_weights_in_funnel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_funnel import (
TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
TFFunnelPreTrainedModel,
)
else:
import sys
A__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 82 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
A__ = logging.get_logger(__name__)
class __lowerCAmelCase ( lowerCamelCase__ ):
def __init__( self , **_snake_case ):
"""simple docstring"""
requires_backends(self , ["""bs4"""] )
super().__init__(**_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = element if element.name else element.parent
for parent in child.parents: # type: bs4.element.Tag
_lowerCAmelCase = parent.find_all(child.name , recursive=_snake_case )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(_snake_case ) else next(i for i, s in enumerate(_snake_case , 1 ) if s is child ) )
_lowerCAmelCase = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = BeautifulSoup(_snake_case , """html.parser""" )
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = []
for element in html_code.descendants:
if type(_snake_case ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
_lowerCAmelCase = html.unescape(_snake_case ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(_snake_case )
_lowerCAmelCase , _lowerCAmelCase = self.xpath_soup(_snake_case )
stringaxtag_seq.append(_snake_case )
stringaxsubs_seq.append(_snake_case )
if len(_snake_case ) != len(_snake_case ):
raise ValueError("""Number of doc strings and xtags does not correspond""" )
if len(_snake_case ) != len(_snake_case ):
raise ValueError("""Number of doc strings and xsubs does not correspond""" )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = """"""
for tagname, subs in zip(_snake_case , _snake_case ):
xpath += F'/{tagname}'
if subs != 0:
xpath += F'[{subs}]'
return xpath
def __call__( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = False
# Check that strings has a valid type
if isinstance(_snake_case , _snake_case ):
_lowerCAmelCase = True
elif isinstance(_snake_case , (list, tuple) ):
if len(_snake_case ) == 0 or isinstance(html_strings[0] , _snake_case ):
_lowerCAmelCase = True
if not valid_strings:
raise ValueError(
"""HTML strings must of type `str`, `List[str]` (batch of examples), """
F'but is of type {type(_snake_case )}.' )
_lowerCAmelCase = bool(isinstance(_snake_case , (list, tuple) ) and (isinstance(html_strings[0] , _snake_case )) )
if not is_batched:
_lowerCAmelCase = [html_strings]
# Get nodes + xpaths
_lowerCAmelCase = []
_lowerCAmelCase = []
for html_string in html_strings:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.get_three_from_single(_snake_case )
nodes.append(_snake_case )
_lowerCAmelCase = []
for node, tag_list, sub_list in zip(_snake_case , _snake_case , _snake_case ):
_lowerCAmelCase = self.construct_xpath(_snake_case , _snake_case )
xpath_strings.append(_snake_case )
xpaths.append(_snake_case )
# return as Dict
_lowerCAmelCase = {"""nodes""": nodes, """xpaths""": xpaths}
_lowerCAmelCase = BatchFeature(data=_snake_case , tensor_type=_snake_case )
return encoded_inputs
| 82 | 1 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
A__ = collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
A__ = """https://storage.googleapis.com/cvdf-datasets/mnist/"""
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = numpy.dtype(numpy.uintaa ).newbyteorder(""">""" )
return numpy.frombuffer(bytestream.read(4 ) , dtype=snake_case )[0]
@deprecated(snake_case , """Please use tf.data to implement this functionality.""" )
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
print("""Extracting""" , f.name )
with gzip.GzipFile(fileobj=snake_case ) as bytestream:
_lowerCAmelCase = _readaa(snake_case )
if magic != 20_51:
raise ValueError(
"""Invalid magic number %d in MNIST image file: %s""" % (magic, f.name) )
_lowerCAmelCase = _readaa(snake_case )
_lowerCAmelCase = _readaa(snake_case )
_lowerCAmelCase = _readaa(snake_case )
_lowerCAmelCase = bytestream.read(rows * cols * num_images )
_lowerCAmelCase = numpy.frombuffer(snake_case , dtype=numpy.uinta )
_lowerCAmelCase = data.reshape(snake_case , snake_case , snake_case , 1 )
return data
@deprecated(snake_case , """Please use tf.one_hot on tensors.""" )
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = labels_dense.shape[0]
_lowerCAmelCase = numpy.arange(snake_case ) * num_classes
_lowerCAmelCase = numpy.zeros((num_labels, num_classes) )
_lowerCAmelCase = 1
return labels_one_hot
@deprecated(snake_case , """Please use tf.data to implement this functionality.""" )
def _UpperCAmelCase ( snake_case , snake_case=False , snake_case=10 ):
"""simple docstring"""
print("""Extracting""" , f.name )
with gzip.GzipFile(fileobj=snake_case ) as bytestream:
_lowerCAmelCase = _readaa(snake_case )
if magic != 20_49:
raise ValueError(
"""Invalid magic number %d in MNIST label file: %s""" % (magic, f.name) )
_lowerCAmelCase = _readaa(snake_case )
_lowerCAmelCase = bytestream.read(snake_case )
_lowerCAmelCase = numpy.frombuffer(snake_case , dtype=numpy.uinta )
if one_hot:
return _dense_to_one_hot(snake_case , snake_case )
return labels
class __lowerCAmelCase :
@deprecated(
_snake_case , """Please use alternatives such as official/mnist/_DataSet.py"""
""" from tensorflow/models.""" , )
def __init__( self , _snake_case , _snake_case , _snake_case=False , _snake_case=False , _snake_case=dtypes.floataa , _snake_case=True , _snake_case=None , ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = random_seed.get_seed(_snake_case )
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seeda if seed is None else seeda )
_lowerCAmelCase = dtypes.as_dtype(_snake_case ).base_dtype
if dtype not in (dtypes.uinta, dtypes.floataa):
raise TypeError("""Invalid image dtype %r, expected uint8 or float32""" % dtype )
if fake_data:
_lowerCAmelCase = 10000
_lowerCAmelCase = one_hot
else:
assert (
images.shape[0] == labels.shape[0]
), F'images.shape: {images.shape} labels.shape: {labels.shape}'
_lowerCAmelCase = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
assert images.shape[3] == 1
_lowerCAmelCase = images.reshape(
images.shape[0] , images.shape[1] * images.shape[2] )
if dtype == dtypes.floataa:
# Convert from [0, 255] -> [0.0, 1.0].
_lowerCAmelCase = images.astype(numpy.floataa )
_lowerCAmelCase = numpy.multiply(_snake_case , 1.0 / 255.0 )
_lowerCAmelCase = images
_lowerCAmelCase = labels
_lowerCAmelCase = 0
_lowerCAmelCase = 0
@property
def snake_case ( self ):
"""simple docstring"""
return self._images
@property
def snake_case ( self ):
"""simple docstring"""
return self._labels
@property
def snake_case ( self ):
"""simple docstring"""
return self._num_examples
@property
def snake_case ( self ):
"""simple docstring"""
return self._epochs_completed
def snake_case ( self , _snake_case , _snake_case=False , _snake_case=True ):
"""simple docstring"""
if fake_data:
_lowerCAmelCase = [1] * 784
_lowerCAmelCase = [1] + [0] * 9 if self.one_hot else 0
return (
[fake_image for _ in range(_snake_case )],
[fake_label for _ in range(_snake_case )],
)
_lowerCAmelCase = self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
_lowerCAmelCase = numpy.arange(self._num_examples )
numpy.random.shuffle(_snake_case )
_lowerCAmelCase = self.images[perma]
_lowerCAmelCase = self.labels[perma]
# Go to the next epoch
if start + batch_size > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Get the rest examples in this epoch
_lowerCAmelCase = self._num_examples - start
_lowerCAmelCase = self._images[start : self._num_examples]
_lowerCAmelCase = self._labels[start : self._num_examples]
# Shuffle the data
if shuffle:
_lowerCAmelCase = numpy.arange(self._num_examples )
numpy.random.shuffle(_snake_case )
_lowerCAmelCase = self.images[perm]
_lowerCAmelCase = self.labels[perm]
# Start next epoch
_lowerCAmelCase = 0
_lowerCAmelCase = batch_size - rest_num_examples
_lowerCAmelCase = self._index_in_epoch
_lowerCAmelCase = self._images[start:end]
_lowerCAmelCase = self._labels[start:end]
return (
numpy.concatenate((images_rest_part, images_new_part) , axis=0 ),
numpy.concatenate((labels_rest_part, labels_new_part) , axis=0 ),
)
else:
self._index_in_epoch += batch_size
_lowerCAmelCase = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
@deprecated(snake_case , """Please write your own downloading logic.""" )
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
if not gfile.Exists(snake_case ):
gfile.MakeDirs(snake_case )
_lowerCAmelCase = os.path.join(snake_case , snake_case )
if not gfile.Exists(snake_case ):
urllib.request.urlretrieve(snake_case , snake_case ) # noqa: S310
with gfile.GFile(snake_case ) as f:
_lowerCAmelCase = f.size()
print("""Successfully downloaded""" , snake_case , snake_case , """bytes.""" )
return filepath
@deprecated(
snake_case , """Please use alternatives such as:""" """ tensorflow_datasets.load('mnist')""" )
def _UpperCAmelCase ( snake_case , snake_case=False , snake_case=False , snake_case=dtypes.floataa , snake_case=True , snake_case=50_00 , snake_case=None , snake_case=DEFAULT_SOURCE_URL , ):
"""simple docstring"""
if fake_data:
def fake():
return _DataSet(
[] , [] , fake_data=snake_case , one_hot=snake_case , dtype=snake_case , seed=snake_case )
_lowerCAmelCase = fake()
_lowerCAmelCase = fake()
_lowerCAmelCase = fake()
return _Datasets(train=snake_case , validation=snake_case , test=snake_case )
if not source_url: # empty string check
_lowerCAmelCase = DEFAULT_SOURCE_URL
_lowerCAmelCase = """train-images-idx3-ubyte.gz"""
_lowerCAmelCase = """train-labels-idx1-ubyte.gz"""
_lowerCAmelCase = """t10k-images-idx3-ubyte.gz"""
_lowerCAmelCase = """t10k-labels-idx1-ubyte.gz"""
_lowerCAmelCase = _maybe_download(
snake_case , snake_case , source_url + train_images_file )
with gfile.Open(snake_case , """rb""" ) as f:
_lowerCAmelCase = _extract_images(snake_case )
_lowerCAmelCase = _maybe_download(
snake_case , snake_case , source_url + train_labels_file )
with gfile.Open(snake_case , """rb""" ) as f:
_lowerCAmelCase = _extract_labels(snake_case , one_hot=snake_case )
_lowerCAmelCase = _maybe_download(
snake_case , snake_case , source_url + test_images_file )
with gfile.Open(snake_case , """rb""" ) as f:
_lowerCAmelCase = _extract_images(snake_case )
_lowerCAmelCase = _maybe_download(
snake_case , snake_case , source_url + test_labels_file )
with gfile.Open(snake_case , """rb""" ) as f:
_lowerCAmelCase = _extract_labels(snake_case , one_hot=snake_case )
if not 0 <= validation_size <= len(snake_case ):
_lowerCAmelCase = (
"""Validation size should be between 0 and """
F'{len(snake_case )}. Received: {validation_size}.'
)
raise ValueError(snake_case )
_lowerCAmelCase = train_images[:validation_size]
_lowerCAmelCase = train_labels[:validation_size]
_lowerCAmelCase = train_images[validation_size:]
_lowerCAmelCase = train_labels[validation_size:]
_lowerCAmelCase = {"""dtype""": dtype, """reshape""": reshape, """seed""": seed}
_lowerCAmelCase = _DataSet(snake_case , snake_case , **snake_case )
_lowerCAmelCase = _DataSet(snake_case , snake_case , **snake_case )
_lowerCAmelCase = _DataSet(snake_case , snake_case , **snake_case )
return _Datasets(train=snake_case , validation=snake_case , test=snake_case )
| 82 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
A__ = TypeVar("""T""")
A__ = TypeVar("""U""")
class __lowerCAmelCase ( Generic[T, U] ):
def __init__( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = key
_lowerCAmelCase = val
_lowerCAmelCase = None
_lowerCAmelCase = None
def __repr__( self ):
"""simple docstring"""
return (
F'Node: key: {self.key}, val: {self.val}, '
F'has next: {bool(self.next )}, has prev: {bool(self.prev )}'
)
class __lowerCAmelCase ( Generic[T, U] ):
def __init__( self ):
"""simple docstring"""
_lowerCAmelCase = DoubleLinkedListNode(_snake_case , _snake_case )
_lowerCAmelCase = DoubleLinkedListNode(_snake_case , _snake_case )
_lowerCAmelCase , _lowerCAmelCase = self.rear, self.head
def __repr__( self ):
"""simple docstring"""
_lowerCAmelCase = ["""DoubleLinkedList"""]
_lowerCAmelCase = self.head
while node.next is not None:
rep.append(str(_snake_case ) )
_lowerCAmelCase = node.next
rep.append(str(self.rear ) )
return ",\n ".join(_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
_lowerCAmelCase = node
_lowerCAmelCase = previous
_lowerCAmelCase = node
_lowerCAmelCase = self.rear
def snake_case ( self , _snake_case ):
"""simple docstring"""
if node.prev is None or node.next is None:
return None
_lowerCAmelCase = node.next
_lowerCAmelCase = node.prev
_lowerCAmelCase = None
_lowerCAmelCase = None
return node
class __lowerCAmelCase ( Generic[T, U] ):
__lowerCamelCase = {}
def __init__( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = DoubleLinkedList()
_lowerCAmelCase = capacity
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = {}
def __repr__( self ):
"""simple docstring"""
return (
F'CacheInfo(hits={self.hits}, misses={self.miss}, '
F'capacity={self.capacity}, current size={self.num_keys})'
)
def __contains__( self , _snake_case ):
"""simple docstring"""
return key in self.cache
def snake_case ( self , _snake_case ):
"""simple docstring"""
if key in self.cache:
self.hits += 1
_lowerCAmelCase = self.cache[key]
_lowerCAmelCase = self.list.remove(self.cache[key] )
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(_snake_case )
return node.val
self.miss += 1
return None
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
_lowerCAmelCase = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(_snake_case ) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
_lowerCAmelCase = DoubleLinkedListNode(_snake_case , _snake_case )
self.list.add(self.cache[key] )
self.num_keys += 1
else:
# bump node to the end of the list, update value
_lowerCAmelCase = self.list.remove(self.cache[key] )
assert node is not None # node guaranteed to be in list
_lowerCAmelCase = value
self.list.add(_snake_case )
@classmethod
def snake_case ( cls , _snake_case = 128 ):
"""simple docstring"""
def cache_decorator_inner(_snake_case ) -> Callable[..., U]:
def cache_decorator_wrapper(*_snake_case ) -> U:
if func not in cls.decorator_function_to_instance_map:
_lowerCAmelCase = LRUCache(_snake_case )
_lowerCAmelCase = cls.decorator_function_to_instance_map[func].get(args[0] )
if result is None:
_lowerCAmelCase = func(*_snake_case )
cls.decorator_function_to_instance_map[func].put(args[0] , _snake_case )
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(_snake_case , """cache_info""" , _snake_case ) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| 82 | 1 |
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = int(snake_case )
if n_element < 1:
_lowerCAmelCase = ValueError("""a should be a positive number""" )
raise my_error
_lowerCAmelCase = [1]
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = (0, 0, 0)
_lowerCAmelCase = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
A__ = input("""Enter the last number (nth term) of the Hamming Number Series: """)
print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
A__ = hamming(int(n))
print("""-----------------------------------------------------""")
print(f"The list with nth numbers is: {hamming_numbers}")
print("""-----------------------------------------------------""")
| 82 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A__ = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""MvpTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = ["""MvpTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = [
"""MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MvpForCausalLM""",
"""MvpForConditionalGeneration""",
"""MvpForQuestionAnswering""",
"""MvpForSequenceClassification""",
"""MvpModel""",
"""MvpPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
A__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 82 | 1 |
A__ = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = [False] * len(snake_case )
_lowerCAmelCase = [s]
_lowerCAmelCase = True
while queue:
_lowerCAmelCase = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(snake_case )
_lowerCAmelCase = True
_lowerCAmelCase = u
return visited[t]
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = [-1] * (len(snake_case ))
_lowerCAmelCase = 0
_lowerCAmelCase = []
_lowerCAmelCase = [i[:] for i in graph] # Record original cut, copy.
while bfs(snake_case , snake_case , snake_case , snake_case ):
_lowerCAmelCase = float("""Inf""" )
_lowerCAmelCase = sink
while s != source:
# Find the minimum value in select path
_lowerCAmelCase = min(snake_case , graph[parent[s]][s] )
_lowerCAmelCase = parent[s]
max_flow += path_flow
_lowerCAmelCase = sink
while v != source:
_lowerCAmelCase = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
_lowerCAmelCase = parent[v]
for i in range(len(snake_case ) ):
for j in range(len(graph[0] ) ):
if graph[i][j] == 0 and temp[i][j] > 0:
res.append((i, j) )
return res
if __name__ == "__main__":
print(mincut(test_graph, source=0, sink=5))
| 82 |
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 0
while number > 0:
_lowerCAmelCase = number % 10
sum_of_digits += last_digit
_lowerCAmelCase = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def _UpperCAmelCase ( snake_case = 1_00 ):
"""simple docstring"""
_lowerCAmelCase = factorial(snake_case )
_lowerCAmelCase = split_and_add(snake_case )
return result
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).strip())))
| 82 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
A__ = logging.get_logger(__name__)
A__ = {
"""post_extract_proj""": """feature_projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.upsample.0""": """encoder.upsample.projection""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """layer_norm""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
for attribute in key.split(""".""" ):
_lowerCAmelCase = getattr(snake_case , snake_case )
if weight_type is not None:
_lowerCAmelCase = getattr(snake_case , snake_case ).shape
else:
_lowerCAmelCase = hf_pointer.shape
assert hf_shape == value.shape, (
F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
F' {value.shape} for {full_name}'
)
if weight_type == "weight":
_lowerCAmelCase = value
elif weight_type == "weight_g":
_lowerCAmelCase = value
elif weight_type == "weight_v":
_lowerCAmelCase = value
elif weight_type == "bias":
_lowerCAmelCase = value
else:
_lowerCAmelCase = value
logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = fairseq_model.state_dict()
_lowerCAmelCase = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
_lowerCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
snake_case , snake_case , snake_case , snake_case , hf_model.config.feat_extract_norm == """group""" , )
_lowerCAmelCase = True
else:
for key, mapped_key in MAPPING.items():
_lowerCAmelCase = """sew.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
_lowerCAmelCase = True
if "*" in mapped_key:
_lowerCAmelCase = name.split(snake_case )[0].split(""".""" )[-2]
_lowerCAmelCase = mapped_key.replace("""*""" , snake_case )
if "weight_g" in name:
_lowerCAmelCase = """weight_g"""
elif "weight_v" in name:
_lowerCAmelCase = """weight_v"""
elif "weight" in name:
_lowerCAmelCase = """weight"""
elif "bias" in name:
_lowerCAmelCase = """bias"""
else:
_lowerCAmelCase = None
set_recursively(snake_case , snake_case , snake_case , snake_case , snake_case )
continue
if not is_used:
unused_weights.append(snake_case )
logger.warning(F'Unused weights: {unused_weights}' )
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = full_name.split("""conv_layers.""" )[-1]
_lowerCAmelCase = name.split(""".""" )
_lowerCAmelCase = int(items[0] )
_lowerCAmelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F'{full_name} has size {value.shape}, but'
F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
)
_lowerCAmelCase = value
logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F'{full_name} has size {value.shape}, but'
F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
)
_lowerCAmelCase = value
logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
" found."
)
_lowerCAmelCase = value
logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F'{full_name} has size {value.shape}, but'
F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
)
_lowerCAmelCase = value
logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(snake_case )
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = SEWConfig()
if is_finetuned:
_lowerCAmelCase = model.wav_encoder.wav_model.cfg
else:
_lowerCAmelCase = model.cfg
_lowerCAmelCase = fs_config.conv_bias
_lowerCAmelCase = eval(fs_config.conv_feature_layers )
_lowerCAmelCase = [x[0] for x in conv_layers]
_lowerCAmelCase = [x[1] for x in conv_layers]
_lowerCAmelCase = [x[2] for x in conv_layers]
_lowerCAmelCase = """gelu"""
_lowerCAmelCase = """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
_lowerCAmelCase = 0.0
_lowerCAmelCase = fs_config.activation_fn.name
_lowerCAmelCase = fs_config.encoder_embed_dim
_lowerCAmelCase = 0.02
_lowerCAmelCase = fs_config.encoder_ffn_embed_dim
_lowerCAmelCase = 1E-5
_lowerCAmelCase = fs_config.encoder_layerdrop
_lowerCAmelCase = fs_config.encoder_attention_heads
_lowerCAmelCase = fs_config.conv_pos_groups
_lowerCAmelCase = fs_config.conv_pos
_lowerCAmelCase = len(snake_case )
_lowerCAmelCase = fs_config.encoder_layers
_lowerCAmelCase = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
_lowerCAmelCase = model.cfg
_lowerCAmelCase = fs_config.final_dropout
_lowerCAmelCase = fs_config.layerdrop
_lowerCAmelCase = fs_config.activation_dropout
_lowerCAmelCase = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
_lowerCAmelCase = fs_config.attention_dropout
_lowerCAmelCase = fs_config.dropout_input
_lowerCAmelCase = fs_config.dropout
_lowerCAmelCase = fs_config.mask_channel_length
_lowerCAmelCase = fs_config.mask_channel_prob
_lowerCAmelCase = fs_config.mask_length
_lowerCAmelCase = fs_config.mask_prob
_lowerCAmelCase = """Wav2Vec2FeatureExtractor"""
_lowerCAmelCase = """Wav2Vec2CTCTokenizer"""
return config
@torch.no_grad()
def _UpperCAmelCase ( snake_case , snake_case , snake_case=None , snake_case=None , snake_case=True ):
"""simple docstring"""
if is_finetuned:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
_lowerCAmelCase = SEWConfig.from_pretrained(snake_case )
else:
_lowerCAmelCase = convert_config(model[0] , snake_case )
_lowerCAmelCase = model[0].eval()
_lowerCAmelCase = True if config.feat_extract_norm == """layer""" else False
_lowerCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=snake_case , return_attention_mask=snake_case , )
if is_finetuned:
if dict_path:
_lowerCAmelCase = Dictionary.load(snake_case )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_lowerCAmelCase = target_dict.pad_index
_lowerCAmelCase = target_dict.bos_index
_lowerCAmelCase = target_dict.pad_index
_lowerCAmelCase = target_dict.bos_index
_lowerCAmelCase = target_dict.eos_index
_lowerCAmelCase = len(target_dict.symbols )
_lowerCAmelCase = os.path.join(snake_case , """vocab.json""" )
if not os.path.isdir(snake_case ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(snake_case ) )
return
os.makedirs(snake_case , exist_ok=snake_case )
with open(snake_case , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(target_dict.indices , snake_case )
_lowerCAmelCase = WavaVecaCTCTokenizer(
snake_case , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=snake_case , )
_lowerCAmelCase = WavaVecaProcessor(feature_extractor=snake_case , tokenizer=snake_case )
processor.save_pretrained(snake_case )
_lowerCAmelCase = SEWForCTC(snake_case )
else:
_lowerCAmelCase = SEWModel(snake_case )
feature_extractor.save_pretrained(snake_case )
recursively_load_weights(snake_case , snake_case , snake_case )
hf_model.save_pretrained(snake_case )
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
A__ = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 82 |
A__ = [0, 2, 4, 6, 8]
A__ = [1, 3, 5, 7, 9]
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 10
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
_lowerCAmelCase = 0
for digit in range(10 ):
_lowerCAmelCase = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 10 , snake_case , snake_case )
return result
_lowerCAmelCase = 0
for digita in range(10 ):
_lowerCAmelCase = digita
if (remainder + digita) % 2 == 0:
_lowerCAmelCase = ODD_DIGITS
else:
_lowerCAmelCase = EVEN_DIGITS
for digita in other_parity_digits:
_lowerCAmelCase = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 10 , snake_case , snake_case , )
return result
def _UpperCAmelCase ( snake_case = 9 ):
"""simple docstring"""
_lowerCAmelCase = 0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(snake_case , 0 , [0] * length , snake_case )
return result
if __name__ == "__main__":
print(f"{solution() = }")
| 82 | 1 |
from collections import defaultdict
class __lowerCAmelCase :
def __init__( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
_lowerCAmelCase = [
[-1 for i in range(total + 1 )] for j in range(2 ** len(_snake_case ) )
]
_lowerCAmelCase = defaultdict(_snake_case ) # stores the list of persons for each task
# final_mask is used to check if all persons are included by setting all bits
# to 1
_lowerCAmelCase = (1 << len(_snake_case )) - 1
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
if mask == self.final_mask:
return 1
# if not everyone gets the task and no more tasks are available, return 0
if task_no > self.total_tasks:
return 0
# if case already considered
if self.dp[mask][task_no] != -1:
return self.dp[mask][task_no]
# Number of ways when we don't this task in the arrangement
_lowerCAmelCase = self.count_ways_until(_snake_case , task_no + 1 )
# now assign the tasks one by one to all possible persons and recursively
# assign for the remaining tasks.
if task_no in self.task:
for p in self.task[task_no]:
# if p is already given a task
if mask & (1 << p):
continue
# assign this task to p and change the mask value. And recursively
# assign tasks with the new mask value.
total_ways_util += self.count_ways_until(mask | (1 << p) , task_no + 1 )
# save the value.
_lowerCAmelCase = total_ways_util
return self.dp[mask][task_no]
def snake_case ( self , _snake_case ):
"""simple docstring"""
for i in range(len(_snake_case ) ):
for j in task_performed[i]:
self.task[j].append(_snake_case )
# call the function to fill the DP table, final answer is stored in dp[0][1]
return self.count_ways_until(0 , 1 )
if __name__ == "__main__":
A__ = 5 # total no of tasks (the value of N)
# the list of tasks that can be done by M persons.
A__ = [[1, 3, 4], [1, 2, 5], [3, 4]]
print(
AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
task_performed
)
)
| 82 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
A__ = logging.get_logger(__name__)
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = OrderedDict()
for key, value in state_dict.items():
if key.startswith("""module.encoder""" ):
_lowerCAmelCase = key.replace("""module.encoder""" , """glpn.encoder""" )
if key.startswith("""module.decoder""" ):
_lowerCAmelCase = key.replace("""module.decoder""" , """decoder.stages""" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
_lowerCAmelCase = key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
_lowerCAmelCase = key.replace(F'patch_embed{idx}' , F'patch_embeddings.{int(snake_case )-1}' )
if "norm" in key:
_lowerCAmelCase = key.replace("""norm""" , """layer_norm""" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
_lowerCAmelCase = key[key.find("""glpn.encoder.layer_norm""" ) + len("""glpn.encoder.layer_norm""" )]
_lowerCAmelCase = key.replace(F'layer_norm{idx}' , F'layer_norm.{int(snake_case )-1}' )
if "layer_norm1" in key:
_lowerCAmelCase = key.replace("""layer_norm1""" , """layer_norm_1""" )
if "layer_norm2" in key:
_lowerCAmelCase = key.replace("""layer_norm2""" , """layer_norm_2""" )
if "block" in key:
# replace for example block1 by block.0
_lowerCAmelCase = key[key.find("""block""" ) + len("""block""" )]
_lowerCAmelCase = key.replace(F'block{idx}' , F'block.{int(snake_case )-1}' )
if "attn.q" in key:
_lowerCAmelCase = key.replace("""attn.q""" , """attention.self.query""" )
if "attn.proj" in key:
_lowerCAmelCase = key.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in key:
_lowerCAmelCase = key.replace("""attn""" , """attention.self""" )
if "fc1" in key:
_lowerCAmelCase = key.replace("""fc1""" , """dense1""" )
if "fc2" in key:
_lowerCAmelCase = key.replace("""fc2""" , """dense2""" )
if "linear_pred" in key:
_lowerCAmelCase = key.replace("""linear_pred""" , """classifier""" )
if "linear_fuse" in key:
_lowerCAmelCase = key.replace("""linear_fuse.conv""" , """linear_fuse""" )
_lowerCAmelCase = key.replace("""linear_fuse.bn""" , """batch_norm""" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
_lowerCAmelCase = key[key.find("""linear_c""" ) + len("""linear_c""" )]
_lowerCAmelCase = key.replace(F'linear_c{idx}' , F'linear_c.{int(snake_case )-1}' )
if "bot_conv" in key:
_lowerCAmelCase = key.replace("""bot_conv""" , """0.convolution""" )
if "skip_conv1" in key:
_lowerCAmelCase = key.replace("""skip_conv1""" , """1.convolution""" )
if "skip_conv2" in key:
_lowerCAmelCase = key.replace("""skip_conv2""" , """2.convolution""" )
if "fusion1" in key:
_lowerCAmelCase = key.replace("""fusion1""" , """1.fusion""" )
if "fusion2" in key:
_lowerCAmelCase = key.replace("""fusion2""" , """2.fusion""" )
if "fusion3" in key:
_lowerCAmelCase = key.replace("""fusion3""" , """3.fusion""" )
if "fusion" in key and "conv" in key:
_lowerCAmelCase = key.replace("""conv""" , """convolutional_layer""" )
if key.startswith("""module.last_layer_depth""" ):
_lowerCAmelCase = key.replace("""module.last_layer_depth""" , """head.head""" )
_lowerCAmelCase = value
return new_state_dict
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
_lowerCAmelCase = state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight' )
_lowerCAmelCase = state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.bias' )
# next, add keys and values (in that order) to the state dict
_lowerCAmelCase = kv_weight[
: config.hidden_sizes[i], :
]
_lowerCAmelCase = kv_bias[: config.hidden_sizes[i]]
_lowerCAmelCase = kv_weight[
config.hidden_sizes[i] :, :
]
_lowerCAmelCase = kv_bias[config.hidden_sizes[i] :]
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_lowerCAmelCase = Image.open(requests.get(snake_case , stream=snake_case ).raw )
return image
@torch.no_grad()
def _UpperCAmelCase ( snake_case , snake_case , snake_case=False , snake_case=None ):
"""simple docstring"""
_lowerCAmelCase = GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12] , decoder_hidden_size=64 , depths=[3, 8, 27, 3] )
# load image processor (only resize + rescale)
_lowerCAmelCase = GLPNImageProcessor()
# prepare image
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(images=snake_case , return_tensors="""pt""" ).pixel_values
logger.info("""Converting model...""" )
# load original state dict
_lowerCAmelCase = torch.load(snake_case , map_location=torch.device("""cpu""" ) )
# rename keys
_lowerCAmelCase = rename_keys(snake_case )
# key and value matrices need special treatment
read_in_k_v(snake_case , snake_case )
# create HuggingFace model and load state dict
_lowerCAmelCase = GLPNForDepthEstimation(snake_case )
model.load_state_dict(snake_case )
model.eval()
# forward pass
_lowerCAmelCase = model(snake_case )
_lowerCAmelCase = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
_lowerCAmelCase = torch.tensor(
[[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]] )
elif "kitti" in model_name:
_lowerCAmelCase = torch.tensor(
[[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]] )
else:
raise ValueError(F'Unknown model name: {model_name}' )
_lowerCAmelCase = torch.Size([1, 4_80, 6_40] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3] , snake_case , atol=1E-4 )
print("""Looks ok!""" )
# finally, push to hub if required
if push_to_hub:
logger.info("""Pushing model and image processor to the hub...""" )
model.push_to_hub(
repo_path_or_name=Path(snake_case , snake_case ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=snake_case , )
image_processor.push_to_hub(
repo_path_or_name=Path(snake_case , snake_case ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=snake_case , )
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""",
default=None,
type=str,
help="""Path to the original PyTorch checkpoint (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
)
parser.add_argument(
"""--model_name""",
default="""glpn-kitti""",
type=str,
help="""Name of the model in case you're pushing to the hub.""",
)
A__ = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 82 | 1 |
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
return x if y == 0 else greatest_common_divisor(snake_case , x % y )
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
return (x * y) // greatest_common_divisor(snake_case , snake_case )
def _UpperCAmelCase ( snake_case = 20 ):
"""simple docstring"""
_lowerCAmelCase = 1
for i in range(1 , n + 1 ):
_lowerCAmelCase = lcm(snake_case , snake_case )
return g
if __name__ == "__main__":
print(f"{solution() = }")
| 82 |
from math import isqrt, loga
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , snake_case , snake_case ):
_lowerCAmelCase = False
return [i for i in range(2 , snake_case ) if is_prime[i]]
def _UpperCAmelCase ( snake_case = 80_08_00 , snake_case = 80_08_00 ):
"""simple docstring"""
_lowerCAmelCase = degree * loga(snake_case )
_lowerCAmelCase = int(snake_case )
_lowerCAmelCase = calculate_prime_numbers(snake_case )
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = len(snake_case ) - 1
while left < right:
while (
prime_numbers[right] * loga(prime_numbers[left] )
+ prime_numbers[left] * loga(prime_numbers[right] )
> upper_bound
):
right -= 1
hybrid_integers_count += right - left
left += 1
return hybrid_integers_count
if __name__ == "__main__":
print(f"{solution() = }")
| 82 | 1 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
A__ = logging.get_logger(__name__)
class __lowerCAmelCase ( lowerCamelCase__ ):
def __init__( self , _snake_case ):
"""simple docstring"""
super().__init__()
_lowerCAmelCase = nn.ModuleList(_snake_case )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = False , _snake_case = True , ):
"""simple docstring"""
for i, (image, scale, controlnet) in enumerate(zip(_snake_case , _snake_case , self.nets ) ):
_lowerCAmelCase , _lowerCAmelCase = controlnet(
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , )
# merge samples
if i == 0:
_lowerCAmelCase , _lowerCAmelCase = down_samples, mid_sample
else:
_lowerCAmelCase = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(_snake_case , _snake_case )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def snake_case ( self , _snake_case , _snake_case = True , _snake_case = None , _snake_case = False , _snake_case = None , ):
"""simple docstring"""
_lowerCAmelCase = 0
_lowerCAmelCase = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
_snake_case , is_main_process=_snake_case , save_function=_snake_case , safe_serialization=_snake_case , variant=_snake_case , )
idx += 1
_lowerCAmelCase = model_path_to_save + F'_{idx}'
@classmethod
def snake_case ( cls , _snake_case , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase = 0
_lowerCAmelCase = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
_lowerCAmelCase = pretrained_model_path
while os.path.isdir(_snake_case ):
_lowerCAmelCase = ControlNetModel.from_pretrained(_snake_case , **_snake_case )
controlnets.append(_snake_case )
idx += 1
_lowerCAmelCase = pretrained_model_path + F'_{idx}'
logger.info(F'{len(_snake_case )} controlnets loaded from {pretrained_model_path}.' )
if len(_snake_case ) == 0:
raise ValueError(
F'No ControlNets found under {os.path.dirname(_snake_case )}. Expected at least {pretrained_model_path + "_0"}.' )
return cls(_snake_case )
| 82 |
from __future__ import annotations
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = str(snake_case )
return n == n[::-1]
def _UpperCAmelCase ( snake_case = 1_00_00_00 ):
"""simple docstring"""
_lowerCAmelCase = 0
for i in range(1 , snake_case ):
if is_palindrome(snake_case ) and is_palindrome(bin(snake_case ).split("""b""" )[1] ):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| 82 | 1 |
from __future__ import annotations
import math
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(snake_case ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = str(snake_case )
_lowerCAmelCase = [n]
for i in range(1 , len(snake_case ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if len(str(snake_case ) ) > 3:
if not is_prime(int(str(snake_case )[-3:] ) ) or not is_prime(int(str(snake_case )[:3] ) ):
return False
return True
def _UpperCAmelCase ( snake_case = 11 ):
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = 13
while len(snake_case ) != count:
if validate(snake_case ):
_lowerCAmelCase = list_truncated_nums(snake_case )
if all(is_prime(snake_case ) for i in list_nums ):
list_truncated_primes.append(snake_case )
num += 2
return list_truncated_primes
def _UpperCAmelCase ( ):
"""simple docstring"""
return sum(compute_truncated_primes(11 ) )
if __name__ == "__main__":
print(f"{sum(compute_truncated_primes(11)) = }")
| 82 |
from collections.abc import Iterable
from typing import Generic, TypeVar
A__ = TypeVar("""_T""")
class __lowerCAmelCase ( Generic[_T] ):
def __init__( self , _snake_case = None ):
"""simple docstring"""
_lowerCAmelCase = list(iterable or [] )
_lowerCAmelCase = []
def __len__( self ):
"""simple docstring"""
return len(self._stacka ) + len(self._stacka )
def __repr__( self ):
"""simple docstring"""
return F'Queue({tuple(self._stacka[::-1] + self._stacka )})'
def snake_case ( self , _snake_case ):
"""simple docstring"""
self._stacka.append(_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self._stacka.pop
_lowerCAmelCase = self._stacka.append
if not self._stacka:
while self._stacka:
stacka_append(stacka_pop() )
if not self._stacka:
raise IndexError("""Queue is empty""" )
return self._stacka.pop()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 82 | 1 |
from collections.abc import Iterable
from typing import Generic, TypeVar
A__ = TypeVar("""_T""")
class __lowerCAmelCase ( Generic[_T] ):
def __init__( self , _snake_case = None ):
"""simple docstring"""
_lowerCAmelCase = list(iterable or [] )
_lowerCAmelCase = []
def __len__( self ):
"""simple docstring"""
return len(self._stacka ) + len(self._stacka )
def __repr__( self ):
"""simple docstring"""
return F'Queue({tuple(self._stacka[::-1] + self._stacka )})'
def snake_case ( self , _snake_case ):
"""simple docstring"""
self._stacka.append(_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self._stacka.pop
_lowerCAmelCase = self._stacka.append
if not self._stacka:
while self._stacka:
stacka_append(stacka_pop() )
if not self._stacka:
raise IndexError("""Queue is empty""" )
return self._stacka.pop()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 82 |
A__ = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A__ = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = True
_lowerCAmelCase = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(snake_case , snake_case , snake_case )
order.append(snake_case )
return order
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = True
_lowerCAmelCase = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(snake_case , snake_case , snake_case )
return component
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = len(snake_case ) * [False]
_lowerCAmelCase = {vert: [] for vert in range(len(snake_case ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(snake_case )
_lowerCAmelCase = []
for i, was_visited in enumerate(snake_case ):
if not was_visited:
order += topology_sort(snake_case , snake_case , snake_case )
_lowerCAmelCase = []
_lowerCAmelCase = len(snake_case ) * [False]
for i in range(len(snake_case ) ):
_lowerCAmelCase = order[len(snake_case ) - i - 1]
if not visited[vert]:
_lowerCAmelCase = find_components(snake_case , snake_case , snake_case )
components_list.append(snake_case )
return components_list
| 82 | 1 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
A__ = subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
A__ = subprocess.check_output(f"git diff --name-only {fork_point_sha}".split()).decode("""utf-8""").split()
A__ = """|""".join(sys.argv[1:])
A__ = re.compile(Rf"^({joined_dirs}).*?\.py$")
A__ = [x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")
| 82 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader
from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
ROUGE_KEYS,
LegacySeqaSeqDataset,
SeqaSeqDataset,
assert_all_frozen,
calculate_bleu,
calculate_rouge,
check_output_dir,
flatten_list,
freeze_embeds,
freeze_params,
get_git_info,
label_smoothed_nll_loss,
lmap,
pickle_save,
save_git_info,
save_json,
use_task_specific_params,
)
# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train # noqa
A__ = logging.getLogger(__name__)
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''summarization'''
__lowerCamelCase = ['''loss''']
__lowerCamelCase = ROUGE_KEYS
__lowerCamelCase = '''rouge2'''
def __init__( self , _snake_case , **_snake_case ):
"""simple docstring"""
if hparams.sortish_sampler and hparams.gpus > 1:
_lowerCAmelCase = False
elif hparams.max_tokens_per_batch is not None:
if hparams.gpus > 1:
raise NotImplementedError("""Dynamic Batch size does not work for multi-gpu training""" )
if hparams.sortish_sampler:
raise ValueError("""--sortish_sampler and --max_tokens_per_batch may not be used simultaneously""" )
super().__init__(_snake_case , num_labels=_snake_case , mode=self.mode , **_snake_case )
use_task_specific_params(self.model , """summarization""" )
save_git_info(self.hparams.output_dir )
_lowerCAmelCase = Path(self.output_dir ) / """metrics.json"""
_lowerCAmelCase = Path(self.output_dir ) / """hparams.pkl"""
pickle_save(self.hparams , self.hparams_save_path )
_lowerCAmelCase = 0
_lowerCAmelCase = defaultdict(_snake_case )
_lowerCAmelCase = self.config.model_type
_lowerCAmelCase = self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size
_lowerCAmelCase = {
"data_dir": self.hparams.data_dir,
"max_source_length": self.hparams.max_source_length,
"prefix": self.model.config.prefix or "",
}
_lowerCAmelCase = {
"""train""": self.hparams.n_train,
"""val""": self.hparams.n_val,
"""test""": self.hparams.n_test,
}
_lowerCAmelCase = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
_lowerCAmelCase = {
"""train""": self.hparams.max_target_length,
"""val""": self.hparams.val_max_target_length,
"""test""": self.hparams.test_max_target_length,
}
assert self.target_lens["train"] <= self.target_lens["val"], F'target_lens: {self.target_lens}'
assert self.target_lens["train"] <= self.target_lens["test"], F'target_lens: {self.target_lens}'
if self.hparams.freeze_embeds:
freeze_embeds(self.model )
if self.hparams.freeze_encoder:
freeze_params(self.model.get_encoder() )
assert_all_frozen(self.model.get_encoder() )
_lowerCAmelCase = get_git_info()["""repo_sha"""]
_lowerCAmelCase = hparams.num_workers
_lowerCAmelCase = None # default to config
if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer , _snake_case ):
_lowerCAmelCase = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
_lowerCAmelCase = self.decoder_start_token_id
_lowerCAmelCase = (
SeqaSeqDataset if hasattr(self.tokenizer , """prepare_seq2seq_batch""" ) else LegacySeqaSeqDataset
)
_lowerCAmelCase = False
_lowerCAmelCase = self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
if self.hparams.eval_max_gen_length is not None:
_lowerCAmelCase = self.hparams.eval_max_gen_length
else:
_lowerCAmelCase = self.model.config.max_length
_lowerCAmelCase = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = {
k: self.tokenizer.batch_decode(v.tolist() ) if """mask""" not in k else v.shape for k, v in batch.items()
}
save_json(_snake_case , Path(self.output_dir ) / """text_batch.json""" )
save_json({k: v.tolist() for k, v in batch.items()} , Path(self.output_dir ) / """tok_batch.json""" )
_lowerCAmelCase = True
return readable_batch
def snake_case ( self , _snake_case , **_snake_case ):
"""simple docstring"""
return self.model(_snake_case , **_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.tokenizer.batch_decode(
_snake_case , skip_special_tokens=_snake_case , clean_up_tokenization_spaces=_snake_case )
return lmap(str.strip , _snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.tokenizer.pad_token_id
_lowerCAmelCase , _lowerCAmelCase = batch["""input_ids"""], batch["""attention_mask"""]
_lowerCAmelCase = batch["""labels"""]
if isinstance(self.model , _snake_case ):
_lowerCAmelCase = self.model._shift_right(_snake_case )
else:
_lowerCAmelCase = shift_tokens_right(_snake_case , _snake_case )
if not self.already_saved_batch: # This would be slightly better if it only happened on rank zero
_lowerCAmelCase = decoder_input_ids
self.save_readable_batch(_snake_case )
_lowerCAmelCase = self(_snake_case , attention_mask=_snake_case , decoder_input_ids=_snake_case , use_cache=_snake_case )
_lowerCAmelCase = outputs["""logits"""]
if self.hparams.label_smoothing == 0:
# Same behavior as modeling_bart.py, besides ignoring pad_token_id
_lowerCAmelCase = nn.CrossEntropyLoss(ignore_index=_snake_case )
assert lm_logits.shape[-1] == self.vocab_size
_lowerCAmelCase = ce_loss_fct(lm_logits.view(-1 , lm_logits.shape[-1] ) , tgt_ids.view(-1 ) )
else:
_lowerCAmelCase = nn.functional.log_softmax(_snake_case , dim=-1 )
_lowerCAmelCase , _lowerCAmelCase = label_smoothed_nll_loss(
_snake_case , _snake_case , self.hparams.label_smoothing , ignore_index=_snake_case )
return (loss,)
@property
def snake_case ( self ):
"""simple docstring"""
return self.tokenizer.pad_token_id
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self._step(_snake_case )
_lowerCAmelCase = dict(zip(self.loss_names , _snake_case ) )
# tokens per batch
_lowerCAmelCase = batch["""input_ids"""].ne(self.pad ).sum() + batch["""labels"""].ne(self.pad ).sum()
_lowerCAmelCase = batch["""input_ids"""].shape[0]
_lowerCAmelCase = batch["""input_ids"""].eq(self.pad ).sum()
_lowerCAmelCase = batch["""input_ids"""].eq(self.pad ).float().mean()
# TODO(SS): make a wandb summary metric for this
return {"loss": loss_tensors[0], "log": logs}
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
return self._generative_step(_snake_case )
def snake_case ( self , _snake_case , _snake_case="val" ):
"""simple docstring"""
self.step_count += 1
_lowerCAmelCase = {k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
_lowerCAmelCase = losses["""loss"""]
_lowerCAmelCase = {
k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
}
_lowerCAmelCase = (
generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
)
_lowerCAmelCase = torch.tensor(_snake_case ).type_as(_snake_case )
generative_metrics.update({k: v.item() for k, v in losses.items()} )
losses.update(_snake_case )
_lowerCAmelCase = {F'{prefix}_avg_{k}': x for k, x in losses.items()}
_lowerCAmelCase = self.step_count
self.metrics[prefix].append(_snake_case ) # callback writes this to self.metrics_save_path
_lowerCAmelCase = flatten_list([x["""preds"""] for x in outputs] )
return {
"log": all_metrics,
"preds": preds,
F'{prefix}_loss': loss,
F'{prefix}_{self.val_metric}': metric_tensor,
}
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
return calculate_rouge(_snake_case , _snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = time.time()
# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
_lowerCAmelCase = self.model.generate(
batch["""input_ids"""] , attention_mask=batch["""attention_mask"""] , use_cache=_snake_case , decoder_start_token_id=self.decoder_start_token_id , num_beams=self.eval_beams , max_length=self.eval_max_length , )
_lowerCAmelCase = (time.time() - ta) / batch["""input_ids"""].shape[0]
_lowerCAmelCase = self.ids_to_clean_text(_snake_case )
_lowerCAmelCase = self.ids_to_clean_text(batch["""labels"""] )
_lowerCAmelCase = self._step(_snake_case )
_lowerCAmelCase = dict(zip(self.loss_names , _snake_case ) )
_lowerCAmelCase = self.calc_generative_metrics(_snake_case , _snake_case )
_lowerCAmelCase = np.mean(lmap(_snake_case , _snake_case ) )
base_metrics.update(gen_time=_snake_case , gen_len=_snake_case , preds=_snake_case , target=_snake_case , **_snake_case )
return base_metrics
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
return self._generative_step(_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
return self.validation_epoch_end(_snake_case , prefix="""test""" )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.n_obs[type_path]
_lowerCAmelCase = self.target_lens[type_path]
_lowerCAmelCase = self.dataset_class(
self.tokenizer , type_path=_snake_case , n_obs=_snake_case , max_target_length=_snake_case , **self.dataset_kwargs , )
return dataset
def snake_case ( self , _snake_case , _snake_case , _snake_case = False ):
"""simple docstring"""
_lowerCAmelCase = self.get_dataset(_snake_case )
if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
_lowerCAmelCase = dataset.make_sortish_sampler(_snake_case , distributed=self.hparams.gpus > 1 )
return DataLoader(
_snake_case , batch_size=_snake_case , collate_fn=dataset.collate_fn , shuffle=_snake_case , num_workers=self.num_workers , sampler=_snake_case , )
elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
_lowerCAmelCase = dataset.make_dynamic_sampler(
self.hparams.max_tokens_per_batch , distributed=self.hparams.gpus > 1 )
return DataLoader(
_snake_case , batch_sampler=_snake_case , collate_fn=dataset.collate_fn , num_workers=self.num_workers , )
else:
return DataLoader(
_snake_case , batch_size=_snake_case , collate_fn=dataset.collate_fn , shuffle=_snake_case , num_workers=self.num_workers , sampler=_snake_case , )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_dataloader("""train""" , batch_size=self.hparams.train_batch_size , shuffle=_snake_case )
return dataloader
def snake_case ( self ):
"""simple docstring"""
return self.get_dataloader("""val""" , batch_size=self.hparams.eval_batch_size )
def snake_case ( self ):
"""simple docstring"""
return self.get_dataloader("""test""" , batch_size=self.hparams.eval_batch_size )
@staticmethod
def snake_case ( _snake_case , _snake_case ):
"""simple docstring"""
BaseTransformer.add_model_specific_args(_snake_case , _snake_case )
add_generic_args(_snake_case , _snake_case )
parser.add_argument(
"""--max_source_length""" , default=1024 , type=_snake_case , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--max_target_length""" , default=56 , type=_snake_case , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--val_max_target_length""" , default=142 , type=_snake_case , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--test_max_target_length""" , default=142 , type=_snake_case , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument("""--freeze_encoder""" , action="""store_true""" )
parser.add_argument("""--freeze_embeds""" , action="""store_true""" )
parser.add_argument("""--sortish_sampler""" , action="""store_true""" , default=_snake_case )
parser.add_argument("""--overwrite_output_dir""" , action="""store_true""" , default=_snake_case )
parser.add_argument("""--max_tokens_per_batch""" , type=_snake_case , default=_snake_case )
parser.add_argument("""--logger_name""" , type=_snake_case , choices=["""default""", """wandb""", """wandb_shared"""] , default="""default""" )
parser.add_argument("""--n_train""" , type=_snake_case , default=-1 , required=_snake_case , help="""# examples. -1 means use all.""" )
parser.add_argument("""--n_val""" , type=_snake_case , default=500 , required=_snake_case , help="""# examples. -1 means use all.""" )
parser.add_argument("""--n_test""" , type=_snake_case , default=-1 , required=_snake_case , help="""# examples. -1 means use all.""" )
parser.add_argument(
"""--task""" , type=_snake_case , default="""summarization""" , required=_snake_case , help="""# examples. -1 means use all.""" )
parser.add_argument("""--label_smoothing""" , type=_snake_case , default=0.0 , required=_snake_case )
parser.add_argument("""--src_lang""" , type=_snake_case , default="""""" , required=_snake_case )
parser.add_argument("""--tgt_lang""" , type=_snake_case , default="""""" , required=_snake_case )
parser.add_argument("""--eval_beams""" , type=_snake_case , default=_snake_case , required=_snake_case )
parser.add_argument(
"""--val_metric""" , type=_snake_case , default=_snake_case , required=_snake_case , choices=["""bleu""", """rouge2""", """loss""", None] )
parser.add_argument("""--eval_max_gen_length""" , type=_snake_case , default=_snake_case , help="""never generate more than n tokens""" )
parser.add_argument("""--save_top_k""" , type=_snake_case , default=1 , required=_snake_case , help="""How many checkpoints to save""" )
parser.add_argument(
"""--early_stopping_patience""" , type=_snake_case , default=-1 , required=_snake_case , help=(
"""-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"""
""" val_check_interval will effect it."""
) , )
return parser
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''translation'''
__lowerCamelCase = ['''loss''']
__lowerCamelCase = ['''bleu''']
__lowerCamelCase = '''bleu'''
def __init__( self , _snake_case , **_snake_case ):
"""simple docstring"""
super().__init__(_snake_case , **_snake_case )
_lowerCAmelCase = hparams.src_lang
_lowerCAmelCase = hparams.tgt_lang
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
return calculate_bleu(_snake_case , _snake_case )
def _UpperCAmelCase ( snake_case , snake_case=None ):
"""simple docstring"""
Path(args.output_dir ).mkdir(exist_ok=snake_case )
check_output_dir(snake_case , expected_items=3 )
if model is None:
if "summarization" in args.task:
_lowerCAmelCase = SummarizationModule(snake_case )
else:
_lowerCAmelCase = TranslationModule(snake_case )
_lowerCAmelCase = Path(args.data_dir ).name
if (
args.logger_name == "default"
or args.fast_dev_run
or str(args.output_dir ).startswith("""/tmp""" )
or str(args.output_dir ).startswith("""/var""" )
):
_lowerCAmelCase = True # don't pollute wandb logs unnecessarily
elif args.logger_name == "wandb":
from pytorch_lightning.loggers import WandbLogger
_lowerCAmelCase = os.environ.get("""WANDB_PROJECT""" , snake_case )
_lowerCAmelCase = WandbLogger(name=model.output_dir.name , project=snake_case )
elif args.logger_name == "wandb_shared":
from pytorch_lightning.loggers import WandbLogger
_lowerCAmelCase = WandbLogger(name=model.output_dir.name , project=F'hf_{dataset}' )
if args.early_stopping_patience >= 0:
_lowerCAmelCase = get_early_stopping_callback(model.val_metric , args.early_stopping_patience )
else:
_lowerCAmelCase = False
_lowerCAmelCase = args.val_metric == """loss"""
_lowerCAmelCase = generic_train(
snake_case , snake_case , logging_callback=SeqaSeqLoggingCallback() , checkpoint_callback=get_checkpoint_callback(
args.output_dir , model.val_metric , args.save_top_k , snake_case ) , early_stopping_callback=snake_case , logger=snake_case , )
pickle_save(model.hparams , model.output_dir / """hparams.pkl""" )
if not args.do_predict:
return model
_lowerCAmelCase = """"""
_lowerCAmelCase = sorted(glob.glob(os.path.join(args.output_dir , """*.ckpt""" ) , recursive=snake_case ) )
if checkpoints:
_lowerCAmelCase = checkpoints[-1]
_lowerCAmelCase = checkpoints[-1]
trainer.logger.log_hyperparams(model.hparams )
# test() without a model tests using the best checkpoint automatically
trainer.test()
return model
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
A__ = pl.Trainer.add_argparse_args(parser)
A__ = SummarizationModule.add_model_specific_args(parser, os.getcwd())
A__ = parser.parse_args()
main(args)
| 82 | 1 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as nn
from ..pytorch_utils import ConvaD
if is_accelerate_available():
from accelerate import init_empty_weights
from accelerate.utils import find_tied_parameters
A__ = logging.get_logger(__name__)
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case=None , snake_case=None ):
"""simple docstring"""
if "." in tensor_name:
_lowerCAmelCase = tensor_name.split(""".""" )
for split in splits[:-1]:
_lowerCAmelCase = getattr(snake_case , snake_case )
if new_module is None:
raise ValueError(F'{module} has no attribute {split}.' )
_lowerCAmelCase = new_module
_lowerCAmelCase = splits[-1]
if tensor_name not in module._parameters and tensor_name not in module._buffers:
raise ValueError(F'{module} does not have a parameter or a buffer named {tensor_name}.' )
_lowerCAmelCase = tensor_name in module._buffers
_lowerCAmelCase = getattr(snake_case , snake_case )
if old_value.device == torch.device("""meta""" ) and device not in ["meta", torch.device("""meta""" )] and value is None:
raise ValueError(F'{tensor_name} is on the meta device, we need a `value` to put in on {device}.' )
_lowerCAmelCase = False
_lowerCAmelCase = False
if is_buffer or not is_bitsandbytes_available():
_lowerCAmelCase = False
_lowerCAmelCase = False
else:
_lowerCAmelCase = hasattr(bnb.nn , """Params4bit""" ) and isinstance(module._parameters[tensor_name] , bnb.nn.Paramsabit )
_lowerCAmelCase = isinstance(module._parameters[tensor_name] , bnb.nn.IntaParams )
if is_abit or is_abit:
_lowerCAmelCase = module._parameters[tensor_name]
if param.device.type != "cuda":
if value is None:
_lowerCAmelCase = old_value.to(snake_case )
elif isinstance(snake_case , torch.Tensor ):
_lowerCAmelCase = value.to("""cpu""" )
if value.dtype == torch.inta:
_lowerCAmelCase = version.parse(importlib.metadata.version("""bitsandbytes""" ) ) > version.parse(
"""0.37.2""" )
if not is_abit_serializable:
raise ValueError(
"""Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. """
"""Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`.""" )
else:
_lowerCAmelCase = torch.tensor(snake_case , device="""cpu""" )
# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
# Since weights are saved in the correct "orientation", we skip transposing when loading.
if issubclass(module.source_cls , snake_case ) and fpaa_statistics is None:
_lowerCAmelCase = new_value.T
_lowerCAmelCase = old_value.__dict__
if is_abit:
_lowerCAmelCase = bnb.nn.IntaParams(snake_case , requires_grad=snake_case , **snake_case ).to(snake_case )
elif is_abit:
_lowerCAmelCase = bnb.nn.Paramsabit(snake_case , requires_grad=snake_case , **snake_case ).to(snake_case )
_lowerCAmelCase = new_value
if fpaa_statistics is not None:
setattr(module.weight , """SCB""" , fpaa_statistics.to(snake_case ) )
else:
if value is None:
_lowerCAmelCase = old_value.to(snake_case )
elif isinstance(snake_case , torch.Tensor ):
_lowerCAmelCase = value.to(snake_case )
else:
_lowerCAmelCase = torch.tensor(snake_case , device=snake_case )
if is_buffer:
_lowerCAmelCase = new_value
else:
_lowerCAmelCase = nn.Parameter(snake_case , requires_grad=old_value.requires_grad )
_lowerCAmelCase = new_value
def _UpperCAmelCase ( snake_case , snake_case=None , snake_case=None , snake_case=None , snake_case=False ):
"""simple docstring"""
for name, module in model.named_children():
if current_key_name is None:
_lowerCAmelCase = []
current_key_name.append(snake_case )
if (isinstance(snake_case , nn.Linear ) or isinstance(snake_case , snake_case )) and name not in modules_to_not_convert:
# Check if the current key is not in the `modules_to_not_convert`
if not any(key in """.""".join(snake_case ) for key in modules_to_not_convert ):
with init_empty_weights():
if isinstance(snake_case , snake_case ):
_lowerCAmelCase , _lowerCAmelCase = module.weight.shape
else:
_lowerCAmelCase = module.in_features
_lowerCAmelCase = module.out_features
if quantization_config.quantization_method() == "llm_int8":
_lowerCAmelCase = bnb.nn.LinearabitLt(
snake_case , snake_case , module.bias is not None , has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight , threshold=quantization_config.llm_inta_threshold , )
_lowerCAmelCase = True
else:
if (
quantization_config.llm_inta_skip_modules is not None
and name in quantization_config.llm_inta_skip_modules
):
pass
else:
_lowerCAmelCase = bnb.nn.Linearabit(
snake_case , snake_case , module.bias is not None , quantization_config.bnb_abit_compute_dtype , compress_statistics=quantization_config.bnb_abit_use_double_quant , quant_type=quantization_config.bnb_abit_quant_type , )
_lowerCAmelCase = True
# Store the module class in case we need to transpose the weight later
_lowerCAmelCase = type(snake_case )
# Force requires grad to False to avoid unexpected errors
model._modules[name].requires_grad_(snake_case )
if len(list(module.children() ) ) > 0:
_lowerCAmelCase , _lowerCAmelCase = _replace_with_bnb_linear(
snake_case , snake_case , snake_case , snake_case , has_been_replaced=snake_case , )
# Remove the last key for recursion
current_key_name.pop(-1 )
return model, has_been_replaced
def _UpperCAmelCase ( snake_case , snake_case=None , snake_case=None , snake_case=None ):
"""simple docstring"""
_lowerCAmelCase = ["""lm_head"""] if modules_to_not_convert is None else modules_to_not_convert
_lowerCAmelCase , _lowerCAmelCase = _replace_with_bnb_linear(
snake_case , snake_case , snake_case , snake_case )
if not has_been_replaced:
logger.warning(
"""You are loading your model in 8bit or 4bit but no linear modules were found in your model."""
""" Please double check your model architecture, or submit an issue on github if you think this is"""
""" a bug.""" )
return model
def _UpperCAmelCase ( *snake_case , **snake_case ):
"""simple docstring"""
warnings.warn(
"""`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead""" , snake_case , )
return replace_with_bnb_linear(*snake_case , **snake_case )
def _UpperCAmelCase ( *snake_case , **snake_case ):
"""simple docstring"""
warnings.warn(
"""`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead""" , snake_case , )
return set_module_quantized_tensor_to_device(*snake_case , **snake_case )
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = deepcopy(snake_case ) # this has 0 cost since it is done inside `init_empty_weights` context manager`
tied_model.tie_weights()
_lowerCAmelCase = find_tied_parameters(snake_case )
# For compatibility with Accelerate < 0.18
if isinstance(snake_case , snake_case ):
_lowerCAmelCase = sum(list(tied_params.values() ) , [] ) + list(tied_params.keys() )
else:
_lowerCAmelCase = sum(snake_case , [] )
_lowerCAmelCase = len(snake_case ) > 0
# Check if it is a base model
_lowerCAmelCase = not hasattr(snake_case , model.base_model_prefix )
# Ignore this for base models (BertModel, GPT2Model, etc.)
if (not has_tied_params) and is_base_model:
return []
# otherwise they have an attached head
_lowerCAmelCase = list(model.named_children() )
_lowerCAmelCase = [list_modules[-1][0]]
# add last module together with tied weights
_lowerCAmelCase = set(snake_case ) - set(snake_case )
_lowerCAmelCase = list(set(snake_case ) ) + list(snake_case )
# remove ".weight" from the keys
_lowerCAmelCase = [""".weight""", """.bias"""]
_lowerCAmelCase = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
_lowerCAmelCase = name.replace(snake_case , """""" )
filtered_module_names.append(snake_case )
return filtered_module_names
| 82 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if isinstance(snake_case , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class __lowerCAmelCase :
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
pass
def snake_case ( self ):
"""simple docstring"""
pass
def snake_case ( self ):
"""simple docstring"""
pass
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase = VisionTextDualEncoderConfig.from_vision_text_configs(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], config.projection_dim) )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_snake_case , text_model=_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = {"""vision_model""": vision_model, """text_model""": text_model}
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_snake_case , text_model=_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
_lowerCAmelCase = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
_lowerCAmelCase = after_output[0].numpy()
_lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_snake_case , 1e-5 )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_snake_case , text_model=_snake_case )
_lowerCAmelCase = model(
input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case , output_attentions=_snake_case )
_lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(_snake_case ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
_lowerCAmelCase = to_atuple(vision_model.config.image_size )
_lowerCAmelCase = to_atuple(vision_model.config.patch_size )
_lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
_lowerCAmelCase = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
_lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(_snake_case ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def snake_case ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = np.abs((a - b) ).max()
self.assertLessEqual(_snake_case , _snake_case , F'Difference between torch and flax is {diff} (>= {tol}).' )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_save_load(**_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**_snake_case )
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_pretrained_model_and_inputs()
_lowerCAmelCase = model_a(**_snake_case )
_lowerCAmelCase = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(_snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(_snake_case )
_lowerCAmelCase = model_a(**_snake_case )
_lowerCAmelCase = after_outputs[0].numpy()
_lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_snake_case , 1e-5 )
@require_tf
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-random-bert""" )
_lowerCAmelCase = 13
_lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_lowerCAmelCase = random_attention_mask([batch_size, 4] )
_lowerCAmelCase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = TFViTModel(_snake_case , name="""vision_model""" )
_lowerCAmelCase = TFBertModel(_snake_case , name="""text_model""" )
return vision_model, text_model
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFViTModelTester(self )
_lowerCAmelCase = TFBertModelTester(self )
_lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
_lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = vision_config_and_inputs
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-deit-tf""" , """hf-internal-testing/tiny-random-roberta""" )
_lowerCAmelCase = 13
_lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_lowerCAmelCase = random_attention_mask([batch_size, 4] )
_lowerCAmelCase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_snake_case , text_model=_snake_case )
_lowerCAmelCase = model(
input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case , output_attentions=_snake_case )
_lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(_snake_case ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
_lowerCAmelCase = to_atuple(vision_model.config.image_size )
_lowerCAmelCase = to_atuple(vision_model.config.patch_size )
_lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
_lowerCAmelCase = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
_lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(_snake_case ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = TFDeiTModel(_snake_case , name="""vision_model""" )
_lowerCAmelCase = TFRobertaModel(_snake_case , name="""text_model""" )
return vision_model, text_model
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFDeiTModelTester(self )
_lowerCAmelCase = TFRobertaModelTester(self )
_lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
_lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = vision_config_and_inputs
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-clip-tf""" , """hf-internal-testing/tiny-random-bert""" )
_lowerCAmelCase = 13
_lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_lowerCAmelCase = random_attention_mask([batch_size, 4] )
_lowerCAmelCase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = TFCLIPVisionModel(_snake_case , name="""vision_model""" )
_lowerCAmelCase = TFBertModel(_snake_case , name="""text_model""" )
return vision_model, text_model
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFCLIPVisionModelTester(self )
_lowerCAmelCase = TFBertModelTester(self )
_lowerCAmelCase = clip_model_tester.prepare_config_and_inputs()
_lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase = vision_config_and_inputs
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(
"""clip-italian/clip-italian""" , logit_scale_init_value=1.0 , from_pt=_snake_case )
_lowerCAmelCase = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
_lowerCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
_lowerCAmelCase = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=_snake_case , padding=_snake_case , return_tensors="""np""" )
_lowerCAmelCase = model(**_snake_case )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
_lowerCAmelCase = np.array([[1.228_4727, 0.310_4122]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , _snake_case , atol=1e-3 ) )
| 82 | 1 |
from ...configuration_utils import PretrainedConfig
A__ = {
"""google/tapas-base-finetuned-sqa""": (
"""https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"""
),
"""google/tapas-base-finetuned-wtq""": (
"""https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"""
),
"""google/tapas-base-finetuned-wikisql-supervised""": (
"""https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"""
),
"""google/tapas-base-finetuned-tabfact""": (
"""https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"""
),
}
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''tapas'''
def __init__( self , _snake_case=30522 , _snake_case=768 , _snake_case=12 , _snake_case=12 , _snake_case=3072 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=1024 , _snake_case=[3, 256, 256, 2, 256, 256, 10] , _snake_case=0.02 , _snake_case=1e-12 , _snake_case=0 , _snake_case=10.0 , _snake_case=0 , _snake_case=1.0 , _snake_case=None , _snake_case=1.0 , _snake_case=False , _snake_case=None , _snake_case=1.0 , _snake_case=1.0 , _snake_case=False , _snake_case=False , _snake_case="ratio" , _snake_case=None , _snake_case=None , _snake_case=64 , _snake_case=32 , _snake_case=False , _snake_case=True , _snake_case=False , _snake_case=False , _snake_case=True , _snake_case=False , _snake_case=None , _snake_case=None , **_snake_case , ):
"""simple docstring"""
super().__init__(pad_token_id=_snake_case , **_snake_case )
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = hidden_act
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = type_vocab_sizes
_lowerCAmelCase = initializer_range
_lowerCAmelCase = layer_norm_eps
# Fine-tuning task hyperparameters
_lowerCAmelCase = positive_label_weight
_lowerCAmelCase = num_aggregation_labels
_lowerCAmelCase = aggregation_loss_weight
_lowerCAmelCase = use_answer_as_supervision
_lowerCAmelCase = answer_loss_importance
_lowerCAmelCase = use_normalized_answer_loss
_lowerCAmelCase = huber_loss_delta
_lowerCAmelCase = temperature
_lowerCAmelCase = aggregation_temperature
_lowerCAmelCase = use_gumbel_for_cells
_lowerCAmelCase = use_gumbel_for_aggregation
_lowerCAmelCase = average_approximation_function
_lowerCAmelCase = cell_selection_preference
_lowerCAmelCase = answer_loss_cutoff
_lowerCAmelCase = max_num_rows
_lowerCAmelCase = max_num_columns
_lowerCAmelCase = average_logits_per_cell
_lowerCAmelCase = select_one_column
_lowerCAmelCase = allow_empty_column_selection
_lowerCAmelCase = init_cell_selection_weights_to_zero
_lowerCAmelCase = reset_position_index_per_cell
_lowerCAmelCase = disable_per_token_loss
# Aggregation hyperparameters
_lowerCAmelCase = aggregation_labels
_lowerCAmelCase = no_aggregation_label_index
if isinstance(self.aggregation_labels , _snake_case ):
_lowerCAmelCase = {int(_snake_case ): v for k, v in aggregation_labels.items()}
| 82 |
def _UpperCAmelCase ( snake_case = 50 ):
"""simple docstring"""
_lowerCAmelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(f"{solution() = }")
| 82 | 1 |
import re
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if len(re.findall("""[ATCG]""" , snake_case ) ) != len(snake_case ):
raise ValueError("""Invalid Strand""" )
return dna.translate(dna.maketrans("""ATCG""" , """TAGC""" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 82 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class __lowerCAmelCase ( unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = {
"""task_specific_params""": {
"""summarization""": {"""length_penalty""": 1.0, """max_length""": 128, """min_length""": 12, """num_beams""": 4},
"""summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 142, """min_length""": 56, """num_beams""": 4},
"""summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
}
}
_lowerCAmelCase = {
"""task_specific_params.summarization.length_penalty""": 1.0,
"""task_specific_params.summarization.max_length""": 128,
"""task_specific_params.summarization.min_length""": 12,
"""task_specific_params.summarization.num_beams""": 4,
"""task_specific_params.summarization_cnn.length_penalty""": 2.0,
"""task_specific_params.summarization_cnn.max_length""": 142,
"""task_specific_params.summarization_cnn.min_length""": 56,
"""task_specific_params.summarization_cnn.num_beams""": 4,
"""task_specific_params.summarization_xsum.length_penalty""": 1.0,
"""task_specific_params.summarization_xsum.max_length""": 62,
"""task_specific_params.summarization_xsum.min_length""": 11,
"""task_specific_params.summarization_xsum.num_beams""": 6,
}
self.assertEqual(flatten_dict(_snake_case ) , _snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(_snake_case ) , x.transpose() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case ) , transpose(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , transpose(_snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case ) , transpose(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , transpose(_snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case ) , np.asarray(transpose(_snake_case ) ) ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , np.asarray(transpose(_snake_case , axes=(1, 2, 0) ) ) ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , np.reshape(_snake_case , (4, 3) ) ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , np.reshape(_snake_case , (12, 5) ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , reshape(_snake_case , (4, 3) ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , reshape(_snake_case , (12, 5) ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , reshape(_snake_case , (4, 3) ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , reshape(_snake_case , (12, 5) ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , np.asarray(reshape(_snake_case , (4, 3) ) ) ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , np.asarray(reshape(_snake_case , (12, 5) ) ) ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(_snake_case ) , np.squeeze(_snake_case ) ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , np.squeeze(_snake_case , axis=2 ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case ) , squeeze(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , squeeze(_snake_case , axis=2 ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case ) , squeeze(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , squeeze(_snake_case , axis=2 ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case ) , np.asarray(squeeze(_snake_case ) ) ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , np.asarray(squeeze(_snake_case , axis=2 ) ) ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , np.expand_dims(_snake_case , axis=1 ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , expand_dims(_snake_case , axis=1 ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , expand_dims(_snake_case , axis=1 ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , np.asarray(expand_dims(_snake_case , axis=1 ) ) ) )
| 82 | 1 |
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor
if is_flax_available():
import jax
from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel
if is_vision_available():
from PIL import Image
from transformers import BeitImageProcessor
class __lowerCAmelCase ( unittest.TestCase ):
def __init__( self , _snake_case , _snake_case=100 , _snake_case=13 , _snake_case=30 , _snake_case=2 , _snake_case=3 , _snake_case=True , _snake_case=True , _snake_case=32 , _snake_case=5 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=10 , _snake_case=0.02 , _snake_case=3 , ):
"""simple docstring"""
_lowerCAmelCase = parent
_lowerCAmelCase = vocab_size
_lowerCAmelCase = batch_size
_lowerCAmelCase = image_size
_lowerCAmelCase = patch_size
_lowerCAmelCase = num_channels
_lowerCAmelCase = is_training
_lowerCAmelCase = use_labels
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_act
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = type_sequence_label_size
_lowerCAmelCase = initializer_range
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
_lowerCAmelCase = (image_size // patch_size) ** 2
_lowerCAmelCase = num_patches + 1
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowerCAmelCase = None
if self.use_labels:
_lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCAmelCase = BeitConfig(
vocab_size=self.vocab_size , image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_snake_case , initializer_range=self.initializer_range , )
return config, pixel_values, labels
def snake_case ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = FlaxBeitModel(config=_snake_case )
_lowerCAmelCase = model(_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = FlaxBeitForMaskedImageModeling(config=_snake_case )
_lowerCAmelCase = model(_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def snake_case ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.type_sequence_label_size
_lowerCAmelCase = FlaxBeitForImageClassification(config=_snake_case )
_lowerCAmelCase = model(_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_lowerCAmelCase = 1
_lowerCAmelCase = FlaxBeitForImageClassification(_snake_case )
_lowerCAmelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_lowerCAmelCase = model(_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = config_and_inputs
_lowerCAmelCase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_flax
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase = (
(FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
)
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = FlaxBeitModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=_snake_case , has_text_modality=_snake_case , hidden_size=37 )
def snake_case ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase = model_class(_snake_case )
_lowerCAmelCase = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCAmelCase = [*signature.parameters.keys()]
_lowerCAmelCase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_lowerCAmelCase = self._prepare_for_class(_snake_case , _snake_case )
_lowerCAmelCase = model_class(_snake_case )
@jax.jit
def model_jitted(_snake_case , **_snake_case ):
return model(pixel_values=_snake_case , **_snake_case )
with self.subTest("""JIT Enabled""" ):
_lowerCAmelCase = model_jitted(**_snake_case ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
_lowerCAmelCase = model_jitted(**_snake_case ).to_tuple()
self.assertEqual(len(_snake_case ) , len(_snake_case ) )
for jitted_output, output in zip(_snake_case , _snake_case ):
self.assertEqual(jitted_output.shape , output.shape )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_snake_case )
@slow
def snake_case ( self ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
_lowerCAmelCase = model_class_name.from_pretrained("""microsoft/beit-base-patch16-224""" )
_lowerCAmelCase = model(np.ones((1, 3, 224, 224) ) )
self.assertIsNotNone(_snake_case )
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_vision
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def snake_case ( self ):
"""simple docstring"""
return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224""" ) if is_vision_available() else None
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = FlaxBeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k""" )
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(images=_snake_case , return_tensors="""np""" ).pixel_values
# prepare bool_masked_pos
_lowerCAmelCase = np.ones((1, 196) , dtype=_snake_case )
# forward pass
_lowerCAmelCase = model(pixel_values=_snake_case , bool_masked_pos=_snake_case )
_lowerCAmelCase = outputs.logits
# verify the logits
_lowerCAmelCase = (1, 196, 8192)
self.assertEqual(logits.shape , _snake_case )
_lowerCAmelCase = np.array(
[[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]] )
self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3] , _snake_case , atol=1e-2 ) )
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224""" )
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(images=_snake_case , return_tensors="""np""" )
# forward pass
_lowerCAmelCase = model(**_snake_case )
_lowerCAmelCase = outputs.logits
# verify the logits
_lowerCAmelCase = (1, 1000)
self.assertEqual(logits.shape , _snake_case )
_lowerCAmelCase = np.array([-1.2385, -1.0987, -1.0108] )
self.assertTrue(np.allclose(logits[0, :3] , _snake_case , atol=1e-4 ) )
_lowerCAmelCase = 281
self.assertEqual(logits.argmax(-1 ).item() , _snake_case )
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k""" )
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(images=_snake_case , return_tensors="""np""" )
# forward pass
_lowerCAmelCase = model(**_snake_case )
_lowerCAmelCase = outputs.logits
# verify the logits
_lowerCAmelCase = (1, 21841)
self.assertEqual(logits.shape , _snake_case )
_lowerCAmelCase = np.array([1.6881, -0.2787, 0.5901] )
self.assertTrue(np.allclose(logits[0, :3] , _snake_case , atol=1e-4 ) )
_lowerCAmelCase = 2396
self.assertEqual(logits.argmax(-1 ).item() , _snake_case )
| 82 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class __lowerCAmelCase ( lowerCamelCase__ ):
@staticmethod
def snake_case ( _snake_case ):
"""simple docstring"""
_lowerCAmelCase = parser.add_parser("""download""" )
download_parser.add_argument(
"""--cache-dir""" , type=_snake_case , default=_snake_case , help="""Path to location to store the models""" )
download_parser.add_argument(
"""--force""" , action="""store_true""" , help="""Force the model to be download even if already in cache-dir""" )
download_parser.add_argument(
"""--trust-remote-code""" , action="""store_true""" , help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine""" , )
download_parser.add_argument("""model""" , type=_snake_case , help="""Name of the model to download""" )
download_parser.set_defaults(func=_snake_case )
def __init__( self , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = model
_lowerCAmelCase = cache
_lowerCAmelCase = force
_lowerCAmelCase = trust_remote_code
def snake_case ( self ):
"""simple docstring"""
from ..models.auto import AutoModel, AutoTokenizer
AutoModel.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
AutoTokenizer.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
| 82 | 1 |
def _UpperCAmelCase ( snake_case = 50_00_00_00 ):
"""simple docstring"""
_lowerCAmelCase = set()
_lowerCAmelCase = int((limit - 24) ** (1 / 2) )
_lowerCAmelCase = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
for p in range(3 , prime_square_limit + 1 , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , prime_square_limit + 1 , snake_case ) ) )
for primea in primes:
_lowerCAmelCase = primea * primea
for primea in primes:
_lowerCAmelCase = primea * primea * primea
if square + cube >= limit - 16:
break
for primea in primes:
_lowerCAmelCase = primea * primea * primea * primea
_lowerCAmelCase = square + cube + tetr
if total >= limit:
break
ret.add(snake_case )
return len(snake_case )
if __name__ == "__main__":
print(f"{solution() = }")
| 82 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = XCLIPTextConfig()
# derive patch size from model name
_lowerCAmelCase = model_name.find("""patch""" )
_lowerCAmelCase = int(model_name[start_idx + len("""patch""" ) : start_idx + len("""patch""" ) + 2] )
_lowerCAmelCase = XCLIPVisionConfig(patch_size=snake_case , num_frames=snake_case )
if "large" in model_name:
_lowerCAmelCase = 7_68
_lowerCAmelCase = 30_72
_lowerCAmelCase = 12
_lowerCAmelCase = 10_24
_lowerCAmelCase = 40_96
_lowerCAmelCase = 16
_lowerCAmelCase = 24
_lowerCAmelCase = 7_68
_lowerCAmelCase = 30_72
if model_name == "xclip-large-patch14-16-frames":
_lowerCAmelCase = 3_36
_lowerCAmelCase = XCLIPConfig.from_text_vision_configs(snake_case , snake_case )
if "large" in model_name:
_lowerCAmelCase = 7_68
return config
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if name == "token_embedding.weight":
_lowerCAmelCase = name.replace("""token_embedding.weight""" , """text_model.embeddings.token_embedding.weight""" )
if name == "positional_embedding":
_lowerCAmelCase = name.replace("""positional_embedding""" , """text_model.embeddings.position_embedding.weight""" )
if "ln_1" in name:
_lowerCAmelCase = name.replace("""ln_1""" , """layer_norm1""" )
if "ln_2" in name:
_lowerCAmelCase = name.replace("""ln_2""" , """layer_norm2""" )
if "c_fc" in name:
_lowerCAmelCase = name.replace("""c_fc""" , """fc1""" )
if "c_proj" in name:
_lowerCAmelCase = name.replace("""c_proj""" , """fc2""" )
if name.startswith("""transformer.resblocks""" ):
_lowerCAmelCase = name.replace("""transformer.resblocks""" , """text_model.encoder.layers""" )
if "attn.out_proj" in name and "message" not in name:
_lowerCAmelCase = name.replace("""attn.out_proj""" , """self_attn.out_proj""" )
if "ln_final" in name:
_lowerCAmelCase = name.replace("""ln_final""" , """text_model.final_layer_norm""" )
# visual encoder
if name == "visual.class_embedding":
_lowerCAmelCase = name.replace("""visual.class_embedding""" , """vision_model.embeddings.class_embedding""" )
if name == "visual.positional_embedding":
_lowerCAmelCase = name.replace("""visual.positional_embedding""" , """vision_model.embeddings.position_embedding.weight""" )
if name.startswith("""visual.transformer.resblocks""" ):
_lowerCAmelCase = name.replace("""visual.transformer.resblocks""" , """vision_model.encoder.layers""" )
if "visual.conv1" in name:
_lowerCAmelCase = name.replace("""visual.conv1""" , """vision_model.embeddings.patch_embedding""" )
if "visual.ln_pre" in name:
_lowerCAmelCase = name.replace("""visual.ln_pre""" , """vision_model.pre_layernorm""" )
if "visual.ln_post" in name:
_lowerCAmelCase = name.replace("""visual.ln_post""" , """vision_model.post_layernorm""" )
if "visual.proj" in name:
_lowerCAmelCase = name.replace("""visual.proj""" , """visual_projection.weight""" )
if "text_projection" in name:
_lowerCAmelCase = name.replace("""text_projection""" , """text_projection.weight""" )
# things on top
if "prompts_visual_proj" in name:
_lowerCAmelCase = name.replace("""prompts_visual_proj""" , """prompts_visual_projection""" )
if "prompts_visual_ln" in name:
_lowerCAmelCase = name.replace("""prompts_visual_ln""" , """prompts_visual_layernorm""" )
# mit
if name == "mit.positional_embedding":
_lowerCAmelCase = name.replace("""positional""" , """position""" )
if name.startswith("""mit.resblocks""" ):
_lowerCAmelCase = name.replace("""mit.resblocks""" , """mit.encoder.layers""" )
# prompts generator
if name.startswith("""prompts_generator.norm""" ):
_lowerCAmelCase = name.replace("""prompts_generator.norm""" , """prompts_generator.layernorm""" )
return name
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_lowerCAmelCase = orig_state_dict.pop(snake_case )
if "attn.in_proj" in key:
_lowerCAmelCase = key.split(""".""" )
if key.startswith("""visual""" ):
_lowerCAmelCase = key_split[3]
_lowerCAmelCase = config.vision_config.hidden_size
if "message_attn" in key:
if "weight" in key:
_lowerCAmelCase = val[
:dim, :
]
_lowerCAmelCase = val[
dim : dim * 2, :
]
_lowerCAmelCase = val[
-dim:, :
]
else:
_lowerCAmelCase = val[
:dim
]
_lowerCAmelCase = val[
dim : dim * 2
]
_lowerCAmelCase = val[
-dim:
]
else:
if "weight" in key:
_lowerCAmelCase = val[
:dim, :
]
_lowerCAmelCase = val[
dim : dim * 2, :
]
_lowerCAmelCase = val[
-dim:, :
]
else:
_lowerCAmelCase = val[:dim]
_lowerCAmelCase = val[
dim : dim * 2
]
_lowerCAmelCase = val[-dim:]
elif key.startswith("""mit""" ):
_lowerCAmelCase = key_split[2]
_lowerCAmelCase = config.vision_config.mit_hidden_size
if "weight" in key:
_lowerCAmelCase = val[:dim, :]
_lowerCAmelCase = val[dim : dim * 2, :]
_lowerCAmelCase = val[-dim:, :]
else:
_lowerCAmelCase = val[:dim]
_lowerCAmelCase = val[dim : dim * 2]
_lowerCAmelCase = val[-dim:]
else:
_lowerCAmelCase = key_split[2]
_lowerCAmelCase = config.text_config.hidden_size
if "weight" in key:
_lowerCAmelCase = val[:dim, :]
_lowerCAmelCase = val[
dim : dim * 2, :
]
_lowerCAmelCase = val[-dim:, :]
else:
_lowerCAmelCase = val[:dim]
_lowerCAmelCase = val[
dim : dim * 2
]
_lowerCAmelCase = val[-dim:]
else:
_lowerCAmelCase = rename_key(snake_case )
if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
_lowerCAmelCase = val.T
_lowerCAmelCase = val
return orig_state_dict
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if num_frames == 8:
_lowerCAmelCase = """eating_spaghetti_8_frames.npy"""
elif num_frames == 16:
_lowerCAmelCase = """eating_spaghetti.npy"""
elif num_frames == 32:
_lowerCAmelCase = """eating_spaghetti_32_frames.npy"""
_lowerCAmelCase = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename=snake_case , repo_type="""dataset""" , )
_lowerCAmelCase = np.load(snake_case )
return list(snake_case )
def _UpperCAmelCase ( snake_case , snake_case=None , snake_case=False ):
"""simple docstring"""
_lowerCAmelCase = {
# fully supervised kinetics-400 checkpoints
"""xclip-base-patch32""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth""",
"""xclip-base-patch32-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"""
),
"""xclip-base-patch16""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth""",
"""xclip-base-patch16-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"""
),
"""xclip-large-patch14""": """https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&export=download&confirm=t&uuid=b26caedc-88e2-473e-830a-9d158b653cdb""",
"""xclip-large-patch14-16-frames""": """https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&export=download&confirm=t&uuid=538fa810-e671-4050-b385-9a623f89804f""",
# fully supervised kinetics-600 checkpoints
"""xclip-base-patch16-kinetics-600""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"""
),
"""xclip-base-patch16-kinetics-600-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"""
),
"""xclip-large-patch14-kinetics-600""": """https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&export=download&confirm=t&uuid=141d4977-4a65-44ae-864f-4b0c19f838be""",
# few shot
"""xclip-base-patch16-hmdb-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"""
),
"""xclip-base-patch16-hmdb-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"""
),
"""xclip-base-patch16-hmdb-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"""
),
"""xclip-base-patch16-hmdb-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"""
),
"""xclip-base-patch16-ucf-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"""
),
"""xclip-base-patch16-ucf-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"""
),
"""xclip-base-patch16-ucf-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"""
),
"""xclip-base-patch16-ucf-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"""
),
# zero shot
"""xclip-base-patch16-zero-shot""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth""",
}
_lowerCAmelCase = model_to_url[model_name]
_lowerCAmelCase = 8
if "16-frames" in model_name:
_lowerCAmelCase = 16
elif "shot" in model_name:
_lowerCAmelCase = 32
_lowerCAmelCase = get_xclip_config(snake_case , snake_case )
_lowerCAmelCase = XCLIPModel(snake_case )
model.eval()
if "drive" in checkpoint_url:
_lowerCAmelCase = """pytorch_model.bin"""
gdown.cached_download(snake_case , snake_case , quiet=snake_case )
_lowerCAmelCase = torch.load(snake_case , map_location="""cpu""" )["""model"""]
else:
_lowerCAmelCase = torch.hub.load_state_dict_from_url(snake_case )["""model"""]
_lowerCAmelCase = convert_state_dict(snake_case , snake_case )
_lowerCAmelCase = XCLIPModel(snake_case )
_lowerCAmelCase , _lowerCAmelCase = model.load_state_dict(snake_case , strict=snake_case )
assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
model.eval()
_lowerCAmelCase = 3_36 if model_name == """xclip-large-patch14-16-frames""" else 2_24
_lowerCAmelCase = VideoMAEImageProcessor(size=snake_case )
_lowerCAmelCase = CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32""" )
_lowerCAmelCase = CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32""" )
_lowerCAmelCase = XCLIPProcessor(image_processor=snake_case , tokenizer=snake_case )
_lowerCAmelCase = prepare_video(snake_case )
_lowerCAmelCase = processor(
text=["""playing sports""", """eating spaghetti""", """go shopping"""] , videos=snake_case , return_tensors="""pt""" , padding=snake_case )
print("""Shape of pixel values:""" , inputs.pixel_values.shape )
with torch.no_grad():
_lowerCAmelCase = model(**snake_case )
# Verify outputs
_lowerCAmelCase = outputs.logits_per_video
_lowerCAmelCase = logits_per_video.softmax(dim=1 )
print("""Probs:""" , snake_case )
# kinetics-400
if model_name == "xclip-base-patch32":
_lowerCAmelCase = torch.tensor([[0.0_019, 0.9_951, 0.0_030]] )
elif model_name == "xclip-base-patch32-16-frames":
_lowerCAmelCase = torch.tensor([[7.09_99E-04, 9.98_83E-01, 4.55_80E-04]] )
elif model_name == "xclip-base-patch16":
_lowerCAmelCase = torch.tensor([[0.0_083, 0.9_681, 0.0_236]] )
elif model_name == "xclip-base-patch16-16-frames":
_lowerCAmelCase = torch.tensor([[7.69_37E-04, 9.97_28E-01, 1.94_73E-03]] )
elif model_name == "xclip-large-patch14":
_lowerCAmelCase = torch.tensor([[0.0_062, 0.9_864, 0.0_075]] )
elif model_name == "xclip-large-patch14-16-frames":
_lowerCAmelCase = torch.tensor([[3.38_77E-04, 9.99_37E-01, 2.88_88E-04]] )
# kinetics-600
elif model_name == "xclip-base-patch16-kinetics-600":
_lowerCAmelCase = torch.tensor([[0.0_555, 0.8_914, 0.0_531]] )
elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
_lowerCAmelCase = torch.tensor([[3.85_54E-04, 9.99_29E-01, 3.27_54E-04]] )
elif model_name == "xclip-large-patch14-kinetics-600":
_lowerCAmelCase = torch.tensor([[0.0_036, 0.9_920, 0.0_045]] )
# few shot
elif model_name == "xclip-base-patch16-hmdb-2-shot":
_lowerCAmelCase = torch.tensor([[7.18_90E-06, 9.99_94E-01, 5.65_59E-05]] )
elif model_name == "xclip-base-patch16-hmdb-4-shot":
_lowerCAmelCase = torch.tensor([[1.03_20E-05, 9.99_93E-01, 6.24_35E-05]] )
elif model_name == "xclip-base-patch16-hmdb-8-shot":
_lowerCAmelCase = torch.tensor([[4.13_77E-06, 9.99_90E-01, 9.83_86E-05]] )
elif model_name == "xclip-base-patch16-hmdb-16-shot":
_lowerCAmelCase = torch.tensor([[4.13_47E-05, 9.99_62E-01, 3.34_11E-04]] )
elif model_name == "xclip-base-patch16-ucf-2-shot":
_lowerCAmelCase = torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]] )
elif model_name == "xclip-base-patch16-ucf-4-shot":
_lowerCAmelCase = torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]] )
elif model_name == "xclip-base-patch16-ucf-8-shot":
_lowerCAmelCase = torch.tensor([[0.0_027, 0.9_904, 0.0_070]] )
elif model_name == "xclip-base-patch16-ucf-16-shot":
_lowerCAmelCase = torch.tensor([[9.82_19E-04, 9.95_93E-01, 3.08_63E-03]] )
# zero shot
elif model_name == "xclip-base-patch16-zero-shot":
_lowerCAmelCase = torch.tensor([[3.50_82E-04, 9.97_85E-01, 1.79_66E-03]] )
else:
raise ValueError(F'Model name {model_name} not supported' )
assert torch.allclose(snake_case , snake_case , atol=1E-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(snake_case )
if push_to_hub:
print("""Pushing model, processor and slow tokenizer files to the hub...""" )
model.push_to_hub(snake_case , organization="""nielsr""" )
processor.push_to_hub(snake_case , organization="""nielsr""" )
slow_tokenizer.push_to_hub(snake_case , organization="""nielsr""" )
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""xclip-base-patch32""",
type=str,
help="""Name of the model.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
A__ = parser.parse_args()
convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 82 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
A__ = {"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitConfig""", """BeitOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = ["""BeitFeatureExtractor"""]
A__ = ["""BeitImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = [
"""BEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BeitForImageClassification""",
"""BeitForMaskedImageModeling""",
"""BeitForSemanticSegmentation""",
"""BeitModel""",
"""BeitPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = [
"""FlaxBeitForImageClassification""",
"""FlaxBeitForMaskedImageModeling""",
"""FlaxBeitModel""",
"""FlaxBeitPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_beit import BeitFeatureExtractor
from .image_processing_beit import BeitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_beit import (
BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
BeitPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_beit import (
FlaxBeitForImageClassification,
FlaxBeitForMaskedImageModeling,
FlaxBeitModel,
FlaxBeitPreTrainedModel,
)
else:
import sys
A__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 82 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@register_to_config
def __init__( self , _snake_case = 768 , ):
"""simple docstring"""
super().__init__()
_lowerCAmelCase = nn.Parameter(torch.zeros(1 , _snake_case ) )
_lowerCAmelCase = nn.Parameter(torch.ones(1 , _snake_case ) )
def snake_case ( self , _snake_case = None , _snake_case = None , ):
"""simple docstring"""
_lowerCAmelCase = nn.Parameter(self.mean.to(_snake_case ).to(_snake_case ) )
_lowerCAmelCase = nn.Parameter(self.std.to(_snake_case ).to(_snake_case ) )
return self
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = (embeds - self.mean) * 1.0 / self.std
return embeds
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = (embeds * self.std) + self.mean
return embeds
| 82 | 1 |
from __future__ import annotations
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
if len(snake_case ) == 0:
raise ValueError("""find_max() arg is an empty sequence""" )
if (
left >= len(snake_case )
or left < -len(snake_case )
or right >= len(snake_case )
or right < -len(snake_case )
):
raise IndexError("""list index out of range""" )
if left == right:
return nums[left]
_lowerCAmelCase = (left + right) >> 1 # the middle
_lowerCAmelCase = find_max(snake_case , snake_case , snake_case ) # find max in range[left, mid]
_lowerCAmelCase = find_max(snake_case , mid + 1 , snake_case ) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 82 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase = AudioLDMPipeline
__lowerCamelCase = TEXT_TO_AUDIO_PARAMS
__lowerCamelCase = TEXT_TO_AUDIO_BATCH_PARAMS
__lowerCamelCase = frozenset(
[
'''num_inference_steps''',
'''num_waveforms_per_prompt''',
'''generator''',
'''latents''',
'''output_type''',
'''return_dict''',
'''callback''',
'''callback_steps''',
] )
def snake_case ( self ):
"""simple docstring"""
torch.manual_seed(0 )
_lowerCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=(32, 64) , class_embed_type="""simple_projection""" , projection_class_embeddings_input_dim=32 , class_embeddings_concat=_snake_case , )
_lowerCAmelCase = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_snake_case , set_alpha_to_one=_snake_case , )
torch.manual_seed(0 )
_lowerCAmelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
_lowerCAmelCase = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , projection_dim=32 , )
_lowerCAmelCase = ClapTextModelWithProjection(_snake_case )
_lowerCAmelCase = RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta""" , model_max_length=77 )
_lowerCAmelCase = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=16000 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=_snake_case , )
_lowerCAmelCase = SpeechTaHifiGan(_snake_case )
_lowerCAmelCase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""vocoder""": vocoder,
}
return components
def snake_case ( self , _snake_case , _snake_case=0 ):
"""simple docstring"""
if str(_snake_case ).startswith("""mps""" ):
_lowerCAmelCase = torch.manual_seed(_snake_case )
else:
_lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
_lowerCAmelCase = {
"""prompt""": """A hammer hitting a wooden surface""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
}
return inputs
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(_snake_case ) == 256
_lowerCAmelCase = audio[:10]
_lowerCAmelCase = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = 3 * [inputs["""prompt"""]]
# forward
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = 3 * [inputs.pop("""prompt""" )]
_lowerCAmelCase = audioldm_pipe.tokenizer(
_snake_case , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=_snake_case , return_tensors="""pt""" , )
_lowerCAmelCase = text_inputs["""input_ids"""].to(_snake_case )
_lowerCAmelCase = audioldm_pipe.text_encoder(
_snake_case , )
_lowerCAmelCase = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
_lowerCAmelCase = F.normalize(_snake_case , dim=-1 )
_lowerCAmelCase = prompt_embeds
# forward
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = 3 * ["""this is a negative prompt"""]
_lowerCAmelCase = negative_prompt
_lowerCAmelCase = 3 * [inputs["""prompt"""]]
# forward
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = 3 * [inputs.pop("""prompt""" )]
_lowerCAmelCase = []
for p in [prompt, negative_prompt]:
_lowerCAmelCase = audioldm_pipe.tokenizer(
_snake_case , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=_snake_case , return_tensors="""pt""" , )
_lowerCAmelCase = text_inputs["""input_ids"""].to(_snake_case )
_lowerCAmelCase = audioldm_pipe.text_encoder(
_snake_case , )
_lowerCAmelCase = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
_lowerCAmelCase = F.normalize(_snake_case , dim=-1 )
embeds.append(_snake_case )
_lowerCAmelCase , _lowerCAmelCase = embeds
# forward
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = PNDMScheduler(skip_prk_steps=_snake_case )
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = """egg cracking"""
_lowerCAmelCase = audioldm_pipe(**_snake_case , negative_prompt=_snake_case )
_lowerCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(_snake_case ) == 256
_lowerCAmelCase = audio[:10]
_lowerCAmelCase = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = PNDMScheduler(skip_prk_steps=_snake_case )
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = """A hammer hitting a wooden surface"""
# test num_waveforms_per_prompt=1 (default)
_lowerCAmelCase = audioldm_pipe(_snake_case , num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
_lowerCAmelCase = 2
_lowerCAmelCase = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
_lowerCAmelCase = 2
_lowerCAmelCase = audioldm_pipe(_snake_case , num_inference_steps=2 , num_waveforms_per_prompt=_snake_case ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
_lowerCAmelCase = 2
_lowerCAmelCase = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=_snake_case ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = audioldm_pipe.vocoder.config.sampling_rate
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = audioldm_pipe(audio_length_in_s=0.016 , **_snake_case )
_lowerCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(_snake_case ) / vocoder_sampling_rate == 0.016
_lowerCAmelCase = audioldm_pipe(audio_length_in_s=0.032 , **_snake_case )
_lowerCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(_snake_case ) / vocoder_sampling_rate == 0.032
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = ["""hey"""]
_lowerCAmelCase = audioldm_pipe(_snake_case , num_inference_steps=1 )
_lowerCAmelCase = output.audios.shape
assert audio_shape == (1, 256)
_lowerCAmelCase = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
_lowerCAmelCase = SpeechTaHifiGan(_snake_case ).to(_snake_case )
_lowerCAmelCase = audioldm_pipe(_snake_case , num_inference_steps=1 )
_lowerCAmelCase = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def snake_case ( self ):
"""simple docstring"""
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._test_inference_batch_single_identical(test_mean_pixel_difference=_snake_case )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def snake_case ( self ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_snake_case )
@slow
class __lowerCAmelCase ( unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case ( self , _snake_case , _snake_case="cpu" , _snake_case=torch.floataa , _snake_case=0 ):
"""simple docstring"""
_lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
_lowerCAmelCase = np.random.RandomState(_snake_case ).standard_normal((1, 8, 128, 16) )
_lowerCAmelCase = torch.from_numpy(_snake_case ).to(device=_snake_case , dtype=_snake_case )
_lowerCAmelCase = {
"""prompt""": """A hammer hitting a wooden surface""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 2.5,
}
return inputs
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_inputs(_snake_case )
_lowerCAmelCase = 25
_lowerCAmelCase = audioldm_pipe(**_snake_case ).audios[0]
assert audio.ndim == 1
assert len(_snake_case ) == 81920
_lowerCAmelCase = audio[77230:77240]
_lowerCAmelCase = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] )
_lowerCAmelCase = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
_lowerCAmelCase = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_inputs(_snake_case )
_lowerCAmelCase = audioldm_pipe(**_snake_case ).audios[0]
assert audio.ndim == 1
assert len(_snake_case ) == 81920
_lowerCAmelCase = audio[27780:27790]
_lowerCAmelCase = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] )
_lowerCAmelCase = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3e-2
| 82 | 1 |
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm
from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = 42
__lowerCamelCase = jnp.floataa
__lowerCamelCase = True
def snake_case ( self ):
"""simple docstring"""
super().setup()
_lowerCAmelCase = nn.Dense(5 , dtype=self.dtype )
def __call__( self , *_snake_case , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase = super().__call__(*_snake_case , **_snake_case )
_lowerCAmelCase = self.cls(outputs[2] )
return outputs[:2] + (cls_out,)
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = FlaxBigBirdForNaturalQuestionsModule
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
def cross_entropy(snake_case , snake_case , snake_case=None ):
_lowerCAmelCase = logits.shape[-1]
_lowerCAmelCase = (labels[..., None] == jnp.arange(snake_case )[None]).astype("""f4""" )
_lowerCAmelCase = jax.nn.log_softmax(snake_case , axis=-1 )
_lowerCAmelCase = -jnp.sum(labels * logits , axis=-1 )
if reduction is not None:
_lowerCAmelCase = reduction(snake_case )
return loss
_lowerCAmelCase = partial(snake_case , reduction=jnp.mean )
_lowerCAmelCase = cross_entropy(snake_case , snake_case )
_lowerCAmelCase = cross_entropy(snake_case , snake_case )
_lowerCAmelCase = cross_entropy(snake_case , snake_case )
return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class __lowerCAmelCase :
__lowerCamelCase = "google/bigbird-roberta-base"
__lowerCamelCase = 3_000
__lowerCamelCase = 10_500
__lowerCamelCase = 128
__lowerCamelCase = 3
__lowerCamelCase = 1
__lowerCamelCase = 5
# tx_args
__lowerCamelCase = 3E-5
__lowerCamelCase = 0.0
__lowerCamelCase = 20_000
__lowerCamelCase = 0.00_95
__lowerCamelCase = "bigbird-roberta-natural-questions"
__lowerCamelCase = "training-expt"
__lowerCamelCase = "data/nq-training.jsonl"
__lowerCamelCase = "data/nq-validation.jsonl"
def snake_case ( self ):
"""simple docstring"""
os.makedirs(self.base_dir , exist_ok=_snake_case )
_lowerCAmelCase = os.path.join(self.base_dir , self.save_dir )
_lowerCAmelCase = self.batch_size_per_device * jax.device_count()
@dataclass
class __lowerCAmelCase :
__lowerCamelCase = 42
__lowerCamelCase = 4_096 # no dynamic padding on TPUs
def __call__( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.collate_fn(_snake_case )
_lowerCAmelCase = jax.tree_util.tree_map(_snake_case , _snake_case )
return batch
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.fetch_inputs(features["""input_ids"""] )
_lowerCAmelCase = {
"""input_ids""": jnp.array(_snake_case , dtype=jnp.intaa ),
"""attention_mask""": jnp.array(_snake_case , dtype=jnp.intaa ),
"""start_labels""": jnp.array(features["""start_token"""] , dtype=jnp.intaa ),
"""end_labels""": jnp.array(features["""end_token"""] , dtype=jnp.intaa ),
"""pooled_labels""": jnp.array(features["""category"""] , dtype=jnp.intaa ),
}
return batch
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = [self._fetch_inputs(_snake_case ) for ids in input_ids]
return zip(*_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = [1 for _ in range(len(_snake_case ) )]
while len(_snake_case ) < self.max_length:
input_ids.append(self.pad_id )
attention_mask.append(0 )
return input_ids, attention_mask
def _UpperCAmelCase ( snake_case , snake_case , snake_case=None ):
"""simple docstring"""
if seed is not None:
_lowerCAmelCase = dataset.shuffle(seed=snake_case )
for i in range(len(snake_case ) // batch_size ):
_lowerCAmelCase = dataset[i * batch_size : (i + 1) * batch_size]
yield dict(snake_case )
@partial(jax.pmap , axis_name="""batch""" )
def _UpperCAmelCase ( snake_case , snake_case , **snake_case ):
"""simple docstring"""
def loss_fn(snake_case ):
_lowerCAmelCase = model_inputs.pop("""start_labels""" )
_lowerCAmelCase = model_inputs.pop("""end_labels""" )
_lowerCAmelCase = model_inputs.pop("""pooled_labels""" )
_lowerCAmelCase = state.apply_fn(**snake_case , params=snake_case , dropout_rng=snake_case , train=snake_case )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = outputs
return state.loss_fn(
snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , )
_lowerCAmelCase , _lowerCAmelCase = jax.random.split(snake_case )
_lowerCAmelCase = jax.value_and_grad(snake_case )
_lowerCAmelCase , _lowerCAmelCase = grad_fn(state.params )
_lowerCAmelCase = jax.lax.pmean({"""loss""": loss} , axis_name="""batch""" )
_lowerCAmelCase = jax.lax.pmean(snake_case , """batch""" )
_lowerCAmelCase = state.apply_gradients(grads=snake_case )
return state, metrics, new_drp_rng
@partial(jax.pmap , axis_name="""batch""" )
def _UpperCAmelCase ( snake_case , **snake_case ):
"""simple docstring"""
_lowerCAmelCase = model_inputs.pop("""start_labels""" )
_lowerCAmelCase = model_inputs.pop("""end_labels""" )
_lowerCAmelCase = model_inputs.pop("""pooled_labels""" )
_lowerCAmelCase = state.apply_fn(**snake_case , params=state.params , train=snake_case )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = outputs
_lowerCAmelCase = state.loss_fn(snake_case , snake_case , snake_case , snake_case , snake_case , snake_case )
_lowerCAmelCase = jax.lax.pmean({"""loss""": loss} , axis_name="""batch""" )
return metrics
class __lowerCAmelCase ( train_state.TrainState ):
__lowerCamelCase = struct.field(pytree_node=lowerCamelCase__ )
@dataclass
class __lowerCAmelCase :
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = None
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case=None ):
"""simple docstring"""
_lowerCAmelCase = model.params
_lowerCAmelCase = TrainState.create(
apply_fn=model.__call__ , params=_snake_case , tx=_snake_case , loss_fn=_snake_case , )
if ckpt_dir is not None:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = restore_checkpoint(_snake_case , _snake_case )
_lowerCAmelCase = {
"""lr""": args.lr,
"""init_lr""": args.init_lr,
"""warmup_steps""": args.warmup_steps,
"""num_train_steps""": num_train_steps,
"""weight_decay""": args.weight_decay,
}
_lowerCAmelCase , _lowerCAmelCase = build_tx(**_snake_case )
_lowerCAmelCase = train_state.TrainState(
step=_snake_case , apply_fn=model.__call__ , params=_snake_case , tx=_snake_case , opt_state=_snake_case , )
_lowerCAmelCase = args
_lowerCAmelCase = data_collator
_lowerCAmelCase = lr
_lowerCAmelCase = params
_lowerCAmelCase = jax_utils.replicate(_snake_case )
return state
def snake_case ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.args
_lowerCAmelCase = len(_snake_case ) // args.batch_size
_lowerCAmelCase = jax.random.PRNGKey(0 )
_lowerCAmelCase = jax.random.split(_snake_case , jax.device_count() )
for epoch in range(args.max_epochs ):
_lowerCAmelCase = jnp.array(0 , dtype=jnp.floataa )
_lowerCAmelCase = get_batched_dataset(_snake_case , args.batch_size , seed=_snake_case )
_lowerCAmelCase = 0
for batch in tqdm(_snake_case , total=_snake_case , desc=F'Running EPOCH-{epoch}' ):
_lowerCAmelCase = self.data_collator(_snake_case )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.train_step_fn(_snake_case , _snake_case , **_snake_case )
running_loss += jax_utils.unreplicate(metrics["""loss"""] )
i += 1
if i % args.logging_steps == 0:
_lowerCAmelCase = jax_utils.unreplicate(state.step )
_lowerCAmelCase = running_loss.item() / i
_lowerCAmelCase = self.scheduler_fn(state_step - 1 )
_lowerCAmelCase = self.evaluate(_snake_case , _snake_case )
_lowerCAmelCase = {
"""step""": state_step.item(),
"""eval_loss""": eval_loss.item(),
"""tr_loss""": tr_loss,
"""lr""": lr.item(),
}
tqdm.write(str(_snake_case ) )
self.logger.log(_snake_case , commit=_snake_case )
if i % args.save_steps == 0:
self.save_checkpoint(args.save_dir + F'-e{epoch}-s{i}' , state=_snake_case )
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = get_batched_dataset(_snake_case , self.args.batch_size )
_lowerCAmelCase = len(_snake_case ) // self.args.batch_size
_lowerCAmelCase = jnp.array(0 , dtype=jnp.floataa )
_lowerCAmelCase = 0
for batch in tqdm(_snake_case , total=_snake_case , desc="""Evaluating ... """ ):
_lowerCAmelCase = self.data_collator(_snake_case )
_lowerCAmelCase = self.val_step_fn(_snake_case , **_snake_case )
running_loss += jax_utils.unreplicate(metrics["""loss"""] )
i += 1
return running_loss / i
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = jax_utils.unreplicate(_snake_case )
print(F'SAVING CHECKPOINT IN {save_dir}' , end=""" ... """ )
self.model_save_fn(_snake_case , params=state.params )
with open(os.path.join(_snake_case , """opt_state.msgpack""" ) , """wb""" ) as f:
f.write(to_bytes(state.opt_state ) )
joblib.dump(self.args , os.path.join(_snake_case , """args.joblib""" ) )
joblib.dump(self.data_collator , os.path.join(_snake_case , """data_collator.joblib""" ) )
with open(os.path.join(_snake_case , """training_state.json""" ) , """w""" ) as f:
json.dump({"""step""": state.step.item()} , _snake_case )
print("""DONE""" )
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
print(F'RESTORING CHECKPOINT FROM {save_dir}' , end=""" ... """ )
with open(os.path.join(snake_case , """flax_model.msgpack""" ) , """rb""" ) as f:
_lowerCAmelCase = from_bytes(state.params , f.read() )
with open(os.path.join(snake_case , """opt_state.msgpack""" ) , """rb""" ) as f:
_lowerCAmelCase = from_bytes(state.opt_state , f.read() )
_lowerCAmelCase = joblib.load(os.path.join(snake_case , """args.joblib""" ) )
_lowerCAmelCase = joblib.load(os.path.join(snake_case , """data_collator.joblib""" ) )
with open(os.path.join(snake_case , """training_state.json""" ) , """r""" ) as f:
_lowerCAmelCase = json.load(snake_case )
_lowerCAmelCase = training_state["""step"""]
print("""DONE""" )
return params, opt_state, step, args, data_collator
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = num_train_steps - warmup_steps
_lowerCAmelCase = optax.linear_schedule(init_value=snake_case , end_value=snake_case , transition_steps=snake_case )
_lowerCAmelCase = optax.linear_schedule(init_value=snake_case , end_value=1E-7 , transition_steps=snake_case )
_lowerCAmelCase = optax.join_schedules(schedules=[warmup_fn, decay_fn] , boundaries=[warmup_steps] )
return lr
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
def weight_decay_mask(snake_case ):
_lowerCAmelCase = traverse_util.flatten_dict(snake_case )
_lowerCAmelCase = {k: (v[-1] != """bias""" and v[-2:] != ("""LayerNorm""", """scale""")) for k, v in params.items()}
return traverse_util.unflatten_dict(snake_case )
_lowerCAmelCase = scheduler_fn(snake_case , snake_case , snake_case , snake_case )
_lowerCAmelCase = optax.adamw(learning_rate=snake_case , weight_decay=snake_case , mask=snake_case )
return tx, lr
| 82 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __lowerCAmelCase ( lowerCamelCase__ ):
# to overwrite at feature extractactor specific tests
__lowerCamelCase = None
__lowerCamelCase = None
@property
def snake_case ( self ):
"""simple docstring"""
return self.feat_extract_tester.prepare_feat_extract_dict()
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(_snake_case , """feature_size""" ) )
self.assertTrue(hasattr(_snake_case , """sampling_rate""" ) )
self.assertTrue(hasattr(_snake_case , """padding_value""" ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(_snake_case ) == len(_snake_case ) for x, y in zip(_snake_case , processed_features[input_name] ) ) )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_snake_case )
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="""np""" )
_lowerCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_lowerCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_snake_case )
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="""pt""" )
_lowerCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_lowerCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_snake_case )
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="""tf""" )
_lowerCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_lowerCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def snake_case ( self , _snake_case=False ):
"""simple docstring"""
def _inputs_have_equal_length(_snake_case ):
_lowerCAmelCase = len(input[0] )
for input_slice in input[1:]:
if len(_snake_case ) != length:
return False
return True
def _inputs_are_equal(_snake_case , _snake_case ):
if len(_snake_case ) != len(_snake_case ):
return False
for input_slice_a, input_slice_a in zip(_snake_case , _snake_case ):
if not np.allclose(np.asarray(_snake_case ) , np.asarray(_snake_case ) , atol=1e-3 ):
return False
return True
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_snake_case )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = self.feat_extract_tester.seq_length_diff
_lowerCAmelCase = self.feat_extract_tester.max_seq_length + pad_diff
_lowerCAmelCase = self.feat_extract_tester.min_seq_length
_lowerCAmelCase = self.feat_extract_tester.batch_size
_lowerCAmelCase = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
_lowerCAmelCase = feat_extract.pad(_snake_case , padding=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""max_length""" , max_length=len(speech_inputs[-1] ) )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""max_length""" )[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=_snake_case , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_are_equal(_snake_case , _snake_case ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
_lowerCAmelCase = feat_extract.pad(_snake_case , pad_to_multiple_of=10 )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , pad_to_multiple_of=10 )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , pad_to_multiple_of=10 , max_length=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , pad_to_multiple_of=10 , max_length=_snake_case , return_tensors="""np""" , )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(all(len(_snake_case ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(_snake_case , _snake_case ) )
_lowerCAmelCase = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(_snake_case ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
_lowerCAmelCase = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1e-3 )
def snake_case ( self , _snake_case=False ):
"""simple docstring"""
def _inputs_have_equal_length(_snake_case ):
_lowerCAmelCase = len(input[0] )
for input_slice in input[1:]:
if len(_snake_case ) != length:
return False
return True
def _inputs_are_equal(_snake_case , _snake_case ):
if len(_snake_case ) != len(_snake_case ):
return False
for input_slice_a, input_slice_a in zip(_snake_case , _snake_case ):
if not np.allclose(np.asarray(_snake_case ) , np.asarray(_snake_case ) , atol=1e-3 ):
return False
return True
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_snake_case )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , truncation=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
# truncate to smallest with np
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" , truncation=_snake_case , )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
# truncate to middle
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=_snake_case , return_tensors="""np""" , )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[1] ) , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_are_equal(_snake_case , _snake_case ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , truncation=_snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""longest""" , truncation=_snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""longest""" , truncation=_snake_case )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""max_length""" , truncation=_snake_case )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
_lowerCAmelCase = 12
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_snake_case , truncation=_snake_case , )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_snake_case , )
_lowerCAmelCase = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
_lowerCAmelCase = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
_lowerCAmelCase = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
def snake_case ( self ):
"""simple docstring"""
self._check_padding(numpify=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._check_padding(numpify=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._check_truncation(numpify=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._check_truncation(numpify=_snake_case )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""pt""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""tf""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_dict
_lowerCAmelCase = True
_lowerCAmelCase = self.feature_extraction_class(**_snake_case )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = [len(_snake_case ) for x in speech_inputs]
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )
self.assertIn("""attention_mask""" , _snake_case )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , _snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_dict
_lowerCAmelCase = True
_lowerCAmelCase = self.feature_extraction_class(**_snake_case )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = [len(_snake_case ) for x in speech_inputs]
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = min(_snake_case )
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=_snake_case , truncation=_snake_case , return_tensors="""np""" )
self.assertIn("""attention_mask""" , _snake_case )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
| 82 | 1 |
import collections
import os
import re
from pathlib import Path
A__ = """src/transformers"""
# Matches is_xxx_available()
A__ = re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
A__ = re.compile(R"""^_import_structure\s+=\s+\{([^\}]+)\}""")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
A__ = re.compile(R"""\s+\"\S*\":\s+\[([^\]]*)\]""")
# Catches a line if not is_foo_available
A__ = re.compile(R"""^\s*if\s+not\s+is\_[a-z_]*\_available\(\)""")
# Catches a line _import_struct["bla"].append("foo")
A__ = re.compile(R"""^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)""")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
A__ = re.compile(R"""^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]""")
# Catches a line with an object between quotes and a comma: "MyModel",
A__ = re.compile(R"""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only: ["foo", "bar"],
A__ = re.compile(R"""^\s+\[([^\]]+)\]""")
# Catches a line with from foo import bar, bla, boo
A__ = re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
# Catches a line with try:
A__ = re.compile(R"""^\s*try:""")
# Catches a line with else:
A__ = re.compile(R"""^\s*else:""")
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if _re_test_backend.search(snake_case ) is None:
return None
_lowerCAmelCase = [b[0] for b in _re_backend.findall(snake_case )]
backends.sort()
return "_and_".join(snake_case )
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
with open(snake_case , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
_lowerCAmelCase = f.readlines()
_lowerCAmelCase = 0
while line_index < len(snake_case ) and not lines[line_index].startswith("""_import_structure = {""" ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(snake_case ):
return None
# First grab the objects without a specific backend in _import_structure
_lowerCAmelCase = []
while not lines[line_index].startswith("""if TYPE_CHECKING""" ) and find_backend(lines[line_index] ) is None:
_lowerCAmelCase = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(snake_case ):
_lowerCAmelCase = _re_one_line_import_struct.search(snake_case ).groups()[0]
_lowerCAmelCase = re.findall(R"""\[([^\]]+)\]""" , snake_case )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(""", """ )] )
line_index += 1
continue
_lowerCAmelCase = _re_import_struct_key_value.search(snake_case )
if single_line_import_search is not None:
_lowerCAmelCase = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """ ) if len(snake_case ) > 0]
objects.extend(snake_case )
elif line.startswith(""" """ * 8 + """\"""" ):
objects.append(line[9:-3] )
line_index += 1
_lowerCAmelCase = {"""none""": objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith("""if TYPE_CHECKING""" ):
# If the line is an if not is_backend_available, we grab all objects associated.
_lowerCAmelCase = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
_lowerCAmelCase = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
_lowerCAmelCase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 4 ):
_lowerCAmelCase = lines[line_index]
if _re_import_struct_add_one.search(snake_case ) is not None:
objects.append(_re_import_struct_add_one.search(snake_case ).groups()[0] )
elif _re_import_struct_add_many.search(snake_case ) is not None:
_lowerCAmelCase = _re_import_struct_add_many.search(snake_case ).groups()[0].split(""", """ )
_lowerCAmelCase = [obj[1:-1] for obj in imports if len(snake_case ) > 0]
objects.extend(snake_case )
elif _re_between_brackets.search(snake_case ) is not None:
_lowerCAmelCase = _re_between_brackets.search(snake_case ).groups()[0].split(""", """ )
_lowerCAmelCase = [obj[1:-1] for obj in imports if len(snake_case ) > 0]
objects.extend(snake_case )
elif _re_quote_object.search(snake_case ) is not None:
objects.append(_re_quote_object.search(snake_case ).groups()[0] )
elif line.startswith(""" """ * 8 + """\"""" ):
objects.append(line[9:-3] )
elif line.startswith(""" """ * 12 + """\"""" ):
objects.append(line[13:-3] )
line_index += 1
_lowerCAmelCase = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
_lowerCAmelCase = []
while (
line_index < len(snake_case )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith("""else""" )
):
_lowerCAmelCase = lines[line_index]
_lowerCAmelCase = _re_import.search(snake_case )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(""", """ ) )
elif line.startswith(""" """ * 8 ):
objects.append(line[8:-2] )
line_index += 1
_lowerCAmelCase = {"""none""": objects}
# Let's continue with backend-specific objects
while line_index < len(snake_case ):
# If the line is an if is_backend_available, we grab all objects associated.
_lowerCAmelCase = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
_lowerCAmelCase = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
_lowerCAmelCase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 8 ):
_lowerCAmelCase = lines[line_index]
_lowerCAmelCase = _re_import.search(snake_case )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(""", """ ) )
elif line.startswith(""" """ * 12 ):
objects.append(line[12:-2] )
line_index += 1
_lowerCAmelCase = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
def find_duplicates(snake_case ):
return [k for k, v in collections.Counter(snake_case ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
_lowerCAmelCase = []
for key in import_dict_objects.keys():
_lowerCAmelCase = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F'Duplicate _import_structure definitions for: {duplicate_imports}' )
_lowerCAmelCase = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F'Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}' )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
_lowerCAmelCase = """base imports""" if key == """none""" else F'{key} backend'
errors.append(F'Differences for {name}:' )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F' {a} in TYPE_HINT but not in _import_structure.' )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F' {a} in _import_structure but not in TYPE_HINT.' )
return errors
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = []
for root, _, files in os.walk(snake_case ):
if "__init__.py" in files:
_lowerCAmelCase = os.path.join(snake_case , """__init__.py""" )
_lowerCAmelCase = parse_init(snake_case )
if objects is not None:
_lowerCAmelCase = analyze_results(*snake_case )
if len(snake_case ) > 0:
_lowerCAmelCase = F'Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'
failures.append("""\n""".join(snake_case ) )
if len(snake_case ) > 0:
raise ValueError("""\n\n""".join(snake_case ) )
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = []
for path, directories, files in os.walk(snake_case ):
for folder in directories:
# Ignore private modules
if folder.startswith("""_""" ):
directories.remove(snake_case )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(snake_case ) / folder).glob("""*.py""" ) ) ) == 0:
continue
_lowerCAmelCase = str((Path(snake_case ) / folder).relative_to(snake_case ) )
_lowerCAmelCase = short_path.replace(os.path.sep , """.""" )
submodules.append(snake_case )
for fname in files:
if fname == "__init__.py":
continue
_lowerCAmelCase = str((Path(snake_case ) / fname).relative_to(snake_case ) )
_lowerCAmelCase = short_path.replace(""".py""" , """""" ).replace(os.path.sep , """.""" )
if len(submodule.split(""".""" ) ) == 1:
submodules.append(snake_case )
return submodules
A__ = [
"""convert_pytorch_checkpoint_to_tf2""",
"""modeling_flax_pytorch_utils""",
"""models.esm.openfold_utils""",
]
def _UpperCAmelCase ( ):
"""simple docstring"""
from transformers.utils import direct_transformers_import
_lowerCAmelCase = direct_transformers_import(snake_case )
_lowerCAmelCase = set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(snake_case , """__init__.py""" ) , """r""" ) as f:
_lowerCAmelCase = f.read()
import_structure_keys.update(set(re.findall(R"""import_structure\[\"([^\"]*)\"\]""" , snake_case ) ) )
_lowerCAmelCase = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(snake_case ) > 0:
_lowerCAmelCase = """\n""".join(F'- {module}' for module in module_not_registered )
raise ValueError(
"""The following submodules are not properly registed in the main init of Transformers:\n"""
F'{list_of_modules}\n'
"""Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.""" )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 82 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''poolformer'''
def __init__( self , _snake_case=3 , _snake_case=16 , _snake_case=16 , _snake_case=3 , _snake_case=4.0 , _snake_case=[2, 2, 6, 2] , _snake_case=[64, 128, 320, 512] , _snake_case=[7, 3, 3, 3] , _snake_case=[4, 2, 2, 2] , _snake_case=[2, 1, 1, 1] , _snake_case=4 , _snake_case=0.0 , _snake_case="gelu" , _snake_case=True , _snake_case=1e-5 , _snake_case=0.02 , **_snake_case , ):
"""simple docstring"""
_lowerCAmelCase = num_channels
_lowerCAmelCase = patch_size
_lowerCAmelCase = stride
_lowerCAmelCase = padding
_lowerCAmelCase = pool_size
_lowerCAmelCase = hidden_sizes
_lowerCAmelCase = mlp_ratio
_lowerCAmelCase = depths
_lowerCAmelCase = patch_sizes
_lowerCAmelCase = strides
_lowerCAmelCase = num_encoder_blocks
_lowerCAmelCase = drop_path_rate
_lowerCAmelCase = hidden_act
_lowerCAmelCase = use_layer_scale
_lowerCAmelCase = layer_scale_init_value
_lowerCAmelCase = initializer_range
super().__init__(**_snake_case )
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = version.parse('''1.11''' )
@property
def snake_case ( self ):
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def snake_case ( self ):
"""simple docstring"""
return 2e-3
| 82 | 1 |
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(snake_case ) )
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
if index == len(snake_case ):
return True
# Recursive Step
for i in range(snake_case ):
if valid_coloring(graph[index] , snake_case , snake_case ):
# Color current vertex
_lowerCAmelCase = i
# Validate coloring
if util_color(snake_case , snake_case , snake_case , index + 1 ):
return True
# Backtrack
_lowerCAmelCase = -1
return False
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = [-1] * len(snake_case )
if util_color(snake_case , snake_case , snake_case , 0 ):
return colored_vertices
return []
| 82 |
def _UpperCAmelCase ( snake_case = 10_00 ):
"""simple docstring"""
_lowerCAmelCase = -1
_lowerCAmelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
_lowerCAmelCase = (n * n - 2 * a * n) // (2 * n - 2 * a)
_lowerCAmelCase = n - a - b
if c * c == (a * a + b * b):
_lowerCAmelCase = a * b * c
if candidate >= product:
_lowerCAmelCase = candidate
return product
if __name__ == "__main__":
print(f"{solution() = }")
| 82 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = ["""NllbTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = ["""NllbTokenizerFast"""]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
A__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 82 |
from __future__ import annotations
import math
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(snake_case ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = str(snake_case )
_lowerCAmelCase = [n]
for i in range(1 , len(snake_case ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if len(str(snake_case ) ) > 3:
if not is_prime(int(str(snake_case )[-3:] ) ) or not is_prime(int(str(snake_case )[:3] ) ):
return False
return True
def _UpperCAmelCase ( snake_case = 11 ):
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = 13
while len(snake_case ) != count:
if validate(snake_case ):
_lowerCAmelCase = list_truncated_nums(snake_case )
if all(is_prime(snake_case ) for i in list_nums ):
list_truncated_primes.append(snake_case )
num += 2
return list_truncated_primes
def _UpperCAmelCase ( ):
"""simple docstring"""
return sum(compute_truncated_primes(11 ) )
if __name__ == "__main__":
print(f"{sum(compute_truncated_primes(11)) = }")
| 82 | 1 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
A__ = logging.get_logger(__name__)
@dataclass
class __lowerCAmelCase :
__lowerCamelCase = field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys() )} )
__lowerCamelCase = field(
metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''} )
__lowerCamelCase = field(
default=128 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.task_name.lower()
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''train'''
__lowerCamelCase = '''dev'''
__lowerCamelCase = '''test'''
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 42
def __init__( self , _snake_case , _snake_case , _snake_case = None , _snake_case = Split.train , _snake_case = None , ):
"""simple docstring"""
warnings.warn(
"""This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
"""library. You can have a look at this example script for pointers: """
"""https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py""" , _snake_case , )
_lowerCAmelCase = args
_lowerCAmelCase = glue_processors[args.task_name]()
_lowerCAmelCase = glue_output_modes[args.task_name]
if isinstance(_snake_case , _snake_case ):
try:
_lowerCAmelCase = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
# Load data features from cache or dataset file
_lowerCAmelCase = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}' , )
_lowerCAmelCase = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
_lowerCAmelCase , _lowerCAmelCase = label_list[2], label_list[1]
_lowerCAmelCase = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
_lowerCAmelCase = cached_features_file + """.lock"""
with FileLock(_snake_case ):
if os.path.exists(_snake_case ) and not args.overwrite_cache:
_lowerCAmelCase = time.time()
_lowerCAmelCase = torch.load(_snake_case )
logger.info(
F'Loading features from cached file {cached_features_file} [took %.3f s]' , time.time() - start )
else:
logger.info(F'Creating features from dataset file at {args.data_dir}' )
if mode == Split.dev:
_lowerCAmelCase = self.processor.get_dev_examples(args.data_dir )
elif mode == Split.test:
_lowerCAmelCase = self.processor.get_test_examples(args.data_dir )
else:
_lowerCAmelCase = self.processor.get_train_examples(args.data_dir )
if limit_length is not None:
_lowerCAmelCase = examples[:limit_length]
_lowerCAmelCase = glue_convert_examples_to_features(
_snake_case , _snake_case , max_length=args.max_seq_length , label_list=_snake_case , output_mode=self.output_mode , )
_lowerCAmelCase = time.time()
torch.save(self.features , _snake_case )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self ):
"""simple docstring"""
return len(self.features )
def __getitem__( self , _snake_case ):
"""simple docstring"""
return self.features[i]
def snake_case ( self ):
"""simple docstring"""
return self.label_list
| 82 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
A__ = logging.get_logger(__name__)
class __lowerCAmelCase ( lowerCamelCase__ ):
def __init__( self , **_snake_case ):
"""simple docstring"""
requires_backends(self , ["""bs4"""] )
super().__init__(**_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = element if element.name else element.parent
for parent in child.parents: # type: bs4.element.Tag
_lowerCAmelCase = parent.find_all(child.name , recursive=_snake_case )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(_snake_case ) else next(i for i, s in enumerate(_snake_case , 1 ) if s is child ) )
_lowerCAmelCase = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = BeautifulSoup(_snake_case , """html.parser""" )
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = []
for element in html_code.descendants:
if type(_snake_case ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
_lowerCAmelCase = html.unescape(_snake_case ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(_snake_case )
_lowerCAmelCase , _lowerCAmelCase = self.xpath_soup(_snake_case )
stringaxtag_seq.append(_snake_case )
stringaxsubs_seq.append(_snake_case )
if len(_snake_case ) != len(_snake_case ):
raise ValueError("""Number of doc strings and xtags does not correspond""" )
if len(_snake_case ) != len(_snake_case ):
raise ValueError("""Number of doc strings and xsubs does not correspond""" )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = """"""
for tagname, subs in zip(_snake_case , _snake_case ):
xpath += F'/{tagname}'
if subs != 0:
xpath += F'[{subs}]'
return xpath
def __call__( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = False
# Check that strings has a valid type
if isinstance(_snake_case , _snake_case ):
_lowerCAmelCase = True
elif isinstance(_snake_case , (list, tuple) ):
if len(_snake_case ) == 0 or isinstance(html_strings[0] , _snake_case ):
_lowerCAmelCase = True
if not valid_strings:
raise ValueError(
"""HTML strings must of type `str`, `List[str]` (batch of examples), """
F'but is of type {type(_snake_case )}.' )
_lowerCAmelCase = bool(isinstance(_snake_case , (list, tuple) ) and (isinstance(html_strings[0] , _snake_case )) )
if not is_batched:
_lowerCAmelCase = [html_strings]
# Get nodes + xpaths
_lowerCAmelCase = []
_lowerCAmelCase = []
for html_string in html_strings:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.get_three_from_single(_snake_case )
nodes.append(_snake_case )
_lowerCAmelCase = []
for node, tag_list, sub_list in zip(_snake_case , _snake_case , _snake_case ):
_lowerCAmelCase = self.construct_xpath(_snake_case , _snake_case )
xpath_strings.append(_snake_case )
xpaths.append(_snake_case )
# return as Dict
_lowerCAmelCase = {"""nodes""": nodes, """xpaths""": xpaths}
_lowerCAmelCase = BatchFeature(data=_snake_case , tensor_type=_snake_case )
return encoded_inputs
| 82 | 1 |
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 0
while number > 0:
_lowerCAmelCase = number % 10
sum_of_digits += last_digit
_lowerCAmelCase = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def _UpperCAmelCase ( snake_case = 1_00 ):
"""simple docstring"""
_lowerCAmelCase = factorial(snake_case )
_lowerCAmelCase = split_and_add(snake_case )
return result
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).strip())))
| 82 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
A__ = TypeVar("""T""")
A__ = TypeVar("""U""")
class __lowerCAmelCase ( Generic[T, U] ):
def __init__( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = key
_lowerCAmelCase = val
_lowerCAmelCase = None
_lowerCAmelCase = None
def __repr__( self ):
"""simple docstring"""
return (
F'Node: key: {self.key}, val: {self.val}, '
F'has next: {bool(self.next )}, has prev: {bool(self.prev )}'
)
class __lowerCAmelCase ( Generic[T, U] ):
def __init__( self ):
"""simple docstring"""
_lowerCAmelCase = DoubleLinkedListNode(_snake_case , _snake_case )
_lowerCAmelCase = DoubleLinkedListNode(_snake_case , _snake_case )
_lowerCAmelCase , _lowerCAmelCase = self.rear, self.head
def __repr__( self ):
"""simple docstring"""
_lowerCAmelCase = ["""DoubleLinkedList"""]
_lowerCAmelCase = self.head
while node.next is not None:
rep.append(str(_snake_case ) )
_lowerCAmelCase = node.next
rep.append(str(self.rear ) )
return ",\n ".join(_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
_lowerCAmelCase = node
_lowerCAmelCase = previous
_lowerCAmelCase = node
_lowerCAmelCase = self.rear
def snake_case ( self , _snake_case ):
"""simple docstring"""
if node.prev is None or node.next is None:
return None
_lowerCAmelCase = node.next
_lowerCAmelCase = node.prev
_lowerCAmelCase = None
_lowerCAmelCase = None
return node
class __lowerCAmelCase ( Generic[T, U] ):
__lowerCamelCase = {}
def __init__( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = DoubleLinkedList()
_lowerCAmelCase = capacity
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = {}
def __repr__( self ):
"""simple docstring"""
return (
F'CacheInfo(hits={self.hits}, misses={self.miss}, '
F'capacity={self.capacity}, current size={self.num_keys})'
)
def __contains__( self , _snake_case ):
"""simple docstring"""
return key in self.cache
def snake_case ( self , _snake_case ):
"""simple docstring"""
if key in self.cache:
self.hits += 1
_lowerCAmelCase = self.cache[key]
_lowerCAmelCase = self.list.remove(self.cache[key] )
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(_snake_case )
return node.val
self.miss += 1
return None
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
_lowerCAmelCase = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(_snake_case ) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
_lowerCAmelCase = DoubleLinkedListNode(_snake_case , _snake_case )
self.list.add(self.cache[key] )
self.num_keys += 1
else:
# bump node to the end of the list, update value
_lowerCAmelCase = self.list.remove(self.cache[key] )
assert node is not None # node guaranteed to be in list
_lowerCAmelCase = value
self.list.add(_snake_case )
@classmethod
def snake_case ( cls , _snake_case = 128 ):
"""simple docstring"""
def cache_decorator_inner(_snake_case ) -> Callable[..., U]:
def cache_decorator_wrapper(*_snake_case ) -> U:
if func not in cls.decorator_function_to_instance_map:
_lowerCAmelCase = LRUCache(_snake_case )
_lowerCAmelCase = cls.decorator_function_to_instance_map[func].get(args[0] )
if result is None:
_lowerCAmelCase = func(*_snake_case )
cls.decorator_function_to_instance_map[func].put(args[0] , _snake_case )
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(_snake_case , """cache_info""" , _snake_case ) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| 82 | 1 |
import argparse
from collections import defaultdict
import yaml
A__ = """docs/source/en/_toctree.yml"""
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = defaultdict(snake_case )
_lowerCAmelCase = []
_lowerCAmelCase = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]} )
else:
new_doc_list.append(snake_case )
_lowerCAmelCase = new_doc_list
_lowerCAmelCase = [key for key, value in counts.items() if value > 1]
_lowerCAmelCase = []
for duplicate_key in duplicates:
_lowerCAmelCase = list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key} )
if len(snake_case ) > 1:
raise ValueError(
F'{duplicate_key} is present several times in the documentation table of content at '
"""`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
"""others.""" )
# Only add this once
new_doc.append({"""local""": duplicate_key, """title""": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1] )
_lowerCAmelCase = sorted(snake_case , key=lambda snake_case : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(snake_case ) > 1:
raise ValueError("""{doc_list} has two 'overview' docs which is not allowed.""" )
overview_doc.extend(snake_case )
# Sort
return overview_doc
def _UpperCAmelCase ( snake_case=False ):
"""simple docstring"""
with open(snake_case , encoding="""utf-8""" ) as f:
_lowerCAmelCase = yaml.safe_load(f.read() )
# Get to the API doc
_lowerCAmelCase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_lowerCAmelCase = content[api_idx]["""sections"""]
# Then to the model doc
_lowerCAmelCase = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
_lowerCAmelCase = api_doc[scheduler_idx]["""sections"""]
_lowerCAmelCase = clean_doc_toc(snake_case )
_lowerCAmelCase = False
if new_scheduler_doc != scheduler_doc:
_lowerCAmelCase = True
if overwrite:
_lowerCAmelCase = new_scheduler_doc
if diff:
if overwrite:
_lowerCAmelCase = api_doc
with open(snake_case , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(snake_case , allow_unicode=snake_case ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
def _UpperCAmelCase ( snake_case=False ):
"""simple docstring"""
with open(snake_case , encoding="""utf-8""" ) as f:
_lowerCAmelCase = yaml.safe_load(f.read() )
# Get to the API doc
_lowerCAmelCase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_lowerCAmelCase = content[api_idx]["""sections"""]
# Then to the model doc
_lowerCAmelCase = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
_lowerCAmelCase = False
_lowerCAmelCase = api_doc[pipeline_idx]["""sections"""]
_lowerCAmelCase = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
_lowerCAmelCase = pipeline_doc["""section"""]
_lowerCAmelCase = clean_doc_toc(snake_case )
if overwrite:
_lowerCAmelCase = new_sub_pipeline_doc
new_pipeline_docs.append(snake_case )
# sort overall pipeline doc
_lowerCAmelCase = clean_doc_toc(snake_case )
if new_pipeline_docs != pipeline_docs:
_lowerCAmelCase = True
if overwrite:
_lowerCAmelCase = new_pipeline_docs
if diff:
if overwrite:
_lowerCAmelCase = api_doc
with open(snake_case , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(snake_case , allow_unicode=snake_case ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
A__ = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 82 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A__ = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""MvpTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = ["""MvpTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = [
"""MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MvpForCausalLM""",
"""MvpForConditionalGeneration""",
"""MvpForQuestionAnswering""",
"""MvpForSequenceClassification""",
"""MvpModel""",
"""MvpPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
A__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 82 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
A__ = logging.get_logger(__name__)
A__ = {
"""Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json""",
# See all Marian models at https://huggingface.co/models?filter=marian
}
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''marian'''
__lowerCamelCase = ['''past_key_values''']
__lowerCamelCase = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self , _snake_case=58101 , _snake_case=None , _snake_case=1024 , _snake_case=12 , _snake_case=4096 , _snake_case=16 , _snake_case=12 , _snake_case=4096 , _snake_case=16 , _snake_case=0.0 , _snake_case=0.0 , _snake_case=True , _snake_case=True , _snake_case="gelu" , _snake_case=1024 , _snake_case=0.1 , _snake_case=0.0 , _snake_case=0.0 , _snake_case=0.02 , _snake_case=58100 , _snake_case=False , _snake_case=58100 , _snake_case=0 , _snake_case=0 , _snake_case=True , **_snake_case , ):
"""simple docstring"""
_lowerCAmelCase = vocab_size
_lowerCAmelCase = decoder_vocab_size or vocab_size
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = d_model
_lowerCAmelCase = encoder_ffn_dim
_lowerCAmelCase = encoder_layers
_lowerCAmelCase = encoder_attention_heads
_lowerCAmelCase = decoder_ffn_dim
_lowerCAmelCase = decoder_layers
_lowerCAmelCase = decoder_attention_heads
_lowerCAmelCase = dropout
_lowerCAmelCase = attention_dropout
_lowerCAmelCase = activation_dropout
_lowerCAmelCase = activation_function
_lowerCAmelCase = init_std
_lowerCAmelCase = encoder_layerdrop
_lowerCAmelCase = decoder_layerdrop
_lowerCAmelCase = use_cache
_lowerCAmelCase = encoder_layers
_lowerCAmelCase = scale_embedding # scale factor will be sqrt(d_model) if True
_lowerCAmelCase = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=_snake_case , eos_token_id=_snake_case , is_encoder_decoder=_snake_case , decoder_start_token_id=_snake_case , forced_eos_token_id=_snake_case , **_snake_case , )
class __lowerCAmelCase ( lowerCamelCase__ ):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def snake_case ( self ):
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
_lowerCAmelCase = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
_lowerCAmelCase = {0: """batch"""}
_lowerCAmelCase = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
_lowerCAmelCase = {0: """batch""", 1: """decoder_sequence"""}
_lowerCAmelCase = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(_snake_case , direction="""inputs""" )
elif self.task == "causal-lm":
# TODO: figure this case out.
_lowerCAmelCase = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
_lowerCAmelCase , _lowerCAmelCase = self.num_layers
for i in range(_snake_case ):
_lowerCAmelCase = {0: """batch""", 2: """past_sequence + sequence"""}
_lowerCAmelCase = {0: """batch""", 2: """past_sequence + sequence"""}
else:
_lowerCAmelCase = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def snake_case ( self ):
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
_lowerCAmelCase = super().outputs
else:
_lowerCAmelCase = super(_snake_case , self ).outputs
if self.use_past:
_lowerCAmelCase , _lowerCAmelCase = self.num_layers
for i in range(_snake_case ):
_lowerCAmelCase = {0: """batch""", 2: """past_sequence + sequence"""}
_lowerCAmelCase = {0: """batch""", 2: """past_sequence + sequence"""}
return common_outputs
def snake_case ( self , _snake_case , _snake_case = -1 , _snake_case = -1 , _snake_case = False , _snake_case = None , ):
"""simple docstring"""
_lowerCAmelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
# Generate decoder inputs
_lowerCAmelCase = seq_length if not self.use_past else 1
_lowerCAmelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
_lowerCAmelCase = {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
_lowerCAmelCase = dict(**_snake_case , **_snake_case )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
_lowerCAmelCase , _lowerCAmelCase = common_inputs["""input_ids"""].shape
_lowerCAmelCase = common_inputs["""decoder_input_ids"""].shape[1]
_lowerCAmelCase , _lowerCAmelCase = self.num_attention_heads
_lowerCAmelCase = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
_lowerCAmelCase = decoder_seq_length + 3
_lowerCAmelCase = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
_lowerCAmelCase = torch.cat(
[common_inputs["""decoder_attention_mask"""], torch.ones(_snake_case , _snake_case )] , dim=1 )
_lowerCAmelCase = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
_lowerCAmelCase , _lowerCAmelCase = self.num_layers
_lowerCAmelCase = min(_snake_case , _snake_case )
_lowerCAmelCase = max(_snake_case , _snake_case ) - min_num_layers
_lowerCAmelCase = """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""
for _ in range(_snake_case ):
common_inputs["past_key_values"].append(
(
torch.zeros(_snake_case ),
torch.zeros(_snake_case ),
torch.zeros(_snake_case ),
torch.zeros(_snake_case ),
) )
# TODO: test this.
_lowerCAmelCase = encoder_shape if remaining_side_name == """encoder""" else decoder_shape
for _ in range(_snake_case , _snake_case ):
common_inputs["past_key_values"].append((torch.zeros(_snake_case ), torch.zeros(_snake_case )) )
return common_inputs
def snake_case ( self , _snake_case , _snake_case = -1 , _snake_case = -1 , _snake_case = False , _snake_case = None , ):
"""simple docstring"""
_lowerCAmelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
_lowerCAmelCase , _lowerCAmelCase = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
_lowerCAmelCase = seqlen + 2
_lowerCAmelCase , _lowerCAmelCase = self.num_layers
_lowerCAmelCase , _lowerCAmelCase = self.num_attention_heads
_lowerCAmelCase = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
_lowerCAmelCase = common_inputs["""attention_mask"""].dtype
_lowerCAmelCase = torch.cat(
[common_inputs["""attention_mask"""], torch.ones(_snake_case , _snake_case , dtype=_snake_case )] , dim=1 )
_lowerCAmelCase = [
(torch.zeros(_snake_case ), torch.zeros(_snake_case )) for _ in range(_snake_case )
]
return common_inputs
def snake_case ( self , _snake_case , _snake_case = -1 , _snake_case = -1 , _snake_case = False , _snake_case = None , ):
"""simple docstring"""
_lowerCAmelCase = compute_effective_axis_dimension(
_snake_case , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
_lowerCAmelCase = tokenizer.num_special_tokens_to_add(_snake_case )
_lowerCAmelCase = compute_effective_axis_dimension(
_snake_case , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=_snake_case )
# Generate dummy inputs according to compute batch and sequence
_lowerCAmelCase = [""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
_lowerCAmelCase = dict(tokenizer(_snake_case , return_tensors=_snake_case ) )
return common_inputs
def snake_case ( self , _snake_case , _snake_case = -1 , _snake_case = -1 , _snake_case = False , _snake_case = None , ):
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
_lowerCAmelCase = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
_snake_case , batch_size=_snake_case , seq_length=_snake_case , is_pair=_snake_case , framework=_snake_case )
else:
_lowerCAmelCase = self._generate_dummy_inputs_for_causal_lm(
_snake_case , batch_size=_snake_case , seq_length=_snake_case , is_pair=_snake_case , framework=_snake_case )
return common_inputs
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
_lowerCAmelCase = super()._flatten_past_key_values_(_snake_case , _snake_case , _snake_case , _snake_case )
else:
_lowerCAmelCase = super(_snake_case , self )._flatten_past_key_values_(
_snake_case , _snake_case , _snake_case , _snake_case )
@property
def snake_case ( self ):
"""simple docstring"""
return 1e-4
| 82 |
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 0
while number > 0:
_lowerCAmelCase = number % 10
sum_of_digits += last_digit
_lowerCAmelCase = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def _UpperCAmelCase ( snake_case = 1_00 ):
"""simple docstring"""
_lowerCAmelCase = factorial(snake_case )
_lowerCAmelCase = split_and_add(snake_case )
return result
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).strip())))
| 82 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
A__ = {
"""configuration_audio_spectrogram_transformer""": [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ASTConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ASTForAudioClassification""",
"""ASTModel""",
"""ASTPreTrainedModel""",
]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = ["""ASTFeatureExtractor"""]
if TYPE_CHECKING:
from .configuration_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
ASTConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ASTForAudioClassification,
ASTModel,
ASTPreTrainedModel,
)
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor
else:
import sys
A__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 82 |
A__ = [0, 2, 4, 6, 8]
A__ = [1, 3, 5, 7, 9]
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 10
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
_lowerCAmelCase = 0
for digit in range(10 ):
_lowerCAmelCase = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 10 , snake_case , snake_case )
return result
_lowerCAmelCase = 0
for digita in range(10 ):
_lowerCAmelCase = digita
if (remainder + digita) % 2 == 0:
_lowerCAmelCase = ODD_DIGITS
else:
_lowerCAmelCase = EVEN_DIGITS
for digita in other_parity_digits:
_lowerCAmelCase = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 10 , snake_case , snake_case , )
return result
def _UpperCAmelCase ( snake_case = 9 ):
"""simple docstring"""
_lowerCAmelCase = 0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(snake_case , 0 , [0] * length , snake_case )
return result
if __name__ == "__main__":
print(f"{solution() = }")
| 82 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
require_torch_gpu,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase = StableUnCLIPImgaImgPipeline
__lowerCamelCase = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
__lowerCamelCase = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
__lowerCamelCase = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
__lowerCamelCase = frozenset([] )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = 32
_lowerCAmelCase = embedder_hidden_size
# image encoding components
_lowerCAmelCase = CLIPImageProcessor(crop_size=32 , size=32 )
torch.manual_seed(0 )
_lowerCAmelCase = CLIPVisionModelWithProjection(
CLIPVisionConfig(
hidden_size=_snake_case , projection_dim=_snake_case , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , ) )
# regular denoising components
torch.manual_seed(0 )
_lowerCAmelCase = StableUnCLIPImageNormalizer(embedding_dim=_snake_case )
_lowerCAmelCase = DDPMScheduler(beta_schedule="""squaredcos_cap_v2""" )
torch.manual_seed(0 )
_lowerCAmelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
torch.manual_seed(0 )
_lowerCAmelCase = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_snake_case , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
_lowerCAmelCase = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""") , up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""") , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type="""projection""" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=_snake_case , layers_per_block=1 , upcast_attention=_snake_case , use_linear_projection=_snake_case , )
torch.manual_seed(0 )
_lowerCAmelCase = DDIMScheduler(
beta_schedule="""scaled_linear""" , beta_start=0.0_0085 , beta_end=0.012 , prediction_type="""v_prediction""" , set_alpha_to_one=_snake_case , steps_offset=1 , )
torch.manual_seed(0 )
_lowerCAmelCase = AutoencoderKL()
_lowerCAmelCase = {
# image encoding components
"""feature_extractor""": feature_extractor,
"""image_encoder""": image_encoder.eval(),
# image noising components
"""image_normalizer""": image_normalizer.eval(),
"""image_noising_scheduler""": image_noising_scheduler,
# regular denoising components
"""tokenizer""": tokenizer,
"""text_encoder""": text_encoder.eval(),
"""unet""": unet.eval(),
"""scheduler""": scheduler,
"""vae""": vae.eval(),
}
return components
def snake_case ( self , _snake_case , _snake_case=0 , _snake_case=True ):
"""simple docstring"""
if str(_snake_case ).startswith("""mps""" ):
_lowerCAmelCase = torch.manual_seed(_snake_case )
else:
_lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
_lowerCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(_snake_case ) ).to(_snake_case )
if pil_image:
_lowerCAmelCase = input_image * 0.5 + 0.5
_lowerCAmelCase = input_image.clamp(0 , 1 )
_lowerCAmelCase = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
_lowerCAmelCase = DiffusionPipeline.numpy_to_pil(_snake_case )[0]
return {
"prompt": "An anime racoon running a marathon",
"image": input_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "np",
}
@skip_mps
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = StableUnCLIPImgaImgPipeline(**_snake_case )
_lowerCAmelCase = sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
inputs.update({"""image_embeds""": None} )
_lowerCAmelCase = sd_pipe(**_snake_case ).images
_lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_lowerCAmelCase = np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = torch_device in ["""cpu""", """mps"""]
self._test_attention_slicing_forward_pass(test_max_difference=_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = torch_device in ["""cpu""", """mps"""]
self._test_inference_batch_single_identical(test_max_difference=_snake_case )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def snake_case ( self ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(test_max_difference=_snake_case )
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png""" )
_lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy""" )
_lowerCAmelCase = StableUnCLIPImgaImgPipeline.from_pretrained(
"""fusing/stable-unclip-2-1-l-img2img""" , torch_dtype=torch.floataa )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_lowerCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
_lowerCAmelCase = pipe(_snake_case , """anime turle""" , generator=_snake_case , output_type="""np""" )
_lowerCAmelCase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_snake_case , _snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png""" )
_lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy""" )
_lowerCAmelCase = StableUnCLIPImgaImgPipeline.from_pretrained(
"""fusing/stable-unclip-2-1-h-img2img""" , torch_dtype=torch.floataa )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_lowerCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
_lowerCAmelCase = pipe(_snake_case , """anime turle""" , generator=_snake_case , output_type="""np""" )
_lowerCAmelCase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_snake_case , _snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png""" )
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_lowerCAmelCase = StableUnCLIPImgaImgPipeline.from_pretrained(
"""fusing/stable-unclip-2-1-h-img2img""" , torch_dtype=torch.floataa )
_lowerCAmelCase = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_lowerCAmelCase = pipe(
_snake_case , """anime turtle""" , num_inference_steps=2 , output_type="""np""" , )
_lowerCAmelCase = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 82 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
A__ = logging.get_logger(__name__)
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = OrderedDict()
for key, value in state_dict.items():
if key.startswith("""module.encoder""" ):
_lowerCAmelCase = key.replace("""module.encoder""" , """glpn.encoder""" )
if key.startswith("""module.decoder""" ):
_lowerCAmelCase = key.replace("""module.decoder""" , """decoder.stages""" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
_lowerCAmelCase = key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
_lowerCAmelCase = key.replace(F'patch_embed{idx}' , F'patch_embeddings.{int(snake_case )-1}' )
if "norm" in key:
_lowerCAmelCase = key.replace("""norm""" , """layer_norm""" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
_lowerCAmelCase = key[key.find("""glpn.encoder.layer_norm""" ) + len("""glpn.encoder.layer_norm""" )]
_lowerCAmelCase = key.replace(F'layer_norm{idx}' , F'layer_norm.{int(snake_case )-1}' )
if "layer_norm1" in key:
_lowerCAmelCase = key.replace("""layer_norm1""" , """layer_norm_1""" )
if "layer_norm2" in key:
_lowerCAmelCase = key.replace("""layer_norm2""" , """layer_norm_2""" )
if "block" in key:
# replace for example block1 by block.0
_lowerCAmelCase = key[key.find("""block""" ) + len("""block""" )]
_lowerCAmelCase = key.replace(F'block{idx}' , F'block.{int(snake_case )-1}' )
if "attn.q" in key:
_lowerCAmelCase = key.replace("""attn.q""" , """attention.self.query""" )
if "attn.proj" in key:
_lowerCAmelCase = key.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in key:
_lowerCAmelCase = key.replace("""attn""" , """attention.self""" )
if "fc1" in key:
_lowerCAmelCase = key.replace("""fc1""" , """dense1""" )
if "fc2" in key:
_lowerCAmelCase = key.replace("""fc2""" , """dense2""" )
if "linear_pred" in key:
_lowerCAmelCase = key.replace("""linear_pred""" , """classifier""" )
if "linear_fuse" in key:
_lowerCAmelCase = key.replace("""linear_fuse.conv""" , """linear_fuse""" )
_lowerCAmelCase = key.replace("""linear_fuse.bn""" , """batch_norm""" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
_lowerCAmelCase = key[key.find("""linear_c""" ) + len("""linear_c""" )]
_lowerCAmelCase = key.replace(F'linear_c{idx}' , F'linear_c.{int(snake_case )-1}' )
if "bot_conv" in key:
_lowerCAmelCase = key.replace("""bot_conv""" , """0.convolution""" )
if "skip_conv1" in key:
_lowerCAmelCase = key.replace("""skip_conv1""" , """1.convolution""" )
if "skip_conv2" in key:
_lowerCAmelCase = key.replace("""skip_conv2""" , """2.convolution""" )
if "fusion1" in key:
_lowerCAmelCase = key.replace("""fusion1""" , """1.fusion""" )
if "fusion2" in key:
_lowerCAmelCase = key.replace("""fusion2""" , """2.fusion""" )
if "fusion3" in key:
_lowerCAmelCase = key.replace("""fusion3""" , """3.fusion""" )
if "fusion" in key and "conv" in key:
_lowerCAmelCase = key.replace("""conv""" , """convolutional_layer""" )
if key.startswith("""module.last_layer_depth""" ):
_lowerCAmelCase = key.replace("""module.last_layer_depth""" , """head.head""" )
_lowerCAmelCase = value
return new_state_dict
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
_lowerCAmelCase = state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight' )
_lowerCAmelCase = state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.bias' )
# next, add keys and values (in that order) to the state dict
_lowerCAmelCase = kv_weight[
: config.hidden_sizes[i], :
]
_lowerCAmelCase = kv_bias[: config.hidden_sizes[i]]
_lowerCAmelCase = kv_weight[
config.hidden_sizes[i] :, :
]
_lowerCAmelCase = kv_bias[config.hidden_sizes[i] :]
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_lowerCAmelCase = Image.open(requests.get(snake_case , stream=snake_case ).raw )
return image
@torch.no_grad()
def _UpperCAmelCase ( snake_case , snake_case , snake_case=False , snake_case=None ):
"""simple docstring"""
_lowerCAmelCase = GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12] , decoder_hidden_size=64 , depths=[3, 8, 27, 3] )
# load image processor (only resize + rescale)
_lowerCAmelCase = GLPNImageProcessor()
# prepare image
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(images=snake_case , return_tensors="""pt""" ).pixel_values
logger.info("""Converting model...""" )
# load original state dict
_lowerCAmelCase = torch.load(snake_case , map_location=torch.device("""cpu""" ) )
# rename keys
_lowerCAmelCase = rename_keys(snake_case )
# key and value matrices need special treatment
read_in_k_v(snake_case , snake_case )
# create HuggingFace model and load state dict
_lowerCAmelCase = GLPNForDepthEstimation(snake_case )
model.load_state_dict(snake_case )
model.eval()
# forward pass
_lowerCAmelCase = model(snake_case )
_lowerCAmelCase = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
_lowerCAmelCase = torch.tensor(
[[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]] )
elif "kitti" in model_name:
_lowerCAmelCase = torch.tensor(
[[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]] )
else:
raise ValueError(F'Unknown model name: {model_name}' )
_lowerCAmelCase = torch.Size([1, 4_80, 6_40] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3] , snake_case , atol=1E-4 )
print("""Looks ok!""" )
# finally, push to hub if required
if push_to_hub:
logger.info("""Pushing model and image processor to the hub...""" )
model.push_to_hub(
repo_path_or_name=Path(snake_case , snake_case ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=snake_case , )
image_processor.push_to_hub(
repo_path_or_name=Path(snake_case , snake_case ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=snake_case , )
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""",
default=None,
type=str,
help="""Path to the original PyTorch checkpoint (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
)
parser.add_argument(
"""--model_name""",
default="""glpn-kitti""",
type=str,
help="""Name of the model in case you're pushing to the hub.""",
)
A__ = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 82 | 1 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = 42
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@register_to_config
def __init__( self , _snake_case = 16 , _snake_case = 88 , _snake_case = None , _snake_case = None , _snake_case = 1 , _snake_case = 0.0 , _snake_case = 32 , _snake_case = None , _snake_case = False , _snake_case = None , _snake_case = "geglu" , _snake_case = True , _snake_case = True , ):
"""simple docstring"""
super().__init__()
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = attention_head_dim
_lowerCAmelCase = num_attention_heads * attention_head_dim
_lowerCAmelCase = in_channels
_lowerCAmelCase = torch.nn.GroupNorm(num_groups=_snake_case , num_channels=_snake_case , eps=1e-6 , affine=_snake_case )
_lowerCAmelCase = nn.Linear(_snake_case , _snake_case )
# 3. Define transformers blocks
_lowerCAmelCase = nn.ModuleList(
[
BasicTransformerBlock(
_snake_case , _snake_case , _snake_case , dropout=_snake_case , cross_attention_dim=_snake_case , activation_fn=_snake_case , attention_bias=_snake_case , double_self_attention=_snake_case , norm_elementwise_affine=_snake_case , )
for d in range(_snake_case )
] )
_lowerCAmelCase = nn.Linear(_snake_case , _snake_case )
def snake_case ( self , _snake_case , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=1 , _snake_case=None , _snake_case = True , ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = hidden_states.shape
_lowerCAmelCase = batch_frames // num_frames
_lowerCAmelCase = hidden_states
_lowerCAmelCase = hidden_states[None, :].reshape(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
_lowerCAmelCase = hidden_states.permute(0 , 2 , 1 , 3 , 4 )
_lowerCAmelCase = self.norm(_snake_case )
_lowerCAmelCase = hidden_states.permute(0 , 3 , 4 , 2 , 1 ).reshape(batch_size * height * width , _snake_case , _snake_case )
_lowerCAmelCase = self.proj_in(_snake_case )
# 2. Blocks
for block in self.transformer_blocks:
_lowerCAmelCase = block(
_snake_case , encoder_hidden_states=_snake_case , timestep=_snake_case , cross_attention_kwargs=_snake_case , class_labels=_snake_case , )
# 3. Output
_lowerCAmelCase = self.proj_out(_snake_case )
_lowerCAmelCase = (
hidden_states[None, None, :]
.reshape(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
.permute(0 , 3 , 4 , 1 , 2 )
.contiguous()
)
_lowerCAmelCase = hidden_states.reshape(_snake_case , _snake_case , _snake_case , _snake_case )
_lowerCAmelCase = hidden_states + residual
if not return_dict:
return (output,)
return TransformerTemporalModelOutput(sample=_snake_case )
| 82 |
from math import isqrt, loga
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , snake_case , snake_case ):
_lowerCAmelCase = False
return [i for i in range(2 , snake_case ) if is_prime[i]]
def _UpperCAmelCase ( snake_case = 80_08_00 , snake_case = 80_08_00 ):
"""simple docstring"""
_lowerCAmelCase = degree * loga(snake_case )
_lowerCAmelCase = int(snake_case )
_lowerCAmelCase = calculate_prime_numbers(snake_case )
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = len(snake_case ) - 1
while left < right:
while (
prime_numbers[right] * loga(prime_numbers[left] )
+ prime_numbers[left] * loga(prime_numbers[right] )
> upper_bound
):
right -= 1
hybrid_integers_count += right - left
left += 1
return hybrid_integers_count
if __name__ == "__main__":
print(f"{solution() = }")
| 82 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
A__ = {"""LayoutLMv2Config""", """LayoutLMv3Config"""}
@is_pipeline_test
class __lowerCAmelCase ( unittest.TestCase ):
__lowerCamelCase = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
__lowerCamelCase = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
__lowerCamelCase = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
__lowerCamelCase = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = pipeline(
task="""text-classification""" , model="""hf-internal-testing/tiny-random-distilbert""" , framework="""pt""" )
_lowerCAmelCase = text_classifier("""This is great !""" )
self.assertEqual(nested_simplify(_snake_case ) , [{"""label""": """LABEL_0""", """score""": 0.504}] )
_lowerCAmelCase = text_classifier("""This is great !""" , top_k=2 )
self.assertEqual(
nested_simplify(_snake_case ) , [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}] )
_lowerCAmelCase = text_classifier(["""This is great !""", """This is bad"""] , top_k=2 )
self.assertEqual(
nested_simplify(_snake_case ) , [
[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
] , )
_lowerCAmelCase = text_classifier("""This is great !""" , top_k=1 )
self.assertEqual(nested_simplify(_snake_case ) , [{"""label""": """LABEL_0""", """score""": 0.504}] )
# Legacy behavior
_lowerCAmelCase = text_classifier("""This is great !""" , return_all_scores=_snake_case )
self.assertEqual(nested_simplify(_snake_case ) , [{"""label""": """LABEL_0""", """score""": 0.504}] )
_lowerCAmelCase = text_classifier("""This is great !""" , return_all_scores=_snake_case )
self.assertEqual(
nested_simplify(_snake_case ) , [[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}]] )
_lowerCAmelCase = text_classifier(["""This is great !""", """Something else"""] , return_all_scores=_snake_case )
self.assertEqual(
nested_simplify(_snake_case ) , [
[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
] , )
_lowerCAmelCase = text_classifier(["""This is great !""", """Something else"""] , return_all_scores=_snake_case )
self.assertEqual(
nested_simplify(_snake_case ) , [
{"""label""": """LABEL_0""", """score""": 0.504},
{"""label""": """LABEL_0""", """score""": 0.504},
] , )
@require_torch
def snake_case ( self ):
"""simple docstring"""
import torch
_lowerCAmelCase = pipeline(
task="""text-classification""" , model="""hf-internal-testing/tiny-random-distilbert""" , framework="""pt""" , device=torch.device("""cpu""" ) , )
_lowerCAmelCase = text_classifier("""This is great !""" )
self.assertEqual(nested_simplify(_snake_case ) , [{"""label""": """LABEL_0""", """score""": 0.504}] )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = pipeline(
task="""text-classification""" , model="""hf-internal-testing/tiny-random-distilbert""" , framework="""tf""" )
_lowerCAmelCase = text_classifier("""This is great !""" )
self.assertEqual(nested_simplify(_snake_case ) , [{"""label""": """LABEL_0""", """score""": 0.504}] )
@slow
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = pipeline("""text-classification""" )
_lowerCAmelCase = text_classifier("""This is great !""" )
self.assertEqual(nested_simplify(_snake_case ) , [{"""label""": """POSITIVE""", """score""": 1.0}] )
_lowerCAmelCase = text_classifier("""This is bad !""" )
self.assertEqual(nested_simplify(_snake_case ) , [{"""label""": """NEGATIVE""", """score""": 1.0}] )
_lowerCAmelCase = text_classifier("""Birds are a type of animal""" )
self.assertEqual(nested_simplify(_snake_case ) , [{"""label""": """POSITIVE""", """score""": 0.988}] )
@slow
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = pipeline("""text-classification""" , framework="""tf""" )
_lowerCAmelCase = text_classifier("""This is great !""" )
self.assertEqual(nested_simplify(_snake_case ) , [{"""label""": """POSITIVE""", """score""": 1.0}] )
_lowerCAmelCase = text_classifier("""This is bad !""" )
self.assertEqual(nested_simplify(_snake_case ) , [{"""label""": """NEGATIVE""", """score""": 1.0}] )
_lowerCAmelCase = text_classifier("""Birds are a type of animal""" )
self.assertEqual(nested_simplify(_snake_case ) , [{"""label""": """POSITIVE""", """score""": 0.988}] )
def snake_case ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = TextClassificationPipeline(model=_snake_case , tokenizer=_snake_case )
return text_classifier, ["HuggingFace is in", "This is another test"]
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = text_classifier.model
# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
_lowerCAmelCase = """HuggingFace is in"""
_lowerCAmelCase = text_classifier(_snake_case )
self.assertEqual(nested_simplify(_snake_case ) , [{"""label""": ANY(_snake_case ), """score""": ANY(_snake_case )}] )
self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values() )
_lowerCAmelCase = ["""HuggingFace is in """, """Paris is in France"""]
_lowerCAmelCase = text_classifier(_snake_case )
self.assertEqual(
nested_simplify(_snake_case ) , [{"""label""": ANY(_snake_case ), """score""": ANY(_snake_case )}, {"""label""": ANY(_snake_case ), """score""": ANY(_snake_case )}] , )
self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values() )
self.assertTrue(outputs[1]["""label"""] in model.config.idalabel.values() )
# Forcing to get all results with `top_k=None`
# This is NOT the legacy format
_lowerCAmelCase = text_classifier(_snake_case , top_k=_snake_case )
_lowerCAmelCase = len(model.config.idalabel.values() )
self.assertEqual(
nested_simplify(_snake_case ) , [[{"""label""": ANY(_snake_case ), """score""": ANY(_snake_case )}] * N, [{"""label""": ANY(_snake_case ), """score""": ANY(_snake_case )}] * N] , )
_lowerCAmelCase = {"""text""": """HuggingFace is in """, """text_pair""": """Paris is in France"""}
_lowerCAmelCase = text_classifier(_snake_case )
self.assertEqual(
nested_simplify(_snake_case ) , {"""label""": ANY(_snake_case ), """score""": ANY(_snake_case )} , )
self.assertTrue(outputs["""label"""] in model.config.idalabel.values() )
# This might be used a text pair, but tokenizer + pipe interaction
# makes it hard to understand that it's not using the pair properly
# https://github.com/huggingface/transformers/issues/17305
# We disabled this usage instead as it was outputting wrong outputs.
_lowerCAmelCase = [["""HuggingFace is in """, """Paris is in France"""]]
with self.assertRaises(_snake_case ):
text_classifier(_snake_case )
# This used to be valid for doing text pairs
# We're keeping it working because of backward compatibility
_lowerCAmelCase = text_classifier([[["""HuggingFace is in """, """Paris is in France"""]]] )
self.assertEqual(
nested_simplify(_snake_case ) , [{"""label""": ANY(_snake_case ), """score""": ANY(_snake_case )}] , )
self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values() )
| 82 |
from __future__ import annotations
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = str(snake_case )
return n == n[::-1]
def _UpperCAmelCase ( snake_case = 1_00_00_00 ):
"""simple docstring"""
_lowerCAmelCase = 0
for i in range(1 , snake_case ):
if is_palindrome(snake_case ) and is_palindrome(bin(snake_case ).split("""b""" )[1] ):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| 82 | 1 |
import comet # From: unbabel-comet
import torch
import datasets
A__ = datasets.logging.get_logger(__name__)
A__ = """\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},
title = {Unbabel's Participation in the WMT20 Metrics Shared Task},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
month = {November},
year = {2020},
address = {Online},
publisher = {Association for Computational Linguistics},
pages = {909--918},
}
@inproceedings{rei-etal-2020-comet,
title = \"{COMET}: A Neural Framework for {MT} Evaluation\",
author = \"Rei, Ricardo and
Stewart, Craig and
Farinha, Ana C and
Lavie, Alon\",
booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",
month = nov,
year = \"2020\",
address = \"Online\",
publisher = \"Association for Computational Linguistics\",
url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",
pages = \"2685--2702\",
}
"""
A__ = """\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.
See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
"""
A__ = """
COMET score.
Args:
`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.
Returns:
`samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
`scores`: List of scores.
Examples:
>>> comet_metric = datasets.load_metric('comet')
>>> # comet_metric = load_metric('comet', 'wmt20-comet-da') # you can also choose which model to use
>>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]
>>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]
>>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]
>>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
>>> print([round(v, 2) for v in results[\"scores\"]])
[0.19, 0.92]
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def snake_case ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="""https://unbabel.github.io/COMET/html/index.html""" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""sources""": datasets.Value("""string""" , id="""sequence""" ),
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , codebase_urls=["""https://github.com/Unbabel/COMET"""] , reference_urls=[
"""https://github.com/Unbabel/COMET""",
"""https://www.aclweb.org/anthology/2020.emnlp-main.213/""",
"""http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6""",
] , )
def snake_case ( self , _snake_case ):
"""simple docstring"""
if self.config_name == "default":
_lowerCAmelCase = comet.load_from_checkpoint(comet.download_model("""wmt20-comet-da""" ) )
else:
_lowerCAmelCase = comet.load_from_checkpoint(comet.download_model(self.config_name ) )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case=None , _snake_case=False ):
"""simple docstring"""
if gpus is None:
_lowerCAmelCase = 1 if torch.cuda.is_available() else 0
_lowerCAmelCase = {"""src""": sources, """mt""": predictions, """ref""": references}
_lowerCAmelCase = [dict(zip(_snake_case , _snake_case ) ) for t in zip(*data.values() )]
_lowerCAmelCase , _lowerCAmelCase = self.scorer.predict(_snake_case , gpus=_snake_case , progress_bar=_snake_case )
return {"mean_score": mean_score, "scores": scores}
| 82 |
from collections.abc import Iterable
from typing import Generic, TypeVar
A__ = TypeVar("""_T""")
class __lowerCAmelCase ( Generic[_T] ):
def __init__( self , _snake_case = None ):
"""simple docstring"""
_lowerCAmelCase = list(iterable or [] )
_lowerCAmelCase = []
def __len__( self ):
"""simple docstring"""
return len(self._stacka ) + len(self._stacka )
def __repr__( self ):
"""simple docstring"""
return F'Queue({tuple(self._stacka[::-1] + self._stacka )})'
def snake_case ( self , _snake_case ):
"""simple docstring"""
self._stacka.append(_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self._stacka.pop
_lowerCAmelCase = self._stacka.append
if not self._stacka:
while self._stacka:
stacka_append(stacka_pop() )
if not self._stacka:
raise IndexError("""Queue is empty""" )
return self._stacka.pop()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 82 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
__lowerCamelCase = 1
@register_to_config
def __init__( self , _snake_case=2000 , _snake_case=0.1 , _snake_case=20 , _snake_case=1e-3 ):
"""simple docstring"""
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
def snake_case ( self , _snake_case , _snake_case = None ):
"""simple docstring"""
_lowerCAmelCase = torch.linspace(1 , self.config.sampling_eps , _snake_case , device=_snake_case )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case=None ):
"""simple docstring"""
if self.timesteps is None:
raise ValueError(
"""`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
_lowerCAmelCase = (
-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
_lowerCAmelCase = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
_lowerCAmelCase = std.flatten()
while len(std.shape ) < len(score.shape ):
_lowerCAmelCase = std.unsqueeze(-1 )
_lowerCAmelCase = -score / std
# compute
_lowerCAmelCase = -1.0 / len(self.timesteps )
_lowerCAmelCase = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
_lowerCAmelCase = beta_t.flatten()
while len(beta_t.shape ) < len(x.shape ):
_lowerCAmelCase = beta_t.unsqueeze(-1 )
_lowerCAmelCase = -0.5 * beta_t * x
_lowerCAmelCase = torch.sqrt(_snake_case )
_lowerCAmelCase = drift - diffusion**2 * score
_lowerCAmelCase = x + drift * dt
# add noise
_lowerCAmelCase = randn_tensor(x.shape , layout=x.layout , generator=_snake_case , device=x.device , dtype=x.dtype )
_lowerCAmelCase = x_mean + diffusion * math.sqrt(-dt ) * noise
return x, x_mean
def __len__( self ):
"""simple docstring"""
return self.config.num_train_timesteps
| 82 |
A__ = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A__ = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = True
_lowerCAmelCase = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(snake_case , snake_case , snake_case )
order.append(snake_case )
return order
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = True
_lowerCAmelCase = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(snake_case , snake_case , snake_case )
return component
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = len(snake_case ) * [False]
_lowerCAmelCase = {vert: [] for vert in range(len(snake_case ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(snake_case )
_lowerCAmelCase = []
for i, was_visited in enumerate(snake_case ):
if not was_visited:
order += topology_sort(snake_case , snake_case , snake_case )
_lowerCAmelCase = []
_lowerCAmelCase = len(snake_case ) * [False]
for i in range(len(snake_case ) ):
_lowerCAmelCase = order[len(snake_case ) - i - 1]
if not visited[vert]:
_lowerCAmelCase = find_components(snake_case , snake_case , snake_case )
components_list.append(snake_case )
return components_list
| 82 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 82 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader
from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
ROUGE_KEYS,
LegacySeqaSeqDataset,
SeqaSeqDataset,
assert_all_frozen,
calculate_bleu,
calculate_rouge,
check_output_dir,
flatten_list,
freeze_embeds,
freeze_params,
get_git_info,
label_smoothed_nll_loss,
lmap,
pickle_save,
save_git_info,
save_json,
use_task_specific_params,
)
# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train # noqa
A__ = logging.getLogger(__name__)
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''summarization'''
__lowerCamelCase = ['''loss''']
__lowerCamelCase = ROUGE_KEYS
__lowerCamelCase = '''rouge2'''
def __init__( self , _snake_case , **_snake_case ):
"""simple docstring"""
if hparams.sortish_sampler and hparams.gpus > 1:
_lowerCAmelCase = False
elif hparams.max_tokens_per_batch is not None:
if hparams.gpus > 1:
raise NotImplementedError("""Dynamic Batch size does not work for multi-gpu training""" )
if hparams.sortish_sampler:
raise ValueError("""--sortish_sampler and --max_tokens_per_batch may not be used simultaneously""" )
super().__init__(_snake_case , num_labels=_snake_case , mode=self.mode , **_snake_case )
use_task_specific_params(self.model , """summarization""" )
save_git_info(self.hparams.output_dir )
_lowerCAmelCase = Path(self.output_dir ) / """metrics.json"""
_lowerCAmelCase = Path(self.output_dir ) / """hparams.pkl"""
pickle_save(self.hparams , self.hparams_save_path )
_lowerCAmelCase = 0
_lowerCAmelCase = defaultdict(_snake_case )
_lowerCAmelCase = self.config.model_type
_lowerCAmelCase = self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size
_lowerCAmelCase = {
"data_dir": self.hparams.data_dir,
"max_source_length": self.hparams.max_source_length,
"prefix": self.model.config.prefix or "",
}
_lowerCAmelCase = {
"""train""": self.hparams.n_train,
"""val""": self.hparams.n_val,
"""test""": self.hparams.n_test,
}
_lowerCAmelCase = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
_lowerCAmelCase = {
"""train""": self.hparams.max_target_length,
"""val""": self.hparams.val_max_target_length,
"""test""": self.hparams.test_max_target_length,
}
assert self.target_lens["train"] <= self.target_lens["val"], F'target_lens: {self.target_lens}'
assert self.target_lens["train"] <= self.target_lens["test"], F'target_lens: {self.target_lens}'
if self.hparams.freeze_embeds:
freeze_embeds(self.model )
if self.hparams.freeze_encoder:
freeze_params(self.model.get_encoder() )
assert_all_frozen(self.model.get_encoder() )
_lowerCAmelCase = get_git_info()["""repo_sha"""]
_lowerCAmelCase = hparams.num_workers
_lowerCAmelCase = None # default to config
if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer , _snake_case ):
_lowerCAmelCase = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
_lowerCAmelCase = self.decoder_start_token_id
_lowerCAmelCase = (
SeqaSeqDataset if hasattr(self.tokenizer , """prepare_seq2seq_batch""" ) else LegacySeqaSeqDataset
)
_lowerCAmelCase = False
_lowerCAmelCase = self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
if self.hparams.eval_max_gen_length is not None:
_lowerCAmelCase = self.hparams.eval_max_gen_length
else:
_lowerCAmelCase = self.model.config.max_length
_lowerCAmelCase = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = {
k: self.tokenizer.batch_decode(v.tolist() ) if """mask""" not in k else v.shape for k, v in batch.items()
}
save_json(_snake_case , Path(self.output_dir ) / """text_batch.json""" )
save_json({k: v.tolist() for k, v in batch.items()} , Path(self.output_dir ) / """tok_batch.json""" )
_lowerCAmelCase = True
return readable_batch
def snake_case ( self , _snake_case , **_snake_case ):
"""simple docstring"""
return self.model(_snake_case , **_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.tokenizer.batch_decode(
_snake_case , skip_special_tokens=_snake_case , clean_up_tokenization_spaces=_snake_case )
return lmap(str.strip , _snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.tokenizer.pad_token_id
_lowerCAmelCase , _lowerCAmelCase = batch["""input_ids"""], batch["""attention_mask"""]
_lowerCAmelCase = batch["""labels"""]
if isinstance(self.model , _snake_case ):
_lowerCAmelCase = self.model._shift_right(_snake_case )
else:
_lowerCAmelCase = shift_tokens_right(_snake_case , _snake_case )
if not self.already_saved_batch: # This would be slightly better if it only happened on rank zero
_lowerCAmelCase = decoder_input_ids
self.save_readable_batch(_snake_case )
_lowerCAmelCase = self(_snake_case , attention_mask=_snake_case , decoder_input_ids=_snake_case , use_cache=_snake_case )
_lowerCAmelCase = outputs["""logits"""]
if self.hparams.label_smoothing == 0:
# Same behavior as modeling_bart.py, besides ignoring pad_token_id
_lowerCAmelCase = nn.CrossEntropyLoss(ignore_index=_snake_case )
assert lm_logits.shape[-1] == self.vocab_size
_lowerCAmelCase = ce_loss_fct(lm_logits.view(-1 , lm_logits.shape[-1] ) , tgt_ids.view(-1 ) )
else:
_lowerCAmelCase = nn.functional.log_softmax(_snake_case , dim=-1 )
_lowerCAmelCase , _lowerCAmelCase = label_smoothed_nll_loss(
_snake_case , _snake_case , self.hparams.label_smoothing , ignore_index=_snake_case )
return (loss,)
@property
def snake_case ( self ):
"""simple docstring"""
return self.tokenizer.pad_token_id
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self._step(_snake_case )
_lowerCAmelCase = dict(zip(self.loss_names , _snake_case ) )
# tokens per batch
_lowerCAmelCase = batch["""input_ids"""].ne(self.pad ).sum() + batch["""labels"""].ne(self.pad ).sum()
_lowerCAmelCase = batch["""input_ids"""].shape[0]
_lowerCAmelCase = batch["""input_ids"""].eq(self.pad ).sum()
_lowerCAmelCase = batch["""input_ids"""].eq(self.pad ).float().mean()
# TODO(SS): make a wandb summary metric for this
return {"loss": loss_tensors[0], "log": logs}
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
return self._generative_step(_snake_case )
def snake_case ( self , _snake_case , _snake_case="val" ):
"""simple docstring"""
self.step_count += 1
_lowerCAmelCase = {k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
_lowerCAmelCase = losses["""loss"""]
_lowerCAmelCase = {
k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
}
_lowerCAmelCase = (
generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
)
_lowerCAmelCase = torch.tensor(_snake_case ).type_as(_snake_case )
generative_metrics.update({k: v.item() for k, v in losses.items()} )
losses.update(_snake_case )
_lowerCAmelCase = {F'{prefix}_avg_{k}': x for k, x in losses.items()}
_lowerCAmelCase = self.step_count
self.metrics[prefix].append(_snake_case ) # callback writes this to self.metrics_save_path
_lowerCAmelCase = flatten_list([x["""preds"""] for x in outputs] )
return {
"log": all_metrics,
"preds": preds,
F'{prefix}_loss': loss,
F'{prefix}_{self.val_metric}': metric_tensor,
}
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
return calculate_rouge(_snake_case , _snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = time.time()
# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
_lowerCAmelCase = self.model.generate(
batch["""input_ids"""] , attention_mask=batch["""attention_mask"""] , use_cache=_snake_case , decoder_start_token_id=self.decoder_start_token_id , num_beams=self.eval_beams , max_length=self.eval_max_length , )
_lowerCAmelCase = (time.time() - ta) / batch["""input_ids"""].shape[0]
_lowerCAmelCase = self.ids_to_clean_text(_snake_case )
_lowerCAmelCase = self.ids_to_clean_text(batch["""labels"""] )
_lowerCAmelCase = self._step(_snake_case )
_lowerCAmelCase = dict(zip(self.loss_names , _snake_case ) )
_lowerCAmelCase = self.calc_generative_metrics(_snake_case , _snake_case )
_lowerCAmelCase = np.mean(lmap(_snake_case , _snake_case ) )
base_metrics.update(gen_time=_snake_case , gen_len=_snake_case , preds=_snake_case , target=_snake_case , **_snake_case )
return base_metrics
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
return self._generative_step(_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
return self.validation_epoch_end(_snake_case , prefix="""test""" )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.n_obs[type_path]
_lowerCAmelCase = self.target_lens[type_path]
_lowerCAmelCase = self.dataset_class(
self.tokenizer , type_path=_snake_case , n_obs=_snake_case , max_target_length=_snake_case , **self.dataset_kwargs , )
return dataset
def snake_case ( self , _snake_case , _snake_case , _snake_case = False ):
"""simple docstring"""
_lowerCAmelCase = self.get_dataset(_snake_case )
if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
_lowerCAmelCase = dataset.make_sortish_sampler(_snake_case , distributed=self.hparams.gpus > 1 )
return DataLoader(
_snake_case , batch_size=_snake_case , collate_fn=dataset.collate_fn , shuffle=_snake_case , num_workers=self.num_workers , sampler=_snake_case , )
elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
_lowerCAmelCase = dataset.make_dynamic_sampler(
self.hparams.max_tokens_per_batch , distributed=self.hparams.gpus > 1 )
return DataLoader(
_snake_case , batch_sampler=_snake_case , collate_fn=dataset.collate_fn , num_workers=self.num_workers , )
else:
return DataLoader(
_snake_case , batch_size=_snake_case , collate_fn=dataset.collate_fn , shuffle=_snake_case , num_workers=self.num_workers , sampler=_snake_case , )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_dataloader("""train""" , batch_size=self.hparams.train_batch_size , shuffle=_snake_case )
return dataloader
def snake_case ( self ):
"""simple docstring"""
return self.get_dataloader("""val""" , batch_size=self.hparams.eval_batch_size )
def snake_case ( self ):
"""simple docstring"""
return self.get_dataloader("""test""" , batch_size=self.hparams.eval_batch_size )
@staticmethod
def snake_case ( _snake_case , _snake_case ):
"""simple docstring"""
BaseTransformer.add_model_specific_args(_snake_case , _snake_case )
add_generic_args(_snake_case , _snake_case )
parser.add_argument(
"""--max_source_length""" , default=1024 , type=_snake_case , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--max_target_length""" , default=56 , type=_snake_case , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--val_max_target_length""" , default=142 , type=_snake_case , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--test_max_target_length""" , default=142 , type=_snake_case , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument("""--freeze_encoder""" , action="""store_true""" )
parser.add_argument("""--freeze_embeds""" , action="""store_true""" )
parser.add_argument("""--sortish_sampler""" , action="""store_true""" , default=_snake_case )
parser.add_argument("""--overwrite_output_dir""" , action="""store_true""" , default=_snake_case )
parser.add_argument("""--max_tokens_per_batch""" , type=_snake_case , default=_snake_case )
parser.add_argument("""--logger_name""" , type=_snake_case , choices=["""default""", """wandb""", """wandb_shared"""] , default="""default""" )
parser.add_argument("""--n_train""" , type=_snake_case , default=-1 , required=_snake_case , help="""# examples. -1 means use all.""" )
parser.add_argument("""--n_val""" , type=_snake_case , default=500 , required=_snake_case , help="""# examples. -1 means use all.""" )
parser.add_argument("""--n_test""" , type=_snake_case , default=-1 , required=_snake_case , help="""# examples. -1 means use all.""" )
parser.add_argument(
"""--task""" , type=_snake_case , default="""summarization""" , required=_snake_case , help="""# examples. -1 means use all.""" )
parser.add_argument("""--label_smoothing""" , type=_snake_case , default=0.0 , required=_snake_case )
parser.add_argument("""--src_lang""" , type=_snake_case , default="""""" , required=_snake_case )
parser.add_argument("""--tgt_lang""" , type=_snake_case , default="""""" , required=_snake_case )
parser.add_argument("""--eval_beams""" , type=_snake_case , default=_snake_case , required=_snake_case )
parser.add_argument(
"""--val_metric""" , type=_snake_case , default=_snake_case , required=_snake_case , choices=["""bleu""", """rouge2""", """loss""", None] )
parser.add_argument("""--eval_max_gen_length""" , type=_snake_case , default=_snake_case , help="""never generate more than n tokens""" )
parser.add_argument("""--save_top_k""" , type=_snake_case , default=1 , required=_snake_case , help="""How many checkpoints to save""" )
parser.add_argument(
"""--early_stopping_patience""" , type=_snake_case , default=-1 , required=_snake_case , help=(
"""-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"""
""" val_check_interval will effect it."""
) , )
return parser
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''translation'''
__lowerCamelCase = ['''loss''']
__lowerCamelCase = ['''bleu''']
__lowerCamelCase = '''bleu'''
def __init__( self , _snake_case , **_snake_case ):
"""simple docstring"""
super().__init__(_snake_case , **_snake_case )
_lowerCAmelCase = hparams.src_lang
_lowerCAmelCase = hparams.tgt_lang
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
return calculate_bleu(_snake_case , _snake_case )
def _UpperCAmelCase ( snake_case , snake_case=None ):
"""simple docstring"""
Path(args.output_dir ).mkdir(exist_ok=snake_case )
check_output_dir(snake_case , expected_items=3 )
if model is None:
if "summarization" in args.task:
_lowerCAmelCase = SummarizationModule(snake_case )
else:
_lowerCAmelCase = TranslationModule(snake_case )
_lowerCAmelCase = Path(args.data_dir ).name
if (
args.logger_name == "default"
or args.fast_dev_run
or str(args.output_dir ).startswith("""/tmp""" )
or str(args.output_dir ).startswith("""/var""" )
):
_lowerCAmelCase = True # don't pollute wandb logs unnecessarily
elif args.logger_name == "wandb":
from pytorch_lightning.loggers import WandbLogger
_lowerCAmelCase = os.environ.get("""WANDB_PROJECT""" , snake_case )
_lowerCAmelCase = WandbLogger(name=model.output_dir.name , project=snake_case )
elif args.logger_name == "wandb_shared":
from pytorch_lightning.loggers import WandbLogger
_lowerCAmelCase = WandbLogger(name=model.output_dir.name , project=F'hf_{dataset}' )
if args.early_stopping_patience >= 0:
_lowerCAmelCase = get_early_stopping_callback(model.val_metric , args.early_stopping_patience )
else:
_lowerCAmelCase = False
_lowerCAmelCase = args.val_metric == """loss"""
_lowerCAmelCase = generic_train(
snake_case , snake_case , logging_callback=SeqaSeqLoggingCallback() , checkpoint_callback=get_checkpoint_callback(
args.output_dir , model.val_metric , args.save_top_k , snake_case ) , early_stopping_callback=snake_case , logger=snake_case , )
pickle_save(model.hparams , model.output_dir / """hparams.pkl""" )
if not args.do_predict:
return model
_lowerCAmelCase = """"""
_lowerCAmelCase = sorted(glob.glob(os.path.join(args.output_dir , """*.ckpt""" ) , recursive=snake_case ) )
if checkpoints:
_lowerCAmelCase = checkpoints[-1]
_lowerCAmelCase = checkpoints[-1]
trainer.logger.log_hyperparams(model.hparams )
# test() without a model tests using the best checkpoint automatically
trainer.test()
return model
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
A__ = pl.Trainer.add_argparse_args(parser)
A__ = SummarizationModule.add_model_specific_args(parser, os.getcwd())
A__ = parser.parse_args()
main(args)
| 82 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
RandomResizedCrop,
Resize,
ToTensor,
)
import transformers
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForImageClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
A__ = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-classification/requirements.txt""")
A__ = list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
A__ = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
with open(snake_case , """rb""" ) as f:
_lowerCAmelCase = Image.open(snake_case )
return im.convert("""RGB""" )
@dataclass
class __lowerCAmelCase :
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={
'''help''': '''Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'''
} , )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
__lowerCamelCase = field(default=lowerCamelCase__ , metadata={'''help''': '''A folder containing the training data.'''} )
__lowerCamelCase = field(default=lowerCamelCase__ , metadata={'''help''': '''A folder containing the validation data.'''} )
__lowerCamelCase = field(
default=0.15 , metadata={'''help''': '''Percent to split off of train for validation.'''} )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def snake_case ( self ):
"""simple docstring"""
if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
raise ValueError(
"""You must specify either a dataset name from the hub or a train and/or validation directory.""" )
@dataclass
class __lowerCAmelCase :
__lowerCamelCase = field(
default='''google/vit-base-patch16-224-in21k''' , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} , )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(lowerCamelCase__ )} , )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''} )
__lowerCamelCase = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
__lowerCamelCase = field(default=lowerCamelCase__ , metadata={'''help''': '''Name or path of preprocessor config.'''} )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''} , )
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = torch.stack([example["""pixel_values"""] for example in examples] )
_lowerCAmelCase = torch.tensor([example["""labels"""] for example in examples] )
return {"pixel_values": pixel_values, "labels": labels}
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_image_classification""" , snake_case , snake_case )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_lowerCAmelCase = training_args.get_process_log_level()
logger.setLevel(snake_case )
transformers.utils.logging.set_verbosity(snake_case )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ F'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
logger.info(F'Training/evaluation parameters {training_args}' )
# Detecting last checkpoint.
_lowerCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_lowerCAmelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. '
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Initialize our dataset and prepare it for the 'image-classification' task.
if data_args.dataset_name is not None:
_lowerCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir , task="""image-classification""" , use_auth_token=True if model_args.use_auth_token else None , )
else:
_lowerCAmelCase = {}
if data_args.train_dir is not None:
_lowerCAmelCase = os.path.join(data_args.train_dir , """**""" )
if data_args.validation_dir is not None:
_lowerCAmelCase = os.path.join(data_args.validation_dir , """**""" )
_lowerCAmelCase = load_dataset(
"""imagefolder""" , data_files=snake_case , cache_dir=model_args.cache_dir , task="""image-classification""" , )
# If we don't have a validation split, split off a percentage of train as validation.
_lowerCAmelCase = None if """validation""" in dataset.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , snake_case ) and data_args.train_val_split > 0.0:
_lowerCAmelCase = dataset["""train"""].train_test_split(data_args.train_val_split )
_lowerCAmelCase = split["""train"""]
_lowerCAmelCase = split["""test"""]
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
_lowerCAmelCase = dataset["""train"""].features["""labels"""].names
_lowerCAmelCase , _lowerCAmelCase = {}, {}
for i, label in enumerate(snake_case ):
_lowerCAmelCase = str(snake_case )
_lowerCAmelCase = label
# Load the accuracy metric from the datasets package
_lowerCAmelCase = evaluate.load("""accuracy""" )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(snake_case ):
return metric.compute(predictions=np.argmax(p.predictions , axis=1 ) , references=p.label_ids )
_lowerCAmelCase = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(snake_case ) , labelaid=snake_case , idalabel=snake_case , finetuning_task="""image-classification""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
_lowerCAmelCase = AutoModelForImageClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=snake_case , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
_lowerCAmelCase = AutoImageProcessor.from_pretrained(
model_args.image_processor_name or model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Define torchvision transforms to be applied to each image.
if "shortest_edge" in image_processor.size:
_lowerCAmelCase = image_processor.size["""shortest_edge"""]
else:
_lowerCAmelCase = (image_processor.size["""height"""], image_processor.size["""width"""])
_lowerCAmelCase = Normalize(mean=image_processor.image_mean , std=image_processor.image_std )
_lowerCAmelCase = Compose(
[
RandomResizedCrop(snake_case ),
RandomHorizontalFlip(),
ToTensor(),
normalize,
] )
_lowerCAmelCase = Compose(
[
Resize(snake_case ),
CenterCrop(snake_case ),
ToTensor(),
normalize,
] )
def train_transforms(snake_case ):
_lowerCAmelCase = [
_train_transforms(pil_img.convert("""RGB""" ) ) for pil_img in example_batch["""image"""]
]
return example_batch
def val_transforms(snake_case ):
_lowerCAmelCase = [_val_transforms(pil_img.convert("""RGB""" ) ) for pil_img in example_batch["""image"""]]
return example_batch
if training_args.do_train:
if "train" not in dataset:
raise ValueError("""--do_train requires a train dataset""" )
if data_args.max_train_samples is not None:
_lowerCAmelCase = (
dataset["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
dataset["train"].set_transform(snake_case )
if training_args.do_eval:
if "validation" not in dataset:
raise ValueError("""--do_eval requires a validation dataset""" )
if data_args.max_eval_samples is not None:
_lowerCAmelCase = (
dataset["""validation"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
dataset["validation"].set_transform(snake_case )
# Initalize our trainer
_lowerCAmelCase = Trainer(
model=snake_case , args=snake_case , train_dataset=dataset["""train"""] if training_args.do_train else None , eval_dataset=dataset["""validation"""] if training_args.do_eval else None , compute_metrics=snake_case , tokenizer=snake_case , data_collator=snake_case , )
# Training
if training_args.do_train:
_lowerCAmelCase = None
if training_args.resume_from_checkpoint is not None:
_lowerCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_lowerCAmelCase = last_checkpoint
_lowerCAmelCase = trainer.train(resume_from_checkpoint=snake_case )
trainer.save_model()
trainer.log_metrics("""train""" , train_result.metrics )
trainer.save_metrics("""train""" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_lowerCAmelCase = trainer.evaluate()
trainer.log_metrics("""eval""" , snake_case )
trainer.save_metrics("""eval""" , snake_case )
# Write model card and (optionally) push to hub
_lowerCAmelCase = {
"""finetuned_from""": model_args.model_name_or_path,
"""tasks""": """image-classification""",
"""dataset""": data_args.dataset_name,
"""tags""": ["""image-classification""", """vision"""],
}
if training_args.push_to_hub:
trainer.push_to_hub(**snake_case )
else:
trainer.create_model_card(**snake_case )
if __name__ == "__main__":
main()
| 82 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if isinstance(snake_case , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class __lowerCAmelCase :
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
pass
def snake_case ( self ):
"""simple docstring"""
pass
def snake_case ( self ):
"""simple docstring"""
pass
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase = VisionTextDualEncoderConfig.from_vision_text_configs(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], config.projection_dim) )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_snake_case , text_model=_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = {"""vision_model""": vision_model, """text_model""": text_model}
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_snake_case , text_model=_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
_lowerCAmelCase = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
_lowerCAmelCase = after_output[0].numpy()
_lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_snake_case , 1e-5 )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_snake_case , text_model=_snake_case )
_lowerCAmelCase = model(
input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case , output_attentions=_snake_case )
_lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(_snake_case ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
_lowerCAmelCase = to_atuple(vision_model.config.image_size )
_lowerCAmelCase = to_atuple(vision_model.config.patch_size )
_lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
_lowerCAmelCase = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
_lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(_snake_case ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def snake_case ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = np.abs((a - b) ).max()
self.assertLessEqual(_snake_case , _snake_case , F'Difference between torch and flax is {diff} (>= {tol}).' )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_save_load(**_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**_snake_case )
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_pretrained_model_and_inputs()
_lowerCAmelCase = model_a(**_snake_case )
_lowerCAmelCase = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(_snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(_snake_case )
_lowerCAmelCase = model_a(**_snake_case )
_lowerCAmelCase = after_outputs[0].numpy()
_lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_snake_case , 1e-5 )
@require_tf
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-random-bert""" )
_lowerCAmelCase = 13
_lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_lowerCAmelCase = random_attention_mask([batch_size, 4] )
_lowerCAmelCase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = TFViTModel(_snake_case , name="""vision_model""" )
_lowerCAmelCase = TFBertModel(_snake_case , name="""text_model""" )
return vision_model, text_model
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFViTModelTester(self )
_lowerCAmelCase = TFBertModelTester(self )
_lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
_lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = vision_config_and_inputs
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-deit-tf""" , """hf-internal-testing/tiny-random-roberta""" )
_lowerCAmelCase = 13
_lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_lowerCAmelCase = random_attention_mask([batch_size, 4] )
_lowerCAmelCase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_snake_case , text_model=_snake_case )
_lowerCAmelCase = model(
input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case , output_attentions=_snake_case )
_lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(_snake_case ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
_lowerCAmelCase = to_atuple(vision_model.config.image_size )
_lowerCAmelCase = to_atuple(vision_model.config.patch_size )
_lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
_lowerCAmelCase = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
_lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(_snake_case ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = TFDeiTModel(_snake_case , name="""vision_model""" )
_lowerCAmelCase = TFRobertaModel(_snake_case , name="""text_model""" )
return vision_model, text_model
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFDeiTModelTester(self )
_lowerCAmelCase = TFRobertaModelTester(self )
_lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
_lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = vision_config_and_inputs
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-clip-tf""" , """hf-internal-testing/tiny-random-bert""" )
_lowerCAmelCase = 13
_lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_lowerCAmelCase = random_attention_mask([batch_size, 4] )
_lowerCAmelCase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = TFCLIPVisionModel(_snake_case , name="""vision_model""" )
_lowerCAmelCase = TFBertModel(_snake_case , name="""text_model""" )
return vision_model, text_model
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFCLIPVisionModelTester(self )
_lowerCAmelCase = TFBertModelTester(self )
_lowerCAmelCase = clip_model_tester.prepare_config_and_inputs()
_lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase = vision_config_and_inputs
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(
"""clip-italian/clip-italian""" , logit_scale_init_value=1.0 , from_pt=_snake_case )
_lowerCAmelCase = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
_lowerCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
_lowerCAmelCase = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=_snake_case , padding=_snake_case , return_tensors="""np""" )
_lowerCAmelCase = model(**_snake_case )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
_lowerCAmelCase = np.array([[1.228_4727, 0.310_4122]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , _snake_case , atol=1e-3 ) )
| 82 | 1 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
A__ = """\
"""
A__ = """
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.
For more information, see https://huggingface.co/docs/transformers/perplexity
"""
A__ = """
Args:
model_id (str): model used for calculating Perplexity
NOTE: Perplexity can only be calculated for causal language models.
This includes models such as gpt2, causal variations of bert,
causal versions of t5, and more (the full list can be found
in the AutoModelForCausalLM documentation here:
https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )
input_texts (list of str): input text, each separate text snippet
is one list entry.
batch_size (int): the batch size to run texts through the model. Defaults to 16.
add_start_token (bool): whether to add the start token to the texts,
so the perplexity can include the probability of the first word. Defaults to True.
device (str): device to run on, defaults to 'cuda' when available
Returns:
perplexity: dictionary containing the perplexity scores for the texts
in the input list, as well as the mean perplexity. If one of the input texts is
longer than the max input length of the model, then it is truncated to the
max length for the perplexity computation.
Examples:
Example 1:
>>> perplexity = datasets.load_metric(\"perplexity\")
>>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]
>>> results = perplexity.compute(model_id='gpt2',
... add_start_token=False,
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
['perplexities', 'mean_perplexity']
>>> print(round(results[\"mean_perplexity\"], 2))
78.22
>>> print(round(results[\"perplexities\"][0], 2))
11.11
Example 2:
>>> perplexity = datasets.load_metric(\"perplexity\")
>>> input_texts = datasets.load_dataset(\"wikitext\",
... \"wikitext-2-raw-v1\",
... split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS
[...]
>>> input_texts = [s for s in input_texts if s!='']
>>> results = perplexity.compute(model_id='gpt2',
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
['perplexities', 'mean_perplexity']
>>> print(round(results[\"mean_perplexity\"], 2))
60.35
>>> print(round(results[\"perplexities\"][0], 2))
81.12
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def snake_case ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""input_texts""": datasets.Value("""string""" ),
} ) , reference_urls=["""https://huggingface.co/docs/transformers/perplexity"""] , )
def snake_case ( self , _snake_case , _snake_case , _snake_case = 16 , _snake_case = True , _snake_case=None ):
"""simple docstring"""
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
_lowerCAmelCase = """cuda"""
else:
_lowerCAmelCase = """cuda""" if torch.cuda.is_available() else """cpu"""
_lowerCAmelCase = AutoModelForCausalLM.from_pretrained(_snake_case )
_lowerCAmelCase = model.to(_snake_case )
_lowerCAmelCase = AutoTokenizer.from_pretrained(_snake_case )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
_lowerCAmelCase = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(_snake_case ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({"""pad_token""": existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
_lowerCAmelCase = model.config.max_length - 1
else:
_lowerCAmelCase = model.config.max_length
_lowerCAmelCase = tokenizer(
_snake_case , add_special_tokens=_snake_case , padding=_snake_case , truncation=_snake_case , max_length=_snake_case , return_tensors="""pt""" , return_attention_mask=_snake_case , ).to(_snake_case )
_lowerCAmelCase = encodings["""input_ids"""]
_lowerCAmelCase = encodings["""attention_mask"""]
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
_lowerCAmelCase = []
_lowerCAmelCase = CrossEntropyLoss(reduction="""none""" )
for start_index in logging.tqdm(range(0 , len(_snake_case ) , _snake_case ) ):
_lowerCAmelCase = min(start_index + batch_size , len(_snake_case ) )
_lowerCAmelCase = encoded_texts[start_index:end_index]
_lowerCAmelCase = attn_masks[start_index:end_index]
if add_start_token:
_lowerCAmelCase = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(_snake_case )
_lowerCAmelCase = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 )
_lowerCAmelCase = torch.cat(
[torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(_snake_case ), attn_mask] , dim=1 )
_lowerCAmelCase = encoded_batch
with torch.no_grad():
_lowerCAmelCase = model(_snake_case , attention_mask=_snake_case ).logits
_lowerCAmelCase = out_logits[..., :-1, :].contiguous()
_lowerCAmelCase = labels[..., 1:].contiguous()
_lowerCAmelCase = attn_mask[..., 1:].contiguous()
_lowerCAmelCase = torch.expa(
(loss_fct(shift_logits.transpose(1 , 2 ) , _snake_case ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(_snake_case )}
| 82 |
def _UpperCAmelCase ( snake_case = 50 ):
"""simple docstring"""
_lowerCAmelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(f"{solution() = }")
| 82 | 1 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
A__ = 20_48
A__ = 40_96
A__ = 42
A__ = os.environ.pop("""PROCESS_TRAIN""", """false""")
A__ = {"""null""": 0, """short""": 1, """long""": 2, """yes""": 3, """no""": 4}
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
def choose_first(snake_case , snake_case=False ):
assert isinstance(snake_case , snake_case )
if len(snake_case ) == 1:
_lowerCAmelCase = answer[0]
return {k: [answer[k]] for k in answer} if is_long_answer else answer
for a in answer:
if is_long_answer:
_lowerCAmelCase = {k: [a[k]] for k in a}
if len(a["""start_token"""] ) > 0:
break
return a
_lowerCAmelCase = {"""id""": example["""id"""]}
_lowerCAmelCase = example["""annotations"""]
_lowerCAmelCase = annotation["""yes_no_answer"""]
if 0 in yes_no_answer or 1 in yes_no_answer:
_lowerCAmelCase = ["""yes"""] if 1 in yes_no_answer else ["""no"""]
_lowerCAmelCase = _lowerCAmelCase = []
_lowerCAmelCase = _lowerCAmelCase = []
_lowerCAmelCase = ["""<cls>"""]
else:
_lowerCAmelCase = ["""short"""]
_lowerCAmelCase = choose_first(annotation["""short_answers"""] )
if len(out["""start_token"""] ) == 0:
# answer will be long if short is not available
_lowerCAmelCase = ["""long"""]
_lowerCAmelCase = choose_first(annotation["""long_answer"""] , is_long_answer=snake_case )
_lowerCAmelCase = []
answer.update(snake_case )
# disregard some samples
if len(answer["""start_token"""] ) > 1 or answer["start_token"] == answer["end_token"]:
_lowerCAmelCase = True
else:
_lowerCAmelCase = False
_lowerCAmelCase = ["""start_token""", """end_token""", """start_byte""", """end_byte""", """text"""]
if not all(isinstance(answer[k] , snake_case ) for k in cols ):
raise ValueError("""Issue in ID""" , example["""id"""] )
return answer
def _UpperCAmelCase ( snake_case , snake_case=False ):
"""simple docstring"""
_lowerCAmelCase = _get_single_answer(snake_case )
# bytes are of no use
del answer["start_byte"]
del answer["end_byte"]
# handle yes_no answers explicitly
if answer["category"][0] in ["yes", "no"]: # category is list with one element
_lowerCAmelCase = example["""document"""]["""tokens"""]
_lowerCAmelCase = []
for i in range(len(doc["""token"""] ) ):
if not doc["is_html"][i]:
context.append(doc["""token"""][i] )
return {
"context": " ".join(snake_case ),
"answer": {
"start_token": -1_00, # ignore index in cross-entropy
"end_token": -1_00, # ignore index in cross-entropy
"category": answer["category"],
"span": answer["category"], # extra
},
}
# later, help in removing all no answers
if answer["start_token"] == [-1]:
return {
"context": "None",
"answer": {
"start_token": -1,
"end_token": -1,
"category": "null",
"span": "None", # extra
},
}
# handling normal samples
_lowerCAmelCase = ["""start_token""", """end_token"""]
answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10
_lowerCAmelCase = example["""document"""]["""tokens"""]
_lowerCAmelCase = answer["""start_token"""]
_lowerCAmelCase = answer["""end_token"""]
_lowerCAmelCase = []
for i in range(len(doc["""token"""] ) ):
if not doc["is_html"][i]:
context.append(doc["""token"""][i] )
else:
if answer["start_token"] > i:
start_token -= 1
if answer["end_token"] > i:
end_token -= 1
_lowerCAmelCase = """ """.join(context[start_token:end_token] )
# checking above code
if assertion:
_lowerCAmelCase = doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]]
_lowerCAmelCase = doc["""token"""][answer["""start_token"""] : answer["""end_token"""]]
_lowerCAmelCase = """ """.join([old[i] for i in range(len(snake_case ) ) if not is_html[i]] )
if new != old:
print("""ID:""" , example["""id"""] )
print("""New:""" , snake_case , end="""\n""" )
print("""Old:""" , snake_case , end="""\n\n""" )
return {
"context": " ".join(snake_case ),
"answer": {
"start_token": start_token,
"end_token": end_token - 1, # this makes it inclusive
"category": answer["category"], # either long or short
"span": new, # extra
},
}
def _UpperCAmelCase ( snake_case , snake_case , snake_case=20_48 , snake_case=40_96 , snake_case=True ):
"""simple docstring"""
_lowerCAmelCase = get_context_and_ans(snake_case , assertion=snake_case )
_lowerCAmelCase = out["""answer"""]
# later, removing these samples
if answer["start_token"] == -1:
return {
"example_id": example["id"],
"input_ids": [[-1]],
"labels": {
"start_token": [-1],
"end_token": [-1],
"category": ["null"],
},
}
_lowerCAmelCase = tokenizer(example["""question"""]["""text"""] , out["""context"""] ).input_ids
_lowerCAmelCase = input_ids.index(tokenizer.sep_token_id ) + 1
# return yes/no
if answer["category"][0] in ["yes", "no"]: # category is list with one element
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = input_ids[:q_len]
_lowerCAmelCase = range(snake_case , len(snake_case ) , max_length - doc_stride )
for i in doc_start_indices:
_lowerCAmelCase = i + max_length - q_len
_lowerCAmelCase = input_ids[i:end_index]
inputs.append(q_indices + slice )
category.append(answer["""category"""][0] )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": [-1_00] * len(snake_case ),
"end_token": [-1_00] * len(snake_case ),
"category": category,
},
}
_lowerCAmelCase = out["""context"""].split()
_lowerCAmelCase = splitted_context[answer["""end_token"""]]
_lowerCAmelCase = len(
tokenizer(
""" """.join(splitted_context[: answer["""start_token"""]] ) , add_special_tokens=snake_case , ).input_ids )
_lowerCAmelCase = len(
tokenizer(""" """.join(splitted_context[: answer["""end_token"""]] ) , add_special_tokens=snake_case ).input_ids )
answer["start_token"] += q_len
answer["end_token"] += q_len
# fixing end token
_lowerCAmelCase = len(tokenizer(snake_case , add_special_tokens=snake_case ).input_ids )
if num_sub_tokens > 1:
answer["end_token"] += num_sub_tokens - 1
_lowerCAmelCase = input_ids[answer["""start_token"""] : answer["""end_token"""] + 1] # right & left are inclusive
_lowerCAmelCase = answer["""start_token"""]
_lowerCAmelCase = answer["""end_token"""]
if assertion:
_lowerCAmelCase = tokenizer.decode(snake_case )
if answer["span"] != new:
print("""ISSUE IN TOKENIZATION""" )
print("""OLD:""" , answer["""span"""] )
print("""NEW:""" , snake_case , end="""\n\n""" )
if len(snake_case ) <= max_length:
return {
"example_id": example["id"],
"input_ids": [input_ids],
"labels": {
"start_token": [answer["start_token"]],
"end_token": [answer["end_token"]],
"category": answer["category"],
},
}
_lowerCAmelCase = input_ids[:q_len]
_lowerCAmelCase = range(snake_case , len(snake_case ) , max_length - doc_stride )
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = [] # null, yes, no, long, short
for i in doc_start_indices:
_lowerCAmelCase = i + max_length - q_len
_lowerCAmelCase = input_ids[i:end_index]
inputs.append(q_indices + slice )
assert len(inputs[-1] ) <= max_length, "Issue in truncating length"
if start_token >= i and end_token <= end_index - 1:
_lowerCAmelCase = start_token - i + q_len
_lowerCAmelCase = end_token - i + q_len
answers_category.append(answer["""category"""][0] ) # ["short"] -> "short"
else:
_lowerCAmelCase = -1_00
_lowerCAmelCase = -1_00
answers_category.append("""null""" )
_lowerCAmelCase = inputs[-1][start_token : end_token + 1]
answers_start_token.append(snake_case )
answers_end_token.append(snake_case )
if assertion:
if new != old and new != [tokenizer.cls_token_id]:
print("""ISSUE in strided for ID:""" , example["""id"""] )
print("""New:""" , tokenizer.decode(snake_case ) )
print("""Old:""" , tokenizer.decode(snake_case ) , end="""\n\n""" )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": answers_start_token,
"end_token": answers_end_token,
"category": answers_category,
},
}
def _UpperCAmelCase ( snake_case , snake_case , snake_case=20_48 , snake_case=40_96 , snake_case=False ):
"""simple docstring"""
_lowerCAmelCase = get_strided_contexts_and_ans(
snake_case , snake_case , doc_stride=snake_case , max_length=snake_case , assertion=snake_case , )
return example
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
with jsonlines.open(snake_case , """a""" ) as writer:
for example in tqdm(snake_case , total=len(snake_case ) , desc="""Saving samples ... """ ):
_lowerCAmelCase = example["""labels"""]
for ids, start, end, cat in zip(
example["""input_ids"""] , labels["""start_token"""] , labels["""end_token"""] , labels["""category"""] , ):
if start == -1 and end == -1:
continue # leave waste samples with no answer
if cat == "null" and np.random.rand() < 0.6:
continue # removing 50 % samples
writer.write(
{
"""input_ids""": ids,
"""start_token""": start,
"""end_token""": end,
"""category""": CATEGORY_MAPPING[cat],
} )
if __name__ == "__main__":
from datasets import load_dataset
from transformers import BigBirdTokenizer
A__ = load_dataset("""natural_questions""")
A__ = BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""")
A__ = data["""train""" if PROCESS_TRAIN == """true""" else """validation"""]
A__ = {
"""tokenizer""": tokenizer,
"""doc_stride""": DOC_STRIDE,
"""max_length""": MAX_LENGTH,
"""assertion""": False,
}
A__ = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
A__ = data.remove_columns(["""annotations""", """document""", """id""", """question"""])
print(data)
np.random.seed(SEED)
A__ = """nq-training.jsonl""" if PROCESS_TRAIN == """true""" else """nq-validation.jsonl"""
save_to_disk(data, file_name=cache_file_name)
| 82 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class __lowerCAmelCase ( unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = {
"""task_specific_params""": {
"""summarization""": {"""length_penalty""": 1.0, """max_length""": 128, """min_length""": 12, """num_beams""": 4},
"""summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 142, """min_length""": 56, """num_beams""": 4},
"""summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
}
}
_lowerCAmelCase = {
"""task_specific_params.summarization.length_penalty""": 1.0,
"""task_specific_params.summarization.max_length""": 128,
"""task_specific_params.summarization.min_length""": 12,
"""task_specific_params.summarization.num_beams""": 4,
"""task_specific_params.summarization_cnn.length_penalty""": 2.0,
"""task_specific_params.summarization_cnn.max_length""": 142,
"""task_specific_params.summarization_cnn.min_length""": 56,
"""task_specific_params.summarization_cnn.num_beams""": 4,
"""task_specific_params.summarization_xsum.length_penalty""": 1.0,
"""task_specific_params.summarization_xsum.max_length""": 62,
"""task_specific_params.summarization_xsum.min_length""": 11,
"""task_specific_params.summarization_xsum.num_beams""": 6,
}
self.assertEqual(flatten_dict(_snake_case ) , _snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(_snake_case ) , x.transpose() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case ) , transpose(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , transpose(_snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case ) , transpose(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , transpose(_snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case ) , np.asarray(transpose(_snake_case ) ) ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , np.asarray(transpose(_snake_case , axes=(1, 2, 0) ) ) ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , np.reshape(_snake_case , (4, 3) ) ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , np.reshape(_snake_case , (12, 5) ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , reshape(_snake_case , (4, 3) ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , reshape(_snake_case , (12, 5) ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , reshape(_snake_case , (4, 3) ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , reshape(_snake_case , (12, 5) ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , np.asarray(reshape(_snake_case , (4, 3) ) ) ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , np.asarray(reshape(_snake_case , (12, 5) ) ) ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(_snake_case ) , np.squeeze(_snake_case ) ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , np.squeeze(_snake_case , axis=2 ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case ) , squeeze(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , squeeze(_snake_case , axis=2 ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case ) , squeeze(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , squeeze(_snake_case , axis=2 ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case ) , np.asarray(squeeze(_snake_case ) ) ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , np.asarray(squeeze(_snake_case , axis=2 ) ) ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , np.expand_dims(_snake_case , axis=1 ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , expand_dims(_snake_case , axis=1 ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , expand_dims(_snake_case , axis=1 ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , np.asarray(expand_dims(_snake_case , axis=1 ) ) ) )
| 82 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = [
"""FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FocalNetForImageClassification""",
"""FocalNetForMaskedImageModeling""",
"""FocalNetBackbone""",
"""FocalNetModel""",
"""FocalNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
A__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 82 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class __lowerCAmelCase ( lowerCamelCase__ ):
@staticmethod
def snake_case ( _snake_case ):
"""simple docstring"""
_lowerCAmelCase = parser.add_parser("""download""" )
download_parser.add_argument(
"""--cache-dir""" , type=_snake_case , default=_snake_case , help="""Path to location to store the models""" )
download_parser.add_argument(
"""--force""" , action="""store_true""" , help="""Force the model to be download even if already in cache-dir""" )
download_parser.add_argument(
"""--trust-remote-code""" , action="""store_true""" , help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine""" , )
download_parser.add_argument("""model""" , type=_snake_case , help="""Name of the model to download""" )
download_parser.set_defaults(func=_snake_case )
def __init__( self , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = model
_lowerCAmelCase = cache
_lowerCAmelCase = force
_lowerCAmelCase = trust_remote_code
def snake_case ( self ):
"""simple docstring"""
from ..models.auto import AutoModel, AutoTokenizer
AutoModel.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
AutoTokenizer.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
| 82 | 1 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
A__ = """src/transformers"""
# This is to make sure the transformers module imported is the one in the repo.
A__ = direct_transformers_import(PATH_TO_TRANSFORMERS)
A__ = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
A__ = re.compile(R"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")
A__ = {
"""DecisionTransformerConfig""",
"""EncoderDecoderConfig""",
"""MusicgenConfig""",
"""RagConfig""",
"""SpeechEncoderDecoderConfig""",
"""TimmBackboneConfig""",
"""VisionEncoderDecoderConfig""",
"""VisionTextDualEncoderConfig""",
"""LlamaConfig""",
}
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = None
# source code of `config_class`
_lowerCAmelCase = inspect.getsource(snake_case )
_lowerCAmelCase = _re_checkpoint.findall(snake_case )
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
for ckpt_name, ckpt_link in checkpoints:
# allow the link to end with `/`
if ckpt_link.endswith("""/""" ):
_lowerCAmelCase = ckpt_link[:-1]
# verify the checkpoint name corresponds to the checkpoint link
_lowerCAmelCase = F'https://huggingface.co/{ckpt_name}'
if ckpt_link == ckpt_link_from_name:
_lowerCAmelCase = ckpt_name
break
return checkpoint
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = []
for config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in config_class.__module__:
continue
_lowerCAmelCase = get_checkpoint_from_config_class(snake_case )
_lowerCAmelCase = config_class.__name__
if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(snake_case )
if len(snake_case ) > 0:
_lowerCAmelCase = """\n""".join(sorted(snake_case ) )
raise ValueError(F'The following configurations don\'t contain any valid checkpoint:\n{message}' )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 82 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = XCLIPTextConfig()
# derive patch size from model name
_lowerCAmelCase = model_name.find("""patch""" )
_lowerCAmelCase = int(model_name[start_idx + len("""patch""" ) : start_idx + len("""patch""" ) + 2] )
_lowerCAmelCase = XCLIPVisionConfig(patch_size=snake_case , num_frames=snake_case )
if "large" in model_name:
_lowerCAmelCase = 7_68
_lowerCAmelCase = 30_72
_lowerCAmelCase = 12
_lowerCAmelCase = 10_24
_lowerCAmelCase = 40_96
_lowerCAmelCase = 16
_lowerCAmelCase = 24
_lowerCAmelCase = 7_68
_lowerCAmelCase = 30_72
if model_name == "xclip-large-patch14-16-frames":
_lowerCAmelCase = 3_36
_lowerCAmelCase = XCLIPConfig.from_text_vision_configs(snake_case , snake_case )
if "large" in model_name:
_lowerCAmelCase = 7_68
return config
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if name == "token_embedding.weight":
_lowerCAmelCase = name.replace("""token_embedding.weight""" , """text_model.embeddings.token_embedding.weight""" )
if name == "positional_embedding":
_lowerCAmelCase = name.replace("""positional_embedding""" , """text_model.embeddings.position_embedding.weight""" )
if "ln_1" in name:
_lowerCAmelCase = name.replace("""ln_1""" , """layer_norm1""" )
if "ln_2" in name:
_lowerCAmelCase = name.replace("""ln_2""" , """layer_norm2""" )
if "c_fc" in name:
_lowerCAmelCase = name.replace("""c_fc""" , """fc1""" )
if "c_proj" in name:
_lowerCAmelCase = name.replace("""c_proj""" , """fc2""" )
if name.startswith("""transformer.resblocks""" ):
_lowerCAmelCase = name.replace("""transformer.resblocks""" , """text_model.encoder.layers""" )
if "attn.out_proj" in name and "message" not in name:
_lowerCAmelCase = name.replace("""attn.out_proj""" , """self_attn.out_proj""" )
if "ln_final" in name:
_lowerCAmelCase = name.replace("""ln_final""" , """text_model.final_layer_norm""" )
# visual encoder
if name == "visual.class_embedding":
_lowerCAmelCase = name.replace("""visual.class_embedding""" , """vision_model.embeddings.class_embedding""" )
if name == "visual.positional_embedding":
_lowerCAmelCase = name.replace("""visual.positional_embedding""" , """vision_model.embeddings.position_embedding.weight""" )
if name.startswith("""visual.transformer.resblocks""" ):
_lowerCAmelCase = name.replace("""visual.transformer.resblocks""" , """vision_model.encoder.layers""" )
if "visual.conv1" in name:
_lowerCAmelCase = name.replace("""visual.conv1""" , """vision_model.embeddings.patch_embedding""" )
if "visual.ln_pre" in name:
_lowerCAmelCase = name.replace("""visual.ln_pre""" , """vision_model.pre_layernorm""" )
if "visual.ln_post" in name:
_lowerCAmelCase = name.replace("""visual.ln_post""" , """vision_model.post_layernorm""" )
if "visual.proj" in name:
_lowerCAmelCase = name.replace("""visual.proj""" , """visual_projection.weight""" )
if "text_projection" in name:
_lowerCAmelCase = name.replace("""text_projection""" , """text_projection.weight""" )
# things on top
if "prompts_visual_proj" in name:
_lowerCAmelCase = name.replace("""prompts_visual_proj""" , """prompts_visual_projection""" )
if "prompts_visual_ln" in name:
_lowerCAmelCase = name.replace("""prompts_visual_ln""" , """prompts_visual_layernorm""" )
# mit
if name == "mit.positional_embedding":
_lowerCAmelCase = name.replace("""positional""" , """position""" )
if name.startswith("""mit.resblocks""" ):
_lowerCAmelCase = name.replace("""mit.resblocks""" , """mit.encoder.layers""" )
# prompts generator
if name.startswith("""prompts_generator.norm""" ):
_lowerCAmelCase = name.replace("""prompts_generator.norm""" , """prompts_generator.layernorm""" )
return name
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_lowerCAmelCase = orig_state_dict.pop(snake_case )
if "attn.in_proj" in key:
_lowerCAmelCase = key.split(""".""" )
if key.startswith("""visual""" ):
_lowerCAmelCase = key_split[3]
_lowerCAmelCase = config.vision_config.hidden_size
if "message_attn" in key:
if "weight" in key:
_lowerCAmelCase = val[
:dim, :
]
_lowerCAmelCase = val[
dim : dim * 2, :
]
_lowerCAmelCase = val[
-dim:, :
]
else:
_lowerCAmelCase = val[
:dim
]
_lowerCAmelCase = val[
dim : dim * 2
]
_lowerCAmelCase = val[
-dim:
]
else:
if "weight" in key:
_lowerCAmelCase = val[
:dim, :
]
_lowerCAmelCase = val[
dim : dim * 2, :
]
_lowerCAmelCase = val[
-dim:, :
]
else:
_lowerCAmelCase = val[:dim]
_lowerCAmelCase = val[
dim : dim * 2
]
_lowerCAmelCase = val[-dim:]
elif key.startswith("""mit""" ):
_lowerCAmelCase = key_split[2]
_lowerCAmelCase = config.vision_config.mit_hidden_size
if "weight" in key:
_lowerCAmelCase = val[:dim, :]
_lowerCAmelCase = val[dim : dim * 2, :]
_lowerCAmelCase = val[-dim:, :]
else:
_lowerCAmelCase = val[:dim]
_lowerCAmelCase = val[dim : dim * 2]
_lowerCAmelCase = val[-dim:]
else:
_lowerCAmelCase = key_split[2]
_lowerCAmelCase = config.text_config.hidden_size
if "weight" in key:
_lowerCAmelCase = val[:dim, :]
_lowerCAmelCase = val[
dim : dim * 2, :
]
_lowerCAmelCase = val[-dim:, :]
else:
_lowerCAmelCase = val[:dim]
_lowerCAmelCase = val[
dim : dim * 2
]
_lowerCAmelCase = val[-dim:]
else:
_lowerCAmelCase = rename_key(snake_case )
if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
_lowerCAmelCase = val.T
_lowerCAmelCase = val
return orig_state_dict
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if num_frames == 8:
_lowerCAmelCase = """eating_spaghetti_8_frames.npy"""
elif num_frames == 16:
_lowerCAmelCase = """eating_spaghetti.npy"""
elif num_frames == 32:
_lowerCAmelCase = """eating_spaghetti_32_frames.npy"""
_lowerCAmelCase = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename=snake_case , repo_type="""dataset""" , )
_lowerCAmelCase = np.load(snake_case )
return list(snake_case )
def _UpperCAmelCase ( snake_case , snake_case=None , snake_case=False ):
"""simple docstring"""
_lowerCAmelCase = {
# fully supervised kinetics-400 checkpoints
"""xclip-base-patch32""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth""",
"""xclip-base-patch32-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"""
),
"""xclip-base-patch16""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth""",
"""xclip-base-patch16-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"""
),
"""xclip-large-patch14""": """https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&export=download&confirm=t&uuid=b26caedc-88e2-473e-830a-9d158b653cdb""",
"""xclip-large-patch14-16-frames""": """https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&export=download&confirm=t&uuid=538fa810-e671-4050-b385-9a623f89804f""",
# fully supervised kinetics-600 checkpoints
"""xclip-base-patch16-kinetics-600""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"""
),
"""xclip-base-patch16-kinetics-600-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"""
),
"""xclip-large-patch14-kinetics-600""": """https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&export=download&confirm=t&uuid=141d4977-4a65-44ae-864f-4b0c19f838be""",
# few shot
"""xclip-base-patch16-hmdb-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"""
),
"""xclip-base-patch16-hmdb-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"""
),
"""xclip-base-patch16-hmdb-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"""
),
"""xclip-base-patch16-hmdb-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"""
),
"""xclip-base-patch16-ucf-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"""
),
"""xclip-base-patch16-ucf-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"""
),
"""xclip-base-patch16-ucf-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"""
),
"""xclip-base-patch16-ucf-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"""
),
# zero shot
"""xclip-base-patch16-zero-shot""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth""",
}
_lowerCAmelCase = model_to_url[model_name]
_lowerCAmelCase = 8
if "16-frames" in model_name:
_lowerCAmelCase = 16
elif "shot" in model_name:
_lowerCAmelCase = 32
_lowerCAmelCase = get_xclip_config(snake_case , snake_case )
_lowerCAmelCase = XCLIPModel(snake_case )
model.eval()
if "drive" in checkpoint_url:
_lowerCAmelCase = """pytorch_model.bin"""
gdown.cached_download(snake_case , snake_case , quiet=snake_case )
_lowerCAmelCase = torch.load(snake_case , map_location="""cpu""" )["""model"""]
else:
_lowerCAmelCase = torch.hub.load_state_dict_from_url(snake_case )["""model"""]
_lowerCAmelCase = convert_state_dict(snake_case , snake_case )
_lowerCAmelCase = XCLIPModel(snake_case )
_lowerCAmelCase , _lowerCAmelCase = model.load_state_dict(snake_case , strict=snake_case )
assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
model.eval()
_lowerCAmelCase = 3_36 if model_name == """xclip-large-patch14-16-frames""" else 2_24
_lowerCAmelCase = VideoMAEImageProcessor(size=snake_case )
_lowerCAmelCase = CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32""" )
_lowerCAmelCase = CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32""" )
_lowerCAmelCase = XCLIPProcessor(image_processor=snake_case , tokenizer=snake_case )
_lowerCAmelCase = prepare_video(snake_case )
_lowerCAmelCase = processor(
text=["""playing sports""", """eating spaghetti""", """go shopping"""] , videos=snake_case , return_tensors="""pt""" , padding=snake_case )
print("""Shape of pixel values:""" , inputs.pixel_values.shape )
with torch.no_grad():
_lowerCAmelCase = model(**snake_case )
# Verify outputs
_lowerCAmelCase = outputs.logits_per_video
_lowerCAmelCase = logits_per_video.softmax(dim=1 )
print("""Probs:""" , snake_case )
# kinetics-400
if model_name == "xclip-base-patch32":
_lowerCAmelCase = torch.tensor([[0.0_019, 0.9_951, 0.0_030]] )
elif model_name == "xclip-base-patch32-16-frames":
_lowerCAmelCase = torch.tensor([[7.09_99E-04, 9.98_83E-01, 4.55_80E-04]] )
elif model_name == "xclip-base-patch16":
_lowerCAmelCase = torch.tensor([[0.0_083, 0.9_681, 0.0_236]] )
elif model_name == "xclip-base-patch16-16-frames":
_lowerCAmelCase = torch.tensor([[7.69_37E-04, 9.97_28E-01, 1.94_73E-03]] )
elif model_name == "xclip-large-patch14":
_lowerCAmelCase = torch.tensor([[0.0_062, 0.9_864, 0.0_075]] )
elif model_name == "xclip-large-patch14-16-frames":
_lowerCAmelCase = torch.tensor([[3.38_77E-04, 9.99_37E-01, 2.88_88E-04]] )
# kinetics-600
elif model_name == "xclip-base-patch16-kinetics-600":
_lowerCAmelCase = torch.tensor([[0.0_555, 0.8_914, 0.0_531]] )
elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
_lowerCAmelCase = torch.tensor([[3.85_54E-04, 9.99_29E-01, 3.27_54E-04]] )
elif model_name == "xclip-large-patch14-kinetics-600":
_lowerCAmelCase = torch.tensor([[0.0_036, 0.9_920, 0.0_045]] )
# few shot
elif model_name == "xclip-base-patch16-hmdb-2-shot":
_lowerCAmelCase = torch.tensor([[7.18_90E-06, 9.99_94E-01, 5.65_59E-05]] )
elif model_name == "xclip-base-patch16-hmdb-4-shot":
_lowerCAmelCase = torch.tensor([[1.03_20E-05, 9.99_93E-01, 6.24_35E-05]] )
elif model_name == "xclip-base-patch16-hmdb-8-shot":
_lowerCAmelCase = torch.tensor([[4.13_77E-06, 9.99_90E-01, 9.83_86E-05]] )
elif model_name == "xclip-base-patch16-hmdb-16-shot":
_lowerCAmelCase = torch.tensor([[4.13_47E-05, 9.99_62E-01, 3.34_11E-04]] )
elif model_name == "xclip-base-patch16-ucf-2-shot":
_lowerCAmelCase = torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]] )
elif model_name == "xclip-base-patch16-ucf-4-shot":
_lowerCAmelCase = torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]] )
elif model_name == "xclip-base-patch16-ucf-8-shot":
_lowerCAmelCase = torch.tensor([[0.0_027, 0.9_904, 0.0_070]] )
elif model_name == "xclip-base-patch16-ucf-16-shot":
_lowerCAmelCase = torch.tensor([[9.82_19E-04, 9.95_93E-01, 3.08_63E-03]] )
# zero shot
elif model_name == "xclip-base-patch16-zero-shot":
_lowerCAmelCase = torch.tensor([[3.50_82E-04, 9.97_85E-01, 1.79_66E-03]] )
else:
raise ValueError(F'Model name {model_name} not supported' )
assert torch.allclose(snake_case , snake_case , atol=1E-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(snake_case )
if push_to_hub:
print("""Pushing model, processor and slow tokenizer files to the hub...""" )
model.push_to_hub(snake_case , organization="""nielsr""" )
processor.push_to_hub(snake_case , organization="""nielsr""" )
slow_tokenizer.push_to_hub(snake_case , organization="""nielsr""" )
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""xclip-base-patch32""",
type=str,
help="""Name of the model.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
A__ = parser.parse_args()
convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 82 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
A__ = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
A__ = TaTokenizerFast
A__ = {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = [
"""MT5EncoderModel""",
"""MT5ForConditionalGeneration""",
"""MT5ForQuestionAnswering""",
"""MT5Model""",
"""MT5PreTrainedModel""",
"""MT5Stack""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = ["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = ["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
A__ = _LazyModule(
__name__,
globals()["""__file__"""],
_import_structure,
extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
module_spec=__spec__,
)
| 82 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@register_to_config
def __init__( self , _snake_case = 768 , ):
"""simple docstring"""
super().__init__()
_lowerCAmelCase = nn.Parameter(torch.zeros(1 , _snake_case ) )
_lowerCAmelCase = nn.Parameter(torch.ones(1 , _snake_case ) )
def snake_case ( self , _snake_case = None , _snake_case = None , ):
"""simple docstring"""
_lowerCAmelCase = nn.Parameter(self.mean.to(_snake_case ).to(_snake_case ) )
_lowerCAmelCase = nn.Parameter(self.std.to(_snake_case ).to(_snake_case ) )
return self
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = (embeds - self.mean) * 1.0 / self.std
return embeds
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = (embeds * self.std) + self.mean
return embeds
| 82 | 1 |
from numpy import exp, pi, sqrt
def _UpperCAmelCase ( snake_case , snake_case = 0.0 , snake_case = 1.0 ):
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 82 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase = AudioLDMPipeline
__lowerCamelCase = TEXT_TO_AUDIO_PARAMS
__lowerCamelCase = TEXT_TO_AUDIO_BATCH_PARAMS
__lowerCamelCase = frozenset(
[
'''num_inference_steps''',
'''num_waveforms_per_prompt''',
'''generator''',
'''latents''',
'''output_type''',
'''return_dict''',
'''callback''',
'''callback_steps''',
] )
def snake_case ( self ):
"""simple docstring"""
torch.manual_seed(0 )
_lowerCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=(32, 64) , class_embed_type="""simple_projection""" , projection_class_embeddings_input_dim=32 , class_embeddings_concat=_snake_case , )
_lowerCAmelCase = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_snake_case , set_alpha_to_one=_snake_case , )
torch.manual_seed(0 )
_lowerCAmelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
_lowerCAmelCase = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , projection_dim=32 , )
_lowerCAmelCase = ClapTextModelWithProjection(_snake_case )
_lowerCAmelCase = RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta""" , model_max_length=77 )
_lowerCAmelCase = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=16000 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=_snake_case , )
_lowerCAmelCase = SpeechTaHifiGan(_snake_case )
_lowerCAmelCase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""vocoder""": vocoder,
}
return components
def snake_case ( self , _snake_case , _snake_case=0 ):
"""simple docstring"""
if str(_snake_case ).startswith("""mps""" ):
_lowerCAmelCase = torch.manual_seed(_snake_case )
else:
_lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
_lowerCAmelCase = {
"""prompt""": """A hammer hitting a wooden surface""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
}
return inputs
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(_snake_case ) == 256
_lowerCAmelCase = audio[:10]
_lowerCAmelCase = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = 3 * [inputs["""prompt"""]]
# forward
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = 3 * [inputs.pop("""prompt""" )]
_lowerCAmelCase = audioldm_pipe.tokenizer(
_snake_case , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=_snake_case , return_tensors="""pt""" , )
_lowerCAmelCase = text_inputs["""input_ids"""].to(_snake_case )
_lowerCAmelCase = audioldm_pipe.text_encoder(
_snake_case , )
_lowerCAmelCase = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
_lowerCAmelCase = F.normalize(_snake_case , dim=-1 )
_lowerCAmelCase = prompt_embeds
# forward
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = 3 * ["""this is a negative prompt"""]
_lowerCAmelCase = negative_prompt
_lowerCAmelCase = 3 * [inputs["""prompt"""]]
# forward
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = 3 * [inputs.pop("""prompt""" )]
_lowerCAmelCase = []
for p in [prompt, negative_prompt]:
_lowerCAmelCase = audioldm_pipe.tokenizer(
_snake_case , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=_snake_case , return_tensors="""pt""" , )
_lowerCAmelCase = text_inputs["""input_ids"""].to(_snake_case )
_lowerCAmelCase = audioldm_pipe.text_encoder(
_snake_case , )
_lowerCAmelCase = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
_lowerCAmelCase = F.normalize(_snake_case , dim=-1 )
embeds.append(_snake_case )
_lowerCAmelCase , _lowerCAmelCase = embeds
# forward
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = PNDMScheduler(skip_prk_steps=_snake_case )
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = """egg cracking"""
_lowerCAmelCase = audioldm_pipe(**_snake_case , negative_prompt=_snake_case )
_lowerCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(_snake_case ) == 256
_lowerCAmelCase = audio[:10]
_lowerCAmelCase = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = PNDMScheduler(skip_prk_steps=_snake_case )
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = """A hammer hitting a wooden surface"""
# test num_waveforms_per_prompt=1 (default)
_lowerCAmelCase = audioldm_pipe(_snake_case , num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
_lowerCAmelCase = 2
_lowerCAmelCase = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
_lowerCAmelCase = 2
_lowerCAmelCase = audioldm_pipe(_snake_case , num_inference_steps=2 , num_waveforms_per_prompt=_snake_case ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
_lowerCAmelCase = 2
_lowerCAmelCase = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=_snake_case ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = audioldm_pipe.vocoder.config.sampling_rate
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = audioldm_pipe(audio_length_in_s=0.016 , **_snake_case )
_lowerCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(_snake_case ) / vocoder_sampling_rate == 0.016
_lowerCAmelCase = audioldm_pipe(audio_length_in_s=0.032 , **_snake_case )
_lowerCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(_snake_case ) / vocoder_sampling_rate == 0.032
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = ["""hey"""]
_lowerCAmelCase = audioldm_pipe(_snake_case , num_inference_steps=1 )
_lowerCAmelCase = output.audios.shape
assert audio_shape == (1, 256)
_lowerCAmelCase = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
_lowerCAmelCase = SpeechTaHifiGan(_snake_case ).to(_snake_case )
_lowerCAmelCase = audioldm_pipe(_snake_case , num_inference_steps=1 )
_lowerCAmelCase = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def snake_case ( self ):
"""simple docstring"""
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._test_inference_batch_single_identical(test_mean_pixel_difference=_snake_case )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def snake_case ( self ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_snake_case )
@slow
class __lowerCAmelCase ( unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case ( self , _snake_case , _snake_case="cpu" , _snake_case=torch.floataa , _snake_case=0 ):
"""simple docstring"""
_lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
_lowerCAmelCase = np.random.RandomState(_snake_case ).standard_normal((1, 8, 128, 16) )
_lowerCAmelCase = torch.from_numpy(_snake_case ).to(device=_snake_case , dtype=_snake_case )
_lowerCAmelCase = {
"""prompt""": """A hammer hitting a wooden surface""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 2.5,
}
return inputs
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_inputs(_snake_case )
_lowerCAmelCase = 25
_lowerCAmelCase = audioldm_pipe(**_snake_case ).audios[0]
assert audio.ndim == 1
assert len(_snake_case ) == 81920
_lowerCAmelCase = audio[77230:77240]
_lowerCAmelCase = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] )
_lowerCAmelCase = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
_lowerCAmelCase = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_inputs(_snake_case )
_lowerCAmelCase = audioldm_pipe(**_snake_case ).audios[0]
assert audio.ndim == 1
assert len(_snake_case ) == 81920
_lowerCAmelCase = audio[27780:27790]
_lowerCAmelCase = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] )
_lowerCAmelCase = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3e-2
| 82 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = """▁"""
A__ = {"""vocab_file""": """spiece.model"""}
A__ = {
"""vocab_file""": {
"""google/reformer-crime-and-punishment""": (
"""https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
)
}
}
A__ = {
"""google/reformer-crime-and-punishment""": 52_42_88,
}
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = VOCAB_FILES_NAMES
__lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase = ['''input_ids''', '''attention_mask''']
def __init__( self , _snake_case , _snake_case="</s>" , _snake_case="<unk>" , _snake_case=[] , _snake_case = None , **_snake_case , ):
"""simple docstring"""
_lowerCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_snake_case , unk_token=_snake_case , additional_special_tokens=_snake_case , sp_model_kwargs=self.sp_model_kwargs , **_snake_case , )
_lowerCAmelCase = vocab_file
_lowerCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_snake_case )
@property
def snake_case ( self ):
"""simple docstring"""
return self.sp_model.get_piece_size()
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = {self.convert_ids_to_tokens(_snake_case ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
"""simple docstring"""
_lowerCAmelCase = self.__dict__.copy()
_lowerCAmelCase = None
return state
def __setstate__( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
_lowerCAmelCase = {}
_lowerCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def snake_case ( self , _snake_case ):
"""simple docstring"""
return self.sp_model.encode(_snake_case , out_type=_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
return self.sp_model.piece_to_id(_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
if index < self.sp_model.get_piece_size():
_lowerCAmelCase = self.sp_model.IdToPiece(_snake_case )
return token
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_snake_case ) + token
_lowerCAmelCase = []
else:
current_sub_tokens.append(_snake_case )
out_string += self.sp_model.decode(_snake_case )
return out_string.strip()
def snake_case ( self , _snake_case , _snake_case = None ):
"""simple docstring"""
if not os.path.isdir(_snake_case ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
_lowerCAmelCase = os.path.join(
_snake_case , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_snake_case ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _snake_case )
elif not os.path.isfile(self.vocab_file ):
with open(_snake_case , """wb""" ) as fi:
_lowerCAmelCase = self.sp_model.serialized_model_proto()
fi.write(_snake_case )
return (out_vocab_file,)
| 82 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __lowerCAmelCase ( lowerCamelCase__ ):
# to overwrite at feature extractactor specific tests
__lowerCamelCase = None
__lowerCamelCase = None
@property
def snake_case ( self ):
"""simple docstring"""
return self.feat_extract_tester.prepare_feat_extract_dict()
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(_snake_case , """feature_size""" ) )
self.assertTrue(hasattr(_snake_case , """sampling_rate""" ) )
self.assertTrue(hasattr(_snake_case , """padding_value""" ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(_snake_case ) == len(_snake_case ) for x, y in zip(_snake_case , processed_features[input_name] ) ) )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_snake_case )
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="""np""" )
_lowerCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_lowerCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_snake_case )
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="""pt""" )
_lowerCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_lowerCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_snake_case )
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="""tf""" )
_lowerCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_lowerCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def snake_case ( self , _snake_case=False ):
"""simple docstring"""
def _inputs_have_equal_length(_snake_case ):
_lowerCAmelCase = len(input[0] )
for input_slice in input[1:]:
if len(_snake_case ) != length:
return False
return True
def _inputs_are_equal(_snake_case , _snake_case ):
if len(_snake_case ) != len(_snake_case ):
return False
for input_slice_a, input_slice_a in zip(_snake_case , _snake_case ):
if not np.allclose(np.asarray(_snake_case ) , np.asarray(_snake_case ) , atol=1e-3 ):
return False
return True
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_snake_case )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = self.feat_extract_tester.seq_length_diff
_lowerCAmelCase = self.feat_extract_tester.max_seq_length + pad_diff
_lowerCAmelCase = self.feat_extract_tester.min_seq_length
_lowerCAmelCase = self.feat_extract_tester.batch_size
_lowerCAmelCase = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
_lowerCAmelCase = feat_extract.pad(_snake_case , padding=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""max_length""" , max_length=len(speech_inputs[-1] ) )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""max_length""" )[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=_snake_case , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_are_equal(_snake_case , _snake_case ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
_lowerCAmelCase = feat_extract.pad(_snake_case , pad_to_multiple_of=10 )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , pad_to_multiple_of=10 )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , pad_to_multiple_of=10 , max_length=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , pad_to_multiple_of=10 , max_length=_snake_case , return_tensors="""np""" , )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(all(len(_snake_case ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(_snake_case , _snake_case ) )
_lowerCAmelCase = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(_snake_case ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
_lowerCAmelCase = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1e-3 )
def snake_case ( self , _snake_case=False ):
"""simple docstring"""
def _inputs_have_equal_length(_snake_case ):
_lowerCAmelCase = len(input[0] )
for input_slice in input[1:]:
if len(_snake_case ) != length:
return False
return True
def _inputs_are_equal(_snake_case , _snake_case ):
if len(_snake_case ) != len(_snake_case ):
return False
for input_slice_a, input_slice_a in zip(_snake_case , _snake_case ):
if not np.allclose(np.asarray(_snake_case ) , np.asarray(_snake_case ) , atol=1e-3 ):
return False
return True
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_snake_case )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , truncation=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
# truncate to smallest with np
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" , truncation=_snake_case , )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
# truncate to middle
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=_snake_case , return_tensors="""np""" , )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[1] ) , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_are_equal(_snake_case , _snake_case ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , truncation=_snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""longest""" , truncation=_snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""longest""" , truncation=_snake_case )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""max_length""" , truncation=_snake_case )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
_lowerCAmelCase = 12
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_snake_case , truncation=_snake_case , )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_snake_case , )
_lowerCAmelCase = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
_lowerCAmelCase = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
_lowerCAmelCase = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
def snake_case ( self ):
"""simple docstring"""
self._check_padding(numpify=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._check_padding(numpify=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._check_truncation(numpify=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._check_truncation(numpify=_snake_case )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""pt""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""tf""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_dict
_lowerCAmelCase = True
_lowerCAmelCase = self.feature_extraction_class(**_snake_case )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = [len(_snake_case ) for x in speech_inputs]
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )
self.assertIn("""attention_mask""" , _snake_case )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , _snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_dict
_lowerCAmelCase = True
_lowerCAmelCase = self.feature_extraction_class(**_snake_case )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = [len(_snake_case ) for x in speech_inputs]
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = min(_snake_case )
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=_snake_case , truncation=_snake_case , return_tensors="""np""" )
self.assertIn("""attention_mask""" , _snake_case )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
| 82 | 1 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
A__ = logging.getLogger(__name__)
A__ = """Hello world! cécé herlolip"""
A__ = namedtuple(
"""BertAbsConfig""",
[
"""temp_dir""",
"""large""",
"""use_bert_emb""",
"""finetune_bert""",
"""encoder""",
"""share_emb""",
"""max_pos""",
"""enc_layers""",
"""enc_hidden_size""",
"""enc_heads""",
"""enc_ff_size""",
"""enc_dropout""",
"""dec_layers""",
"""dec_hidden_size""",
"""dec_heads""",
"""dec_ff_size""",
"""dec_dropout""",
],
)
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = BertAbsConfig(
temp_dir=""".""" , finetune_bert=snake_case , large=snake_case , share_emb=snake_case , use_bert_emb=snake_case , encoder="""bert""" , max_pos=5_12 , enc_layers=6 , enc_hidden_size=5_12 , enc_heads=8 , enc_ff_size=5_12 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=7_68 , dec_heads=8 , dec_ff_size=20_48 , dec_dropout=0.2 , )
_lowerCAmelCase = torch.load(snake_case , lambda snake_case , snake_case : storage )
_lowerCAmelCase = AbsSummarizer(snake_case , torch.device("""cpu""" ) , snake_case )
original.eval()
_lowerCAmelCase = BertAbsSummarizer(snake_case , torch.device("""cpu""" ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("""convert the model""" )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("""Make sure that the models' outputs are identical""" )
_lowerCAmelCase = BertTokenizer.from_pretrained("""bert-base-uncased""" )
# prepare the model inputs
_lowerCAmelCase = tokenizer.encode("""This is sample éàalj'-.""" )
encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(snake_case )) )
_lowerCAmelCase = torch.tensor(snake_case ).unsqueeze(0 )
_lowerCAmelCase = tokenizer.encode("""This is sample 3 éàalj'-.""" )
decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(snake_case )) )
_lowerCAmelCase = torch.tensor(snake_case ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
_lowerCAmelCase = encoder_input_ids
_lowerCAmelCase = decoder_input_ids
_lowerCAmelCase = _lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = _lowerCAmelCase = None
_lowerCAmelCase = _lowerCAmelCase = None
_lowerCAmelCase = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
_lowerCAmelCase = original(snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case )[0]
_lowerCAmelCase = original.generator(snake_case )
_lowerCAmelCase = new_model(
snake_case , snake_case , snake_case , snake_case , snake_case )[0]
_lowerCAmelCase = new_model.generator(snake_case )
_lowerCAmelCase = torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print("""Maximum absolute difference beween weights: {:.2f}""".format(snake_case ) )
_lowerCAmelCase = torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print("""Maximum absolute difference beween weights: {:.2f}""".format(snake_case ) )
_lowerCAmelCase = torch.allclose(snake_case , snake_case , atol=1E-3 )
if are_identical:
logging.info("""all weights are equal up to 1e-3""" )
else:
raise ValueError("""the weights are different. The new model is likely different from the original one.""" )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("""saving the model's state dictionary""" )
torch.save(
new_model.state_dict() , """./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin""" )
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
parser.add_argument(
"""--bertabs_checkpoint_path""",
default=None,
type=str,
required=True,
help="""Path the official PyTorch dump.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=str,
required=True,
help="""Path to the output PyTorch model.""",
)
A__ = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 82 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''poolformer'''
def __init__( self , _snake_case=3 , _snake_case=16 , _snake_case=16 , _snake_case=3 , _snake_case=4.0 , _snake_case=[2, 2, 6, 2] , _snake_case=[64, 128, 320, 512] , _snake_case=[7, 3, 3, 3] , _snake_case=[4, 2, 2, 2] , _snake_case=[2, 1, 1, 1] , _snake_case=4 , _snake_case=0.0 , _snake_case="gelu" , _snake_case=True , _snake_case=1e-5 , _snake_case=0.02 , **_snake_case , ):
"""simple docstring"""
_lowerCAmelCase = num_channels
_lowerCAmelCase = patch_size
_lowerCAmelCase = stride
_lowerCAmelCase = padding
_lowerCAmelCase = pool_size
_lowerCAmelCase = hidden_sizes
_lowerCAmelCase = mlp_ratio
_lowerCAmelCase = depths
_lowerCAmelCase = patch_sizes
_lowerCAmelCase = strides
_lowerCAmelCase = num_encoder_blocks
_lowerCAmelCase = drop_path_rate
_lowerCAmelCase = hidden_act
_lowerCAmelCase = use_layer_scale
_lowerCAmelCase = layer_scale_init_value
_lowerCAmelCase = initializer_range
super().__init__(**_snake_case )
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = version.parse('''1.11''' )
@property
def snake_case ( self ):
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def snake_case ( self ):
"""simple docstring"""
return 2e-3
| 82 | 1 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
A__ = logging.getLogger(__name__)
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
return (preds == labels).mean()
@dataclass
class __lowerCAmelCase :
__lowerCamelCase = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
@dataclass
class __lowerCAmelCase :
__lowerCamelCase = field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys() )} )
__lowerCamelCase = field(metadata={'''help''': '''Should contain the data files for the task.'''} )
__lowerCamelCase = field(
default=128 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
""" --overwrite_output_dir to overcome.""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , snake_case )
# Set seed
set_seed(training_args.seed )
try:
_lowerCAmelCase = processors[data_args.task_name]()
_lowerCAmelCase = processor.get_labels()
_lowerCAmelCase = len(snake_case )
except KeyError:
raise ValueError("""Task not found: %s""" % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_lowerCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=snake_case , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
_lowerCAmelCase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
_lowerCAmelCase = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=snake_case , cache_dir=model_args.cache_dir , )
# Get datasets
_lowerCAmelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=snake_case , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
_lowerCAmelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=snake_case , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(snake_case ) -> Dict:
_lowerCAmelCase = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(snake_case , p.label_ids )}
# Data collator
_lowerCAmelCase = DataCollatorWithPadding(snake_case , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
_lowerCAmelCase = Trainer(
model=snake_case , args=snake_case , train_dataset=snake_case , eval_dataset=snake_case , compute_metrics=snake_case , data_collator=snake_case , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
_lowerCAmelCase = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
_lowerCAmelCase = trainer.evaluate()
_lowerCAmelCase = os.path.join(training_args.output_dir , """eval_results.txt""" )
if trainer.is_world_master():
with open(snake_case , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key, value in result.items():
logger.info(""" %s = %s""" , snake_case , snake_case )
writer.write("""%s = %s\n""" % (key, value) )
results.update(snake_case )
return results
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 82 |
def _UpperCAmelCase ( snake_case = 10_00 ):
"""simple docstring"""
_lowerCAmelCase = -1
_lowerCAmelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
_lowerCAmelCase = (n * n - 2 * a * n) // (2 * n - 2 * a)
_lowerCAmelCase = n - a - b
if c * c == (a * a + b * b):
_lowerCAmelCase = a * b * c
if candidate >= product:
_lowerCAmelCase = candidate
return product
if __name__ == "__main__":
print(f"{solution() = }")
| 82 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
A__ = {"""LayoutLMv2Config""", """LayoutLMv3Config"""}
@is_pipeline_test
class __lowerCAmelCase ( unittest.TestCase ):
__lowerCamelCase = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
__lowerCamelCase = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
__lowerCamelCase = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
__lowerCamelCase = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def snake_case ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = ZeroShotClassificationPipeline(
model=_snake_case , tokenizer=_snake_case , candidate_labels=["""polics""", """health"""] )
return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = classifier("""Who are you voting for in 2020?""" , candidate_labels="""politics""" )
self.assertEqual(_snake_case , {"""sequence""": ANY(_snake_case ), """labels""": [ANY(_snake_case )], """scores""": [ANY(_snake_case )]} )
# No kwarg
_lowerCAmelCase = classifier("""Who are you voting for in 2020?""" , ["""politics"""] )
self.assertEqual(_snake_case , {"""sequence""": ANY(_snake_case ), """labels""": [ANY(_snake_case )], """scores""": [ANY(_snake_case )]} )
_lowerCAmelCase = classifier("""Who are you voting for in 2020?""" , candidate_labels=["""politics"""] )
self.assertEqual(_snake_case , {"""sequence""": ANY(_snake_case ), """labels""": [ANY(_snake_case )], """scores""": [ANY(_snake_case )]} )
_lowerCAmelCase = classifier("""Who are you voting for in 2020?""" , candidate_labels="""politics, public health""" )
self.assertEqual(
_snake_case , {"""sequence""": ANY(_snake_case ), """labels""": [ANY(_snake_case ), ANY(_snake_case )], """scores""": [ANY(_snake_case ), ANY(_snake_case )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""] ) ) , 1.0 )
_lowerCAmelCase = classifier("""Who are you voting for in 2020?""" , candidate_labels=["""politics""", """public health"""] )
self.assertEqual(
_snake_case , {"""sequence""": ANY(_snake_case ), """labels""": [ANY(_snake_case ), ANY(_snake_case )], """scores""": [ANY(_snake_case ), ANY(_snake_case )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""] ) ) , 1.0 )
_lowerCAmelCase = classifier(
"""Who are you voting for in 2020?""" , candidate_labels="""politics""" , hypothesis_template="""This text is about {}""" )
self.assertEqual(_snake_case , {"""sequence""": ANY(_snake_case ), """labels""": [ANY(_snake_case )], """scores""": [ANY(_snake_case )]} )
# https://github.com/huggingface/transformers/issues/13846
_lowerCAmelCase = classifier(["""I am happy"""] , ["""positive""", """negative"""] )
self.assertEqual(
_snake_case , [
{"""sequence""": ANY(_snake_case ), """labels""": [ANY(_snake_case ), ANY(_snake_case )], """scores""": [ANY(_snake_case ), ANY(_snake_case )]}
for i in range(1 )
] , )
_lowerCAmelCase = classifier(["""I am happy""", """I am sad"""] , ["""positive""", """negative"""] )
self.assertEqual(
_snake_case , [
{"""sequence""": ANY(_snake_case ), """labels""": [ANY(_snake_case ), ANY(_snake_case )], """scores""": [ANY(_snake_case ), ANY(_snake_case )]}
for i in range(2 )
] , )
with self.assertRaises(_snake_case ):
classifier("""""" , candidate_labels="""politics""" )
with self.assertRaises(_snake_case ):
classifier(_snake_case , candidate_labels="""politics""" )
with self.assertRaises(_snake_case ):
classifier("""Who are you voting for in 2020?""" , candidate_labels="""""" )
with self.assertRaises(_snake_case ):
classifier("""Who are you voting for in 2020?""" , candidate_labels=_snake_case )
with self.assertRaises(_snake_case ):
classifier(
"""Who are you voting for in 2020?""" , candidate_labels="""politics""" , hypothesis_template="""Not formatting template""" , )
with self.assertRaises(_snake_case ):
classifier(
"""Who are you voting for in 2020?""" , candidate_labels="""politics""" , hypothesis_template=_snake_case , )
self.run_entailment_id(_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = zero_shot_classifier.model.config
_lowerCAmelCase = config.labelaid
_lowerCAmelCase = zero_shot_classifier.entailment_id
_lowerCAmelCase = {"""LABEL_0""": 0, """LABEL_1""": 1, """LABEL_2""": 2}
self.assertEqual(zero_shot_classifier.entailment_id , -1 )
_lowerCAmelCase = {"""entailment""": 0, """neutral""": 1, """contradiction""": 2}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
_lowerCAmelCase = {"""ENTAIL""": 0, """NON-ENTAIL""": 1}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
_lowerCAmelCase = {"""ENTAIL""": 2, """NEUTRAL""": 1, """CONTR""": 0}
self.assertEqual(zero_shot_classifier.entailment_id , 2 )
_lowerCAmelCase = original_labelaid
self.assertEqual(_snake_case , zero_shot_classifier.entailment_id )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = pipeline(
"""zero-shot-classification""" , model="""sshleifer/tiny-distilbert-base-cased-distilled-squad""" , framework="""pt""" , )
# There was a regression in 4.10 for this
# Adding a test so we don't make the mistake again.
# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
zero_shot_classifier(
"""Who are you voting for in 2020?""" * 100 , candidate_labels=["""politics""", """public health""", """science"""] )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = pipeline(
"""zero-shot-classification""" , model="""sshleifer/tiny-distilbert-base-cased-distilled-squad""" , framework="""pt""" , )
_lowerCAmelCase = zero_shot_classifier(
"""Who are you voting for in 2020?""" , candidate_labels=["""politics""", """public health""", """science"""] )
self.assertEqual(
nested_simplify(_snake_case ) , {
"""sequence""": """Who are you voting for in 2020?""",
"""labels""": ["""science""", """public health""", """politics"""],
"""scores""": [0.333, 0.333, 0.333],
} , )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = pipeline(
"""zero-shot-classification""" , model="""sshleifer/tiny-distilbert-base-cased-distilled-squad""" , framework="""tf""" , )
_lowerCAmelCase = zero_shot_classifier(
"""Who are you voting for in 2020?""" , candidate_labels=["""politics""", """public health""", """science"""] )
self.assertEqual(
nested_simplify(_snake_case ) , {
"""sequence""": """Who are you voting for in 2020?""",
"""labels""": ["""science""", """public health""", """politics"""],
"""scores""": [0.333, 0.333, 0.333],
} , )
@slow
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = pipeline("""zero-shot-classification""" , model="""roberta-large-mnli""" , framework="""pt""" )
_lowerCAmelCase = zero_shot_classifier(
"""Who are you voting for in 2020?""" , candidate_labels=["""politics""", """public health""", """science"""] )
self.assertEqual(
nested_simplify(_snake_case ) , {
"""sequence""": """Who are you voting for in 2020?""",
"""labels""": ["""politics""", """public health""", """science"""],
"""scores""": [0.976, 0.015, 0.009],
} , )
_lowerCAmelCase = zero_shot_classifier(
"""The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
""" in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
""" through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
""" solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
""" machine translation tasks show these models to be superior in quality while being more parallelizable"""
""" and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
""" English-to-German translation task, improving over the existing best results, including ensembles by"""
""" over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
""" single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
""" fraction of the training costs of the best models from the literature. We show that the Transformer"""
""" generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
""" large and limited training data.""" , candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""] , multi_label=_snake_case , )
self.assertEqual(
nested_simplify(_snake_case ) , {
"""sequence""": (
"""The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
""" networks in an encoder-decoder configuration. The best performing models also connect the"""
""" encoder and decoder through an attention mechanism. We propose a new simple network"""
""" architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
""" and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
""" superior in quality while being more parallelizable and requiring significantly less time to"""
""" train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
""" improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
""" English-to-French translation task, our model establishes a new single-model state-of-the-art"""
""" BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
""" costs of the best models from the literature. We show that the Transformer generalizes well to"""
""" other tasks by applying it successfully to English constituency parsing both with large and"""
""" limited training data."""
),
"""labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
"""scores""": [0.817, 0.713, 0.018, 0.018],
} , )
@slow
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = pipeline("""zero-shot-classification""" , model="""roberta-large-mnli""" , framework="""tf""" )
_lowerCAmelCase = zero_shot_classifier(
"""Who are you voting for in 2020?""" , candidate_labels=["""politics""", """public health""", """science"""] )
self.assertEqual(
nested_simplify(_snake_case ) , {
"""sequence""": """Who are you voting for in 2020?""",
"""labels""": ["""politics""", """public health""", """science"""],
"""scores""": [0.976, 0.015, 0.009],
} , )
_lowerCAmelCase = zero_shot_classifier(
"""The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
""" in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
""" through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
""" solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
""" machine translation tasks show these models to be superior in quality while being more parallelizable"""
""" and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
""" English-to-German translation task, improving over the existing best results, including ensembles by"""
""" over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
""" single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
""" fraction of the training costs of the best models from the literature. We show that the Transformer"""
""" generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
""" large and limited training data.""" , candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""] , multi_label=_snake_case , )
self.assertEqual(
nested_simplify(_snake_case ) , {
"""sequence""": (
"""The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
""" networks in an encoder-decoder configuration. The best performing models also connect the"""
""" encoder and decoder through an attention mechanism. We propose a new simple network"""
""" architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
""" and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
""" superior in quality while being more parallelizable and requiring significantly less time to"""
""" train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
""" improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
""" English-to-French translation task, our model establishes a new single-model state-of-the-art"""
""" BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
""" costs of the best models from the literature. We show that the Transformer generalizes well to"""
""" other tasks by applying it successfully to English constituency parsing both with large and"""
""" limited training data."""
),
"""labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
"""scores""": [0.817, 0.713, 0.018, 0.018],
} , )
| 82 |
from __future__ import annotations
import math
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(snake_case ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = str(snake_case )
_lowerCAmelCase = [n]
for i in range(1 , len(snake_case ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if len(str(snake_case ) ) > 3:
if not is_prime(int(str(snake_case )[-3:] ) ) or not is_prime(int(str(snake_case )[:3] ) ):
return False
return True
def _UpperCAmelCase ( snake_case = 11 ):
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = 13
while len(snake_case ) != count:
if validate(snake_case ):
_lowerCAmelCase = list_truncated_nums(snake_case )
if all(is_prime(snake_case ) for i in list_nums ):
list_truncated_primes.append(snake_case )
num += 2
return list_truncated_primes
def _UpperCAmelCase ( ):
"""simple docstring"""
return sum(compute_truncated_primes(11 ) )
if __name__ == "__main__":
print(f"{sum(compute_truncated_primes(11)) = }")
| 82 | 1 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = 42
__lowerCamelCase = None
def _UpperCAmelCase ( snake_case , snake_case=0.999 , snake_case="cosine" , ):
"""simple docstring"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(snake_case ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(snake_case ):
return math.exp(t * -12.0 )
else:
raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}' )
_lowerCAmelCase = []
for i in range(snake_case ):
_lowerCAmelCase = i / num_diffusion_timesteps
_lowerCAmelCase = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(snake_case ) / alpha_bar_fn(snake_case ) , snake_case ) )
return torch.tensor(snake_case , dtype=torch.floataa )
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@register_to_config
def __init__( self , _snake_case = 1000 , _snake_case = "fixed_small_log" , _snake_case = True , _snake_case = 1.0 , _snake_case = "epsilon" , _snake_case = "squaredcos_cap_v2" , ):
"""simple docstring"""
if beta_schedule != "squaredcos_cap_v2":
raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'""" )
_lowerCAmelCase = betas_for_alpha_bar(_snake_case )
_lowerCAmelCase = 1.0 - self.betas
_lowerCAmelCase = torch.cumprod(self.alphas , dim=0 )
_lowerCAmelCase = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
_lowerCAmelCase = 1.0
# setable values
_lowerCAmelCase = None
_lowerCAmelCase = torch.from_numpy(np.arange(0 , _snake_case )[::-1].copy() )
_lowerCAmelCase = variance_type
def snake_case ( self , _snake_case , _snake_case = None ):
"""simple docstring"""
return sample
def snake_case ( self , _snake_case , _snake_case = None ):
"""simple docstring"""
_lowerCAmelCase = num_inference_steps
_lowerCAmelCase = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
_lowerCAmelCase = (np.arange(0 , _snake_case ) * step_ratio).round()[::-1].copy().astype(np.intaa )
_lowerCAmelCase = torch.from_numpy(_snake_case ).to(_snake_case )
def snake_case ( self , _snake_case , _snake_case=None , _snake_case=None , _snake_case=None ):
"""simple docstring"""
if prev_timestep is None:
_lowerCAmelCase = t - 1
_lowerCAmelCase = self.alphas_cumprod[t]
_lowerCAmelCase = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
_lowerCAmelCase = 1 - alpha_prod_t
_lowerCAmelCase = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
_lowerCAmelCase = self.betas[t]
else:
_lowerCAmelCase = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
_lowerCAmelCase = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
_lowerCAmelCase = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
_lowerCAmelCase = torch.log(torch.clamp(_snake_case , min=1e-20 ) )
_lowerCAmelCase = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
_lowerCAmelCase = variance.log()
_lowerCAmelCase = beta.log()
_lowerCAmelCase = (predicted_variance + 1) / 2
_lowerCAmelCase = frac * max_log + (1 - frac) * min_log
return variance
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case = None , _snake_case=None , _snake_case = True , ):
"""simple docstring"""
_lowerCAmelCase = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
_lowerCAmelCase , _lowerCAmelCase = torch.split(_snake_case , sample.shape[1] , dim=1 )
else:
_lowerCAmelCase = None
# 1. compute alphas, betas
if prev_timestep is None:
_lowerCAmelCase = t - 1
_lowerCAmelCase = self.alphas_cumprod[t]
_lowerCAmelCase = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
_lowerCAmelCase = 1 - alpha_prod_t
_lowerCAmelCase = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
_lowerCAmelCase = self.betas[t]
_lowerCAmelCase = self.alphas[t]
else:
_lowerCAmelCase = 1 - alpha_prod_t / alpha_prod_t_prev
_lowerCAmelCase = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
_lowerCAmelCase = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
_lowerCAmelCase = model_output
else:
raise ValueError(
F'prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'
""" for the UnCLIPScheduler.""" )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
_lowerCAmelCase = torch.clamp(
_snake_case , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
_lowerCAmelCase = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
_lowerCAmelCase = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
_lowerCAmelCase = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
_lowerCAmelCase = 0
if t > 0:
_lowerCAmelCase = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=_snake_case , device=model_output.device )
_lowerCAmelCase = self._get_variance(
_snake_case , predicted_variance=_snake_case , prev_timestep=_snake_case , )
if self.variance_type == "fixed_small_log":
_lowerCAmelCase = variance
elif self.variance_type == "learned_range":
_lowerCAmelCase = (0.5 * variance).exp()
else:
raise ValueError(
F'variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'
""" for the UnCLIPScheduler.""" )
_lowerCAmelCase = variance * variance_noise
_lowerCAmelCase = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=_snake_case , pred_original_sample=_snake_case )
def snake_case ( self , _snake_case , _snake_case , _snake_case , ):
"""simple docstring"""
_lowerCAmelCase = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
_lowerCAmelCase = timesteps.to(original_samples.device )
_lowerCAmelCase = alphas_cumprod[timesteps] ** 0.5
_lowerCAmelCase = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
_lowerCAmelCase = sqrt_alpha_prod.unsqueeze(-1 )
_lowerCAmelCase = (1 - alphas_cumprod[timesteps]) ** 0.5
_lowerCAmelCase = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
_lowerCAmelCase = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
_lowerCAmelCase = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 82 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
A__ = logging.get_logger(__name__)
class __lowerCAmelCase ( lowerCamelCase__ ):
def __init__( self , **_snake_case ):
"""simple docstring"""
requires_backends(self , ["""bs4"""] )
super().__init__(**_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = element if element.name else element.parent
for parent in child.parents: # type: bs4.element.Tag
_lowerCAmelCase = parent.find_all(child.name , recursive=_snake_case )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(_snake_case ) else next(i for i, s in enumerate(_snake_case , 1 ) if s is child ) )
_lowerCAmelCase = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = BeautifulSoup(_snake_case , """html.parser""" )
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = []
for element in html_code.descendants:
if type(_snake_case ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
_lowerCAmelCase = html.unescape(_snake_case ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(_snake_case )
_lowerCAmelCase , _lowerCAmelCase = self.xpath_soup(_snake_case )
stringaxtag_seq.append(_snake_case )
stringaxsubs_seq.append(_snake_case )
if len(_snake_case ) != len(_snake_case ):
raise ValueError("""Number of doc strings and xtags does not correspond""" )
if len(_snake_case ) != len(_snake_case ):
raise ValueError("""Number of doc strings and xsubs does not correspond""" )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = """"""
for tagname, subs in zip(_snake_case , _snake_case ):
xpath += F'/{tagname}'
if subs != 0:
xpath += F'[{subs}]'
return xpath
def __call__( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = False
# Check that strings has a valid type
if isinstance(_snake_case , _snake_case ):
_lowerCAmelCase = True
elif isinstance(_snake_case , (list, tuple) ):
if len(_snake_case ) == 0 or isinstance(html_strings[0] , _snake_case ):
_lowerCAmelCase = True
if not valid_strings:
raise ValueError(
"""HTML strings must of type `str`, `List[str]` (batch of examples), """
F'but is of type {type(_snake_case )}.' )
_lowerCAmelCase = bool(isinstance(_snake_case , (list, tuple) ) and (isinstance(html_strings[0] , _snake_case )) )
if not is_batched:
_lowerCAmelCase = [html_strings]
# Get nodes + xpaths
_lowerCAmelCase = []
_lowerCAmelCase = []
for html_string in html_strings:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.get_three_from_single(_snake_case )
nodes.append(_snake_case )
_lowerCAmelCase = []
for node, tag_list, sub_list in zip(_snake_case , _snake_case , _snake_case ):
_lowerCAmelCase = self.construct_xpath(_snake_case , _snake_case )
xpath_strings.append(_snake_case )
xpaths.append(_snake_case )
# return as Dict
_lowerCAmelCase = {"""nodes""": nodes, """xpaths""": xpaths}
_lowerCAmelCase = BatchFeature(data=_snake_case , tensor_type=_snake_case )
return encoded_inputs
| 82 | 1 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
A__ = 16
A__ = 32
def _UpperCAmelCase ( snake_case , snake_case = 16 , snake_case = "bert-base-cased" ):
"""simple docstring"""
_lowerCAmelCase = AutoTokenizer.from_pretrained(snake_case )
_lowerCAmelCase = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(snake_case ):
# max_length=None => use the model max length (it's actually the default)
_lowerCAmelCase = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=snake_case , max_length=snake_case )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
_lowerCAmelCase = datasets.map(
snake_case , batched=snake_case , remove_columns=["""idx""", """sentence1""", """sentence2"""] , load_from_cache_file=snake_case )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_lowerCAmelCase = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(snake_case ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(snake_case , padding="""max_length""" , max_length=1_28 , return_tensors="""pt""" )
return tokenizer.pad(snake_case , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
_lowerCAmelCase = DataLoader(
tokenized_datasets["""train"""] , shuffle=snake_case , collate_fn=snake_case , batch_size=snake_case )
_lowerCAmelCase = DataLoader(
tokenized_datasets["""validation"""] , shuffle=snake_case , collate_fn=snake_case , batch_size=snake_case )
return train_dataloader, eval_dataloader
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
model.eval()
_lowerCAmelCase = 0
for step, batch in enumerate(snake_case ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_lowerCAmelCase = model(**snake_case )
_lowerCAmelCase = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
_lowerCAmelCase , _lowerCAmelCase = accelerator.gather(
(predictions, batch["""labels"""]) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(snake_case ) - 1:
_lowerCAmelCase = predictions[: len(eval_dataloader.dataset ) - samples_seen]
_lowerCAmelCase = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=snake_case , references=snake_case , )
_lowerCAmelCase = metric.compute()
return eval_metric["accuracy"]
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_lowerCAmelCase = config["""lr"""]
_lowerCAmelCase = int(config["""num_epochs"""] )
_lowerCAmelCase = int(config["""seed"""] )
_lowerCAmelCase = int(config["""batch_size"""] )
_lowerCAmelCase = args.model_name_or_path
set_seed(snake_case )
_lowerCAmelCase , _lowerCAmelCase = get_dataloaders(snake_case , snake_case , snake_case )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_lowerCAmelCase = AutoModelForSequenceClassification.from_pretrained(snake_case , return_dict=snake_case )
# Instantiate optimizer
_lowerCAmelCase = (
AdamW
if accelerator.state.deepspeed_plugin is None
or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
_lowerCAmelCase = optimizer_cls(params=model.parameters() , lr=snake_case )
if accelerator.state.deepspeed_plugin is not None:
_lowerCAmelCase = accelerator.state.deepspeed_plugin.deepspeed_config[
"""gradient_accumulation_steps"""
]
else:
_lowerCAmelCase = 1
_lowerCAmelCase = (len(snake_case ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
_lowerCAmelCase = get_linear_schedule_with_warmup(
optimizer=snake_case , num_warmup_steps=0 , num_training_steps=snake_case , )
else:
_lowerCAmelCase = DummyScheduler(snake_case , total_num_steps=snake_case , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = accelerator.prepare(
snake_case , snake_case , snake_case , snake_case , snake_case )
# We need to keep track of how many total steps we have iterated over
_lowerCAmelCase = 0
# We also need to keep track of the stating epoch so files are named properly
_lowerCAmelCase = 0
_lowerCAmelCase = evaluate.load("""glue""" , """mrpc""" )
_lowerCAmelCase = num_epochs
if args.partial_train_epoch is not None:
_lowerCAmelCase = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
_lowerCAmelCase = args.resume_from_checkpoint.split("""epoch_""" )[1]
_lowerCAmelCase = """"""
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
_lowerCAmelCase = int(snake_case ) + 1
_lowerCAmelCase = evaluation_loop(snake_case , snake_case , snake_case , snake_case )
accelerator.print("""resumed checkpoint performance:""" , snake_case )
accelerator.print("""resumed checkpoint's scheduler's lr:""" , lr_scheduler.get_lr()[0] )
accelerator.print("""resumed optimizers's lr:""" , optimizer.param_groups[0]["""lr"""] )
with open(os.path.join(args.output_dir , F'state_{starting_epoch-1}.json' ) , """r""" ) as f:
_lowerCAmelCase = json.load(snake_case )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
_lowerCAmelCase = {}
for epoch in range(snake_case , snake_case ):
model.train()
for step, batch in enumerate(snake_case ):
_lowerCAmelCase = model(**snake_case )
_lowerCAmelCase = outputs.loss
_lowerCAmelCase = loss / gradient_accumulation_steps
accelerator.backward(snake_case )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
_lowerCAmelCase = F'epoch_{epoch}'
_lowerCAmelCase = os.path.join(args.output_dir , snake_case )
accelerator.save_state(snake_case )
_lowerCAmelCase = evaluation_loop(snake_case , snake_case , snake_case , snake_case )
_lowerCAmelCase = accuracy
_lowerCAmelCase = lr_scheduler.get_lr()[0]
_lowerCAmelCase = optimizer.param_groups[0]["""lr"""]
_lowerCAmelCase = epoch
_lowerCAmelCase = overall_step
accelerator.print(F'epoch {epoch}:' , snake_case )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , F'state_{epoch}.json' ) , """w""" ) as f:
json.dump(snake_case , snake_case )
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
parser.add_argument(
"""--model_name_or_path""" , type=snake_case , default="""bert-base-cased""" , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , required=snake_case , )
parser.add_argument(
"""--output_dir""" , type=snake_case , default=""".""" , help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" , )
parser.add_argument(
"""--resume_from_checkpoint""" , type=snake_case , default=snake_case , help="""If the training should continue from a checkpoint folder.""" , )
parser.add_argument(
"""--partial_train_epoch""" , type=snake_case , default=snake_case , help="""If passed, the training will stop after this number of epochs.""" , )
parser.add_argument(
"""--num_epochs""" , type=snake_case , default=2 , help="""Number of train epochs.""" , )
_lowerCAmelCase = parser.parse_args()
_lowerCAmelCase = {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
training_function(snake_case , snake_case )
if __name__ == "__main__":
main()
| 82 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
A__ = TypeVar("""T""")
A__ = TypeVar("""U""")
class __lowerCAmelCase ( Generic[T, U] ):
def __init__( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = key
_lowerCAmelCase = val
_lowerCAmelCase = None
_lowerCAmelCase = None
def __repr__( self ):
"""simple docstring"""
return (
F'Node: key: {self.key}, val: {self.val}, '
F'has next: {bool(self.next )}, has prev: {bool(self.prev )}'
)
class __lowerCAmelCase ( Generic[T, U] ):
def __init__( self ):
"""simple docstring"""
_lowerCAmelCase = DoubleLinkedListNode(_snake_case , _snake_case )
_lowerCAmelCase = DoubleLinkedListNode(_snake_case , _snake_case )
_lowerCAmelCase , _lowerCAmelCase = self.rear, self.head
def __repr__( self ):
"""simple docstring"""
_lowerCAmelCase = ["""DoubleLinkedList"""]
_lowerCAmelCase = self.head
while node.next is not None:
rep.append(str(_snake_case ) )
_lowerCAmelCase = node.next
rep.append(str(self.rear ) )
return ",\n ".join(_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
_lowerCAmelCase = node
_lowerCAmelCase = previous
_lowerCAmelCase = node
_lowerCAmelCase = self.rear
def snake_case ( self , _snake_case ):
"""simple docstring"""
if node.prev is None or node.next is None:
return None
_lowerCAmelCase = node.next
_lowerCAmelCase = node.prev
_lowerCAmelCase = None
_lowerCAmelCase = None
return node
class __lowerCAmelCase ( Generic[T, U] ):
__lowerCamelCase = {}
def __init__( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = DoubleLinkedList()
_lowerCAmelCase = capacity
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = {}
def __repr__( self ):
"""simple docstring"""
return (
F'CacheInfo(hits={self.hits}, misses={self.miss}, '
F'capacity={self.capacity}, current size={self.num_keys})'
)
def __contains__( self , _snake_case ):
"""simple docstring"""
return key in self.cache
def snake_case ( self , _snake_case ):
"""simple docstring"""
if key in self.cache:
self.hits += 1
_lowerCAmelCase = self.cache[key]
_lowerCAmelCase = self.list.remove(self.cache[key] )
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(_snake_case )
return node.val
self.miss += 1
return None
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
_lowerCAmelCase = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(_snake_case ) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
_lowerCAmelCase = DoubleLinkedListNode(_snake_case , _snake_case )
self.list.add(self.cache[key] )
self.num_keys += 1
else:
# bump node to the end of the list, update value
_lowerCAmelCase = self.list.remove(self.cache[key] )
assert node is not None # node guaranteed to be in list
_lowerCAmelCase = value
self.list.add(_snake_case )
@classmethod
def snake_case ( cls , _snake_case = 128 ):
"""simple docstring"""
def cache_decorator_inner(_snake_case ) -> Callable[..., U]:
def cache_decorator_wrapper(*_snake_case ) -> U:
if func not in cls.decorator_function_to_instance_map:
_lowerCAmelCase = LRUCache(_snake_case )
_lowerCAmelCase = cls.decorator_function_to_instance_map[func].get(args[0] )
if result is None:
_lowerCAmelCase = func(*_snake_case )
cls.decorator_function_to_instance_map[func].put(args[0] , _snake_case )
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(_snake_case , """cache_info""" , _snake_case ) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| 82 | 1 |
def _UpperCAmelCase ( snake_case = 50 ):
"""simple docstring"""
_lowerCAmelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(f"{solution() = }")
| 82 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A__ = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""MvpTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = ["""MvpTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = [
"""MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MvpForCausalLM""",
"""MvpForConditionalGeneration""",
"""MvpForQuestionAnswering""",
"""MvpForSequenceClassification""",
"""MvpModel""",
"""MvpPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
A__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 82 | 1 |
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = """"""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
_lowerCAmelCase = remove_duplicates(key.upper() )
_lowerCAmelCase = len(snake_case )
# First fill cipher with key characters
_lowerCAmelCase = {alphabet[i]: char for i, char in enumerate(snake_case )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(snake_case ) , 26 ):
_lowerCAmelCase = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
_lowerCAmelCase = alphabet[i - offset]
_lowerCAmelCase = char
return cipher_alphabet
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
return "".join(cipher_map.get(snake_case , snake_case ) for ch in message.upper() )
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(snake_case , snake_case ) for ch in message.upper() )
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = input("""Enter message to encode or decode: """ ).strip()
_lowerCAmelCase = input("""Enter keyword: """ ).strip()
_lowerCAmelCase = input("""Encipher or decipher? E/D:""" ).strip()[0].lower()
try:
_lowerCAmelCase = {"""e""": encipher, """d""": decipher}[option]
except KeyError:
raise KeyError("""invalid input option""" )
_lowerCAmelCase = create_cipher_map(snake_case )
print(func(snake_case , snake_case ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 82 |
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 0
while number > 0:
_lowerCAmelCase = number % 10
sum_of_digits += last_digit
_lowerCAmelCase = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def _UpperCAmelCase ( snake_case = 1_00 ):
"""simple docstring"""
_lowerCAmelCase = factorial(snake_case )
_lowerCAmelCase = split_and_add(snake_case )
return result
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).strip())))
| 82 | 1 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
A__ = TypeVar("""KT""")
A__ = TypeVar("""VT""")
class __lowerCAmelCase ( Generic[KT, VT] ):
def __init__( self , _snake_case = "root" , _snake_case = None ):
"""simple docstring"""
_lowerCAmelCase = key
_lowerCAmelCase = value
_lowerCAmelCase = []
def __repr__( self ):
"""simple docstring"""
return F'Node({self.key}: {self.value})'
@property
def snake_case ( self ):
"""simple docstring"""
return len(self.forward )
class __lowerCAmelCase ( Generic[KT, VT] ):
def __init__( self , _snake_case = 0.5 , _snake_case = 16 ):
"""simple docstring"""
_lowerCAmelCase = Node[KT, VT]()
_lowerCAmelCase = 0
_lowerCAmelCase = p
_lowerCAmelCase = max_level
def __str__( self ):
"""simple docstring"""
_lowerCAmelCase = list(self )
if len(_snake_case ) == 0:
return F'SkipList(level={self.level})'
_lowerCAmelCase = max((len(str(_snake_case ) ) for item in items) , default=4 )
_lowerCAmelCase = max(_snake_case , 4 ) + 4
_lowerCAmelCase = self.head
_lowerCAmelCase = []
_lowerCAmelCase = node.forward.copy()
lines.append(F'[{node.key}]'.ljust(_snake_case , """-""" ) + """* """ * len(_snake_case ) )
lines.append(""" """ * label_size + """| """ * len(_snake_case ) )
while len(node.forward ) != 0:
_lowerCAmelCase = node.forward[0]
lines.append(
F'[{node.key}]'.ljust(_snake_case , """-""" )
+ """ """.join(str(n.key ) if n.key == node.key else """|""" for n in forwards ) )
lines.append(""" """ * label_size + """| """ * len(_snake_case ) )
_lowerCAmelCase = node.forward
lines.append("""None""".ljust(_snake_case ) + """* """ * len(_snake_case ) )
return F'SkipList(level={self.level})\n' + "\n".join(_snake_case )
def __iter__( self ):
"""simple docstring"""
_lowerCAmelCase = self.head
while len(node.forward ) != 0:
yield node.forward[0].key
_lowerCAmelCase = node.forward[0]
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = 1
while random() < self.p and level < self.max_level:
level += 1
return level
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = self.head
for i in reversed(range(self.level ) ):
# i < node.level - When node level is lesser than `i` decrement `i`.
# node.forward[i].key < key - Jumping to node with key value higher
# or equal to searched key would result
# in skipping searched key.
while i < node.level and node.forward[i].key < key:
_lowerCAmelCase = node.forward[i]
# Each leftmost node (relative to searched node) will potentially have to
# be updated.
update_vector.append(_snake_case )
update_vector.reverse() # Note that we were inserting values in reverse order.
# len(node.forward) != 0 - If current node doesn't contain any further
# references then searched key is not present.
# node.forward[0].key == key - Next node key should be equal to search key
# if key is present.
if len(node.forward ) != 0 and node.forward[0].key == key:
return node.forward[0], update_vector
else:
return None, update_vector
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self._locate_node(_snake_case )
if node is not None:
for i, update_node in enumerate(_snake_case ):
# Remove or replace all references to removed node.
if update_node.level > i and update_node.forward[i].key == key:
if node.level > i:
_lowerCAmelCase = node.forward[i]
else:
_lowerCAmelCase = update_node.forward[:i]
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self._locate_node(_snake_case )
if node is not None:
_lowerCAmelCase = value
else:
_lowerCAmelCase = self.random_level()
if level > self.level:
# After level increase we have to add additional nodes to head.
for _ in range(self.level - 1 , _snake_case ):
update_vector.append(self.head )
_lowerCAmelCase = level
_lowerCAmelCase = Node(_snake_case , _snake_case )
for i, update_node in enumerate(update_vector[:level] ):
# Change references to pass through new node.
if update_node.level > i:
new_node.forward.append(update_node.forward[i] )
if update_node.level < i + 1:
update_node.forward.append(_snake_case )
else:
_lowerCAmelCase = new_node
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self._locate_node(_snake_case )
if node is not None:
return node.value
return None
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = SkipList()
skip_list.insert("""Key1""" , 3 )
skip_list.insert("""Key2""" , 12 )
skip_list.insert("""Key3""" , 41 )
skip_list.insert("""Key4""" , -19 )
_lowerCAmelCase = skip_list.head
_lowerCAmelCase = {}
while node.level != 0:
_lowerCAmelCase = node.forward[0]
_lowerCAmelCase = node.value
assert len(snake_case ) == 4
assert all_values["Key1"] == 3
assert all_values["Key2"] == 12
assert all_values["Key3"] == 41
assert all_values["Key4"] == -19
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = SkipList()
skip_list.insert("""Key1""" , 10 )
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""Key5""" , 7 )
skip_list.insert("""Key7""" , 10 )
skip_list.insert("""Key10""" , 5 )
skip_list.insert("""Key7""" , 7 )
skip_list.insert("""Key5""" , 5 )
skip_list.insert("""Key10""" , 10 )
_lowerCAmelCase = skip_list.head
_lowerCAmelCase = {}
while node.level != 0:
_lowerCAmelCase = node.forward[0]
_lowerCAmelCase = node.value
if len(snake_case ) != 4:
print()
assert len(snake_case ) == 4
assert all_values["Key1"] == 12
assert all_values["Key7"] == 7
assert all_values["Key5"] == 5
assert all_values["Key10"] == 10
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = SkipList()
assert skip_list.find("""Some key""" ) is None
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = SkipList()
skip_list.insert("""Key2""" , 20 )
assert skip_list.find("""Key2""" ) == 20
skip_list.insert("""Some Key""" , 10 )
skip_list.insert("""Key2""" , 8 )
skip_list.insert("""V""" , 13 )
assert skip_list.find("""Y""" ) is None
assert skip_list.find("""Key2""" ) == 8
assert skip_list.find("""Some Key""" ) == 10
assert skip_list.find("""V""" ) == 13
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = SkipList()
skip_list.delete("""Some key""" )
assert len(skip_list.head.forward ) == 0
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = SkipList()
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""V""" , 13 )
skip_list.insert("""X""" , 14 )
skip_list.insert("""Key2""" , 15 )
skip_list.delete("""V""" )
skip_list.delete("""Key2""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""Key2""" ) is None
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = SkipList()
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""V""" , 13 )
skip_list.insert("""X""" , 14 )
skip_list.insert("""Key2""" , 15 )
skip_list.delete("""V""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) == 14
assert skip_list.find("""Key1""" ) == 12
assert skip_list.find("""Key2""" ) == 15
skip_list.delete("""X""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) is None
assert skip_list.find("""Key1""" ) == 12
assert skip_list.find("""Key2""" ) == 15
skip_list.delete("""Key1""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) is None
assert skip_list.find("""Key1""" ) is None
assert skip_list.find("""Key2""" ) == 15
skip_list.delete("""Key2""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) is None
assert skip_list.find("""Key1""" ) is None
assert skip_list.find("""Key2""" ) is None
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = SkipList()
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""V""" , 13 )
skip_list.insert("""X""" , 1_42 )
skip_list.insert("""Key2""" , 15 )
skip_list.delete("""X""" )
def traverse_keys(snake_case ):
yield node.key
for forward_node in node.forward:
yield from traverse_keys(snake_case )
assert len(set(traverse_keys(skip_list.head ) ) ) == 4
def _UpperCAmelCase ( ):
"""simple docstring"""
def is_sorted(snake_case ):
return all(next_item >= item for item, next_item in zip(snake_case , lst[1:] ) )
_lowerCAmelCase = SkipList()
for i in range(10 ):
skip_list.insert(snake_case , snake_case )
assert is_sorted(list(snake_case ) )
skip_list.delete(5 )
skip_list.delete(8 )
skip_list.delete(2 )
assert is_sorted(list(snake_case ) )
skip_list.insert(-12 , -12 )
skip_list.insert(77 , 77 )
assert is_sorted(list(snake_case ) )
def _UpperCAmelCase ( ):
"""simple docstring"""
for _ in range(1_00 ):
# Repeat test 100 times due to the probabilistic nature of skip list
# random values == random bugs
test_insert()
test_insert_overrides_existing_value()
test_searching_empty_list_returns_none()
test_search()
test_deleting_item_from_empty_list_do_nothing()
test_deleted_items_are_not_founded_by_find_method()
test_delete_removes_only_given_key()
test_delete_doesnt_leave_dead_nodes()
test_iter_always_yields_sorted_values()
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = SkipList()
skip_list.insert(2 , """2""" )
skip_list.insert(4 , """4""" )
skip_list.insert(6 , """4""" )
skip_list.insert(4 , """5""" )
skip_list.insert(8 , """4""" )
skip_list.insert(9 , """4""" )
skip_list.delete(4 )
print(snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 82 |
A__ = [0, 2, 4, 6, 8]
A__ = [1, 3, 5, 7, 9]
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 10
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
_lowerCAmelCase = 0
for digit in range(10 ):
_lowerCAmelCase = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 10 , snake_case , snake_case )
return result
_lowerCAmelCase = 0
for digita in range(10 ):
_lowerCAmelCase = digita
if (remainder + digita) % 2 == 0:
_lowerCAmelCase = ODD_DIGITS
else:
_lowerCAmelCase = EVEN_DIGITS
for digita in other_parity_digits:
_lowerCAmelCase = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 10 , snake_case , snake_case , )
return result
def _UpperCAmelCase ( snake_case = 9 ):
"""simple docstring"""
_lowerCAmelCase = 0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(snake_case , 0 , [0] * length , snake_case )
return result
if __name__ == "__main__":
print(f"{solution() = }")
| 82 | 1 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = XCLIPTextConfig()
# derive patch size from model name
_lowerCAmelCase = model_name.find("""patch""" )
_lowerCAmelCase = int(model_name[start_idx + len("""patch""" ) : start_idx + len("""patch""" ) + 2] )
_lowerCAmelCase = XCLIPVisionConfig(patch_size=snake_case , num_frames=snake_case )
if "large" in model_name:
_lowerCAmelCase = 7_68
_lowerCAmelCase = 30_72
_lowerCAmelCase = 12
_lowerCAmelCase = 10_24
_lowerCAmelCase = 40_96
_lowerCAmelCase = 16
_lowerCAmelCase = 24
_lowerCAmelCase = 7_68
_lowerCAmelCase = 30_72
if model_name == "xclip-large-patch14-16-frames":
_lowerCAmelCase = 3_36
_lowerCAmelCase = XCLIPConfig.from_text_vision_configs(snake_case , snake_case )
if "large" in model_name:
_lowerCAmelCase = 7_68
return config
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if name == "token_embedding.weight":
_lowerCAmelCase = name.replace("""token_embedding.weight""" , """text_model.embeddings.token_embedding.weight""" )
if name == "positional_embedding":
_lowerCAmelCase = name.replace("""positional_embedding""" , """text_model.embeddings.position_embedding.weight""" )
if "ln_1" in name:
_lowerCAmelCase = name.replace("""ln_1""" , """layer_norm1""" )
if "ln_2" in name:
_lowerCAmelCase = name.replace("""ln_2""" , """layer_norm2""" )
if "c_fc" in name:
_lowerCAmelCase = name.replace("""c_fc""" , """fc1""" )
if "c_proj" in name:
_lowerCAmelCase = name.replace("""c_proj""" , """fc2""" )
if name.startswith("""transformer.resblocks""" ):
_lowerCAmelCase = name.replace("""transformer.resblocks""" , """text_model.encoder.layers""" )
if "attn.out_proj" in name and "message" not in name:
_lowerCAmelCase = name.replace("""attn.out_proj""" , """self_attn.out_proj""" )
if "ln_final" in name:
_lowerCAmelCase = name.replace("""ln_final""" , """text_model.final_layer_norm""" )
# visual encoder
if name == "visual.class_embedding":
_lowerCAmelCase = name.replace("""visual.class_embedding""" , """vision_model.embeddings.class_embedding""" )
if name == "visual.positional_embedding":
_lowerCAmelCase = name.replace("""visual.positional_embedding""" , """vision_model.embeddings.position_embedding.weight""" )
if name.startswith("""visual.transformer.resblocks""" ):
_lowerCAmelCase = name.replace("""visual.transformer.resblocks""" , """vision_model.encoder.layers""" )
if "visual.conv1" in name:
_lowerCAmelCase = name.replace("""visual.conv1""" , """vision_model.embeddings.patch_embedding""" )
if "visual.ln_pre" in name:
_lowerCAmelCase = name.replace("""visual.ln_pre""" , """vision_model.pre_layernorm""" )
if "visual.ln_post" in name:
_lowerCAmelCase = name.replace("""visual.ln_post""" , """vision_model.post_layernorm""" )
if "visual.proj" in name:
_lowerCAmelCase = name.replace("""visual.proj""" , """visual_projection.weight""" )
if "text_projection" in name:
_lowerCAmelCase = name.replace("""text_projection""" , """text_projection.weight""" )
# things on top
if "prompts_visual_proj" in name:
_lowerCAmelCase = name.replace("""prompts_visual_proj""" , """prompts_visual_projection""" )
if "prompts_visual_ln" in name:
_lowerCAmelCase = name.replace("""prompts_visual_ln""" , """prompts_visual_layernorm""" )
# mit
if name == "mit.positional_embedding":
_lowerCAmelCase = name.replace("""positional""" , """position""" )
if name.startswith("""mit.resblocks""" ):
_lowerCAmelCase = name.replace("""mit.resblocks""" , """mit.encoder.layers""" )
# prompts generator
if name.startswith("""prompts_generator.norm""" ):
_lowerCAmelCase = name.replace("""prompts_generator.norm""" , """prompts_generator.layernorm""" )
return name
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_lowerCAmelCase = orig_state_dict.pop(snake_case )
if "attn.in_proj" in key:
_lowerCAmelCase = key.split(""".""" )
if key.startswith("""visual""" ):
_lowerCAmelCase = key_split[3]
_lowerCAmelCase = config.vision_config.hidden_size
if "message_attn" in key:
if "weight" in key:
_lowerCAmelCase = val[
:dim, :
]
_lowerCAmelCase = val[
dim : dim * 2, :
]
_lowerCAmelCase = val[
-dim:, :
]
else:
_lowerCAmelCase = val[
:dim
]
_lowerCAmelCase = val[
dim : dim * 2
]
_lowerCAmelCase = val[
-dim:
]
else:
if "weight" in key:
_lowerCAmelCase = val[
:dim, :
]
_lowerCAmelCase = val[
dim : dim * 2, :
]
_lowerCAmelCase = val[
-dim:, :
]
else:
_lowerCAmelCase = val[:dim]
_lowerCAmelCase = val[
dim : dim * 2
]
_lowerCAmelCase = val[-dim:]
elif key.startswith("""mit""" ):
_lowerCAmelCase = key_split[2]
_lowerCAmelCase = config.vision_config.mit_hidden_size
if "weight" in key:
_lowerCAmelCase = val[:dim, :]
_lowerCAmelCase = val[dim : dim * 2, :]
_lowerCAmelCase = val[-dim:, :]
else:
_lowerCAmelCase = val[:dim]
_lowerCAmelCase = val[dim : dim * 2]
_lowerCAmelCase = val[-dim:]
else:
_lowerCAmelCase = key_split[2]
_lowerCAmelCase = config.text_config.hidden_size
if "weight" in key:
_lowerCAmelCase = val[:dim, :]
_lowerCAmelCase = val[
dim : dim * 2, :
]
_lowerCAmelCase = val[-dim:, :]
else:
_lowerCAmelCase = val[:dim]
_lowerCAmelCase = val[
dim : dim * 2
]
_lowerCAmelCase = val[-dim:]
else:
_lowerCAmelCase = rename_key(snake_case )
if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
_lowerCAmelCase = val.T
_lowerCAmelCase = val
return orig_state_dict
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if num_frames == 8:
_lowerCAmelCase = """eating_spaghetti_8_frames.npy"""
elif num_frames == 16:
_lowerCAmelCase = """eating_spaghetti.npy"""
elif num_frames == 32:
_lowerCAmelCase = """eating_spaghetti_32_frames.npy"""
_lowerCAmelCase = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename=snake_case , repo_type="""dataset""" , )
_lowerCAmelCase = np.load(snake_case )
return list(snake_case )
def _UpperCAmelCase ( snake_case , snake_case=None , snake_case=False ):
"""simple docstring"""
_lowerCAmelCase = {
# fully supervised kinetics-400 checkpoints
"""xclip-base-patch32""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth""",
"""xclip-base-patch32-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"""
),
"""xclip-base-patch16""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth""",
"""xclip-base-patch16-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"""
),
"""xclip-large-patch14""": """https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&export=download&confirm=t&uuid=b26caedc-88e2-473e-830a-9d158b653cdb""",
"""xclip-large-patch14-16-frames""": """https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&export=download&confirm=t&uuid=538fa810-e671-4050-b385-9a623f89804f""",
# fully supervised kinetics-600 checkpoints
"""xclip-base-patch16-kinetics-600""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"""
),
"""xclip-base-patch16-kinetics-600-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"""
),
"""xclip-large-patch14-kinetics-600""": """https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&export=download&confirm=t&uuid=141d4977-4a65-44ae-864f-4b0c19f838be""",
# few shot
"""xclip-base-patch16-hmdb-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"""
),
"""xclip-base-patch16-hmdb-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"""
),
"""xclip-base-patch16-hmdb-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"""
),
"""xclip-base-patch16-hmdb-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"""
),
"""xclip-base-patch16-ucf-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"""
),
"""xclip-base-patch16-ucf-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"""
),
"""xclip-base-patch16-ucf-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"""
),
"""xclip-base-patch16-ucf-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"""
),
# zero shot
"""xclip-base-patch16-zero-shot""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth""",
}
_lowerCAmelCase = model_to_url[model_name]
_lowerCAmelCase = 8
if "16-frames" in model_name:
_lowerCAmelCase = 16
elif "shot" in model_name:
_lowerCAmelCase = 32
_lowerCAmelCase = get_xclip_config(snake_case , snake_case )
_lowerCAmelCase = XCLIPModel(snake_case )
model.eval()
if "drive" in checkpoint_url:
_lowerCAmelCase = """pytorch_model.bin"""
gdown.cached_download(snake_case , snake_case , quiet=snake_case )
_lowerCAmelCase = torch.load(snake_case , map_location="""cpu""" )["""model"""]
else:
_lowerCAmelCase = torch.hub.load_state_dict_from_url(snake_case )["""model"""]
_lowerCAmelCase = convert_state_dict(snake_case , snake_case )
_lowerCAmelCase = XCLIPModel(snake_case )
_lowerCAmelCase , _lowerCAmelCase = model.load_state_dict(snake_case , strict=snake_case )
assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
model.eval()
_lowerCAmelCase = 3_36 if model_name == """xclip-large-patch14-16-frames""" else 2_24
_lowerCAmelCase = VideoMAEImageProcessor(size=snake_case )
_lowerCAmelCase = CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32""" )
_lowerCAmelCase = CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32""" )
_lowerCAmelCase = XCLIPProcessor(image_processor=snake_case , tokenizer=snake_case )
_lowerCAmelCase = prepare_video(snake_case )
_lowerCAmelCase = processor(
text=["""playing sports""", """eating spaghetti""", """go shopping"""] , videos=snake_case , return_tensors="""pt""" , padding=snake_case )
print("""Shape of pixel values:""" , inputs.pixel_values.shape )
with torch.no_grad():
_lowerCAmelCase = model(**snake_case )
# Verify outputs
_lowerCAmelCase = outputs.logits_per_video
_lowerCAmelCase = logits_per_video.softmax(dim=1 )
print("""Probs:""" , snake_case )
# kinetics-400
if model_name == "xclip-base-patch32":
_lowerCAmelCase = torch.tensor([[0.0_019, 0.9_951, 0.0_030]] )
elif model_name == "xclip-base-patch32-16-frames":
_lowerCAmelCase = torch.tensor([[7.09_99E-04, 9.98_83E-01, 4.55_80E-04]] )
elif model_name == "xclip-base-patch16":
_lowerCAmelCase = torch.tensor([[0.0_083, 0.9_681, 0.0_236]] )
elif model_name == "xclip-base-patch16-16-frames":
_lowerCAmelCase = torch.tensor([[7.69_37E-04, 9.97_28E-01, 1.94_73E-03]] )
elif model_name == "xclip-large-patch14":
_lowerCAmelCase = torch.tensor([[0.0_062, 0.9_864, 0.0_075]] )
elif model_name == "xclip-large-patch14-16-frames":
_lowerCAmelCase = torch.tensor([[3.38_77E-04, 9.99_37E-01, 2.88_88E-04]] )
# kinetics-600
elif model_name == "xclip-base-patch16-kinetics-600":
_lowerCAmelCase = torch.tensor([[0.0_555, 0.8_914, 0.0_531]] )
elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
_lowerCAmelCase = torch.tensor([[3.85_54E-04, 9.99_29E-01, 3.27_54E-04]] )
elif model_name == "xclip-large-patch14-kinetics-600":
_lowerCAmelCase = torch.tensor([[0.0_036, 0.9_920, 0.0_045]] )
# few shot
elif model_name == "xclip-base-patch16-hmdb-2-shot":
_lowerCAmelCase = torch.tensor([[7.18_90E-06, 9.99_94E-01, 5.65_59E-05]] )
elif model_name == "xclip-base-patch16-hmdb-4-shot":
_lowerCAmelCase = torch.tensor([[1.03_20E-05, 9.99_93E-01, 6.24_35E-05]] )
elif model_name == "xclip-base-patch16-hmdb-8-shot":
_lowerCAmelCase = torch.tensor([[4.13_77E-06, 9.99_90E-01, 9.83_86E-05]] )
elif model_name == "xclip-base-patch16-hmdb-16-shot":
_lowerCAmelCase = torch.tensor([[4.13_47E-05, 9.99_62E-01, 3.34_11E-04]] )
elif model_name == "xclip-base-patch16-ucf-2-shot":
_lowerCAmelCase = torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]] )
elif model_name == "xclip-base-patch16-ucf-4-shot":
_lowerCAmelCase = torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]] )
elif model_name == "xclip-base-patch16-ucf-8-shot":
_lowerCAmelCase = torch.tensor([[0.0_027, 0.9_904, 0.0_070]] )
elif model_name == "xclip-base-patch16-ucf-16-shot":
_lowerCAmelCase = torch.tensor([[9.82_19E-04, 9.95_93E-01, 3.08_63E-03]] )
# zero shot
elif model_name == "xclip-base-patch16-zero-shot":
_lowerCAmelCase = torch.tensor([[3.50_82E-04, 9.97_85E-01, 1.79_66E-03]] )
else:
raise ValueError(F'Model name {model_name} not supported' )
assert torch.allclose(snake_case , snake_case , atol=1E-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(snake_case )
if push_to_hub:
print("""Pushing model, processor and slow tokenizer files to the hub...""" )
model.push_to_hub(snake_case , organization="""nielsr""" )
processor.push_to_hub(snake_case , organization="""nielsr""" )
slow_tokenizer.push_to_hub(snake_case , organization="""nielsr""" )
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""xclip-base-patch32""",
type=str,
help="""Name of the model.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
A__ = parser.parse_args()
convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 82 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
A__ = logging.get_logger(__name__)
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = OrderedDict()
for key, value in state_dict.items():
if key.startswith("""module.encoder""" ):
_lowerCAmelCase = key.replace("""module.encoder""" , """glpn.encoder""" )
if key.startswith("""module.decoder""" ):
_lowerCAmelCase = key.replace("""module.decoder""" , """decoder.stages""" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
_lowerCAmelCase = key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
_lowerCAmelCase = key.replace(F'patch_embed{idx}' , F'patch_embeddings.{int(snake_case )-1}' )
if "norm" in key:
_lowerCAmelCase = key.replace("""norm""" , """layer_norm""" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
_lowerCAmelCase = key[key.find("""glpn.encoder.layer_norm""" ) + len("""glpn.encoder.layer_norm""" )]
_lowerCAmelCase = key.replace(F'layer_norm{idx}' , F'layer_norm.{int(snake_case )-1}' )
if "layer_norm1" in key:
_lowerCAmelCase = key.replace("""layer_norm1""" , """layer_norm_1""" )
if "layer_norm2" in key:
_lowerCAmelCase = key.replace("""layer_norm2""" , """layer_norm_2""" )
if "block" in key:
# replace for example block1 by block.0
_lowerCAmelCase = key[key.find("""block""" ) + len("""block""" )]
_lowerCAmelCase = key.replace(F'block{idx}' , F'block.{int(snake_case )-1}' )
if "attn.q" in key:
_lowerCAmelCase = key.replace("""attn.q""" , """attention.self.query""" )
if "attn.proj" in key:
_lowerCAmelCase = key.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in key:
_lowerCAmelCase = key.replace("""attn""" , """attention.self""" )
if "fc1" in key:
_lowerCAmelCase = key.replace("""fc1""" , """dense1""" )
if "fc2" in key:
_lowerCAmelCase = key.replace("""fc2""" , """dense2""" )
if "linear_pred" in key:
_lowerCAmelCase = key.replace("""linear_pred""" , """classifier""" )
if "linear_fuse" in key:
_lowerCAmelCase = key.replace("""linear_fuse.conv""" , """linear_fuse""" )
_lowerCAmelCase = key.replace("""linear_fuse.bn""" , """batch_norm""" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
_lowerCAmelCase = key[key.find("""linear_c""" ) + len("""linear_c""" )]
_lowerCAmelCase = key.replace(F'linear_c{idx}' , F'linear_c.{int(snake_case )-1}' )
if "bot_conv" in key:
_lowerCAmelCase = key.replace("""bot_conv""" , """0.convolution""" )
if "skip_conv1" in key:
_lowerCAmelCase = key.replace("""skip_conv1""" , """1.convolution""" )
if "skip_conv2" in key:
_lowerCAmelCase = key.replace("""skip_conv2""" , """2.convolution""" )
if "fusion1" in key:
_lowerCAmelCase = key.replace("""fusion1""" , """1.fusion""" )
if "fusion2" in key:
_lowerCAmelCase = key.replace("""fusion2""" , """2.fusion""" )
if "fusion3" in key:
_lowerCAmelCase = key.replace("""fusion3""" , """3.fusion""" )
if "fusion" in key and "conv" in key:
_lowerCAmelCase = key.replace("""conv""" , """convolutional_layer""" )
if key.startswith("""module.last_layer_depth""" ):
_lowerCAmelCase = key.replace("""module.last_layer_depth""" , """head.head""" )
_lowerCAmelCase = value
return new_state_dict
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
_lowerCAmelCase = state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight' )
_lowerCAmelCase = state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.bias' )
# next, add keys and values (in that order) to the state dict
_lowerCAmelCase = kv_weight[
: config.hidden_sizes[i], :
]
_lowerCAmelCase = kv_bias[: config.hidden_sizes[i]]
_lowerCAmelCase = kv_weight[
config.hidden_sizes[i] :, :
]
_lowerCAmelCase = kv_bias[config.hidden_sizes[i] :]
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_lowerCAmelCase = Image.open(requests.get(snake_case , stream=snake_case ).raw )
return image
@torch.no_grad()
def _UpperCAmelCase ( snake_case , snake_case , snake_case=False , snake_case=None ):
"""simple docstring"""
_lowerCAmelCase = GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12] , decoder_hidden_size=64 , depths=[3, 8, 27, 3] )
# load image processor (only resize + rescale)
_lowerCAmelCase = GLPNImageProcessor()
# prepare image
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(images=snake_case , return_tensors="""pt""" ).pixel_values
logger.info("""Converting model...""" )
# load original state dict
_lowerCAmelCase = torch.load(snake_case , map_location=torch.device("""cpu""" ) )
# rename keys
_lowerCAmelCase = rename_keys(snake_case )
# key and value matrices need special treatment
read_in_k_v(snake_case , snake_case )
# create HuggingFace model and load state dict
_lowerCAmelCase = GLPNForDepthEstimation(snake_case )
model.load_state_dict(snake_case )
model.eval()
# forward pass
_lowerCAmelCase = model(snake_case )
_lowerCAmelCase = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
_lowerCAmelCase = torch.tensor(
[[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]] )
elif "kitti" in model_name:
_lowerCAmelCase = torch.tensor(
[[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]] )
else:
raise ValueError(F'Unknown model name: {model_name}' )
_lowerCAmelCase = torch.Size([1, 4_80, 6_40] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3] , snake_case , atol=1E-4 )
print("""Looks ok!""" )
# finally, push to hub if required
if push_to_hub:
logger.info("""Pushing model and image processor to the hub...""" )
model.push_to_hub(
repo_path_or_name=Path(snake_case , snake_case ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=snake_case , )
image_processor.push_to_hub(
repo_path_or_name=Path(snake_case , snake_case ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=snake_case , )
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""",
default=None,
type=str,
help="""Path to the original PyTorch checkpoint (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
)
parser.add_argument(
"""--model_name""",
default="""glpn-kitti""",
type=str,
help="""Name of the model in case you're pushing to the hub.""",
)
A__ = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 82 | 1 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
if isinstance(snake_case , torch.Tensor ):
return image
elif isinstance(snake_case , PIL.Image.Image ):
_lowerCAmelCase = [image]
if isinstance(image[0] , PIL.Image.Image ):
_lowerCAmelCase = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION["""lanczos"""] ) )[None, :] for i in image]
_lowerCAmelCase = np.concatenate(snake_case , axis=0 )
_lowerCAmelCase = np.array(snake_case ).astype(np.floataa ) / 255.0
_lowerCAmelCase = image.transpose(0 , 3 , 1 , 2 )
_lowerCAmelCase = 2.0 * image - 1.0
_lowerCAmelCase = torch.from_numpy(snake_case )
elif isinstance(image[0] , torch.Tensor ):
_lowerCAmelCase = torch.cat(snake_case , dim=0 )
return image
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case=0.9_995 ):
"""simple docstring"""
if not isinstance(snake_case , np.ndarray ):
_lowerCAmelCase = True
_lowerCAmelCase = va.device
_lowerCAmelCase = va.cpu().numpy()
_lowerCAmelCase = va.cpu().numpy()
_lowerCAmelCase = np.sum(va * va / (np.linalg.norm(snake_case ) * np.linalg.norm(snake_case )) )
if np.abs(snake_case ) > DOT_THRESHOLD:
_lowerCAmelCase = (1 - t) * va + t * va
else:
_lowerCAmelCase = np.arccos(snake_case )
_lowerCAmelCase = np.sin(snake_case )
_lowerCAmelCase = theta_a * t
_lowerCAmelCase = np.sin(snake_case )
_lowerCAmelCase = np.sin(theta_a - theta_t ) / sin_theta_a
_lowerCAmelCase = sin_theta_t / sin_theta_a
_lowerCAmelCase = sa * va + sa * va
if inputs_are_torch:
_lowerCAmelCase = torch.from_numpy(snake_case ).to(snake_case )
return va
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = F.normalize(snake_case , dim=-1 )
_lowerCAmelCase = F.normalize(snake_case , dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
for param in model.parameters():
_lowerCAmelCase = value
class __lowerCAmelCase ( lowerCamelCase__ ):
def __init__( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , _snake_case=None , _snake_case=None , ):
"""simple docstring"""
super().__init__()
self.register_modules(
vae=_snake_case , text_encoder=_snake_case , clip_model=_snake_case , tokenizer=_snake_case , unet=_snake_case , scheduler=_snake_case , feature_extractor=_snake_case , coca_model=_snake_case , coca_tokenizer=_snake_case , coca_transform=_snake_case , )
_lowerCAmelCase = (
feature_extractor.size
if isinstance(feature_extractor.size , _snake_case )
else feature_extractor.size["""shortest_edge"""]
)
_lowerCAmelCase = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , _snake_case )
set_requires_grad(self.clip_model , _snake_case )
def snake_case ( self , _snake_case = "auto" ):
"""simple docstring"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
_lowerCAmelCase = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(_snake_case )
def snake_case ( self ):
"""simple docstring"""
self.enable_attention_slicing(_snake_case )
def snake_case ( self ):
"""simple docstring"""
set_requires_grad(self.vae , _snake_case )
def snake_case ( self ):
"""simple docstring"""
set_requires_grad(self.vae , _snake_case )
def snake_case ( self ):
"""simple docstring"""
set_requires_grad(self.unet , _snake_case )
def snake_case ( self ):
"""simple docstring"""
set_requires_grad(self.unet , _snake_case )
def snake_case ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = min(int(num_inference_steps * strength ) , _snake_case )
_lowerCAmelCase = max(num_inference_steps - init_timestep , 0 )
_lowerCAmelCase = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None ):
"""simple docstring"""
if not isinstance(_snake_case , torch.Tensor ):
raise ValueError(F'`image` has to be of type `torch.Tensor` but is {type(_snake_case )}' )
_lowerCAmelCase = image.to(device=_snake_case , dtype=_snake_case )
if isinstance(_snake_case , _snake_case ):
_lowerCAmelCase = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(_snake_case )
]
_lowerCAmelCase = torch.cat(_snake_case , dim=0 )
else:
_lowerCAmelCase = self.vae.encode(_snake_case ).latent_dist.sample(_snake_case )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_lowerCAmelCase = 0.1_8215 * init_latents
_lowerCAmelCase = init_latents.repeat_interleave(_snake_case , dim=0 )
_lowerCAmelCase = randn_tensor(init_latents.shape , generator=_snake_case , device=_snake_case , dtype=_snake_case )
# get latents
_lowerCAmelCase = self.scheduler.add_noise(_snake_case , _snake_case , _snake_case )
_lowerCAmelCase = init_latents
return latents
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.coca_transform(_snake_case ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
_lowerCAmelCase = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
_lowerCAmelCase = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split("""<end_of_text>""" )[0].replace("""<start_of_text>""" , """""" ).rstrip(""" .,""" )
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.feature_extractor.preprocess(_snake_case )
_lowerCAmelCase = torch.from_numpy(clip_image_input["""pixel_values"""][0] ).unsqueeze(0 ).to(self.device ).half()
_lowerCAmelCase = self.clip_model.get_image_features(_snake_case )
_lowerCAmelCase = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=_snake_case )
_lowerCAmelCase = image_embeddings_clip.repeat_interleave(_snake_case , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , ):
"""simple docstring"""
_lowerCAmelCase = latents.detach().requires_grad_()
_lowerCAmelCase = self.scheduler.scale_model_input(_snake_case , _snake_case )
# predict the noise residual
_lowerCAmelCase = self.unet(_snake_case , _snake_case , encoder_hidden_states=_snake_case ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
_lowerCAmelCase = self.scheduler.alphas_cumprod[timestep]
_lowerCAmelCase = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_lowerCAmelCase = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
_lowerCAmelCase = torch.sqrt(_snake_case )
_lowerCAmelCase = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , _snake_case ):
_lowerCAmelCase = self.scheduler.sigmas[index]
_lowerCAmelCase = latents - sigma * noise_pred
else:
raise ValueError(F'scheduler type {type(self.scheduler )} not supported' )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_lowerCAmelCase = 1 / 0.1_8215 * sample
_lowerCAmelCase = self.vae.decode(_snake_case ).sample
_lowerCAmelCase = (image / 2 + 0.5).clamp(0 , 1 )
_lowerCAmelCase = transforms.Resize(self.feature_extractor_size )(_snake_case )
_lowerCAmelCase = self.normalize(_snake_case ).to(latents.dtype )
_lowerCAmelCase = self.clip_model.get_image_features(_snake_case )
_lowerCAmelCase = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=_snake_case )
_lowerCAmelCase = spherical_dist_loss(_snake_case , _snake_case ).mean() * clip_guidance_scale
_lowerCAmelCase = -torch.autograd.grad(_snake_case , _snake_case )[0]
if isinstance(self.scheduler , _snake_case ):
_lowerCAmelCase = latents.detach() + grads * (sigma**2)
_lowerCAmelCase = noise_pred_original
else:
_lowerCAmelCase = noise_pred_original - torch.sqrt(_snake_case ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self , _snake_case , _snake_case , _snake_case = None , _snake_case = None , _snake_case = 512 , _snake_case = 512 , _snake_case = 0.6 , _snake_case = 50 , _snake_case = 7.5 , _snake_case = 1 , _snake_case = 0.0 , _snake_case = 100 , _snake_case = None , _snake_case = "pil" , _snake_case = True , _snake_case = 0.8 , _snake_case = 0.1 , _snake_case = 0.1 , ):
"""simple docstring"""
if isinstance(_snake_case , _snake_case ) and len(_snake_case ) != batch_size:
raise ValueError(F'You have passed {batch_size} batch_size, but only {len(_snake_case )} generators.' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.' )
if isinstance(_snake_case , torch.Generator ) and batch_size > 1:
_lowerCAmelCase = [generator] + [None] * (batch_size - 1)
_lowerCAmelCase = [
("""model""", self.coca_model is None),
("""tokenizer""", self.coca_tokenizer is None),
("""transform""", self.coca_transform is None),
]
_lowerCAmelCase = [x[0] for x in coca_is_none if x[1]]
_lowerCAmelCase = """, """.join(_snake_case )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(_snake_case ):
raise ValueError(
F'Content prompt is None and CoCa [{coca_is_none_str}] is None.'
F'Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.' )
_lowerCAmelCase = self.get_image_description(_snake_case )
if style_prompt is None:
if len(_snake_case ):
raise ValueError(
F'Style prompt is None and CoCa [{coca_is_none_str}] is None.'
F' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.' )
_lowerCAmelCase = self.get_image_description(_snake_case )
# get prompt text embeddings for content and style
_lowerCAmelCase = self.tokenizer(
_snake_case , padding="""max_length""" , max_length=self.tokenizer.model_max_length , truncation=_snake_case , return_tensors="""pt""" , )
_lowerCAmelCase = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
_lowerCAmelCase = self.tokenizer(
_snake_case , padding="""max_length""" , max_length=self.tokenizer.model_max_length , truncation=_snake_case , return_tensors="""pt""" , )
_lowerCAmelCase = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
_lowerCAmelCase = slerp(_snake_case , _snake_case , _snake_case )
# duplicate text embeddings for each generation per prompt
_lowerCAmelCase = text_embeddings.repeat_interleave(_snake_case , dim=0 )
# set timesteps
_lowerCAmelCase = """offset""" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
_lowerCAmelCase = {}
if accepts_offset:
_lowerCAmelCase = 1
self.scheduler.set_timesteps(_snake_case , **_snake_case )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
_lowerCAmelCase , _lowerCAmelCase = self.get_timesteps(_snake_case , _snake_case , self.device )
_lowerCAmelCase = timesteps[:1].repeat(_snake_case )
# Preprocess image
_lowerCAmelCase = preprocess(_snake_case , _snake_case , _snake_case )
_lowerCAmelCase = self.prepare_latents(
_snake_case , _snake_case , _snake_case , text_embeddings.dtype , self.device , _snake_case )
_lowerCAmelCase = preprocess(_snake_case , _snake_case , _snake_case )
_lowerCAmelCase = self.prepare_latents(
_snake_case , _snake_case , _snake_case , text_embeddings.dtype , self.device , _snake_case )
_lowerCAmelCase = slerp(_snake_case , _snake_case , _snake_case )
if clip_guidance_scale > 0:
_lowerCAmelCase = self.get_clip_image_embeddings(_snake_case , _snake_case )
_lowerCAmelCase = self.get_clip_image_embeddings(_snake_case , _snake_case )
_lowerCAmelCase = slerp(
_snake_case , _snake_case , _snake_case )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
_lowerCAmelCase = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
_lowerCAmelCase = content_text_input.input_ids.shape[-1]
_lowerCAmelCase = self.tokenizer([""""""] , padding="""max_length""" , max_length=_snake_case , return_tensors="""pt""" )
_lowerCAmelCase = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
_lowerCAmelCase = uncond_embeddings.repeat_interleave(_snake_case , dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_lowerCAmelCase = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
_lowerCAmelCase = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
_lowerCAmelCase = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
_lowerCAmelCase = torch.randn(_snake_case , generator=_snake_case , device="""cpu""" , dtype=_snake_case ).to(
self.device )
else:
_lowerCAmelCase = torch.randn(_snake_case , generator=_snake_case , device=self.device , dtype=_snake_case )
else:
if latents.shape != latents_shape:
raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}' )
_lowerCAmelCase = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
_lowerCAmelCase = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_lowerCAmelCase = """eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_lowerCAmelCase = {}
if accepts_eta:
_lowerCAmelCase = eta
# check if the scheduler accepts generator
_lowerCAmelCase = """generator""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
_lowerCAmelCase = generator
with self.progress_bar(total=_snake_case ):
for i, t in enumerate(_snake_case ):
# expand the latents if we are doing classifier free guidance
_lowerCAmelCase = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_lowerCAmelCase = self.scheduler.scale_model_input(_snake_case , _snake_case )
# predict the noise residual
_lowerCAmelCase = self.unet(_snake_case , _snake_case , encoder_hidden_states=_snake_case ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
_lowerCAmelCase , _lowerCAmelCase = noise_pred.chunk(2 )
_lowerCAmelCase = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
_lowerCAmelCase = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
_lowerCAmelCase , _lowerCAmelCase = self.cond_fn(
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , )
# compute the previous noisy sample x_t -> x_t-1
_lowerCAmelCase = self.scheduler.step(_snake_case , _snake_case , _snake_case , **_snake_case ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_lowerCAmelCase = 1 / 0.1_8215 * latents
_lowerCAmelCase = self.vae.decode(_snake_case ).sample
_lowerCAmelCase = (image / 2 + 0.5).clamp(0 , 1 )
_lowerCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_lowerCAmelCase = self.numpy_to_pil(_snake_case )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=_snake_case , nsfw_content_detected=_snake_case )
| 82 |
from math import isqrt, loga
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , snake_case , snake_case ):
_lowerCAmelCase = False
return [i for i in range(2 , snake_case ) if is_prime[i]]
def _UpperCAmelCase ( snake_case = 80_08_00 , snake_case = 80_08_00 ):
"""simple docstring"""
_lowerCAmelCase = degree * loga(snake_case )
_lowerCAmelCase = int(snake_case )
_lowerCAmelCase = calculate_prime_numbers(snake_case )
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = len(snake_case ) - 1
while left < right:
while (
prime_numbers[right] * loga(prime_numbers[left] )
+ prime_numbers[left] * loga(prime_numbers[right] )
> upper_bound
):
right -= 1
hybrid_integers_count += right - left
left += 1
return hybrid_integers_count
if __name__ == "__main__":
print(f"{solution() = }")
| 82 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
A__ = TypeVar("""T""")
A__ = TypeVar("""U""")
class __lowerCAmelCase ( Generic[T, U] ):
def __init__( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = key
_lowerCAmelCase = val
_lowerCAmelCase = None
_lowerCAmelCase = None
def __repr__( self ):
"""simple docstring"""
return (
F'Node: key: {self.key}, val: {self.val}, '
F'has next: {bool(self.next )}, has prev: {bool(self.prev )}'
)
class __lowerCAmelCase ( Generic[T, U] ):
def __init__( self ):
"""simple docstring"""
_lowerCAmelCase = DoubleLinkedListNode(_snake_case , _snake_case )
_lowerCAmelCase = DoubleLinkedListNode(_snake_case , _snake_case )
_lowerCAmelCase , _lowerCAmelCase = self.rear, self.head
def __repr__( self ):
"""simple docstring"""
_lowerCAmelCase = ["""DoubleLinkedList"""]
_lowerCAmelCase = self.head
while node.next is not None:
rep.append(str(_snake_case ) )
_lowerCAmelCase = node.next
rep.append(str(self.rear ) )
return ",\n ".join(_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
_lowerCAmelCase = node
_lowerCAmelCase = previous
_lowerCAmelCase = node
_lowerCAmelCase = self.rear
def snake_case ( self , _snake_case ):
"""simple docstring"""
if node.prev is None or node.next is None:
return None
_lowerCAmelCase = node.next
_lowerCAmelCase = node.prev
_lowerCAmelCase = None
_lowerCAmelCase = None
return node
class __lowerCAmelCase ( Generic[T, U] ):
__lowerCamelCase = {}
def __init__( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = DoubleLinkedList()
_lowerCAmelCase = capacity
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = {}
def __repr__( self ):
"""simple docstring"""
return (
F'CacheInfo(hits={self.hits}, misses={self.miss}, '
F'capacity={self.capacity}, current size={self.num_keys})'
)
def __contains__( self , _snake_case ):
"""simple docstring"""
return key in self.cache
def snake_case ( self , _snake_case ):
"""simple docstring"""
if key in self.cache:
self.hits += 1
_lowerCAmelCase = self.cache[key]
_lowerCAmelCase = self.list.remove(self.cache[key] )
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(_snake_case )
return node.val
self.miss += 1
return None
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
_lowerCAmelCase = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(_snake_case ) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
_lowerCAmelCase = DoubleLinkedListNode(_snake_case , _snake_case )
self.list.add(self.cache[key] )
self.num_keys += 1
else:
# bump node to the end of the list, update value
_lowerCAmelCase = self.list.remove(self.cache[key] )
assert node is not None # node guaranteed to be in list
_lowerCAmelCase = value
self.list.add(_snake_case )
@classmethod
def snake_case ( cls , _snake_case = 128 ):
"""simple docstring"""
def cache_decorator_inner(_snake_case ) -> Callable[..., U]:
def cache_decorator_wrapper(*_snake_case ) -> U:
if func not in cls.decorator_function_to_instance_map:
_lowerCAmelCase = LRUCache(_snake_case )
_lowerCAmelCase = cls.decorator_function_to_instance_map[func].get(args[0] )
if result is None:
_lowerCAmelCase = func(*_snake_case )
cls.decorator_function_to_instance_map[func].put(args[0] , _snake_case )
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(_snake_case , """cache_info""" , _snake_case ) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| 82 |
from __future__ import annotations
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = str(snake_case )
return n == n[::-1]
def _UpperCAmelCase ( snake_case = 1_00_00_00 ):
"""simple docstring"""
_lowerCAmelCase = 0
for i in range(1 , snake_case ):
if is_palindrome(snake_case ) and is_palindrome(bin(snake_case ).split("""b""" )[1] ):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| 82 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""facebook/timesformer""": """https://huggingface.co/facebook/timesformer/resolve/main/config.json""",
}
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''timesformer'''
def __init__( self , _snake_case=224 , _snake_case=16 , _snake_case=3 , _snake_case=8 , _snake_case=768 , _snake_case=12 , _snake_case=12 , _snake_case=3072 , _snake_case="gelu" , _snake_case=0.0 , _snake_case=0.0 , _snake_case=0.02 , _snake_case=1e-6 , _snake_case=True , _snake_case="divided_space_time" , _snake_case=0 , **_snake_case , ):
"""simple docstring"""
super().__init__(**_snake_case )
_lowerCAmelCase = image_size
_lowerCAmelCase = patch_size
_lowerCAmelCase = num_channels
_lowerCAmelCase = num_frames
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_act
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = initializer_range
_lowerCAmelCase = layer_norm_eps
_lowerCAmelCase = qkv_bias
_lowerCAmelCase = attention_type
_lowerCAmelCase = drop_path_rate
| 82 |
from collections.abc import Iterable
from typing import Generic, TypeVar
A__ = TypeVar("""_T""")
class __lowerCAmelCase ( Generic[_T] ):
def __init__( self , _snake_case = None ):
"""simple docstring"""
_lowerCAmelCase = list(iterable or [] )
_lowerCAmelCase = []
def __len__( self ):
"""simple docstring"""
return len(self._stacka ) + len(self._stacka )
def __repr__( self ):
"""simple docstring"""
return F'Queue({tuple(self._stacka[::-1] + self._stacka )})'
def snake_case ( self , _snake_case ):
"""simple docstring"""
self._stacka.append(_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self._stacka.pop
_lowerCAmelCase = self._stacka.append
if not self._stacka:
while self._stacka:
stacka_append(stacka_pop() )
if not self._stacka:
raise IndexError("""Queue is empty""" )
return self._stacka.pop()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 82 | 1 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
find_tied_parameters,
get_balanced_memory,
infer_auto_device_map,
load_checkpoint_in_model,
offload_weight,
set_module_tensor_to_device,
)
if is_bnb_available():
import bitsandbytes as bnb
from copy import deepcopy
A__ = logging.getLogger(__name__)
def _UpperCAmelCase ( snake_case , snake_case , snake_case = None , snake_case = None , snake_case = None , snake_case = None , snake_case = None , snake_case = False , ):
"""simple docstring"""
_lowerCAmelCase = bnb_quantization_config.load_in_abit
_lowerCAmelCase = bnb_quantization_config.load_in_abit
if load_in_abit and not is_abit_bnb_available():
raise ImportError(
"""You have a version of `bitsandbytes` that is not compatible with 8bit quantization,"""
""" make sure you have the latest version of `bitsandbytes` installed.""" )
if load_in_abit and not is_abit_bnb_available():
raise ValueError(
"""You have a version of `bitsandbytes` that is not compatible with 4bit quantization,"""
"""make sure you have the latest version of `bitsandbytes` installed.""" )
_lowerCAmelCase = []
# custom device map
if isinstance(snake_case , snake_case ) and len(device_map.keys() ) > 1:
_lowerCAmelCase = [key for key, value in device_map.items() if value in ["""disk""", """cpu"""]]
# We keep some modules such as the lm_head in their original dtype for numerical stability reasons
if bnb_quantization_config.skip_modules is None:
_lowerCAmelCase = get_keys_to_not_convert(snake_case )
# add cpu modules to skip modules only for 4-bit modules
if load_in_abit:
bnb_quantization_config.skip_modules.extend(snake_case )
_lowerCAmelCase = bnb_quantization_config.skip_modules
# We add the modules we want to keep in full precision
if bnb_quantization_config.keep_in_fpaa_modules is None:
_lowerCAmelCase = []
_lowerCAmelCase = bnb_quantization_config.keep_in_fpaa_modules
modules_to_not_convert.extend(snake_case )
# compatibility with peft
_lowerCAmelCase = load_in_abit
_lowerCAmelCase = load_in_abit
_lowerCAmelCase = get_parameter_device(snake_case )
if model_device.type != "meta":
# quantization of an already loaded model
logger.warning(
"""It is not recommended to quantize a loaded model. """
"""The model should be instantiated under the `init_empty_weights` context manager.""" )
_lowerCAmelCase = replace_with_bnb_layers(snake_case , snake_case , modules_to_not_convert=snake_case )
# convert param to the right dtype
_lowerCAmelCase = bnb_quantization_config.torch_dtype
for name, param in model.state_dict().items():
if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules ):
param.to(torch.floataa )
if param.dtype != torch.floataa:
_lowerCAmelCase = name.replace(""".weight""" , """""" ).replace(""".bias""" , """""" )
_lowerCAmelCase = getattr(snake_case , snake_case , snake_case )
if param is not None:
param.to(torch.floataa )
elif torch.is_floating_point(snake_case ):
param.to(snake_case )
if model_device.type == "cuda":
# move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
model.cuda(torch.cuda.current_device() )
torch.cuda.empty_cache()
elif torch.cuda.is_available():
model.to(torch.cuda.current_device() )
else:
raise RuntimeError("""No GPU found. A GPU is needed for quantization.""" )
logger.info(
F'The model device type is {model_device.type}. However, cuda is needed for quantization.'
"""We move the model to cuda.""" )
return model
elif weights_location is None:
raise RuntimeError(
F'`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} ' )
else:
with init_empty_weights():
_lowerCAmelCase = replace_with_bnb_layers(
snake_case , snake_case , modules_to_not_convert=snake_case )
_lowerCAmelCase = get_quantized_model_device_map(
snake_case , snake_case , snake_case , max_memory=snake_case , no_split_module_classes=snake_case , )
if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
_lowerCAmelCase = True
_lowerCAmelCase = any(x in list(device_map.values() ) for x in ["""cpu""", """disk"""] )
load_checkpoint_in_model(
snake_case , snake_case , snake_case , dtype=bnb_quantization_config.torch_dtype , offload_folder=snake_case , offload_state_dict=snake_case , keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules , offload_abit_bnb=load_in_abit and offload , )
return dispatch_model(snake_case , device_map=snake_case , offload_dir=snake_case )
def _UpperCAmelCase ( snake_case , snake_case , snake_case=None , snake_case=None , snake_case=None ):
"""simple docstring"""
if device_map is None:
if torch.cuda.is_available():
_lowerCAmelCase = {"""""": torch.cuda.current_device()}
else:
raise RuntimeError("""No GPU found. A GPU is needed for quantization.""" )
logger.info("""The device_map was not initialized.""" """Setting device_map to `{'':torch.cuda.current_device()}`.""" )
if isinstance(snake_case , snake_case ):
if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
raise ValueError(
"""If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or """
"""'sequential'.""" )
_lowerCAmelCase = {}
special_dtypes.update(
{
name: bnb_quantization_config.torch_dtype
for name, _ in model.named_parameters()
if any(m in name for m in bnb_quantization_config.skip_modules )
} )
special_dtypes.update(
{
name: torch.floataa
for name, _ in model.named_parameters()
if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules )
} )
_lowerCAmelCase = {}
_lowerCAmelCase = special_dtypes
_lowerCAmelCase = no_split_module_classes
_lowerCAmelCase = bnb_quantization_config.target_dtype
# get max_memory for each device.
if device_map != "sequential":
_lowerCAmelCase = get_balanced_memory(
snake_case , low_zero=(device_map == """balanced_low_0""") , max_memory=snake_case , **snake_case , )
_lowerCAmelCase = max_memory
_lowerCAmelCase = infer_auto_device_map(snake_case , **snake_case )
if isinstance(snake_case , snake_case ):
# check if don't have any quantized module on the cpu
_lowerCAmelCase = bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules
_lowerCAmelCase = {
key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
}
for device in ["cpu", "disk"]:
if device in device_map_without_some_modules.values():
if bnb_quantization_config.load_in_abit:
raise ValueError(
"""
Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
these modules in `torch_dtype`, you need to pass a custom `device_map` to
`load_and_quantize_model`. Check
https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk
for more details.
""" )
else:
logger.info(
"""Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit""" )
del device_map_without_some_modules
return device_map
def _UpperCAmelCase ( snake_case , snake_case , snake_case=None , snake_case=None ):
"""simple docstring"""
if modules_to_not_convert is None:
_lowerCAmelCase = []
_lowerCAmelCase , _lowerCAmelCase = _replace_with_bnb_layers(
snake_case , snake_case , snake_case , snake_case )
if not has_been_replaced:
logger.warning(
"""You are loading your model in 8bit or 4bit but no linear modules were found in your model."""
""" this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers."""
""" Please double check your model architecture, or submit an issue on github if you think this is"""
""" a bug.""" )
return model
def _UpperCAmelCase ( snake_case , snake_case , snake_case=None , snake_case=None , ):
"""simple docstring"""
_lowerCAmelCase = False
for name, module in model.named_children():
if current_key_name is None:
_lowerCAmelCase = []
current_key_name.append(snake_case )
if isinstance(snake_case , nn.Linear ) and name not in modules_to_not_convert:
# Check if the current key is not in the `modules_to_not_convert`
_lowerCAmelCase = """.""".join(snake_case )
_lowerCAmelCase = True
for key in modules_to_not_convert:
if (
(key in current_key_name_str) and (key + "." in current_key_name_str)
) or key == current_key_name_str:
_lowerCAmelCase = False
break
if proceed:
# Load bnb module with empty weight and replace ``nn.Linear` module
if bnb_quantization_config.load_in_abit:
_lowerCAmelCase = bnb.nn.LinearabitLt(
module.in_features , module.out_features , module.bias is not None , has_fpaa_weights=snake_case , threshold=bnb_quantization_config.llm_inta_threshold , )
elif bnb_quantization_config.load_in_abit:
_lowerCAmelCase = bnb.nn.Linearabit(
module.in_features , module.out_features , module.bias is not None , bnb_quantization_config.bnb_abit_compute_dtype , compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant , quant_type=bnb_quantization_config.bnb_abit_quant_type , )
else:
raise ValueError("""load_in_8bit and load_in_4bit can't be both False""" )
_lowerCAmelCase = module.weight.data
if module.bias is not None:
_lowerCAmelCase = module.bias.data
bnb_module.requires_grad_(snake_case )
setattr(snake_case , snake_case , snake_case )
_lowerCAmelCase = True
if len(list(module.children() ) ) > 0:
_lowerCAmelCase , _lowerCAmelCase = _replace_with_bnb_layers(
snake_case , snake_case , snake_case , snake_case )
_lowerCAmelCase = has_been_replaced | _has_been_replaced
# Remove the last key for recursion
current_key_name.pop(-1 )
return model, has_been_replaced
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
with init_empty_weights():
_lowerCAmelCase = deepcopy(snake_case ) # this has 0 cost since it is done inside `init_empty_weights` context manager`
_lowerCAmelCase = find_tied_parameters(snake_case )
# For compatibility with Accelerate < 0.18
if isinstance(snake_case , snake_case ):
_lowerCAmelCase = sum(list(tied_params.values() ) , [] ) + list(tied_params.keys() )
else:
_lowerCAmelCase = sum(snake_case , [] )
_lowerCAmelCase = len(snake_case ) > 0
# Check if it is a base model
_lowerCAmelCase = False
if hasattr(snake_case , """base_model_prefix""" ):
_lowerCAmelCase = not hasattr(snake_case , model.base_model_prefix )
# Ignore this for base models (BertModel, GPT2Model, etc.)
if (not has_tied_params) and is_base_model:
return []
# otherwise they have an attached head
_lowerCAmelCase = list(model.named_children() )
_lowerCAmelCase = [list_modules[-1][0]]
# add last module together with tied weights
_lowerCAmelCase = set(snake_case ) - set(snake_case )
_lowerCAmelCase = list(set(snake_case ) ) + list(snake_case )
# remove ".weight" from the keys
_lowerCAmelCase = [""".weight""", """.bias"""]
_lowerCAmelCase = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
_lowerCAmelCase = name.replace(snake_case , """""" )
filtered_module_names.append(snake_case )
return filtered_module_names
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
for m in model.modules():
if isinstance(snake_case , bnb.nn.Linearabit ):
return True
return False
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
return next(parameter.parameters() ).device
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
if fpaa_statistics is None:
set_module_tensor_to_device(snake_case , snake_case , 0 , dtype=snake_case , value=snake_case )
_lowerCAmelCase = param_name
_lowerCAmelCase = model
if "." in tensor_name:
_lowerCAmelCase = tensor_name.split(""".""" )
for split in splits[:-1]:
_lowerCAmelCase = getattr(snake_case , snake_case )
if new_module is None:
raise ValueError(F'{module} has no attribute {split}.' )
_lowerCAmelCase = new_module
_lowerCAmelCase = splits[-1]
# offload weights
_lowerCAmelCase = False
offload_weight(module._parameters[tensor_name] , snake_case , snake_case , index=snake_case )
if hasattr(module._parameters[tensor_name] , """SCB""" ):
offload_weight(
module._parameters[tensor_name].SCB , param_name.replace("""weight""" , """SCB""" ) , snake_case , index=snake_case , )
else:
offload_weight(snake_case , snake_case , snake_case , index=snake_case )
offload_weight(snake_case , param_name.replace("""weight""" , """SCB""" ) , snake_case , index=snake_case )
set_module_tensor_to_device(snake_case , snake_case , """meta""" , dtype=snake_case , value=torch.empty(*param.size() ) )
| 82 |
A__ = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A__ = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = True
_lowerCAmelCase = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(snake_case , snake_case , snake_case )
order.append(snake_case )
return order
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = True
_lowerCAmelCase = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(snake_case , snake_case , snake_case )
return component
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = len(snake_case ) * [False]
_lowerCAmelCase = {vert: [] for vert in range(len(snake_case ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(snake_case )
_lowerCAmelCase = []
for i, was_visited in enumerate(snake_case ):
if not was_visited:
order += topology_sort(snake_case , snake_case , snake_case )
_lowerCAmelCase = []
_lowerCAmelCase = len(snake_case ) * [False]
for i in range(len(snake_case ) ):
_lowerCAmelCase = order[len(snake_case ) - i - 1]
if not visited[vert]:
_lowerCAmelCase = find_components(snake_case , snake_case , snake_case )
components_list.append(snake_case )
return components_list
| 82 | 1 |
from collections import defaultdict
from math import ceil, sqrt
def _UpperCAmelCase ( snake_case = 1_00_00_00 , snake_case = 10 ):
"""simple docstring"""
_lowerCAmelCase = defaultdict(snake_case )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
_lowerCAmelCase = max(
ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 )
else:
_lowerCAmelCase = 1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(snake_case , outer_width - 1 , 2 ):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 10 )
if __name__ == "__main__":
print(f"{solution() = }")
| 82 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader
from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
ROUGE_KEYS,
LegacySeqaSeqDataset,
SeqaSeqDataset,
assert_all_frozen,
calculate_bleu,
calculate_rouge,
check_output_dir,
flatten_list,
freeze_embeds,
freeze_params,
get_git_info,
label_smoothed_nll_loss,
lmap,
pickle_save,
save_git_info,
save_json,
use_task_specific_params,
)
# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train # noqa
A__ = logging.getLogger(__name__)
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''summarization'''
__lowerCamelCase = ['''loss''']
__lowerCamelCase = ROUGE_KEYS
__lowerCamelCase = '''rouge2'''
def __init__( self , _snake_case , **_snake_case ):
"""simple docstring"""
if hparams.sortish_sampler and hparams.gpus > 1:
_lowerCAmelCase = False
elif hparams.max_tokens_per_batch is not None:
if hparams.gpus > 1:
raise NotImplementedError("""Dynamic Batch size does not work for multi-gpu training""" )
if hparams.sortish_sampler:
raise ValueError("""--sortish_sampler and --max_tokens_per_batch may not be used simultaneously""" )
super().__init__(_snake_case , num_labels=_snake_case , mode=self.mode , **_snake_case )
use_task_specific_params(self.model , """summarization""" )
save_git_info(self.hparams.output_dir )
_lowerCAmelCase = Path(self.output_dir ) / """metrics.json"""
_lowerCAmelCase = Path(self.output_dir ) / """hparams.pkl"""
pickle_save(self.hparams , self.hparams_save_path )
_lowerCAmelCase = 0
_lowerCAmelCase = defaultdict(_snake_case )
_lowerCAmelCase = self.config.model_type
_lowerCAmelCase = self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size
_lowerCAmelCase = {
"data_dir": self.hparams.data_dir,
"max_source_length": self.hparams.max_source_length,
"prefix": self.model.config.prefix or "",
}
_lowerCAmelCase = {
"""train""": self.hparams.n_train,
"""val""": self.hparams.n_val,
"""test""": self.hparams.n_test,
}
_lowerCAmelCase = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
_lowerCAmelCase = {
"""train""": self.hparams.max_target_length,
"""val""": self.hparams.val_max_target_length,
"""test""": self.hparams.test_max_target_length,
}
assert self.target_lens["train"] <= self.target_lens["val"], F'target_lens: {self.target_lens}'
assert self.target_lens["train"] <= self.target_lens["test"], F'target_lens: {self.target_lens}'
if self.hparams.freeze_embeds:
freeze_embeds(self.model )
if self.hparams.freeze_encoder:
freeze_params(self.model.get_encoder() )
assert_all_frozen(self.model.get_encoder() )
_lowerCAmelCase = get_git_info()["""repo_sha"""]
_lowerCAmelCase = hparams.num_workers
_lowerCAmelCase = None # default to config
if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer , _snake_case ):
_lowerCAmelCase = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
_lowerCAmelCase = self.decoder_start_token_id
_lowerCAmelCase = (
SeqaSeqDataset if hasattr(self.tokenizer , """prepare_seq2seq_batch""" ) else LegacySeqaSeqDataset
)
_lowerCAmelCase = False
_lowerCAmelCase = self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
if self.hparams.eval_max_gen_length is not None:
_lowerCAmelCase = self.hparams.eval_max_gen_length
else:
_lowerCAmelCase = self.model.config.max_length
_lowerCAmelCase = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = {
k: self.tokenizer.batch_decode(v.tolist() ) if """mask""" not in k else v.shape for k, v in batch.items()
}
save_json(_snake_case , Path(self.output_dir ) / """text_batch.json""" )
save_json({k: v.tolist() for k, v in batch.items()} , Path(self.output_dir ) / """tok_batch.json""" )
_lowerCAmelCase = True
return readable_batch
def snake_case ( self , _snake_case , **_snake_case ):
"""simple docstring"""
return self.model(_snake_case , **_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.tokenizer.batch_decode(
_snake_case , skip_special_tokens=_snake_case , clean_up_tokenization_spaces=_snake_case )
return lmap(str.strip , _snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.tokenizer.pad_token_id
_lowerCAmelCase , _lowerCAmelCase = batch["""input_ids"""], batch["""attention_mask"""]
_lowerCAmelCase = batch["""labels"""]
if isinstance(self.model , _snake_case ):
_lowerCAmelCase = self.model._shift_right(_snake_case )
else:
_lowerCAmelCase = shift_tokens_right(_snake_case , _snake_case )
if not self.already_saved_batch: # This would be slightly better if it only happened on rank zero
_lowerCAmelCase = decoder_input_ids
self.save_readable_batch(_snake_case )
_lowerCAmelCase = self(_snake_case , attention_mask=_snake_case , decoder_input_ids=_snake_case , use_cache=_snake_case )
_lowerCAmelCase = outputs["""logits"""]
if self.hparams.label_smoothing == 0:
# Same behavior as modeling_bart.py, besides ignoring pad_token_id
_lowerCAmelCase = nn.CrossEntropyLoss(ignore_index=_snake_case )
assert lm_logits.shape[-1] == self.vocab_size
_lowerCAmelCase = ce_loss_fct(lm_logits.view(-1 , lm_logits.shape[-1] ) , tgt_ids.view(-1 ) )
else:
_lowerCAmelCase = nn.functional.log_softmax(_snake_case , dim=-1 )
_lowerCAmelCase , _lowerCAmelCase = label_smoothed_nll_loss(
_snake_case , _snake_case , self.hparams.label_smoothing , ignore_index=_snake_case )
return (loss,)
@property
def snake_case ( self ):
"""simple docstring"""
return self.tokenizer.pad_token_id
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self._step(_snake_case )
_lowerCAmelCase = dict(zip(self.loss_names , _snake_case ) )
# tokens per batch
_lowerCAmelCase = batch["""input_ids"""].ne(self.pad ).sum() + batch["""labels"""].ne(self.pad ).sum()
_lowerCAmelCase = batch["""input_ids"""].shape[0]
_lowerCAmelCase = batch["""input_ids"""].eq(self.pad ).sum()
_lowerCAmelCase = batch["""input_ids"""].eq(self.pad ).float().mean()
# TODO(SS): make a wandb summary metric for this
return {"loss": loss_tensors[0], "log": logs}
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
return self._generative_step(_snake_case )
def snake_case ( self , _snake_case , _snake_case="val" ):
"""simple docstring"""
self.step_count += 1
_lowerCAmelCase = {k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
_lowerCAmelCase = losses["""loss"""]
_lowerCAmelCase = {
k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
}
_lowerCAmelCase = (
generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
)
_lowerCAmelCase = torch.tensor(_snake_case ).type_as(_snake_case )
generative_metrics.update({k: v.item() for k, v in losses.items()} )
losses.update(_snake_case )
_lowerCAmelCase = {F'{prefix}_avg_{k}': x for k, x in losses.items()}
_lowerCAmelCase = self.step_count
self.metrics[prefix].append(_snake_case ) # callback writes this to self.metrics_save_path
_lowerCAmelCase = flatten_list([x["""preds"""] for x in outputs] )
return {
"log": all_metrics,
"preds": preds,
F'{prefix}_loss': loss,
F'{prefix}_{self.val_metric}': metric_tensor,
}
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
return calculate_rouge(_snake_case , _snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = time.time()
# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
_lowerCAmelCase = self.model.generate(
batch["""input_ids"""] , attention_mask=batch["""attention_mask"""] , use_cache=_snake_case , decoder_start_token_id=self.decoder_start_token_id , num_beams=self.eval_beams , max_length=self.eval_max_length , )
_lowerCAmelCase = (time.time() - ta) / batch["""input_ids"""].shape[0]
_lowerCAmelCase = self.ids_to_clean_text(_snake_case )
_lowerCAmelCase = self.ids_to_clean_text(batch["""labels"""] )
_lowerCAmelCase = self._step(_snake_case )
_lowerCAmelCase = dict(zip(self.loss_names , _snake_case ) )
_lowerCAmelCase = self.calc_generative_metrics(_snake_case , _snake_case )
_lowerCAmelCase = np.mean(lmap(_snake_case , _snake_case ) )
base_metrics.update(gen_time=_snake_case , gen_len=_snake_case , preds=_snake_case , target=_snake_case , **_snake_case )
return base_metrics
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
return self._generative_step(_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
return self.validation_epoch_end(_snake_case , prefix="""test""" )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.n_obs[type_path]
_lowerCAmelCase = self.target_lens[type_path]
_lowerCAmelCase = self.dataset_class(
self.tokenizer , type_path=_snake_case , n_obs=_snake_case , max_target_length=_snake_case , **self.dataset_kwargs , )
return dataset
def snake_case ( self , _snake_case , _snake_case , _snake_case = False ):
"""simple docstring"""
_lowerCAmelCase = self.get_dataset(_snake_case )
if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
_lowerCAmelCase = dataset.make_sortish_sampler(_snake_case , distributed=self.hparams.gpus > 1 )
return DataLoader(
_snake_case , batch_size=_snake_case , collate_fn=dataset.collate_fn , shuffle=_snake_case , num_workers=self.num_workers , sampler=_snake_case , )
elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
_lowerCAmelCase = dataset.make_dynamic_sampler(
self.hparams.max_tokens_per_batch , distributed=self.hparams.gpus > 1 )
return DataLoader(
_snake_case , batch_sampler=_snake_case , collate_fn=dataset.collate_fn , num_workers=self.num_workers , )
else:
return DataLoader(
_snake_case , batch_size=_snake_case , collate_fn=dataset.collate_fn , shuffle=_snake_case , num_workers=self.num_workers , sampler=_snake_case , )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_dataloader("""train""" , batch_size=self.hparams.train_batch_size , shuffle=_snake_case )
return dataloader
def snake_case ( self ):
"""simple docstring"""
return self.get_dataloader("""val""" , batch_size=self.hparams.eval_batch_size )
def snake_case ( self ):
"""simple docstring"""
return self.get_dataloader("""test""" , batch_size=self.hparams.eval_batch_size )
@staticmethod
def snake_case ( _snake_case , _snake_case ):
"""simple docstring"""
BaseTransformer.add_model_specific_args(_snake_case , _snake_case )
add_generic_args(_snake_case , _snake_case )
parser.add_argument(
"""--max_source_length""" , default=1024 , type=_snake_case , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--max_target_length""" , default=56 , type=_snake_case , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--val_max_target_length""" , default=142 , type=_snake_case , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--test_max_target_length""" , default=142 , type=_snake_case , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument("""--freeze_encoder""" , action="""store_true""" )
parser.add_argument("""--freeze_embeds""" , action="""store_true""" )
parser.add_argument("""--sortish_sampler""" , action="""store_true""" , default=_snake_case )
parser.add_argument("""--overwrite_output_dir""" , action="""store_true""" , default=_snake_case )
parser.add_argument("""--max_tokens_per_batch""" , type=_snake_case , default=_snake_case )
parser.add_argument("""--logger_name""" , type=_snake_case , choices=["""default""", """wandb""", """wandb_shared"""] , default="""default""" )
parser.add_argument("""--n_train""" , type=_snake_case , default=-1 , required=_snake_case , help="""# examples. -1 means use all.""" )
parser.add_argument("""--n_val""" , type=_snake_case , default=500 , required=_snake_case , help="""# examples. -1 means use all.""" )
parser.add_argument("""--n_test""" , type=_snake_case , default=-1 , required=_snake_case , help="""# examples. -1 means use all.""" )
parser.add_argument(
"""--task""" , type=_snake_case , default="""summarization""" , required=_snake_case , help="""# examples. -1 means use all.""" )
parser.add_argument("""--label_smoothing""" , type=_snake_case , default=0.0 , required=_snake_case )
parser.add_argument("""--src_lang""" , type=_snake_case , default="""""" , required=_snake_case )
parser.add_argument("""--tgt_lang""" , type=_snake_case , default="""""" , required=_snake_case )
parser.add_argument("""--eval_beams""" , type=_snake_case , default=_snake_case , required=_snake_case )
parser.add_argument(
"""--val_metric""" , type=_snake_case , default=_snake_case , required=_snake_case , choices=["""bleu""", """rouge2""", """loss""", None] )
parser.add_argument("""--eval_max_gen_length""" , type=_snake_case , default=_snake_case , help="""never generate more than n tokens""" )
parser.add_argument("""--save_top_k""" , type=_snake_case , default=1 , required=_snake_case , help="""How many checkpoints to save""" )
parser.add_argument(
"""--early_stopping_patience""" , type=_snake_case , default=-1 , required=_snake_case , help=(
"""-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"""
""" val_check_interval will effect it."""
) , )
return parser
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''translation'''
__lowerCamelCase = ['''loss''']
__lowerCamelCase = ['''bleu''']
__lowerCamelCase = '''bleu'''
def __init__( self , _snake_case , **_snake_case ):
"""simple docstring"""
super().__init__(_snake_case , **_snake_case )
_lowerCAmelCase = hparams.src_lang
_lowerCAmelCase = hparams.tgt_lang
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
return calculate_bleu(_snake_case , _snake_case )
def _UpperCAmelCase ( snake_case , snake_case=None ):
"""simple docstring"""
Path(args.output_dir ).mkdir(exist_ok=snake_case )
check_output_dir(snake_case , expected_items=3 )
if model is None:
if "summarization" in args.task:
_lowerCAmelCase = SummarizationModule(snake_case )
else:
_lowerCAmelCase = TranslationModule(snake_case )
_lowerCAmelCase = Path(args.data_dir ).name
if (
args.logger_name == "default"
or args.fast_dev_run
or str(args.output_dir ).startswith("""/tmp""" )
or str(args.output_dir ).startswith("""/var""" )
):
_lowerCAmelCase = True # don't pollute wandb logs unnecessarily
elif args.logger_name == "wandb":
from pytorch_lightning.loggers import WandbLogger
_lowerCAmelCase = os.environ.get("""WANDB_PROJECT""" , snake_case )
_lowerCAmelCase = WandbLogger(name=model.output_dir.name , project=snake_case )
elif args.logger_name == "wandb_shared":
from pytorch_lightning.loggers import WandbLogger
_lowerCAmelCase = WandbLogger(name=model.output_dir.name , project=F'hf_{dataset}' )
if args.early_stopping_patience >= 0:
_lowerCAmelCase = get_early_stopping_callback(model.val_metric , args.early_stopping_patience )
else:
_lowerCAmelCase = False
_lowerCAmelCase = args.val_metric == """loss"""
_lowerCAmelCase = generic_train(
snake_case , snake_case , logging_callback=SeqaSeqLoggingCallback() , checkpoint_callback=get_checkpoint_callback(
args.output_dir , model.val_metric , args.save_top_k , snake_case ) , early_stopping_callback=snake_case , logger=snake_case , )
pickle_save(model.hparams , model.output_dir / """hparams.pkl""" )
if not args.do_predict:
return model
_lowerCAmelCase = """"""
_lowerCAmelCase = sorted(glob.glob(os.path.join(args.output_dir , """*.ckpt""" ) , recursive=snake_case ) )
if checkpoints:
_lowerCAmelCase = checkpoints[-1]
_lowerCAmelCase = checkpoints[-1]
trainer.logger.log_hyperparams(model.hparams )
# test() without a model tests using the best checkpoint automatically
trainer.test()
return model
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
A__ = pl.Trainer.add_argparse_args(parser)
A__ = SummarizationModule.add_model_specific_args(parser, os.getcwd())
A__ = parser.parse_args()
main(args)
| 82 | 1 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( snake_case , snake_case , snake_case=None ):
"""simple docstring"""
assert torch_layer.weight.shape == weight.shape, F'{torch_layer} layer.weight does not match'
_lowerCAmelCase = nn.Parameter(snake_case )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, F'{torch_layer} layer.bias does not match'
_lowerCAmelCase = nn.Parameter(snake_case )
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = np.asarray(weights[0] )
_lowerCAmelCase = np.asarray(weights[1] )
_lowerCAmelCase = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(snake_case ).transpose(1 , 2 ).contiguous().view(-1 , snake_case ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(snake_case ).transpose(1 , 2 ).contiguous().view(-1 , snake_case ) , )
set_param(
torch_layer.output.dense , torch.tensor(snake_case ).view(-1 , snake_case ).contiguous().transpose(0 , 1 ) , )
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = np.asarray(weights[0] )
_lowerCAmelCase = np.asarray(weights[1] )
_lowerCAmelCase = np.asarray(weights[2] )
_lowerCAmelCase = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(snake_case ).transpose(1 , 2 ).contiguous().view(-1 , snake_case ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(snake_case ).transpose(1 , 2 ).contiguous().view(-1 , snake_case ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(snake_case ).transpose(1 , 2 ).contiguous().view(-1 , snake_case ) , )
set_param(
torch_layer.output.dense , torch.tensor(snake_case ).view(-1 , snake_case ).contiguous().transpose(0 , 1 ) , )
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = weights[0][0][0]
_lowerCAmelCase = np.asarray(layer_norm_a[0] )
_lowerCAmelCase = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(snake_case ) , torch.tensor(snake_case ) , )
# lsh weights + output
_lowerCAmelCase = weights[0][1]
if len(snake_case ) < 4:
set_layer_weights_in_torch_lsh(snake_case , torch_block.attention , snake_case )
else:
set_layer_weights_in_torch_local(snake_case , torch_block.attention , snake_case )
# intermediate weighs
_lowerCAmelCase = weights[2][0][1][2]
# Chunked Feed Forward
if len(snake_case ) == 4:
_lowerCAmelCase = intermediate_weights[2]
# layernorm 2
_lowerCAmelCase = np.asarray(intermediate_weights[0][0] )
_lowerCAmelCase = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(snake_case ) , torch.tensor(snake_case ) , )
# intermediate dense
_lowerCAmelCase = np.asarray(intermediate_weights[1][0] )
_lowerCAmelCase = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(snake_case ).transpose(0 , 1 ).contiguous() , torch.tensor(snake_case ) , )
# intermediate out
_lowerCAmelCase = np.asarray(intermediate_weights[4][0] )
_lowerCAmelCase = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(snake_case ).transpose(0 , 1 ).contiguous() , torch.tensor(snake_case ) , )
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = torch_model.reformer
# word embeds
_lowerCAmelCase = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(snake_case ) , )
if isinstance(weights[3] , snake_case ):
_lowerCAmelCase = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
_lowerCAmelCase = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), F'{position_embeddings[emb_idx]} emb does not match'
_lowerCAmelCase = nn.Parameter(torch.tensor(snake_case ) )
_lowerCAmelCase = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
snake_case ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
_lowerCAmelCase = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(snake_case , snake_case , snake_case )
# output layer norm
_lowerCAmelCase = np.asarray(weights[7][0] )
_lowerCAmelCase = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(snake_case ) , torch.tensor(snake_case ) , )
# output embeddings
_lowerCAmelCase = np.asarray(weights[9][0] )
_lowerCAmelCase = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(snake_case ).transpose(0 , 1 ).contiguous() , torch.tensor(snake_case ) , )
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = ReformerConfig.from_json_file(snake_case )
print(F'Building PyTorch model from configuration: {config}' )
_lowerCAmelCase = ReformerModelWithLMHead(snake_case )
with open(snake_case , """rb""" ) as f:
_lowerCAmelCase = pickle.load(snake_case )["""weights"""]
set_model_weights_in_torch(snake_case , snake_case , config.hidden_size )
# Save pytorch-model
print(F'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() , snake_case )
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--trax_model_pkl_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained Reformer model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
A__ = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 82 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if isinstance(snake_case , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class __lowerCAmelCase :
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
pass
def snake_case ( self ):
"""simple docstring"""
pass
def snake_case ( self ):
"""simple docstring"""
pass
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase = VisionTextDualEncoderConfig.from_vision_text_configs(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], config.projection_dim) )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_snake_case , text_model=_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = {"""vision_model""": vision_model, """text_model""": text_model}
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_snake_case , text_model=_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
_lowerCAmelCase = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(_snake_case )
_lowerCAmelCase = model(input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case )
_lowerCAmelCase = after_output[0].numpy()
_lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_snake_case , 1e-5 )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_snake_case , text_model=_snake_case )
_lowerCAmelCase = model(
input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case , output_attentions=_snake_case )
_lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(_snake_case ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
_lowerCAmelCase = to_atuple(vision_model.config.image_size )
_lowerCAmelCase = to_atuple(vision_model.config.patch_size )
_lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
_lowerCAmelCase = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
_lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(_snake_case ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def snake_case ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = np.abs((a - b) ).max()
self.assertLessEqual(_snake_case , _snake_case , F'Difference between torch and flax is {diff} (>= {tol}).' )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_save_load(**_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**_snake_case )
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_pretrained_model_and_inputs()
_lowerCAmelCase = model_a(**_snake_case )
_lowerCAmelCase = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(_snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(_snake_case )
_lowerCAmelCase = model_a(**_snake_case )
_lowerCAmelCase = after_outputs[0].numpy()
_lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_snake_case , 1e-5 )
@require_tf
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-random-bert""" )
_lowerCAmelCase = 13
_lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_lowerCAmelCase = random_attention_mask([batch_size, 4] )
_lowerCAmelCase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = TFViTModel(_snake_case , name="""vision_model""" )
_lowerCAmelCase = TFBertModel(_snake_case , name="""text_model""" )
return vision_model, text_model
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFViTModelTester(self )
_lowerCAmelCase = TFBertModelTester(self )
_lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
_lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = vision_config_and_inputs
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-deit-tf""" , """hf-internal-testing/tiny-random-roberta""" )
_lowerCAmelCase = 13
_lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_lowerCAmelCase = random_attention_mask([batch_size, 4] )
_lowerCAmelCase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=None , **_snake_case ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_snake_case , _snake_case )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_snake_case , text_model=_snake_case )
_lowerCAmelCase = model(
input_ids=_snake_case , pixel_values=_snake_case , attention_mask=_snake_case , output_attentions=_snake_case )
_lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(_snake_case ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
_lowerCAmelCase = to_atuple(vision_model.config.image_size )
_lowerCAmelCase = to_atuple(vision_model.config.patch_size )
_lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
_lowerCAmelCase = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
_lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(_snake_case ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = TFDeiTModel(_snake_case , name="""vision_model""" )
_lowerCAmelCase = TFRobertaModel(_snake_case , name="""text_model""" )
return vision_model, text_model
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFDeiTModelTester(self )
_lowerCAmelCase = TFRobertaModelTester(self )
_lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
_lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = vision_config_and_inputs
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-clip-tf""" , """hf-internal-testing/tiny-random-bert""" )
_lowerCAmelCase = 13
_lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_lowerCAmelCase = random_attention_mask([batch_size, 4] )
_lowerCAmelCase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = TFCLIPVisionModel(_snake_case , name="""vision_model""" )
_lowerCAmelCase = TFBertModel(_snake_case , name="""text_model""" )
return vision_model, text_model
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFCLIPVisionModelTester(self )
_lowerCAmelCase = TFBertModelTester(self )
_lowerCAmelCase = clip_model_tester.prepare_config_and_inputs()
_lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase = vision_config_and_inputs
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(
"""clip-italian/clip-italian""" , logit_scale_init_value=1.0 , from_pt=_snake_case )
_lowerCAmelCase = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
_lowerCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
_lowerCAmelCase = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=_snake_case , padding=_snake_case , return_tensors="""np""" )
_lowerCAmelCase = model(**_snake_case )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
_lowerCAmelCase = np.array([[1.228_4727, 0.310_4122]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , _snake_case , atol=1e-3 ) )
| 82 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''gpt_neox'''
def __init__( self , _snake_case=50432 , _snake_case=6144 , _snake_case=44 , _snake_case=64 , _snake_case=24576 , _snake_case="gelu" , _snake_case=0.25 , _snake_case=10000 , _snake_case=0.0 , _snake_case=0.0 , _snake_case=0.1 , _snake_case=2048 , _snake_case=0.02 , _snake_case=1e-5 , _snake_case=True , _snake_case=0 , _snake_case=2 , _snake_case=False , _snake_case=True , _snake_case=None , **_snake_case , ):
"""simple docstring"""
super().__init__(bos_token_id=_snake_case , eos_token_id=_snake_case , **_snake_case )
_lowerCAmelCase = vocab_size
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_act
_lowerCAmelCase = rotary_pct
_lowerCAmelCase = rotary_emb_base
_lowerCAmelCase = attention_dropout
_lowerCAmelCase = hidden_dropout
_lowerCAmelCase = classifier_dropout
_lowerCAmelCase = initializer_range
_lowerCAmelCase = layer_norm_eps
_lowerCAmelCase = use_cache
_lowerCAmelCase = tie_word_embeddings
_lowerCAmelCase = use_parallel_residual
_lowerCAmelCase = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
"""The hidden size is not divisble by the number of attention heads! Make sure to update them!""" )
def snake_case ( self ):
"""simple docstring"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , _snake_case ) or len(self.rope_scaling ) != 2:
raise ValueError(
"""`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
F'got {self.rope_scaling}' )
_lowerCAmelCase = self.rope_scaling.get("""type""" , _snake_case )
_lowerCAmelCase = self.rope_scaling.get("""factor""" , _snake_case )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}' )
if rope_scaling_factor is None or not isinstance(_snake_case , _snake_case ) or rope_scaling_factor <= 1.0:
raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}' )
| 82 |
def _UpperCAmelCase ( snake_case = 50 ):
"""simple docstring"""
_lowerCAmelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(f"{solution() = }")
| 82 | 1 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
A__ = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def _UpperCAmelCase ( snake_case = "mumbai" ):
"""simple docstring"""
_lowerCAmelCase = BeautifulSoup(requests.get(url + location ).content , """html.parser""" )
# This attribute finds out all the specifics listed in a job
for job in soup.find_all("""div""" , attrs={"""data-tn-component""": """organicJob"""} ):
_lowerCAmelCase = job.find("""a""" , attrs={"""data-tn-element""": """jobTitle"""} ).text.strip()
_lowerCAmelCase = job.find("""span""" , {"""class""": """company"""} ).text.strip()
yield job_title, company_name
if __name__ == "__main__":
for i, job in enumerate(fetch_jobs("""Bangalore"""), 1):
print(f"Job {i:>2} is {job[0]} at {job[1]}")
| 82 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class __lowerCAmelCase ( unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = {
"""task_specific_params""": {
"""summarization""": {"""length_penalty""": 1.0, """max_length""": 128, """min_length""": 12, """num_beams""": 4},
"""summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 142, """min_length""": 56, """num_beams""": 4},
"""summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
}
}
_lowerCAmelCase = {
"""task_specific_params.summarization.length_penalty""": 1.0,
"""task_specific_params.summarization.max_length""": 128,
"""task_specific_params.summarization.min_length""": 12,
"""task_specific_params.summarization.num_beams""": 4,
"""task_specific_params.summarization_cnn.length_penalty""": 2.0,
"""task_specific_params.summarization_cnn.max_length""": 142,
"""task_specific_params.summarization_cnn.min_length""": 56,
"""task_specific_params.summarization_cnn.num_beams""": 4,
"""task_specific_params.summarization_xsum.length_penalty""": 1.0,
"""task_specific_params.summarization_xsum.max_length""": 62,
"""task_specific_params.summarization_xsum.min_length""": 11,
"""task_specific_params.summarization_xsum.num_beams""": 6,
}
self.assertEqual(flatten_dict(_snake_case ) , _snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(_snake_case ) , x.transpose() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case ) , transpose(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , transpose(_snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case ) , transpose(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , transpose(_snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case ) , np.asarray(transpose(_snake_case ) ) ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(transpose(_snake_case , axes=(1, 2, 0) ) , np.asarray(transpose(_snake_case , axes=(1, 2, 0) ) ) ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , np.reshape(_snake_case , (4, 3) ) ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , np.reshape(_snake_case , (12, 5) ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , reshape(_snake_case , (4, 3) ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , reshape(_snake_case , (12, 5) ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , reshape(_snake_case , (4, 3) ).numpy() ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , reshape(_snake_case , (12, 5) ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (4, 3) ) , np.asarray(reshape(_snake_case , (4, 3) ) ) ) )
_lowerCAmelCase = np.random.randn(3 , 4 , 5 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(reshape(_snake_case , (12, 5) ) , np.asarray(reshape(_snake_case , (12, 5) ) ) ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(_snake_case ) , np.squeeze(_snake_case ) ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , np.squeeze(_snake_case , axis=2 ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case ) , squeeze(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , squeeze(_snake_case , axis=2 ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case ) , squeeze(_snake_case ).numpy() ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , squeeze(_snake_case , axis=2 ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(1 , 3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case ) , np.asarray(squeeze(_snake_case ) ) ) )
_lowerCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(squeeze(_snake_case , axis=2 ) , np.asarray(squeeze(_snake_case , axis=2 ) ) ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , np.expand_dims(_snake_case , axis=1 ) ) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = torch.tensor(_snake_case )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , expand_dims(_snake_case , axis=1 ).numpy() ) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = tf.constant(_snake_case )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , expand_dims(_snake_case , axis=1 ).numpy() ) )
@require_flax
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = np.random.randn(3 , 4 )
_lowerCAmelCase = jnp.array(_snake_case )
self.assertTrue(np.allclose(expand_dims(_snake_case , axis=1 ) , np.asarray(expand_dims(_snake_case , axis=1 ) ) ) )
| 82 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A__ = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""MvpTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = ["""MvpTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = [
"""MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MvpForCausalLM""",
"""MvpForConditionalGeneration""",
"""MvpForQuestionAnswering""",
"""MvpForSequenceClassification""",
"""MvpModel""",
"""MvpPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
A__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 82 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class __lowerCAmelCase ( lowerCamelCase__ ):
@staticmethod
def snake_case ( _snake_case ):
"""simple docstring"""
_lowerCAmelCase = parser.add_parser("""download""" )
download_parser.add_argument(
"""--cache-dir""" , type=_snake_case , default=_snake_case , help="""Path to location to store the models""" )
download_parser.add_argument(
"""--force""" , action="""store_true""" , help="""Force the model to be download even if already in cache-dir""" )
download_parser.add_argument(
"""--trust-remote-code""" , action="""store_true""" , help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine""" , )
download_parser.add_argument("""model""" , type=_snake_case , help="""Name of the model to download""" )
download_parser.set_defaults(func=_snake_case )
def __init__( self , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = model
_lowerCAmelCase = cache
_lowerCAmelCase = force
_lowerCAmelCase = trust_remote_code
def snake_case ( self ):
"""simple docstring"""
from ..models.auto import AutoModel, AutoTokenizer
AutoModel.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
AutoTokenizer.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
| 82 | 1 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
A__ = logging.get_logger(__name__)
A__ = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
A__ = {
"""vocab_file""": {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"""
),
"""facebook/dpr-ctx_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"""
),
"""facebook/dpr-ctx_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"""
),
},
}
A__ = {
"""vocab_file""": {
"""facebook/dpr-question_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"""
),
"""facebook/dpr-question_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""facebook/dpr-question_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"""
),
"""facebook/dpr-question_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"""
),
},
}
A__ = {
"""vocab_file""": {
"""facebook/dpr-reader-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"""
),
"""facebook/dpr-reader-multiset-base""": (
"""https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""facebook/dpr-reader-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"""
),
"""facebook/dpr-reader-multiset-base""": (
"""https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"""
),
},
}
A__ = {
"""facebook/dpr-ctx_encoder-single-nq-base""": 5_12,
"""facebook/dpr-ctx_encoder-multiset-base""": 5_12,
}
A__ = {
"""facebook/dpr-question_encoder-single-nq-base""": 5_12,
"""facebook/dpr-question_encoder-multiset-base""": 5_12,
}
A__ = {
"""facebook/dpr-reader-single-nq-base""": 5_12,
"""facebook/dpr-reader-multiset-base""": 5_12,
}
A__ = {
"""facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
"""facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
A__ = {
"""facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
"""facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
A__ = {
"""facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
"""facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = VOCAB_FILES_NAMES
__lowerCamelCase = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = VOCAB_FILES_NAMES
__lowerCamelCase = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
A__ = collections.namedtuple(
"""DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)
A__ = collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])
A__ = R"""
Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
with the format:
```
[CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
```
Args:
questions (`str` or `List[str]`):
The questions to be encoded. You can specify one question for many passages. In this case, the question
will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
`titles` or `texts`.
titles (`str` or `List[str]`):
The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
texts (`str` or `List[str]`):
The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Activates and controls padding. Accepts the following values:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
Activates and controls truncation. Accepts the following values:
- `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to
the maximum acceptable input length for the model if that argument is not provided. This will truncate
token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
of pairs) is provided.
- `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the first
sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the
second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
max_length (`int`, *optional*):
Controls the maximum length to use by one of the truncation/padding parameters.
If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return Numpy `np.ndarray` objects.
return_attention_mask (`bool`, *optional*):
Whether or not to return the attention mask. If not set, will return the attention mask according to the
specific tokenizer's default, defined by the `return_outputs` attribute.
[What are attention masks?](../glossary#attention-mask)
Returns:
`Dict[str, List[List[int]]]`: A dictionary with the following keys:
- `input_ids`: List of token ids to be fed to a model.
- `attention_mask`: List of indices specifying which tokens should be attended to by the model.
"""
@add_start_docstrings(lowerCamelCase__ )
class __lowerCAmelCase :
def __call__( self , _snake_case , _snake_case = None , _snake_case = None , _snake_case = False , _snake_case = False , _snake_case = None , _snake_case = None , _snake_case = None , **_snake_case , ):
"""simple docstring"""
if titles is None and texts is None:
return super().__call__(
_snake_case , padding=_snake_case , truncation=_snake_case , max_length=_snake_case , return_tensors=_snake_case , return_attention_mask=_snake_case , **_snake_case , )
elif titles is None or texts is None:
_lowerCAmelCase = titles if texts is None else texts
return super().__call__(
_snake_case , _snake_case , padding=_snake_case , truncation=_snake_case , max_length=_snake_case , return_tensors=_snake_case , return_attention_mask=_snake_case , **_snake_case , )
_lowerCAmelCase = titles if not isinstance(_snake_case , _snake_case ) else [titles]
_lowerCAmelCase = texts if not isinstance(_snake_case , _snake_case ) else [texts]
_lowerCAmelCase = len(_snake_case )
_lowerCAmelCase = questions if not isinstance(_snake_case , _snake_case ) else [questions] * n_passages
if len(_snake_case ) != len(_snake_case ):
raise ValueError(
F'There should be as many titles than texts but got {len(_snake_case )} titles and {len(_snake_case )} texts.' )
_lowerCAmelCase = super().__call__(_snake_case , _snake_case , padding=_snake_case , truncation=_snake_case )["""input_ids"""]
_lowerCAmelCase = super().__call__(_snake_case , add_special_tokens=_snake_case , padding=_snake_case , truncation=_snake_case )["""input_ids"""]
_lowerCAmelCase = {
"""input_ids""": [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(_snake_case , _snake_case )
]
}
if return_attention_mask is not False:
_lowerCAmelCase = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
_lowerCAmelCase = attention_mask
return self.pad(_snake_case , padding=_snake_case , max_length=_snake_case , return_tensors=_snake_case )
def snake_case ( self , _snake_case , _snake_case , _snake_case = 16 , _snake_case = 64 , _snake_case = 4 , ):
"""simple docstring"""
_lowerCAmelCase = reader_input["""input_ids"""]
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = reader_output[:3]
_lowerCAmelCase = len(_snake_case )
_lowerCAmelCase = sorted(range(_snake_case ) , reverse=_snake_case , key=relevance_logits.__getitem__ )
_lowerCAmelCase = []
for doc_id in sorted_docs:
_lowerCAmelCase = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
_lowerCAmelCase = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
_lowerCAmelCase = sequence_ids.index(self.pad_token_id )
else:
_lowerCAmelCase = len(_snake_case )
_lowerCAmelCase = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=_snake_case , top_spans=_snake_case , )
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=_snake_case , start_index=_snake_case , end_index=_snake_case , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) )
if len(_snake_case ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , ):
"""simple docstring"""
_lowerCAmelCase = []
for start_index, start_score in enumerate(_snake_case ):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
scores.append(((start_index, start_index + answer_length), start_score + end_score) )
_lowerCAmelCase = sorted(_snake_case , key=lambda _snake_case : x[1] , reverse=_snake_case )
_lowerCAmelCase = []
for (start_index, end_index), score in scores:
if start_index > end_index:
raise ValueError(F'Wrong span indices: [{start_index}:{end_index}]' )
_lowerCAmelCase = end_index - start_index + 1
if length > max_answer_length:
raise ValueError(F'Span is too long: {length} > {max_answer_length}' )
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals ):
continue
chosen_span_intervals.append((start_index, end_index) )
if len(_snake_case ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(lowerCamelCase__ )
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
__lowerCamelCase = VOCAB_FILES_NAMES
__lowerCamelCase = READER_PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase = READER_PRETRAINED_INIT_CONFIGURATION
__lowerCamelCase = ['''input_ids''', '''attention_mask''']
| 82 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = XCLIPTextConfig()
# derive patch size from model name
_lowerCAmelCase = model_name.find("""patch""" )
_lowerCAmelCase = int(model_name[start_idx + len("""patch""" ) : start_idx + len("""patch""" ) + 2] )
_lowerCAmelCase = XCLIPVisionConfig(patch_size=snake_case , num_frames=snake_case )
if "large" in model_name:
_lowerCAmelCase = 7_68
_lowerCAmelCase = 30_72
_lowerCAmelCase = 12
_lowerCAmelCase = 10_24
_lowerCAmelCase = 40_96
_lowerCAmelCase = 16
_lowerCAmelCase = 24
_lowerCAmelCase = 7_68
_lowerCAmelCase = 30_72
if model_name == "xclip-large-patch14-16-frames":
_lowerCAmelCase = 3_36
_lowerCAmelCase = XCLIPConfig.from_text_vision_configs(snake_case , snake_case )
if "large" in model_name:
_lowerCAmelCase = 7_68
return config
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if name == "token_embedding.weight":
_lowerCAmelCase = name.replace("""token_embedding.weight""" , """text_model.embeddings.token_embedding.weight""" )
if name == "positional_embedding":
_lowerCAmelCase = name.replace("""positional_embedding""" , """text_model.embeddings.position_embedding.weight""" )
if "ln_1" in name:
_lowerCAmelCase = name.replace("""ln_1""" , """layer_norm1""" )
if "ln_2" in name:
_lowerCAmelCase = name.replace("""ln_2""" , """layer_norm2""" )
if "c_fc" in name:
_lowerCAmelCase = name.replace("""c_fc""" , """fc1""" )
if "c_proj" in name:
_lowerCAmelCase = name.replace("""c_proj""" , """fc2""" )
if name.startswith("""transformer.resblocks""" ):
_lowerCAmelCase = name.replace("""transformer.resblocks""" , """text_model.encoder.layers""" )
if "attn.out_proj" in name and "message" not in name:
_lowerCAmelCase = name.replace("""attn.out_proj""" , """self_attn.out_proj""" )
if "ln_final" in name:
_lowerCAmelCase = name.replace("""ln_final""" , """text_model.final_layer_norm""" )
# visual encoder
if name == "visual.class_embedding":
_lowerCAmelCase = name.replace("""visual.class_embedding""" , """vision_model.embeddings.class_embedding""" )
if name == "visual.positional_embedding":
_lowerCAmelCase = name.replace("""visual.positional_embedding""" , """vision_model.embeddings.position_embedding.weight""" )
if name.startswith("""visual.transformer.resblocks""" ):
_lowerCAmelCase = name.replace("""visual.transformer.resblocks""" , """vision_model.encoder.layers""" )
if "visual.conv1" in name:
_lowerCAmelCase = name.replace("""visual.conv1""" , """vision_model.embeddings.patch_embedding""" )
if "visual.ln_pre" in name:
_lowerCAmelCase = name.replace("""visual.ln_pre""" , """vision_model.pre_layernorm""" )
if "visual.ln_post" in name:
_lowerCAmelCase = name.replace("""visual.ln_post""" , """vision_model.post_layernorm""" )
if "visual.proj" in name:
_lowerCAmelCase = name.replace("""visual.proj""" , """visual_projection.weight""" )
if "text_projection" in name:
_lowerCAmelCase = name.replace("""text_projection""" , """text_projection.weight""" )
# things on top
if "prompts_visual_proj" in name:
_lowerCAmelCase = name.replace("""prompts_visual_proj""" , """prompts_visual_projection""" )
if "prompts_visual_ln" in name:
_lowerCAmelCase = name.replace("""prompts_visual_ln""" , """prompts_visual_layernorm""" )
# mit
if name == "mit.positional_embedding":
_lowerCAmelCase = name.replace("""positional""" , """position""" )
if name.startswith("""mit.resblocks""" ):
_lowerCAmelCase = name.replace("""mit.resblocks""" , """mit.encoder.layers""" )
# prompts generator
if name.startswith("""prompts_generator.norm""" ):
_lowerCAmelCase = name.replace("""prompts_generator.norm""" , """prompts_generator.layernorm""" )
return name
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_lowerCAmelCase = orig_state_dict.pop(snake_case )
if "attn.in_proj" in key:
_lowerCAmelCase = key.split(""".""" )
if key.startswith("""visual""" ):
_lowerCAmelCase = key_split[3]
_lowerCAmelCase = config.vision_config.hidden_size
if "message_attn" in key:
if "weight" in key:
_lowerCAmelCase = val[
:dim, :
]
_lowerCAmelCase = val[
dim : dim * 2, :
]
_lowerCAmelCase = val[
-dim:, :
]
else:
_lowerCAmelCase = val[
:dim
]
_lowerCAmelCase = val[
dim : dim * 2
]
_lowerCAmelCase = val[
-dim:
]
else:
if "weight" in key:
_lowerCAmelCase = val[
:dim, :
]
_lowerCAmelCase = val[
dim : dim * 2, :
]
_lowerCAmelCase = val[
-dim:, :
]
else:
_lowerCAmelCase = val[:dim]
_lowerCAmelCase = val[
dim : dim * 2
]
_lowerCAmelCase = val[-dim:]
elif key.startswith("""mit""" ):
_lowerCAmelCase = key_split[2]
_lowerCAmelCase = config.vision_config.mit_hidden_size
if "weight" in key:
_lowerCAmelCase = val[:dim, :]
_lowerCAmelCase = val[dim : dim * 2, :]
_lowerCAmelCase = val[-dim:, :]
else:
_lowerCAmelCase = val[:dim]
_lowerCAmelCase = val[dim : dim * 2]
_lowerCAmelCase = val[-dim:]
else:
_lowerCAmelCase = key_split[2]
_lowerCAmelCase = config.text_config.hidden_size
if "weight" in key:
_lowerCAmelCase = val[:dim, :]
_lowerCAmelCase = val[
dim : dim * 2, :
]
_lowerCAmelCase = val[-dim:, :]
else:
_lowerCAmelCase = val[:dim]
_lowerCAmelCase = val[
dim : dim * 2
]
_lowerCAmelCase = val[-dim:]
else:
_lowerCAmelCase = rename_key(snake_case )
if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
_lowerCAmelCase = val.T
_lowerCAmelCase = val
return orig_state_dict
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if num_frames == 8:
_lowerCAmelCase = """eating_spaghetti_8_frames.npy"""
elif num_frames == 16:
_lowerCAmelCase = """eating_spaghetti.npy"""
elif num_frames == 32:
_lowerCAmelCase = """eating_spaghetti_32_frames.npy"""
_lowerCAmelCase = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename=snake_case , repo_type="""dataset""" , )
_lowerCAmelCase = np.load(snake_case )
return list(snake_case )
def _UpperCAmelCase ( snake_case , snake_case=None , snake_case=False ):
"""simple docstring"""
_lowerCAmelCase = {
# fully supervised kinetics-400 checkpoints
"""xclip-base-patch32""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth""",
"""xclip-base-patch32-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"""
),
"""xclip-base-patch16""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth""",
"""xclip-base-patch16-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"""
),
"""xclip-large-patch14""": """https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&export=download&confirm=t&uuid=b26caedc-88e2-473e-830a-9d158b653cdb""",
"""xclip-large-patch14-16-frames""": """https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&export=download&confirm=t&uuid=538fa810-e671-4050-b385-9a623f89804f""",
# fully supervised kinetics-600 checkpoints
"""xclip-base-patch16-kinetics-600""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"""
),
"""xclip-base-patch16-kinetics-600-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"""
),
"""xclip-large-patch14-kinetics-600""": """https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&export=download&confirm=t&uuid=141d4977-4a65-44ae-864f-4b0c19f838be""",
# few shot
"""xclip-base-patch16-hmdb-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"""
),
"""xclip-base-patch16-hmdb-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"""
),
"""xclip-base-patch16-hmdb-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"""
),
"""xclip-base-patch16-hmdb-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"""
),
"""xclip-base-patch16-ucf-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"""
),
"""xclip-base-patch16-ucf-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"""
),
"""xclip-base-patch16-ucf-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"""
),
"""xclip-base-patch16-ucf-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"""
),
# zero shot
"""xclip-base-patch16-zero-shot""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth""",
}
_lowerCAmelCase = model_to_url[model_name]
_lowerCAmelCase = 8
if "16-frames" in model_name:
_lowerCAmelCase = 16
elif "shot" in model_name:
_lowerCAmelCase = 32
_lowerCAmelCase = get_xclip_config(snake_case , snake_case )
_lowerCAmelCase = XCLIPModel(snake_case )
model.eval()
if "drive" in checkpoint_url:
_lowerCAmelCase = """pytorch_model.bin"""
gdown.cached_download(snake_case , snake_case , quiet=snake_case )
_lowerCAmelCase = torch.load(snake_case , map_location="""cpu""" )["""model"""]
else:
_lowerCAmelCase = torch.hub.load_state_dict_from_url(snake_case )["""model"""]
_lowerCAmelCase = convert_state_dict(snake_case , snake_case )
_lowerCAmelCase = XCLIPModel(snake_case )
_lowerCAmelCase , _lowerCAmelCase = model.load_state_dict(snake_case , strict=snake_case )
assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
model.eval()
_lowerCAmelCase = 3_36 if model_name == """xclip-large-patch14-16-frames""" else 2_24
_lowerCAmelCase = VideoMAEImageProcessor(size=snake_case )
_lowerCAmelCase = CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32""" )
_lowerCAmelCase = CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32""" )
_lowerCAmelCase = XCLIPProcessor(image_processor=snake_case , tokenizer=snake_case )
_lowerCAmelCase = prepare_video(snake_case )
_lowerCAmelCase = processor(
text=["""playing sports""", """eating spaghetti""", """go shopping"""] , videos=snake_case , return_tensors="""pt""" , padding=snake_case )
print("""Shape of pixel values:""" , inputs.pixel_values.shape )
with torch.no_grad():
_lowerCAmelCase = model(**snake_case )
# Verify outputs
_lowerCAmelCase = outputs.logits_per_video
_lowerCAmelCase = logits_per_video.softmax(dim=1 )
print("""Probs:""" , snake_case )
# kinetics-400
if model_name == "xclip-base-patch32":
_lowerCAmelCase = torch.tensor([[0.0_019, 0.9_951, 0.0_030]] )
elif model_name == "xclip-base-patch32-16-frames":
_lowerCAmelCase = torch.tensor([[7.09_99E-04, 9.98_83E-01, 4.55_80E-04]] )
elif model_name == "xclip-base-patch16":
_lowerCAmelCase = torch.tensor([[0.0_083, 0.9_681, 0.0_236]] )
elif model_name == "xclip-base-patch16-16-frames":
_lowerCAmelCase = torch.tensor([[7.69_37E-04, 9.97_28E-01, 1.94_73E-03]] )
elif model_name == "xclip-large-patch14":
_lowerCAmelCase = torch.tensor([[0.0_062, 0.9_864, 0.0_075]] )
elif model_name == "xclip-large-patch14-16-frames":
_lowerCAmelCase = torch.tensor([[3.38_77E-04, 9.99_37E-01, 2.88_88E-04]] )
# kinetics-600
elif model_name == "xclip-base-patch16-kinetics-600":
_lowerCAmelCase = torch.tensor([[0.0_555, 0.8_914, 0.0_531]] )
elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
_lowerCAmelCase = torch.tensor([[3.85_54E-04, 9.99_29E-01, 3.27_54E-04]] )
elif model_name == "xclip-large-patch14-kinetics-600":
_lowerCAmelCase = torch.tensor([[0.0_036, 0.9_920, 0.0_045]] )
# few shot
elif model_name == "xclip-base-patch16-hmdb-2-shot":
_lowerCAmelCase = torch.tensor([[7.18_90E-06, 9.99_94E-01, 5.65_59E-05]] )
elif model_name == "xclip-base-patch16-hmdb-4-shot":
_lowerCAmelCase = torch.tensor([[1.03_20E-05, 9.99_93E-01, 6.24_35E-05]] )
elif model_name == "xclip-base-patch16-hmdb-8-shot":
_lowerCAmelCase = torch.tensor([[4.13_77E-06, 9.99_90E-01, 9.83_86E-05]] )
elif model_name == "xclip-base-patch16-hmdb-16-shot":
_lowerCAmelCase = torch.tensor([[4.13_47E-05, 9.99_62E-01, 3.34_11E-04]] )
elif model_name == "xclip-base-patch16-ucf-2-shot":
_lowerCAmelCase = torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]] )
elif model_name == "xclip-base-patch16-ucf-4-shot":
_lowerCAmelCase = torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]] )
elif model_name == "xclip-base-patch16-ucf-8-shot":
_lowerCAmelCase = torch.tensor([[0.0_027, 0.9_904, 0.0_070]] )
elif model_name == "xclip-base-patch16-ucf-16-shot":
_lowerCAmelCase = torch.tensor([[9.82_19E-04, 9.95_93E-01, 3.08_63E-03]] )
# zero shot
elif model_name == "xclip-base-patch16-zero-shot":
_lowerCAmelCase = torch.tensor([[3.50_82E-04, 9.97_85E-01, 1.79_66E-03]] )
else:
raise ValueError(F'Model name {model_name} not supported' )
assert torch.allclose(snake_case , snake_case , atol=1E-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(snake_case )
if push_to_hub:
print("""Pushing model, processor and slow tokenizer files to the hub...""" )
model.push_to_hub(snake_case , organization="""nielsr""" )
processor.push_to_hub(snake_case , organization="""nielsr""" )
slow_tokenizer.push_to_hub(snake_case , organization="""nielsr""" )
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""xclip-base-patch32""",
type=str,
help="""Name of the model.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
A__ = parser.parse_args()
convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 82 | 1 |
from collections.abc import Sequence
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(snake_case ) )
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = 0.0
for coeff in reversed(snake_case ):
_lowerCAmelCase = result * x + coeff
return result
if __name__ == "__main__":
A__ = (0.0, 0.0, 5.0, 9.3, 7.0)
A__ = 1_0.0
print(evaluate_poly(poly, x))
print(horner(poly, x))
| 82 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@register_to_config
def __init__( self , _snake_case = 768 , ):
"""simple docstring"""
super().__init__()
_lowerCAmelCase = nn.Parameter(torch.zeros(1 , _snake_case ) )
_lowerCAmelCase = nn.Parameter(torch.ones(1 , _snake_case ) )
def snake_case ( self , _snake_case = None , _snake_case = None , ):
"""simple docstring"""
_lowerCAmelCase = nn.Parameter(self.mean.to(_snake_case ).to(_snake_case ) )
_lowerCAmelCase = nn.Parameter(self.std.to(_snake_case ).to(_snake_case ) )
return self
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = (embeds - self.mean) * 1.0 / self.std
return embeds
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = (embeds * self.std) + self.mean
return embeds
| 82 | 1 |
from functools import lru_cache
@lru_cache
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 82 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase = AudioLDMPipeline
__lowerCamelCase = TEXT_TO_AUDIO_PARAMS
__lowerCamelCase = TEXT_TO_AUDIO_BATCH_PARAMS
__lowerCamelCase = frozenset(
[
'''num_inference_steps''',
'''num_waveforms_per_prompt''',
'''generator''',
'''latents''',
'''output_type''',
'''return_dict''',
'''callback''',
'''callback_steps''',
] )
def snake_case ( self ):
"""simple docstring"""
torch.manual_seed(0 )
_lowerCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=(32, 64) , class_embed_type="""simple_projection""" , projection_class_embeddings_input_dim=32 , class_embeddings_concat=_snake_case , )
_lowerCAmelCase = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_snake_case , set_alpha_to_one=_snake_case , )
torch.manual_seed(0 )
_lowerCAmelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
_lowerCAmelCase = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , projection_dim=32 , )
_lowerCAmelCase = ClapTextModelWithProjection(_snake_case )
_lowerCAmelCase = RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta""" , model_max_length=77 )
_lowerCAmelCase = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=16000 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=_snake_case , )
_lowerCAmelCase = SpeechTaHifiGan(_snake_case )
_lowerCAmelCase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""vocoder""": vocoder,
}
return components
def snake_case ( self , _snake_case , _snake_case=0 ):
"""simple docstring"""
if str(_snake_case ).startswith("""mps""" ):
_lowerCAmelCase = torch.manual_seed(_snake_case )
else:
_lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
_lowerCAmelCase = {
"""prompt""": """A hammer hitting a wooden surface""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
}
return inputs
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(_snake_case ) == 256
_lowerCAmelCase = audio[:10]
_lowerCAmelCase = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = 3 * [inputs["""prompt"""]]
# forward
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = 3 * [inputs.pop("""prompt""" )]
_lowerCAmelCase = audioldm_pipe.tokenizer(
_snake_case , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=_snake_case , return_tensors="""pt""" , )
_lowerCAmelCase = text_inputs["""input_ids"""].to(_snake_case )
_lowerCAmelCase = audioldm_pipe.text_encoder(
_snake_case , )
_lowerCAmelCase = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
_lowerCAmelCase = F.normalize(_snake_case , dim=-1 )
_lowerCAmelCase = prompt_embeds
# forward
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = 3 * ["""this is a negative prompt"""]
_lowerCAmelCase = negative_prompt
_lowerCAmelCase = 3 * [inputs["""prompt"""]]
# forward
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = 3 * [inputs.pop("""prompt""" )]
_lowerCAmelCase = []
for p in [prompt, negative_prompt]:
_lowerCAmelCase = audioldm_pipe.tokenizer(
_snake_case , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=_snake_case , return_tensors="""pt""" , )
_lowerCAmelCase = text_inputs["""input_ids"""].to(_snake_case )
_lowerCAmelCase = audioldm_pipe.text_encoder(
_snake_case , )
_lowerCAmelCase = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
_lowerCAmelCase = F.normalize(_snake_case , dim=-1 )
embeds.append(_snake_case )
_lowerCAmelCase , _lowerCAmelCase = embeds
# forward
_lowerCAmelCase = audioldm_pipe(**_snake_case )
_lowerCAmelCase = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = PNDMScheduler(skip_prk_steps=_snake_case )
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = """egg cracking"""
_lowerCAmelCase = audioldm_pipe(**_snake_case , negative_prompt=_snake_case )
_lowerCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(_snake_case ) == 256
_lowerCAmelCase = audio[:10]
_lowerCAmelCase = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = PNDMScheduler(skip_prk_steps=_snake_case )
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = """A hammer hitting a wooden surface"""
# test num_waveforms_per_prompt=1 (default)
_lowerCAmelCase = audioldm_pipe(_snake_case , num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
_lowerCAmelCase = 2
_lowerCAmelCase = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
_lowerCAmelCase = 2
_lowerCAmelCase = audioldm_pipe(_snake_case , num_inference_steps=2 , num_waveforms_per_prompt=_snake_case ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
_lowerCAmelCase = 2
_lowerCAmelCase = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=_snake_case ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = audioldm_pipe.vocoder.config.sampling_rate
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = audioldm_pipe(audio_length_in_s=0.016 , **_snake_case )
_lowerCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(_snake_case ) / vocoder_sampling_rate == 0.016
_lowerCAmelCase = audioldm_pipe(audio_length_in_s=0.032 , **_snake_case )
_lowerCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(_snake_case ) / vocoder_sampling_rate == 0.032
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = AudioLDMPipeline(**_snake_case )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = ["""hey"""]
_lowerCAmelCase = audioldm_pipe(_snake_case , num_inference_steps=1 )
_lowerCAmelCase = output.audios.shape
assert audio_shape == (1, 256)
_lowerCAmelCase = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
_lowerCAmelCase = SpeechTaHifiGan(_snake_case ).to(_snake_case )
_lowerCAmelCase = audioldm_pipe(_snake_case , num_inference_steps=1 )
_lowerCAmelCase = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def snake_case ( self ):
"""simple docstring"""
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._test_inference_batch_single_identical(test_mean_pixel_difference=_snake_case )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def snake_case ( self ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_snake_case )
@slow
class __lowerCAmelCase ( unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case ( self , _snake_case , _snake_case="cpu" , _snake_case=torch.floataa , _snake_case=0 ):
"""simple docstring"""
_lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
_lowerCAmelCase = np.random.RandomState(_snake_case ).standard_normal((1, 8, 128, 16) )
_lowerCAmelCase = torch.from_numpy(_snake_case ).to(device=_snake_case , dtype=_snake_case )
_lowerCAmelCase = {
"""prompt""": """A hammer hitting a wooden surface""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 2.5,
}
return inputs
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_inputs(_snake_case )
_lowerCAmelCase = 25
_lowerCAmelCase = audioldm_pipe(**_snake_case ).audios[0]
assert audio.ndim == 1
assert len(_snake_case ) == 81920
_lowerCAmelCase = audio[77230:77240]
_lowerCAmelCase = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] )
_lowerCAmelCase = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
_lowerCAmelCase = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
_lowerCAmelCase = audioldm_pipe.to(_snake_case )
audioldm_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_inputs(_snake_case )
_lowerCAmelCase = audioldm_pipe(**_snake_case ).audios[0]
assert audio.ndim == 1
assert len(_snake_case ) == 81920
_lowerCAmelCase = audio[27780:27790]
_lowerCAmelCase = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] )
_lowerCAmelCase = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3e-2
| 82 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase = StableDiffusionInstructPixaPixPipeline
__lowerCamelCase = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
__lowerCamelCase = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
__lowerCamelCase = IMAGE_TO_IMAGE_IMAGE_PARAMS
__lowerCamelCase = IMAGE_TO_IMAGE_IMAGE_PARAMS
def snake_case ( self ):
"""simple docstring"""
torch.manual_seed(0 )
_lowerCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=8 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
_lowerCAmelCase = PNDMScheduler(skip_prk_steps=_snake_case )
torch.manual_seed(0 )
_lowerCAmelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
_lowerCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
_lowerCAmelCase = CLIPTextModel(_snake_case )
_lowerCAmelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
_lowerCAmelCase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def snake_case ( self , _snake_case , _snake_case=0 ):
"""simple docstring"""
_lowerCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(_snake_case ) ).to(_snake_case )
_lowerCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_lowerCAmelCase = Image.fromarray(np.uinta(_snake_case ) ).convert("""RGB""" )
if str(_snake_case ).startswith("""mps""" ):
_lowerCAmelCase = torch.manual_seed(_snake_case )
else:
_lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
_lowerCAmelCase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""image_guidance_scale""": 1,
"""output_type""": """numpy""",
}
return inputs
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = StableDiffusionInstructPixaPixPipeline(**_snake_case )
_lowerCAmelCase = sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = sd_pipe(**_snake_case ).images
_lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_lowerCAmelCase = np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = StableDiffusionInstructPixaPixPipeline(**_snake_case )
_lowerCAmelCase = sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = """french fries"""
_lowerCAmelCase = sd_pipe(**_snake_case , negative_prompt=_snake_case )
_lowerCAmelCase = output.images
_lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_lowerCAmelCase = np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = StableDiffusionInstructPixaPixPipeline(**_snake_case )
_lowerCAmelCase = sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = [inputs["""prompt"""]] * 2
_lowerCAmelCase = np.array(inputs["""image"""] ).astype(np.floataa ) / 255.0
_lowerCAmelCase = torch.from_numpy(_snake_case ).unsqueeze(0 ).to(_snake_case )
_lowerCAmelCase = image / 2 + 0.5
_lowerCAmelCase = image.permute(0 , 3 , 1 , 2 )
_lowerCAmelCase = image.repeat(2 , 1 , 1 , 1 )
_lowerCAmelCase = sd_pipe(**_snake_case ).images
_lowerCAmelCase = image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
_lowerCAmelCase = np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = EulerAncestralDiscreteScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" )
_lowerCAmelCase = StableDiffusionInstructPixaPixPipeline(**_snake_case )
_lowerCAmelCase = sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = sd_pipe(**_snake_case ).images
_lowerCAmelCase = image[0, -3:, -3:, -1]
_lowerCAmelCase = [round(_snake_case , 4 ) for x in image_slice.flatten().tolist()]
print(""",""".join([str(_snake_case ) for x in slice] ) )
assert image.shape == (1, 32, 32, 3)
_lowerCAmelCase = np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case ( self ):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = StableDiffusionInstructPixaPixPipeline(**_snake_case )
_lowerCAmelCase = VaeImageProcessor(do_resize=_snake_case , do_normalize=_snake_case )
_lowerCAmelCase = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = pipe(**self.get_dummy_inputs_by_type(_snake_case , input_image_type="""pt""" ) )[0]
_lowerCAmelCase = components["""vae"""]
_lowerCAmelCase = self.get_dummy_inputs_by_type(_snake_case , input_image_type="""pt""" )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
_lowerCAmelCase = vae.encode(inputs[image_param] ).latent_dist.mode()
_lowerCAmelCase = pipe(**_snake_case )[0]
_lowerCAmelCase = np.abs(out - out_latents_inputs ).max()
self.assertLess(_snake_case , 1e-4 , """passing latents as image input generate different result from passing image""" )
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case ( self , _snake_case=0 ):
"""simple docstring"""
_lowerCAmelCase = torch.manual_seed(_snake_case )
_lowerCAmelCase = load_image(
"""https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg""" )
_lowerCAmelCase = {
"""prompt""": """turn him into a cyborg""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""image_guidance_scale""": 1.0,
"""output_type""": """numpy""",
}
return inputs
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=_snake_case )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing()
_lowerCAmelCase = self.get_inputs()
_lowerCAmelCase = pipe(**_snake_case ).images
_lowerCAmelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
_lowerCAmelCase = np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=_snake_case )
_lowerCAmelCase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing()
_lowerCAmelCase = self.get_inputs()
_lowerCAmelCase = pipe(**_snake_case ).images
_lowerCAmelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
_lowerCAmelCase = np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=_snake_case )
_lowerCAmelCase = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing()
_lowerCAmelCase = self.get_inputs()
_lowerCAmelCase = pipe(**_snake_case ).images
_lowerCAmelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
_lowerCAmelCase = np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = 0
def callback_fn(_snake_case , _snake_case , _snake_case ) -> None:
_lowerCAmelCase = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
_lowerCAmelCase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
_lowerCAmelCase = latents[0, -3:, -3:, -1]
_lowerCAmelCase = np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
_lowerCAmelCase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
_lowerCAmelCase = latents[0, -3:, -3:, -1]
_lowerCAmelCase = np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
_lowerCAmelCase = False
_lowerCAmelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=_snake_case , torch_dtype=torch.floataa )
_lowerCAmelCase = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing()
_lowerCAmelCase = self.get_inputs()
pipe(**_snake_case , callback=_snake_case , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def snake_case ( self ):
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_lowerCAmelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=_snake_case , torch_dtype=torch.floataa )
_lowerCAmelCase = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
_lowerCAmelCase = self.get_inputs()
_lowerCAmelCase = pipe(**_snake_case )
_lowerCAmelCase = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
_lowerCAmelCase = inputs["""image"""].resize((504, 504) )
_lowerCAmelCase = """timbrooks/instruct-pix2pix"""
_lowerCAmelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
_snake_case , safety_checker=_snake_case , )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing()
_lowerCAmelCase = pipe(**_snake_case )
_lowerCAmelCase = output.images[0]
_lowerCAmelCase = image[255:258, 383:386, -1]
assert image.shape == (504, 504, 3)
_lowerCAmelCase = np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 82 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __lowerCAmelCase ( lowerCamelCase__ ):
# to overwrite at feature extractactor specific tests
__lowerCamelCase = None
__lowerCamelCase = None
@property
def snake_case ( self ):
"""simple docstring"""
return self.feat_extract_tester.prepare_feat_extract_dict()
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(_snake_case , """feature_size""" ) )
self.assertTrue(hasattr(_snake_case , """sampling_rate""" ) )
self.assertTrue(hasattr(_snake_case , """padding_value""" ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(_snake_case ) == len(_snake_case ) for x, y in zip(_snake_case , processed_features[input_name] ) ) )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_snake_case )
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="""np""" )
_lowerCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_lowerCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_snake_case )
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="""pt""" )
_lowerCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_lowerCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_snake_case )
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="""tf""" )
_lowerCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_lowerCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def snake_case ( self , _snake_case=False ):
"""simple docstring"""
def _inputs_have_equal_length(_snake_case ):
_lowerCAmelCase = len(input[0] )
for input_slice in input[1:]:
if len(_snake_case ) != length:
return False
return True
def _inputs_are_equal(_snake_case , _snake_case ):
if len(_snake_case ) != len(_snake_case ):
return False
for input_slice_a, input_slice_a in zip(_snake_case , _snake_case ):
if not np.allclose(np.asarray(_snake_case ) , np.asarray(_snake_case ) , atol=1e-3 ):
return False
return True
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_snake_case )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = self.feat_extract_tester.seq_length_diff
_lowerCAmelCase = self.feat_extract_tester.max_seq_length + pad_diff
_lowerCAmelCase = self.feat_extract_tester.min_seq_length
_lowerCAmelCase = self.feat_extract_tester.batch_size
_lowerCAmelCase = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
_lowerCAmelCase = feat_extract.pad(_snake_case , padding=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""max_length""" , max_length=len(speech_inputs[-1] ) )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""max_length""" )[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=_snake_case , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_are_equal(_snake_case , _snake_case ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
_lowerCAmelCase = feat_extract.pad(_snake_case , pad_to_multiple_of=10 )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , pad_to_multiple_of=10 )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , pad_to_multiple_of=10 , max_length=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , pad_to_multiple_of=10 , max_length=_snake_case , return_tensors="""np""" , )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(all(len(_snake_case ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(_snake_case , _snake_case ) )
_lowerCAmelCase = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(_snake_case ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
_lowerCAmelCase = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1e-3 )
def snake_case ( self , _snake_case=False ):
"""simple docstring"""
def _inputs_have_equal_length(_snake_case ):
_lowerCAmelCase = len(input[0] )
for input_slice in input[1:]:
if len(_snake_case ) != length:
return False
return True
def _inputs_are_equal(_snake_case , _snake_case ):
if len(_snake_case ) != len(_snake_case ):
return False
for input_slice_a, input_slice_a in zip(_snake_case , _snake_case ):
if not np.allclose(np.asarray(_snake_case ) , np.asarray(_snake_case ) , atol=1e-3 ):
return False
return True
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_snake_case )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , truncation=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
# truncate to smallest with np
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" , truncation=_snake_case , )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
# truncate to middle
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=_snake_case , return_tensors="""np""" , )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[1] ) , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_are_equal(_snake_case , _snake_case ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , truncation=_snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""longest""" , truncation=_snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""longest""" , truncation=_snake_case )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""max_length""" , truncation=_snake_case )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
_lowerCAmelCase = 12
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_snake_case , truncation=_snake_case , )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_snake_case , )
_lowerCAmelCase = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
_lowerCAmelCase = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
_lowerCAmelCase = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
def snake_case ( self ):
"""simple docstring"""
self._check_padding(numpify=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._check_padding(numpify=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._check_truncation(numpify=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._check_truncation(numpify=_snake_case )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""pt""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""tf""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_dict
_lowerCAmelCase = True
_lowerCAmelCase = self.feature_extraction_class(**_snake_case )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = [len(_snake_case ) for x in speech_inputs]
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )
self.assertIn("""attention_mask""" , _snake_case )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , _snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_dict
_lowerCAmelCase = True
_lowerCAmelCase = self.feature_extraction_class(**_snake_case )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = [len(_snake_case ) for x in speech_inputs]
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = min(_snake_case )
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=_snake_case , truncation=_snake_case , return_tensors="""np""" )
self.assertIn("""attention_mask""" , _snake_case )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
| 82 | 1 |
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def _UpperCAmelCase ( snake_case = 50_00 ):
"""simple docstring"""
_lowerCAmelCase = [(i * (3 * i - 1)) // 2 for i in range(1 , snake_case )]
for i, pentagonal_i in enumerate(snake_case ):
for j in range(snake_case , len(snake_case ) ):
_lowerCAmelCase = pentagonal_nums[j]
_lowerCAmelCase = pentagonal_i + pentagonal_j
_lowerCAmelCase = pentagonal_j - pentagonal_i
if is_pentagonal(snake_case ) and is_pentagonal(snake_case ):
return b
return -1
if __name__ == "__main__":
print(f"{solution() = }")
| 82 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''poolformer'''
def __init__( self , _snake_case=3 , _snake_case=16 , _snake_case=16 , _snake_case=3 , _snake_case=4.0 , _snake_case=[2, 2, 6, 2] , _snake_case=[64, 128, 320, 512] , _snake_case=[7, 3, 3, 3] , _snake_case=[4, 2, 2, 2] , _snake_case=[2, 1, 1, 1] , _snake_case=4 , _snake_case=0.0 , _snake_case="gelu" , _snake_case=True , _snake_case=1e-5 , _snake_case=0.02 , **_snake_case , ):
"""simple docstring"""
_lowerCAmelCase = num_channels
_lowerCAmelCase = patch_size
_lowerCAmelCase = stride
_lowerCAmelCase = padding
_lowerCAmelCase = pool_size
_lowerCAmelCase = hidden_sizes
_lowerCAmelCase = mlp_ratio
_lowerCAmelCase = depths
_lowerCAmelCase = patch_sizes
_lowerCAmelCase = strides
_lowerCAmelCase = num_encoder_blocks
_lowerCAmelCase = drop_path_rate
_lowerCAmelCase = hidden_act
_lowerCAmelCase = use_layer_scale
_lowerCAmelCase = layer_scale_init_value
_lowerCAmelCase = initializer_range
super().__init__(**_snake_case )
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = version.parse('''1.11''' )
@property
def snake_case ( self ):
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def snake_case ( self ):
"""simple docstring"""
return 2e-3
| 82 | 1 |
import os
A__ = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 1_00, """D""": 5_00, """M""": 10_00}
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 0
_lowerCAmelCase = 0
while index < len(snake_case ) - 1:
_lowerCAmelCase = SYMBOLS[numerals[index]]
_lowerCAmelCase = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = """"""
_lowerCAmelCase = num // 10_00
numerals += m_count * "M"
num %= 10_00
_lowerCAmelCase = num // 1_00
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 1_00
_lowerCAmelCase = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def _UpperCAmelCase ( snake_case = "/p089_roman.txt" ):
"""simple docstring"""
_lowerCAmelCase = 0
with open(os.path.dirname(snake_case ) + roman_numerals_filename ) as filea:
_lowerCAmelCase = filea.readlines()
for line in lines:
_lowerCAmelCase = line.strip()
_lowerCAmelCase = parse_roman_numerals(snake_case )
_lowerCAmelCase = generate_roman_numerals(snake_case )
savings += len(snake_case ) - len(snake_case )
return savings
if __name__ == "__main__":
print(f"{solution() = }")
| 82 |
def _UpperCAmelCase ( snake_case = 10_00 ):
"""simple docstring"""
_lowerCAmelCase = -1
_lowerCAmelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
_lowerCAmelCase = (n * n - 2 * a * n) // (2 * n - 2 * a)
_lowerCAmelCase = n - a - b
if c * c == (a * a + b * b):
_lowerCAmelCase = a * b * c
if candidate >= product:
_lowerCAmelCase = candidate
return product
if __name__ == "__main__":
print(f"{solution() = }")
| 82 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {
"""configuration_trajectory_transformer""": [
"""TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TrajectoryTransformerConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = [
"""TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TrajectoryTransformerModel""",
"""TrajectoryTransformerPreTrainedModel""",
"""load_tf_weights_in_trajectory_transformer""",
]
if TYPE_CHECKING:
from .configuration_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TrajectoryTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TrajectoryTransformerModel,
TrajectoryTransformerPreTrainedModel,
load_tf_weights_in_trajectory_transformer,
)
else:
import sys
A__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 82 |
from __future__ import annotations
import math
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(snake_case ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = str(snake_case )
_lowerCAmelCase = [n]
for i in range(1 , len(snake_case ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if len(str(snake_case ) ) > 3:
if not is_prime(int(str(snake_case )[-3:] ) ) or not is_prime(int(str(snake_case )[:3] ) ):
return False
return True
def _UpperCAmelCase ( snake_case = 11 ):
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = 13
while len(snake_case ) != count:
if validate(snake_case ):
_lowerCAmelCase = list_truncated_nums(snake_case )
if all(is_prime(snake_case ) for i in list_nums ):
list_truncated_primes.append(snake_case )
num += 2
return list_truncated_primes
def _UpperCAmelCase ( ):
"""simple docstring"""
return sum(compute_truncated_primes(11 ) )
if __name__ == "__main__":
print(f"{sum(compute_truncated_primes(11)) = }")
| 82 | 1 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"""split_dict""" , [
SplitDict(),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=13_37 , num_examples=42 , dataset_name="""my_dataset""" )} ),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=13_37 , num_examples=42 )} ),
SplitDict({"""train""": SplitInfo()} ),
] , )
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = split_dict._to_yaml_list()
assert len(snake_case ) == len(snake_case )
_lowerCAmelCase = SplitDict._from_yaml_list(snake_case )
for split_name, split_info in split_dict.items():
# dataset_name field is deprecated, and is therefore not part of the YAML dump
_lowerCAmelCase = None
# the split name of split_dict takes over the name of the split info object
_lowerCAmelCase = split_name
assert split_dict == reloaded
@pytest.mark.parametrize(
"""split_info""" , [SplitInfo(), SplitInfo(dataset_name=snake_case ), SplitInfo(dataset_name="""my_dataset""" )] )
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = asdict(SplitDict({"""train""": split_info} ) )
assert "dataset_name" in split_dict_asdict["train"]
assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
| 82 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
A__ = logging.get_logger(__name__)
class __lowerCAmelCase ( lowerCamelCase__ ):
def __init__( self , **_snake_case ):
"""simple docstring"""
requires_backends(self , ["""bs4"""] )
super().__init__(**_snake_case )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = element if element.name else element.parent
for parent in child.parents: # type: bs4.element.Tag
_lowerCAmelCase = parent.find_all(child.name , recursive=_snake_case )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(_snake_case ) else next(i for i, s in enumerate(_snake_case , 1 ) if s is child ) )
_lowerCAmelCase = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = BeautifulSoup(_snake_case , """html.parser""" )
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = []
for element in html_code.descendants:
if type(_snake_case ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
_lowerCAmelCase = html.unescape(_snake_case ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(_snake_case )
_lowerCAmelCase , _lowerCAmelCase = self.xpath_soup(_snake_case )
stringaxtag_seq.append(_snake_case )
stringaxsubs_seq.append(_snake_case )
if len(_snake_case ) != len(_snake_case ):
raise ValueError("""Number of doc strings and xtags does not correspond""" )
if len(_snake_case ) != len(_snake_case ):
raise ValueError("""Number of doc strings and xsubs does not correspond""" )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = """"""
for tagname, subs in zip(_snake_case , _snake_case ):
xpath += F'/{tagname}'
if subs != 0:
xpath += F'[{subs}]'
return xpath
def __call__( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = False
# Check that strings has a valid type
if isinstance(_snake_case , _snake_case ):
_lowerCAmelCase = True
elif isinstance(_snake_case , (list, tuple) ):
if len(_snake_case ) == 0 or isinstance(html_strings[0] , _snake_case ):
_lowerCAmelCase = True
if not valid_strings:
raise ValueError(
"""HTML strings must of type `str`, `List[str]` (batch of examples), """
F'but is of type {type(_snake_case )}.' )
_lowerCAmelCase = bool(isinstance(_snake_case , (list, tuple) ) and (isinstance(html_strings[0] , _snake_case )) )
if not is_batched:
_lowerCAmelCase = [html_strings]
# Get nodes + xpaths
_lowerCAmelCase = []
_lowerCAmelCase = []
for html_string in html_strings:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.get_three_from_single(_snake_case )
nodes.append(_snake_case )
_lowerCAmelCase = []
for node, tag_list, sub_list in zip(_snake_case , _snake_case , _snake_case ):
_lowerCAmelCase = self.construct_xpath(_snake_case , _snake_case )
xpath_strings.append(_snake_case )
xpaths.append(_snake_case )
# return as Dict
_lowerCAmelCase = {"""nodes""": nodes, """xpaths""": xpaths}
_lowerCAmelCase = BatchFeature(data=_snake_case , tensor_type=_snake_case )
return encoded_inputs
| 82 | 1 |
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