code
stringlengths 82
53.2k
| code_codestyle
int64 0
721
| style_context
stringlengths 91
41.9k
| style_context_codestyle
int64 0
699
| label
int64 0
1
|
|---|---|---|---|---|
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
A_ = ""
A_ = ""
A_ = ""
A_ = 1 # (0 is vertical, 1 is horizontal)
def _UpperCamelCase ( ) -> None:
lowerCamelCase_ ,lowerCamelCase_ = get_dataset(__UpperCamelCase ,__UpperCamelCase )
print('Processing...' )
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = update_image_and_anno(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
for index, image in enumerate(__UpperCamelCase ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
lowerCamelCase_ = random_chars(32 )
lowerCamelCase_ = paths[index].split(os.sep )[-1].rsplit('.' ,1 )[0]
lowerCamelCase_ = f'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
cva.imwrite(f'''/{file_root}.jpg''' ,__UpperCamelCase ,[cva.IMWRITE_JPEG_QUALITY, 85] )
print(f'''Success {index+1}/{len(__UpperCamelCase )} with {file_name}''' )
lowerCamelCase_ = []
for anno in new_annos[index]:
lowerCamelCase_ = f'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
annos_list.append(__UpperCamelCase )
with open(f'''/{file_root}.txt''' ,'w' ) as outfile:
outfile.write('\n'.join(line for line in annos_list ) )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> tuple[list, list]:
lowerCamelCase_ = []
lowerCamelCase_ = []
for label_file in glob.glob(os.path.join(__UpperCamelCase ,'*.txt' ) ):
lowerCamelCase_ = label_file.split(os.sep )[-1].rsplit('.' ,1 )[0]
with open(__UpperCamelCase ) as in_file:
lowerCamelCase_ = in_file.readlines()
lowerCamelCase_ = os.path.join(__UpperCamelCase ,f'''{label_name}.jpg''' )
lowerCamelCase_ = []
for obj_list in obj_lists:
lowerCamelCase_ = obj_list.rstrip('\n' ).split(' ' )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(__UpperCamelCase )
labels.append(__UpperCamelCase )
return img_paths, labels
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase = 1 ) -> tuple[list, list, list]:
lowerCamelCase_ = []
lowerCamelCase_ = []
lowerCamelCase_ = []
for idx in range(len(__UpperCamelCase ) ):
lowerCamelCase_ = []
lowerCamelCase_ = img_list[idx]
path_list.append(__UpperCamelCase )
lowerCamelCase_ = anno_list[idx]
lowerCamelCase_ = cva.imread(__UpperCamelCase )
if flip_type == 1:
lowerCamelCase_ = cva.flip(__UpperCamelCase ,__UpperCamelCase )
for bbox in img_annos:
lowerCamelCase_ = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
lowerCamelCase_ = cva.flip(__UpperCamelCase ,__UpperCamelCase )
for bbox in img_annos:
lowerCamelCase_ = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(__UpperCamelCase )
new_imgs_list.append(__UpperCamelCase )
return new_imgs_list, new_annos_lists, path_list
def _UpperCamelCase ( __UpperCamelCase = 32 ) -> str:
assert number_char > 1, "The number of character should greater than 1"
lowerCamelCase_ = ascii_lowercase + digits
return "".join(random.choice(__UpperCamelCase ) for _ in range(__UpperCamelCase ) )
if __name__ == "__main__":
main()
print("DONE ✅")
| 42
|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class __UpperCamelCase ( unittest.TestCase ):
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = TFCamembertModel.from_pretrained('jplu/tf-camembert-base' )
__a : str = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
__a : Optional[int] = model(__a )['last_hidden_state']
__a : Optional[int] = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , __a )
# compare the actual values for a slice.
__a : List[str] = tf.convert_to_tensor(
[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 476
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
A_ : Union[str, Any] = {
"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_ : Tuple = [
"AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"ASTForAudioClassification",
"ASTModel",
"ASTPreTrainedModel",
]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Dict = ["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_ : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 696
|
"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
A_ : int = {
"return_dict": False,
"output_hidden_states": True,
"output_attentions": True,
"torchscript": True,
"torch_dtype": "float16",
"use_bfloat16": True,
"tf_legacy_loss": True,
"pruned_heads": {"a": 1},
"tie_word_embeddings": False,
"is_decoder": True,
"cross_attention_hidden_size": 1_28,
"add_cross_attention": True,
"tie_encoder_decoder": True,
"max_length": 50,
"min_length": 3,
"do_sample": True,
"early_stopping": True,
"num_beams": 3,
"num_beam_groups": 3,
"diversity_penalty": 0.5,
"temperature": 2.0,
"top_k": 10,
"top_p": 0.7,
"typical_p": 0.2,
"repetition_penalty": 0.8,
"length_penalty": 0.8,
"no_repeat_ngram_size": 5,
"encoder_no_repeat_ngram_size": 5,
"bad_words_ids": [1, 2, 3],
"num_return_sequences": 3,
"chunk_size_feed_forward": 5,
"output_scores": True,
"return_dict_in_generate": True,
"forced_bos_token_id": 2,
"forced_eos_token_id": 3,
"remove_invalid_values": True,
"architectures": ["BertModel"],
"finetuning_task": "translation",
"id2label": {0: "label"},
"label2id": {"label": "0"},
"tokenizer_class": "BertTokenizerFast",
"prefix": "prefix",
"bos_token_id": 6,
"pad_token_id": 7,
"eos_token_id": 8,
"sep_token_id": 9,
"decoder_start_token_id": 10,
"exponential_decay_length_penalty": (5, 1.01),
"suppress_tokens": [0, 1],
"begin_suppress_tokens": 2,
"task_specific_params": {"translation": "some_params"},
"problem_type": "regression",
}
@is_staging_test
class a_ ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def a__ (cls ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = TOKEN
HfFolder.save_token(lowerCamelCase_ )
@classmethod
def a__ (cls ):
'''simple docstring'''
try:
delete_repo(token=cls._token, repo_id='test-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id='valid_org/test-config-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id='test-dynamic-config' )
except HTTPError:
pass
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = BertConfig(
vocab_size=9_9, hidden_size=3_2, num_hidden_layers=5, num_attention_heads=4, intermediate_size=3_7 )
config.push_to_hub('test-config', use_auth_token=self._token )
lowerCamelCase__ : Optional[int] = BertConfig.from_pretrained(f'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_, getattr(lowerCamelCase_, lowerCamelCase_ ) )
# Reset repo
delete_repo(token=self._token, repo_id='test-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase_, repo_id='test-config', push_to_hub=lowerCamelCase_, use_auth_token=self._token )
lowerCamelCase__ : List[str] = BertConfig.from_pretrained(f'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_, getattr(lowerCamelCase_, lowerCamelCase_ ) )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = BertConfig(
vocab_size=9_9, hidden_size=3_2, num_hidden_layers=5, num_attention_heads=4, intermediate_size=3_7 )
config.push_to_hub('valid_org/test-config-org', use_auth_token=self._token )
lowerCamelCase__ : Union[str, Any] = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_, getattr(lowerCamelCase_, lowerCamelCase_ ) )
# Reset repo
delete_repo(token=self._token, repo_id='valid_org/test-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowerCamelCase_, repo_id='valid_org/test-config-org', push_to_hub=lowerCamelCase_, use_auth_token=self._token )
lowerCamelCase__ : str = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_, getattr(lowerCamelCase_, lowerCamelCase_ ) )
def a__ (self ):
'''simple docstring'''
CustomConfig.register_for_auto_class()
lowerCamelCase__ : Optional[int] = CustomConfig(attribute=4_2 )
config.push_to_hub('test-dynamic-config', use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map, {'AutoConfig': 'custom_configuration.CustomConfig'} )
lowerCamelCase__ : List[str] = AutoConfig.from_pretrained(f'''{USER}/test-dynamic-config''', trust_remote_code=lowerCamelCase_ )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__, 'CustomConfig' )
self.assertEqual(new_config.attribute, 4_2 )
class a_ ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
lowerCamelCase__ : Tuple = c.n_embd + 1 # int
lowerCamelCase__ : Union[str, Any] = c.resid_pdrop + 1.0 # float
lowerCamelCase__ : List[Any] = not c.scale_attn_weights # bool
lowerCamelCase__ : List[Any] = c.summary_type + 'foo' # str
c.update_from_string(
f'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}''' )
self.assertEqual(lowerCamelCase_, c.n_embd, 'mismatch for key: n_embd' )
self.assertEqual(lowerCamelCase_, c.resid_pdrop, 'mismatch for key: resid_pdrop' )
self.assertEqual(lowerCamelCase_, c.scale_attn_weights, 'mismatch for key: scale_attn_weights' )
self.assertEqual(lowerCamelCase_, c.summary_type, 'mismatch for key: summary_type' )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[str] = PretrainedConfig()
lowerCamelCase__ : Optional[Any] = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
lowerCamelCase_, ['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version'] )
lowerCamelCase__ : Any = [key for key, value in config_common_kwargs.items() if value == getattr(lowerCamelCase_, lowerCamelCase_ )]
if len(lowerCamelCase_ ) > 0:
raise ValueError(
'The following keys are set with the default values in'
' `test_configuration_common.config_common_kwargs` pick another value for them:'
f''' {', '.join(lowerCamelCase_ )}.''' )
def a__ (self ):
'''simple docstring'''
with self.assertRaises(lowerCamelCase_ ):
# config is in subfolder, the following should not work without specifying the subfolder
lowerCamelCase__ : Union[str, Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' )
lowerCamelCase__ : int = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder', subfolder='bert' )
self.assertIsNotNone(lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = mock.Mock()
lowerCamelCase__ : List[str] = 5_0_0
lowerCamelCase__ : Any = {}
lowerCamelCase__ : int = HTTPError
lowerCamelCase__ : Optional[Any] = {}
# Download this model to make sure it's in the cache.
lowerCamelCase__ : Any = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request', return_value=lowerCamelCase_ ) as mock_head:
lowerCamelCase__ : List[str] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Dict = BertConfig.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json' )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = AutoConfig.from_pretrained('bert-base-cased' )
lowerCamelCase__ : str = ['config.4.0.0.json']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = 2
json.dump(configuration.to_dict(), open(os.path.join(lowerCamelCase_, 'config.4.0.0.json' ), 'w' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
lowerCamelCase__ : Union[str, Any] = AutoConfig.from_pretrained(lowerCamelCase_ )
self.assertEqual(new_configuration.hidden_size, 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
lowerCamelCase__ : str = ['config.42.0.0.json']
lowerCamelCase__ : Union[str, Any] = 7_6_8
configuration.save_pretrained(lowerCamelCase_ )
shutil.move(os.path.join(lowerCamelCase_, 'config.4.0.0.json' ), os.path.join(lowerCamelCase_, 'config.42.0.0.json' ) )
lowerCamelCase__ : Union[str, Any] = AutoConfig.from_pretrained(lowerCamelCase_ )
self.assertEqual(new_configuration.hidden_size, 7_6_8 )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = 'hf-internal-testing/test-two-configs'
import transformers as new_transformers
lowerCamelCase__ : Optional[int] = 'v4.0.0'
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = new_transformers.models.auto.AutoConfig.from_pretrained(
lowerCamelCase_, return_unused_kwargs=lowerCamelCase_ )
self.assertEqual(new_configuration.hidden_size, 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(lowerCamelCase_, {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
lowerCamelCase__ : Dict = 'v3.0.0'
lowerCamelCase__ : List[str] = old_transformers.models.auto.AutoConfig.from_pretrained(lowerCamelCase_ )
self.assertEqual(old_configuration.hidden_size, 7_6_8 )
| 696
| 1
|
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self ,lowerCamelCase_ ,lowerCamelCase_=1_3 ,lowerCamelCase_=7 ,lowerCamelCase_=True ,lowerCamelCase_=True ,lowerCamelCase_=True ,lowerCamelCase_=True ,lowerCamelCase_=9_9 ,lowerCamelCase_=3_2 ,lowerCamelCase_=5 ,lowerCamelCase_=4 ,lowerCamelCase_=3_7 ,lowerCamelCase_="gelu" ,lowerCamelCase_=0.1 ,lowerCamelCase_=0.1 ,lowerCamelCase_=5_1_2 ,lowerCamelCase_=1_6 ,lowerCamelCase_=2 ,lowerCamelCase_=0.02 ,lowerCamelCase_=4 ,) -> Dict:
A = parent
A = batch_size
A = seq_length
A = is_training
A = use_attention_mask
A = use_token_type_ids
A = use_labels
A = vocab_size
A = hidden_size
A = num_hidden_layers
A = num_attention_heads
A = intermediate_size
A = hidden_act
A = hidden_dropout_prob
A = attention_probs_dropout_prob
A = max_position_embeddings
A = type_vocab_size
A = type_sequence_label_size
A = initializer_range
A = num_choices
def UpperCamelCase__ ( self ) -> int:
A = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
A = None
if self.use_attention_mask:
A = random_attention_mask([self.batch_size, self.seq_length] )
A = None
if self.use_token_type_ids:
A = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
A = RoFormerConfig(
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=lowerCamelCase_ ,initializer_range=self.initializer_range ,)
return config, input_ids, token_type_ids, attention_mask
def UpperCamelCase__ ( self ) -> Optional[Any]:
A = self.prepare_config_and_inputs()
A , A , A , A = config_and_inputs
A = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
_lowerCamelCase = True
_lowerCamelCase = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCamelCase__ ( self ) -> int:
A = FlaxRoFormerModelTester(self )
@slow
def UpperCamelCase__ ( self ) -> Optional[int]:
for model_class_name in self.all_model_classes:
A = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" ,from_pt=lowerCamelCase_ )
A = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCamelCase_ )
@require_flax
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCamelCase__ ( self ) -> Dict:
A = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
A = jnp.array([[0, 1, 2, 3, 4, 5]] )
A = model(lowerCamelCase_ )[0]
A = 5_0_0_0_0
A = (1, 6, vocab_size)
self.assertEqual(output.shape ,lowerCamelCase_ )
A = jnp.array(
[[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] ,lowerCamelCase_ ,atol=1E-4 ) )
| 617
|
"""simple docstring"""
from statistics import mean, stdev
def _A ( _a : list , _a : int = 3 ):
"""simple docstring"""
A = min(_a )
A = max(_a )
# normalize data
return [round((x - x_min) / (x_max - x_min) , _a ) for x in data]
def _A ( _a : list , _a : int = 3 ):
"""simple docstring"""
A = mean(_a )
A = stdev(_a )
# standardize data
return [round((x - mu) / (sigma) , _a ) for x in data]
| 617
| 1
|
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
def __UpperCAmelCase ( _snake_case : int, _snake_case : int ):
return (
num != den and num % 1_0 == den // 1_0 and (num // 1_0) / (den % 1_0) == num / den
)
def __UpperCAmelCase ( _snake_case : int ):
_lowercase = []
_lowercase = 1_1
_lowercase = int("1" + "0" * digit_len )
for num in range(_snake_case, _snake_case ):
while den <= 9_9:
if (num != den) and (num % 1_0 == den // 1_0) and (den % 1_0 != 0):
if is_digit_cancelling(_snake_case, _snake_case ):
solutions.append(f"""{num}/{den}""" )
den += 1
num += 1
_lowercase = 1_0
return solutions
def __UpperCAmelCase ( _snake_case : int = 2 ):
_lowercase = 1.0
for fraction in fraction_list(_snake_case ):
_lowercase = Fraction(_snake_case )
result *= frac.denominator / frac.numerator
return int(_snake_case )
if __name__ == "__main__":
print(solution())
| 227
|
"""simple docstring"""
import argparse
import datetime
def __UpperCAmelCase ( _snake_case : str ):
_lowercase = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wednesday",
"4": "Thursday",
"5": "Friday",
"6": "Saturday",
}
_lowercase = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}
# Validate
if not 0 < len(_snake_case ) < 1_1:
raise ValueError("Must be 10 characters long" )
# Get month
_lowercase = int(date_input[0] + date_input[1] )
# Validate
if not 0 < m < 1_3:
raise ValueError("Month must be between 1 - 12" )
_lowercase = date_input[2]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError("Date separator must be '-' or '/'" )
# Get day
_lowercase = int(date_input[3] + date_input[4] )
# Validate
if not 0 < d < 3_2:
raise ValueError("Date must be between 1 - 31" )
# Get second separator
_lowercase = date_input[5]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError("Date separator must be '-' or '/'" )
# Get year
_lowercase = int(date_input[6] + date_input[7] + date_input[8] + date_input[9] )
# Arbitrary year range
if not 4_5 < y < 8_5_0_0:
raise ValueError(
"Year out of range. There has to be some sort of limit...right?" )
# Get datetime obj for validation
_lowercase = datetime.date(int(_snake_case ), int(_snake_case ), int(_snake_case ) )
# Start math
if m <= 2:
_lowercase = y - 1
_lowercase = m + 1_2
# maths var
_lowercase = int(str(_snake_case )[:2] )
_lowercase = int(str(_snake_case )[2:] )
_lowercase = int(2.6 * m - 5.3_9 )
_lowercase = int(c / 4 )
_lowercase = int(k / 4 )
_lowercase = int(d + k )
_lowercase = int(t + u + v + x )
_lowercase = int(z - (2 * c) )
_lowercase = round(w % 7 )
# End math
# Validate math
if f != convert_datetime_days[dt_ck.weekday()]:
raise AssertionError("The date was evaluated incorrectly. Contact developer." )
# Response
_lowercase = f"""Your date {date_input}, is a {days[str(_snake_case )]}!"""
return response
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCamelCase : Any = argparse.ArgumentParser(
description=(
"Find out what day of the week nearly any date is or was. Enter "
"date as a string in the mm-dd-yyyy or mm/dd/yyyy format"
)
)
parser.add_argument(
"date_input", type=str, help="Date as a string (mm-dd-yyyy or mm/dd/yyyy)"
)
__UpperCamelCase : str = parser.parse_args()
zeller(args.date_input)
| 227
| 1
|
"""simple docstring"""
from __future__ import annotations
def __A ( a_ :list[int] , a_ :int) -> list[int]:
__a : int = 0
__a : Union[str, Any] = len(a_) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
__a : Optional[Any] = i + 1
else:
__a : Optional[int] = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'{two_pointer([2, 7, 11, 15], 9) = }')
| 52
|
'''simple docstring'''
from typing import Dict, Iterable, List, Optional, 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_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
UpperCamelCase__ = logging.get_logger(__name__)
class lowerCamelCase_ ( __a ):
lowerCAmelCase__ = ['pixel_values']
def __init__( self : List[str] , _A : bool = True , _A : Dict[str, int] = None , _A : PILImageResampling = PILImageResampling.BICUBIC , _A : bool = True , _A : Dict[str, int] = None , _A : bool = True , _A : Union[int, float] = 1 / 255 , _A : bool = True , _A : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN , _A : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD , **_A : int , ):
'''simple docstring'''
super().__init__(**_A )
UpperCAmelCase__ : Dict = size if size is not None else {'''shortest_edge''': 224}
UpperCAmelCase__ : int = get_size_dict(_A , default_to_square=_A )
UpperCAmelCase__ : Union[str, Any] = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
UpperCAmelCase__ : List[str] = get_size_dict(_A , param_name='''crop_size''' )
UpperCAmelCase__ : str = do_resize
UpperCAmelCase__ : List[Any] = size
UpperCAmelCase__ : int = resample
UpperCAmelCase__ : int = do_center_crop
UpperCAmelCase__ : List[str] = crop_size
UpperCAmelCase__ : Union[str, Any] = do_rescale
UpperCAmelCase__ : Optional[int] = rescale_factor
UpperCAmelCase__ : List[Any] = do_normalize
UpperCAmelCase__ : Dict = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
UpperCAmelCase__ : Dict = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def lowercase_ ( self : str , _A : np.ndarray , _A : Dict[str, int] , _A : PILImageResampling = PILImageResampling.BICUBIC , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = get_size_dict(_A , default_to_square=_A )
# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
if "shortest_edge" in size:
UpperCAmelCase__ : Tuple = int((256 / 224) * size['''shortest_edge'''] )
UpperCAmelCase__ : Tuple = get_resize_output_image_size(_A , size=_A , default_to_square=_A )
UpperCAmelCase__ : Dict = {'''height''': output_size[0], '''width''': output_size[1]}
if "height" not in size_dict or "width" not in size_dict:
raise ValueError(
f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}""" )
return resize(
_A , size=(size_dict['''height'''], size_dict['''width''']) , resample=_A , data_format=_A , **_A )
def lowercase_ ( self : Optional[Any] , _A : np.ndarray , _A : Dict[str, int] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Optional[int] , ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = get_size_dict(_A )
if "height" not in size or "width" not in size:
raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}""" )
return center_crop(_A , size=(size['''height'''], size['''width''']) , data_format=_A , **_A )
def lowercase_ ( self : List[str] , _A : np.ndarray , _A : Union[int, float] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Dict , ):
'''simple docstring'''
return rescale(_A , scale=_A , data_format=_A , **_A )
def lowercase_ ( self : Dict , _A : np.ndarray , _A : Union[float, List[float]] , _A : Union[float, List[float]] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Optional[int] , ):
'''simple docstring'''
return normalize(_A , mean=_A , std=_A , data_format=_A , **_A )
def lowercase_ ( self : Optional[Any] , _A : ImageInput , _A : Optional[bool] = None , _A : Optional[Dict[str, int]] = None , _A : PILImageResampling = None , _A : Optional[bool] = None , _A : Optional[Dict[str, int]] = None , _A : Optional[bool] = None , _A : Optional[float] = None , _A : Optional[bool] = None , _A : Optional[Union[float, Iterable[float]]] = None , _A : Optional[Union[float, Iterable[float]]] = None , _A : Optional[TensorType] = None , _A : ChannelDimension = ChannelDimension.FIRST , **_A : str , ):
'''simple docstring'''
UpperCAmelCase__ : str = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase__ : Optional[int] = resample if resample is not None else self.resample
UpperCAmelCase__ : List[str] = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase__ : Tuple = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase__ : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase__ : List[str] = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase__ : Tuple = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase__ : List[str] = image_std if image_std is not None else self.image_std
UpperCAmelCase__ : Tuple = size if size is not None else self.size
UpperCAmelCase__ : int = get_size_dict(_A , default_to_square=_A )
UpperCAmelCase__ : Union[str, Any] = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase__ : int = get_size_dict(_A , param_name='''crop_size''' )
UpperCAmelCase__ : Union[str, Any] = make_list_of_images(_A )
if not valid_images(_A ):
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.
UpperCAmelCase__ : int = [to_numpy_array(_A ) for image in images]
if do_resize:
UpperCAmelCase__ : str = [self.resize(_A , _A , _A ) for image in images]
if do_center_crop:
UpperCAmelCase__ : Tuple = [self.center_crop(_A , _A ) for image in images]
if do_rescale:
UpperCAmelCase__ : Optional[int] = [self.rescale(_A , _A ) for image in images]
if do_normalize:
UpperCAmelCase__ : Any = [self.normalize(_A , _A , _A ) for image in images]
UpperCAmelCase__ : Tuple = [to_channel_dimension_format(_A , _A ) for image in images]
UpperCAmelCase__ : Dict = {'''pixel_values''': images}
return BatchFeature(data=_A , tensor_type=_A )
| 75
| 0
|
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class lowerCamelCase_ ( unittest.TestCase , UpperCAmelCase_ ):
'''simple docstring'''
def UpperCamelCase__ ( self) -> Union[str, Any]:
__UpperCamelCase :Any = load_tool('''text-classification''')
self.tool.setup()
__UpperCamelCase :Tuple = load_tool('''text-classification''' , remote=__lowercase)
def UpperCamelCase__ ( self) -> List[Any]:
__UpperCamelCase :Dict = self.tool('''That\'s quite cool''' , ['''positive''', '''negative'''])
self.assertEqual(__lowercase , '''positive''')
def UpperCamelCase__ ( self) -> Tuple:
__UpperCamelCase :List[Any] = self.remote_tool('''That\'s quite cool''' , ['''positive''', '''negative'''])
self.assertEqual(__lowercase , '''positive''')
def UpperCamelCase__ ( self) -> int:
__UpperCamelCase :Optional[int] = self.tool(text='''That\'s quite cool''' , labels=['''positive''', '''negative'''])
self.assertEqual(__lowercase , '''positive''')
def UpperCamelCase__ ( self) -> Union[str, Any]:
__UpperCamelCase :Optional[Any] = self.remote_tool(text='''That\'s quite cool''' , labels=['''positive''', '''negative'''])
self.assertEqual(__lowercase , '''positive''')
| 452
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : List[Any] = """wavlm"""
def __init__( self , __lowercase=32 , __lowercase=768 , __lowercase=12 , __lowercase=12 , __lowercase=3_072 , __lowercase="gelu" , __lowercase=0.1 , __lowercase=0.1 , __lowercase=0.1 , __lowercase=0.0 , __lowercase=0.1 , __lowercase=0.1 , __lowercase=0.02 , __lowercase=1E-5 , __lowercase="group" , __lowercase="gelu" , __lowercase=(512, 512, 512, 512, 512, 512, 512) , __lowercase=(5, 2, 2, 2, 2, 2, 2) , __lowercase=(10, 3, 3, 3, 3, 2, 2) , __lowercase=False , __lowercase=128 , __lowercase=16 , __lowercase=320 , __lowercase=800 , __lowercase=False , __lowercase=True , __lowercase=0.05 , __lowercase=10 , __lowercase=2 , __lowercase=0.0 , __lowercase=10 , __lowercase=320 , __lowercase=2 , __lowercase=0.1 , __lowercase=100 , __lowercase=256 , __lowercase=256 , __lowercase=0.1 , __lowercase="mean" , __lowercase=False , __lowercase=False , __lowercase=256 , __lowercase=(512, 512, 512, 512, 1_500) , __lowercase=(5, 3, 3, 1, 1) , __lowercase=(1, 2, 3, 1, 1) , __lowercase=512 , __lowercase=80 , __lowercase=0 , __lowercase=1 , __lowercase=2 , __lowercase=False , __lowercase=3 , __lowercase=2 , __lowercase=3 , __lowercase=None , **__lowercase , ) -> Dict:
super().__init__(**__lowercase , pad_token_id=__lowercase , bos_token_id=__lowercase , eos_token_id=__lowercase)
__UpperCamelCase :Any = hidden_size
__UpperCamelCase :Tuple = feat_extract_norm
__UpperCamelCase :List[str] = feat_extract_activation
__UpperCamelCase :int = list(__lowercase)
__UpperCamelCase :List[Any] = list(__lowercase)
__UpperCamelCase :Union[str, Any] = list(__lowercase)
__UpperCamelCase :Optional[Any] = conv_bias
__UpperCamelCase :Tuple = num_buckets
__UpperCamelCase :Optional[int] = max_bucket_distance
__UpperCamelCase :Union[str, Any] = num_conv_pos_embeddings
__UpperCamelCase :Optional[Any] = num_conv_pos_embedding_groups
__UpperCamelCase :List[Any] = len(self.conv_dim)
__UpperCamelCase :Tuple = num_hidden_layers
__UpperCamelCase :str = intermediate_size
__UpperCamelCase :Union[str, Any] = hidden_act
__UpperCamelCase :Optional[int] = num_attention_heads
__UpperCamelCase :str = hidden_dropout
__UpperCamelCase :int = attention_dropout
__UpperCamelCase :Optional[int] = activation_dropout
__UpperCamelCase :str = feat_proj_dropout
__UpperCamelCase :List[Any] = final_dropout
__UpperCamelCase :int = layerdrop
__UpperCamelCase :List[Any] = layer_norm_eps
__UpperCamelCase :Optional[int] = initializer_range
__UpperCamelCase :Any = num_ctc_classes
__UpperCamelCase :Optional[int] = vocab_size
__UpperCamelCase :List[Any] = do_stable_layer_norm
__UpperCamelCase :str = use_weighted_layer_sum
__UpperCamelCase :Any = classifier_proj_size
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
f""" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"""
f""" `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__UpperCamelCase :Union[str, Any] = apply_spec_augment
__UpperCamelCase :Optional[Any] = mask_time_prob
__UpperCamelCase :Union[str, Any] = mask_time_length
__UpperCamelCase :Optional[int] = mask_time_min_masks
__UpperCamelCase :str = mask_feature_prob
__UpperCamelCase :Tuple = mask_feature_length
# parameters for pretraining with codevector quantized representations
__UpperCamelCase :Optional[Any] = num_codevectors_per_group
__UpperCamelCase :List[Any] = num_codevector_groups
__UpperCamelCase :str = contrastive_logits_temperature
__UpperCamelCase :Tuple = num_negatives
__UpperCamelCase :Any = codevector_dim
__UpperCamelCase :Union[str, Any] = proj_codevector_dim
__UpperCamelCase :Tuple = diversity_loss_weight
# ctc loss
__UpperCamelCase :int = ctc_loss_reduction
__UpperCamelCase :Any = ctc_zero_infinity
# adapter
__UpperCamelCase :List[Any] = add_adapter
__UpperCamelCase :Dict = adapter_kernel_size
__UpperCamelCase :Any = adapter_stride
__UpperCamelCase :Optional[int] = num_adapter_layers
__UpperCamelCase :Union[str, Any] = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
__UpperCamelCase :int = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
__UpperCamelCase :Optional[Any] = list(__lowercase)
__UpperCamelCase :Optional[Any] = list(__lowercase)
__UpperCamelCase :List[str] = list(__lowercase)
__UpperCamelCase :List[Any] = xvector_output_dim
@property
def UpperCamelCase__ ( self) -> Any:
return functools.reduce(operator.mul , self.conv_stride , 1)
| 452
| 1
|
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
UpperCAmelCase__ = abspath(join(dirname(__file__), "src"))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)
def _a ( a :Tuple ) -> Union[str, Any]:
config.addinivalue_line(
'''markers''' , '''is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested''' )
config.addinivalue_line(
'''markers''' , '''is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested''' )
config.addinivalue_line('''markers''' , '''is_pipeline_test: mark test to run only when pipelines are tested''' )
config.addinivalue_line('''markers''' , '''is_staging_test: mark test to run only in the staging environment''' )
config.addinivalue_line('''markers''' , '''accelerate_tests: mark test that require accelerate''' )
config.addinivalue_line('''markers''' , '''tool_tests: mark the tool tests that are run on their specific schedule''' )
def _a ( a :Tuple ) -> List[Any]:
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(_a )
def _a ( a :Union[str, Any] ) -> Optional[Any]:
from transformers.testing_utils import pytest_terminal_summary_main
a = terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(_a , id=_a )
def _a ( a :Tuple , a :Union[str, Any] ) -> Any:
if exitstatus == 5:
a = 0
# Doctest custom flag to ignore output.
UpperCAmelCase__ = doctest.register_optionflag("IGNORE_RESULT")
UpperCAmelCase__ = doctest.OutputChecker
class lowercase_ ( lowercase__ ):
'''simple docstring'''
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Tuple , __UpperCAmelCase : List[str] ) ->Any:
"""simple docstring"""
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
UpperCAmelCase__ = CustomOutputChecker
UpperCAmelCase__ = HfDoctestModule
UpperCAmelCase__ = HfDocTestParser
| 117
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE : List[Any] = {
"configuration_longformer": [
"LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"LongformerConfig",
"LongformerOnnxConfig",
],
"tokenization_longformer": ["LongformerTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Optional[int] = ["LongformerTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Dict = [
"LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"LongformerForMaskedLM",
"LongformerForMultipleChoice",
"LongformerForQuestionAnswering",
"LongformerForSequenceClassification",
"LongformerForTokenClassification",
"LongformerModel",
"LongformerPreTrainedModel",
"LongformerSelfAttention",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Dict = [
"TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFLongformerForMaskedLM",
"TFLongformerForMultipleChoice",
"TFLongformerForQuestionAnswering",
"TFLongformerForSequenceClassification",
"TFLongformerForTokenClassification",
"TFLongformerModel",
"TFLongformerPreTrainedModel",
"TFLongformerSelfAttention",
]
if TYPE_CHECKING:
from .configuration_longformer import (
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
LongformerConfig,
LongformerOnnxConfig,
)
from .tokenization_longformer import LongformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
LongformerSelfAttention,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
TFLongformerSelfAttention,
)
else:
import sys
SCREAMING_SNAKE_CASE : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 257
| 0
|
'''simple docstring'''
from math import factorial
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ = 100 ) -> int:
return sum(int(_lowerCAmelCase ) for x in str(factorial(_lowerCAmelCase ) ) )
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).strip())))
| 716
|
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class A__ ( __magic_name__ ):
def __init__( self : Union[str, Any] , a : str="" , a : str="train" ):
'''simple docstring'''
assert os.path.isdir(a )
lowerCAmelCase__ : Optional[Any] = []
lowerCAmelCase__ : Dict = os.listdir(a )
for story_filename in story_filenames_list:
if "summary" in story_filename:
continue
lowerCAmelCase__ : Union[str, Any] = os.path.join(a , a )
if not os.path.isfile(a ):
continue
self.documents.append(a )
def __len__( self : Any ):
'''simple docstring'''
return len(self.documents )
def __getitem__( self : Dict , a : Any ):
'''simple docstring'''
lowerCAmelCase__ : Optional[int] = self.documents[idx]
lowerCAmelCase__ : Union[str, Any] = document_path.split('/' )[-1]
with open(a , encoding='utf-8' ) as source:
lowerCAmelCase__ : List[Any] = source.read()
lowerCAmelCase__ , lowerCAmelCase__ : List[Any] = process_story(a )
return document_name, story_lines, summary_lines
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> Tuple:
lowerCAmelCase__ : Optional[int] = list(filter(lambda SCREAMING_SNAKE_CASE_ : len(SCREAMING_SNAKE_CASE_ ) != 0 , [line.strip() for line in raw_story.split('\n' )] ) )
# for some unknown reason some lines miss a period, add it
lowerCAmelCase__ : List[Any] = [_add_missing_period(SCREAMING_SNAKE_CASE_ ) for line in nonempty_lines]
# gather article lines
lowerCAmelCase__ : int = []
lowerCAmelCase__ : Any = deque(SCREAMING_SNAKE_CASE_ )
while True:
try:
lowerCAmelCase__ : int = lines.popleft()
if element.startswith('@highlight' ):
break
story_lines.append(SCREAMING_SNAKE_CASE_ )
except IndexError:
# if "@highlight" is absent from the file we pop
# all elements until there is None, raising an exception.
return story_lines, []
# gather summary lines
lowerCAmelCase__ : Tuple = list(filter(lambda SCREAMING_SNAKE_CASE_ : not t.startswith('@highlight' ) , SCREAMING_SNAKE_CASE_ ) )
return story_lines, summary_lines
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> Any:
lowerCAmelCase__ : int = ['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
if line.startswith('@highlight' ):
return line
if line[-1] in END_TOKENS:
return line
return line + "."
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
if len(SCREAMING_SNAKE_CASE_ ) > block_size:
return sequence[:block_size]
else:
sequence.extend([pad_token_id] * (block_size - len(SCREAMING_SNAKE_CASE_ )) )
return sequence
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
lowerCAmelCase__ : str = torch.ones_like(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : int = sequence == pad_token_id
lowerCAmelCase__ : Optional[int] = 0
return mask
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
lowerCAmelCase__ : Any = [tokenizer.encode(SCREAMING_SNAKE_CASE_ ) for line in story_lines]
lowerCAmelCase__ : str = [token for sentence in story_lines_token_ids for token in sentence]
lowerCAmelCase__ : Dict = [tokenizer.encode(SCREAMING_SNAKE_CASE_ ) for line in summary_lines]
lowerCAmelCase__ : str = [token for sentence in summary_lines_token_ids for token in sentence]
return story_token_ids, summary_token_ids
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
lowerCAmelCase__ : Optional[int] = []
for sequence in batch:
lowerCAmelCase__ : Union[str, Any] = -1
lowerCAmelCase__ : int = []
for s in sequence:
if s == separator_token_id:
sentence_num += 1
embeddings.append(sentence_num % 2 )
batch_embeddings.append(SCREAMING_SNAKE_CASE_ )
return torch.tensor(SCREAMING_SNAKE_CASE_ )
| 69
| 0
|
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : str ) -> int:
assert column_title.isupper()
__snake_case = 0
__snake_case = len(_UpperCAmelCase ) - 1
__snake_case = 0
while index >= 0:
__snake_case = (ord(column_title[index] ) - 64) * pow(26 , _UpperCAmelCase )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 69
|
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
a : Tuple = get_tests_dir('''fixtures''')
a : Dict = get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
a : int = get_tests_dir('''fixtures/dummy-config.json''')
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def A ( self : Tuple ):
"""simple docstring"""
__snake_case = 0
def A ( self : str ):
"""simple docstring"""
__snake_case = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h" )
self.assertIsInstance(a_ , a_ )
def A ( self : str ):
"""simple docstring"""
__snake_case = AutoFeatureExtractor.from_pretrained(a_ )
self.assertIsInstance(a_ , a_ )
def A ( self : str ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
__snake_case = WavaVecaConfig()
# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
__snake_case = AutoFeatureExtractor.from_pretrained(a_ ).to_dict()
config_dict.pop("feature_extractor_type" )
__snake_case = WavaVecaFeatureExtractor(**a_ )
# save in new folder
model_config.save_pretrained(a_ )
config.save_pretrained(a_ )
__snake_case = AutoFeatureExtractor.from_pretrained(a_ )
# make sure private variable is not incorrectly saved
__snake_case = json.loads(config.to_json_string() )
self.assertTrue("_processor_class" not in dict_as_saved )
self.assertIsInstance(a_ , a_ )
def A ( self : List[Any] ):
"""simple docstring"""
__snake_case = AutoFeatureExtractor.from_pretrained(a_ )
self.assertIsInstance(a_ , a_ )
def A ( self : Optional[Any] ):
"""simple docstring"""
with self.assertRaisesRegex(
a_ , "bert-base is not a local folder and is not a valid model identifier" ):
__snake_case = AutoFeatureExtractor.from_pretrained("bert-base" )
def A ( self : Dict ):
"""simple docstring"""
with self.assertRaisesRegex(
a_ , r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ):
__snake_case = AutoFeatureExtractor.from_pretrained(a_ , revision="aaaaaa" )
def A ( self : Tuple ):
"""simple docstring"""
with self.assertRaisesRegex(
a_ , "hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json." , ):
__snake_case = AutoFeatureExtractor.from_pretrained("hf-internal-testing/config-no-model" )
def A ( self : Tuple ):
"""simple docstring"""
with self.assertRaises(a_ ):
__snake_case = AutoFeatureExtractor.from_pretrained(
"hf-internal-testing/test_dynamic_feature_extractor" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(a_ ):
__snake_case = AutoFeatureExtractor.from_pretrained(
"hf-internal-testing/test_dynamic_feature_extractor" , trust_remote_code=a_ )
__snake_case = AutoFeatureExtractor.from_pretrained(
"hf-internal-testing/test_dynamic_feature_extractor" , trust_remote_code=a_ )
self.assertEqual(feature_extractor.__class__.__name__ , "NewFeatureExtractor" )
# Test feature extractor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(a_ )
__snake_case = AutoFeatureExtractor.from_pretrained(a_ , trust_remote_code=a_ )
self.assertEqual(reloaded_feature_extractor.__class__.__name__ , "NewFeatureExtractor" )
def A ( self : int ):
"""simple docstring"""
try:
AutoConfig.register("custom" , a_ )
AutoFeatureExtractor.register(a_ , a_ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(a_ ):
AutoFeatureExtractor.register(a_ , a_ )
# Now that the config is registered, it can be used as any other config with the auto-API
__snake_case = CustomFeatureExtractor.from_pretrained(a_ )
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(a_ )
__snake_case = AutoFeatureExtractor.from_pretrained(a_ )
self.assertIsInstance(a_ , a_ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
def A ( self : Dict ):
"""simple docstring"""
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
__SCREAMING_SNAKE_CASE = True
try:
AutoConfig.register("custom" , a_ )
AutoFeatureExtractor.register(a_ , a_ )
# If remote code is not set, the default is to use local
__snake_case = AutoFeatureExtractor.from_pretrained(
"hf-internal-testing/test_dynamic_feature_extractor" )
self.assertEqual(feature_extractor.__class__.__name__ , "NewFeatureExtractor" )
self.assertTrue(feature_extractor.is_local )
# If remote code is disabled, we load the local one.
__snake_case = AutoFeatureExtractor.from_pretrained(
"hf-internal-testing/test_dynamic_feature_extractor" , trust_remote_code=a_ )
self.assertEqual(feature_extractor.__class__.__name__ , "NewFeatureExtractor" )
self.assertTrue(feature_extractor.is_local )
# If remote is enabled, we load from the Hub
__snake_case = AutoFeatureExtractor.from_pretrained(
"hf-internal-testing/test_dynamic_feature_extractor" , trust_remote_code=a_ )
self.assertEqual(feature_extractor.__class__.__name__ , "NewFeatureExtractor" )
self.assertTrue(not hasattr(a_ , "is_local" ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
| 69
| 1
|
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
def A_ ( lowercase_ , lowercase_ , lowercase_ ) ->List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = os.path.abspath(lowercase_ )
logger.info(f'''Converting TensorFlow checkpoint from {tf_path}''' )
# Load weights from TF model
SCREAMING_SNAKE_CASE = tf.train.list_variables(lowercase_ )
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
for full_name, shape in init_vars:
# logger.info(f"Loading TF weight {name} with shape {shape}")
SCREAMING_SNAKE_CASE = full_name.split('/' )
if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
logger.info(f'''Skipping non-model layer {full_name}''' )
continue
if "optimizer" in full_name:
logger.info(f'''Skipping optimization layer {full_name}''' )
continue
if name[0] == "model":
# ignore initial 'model'
SCREAMING_SNAKE_CASE = name[1:]
# figure out how many levels deep the name is
SCREAMING_SNAKE_CASE = 0
for _name in name:
if _name.startswith('layer_with_weights' ):
depth += 1
else:
break
layer_depth.append(lowercase_ )
# read data
SCREAMING_SNAKE_CASE = tf.train.load_variable(lowercase_ , lowercase_ )
names.append('/'.join(lowercase_ ) )
arrays.append(lowercase_ )
logger.info(f'''Read a total of {len(lowercase_ ):,} layers''' )
# Sanity check
if len(set(lowercase_ ) ) != 1:
raise ValueError(f'''Found layer names with different depths (layer depth {list(set(lowercase_ ) )})''' )
SCREAMING_SNAKE_CASE = list(set(lowercase_ ) )[0]
if layer_depth != 1:
raise ValueError(
'The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'
' heads.' )
# convert layers
logger.info('Converting weights...' )
for full_name, array in zip(lowercase_ , lowercase_ ):
SCREAMING_SNAKE_CASE = full_name.split('/' )
SCREAMING_SNAKE_CASE = model
SCREAMING_SNAKE_CASE = []
for i, m_name in enumerate(lowercase_ ):
if m_name == ".ATTRIBUTES":
# variable names end with .ATTRIBUTES/VARIABLE_VALUE
break
if m_name.startswith('layer_with_weights' ):
SCREAMING_SNAKE_CASE = int(m_name.split('-' )[-1] )
if layer_num <= 2:
# embedding layers
# layer_num 0: word_embeddings
# layer_num 1: position_embeddings
# layer_num 2: token_type_embeddings
continue
elif layer_num == 3:
# embedding LayerNorm
trace.extend(['embeddings', 'LayerNorm'] )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'embeddings' )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'LayerNorm' )
elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
# encoder layers
trace.extend(['encoder', 'layer', str(layer_num - 4 )] )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'encoder' )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'layer' )
SCREAMING_SNAKE_CASE = pointer[layer_num - 4]
elif layer_num == config.num_hidden_layers + 4:
# pooler layer
trace.extend(['pooler', 'dense'] )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'pooler' )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'dense' )
elif m_name == "embeddings":
trace.append('embeddings' )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'embeddings' )
if layer_num == 0:
trace.append('word_embeddings' )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'word_embeddings' )
elif layer_num == 1:
trace.append('position_embeddings' )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'position_embeddings' )
elif layer_num == 2:
trace.append('token_type_embeddings' )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'token_type_embeddings' )
else:
raise ValueError(f'''Unknown embedding layer with name {full_name}''' )
trace.append('weight' )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'weight' )
elif m_name == "_attention_layer":
# self-attention layer
trace.extend(['attention', 'self'] )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'attention' )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'self' )
elif m_name == "_attention_layer_norm":
# output attention norm
trace.extend(['attention', 'output', 'LayerNorm'] )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'attention' )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'output' )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'LayerNorm' )
elif m_name == "_attention_output_dense":
# output attention dense
trace.extend(['attention', 'output', 'dense'] )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'attention' )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'output' )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'dense' )
elif m_name == "_output_dense":
# output dense
trace.extend(['output', 'dense'] )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'output' )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'dense' )
elif m_name == "_output_layer_norm":
# output dense
trace.extend(['output', 'LayerNorm'] )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'output' )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'LayerNorm' )
elif m_name == "_key_dense":
# attention key
trace.append('key' )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'key' )
elif m_name == "_query_dense":
# attention query
trace.append('query' )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'query' )
elif m_name == "_value_dense":
# attention value
trace.append('value' )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'value' )
elif m_name == "_intermediate_dense":
# attention intermediate dense
trace.extend(['intermediate', 'dense'] )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'intermediate' )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'dense' )
elif m_name == "_output_layer_norm":
# output layer norm
trace.append('output' )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'output' )
# weights & biases
elif m_name in ["bias", "beta"]:
trace.append('bias' )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'bias' )
elif m_name in ["kernel", "gamma"]:
trace.append('weight' )
SCREAMING_SNAKE_CASE = getattr(lowercase_ , 'weight' )
else:
logger.warning(f'''Ignored {m_name}''' )
# for certain layers reshape is necessary
SCREAMING_SNAKE_CASE = '.'.join(lowercase_ )
if re.match(r'(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)' , lowercase_ ) or re.match(
r'(\S+)\.attention\.output\.dense\.weight' , lowercase_ ):
SCREAMING_SNAKE_CASE = array.reshape(pointer.data.shape )
if "kernel" in full_name:
SCREAMING_SNAKE_CASE = array.transpose()
if pointer.shape == array.shape:
SCREAMING_SNAKE_CASE = torch.from_numpy(lowercase_ )
else:
raise ValueError(
f'''Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'''
f''' {array.shape}''' )
logger.info(f'''Successfully set variable {full_name} to PyTorch layer {trace}''' )
return model
def A_ ( lowercase_ , lowercase_ , lowercase_ ) ->Any:
"""simple docstring"""
logger.info(f'''Loading model based on config from {config_path}...''' )
SCREAMING_SNAKE_CASE = BertConfig.from_json_file(lowercase_ )
SCREAMING_SNAKE_CASE = BertModel(lowercase_ )
# Load weights from checkpoint
logger.info(f'''Loading weights from checkpoint {tf_checkpoint_path}...''' )
load_tfa_weights_in_bert(lowercase_ , lowercase_ , lowercase_ )
# Save pytorch-model
logger.info(f'''Saving PyTorch model to {pytorch_dump_path}...''' )
torch.save(model.state_dict() , lowercase_ )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow 2.x checkpoint path."
)
parser.add_argument(
"--bert_config_file",
type=str,
required=True,
help="The config json file corresponding to the BERT model. This specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path",
type=str,
required=True,
help="Path to the output PyTorch model (must include filename).",
)
__UpperCAmelCase = parser.parse_args()
convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 259
|
import numpy as np
class a_:
"""simple docstring"""
def __init__( self : Any) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE = (0, 0)
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = 0
def __eq__( self : int , lowerCAmelCase__ : List[Any]) -> Optional[int]:
"""simple docstring"""
return self.position == cell.position
def __UpperCamelCase ( self : int) -> int:
"""simple docstring"""
print(self.position)
class a_:
"""simple docstring"""
def __init__( self : Optional[int] , lowerCAmelCase__ : List[str]=(5, 5)) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = np.zeros(lowerCAmelCase__)
SCREAMING_SNAKE_CASE = world_size[0]
SCREAMING_SNAKE_CASE = world_size[1]
def __UpperCamelCase ( self : Optional[Any]) -> Tuple:
"""simple docstring"""
print(self.w)
def __UpperCamelCase ( self : Union[str, Any] , lowerCAmelCase__ : Tuple) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
SCREAMING_SNAKE_CASE = cell.position[0]
SCREAMING_SNAKE_CASE = cell.position[1]
SCREAMING_SNAKE_CASE = []
for n in neughbour_cord:
SCREAMING_SNAKE_CASE = current_x + n[0]
SCREAMING_SNAKE_CASE = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
SCREAMING_SNAKE_CASE = Cell()
SCREAMING_SNAKE_CASE = (x, y)
SCREAMING_SNAKE_CASE = cell
neighbours.append(lowerCAmelCase__)
return neighbours
def A_ ( lowercase_ , lowercase_ , lowercase_ ) ->Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
_open.append(lowercase_ )
while _open:
SCREAMING_SNAKE_CASE = np.argmin([n.f for n in _open] )
SCREAMING_SNAKE_CASE = _open[min_f]
_closed.append(_open.pop(lowercase_ ) )
if current == goal:
break
for n in world.get_neigbours(lowercase_ ):
for c in _closed:
if c == n:
continue
SCREAMING_SNAKE_CASE = current.g + 1
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = n.position
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = goal.position
SCREAMING_SNAKE_CASE = (ya - ya) ** 2 + (xa - xa) ** 2
SCREAMING_SNAKE_CASE = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(lowercase_ )
SCREAMING_SNAKE_CASE = []
while current.parent is not None:
path.append(current.position )
SCREAMING_SNAKE_CASE = current.parent
path.append(current.position )
return path[::-1]
if __name__ == "__main__":
__UpperCAmelCase = Gridworld()
# Start position and goal
__UpperCAmelCase = Cell()
__UpperCAmelCase = (0, 0)
__UpperCAmelCase = Cell()
__UpperCAmelCase = (4, 4)
print(f'path from {start.position} to {goal.position}')
__UpperCAmelCase = astar(world, start, goal)
# Just for visual reasons.
for i in s:
__UpperCAmelCase = 1
print(world.w)
| 259
| 1
|
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def a_ ( UpperCamelCase_ : str ) -> int:
"""simple docstring"""
return EnvironmentCommand()
class lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
@staticmethod
def lowerCamelCase__ ( __snake_case : ArgumentParser ) -> Dict:
'''simple docstring'''
lowerCamelCase = parser.add_parser('env' )
download_parser.set_defaults(func=__snake_case )
def lowerCamelCase__ ( self : str ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase = huggingface_hub.__version__
lowerCamelCase = 'not installed'
lowerCamelCase = 'NA'
if is_torch_available():
import torch
lowerCamelCase = torch.__version__
lowerCamelCase = torch.cuda.is_available()
lowerCamelCase = 'not installed'
if is_transformers_available():
import transformers
lowerCamelCase = transformers.__version__
lowerCamelCase = 'not installed'
if is_accelerate_available():
import accelerate
lowerCamelCase = accelerate.__version__
lowerCamelCase = 'not installed'
if is_xformers_available():
import xformers
lowerCamelCase = xformers.__version__
lowerCamelCase = {
'`diffusers` version': version,
'Platform': platform.platform(),
'Python version': platform.python_version(),
'PyTorch version (GPU?)': F'''{pt_version} ({pt_cuda_available})''',
'Huggingface_hub version': hub_version,
'Transformers version': transformers_version,
'Accelerate version': accelerate_version,
'xFormers version': xformers_version,
'Using GPU in script?': '<fill in>',
'Using distributed or parallel set-up in script?': '<fill in>',
}
print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n' )
print(self.format_dict(__snake_case ) )
return info
@staticmethod
def lowerCamelCase__ ( __snake_case : Optional[Any] ) -> Tuple:
'''simple docstring'''
return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()] ) + "\n"
| 246
|
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
TFDebertaVaModel,
)
class lowerCAmelCase :
'''simple docstring'''
def __init__( self : Dict , __snake_case : Tuple , __snake_case : Optional[int]=13 , __snake_case : int=7 , __snake_case : Tuple=True , __snake_case : Optional[Any]=True , __snake_case : List[Any]=True , __snake_case : Optional[Any]=True , __snake_case : Optional[Any]=99 , __snake_case : Union[str, Any]=32 , __snake_case : str=2 , __snake_case : Tuple=4 , __snake_case : Tuple=37 , __snake_case : Any="gelu" , __snake_case : List[Any]=0.1 , __snake_case : Optional[int]=0.1 , __snake_case : Tuple=512 , __snake_case : str=16 , __snake_case : str=2 , __snake_case : Dict=0.02 , __snake_case : Union[str, Any]=False , __snake_case : Tuple=True , __snake_case : Union[str, Any]="None" , __snake_case : Union[str, Any]=3 , __snake_case : Optional[Any]=4 , __snake_case : List[str]=None , ) -> Dict:
'''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 = relative_attention
lowerCamelCase = position_biased_input
lowerCamelCase = pos_att_type
lowerCamelCase = scope
def lowerCamelCase__ ( self : Union[str, Any] ) -> Optional[int]:
'''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 = DebertaVaConfig(
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 , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , initializer_range=self.initializer_range , return_dict=__snake_case , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase__ ( self : Optional[Any] , __snake_case : List[str] , __snake_case : Optional[Any] , __snake_case : Optional[Any] , __snake_case : str , __snake_case : Tuple , __snake_case : List[Any] , __snake_case : Optional[Any] ) -> Any:
'''simple docstring'''
lowerCamelCase = TFDebertaVaModel(config=__snake_case )
lowerCamelCase = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
lowerCamelCase = [input_ids, input_mask]
lowerCamelCase = model(__snake_case )
lowerCamelCase = model(__snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase__ ( self : int , __snake_case : Optional[int] , __snake_case : str , __snake_case : Any , __snake_case : List[str] , __snake_case : Optional[int] , __snake_case : Optional[int] , __snake_case : Union[str, Any] ) -> Any:
'''simple docstring'''
lowerCamelCase = TFDebertaVaForMaskedLM(config=__snake_case )
lowerCamelCase = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
lowerCamelCase = model(__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase__ ( self : Optional[int] , __snake_case : str , __snake_case : Union[str, Any] , __snake_case : Optional[Any] , __snake_case : List[Any] , __snake_case : Optional[Any] , __snake_case : Dict , __snake_case : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
lowerCamelCase = self.num_labels
lowerCamelCase = TFDebertaVaForSequenceClassification(config=__snake_case )
lowerCamelCase = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
lowerCamelCase = model(__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase__ ( self : Tuple , __snake_case : List[Any] , __snake_case : Optional[Any] , __snake_case : Optional[Any] , __snake_case : Optional[int] , __snake_case : Dict , __snake_case : str , __snake_case : List[str] ) -> int:
'''simple docstring'''
lowerCamelCase = self.num_labels
lowerCamelCase = TFDebertaVaForTokenClassification(config=__snake_case )
lowerCamelCase = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
lowerCamelCase = model(__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase__ ( self : Tuple , __snake_case : Dict , __snake_case : str , __snake_case : Tuple , __snake_case : str , __snake_case : List[str] , __snake_case : int , __snake_case : Any ) -> Dict:
'''simple docstring'''
lowerCamelCase = TFDebertaVaForQuestionAnswering(config=__snake_case )
lowerCamelCase = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
lowerCamelCase = model(__snake_case )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase__ ( self : Any ) -> List[Any]:
'''simple docstring'''
lowerCamelCase = self.prepare_config_and_inputs()
(
(
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) ,
) = config_and_inputs
lowerCamelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_tf
class lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
snake_case = (
(
TFDebertaVaModel,
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
)
if is_tf_available()
else ()
)
snake_case = (
{
'feature-extraction': TFDebertaVaModel,
'fill-mask': TFDebertaVaForMaskedLM,
'question-answering': TFDebertaVaForQuestionAnswering,
'text-classification': TFDebertaVaForSequenceClassification,
'token-classification': TFDebertaVaForTokenClassification,
'zero-shot': TFDebertaVaForSequenceClassification,
}
if is_tf_available()
else {}
)
snake_case = False
snake_case = False
def lowerCamelCase__ ( self : List[Any] ) -> List[str]:
'''simple docstring'''
lowerCamelCase = TFDebertaVaModelTester(self )
lowerCamelCase = ConfigTester(self , config_class=__snake_case , hidden_size=37 )
def lowerCamelCase__ ( self : Any ) -> int:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase__ ( self : List[Any] ) -> Tuple:
'''simple docstring'''
lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__snake_case )
def lowerCamelCase__ ( self : int ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__snake_case )
def lowerCamelCase__ ( self : Tuple ) -> List[str]:
'''simple docstring'''
lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__snake_case )
def lowerCamelCase__ ( self : Optional[Any] ) -> Tuple:
'''simple docstring'''
lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__snake_case )
def lowerCamelCase__ ( self : Tuple ) -> str:
'''simple docstring'''
lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__snake_case )
@slow
def lowerCamelCase__ ( self : Dict ) -> List[str]:
'''simple docstring'''
lowerCamelCase = TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge' )
self.assertIsNotNone(__snake_case )
@require_tf
class lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@unittest.skip(reason='Model not available yet' )
def lowerCamelCase__ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
pass
@slow
def lowerCamelCase__ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
lowerCamelCase = TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge' )
lowerCamelCase = tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
lowerCamelCase = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
lowerCamelCase = model(__snake_case , attention_mask=__snake_case )[0]
lowerCamelCase = tf.constant(
[[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]] )
tf.debugging.assert_near(output[:, 1:4, 1:4] , __snake_case , atol=1e-4 )
| 246
| 1
|
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
_a : List[str] = logging.get_logger(__name__)
_a : Tuple = [
("""bert.bert""", """visual_bert"""),
("""bert.cls""", """cls"""),
("""bert.classifier""", """cls"""),
("""token_type_embeddings_visual""", """visual_token_type_embeddings"""),
("""position_embeddings_visual""", """visual_position_embeddings"""),
("""projection""", """visual_projection"""),
]
_a : Optional[int] = [
"""nlvr2_coco_pre_trained.th""",
"""nlvr2_fine_tuned.th""",
"""nlvr2_pre_trained.th""",
"""vcr_coco_pre_train.th""",
"""vcr_fine_tune.th""",
"""vcr_pre_train.th""",
"""vqa_coco_pre_trained.th""",
"""vqa_fine_tuned.th""",
"""vqa_pre_trained.th""",
]
def a__ ( a : Optional[int] ):
"""simple docstring"""
_snake_case : Any = torch.load(UpperCamelCase__ , map_location="cpu" )
return sd
def a__ ( a : Optional[int] , a : Union[str, Any] , a : Dict=rename_keys_prefix ):
"""simple docstring"""
_snake_case : Tuple = OrderedDict()
_snake_case : List[Any] = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
_snake_case : Union[str, Any] = key
for name_pair in rename_keys_prefix:
_snake_case : Optional[int] = new_key.replace(name_pair[0] , name_pair[1] )
_snake_case : List[str] = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
_snake_case : Tuple = new_d["cls.predictions.bias"]
return new_d
@torch.no_grad()
def a__ ( a : Any , a : Dict ):
"""simple docstring"""
assert (
checkpoint_path.split("/" )[-1] in ACCEPTABLE_CHECKPOINTS
), f'The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'
# Get Config
if "pre" in checkpoint_path:
_snake_case : Any = "pretraining"
if "vcr" in checkpoint_path:
_snake_case : List[Any] = {"visual_embedding_dim": 512}
elif "vqa_advanced" in checkpoint_path:
_snake_case : Any = {"visual_embedding_dim": 2_048}
elif "vqa" in checkpoint_path:
_snake_case : Any = {"visual_embedding_dim": 2_048}
elif "nlvr" in checkpoint_path:
_snake_case : Optional[Any] = {"visual_embedding_dim": 1_024}
else:
raise NotImplementedError(f'No implementation found for `{checkpoint_path}`.' )
else:
if "vcr" in checkpoint_path:
_snake_case : Union[str, Any] = {"visual_embedding_dim": 512}
_snake_case : Tuple = "multichoice"
elif "vqa_advanced" in checkpoint_path:
_snake_case : List[str] = {"visual_embedding_dim": 2_048}
_snake_case : List[str] = "vqa_advanced"
elif "vqa" in checkpoint_path:
_snake_case : List[Any] = {"visual_embedding_dim": 2_048, "num_labels": 3_129}
_snake_case : List[str] = "vqa"
elif "nlvr" in checkpoint_path:
_snake_case : List[Any] = {
"visual_embedding_dim": 1_024,
"num_labels": 2,
}
_snake_case : Any = "nlvr"
_snake_case : Tuple = VisualBertConfig(**UpperCamelCase__ )
# Load State Dict
_snake_case : Dict = load_state_dict(UpperCamelCase__ )
_snake_case : Any = get_new_dict(UpperCamelCase__ , UpperCamelCase__ )
if model_type == "pretraining":
_snake_case : List[Any] = VisualBertForPreTraining(UpperCamelCase__ )
elif model_type == "vqa":
_snake_case : Optional[Any] = VisualBertForQuestionAnswering(UpperCamelCase__ )
elif model_type == "nlvr":
_snake_case : Dict = VisualBertForVisualReasoning(UpperCamelCase__ )
elif model_type == "multichoice":
_snake_case : Tuple = VisualBertForMultipleChoice(UpperCamelCase__ )
model.load_state_dict(UpperCamelCase__ )
# Save Checkpoints
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
model.save_pretrained(UpperCamelCase__ )
if __name__ == "__main__":
_a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""orig_checkpoint_path""", type=str, help="""A path to .th on local filesystem.""")
parser.add_argument("""pytorch_dump_folder_path""", type=str, help="""Path to the output PyTorch model.""")
_a : List[Any] = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 719
|
"""simple docstring"""
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
_a : Dict = input("""Enter image url: """).strip()
print(f'Downloading image from {url} ...')
_a : str = BeautifulSoup(requests.get(url).content, """html.parser""")
# The image URL is in the content field of the first meta tag with property og:image
_a : str = soup.find("""meta""", {"""property""": """og:image"""})["""content"""]
_a : Dict = requests.get(image_url).content
_a : str = f'{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'
with open(file_name, """wb""") as fp:
fp.write(image_data)
print(f'Done. Image saved to disk as {file_name}.')
| 87
| 0
|
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ ) -> Optional[int]:
lowerCAmelCase__ : str = len(lowerCAmelCase__ )
lowerCAmelCase__ : Any = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for i in range(arr_len + 1 ):
lowerCAmelCase__ : List[str] = True
# sum is not zero and set is empty then false
for i in range(1 , required_sum + 1 ):
lowerCAmelCase__ : List[Any] = False
for i in range(1 , arr_len + 1 ):
for j in range(1 , required_sum + 1 ):
if arr[i - 1] > j:
lowerCAmelCase__ : Optional[Any] = subset[i - 1][j]
if arr[i - 1] <= j:
lowerCAmelCase__ : str = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]
return subset[arr_len][required_sum]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 453
|
"""simple docstring"""
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
'''stable diffusion controlnet''',
'''0.22.0''',
'''Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.''',
standard_warn=False,
stacklevel=3,
)
| 260
| 0
|
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> list:
lowerCAmelCase__ : Any = [0] * len(SCREAMING_SNAKE_CASE_ )
for i in range(1 , len(SCREAMING_SNAKE_CASE_ ) ):
# use last results for better performance - dynamic programming
lowerCAmelCase__ : Optional[int] = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
lowerCAmelCase__ : Union[str, Any] = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
lowerCAmelCase__ : Any = j
return prefix_result
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> int:
return max(prefix_function(SCREAMING_SNAKE_CASE_ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 69
|
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class A__ ( __magic_name__ , unittest.TestCase ):
lowercase = XLMTokenizer
lowercase = False
def _lowerCamelCase ( self : int ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCAmelCase__ : List[str] = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'w</w>',
'r</w>',
't</w>',
'lo',
'low',
'er</w>',
'low</w>',
'lowest</w>',
'newer</w>',
'wider</w>',
'<unk>',
]
lowerCAmelCase__ : Any = dict(zip(a , range(len(a ) ) ) )
lowerCAmelCase__ : Optional[int] = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']
lowerCAmelCase__ : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowerCAmelCase__ : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' ) as fp:
fp.write(json.dumps(a ) )
with open(self.merges_file , 'w' ) as fp:
fp.write('\n'.join(a ) )
def _lowerCamelCase ( self : List[str] , a : Dict ):
'''simple docstring'''
lowerCAmelCase__ : List[Any] = 'lower newer'
lowerCAmelCase__ : Any = 'lower newer'
return input_text, output_text
def _lowerCamelCase ( self : int ):
'''simple docstring'''
lowerCAmelCase__ : Tuple = XLMTokenizer(self.vocab_file , self.merges_file )
lowerCAmelCase__ : Optional[int] = 'lower'
lowerCAmelCase__ : Optional[Any] = ['low', 'er</w>']
lowerCAmelCase__ : Dict = tokenizer.tokenize(a )
self.assertListEqual(a , a )
lowerCAmelCase__ : Tuple = tokens + ['<unk>']
lowerCAmelCase__ : Optional[int] = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(a ) , a )
@slow
def _lowerCamelCase ( self : Any ):
'''simple docstring'''
lowerCAmelCase__ : List[Any] = XLMTokenizer.from_pretrained('xlm-mlm-en-2048' )
lowerCAmelCase__ : Any = tokenizer.encode('sequence builders' , add_special_tokens=a )
lowerCAmelCase__ : Union[str, Any] = tokenizer.encode('multi-sequence build' , add_special_tokens=a )
lowerCAmelCase__ : List[Any] = tokenizer.build_inputs_with_special_tokens(a )
lowerCAmelCase__ : List[str] = tokenizer.build_inputs_with_special_tokens(a , a )
assert encoded_sentence == [0] + text + [1]
assert encoded_pair == [0] + text + [1] + text_a + [1]
| 69
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {
"""configuration_conditional_detr""": [
"""CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ConditionalDetrConfig""",
"""ConditionalDetrOnnxConfig""",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""ConditionalDetrFeatureExtractor"""]
a_ = ["""ConditionalDetrImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"""CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ConditionalDetrForObjectDetection""",
"""ConditionalDetrForSegmentation""",
"""ConditionalDetrModel""",
"""ConditionalDetrPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 437
|
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"""
),
}
class A_(SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
a_ : List[Any] = """xlm-prophetnet"""
a_ : Any = ["""past_key_values"""]
a_ : Optional[int] = {
"""num_attention_heads""": """num_encoder_attention_heads""",
}
def __init__( self , A = 0.1 , A = "gelu" , A = 3_0522 , A = 1024 , A = 4096 , A = 12 , A = 16 , A = 4096 , A = 12 , A = 16 , A = 0.1 , A = 0.1 , A = 512 , A = 0.0_2 , A = True , A = True , A = 0 , A = 2 , A = 32 , A = 128 , A = False , A = 0.0 , A = True , A = 0 , A = 1 , A = 2 , **A , ):
_lowerCamelCase : Dict = vocab_size
_lowerCamelCase : List[Any] = hidden_size
_lowerCamelCase : List[Any] = encoder_ffn_dim
_lowerCamelCase : Any = num_encoder_layers
_lowerCamelCase : Any = num_encoder_attention_heads
_lowerCamelCase : List[str] = decoder_ffn_dim
_lowerCamelCase : Optional[int] = num_decoder_layers
_lowerCamelCase : List[str] = num_decoder_attention_heads
_lowerCamelCase : Optional[Any] = max_position_embeddings
_lowerCamelCase : Optional[Any] = init_std # Normal(0, this parameter)
_lowerCamelCase : Optional[int] = activation_function
# parameters for xlmprophetnet
_lowerCamelCase : Any = ngram
_lowerCamelCase : Dict = num_buckets
_lowerCamelCase : Dict = relative_max_distance
_lowerCamelCase : Optional[Any] = disable_ngram_loss
_lowerCamelCase : Union[str, Any] = eps
# 3 Types of Dropout
_lowerCamelCase : int = attention_dropout
_lowerCamelCase : str = activation_dropout
_lowerCamelCase : Optional[int] = dropout
_lowerCamelCase : List[Any] = use_cache
super().__init__(
pad_token_id=A , bos_token_id=A , eos_token_id=A , is_encoder_decoder=A , add_cross_attention=A , decoder_start_token_id=A , **A , )
@property
def _lowerCAmelCase ( self ):
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def _lowerCAmelCase ( self , A ):
raise NotImplementedError(
'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
' `num_decoder_layers`.' )
| 437
| 1
|
from __future__ import annotations
snake_case__ : Tuple = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ):
__lowercase = [
[0 for col in range(len(grid[0] ) )] for row in range(len(_SCREAMING_SNAKE_CASE ) )
] # the reference grid
__lowercase = 1
__lowercase = [
[0 for col in range(len(grid[0] ) )] for row in range(len(_SCREAMING_SNAKE_CASE ) )
] # the action grid
__lowercase = init[0]
__lowercase = init[1]
__lowercase = 0
__lowercase = g + heuristic[x][y] # cost from starting cell to destination cell
__lowercase = [[f, g, x, y]]
__lowercase = False # flag that is set when search is complete
__lowercase = False # flag set if we can't find expand
while not found and not resign:
if len(_SCREAMING_SNAKE_CASE ) == 0:
raise ValueError("Algorithm is unable to find solution" )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
__lowercase = cell.pop()
__lowercase = next_cell[2]
__lowercase = next_cell[3]
__lowercase = next_cell[1]
if x == goal[0] and y == goal[1]:
__lowercase = True
else:
for i in range(len(_SCREAMING_SNAKE_CASE ) ): # to try out different valid actions
__lowercase = x + DIRECTIONS[i][0]
__lowercase = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(_SCREAMING_SNAKE_CASE ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
__lowercase = g + cost
__lowercase = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
__lowercase = 1
__lowercase = i
__lowercase = []
__lowercase = goal[0]
__lowercase = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
__lowercase = x - DIRECTIONS[action[x][y]][0]
__lowercase = y - DIRECTIONS[action[x][y]][1]
__lowercase = xa
__lowercase = ya
invpath.append([x, y] )
__lowercase = []
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
path.append(invpath[len(_SCREAMING_SNAKE_CASE ) - 1 - i] )
return path, action
if __name__ == "__main__":
snake_case__ : List[str] = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
snake_case__ : Optional[int] = [0, 0]
# all coordinates are given in format [y,x]
snake_case__ : Union[str, Any] = [len(grid) - 1, len(grid[0]) - 1]
snake_case__ : Dict = 1
# the cost map which pushes the path closer to the goal
snake_case__ : Tuple = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
snake_case__ : Optional[int] = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
snake_case__ : List[Any] = 99
snake_case__ , snake_case__ : Union[str, Any] = search(grid, init, goal, cost, heuristic)
print("""ACTION MAP""")
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 655
|
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _A ( _lowercase , _lowercase ):
'''simple docstring'''
@register_to_config
def __init__( self : Optional[Any] , *,
lowerCamelCase : int = 4 , lowerCamelCase : int = 768 , lowerCamelCase : int , lowerCamelCase : Optional[int] , ):
'''simple docstring'''
super().__init__()
__lowercase = nn.Parameter(torch.zeros(lowerCamelCase ) )
# parameters for additional clip time embeddings
__lowercase = nn.Linear(lowerCamelCase , lowerCamelCase )
__lowercase = nn.Linear(lowerCamelCase , lowerCamelCase )
# parameters for encoder hidden states
__lowercase = clip_extra_context_tokens
__lowercase = nn.Linear(
lowerCamelCase , self.clip_extra_context_tokens * cross_attention_dim )
__lowercase = nn.Linear(lowerCamelCase , lowerCamelCase )
__lowercase = nn.LayerNorm(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , *, lowerCamelCase : Any , lowerCamelCase : Tuple , lowerCamelCase : Optional[int] , lowerCamelCase : Tuple ):
'''simple docstring'''
if do_classifier_free_guidance:
# Add the classifier free guidance embeddings to the image embeddings
__lowercase = image_embeddings.shape[0]
__lowercase = self.learned_classifier_free_guidance_embeddings.unsqueeze(0 )
__lowercase = classifier_free_guidance_embeddings.expand(
lowerCamelCase , -1 )
__lowercase = torch.cat([classifier_free_guidance_embeddings, image_embeddings] , dim=0 )
# The image embeddings batch size and the text embeddings batch size are equal
assert image_embeddings.shape[0] == prompt_embeds.shape[0]
__lowercase = prompt_embeds.shape[0]
# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
# adding CLIP embeddings to the existing timestep embedding, ...
__lowercase = self.embedding_proj(lowerCamelCase )
__lowercase = self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase )
__lowercase = time_projected_image_embeddings + time_projected_prompt_embeds
# ... and by projecting CLIP embeddings into four
# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
__lowercase = self.clip_extra_context_tokens_proj(lowerCamelCase )
__lowercase = clip_extra_context_tokens.reshape(lowerCamelCase , -1 , self.clip_extra_context_tokens )
__lowercase = clip_extra_context_tokens.permute(0 , 2 , 1 )
__lowercase = self.encoder_hidden_states_proj(lowerCamelCase )
__lowercase = self.text_encoder_hidden_states_norm(lowerCamelCase )
__lowercase = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] , dim=1 )
return text_encoder_hidden_states, additive_clip_time_embeddings
| 655
| 1
|
from math import factorial
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : int = 20 ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] =2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
SCREAMING_SNAKE_CASE_ : List[Any] =n // 2
return int(factorial(lowerCAmelCase_ ) / (factorial(lowerCAmelCase_ ) * factorial(n - k )) )
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution(20))
else:
try:
__SCREAMING_SNAKE_CASE = int(sys.argv[1])
print(solution(n))
except ValueError:
print('Invalid entry - please enter a number.')
| 220
|
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__A )
class lowerCAmelCase_ ( __A ):
'''simple docstring'''
_lowercase = field(default='text-classification' , metadata={'include_in_asdict_even_if_is_default': True} )
_lowercase = Features({'text': Value('string' )} )
_lowercase = Features({'labels': ClassLabel} )
_lowercase = "text"
_lowercase = "labels"
def __lowerCamelCase ( self , __UpperCAmelCase ):
if self.label_column not in features:
raise ValueError(F"""Column {self.label_column} is not present in features.""" )
if not isinstance(features[self.label_column] , __UpperCAmelCase ):
raise ValueError(F"""Column {self.label_column} is not a ClassLabel.""" )
SCREAMING_SNAKE_CASE_ : Optional[int] =copy.deepcopy(self )
SCREAMING_SNAKE_CASE_ : Any =self.label_schema.copy()
SCREAMING_SNAKE_CASE_ : Any =features[self.label_column]
SCREAMING_SNAKE_CASE_ : int =label_schema
return task_template
@property
def __lowerCamelCase ( self ):
return {
self.text_column: "text",
self.label_column: "labels",
}
| 220
| 1
|
'''simple docstring'''
from math import sqrt
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =0
for i in range(1 , int(sqrt(a__ ) + 1 ) ):
if n % i == 0 and i != sqrt(a__ ):
total += i + n // i
elif i == sqrt(a__ ):
total += i
return total - n
def UpperCamelCase__ ( a__ = 1_0_0_0_0 ):
'''simple docstring'''
_lowerCAmelCase =sum(
i
for i in range(1 , a__ )
if sum_of_divisors(sum_of_divisors(a__ ) ) == i and sum_of_divisors(a__ ) != i )
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 58
|
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
lowercase_ = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : int = 'sequence-classification'
def __init__( self , __A ) -> List[Any]:
if type(__A ) == dict:
_lowerCAmelCase =Namespace(**__A )
_lowerCAmelCase =glue_output_modes[hparams.task]
_lowerCAmelCase =glue_tasks_num_labels[hparams.task]
super().__init__(__A , __A , self.mode )
def UpperCamelCase__ ( self , **__A ) -> Any:
return self.model(**__A )
def UpperCamelCase__ ( self , __A , __A ) -> Union[str, Any]:
_lowerCAmelCase ={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_lowerCAmelCase =batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None
_lowerCAmelCase =self(**__A )
_lowerCAmelCase =outputs[0]
_lowerCAmelCase =self.trainer.lr_schedulers[0]['scheduler']
_lowerCAmelCase ={'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def UpperCamelCase__ ( self ) -> Any:
_lowerCAmelCase =self.hparams
_lowerCAmelCase =processors[args.task]()
_lowerCAmelCase =processor.get_labels()
for mode in ["train", "dev"]:
_lowerCAmelCase =self._feature_file(__A )
if os.path.exists(__A ) and not args.overwrite_cache:
logger.info('Loading features from cached file %s' , __A )
else:
logger.info('Creating features from dataset file at %s' , args.data_dir )
_lowerCAmelCase =(
processor.get_dev_examples(args.data_dir )
if mode == 'dev'
else processor.get_train_examples(args.data_dir )
)
_lowerCAmelCase =convert_examples_to_features(
__A , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info('Saving features into cached file %s' , __A )
torch.save(__A , __A )
def UpperCamelCase__ ( self , __A , __A , __A = False ) -> DataLoader:
_lowerCAmelCase ='dev' if mode == 'test' else mode
_lowerCAmelCase =self._feature_file(__A )
logger.info('Loading features from cached file %s' , __A )
_lowerCAmelCase =torch.load(__A )
_lowerCAmelCase =torch.tensor([f.input_ids for f in features] , dtype=torch.long )
_lowerCAmelCase =torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
_lowerCAmelCase =torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_lowerCAmelCase =torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_lowerCAmelCase =torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(__A , __A , __A , __A ) , batch_size=__A , shuffle=__A , )
def UpperCamelCase__ ( self , __A , __A ) -> List[str]:
_lowerCAmelCase ={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_lowerCAmelCase =batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None
_lowerCAmelCase =self(**__A )
_lowerCAmelCase , _lowerCAmelCase =outputs[:2]
_lowerCAmelCase =logits.detach().cpu().numpy()
_lowerCAmelCase =inputs['labels'].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def UpperCamelCase__ ( self , __A ) -> tuple:
_lowerCAmelCase =torch.stack([x['val_loss'] for x in outputs] ).mean().detach().cpu().item()
_lowerCAmelCase =np.concatenate([x['pred'] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
_lowerCAmelCase =np.argmax(__A , axis=1 )
elif self.hparams.glue_output_mode == "regression":
_lowerCAmelCase =np.squeeze(__A )
_lowerCAmelCase =np.concatenate([x['target'] for x in outputs] , axis=0 )
_lowerCAmelCase =[[] for _ in range(out_label_ids.shape[0] )]
_lowerCAmelCase =[[] for _ in range(out_label_ids.shape[0] )]
_lowerCAmelCase ={**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task , __A , __A )}
_lowerCAmelCase =dict(results.items() )
_lowerCAmelCase =results
return ret, preds_list, out_label_list
def UpperCamelCase__ ( self , __A ) -> dict:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =self._eval_end(__A )
_lowerCAmelCase =ret['log']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def UpperCamelCase__ ( self , __A ) -> dict:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =self._eval_end(__A )
_lowerCAmelCase =ret['log']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def UpperCamelCase__ ( __A , __A ) -> Any:
BaseTransformer.add_model_specific_args(__A , __A )
parser.add_argument(
'--max_seq_length' , default=128 , type=__A , help=(
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
) , )
parser.add_argument(
'--task' , default='' , type=__A , required=__A , help='The GLUE task to run' , )
parser.add_argument(
'--gpus' , default=0 , type=__A , help='The number of GPUs allocated for this, it is by default 0 meaning none' , )
parser.add_argument(
'--overwrite_cache' , action='store_true' , help='Overwrite the cached training and evaluation sets' )
return parser
def UpperCamelCase__ ( ):
'''simple docstring'''
_lowerCAmelCase =argparse.ArgumentParser()
add_generic_args(a__ , os.getcwd() )
_lowerCAmelCase =GLUETransformer.add_model_specific_args(a__ , os.getcwd() )
_lowerCAmelCase =parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_lowerCAmelCase =os.path.join(
'./results' , F'''{args.task}_{time.strftime('%Y%m%d_%H%M%S' )}''' , )
os.makedirs(args.output_dir )
_lowerCAmelCase =GLUETransformer(a__ )
_lowerCAmelCase =generic_train(a__ , a__ )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_lowerCAmelCase =sorted(glob.glob(os.path.join(args.output_dir , 'checkpoint-epoch=*.ckpt' ) , recursive=a__ ) )
_lowerCAmelCase =model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(a__ )
if __name__ == "__main__":
main()
| 58
| 1
|
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class A__:
@staticmethod
def _a ( *__SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : str ) -> List[str]:
"""simple docstring"""
pass
def _a ( UpperCAmelCase__ ) -> Any:
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
lowerCAmelCase__ =(
"https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"
)
@is_pipeline_test
@require_torch
@require_vision
class A__( unittest.TestCase ):
lowerCAmelCase = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : str ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = pipeline(
'''document-question-answering''' , model=__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = INVOICE_URL
__SCREAMING_SNAKE_CASE = list(zip(*apply_tesseract(load_image(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE , '''''' ) ) )
__SCREAMING_SNAKE_CASE = '''What is the placebo?'''
__SCREAMING_SNAKE_CASE = [
{
'''image''': load_image(__SCREAMING_SNAKE_CASE ),
'''question''': question,
},
{
'''image''': image,
'''question''': question,
},
{
'''image''': image,
'''question''': question,
'''word_boxes''': word_boxes,
},
]
return dqa_pipeline, examples
def _a ( self : str , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = dqa_pipeline(__SCREAMING_SNAKE_CASE , top_k=2 )
self.assertEqual(
__SCREAMING_SNAKE_CASE , [
[
{'''score''': ANY(__SCREAMING_SNAKE_CASE ), '''answer''': ANY(__SCREAMING_SNAKE_CASE ), '''start''': ANY(__SCREAMING_SNAKE_CASE ), '''end''': ANY(__SCREAMING_SNAKE_CASE )},
{'''score''': ANY(__SCREAMING_SNAKE_CASE ), '''answer''': ANY(__SCREAMING_SNAKE_CASE ), '''start''': ANY(__SCREAMING_SNAKE_CASE ), '''end''': ANY(__SCREAMING_SNAKE_CASE )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def _a ( self : List[str] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = pipeline('''document-question-answering''' , model='''hf-internal-testing/tiny-random-layoutlmv2''' )
__SCREAMING_SNAKE_CASE = INVOICE_URL
__SCREAMING_SNAKE_CASE = '''How many cats are there?'''
__SCREAMING_SNAKE_CASE = [
{'''score''': 0.00_01, '''answer''': '''oy 2312/2019''', '''start''': 38, '''end''': 39},
{'''score''': 0.00_01, '''answer''': '''oy 2312/2019 DUE''', '''start''': 38, '''end''': 40},
]
__SCREAMING_SNAKE_CASE = dqa_pipeline(image=__SCREAMING_SNAKE_CASE , question=__SCREAMING_SNAKE_CASE , top_k=2 )
self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE , decimals=4 ) , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE , decimals=4 ) , __SCREAMING_SNAKE_CASE )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
__SCREAMING_SNAKE_CASE = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
__SCREAMING_SNAKE_CASE = dqa_pipeline(image=__SCREAMING_SNAKE_CASE , question=__SCREAMING_SNAKE_CASE , top_k=2 )
self.assertEqual(__SCREAMING_SNAKE_CASE , [] )
# We can optionnally pass directly the words and bounding boxes
__SCREAMING_SNAKE_CASE = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = dqa_pipeline(image=__SCREAMING_SNAKE_CASE , question=__SCREAMING_SNAKE_CASE , words=__SCREAMING_SNAKE_CASE , boxes=__SCREAMING_SNAKE_CASE , top_k=2 )
self.assertEqual(__SCREAMING_SNAKE_CASE , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _a ( self : int ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = pipeline(
'''document-question-answering''' , model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa''' , revision='''9977165''' , )
__SCREAMING_SNAKE_CASE = INVOICE_URL
__SCREAMING_SNAKE_CASE = '''What is the invoice number?'''
__SCREAMING_SNAKE_CASE = dqa_pipeline(image=__SCREAMING_SNAKE_CASE , question=__SCREAMING_SNAKE_CASE , top_k=2 )
self.assertEqual(
nested_simplify(__SCREAMING_SNAKE_CASE , decimals=4 ) , [
{'''score''': 0.99_44, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.00_09, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__SCREAMING_SNAKE_CASE = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__SCREAMING_SNAKE_CASE , decimals=4 ) , [
{'''score''': 0.99_44, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.00_09, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__SCREAMING_SNAKE_CASE = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__SCREAMING_SNAKE_CASE , decimals=4 ) , [
[
{'''score''': 0.99_44, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.00_09, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _a ( self : Dict ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = pipeline(
'''document-question-answering''' , model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa''' , revision='''9977165''' , max_seq_len=50 , )
__SCREAMING_SNAKE_CASE = INVOICE_URL
__SCREAMING_SNAKE_CASE = '''What is the invoice number?'''
__SCREAMING_SNAKE_CASE = dqa_pipeline(image=__SCREAMING_SNAKE_CASE , question=__SCREAMING_SNAKE_CASE , top_k=2 )
self.assertEqual(
nested_simplify(__SCREAMING_SNAKE_CASE , decimals=4 ) , [
{'''score''': 0.99_74, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.99_48, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__SCREAMING_SNAKE_CASE = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__SCREAMING_SNAKE_CASE , decimals=4 ) , [
{'''score''': 0.99_74, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.99_48, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__SCREAMING_SNAKE_CASE = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__SCREAMING_SNAKE_CASE , decimals=4 ) , [
[
{'''score''': 0.99_74, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.99_48, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
'''impira/layoutlm-document-qa''' , revision='''3dc6de3''' , add_prefix_space=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pipeline(
'''document-question-answering''' , model='''impira/layoutlm-document-qa''' , tokenizer=__SCREAMING_SNAKE_CASE , revision='''3dc6de3''' , )
__SCREAMING_SNAKE_CASE = INVOICE_URL
__SCREAMING_SNAKE_CASE = '''What is the invoice number?'''
__SCREAMING_SNAKE_CASE = dqa_pipeline(image=__SCREAMING_SNAKE_CASE , question=__SCREAMING_SNAKE_CASE , top_k=2 )
self.assertEqual(
nested_simplify(__SCREAMING_SNAKE_CASE , decimals=4 ) , [
{'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
__SCREAMING_SNAKE_CASE = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__SCREAMING_SNAKE_CASE , decimals=4 ) , [
{'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
__SCREAMING_SNAKE_CASE = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__SCREAMING_SNAKE_CASE , decimals=4 ) , [
[
{'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
]
]
* 2 , )
__SCREAMING_SNAKE_CASE = list(zip(*apply_tesseract(load_image(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE , '''''' ) ) )
# This model should also work if `image` is set to None
__SCREAMING_SNAKE_CASE = dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__SCREAMING_SNAKE_CASE , decimals=4 ) , [
{'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def _a ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
'''impira/layoutlm-document-qa''' , revision='''3dc6de3''' , add_prefix_space=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pipeline(
'''document-question-answering''' , model='''impira/layoutlm-document-qa''' , tokenizer=__SCREAMING_SNAKE_CASE , revision='''3dc6de3''' , max_seq_len=50 , )
__SCREAMING_SNAKE_CASE = INVOICE_URL
__SCREAMING_SNAKE_CASE = '''What is the invoice number?'''
__SCREAMING_SNAKE_CASE = dqa_pipeline(image=__SCREAMING_SNAKE_CASE , question=__SCREAMING_SNAKE_CASE , top_k=2 )
self.assertEqual(
nested_simplify(__SCREAMING_SNAKE_CASE , decimals=4 ) , [
{'''score''': 0.99_99, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.99_98, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__SCREAMING_SNAKE_CASE = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__SCREAMING_SNAKE_CASE , decimals=4 ) , [
[
{'''score''': 0.99_99, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.99_98, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
]
]
* 2 , )
__SCREAMING_SNAKE_CASE = list(zip(*apply_tesseract(load_image(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE , '''''' ) ) )
# This model should also work if `image` is set to None
__SCREAMING_SNAKE_CASE = dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__SCREAMING_SNAKE_CASE , decimals=4 ) , [
{'''score''': 0.99_99, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.99_98, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
@slow
@require_torch
def _a ( self : str ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = pipeline(
'''document-question-answering''' , model='''naver-clova-ix/donut-base-finetuned-docvqa''' , tokenizer=AutoTokenizer.from_pretrained('''naver-clova-ix/donut-base-finetuned-docvqa''' ) , feature_extractor='''naver-clova-ix/donut-base-finetuned-docvqa''' , )
__SCREAMING_SNAKE_CASE = INVOICE_URL
__SCREAMING_SNAKE_CASE = '''What is the invoice number?'''
__SCREAMING_SNAKE_CASE = dqa_pipeline(image=__SCREAMING_SNAKE_CASE , question=__SCREAMING_SNAKE_CASE , top_k=2 )
self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE , decimals=4 ) , [{'''answer''': '''us-001'''}] )
@require_tf
@unittest.skip('''Document question answering not implemented in TF''' )
def _a ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
pass
| 482
|
"""simple docstring"""
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ =OrderedDict(
[
# Base model mapping
("albert", "FlaxAlbertModel"),
("bart", "FlaxBartModel"),
("beit", "FlaxBeitModel"),
("bert", "FlaxBertModel"),
("big_bird", "FlaxBigBirdModel"),
("blenderbot", "FlaxBlenderbotModel"),
("blenderbot-small", "FlaxBlenderbotSmallModel"),
("clip", "FlaxCLIPModel"),
("distilbert", "FlaxDistilBertModel"),
("electra", "FlaxElectraModel"),
("gpt-sw3", "FlaxGPT2Model"),
("gpt2", "FlaxGPT2Model"),
("gpt_neo", "FlaxGPTNeoModel"),
("gptj", "FlaxGPTJModel"),
("longt5", "FlaxLongT5Model"),
("marian", "FlaxMarianModel"),
("mbart", "FlaxMBartModel"),
("mt5", "FlaxMT5Model"),
("opt", "FlaxOPTModel"),
("pegasus", "FlaxPegasusModel"),
("regnet", "FlaxRegNetModel"),
("resnet", "FlaxResNetModel"),
("roberta", "FlaxRobertaModel"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormModel"),
("roformer", "FlaxRoFormerModel"),
("t5", "FlaxT5Model"),
("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"),
("vit", "FlaxViTModel"),
("wav2vec2", "FlaxWav2Vec2Model"),
("whisper", "FlaxWhisperModel"),
("xglm", "FlaxXGLMModel"),
("xlm-roberta", "FlaxXLMRobertaModel"),
]
)
lowerCAmelCase__ =OrderedDict(
[
# Model for pre-training mapping
("albert", "FlaxAlbertForPreTraining"),
("bart", "FlaxBartForConditionalGeneration"),
("bert", "FlaxBertForPreTraining"),
("big_bird", "FlaxBigBirdForPreTraining"),
("electra", "FlaxElectraForPreTraining"),
("longt5", "FlaxLongT5ForConditionalGeneration"),
("mbart", "FlaxMBartForConditionalGeneration"),
("mt5", "FlaxMT5ForConditionalGeneration"),
("roberta", "FlaxRobertaForMaskedLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
("roformer", "FlaxRoFormerForMaskedLM"),
("t5", "FlaxT5ForConditionalGeneration"),
("wav2vec2", "FlaxWav2Vec2ForPreTraining"),
("whisper", "FlaxWhisperForConditionalGeneration"),
("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
]
)
lowerCAmelCase__ =OrderedDict(
[
# Model for Masked LM mapping
("albert", "FlaxAlbertForMaskedLM"),
("bart", "FlaxBartForConditionalGeneration"),
("bert", "FlaxBertForMaskedLM"),
("big_bird", "FlaxBigBirdForMaskedLM"),
("distilbert", "FlaxDistilBertForMaskedLM"),
("electra", "FlaxElectraForMaskedLM"),
("mbart", "FlaxMBartForConditionalGeneration"),
("roberta", "FlaxRobertaForMaskedLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
("roformer", "FlaxRoFormerForMaskedLM"),
("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
]
)
lowerCAmelCase__ =OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("bart", "FlaxBartForConditionalGeneration"),
("blenderbot", "FlaxBlenderbotForConditionalGeneration"),
("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"),
("encoder-decoder", "FlaxEncoderDecoderModel"),
("longt5", "FlaxLongT5ForConditionalGeneration"),
("marian", "FlaxMarianMTModel"),
("mbart", "FlaxMBartForConditionalGeneration"),
("mt5", "FlaxMT5ForConditionalGeneration"),
("pegasus", "FlaxPegasusForConditionalGeneration"),
("t5", "FlaxT5ForConditionalGeneration"),
]
)
lowerCAmelCase__ =OrderedDict(
[
# Model for Image-classsification
("beit", "FlaxBeitForImageClassification"),
("regnet", "FlaxRegNetForImageClassification"),
("resnet", "FlaxResNetForImageClassification"),
("vit", "FlaxViTForImageClassification"),
]
)
lowerCAmelCase__ =OrderedDict(
[
("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
]
)
lowerCAmelCase__ =OrderedDict(
[
# Model for Causal LM mapping
("bart", "FlaxBartForCausalLM"),
("bert", "FlaxBertForCausalLM"),
("big_bird", "FlaxBigBirdForCausalLM"),
("electra", "FlaxElectraForCausalLM"),
("gpt-sw3", "FlaxGPT2LMHeadModel"),
("gpt2", "FlaxGPT2LMHeadModel"),
("gpt_neo", "FlaxGPTNeoForCausalLM"),
("gptj", "FlaxGPTJForCausalLM"),
("opt", "FlaxOPTForCausalLM"),
("roberta", "FlaxRobertaForCausalLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForCausalLM"),
("xglm", "FlaxXGLMForCausalLM"),
("xlm-roberta", "FlaxXLMRobertaForCausalLM"),
]
)
lowerCAmelCase__ =OrderedDict(
[
# Model for Sequence Classification mapping
("albert", "FlaxAlbertForSequenceClassification"),
("bart", "FlaxBartForSequenceClassification"),
("bert", "FlaxBertForSequenceClassification"),
("big_bird", "FlaxBigBirdForSequenceClassification"),
("distilbert", "FlaxDistilBertForSequenceClassification"),
("electra", "FlaxElectraForSequenceClassification"),
("mbart", "FlaxMBartForSequenceClassification"),
("roberta", "FlaxRobertaForSequenceClassification"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForSequenceClassification"),
("roformer", "FlaxRoFormerForSequenceClassification"),
("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"),
]
)
lowerCAmelCase__ =OrderedDict(
[
# Model for Question Answering mapping
("albert", "FlaxAlbertForQuestionAnswering"),
("bart", "FlaxBartForQuestionAnswering"),
("bert", "FlaxBertForQuestionAnswering"),
("big_bird", "FlaxBigBirdForQuestionAnswering"),
("distilbert", "FlaxDistilBertForQuestionAnswering"),
("electra", "FlaxElectraForQuestionAnswering"),
("mbart", "FlaxMBartForQuestionAnswering"),
("roberta", "FlaxRobertaForQuestionAnswering"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForQuestionAnswering"),
("roformer", "FlaxRoFormerForQuestionAnswering"),
("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"),
]
)
lowerCAmelCase__ =OrderedDict(
[
# Model for Token Classification mapping
("albert", "FlaxAlbertForTokenClassification"),
("bert", "FlaxBertForTokenClassification"),
("big_bird", "FlaxBigBirdForTokenClassification"),
("distilbert", "FlaxDistilBertForTokenClassification"),
("electra", "FlaxElectraForTokenClassification"),
("roberta", "FlaxRobertaForTokenClassification"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForTokenClassification"),
("roformer", "FlaxRoFormerForTokenClassification"),
("xlm-roberta", "FlaxXLMRobertaForTokenClassification"),
]
)
lowerCAmelCase__ =OrderedDict(
[
# Model for Multiple Choice mapping
("albert", "FlaxAlbertForMultipleChoice"),
("bert", "FlaxBertForMultipleChoice"),
("big_bird", "FlaxBigBirdForMultipleChoice"),
("distilbert", "FlaxDistilBertForMultipleChoice"),
("electra", "FlaxElectraForMultipleChoice"),
("roberta", "FlaxRobertaForMultipleChoice"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMultipleChoice"),
("roformer", "FlaxRoFormerForMultipleChoice"),
("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"),
]
)
lowerCAmelCase__ =OrderedDict(
[
("bert", "FlaxBertForNextSentencePrediction"),
]
)
lowerCAmelCase__ =OrderedDict(
[
("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
("whisper", "FlaxWhisperForConditionalGeneration"),
]
)
lowerCAmelCase__ =OrderedDict(
[
("whisper", "FlaxWhisperForAudioClassification"),
]
)
lowerCAmelCase__ =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
lowerCAmelCase__ =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
lowerCAmelCase__ =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
lowerCAmelCase__ =_LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
lowerCAmelCase__ =_LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
lowerCAmelCase__ =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
lowerCAmelCase__ =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
lowerCAmelCase__ =_LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
lowerCAmelCase__ =_LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
lowerCAmelCase__ =_LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
lowerCAmelCase__ =_LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
lowerCAmelCase__ =_LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
lowerCAmelCase__ =_LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
lowerCAmelCase__ =_LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class A__( _BaseAutoModelClass ):
lowerCAmelCase = FLAX_MODEL_MAPPING
lowerCAmelCase__ =auto_class_update(FlaxAutoModel)
class A__( _BaseAutoModelClass ):
lowerCAmelCase = FLAX_MODEL_FOR_PRETRAINING_MAPPING
lowerCAmelCase__ =auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")
class A__( _BaseAutoModelClass ):
lowerCAmelCase = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
lowerCAmelCase__ =auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")
class A__( _BaseAutoModelClass ):
lowerCAmelCase = FLAX_MODEL_FOR_MASKED_LM_MAPPING
lowerCAmelCase__ =auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")
class A__( _BaseAutoModelClass ):
lowerCAmelCase = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
lowerCAmelCase__ =auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)
class A__( _BaseAutoModelClass ):
lowerCAmelCase = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
lowerCAmelCase__ =auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)
class A__( _BaseAutoModelClass ):
lowerCAmelCase = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
lowerCAmelCase__ =auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")
class A__( _BaseAutoModelClass ):
lowerCAmelCase = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
lowerCAmelCase__ =auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="token classification"
)
class A__( _BaseAutoModelClass ):
lowerCAmelCase = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
lowerCAmelCase__ =auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")
class A__( _BaseAutoModelClass ):
lowerCAmelCase = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
lowerCAmelCase__ =auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)
class A__( _BaseAutoModelClass ):
lowerCAmelCase = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
lowerCAmelCase__ =auto_class_update(
FlaxAutoModelForImageClassification, head_doc="image classification"
)
class A__( _BaseAutoModelClass ):
lowerCAmelCase = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
lowerCAmelCase__ =auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")
class A__( _BaseAutoModelClass ):
lowerCAmelCase = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
lowerCAmelCase__ =auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
)
| 482
| 1
|
'''simple docstring'''
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCAmelCase__ ( UpperCAmelCase , UpperCAmelCase ):
"""simple docstring"""
snake_case__ : Any = torch.load(UpperCAmelCase , map_location="""cpu""" )
snake_case__ : List[Any] = chkpt["""model"""]
# We have the base model one level deeper than the original XLM repository
snake_case__ : List[Any] = {}
for k, v in state_dict.items():
if "pred_layer" in k:
snake_case__ : Union[str, Any] = v
else:
snake_case__ : str = v
snake_case__ : Optional[int] = chkpt["""params"""]
snake_case__ : List[Any] = {n: v for n, v in config.items() if not isinstance(UpperCAmelCase , (torch.FloatTensor, numpy.ndarray) )}
snake_case__ : Any = chkpt["""dico_word2id"""]
snake_case__ : Optional[int] = {s + """</w>""" if s.find("""@@""" ) == -1 and i > 13 else s.replace("""@@""" , """""" ): i for s, i in vocab.items()}
# Save pytorch-model
snake_case__ : str = pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
snake_case__ : Optional[int] = pytorch_dump_folder_path + """/""" + CONFIG_NAME
snake_case__ : str = pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""vocab_file"""]
print(f"""Save PyTorch model to {pytorch_weights_dump_path}""" )
torch.save(UpperCAmelCase , UpperCAmelCase )
print(f"""Save configuration file to {pytorch_config_dump_path}""" )
with open(UpperCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(UpperCAmelCase , indent=2 ) + """\n""" )
print(f"""Save vocab file to {pytorch_config_dump_path}""" )
with open(UpperCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(UpperCAmelCase , indent=2 ) + """\n""" )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--xlm_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.'
)
lowerCAmelCase__ = parser.parse_args()
convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
| 172
|
'''simple docstring'''
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
lowerCAmelCase__ = logging.getLogger()
@unittest.skip('Temporarily disable the doc tests.' )
@require_torch
@require_tf
@slow
class _A ( unittest.TestCase ):
'''simple docstring'''
def __lowerCAmelCase ( self : List[str] , lowerCamelCase : Path , lowerCamelCase : Union[str, None] = None , lowerCamelCase : Union[List[str], None] = None , lowerCamelCase : Union[str, List[str], None] = None , lowerCamelCase : bool = True , )-> Dict:
snake_case__ : int = [file for file in os.listdir(lowerCamelCase ) if os.path.isfile(os.path.join(lowerCamelCase , lowerCamelCase ) )]
if identifier is not None:
snake_case__ : List[Any] = [file for file in files if identifier in file]
if n_identifier is not None:
if isinstance(lowerCamelCase , lowerCamelCase ):
for n_ in n_identifier:
snake_case__ : Union[str, Any] = [file for file in files if n_ not in file]
else:
snake_case__ : Optional[Any] = [file for file in files if n_identifier not in file]
snake_case__ : Tuple = ignore_files or []
ignore_files.append("""__init__.py""" )
snake_case__ : int = [file for file in files if file not in ignore_files]
for file in files:
# Open all files
print("""Testing""" , lowerCamelCase )
if only_modules:
snake_case__ : Union[str, Any] = file.split(""".""" )[0]
try:
snake_case__ : Any = getattr(lowerCamelCase , lowerCamelCase )
snake_case__ : Optional[Any] = doctest.DocTestSuite(lowerCamelCase )
snake_case__ : int = unittest.TextTestRunner().run(lowerCamelCase )
self.assertIs(len(result.failures ) , 0 )
except AttributeError:
logger.info(F"""{module_identifier} is not a module.""" )
else:
snake_case__ : List[Any] = doctest.testfile(str("""..""" / directory / file ) , optionflags=doctest.ELLIPSIS )
self.assertIs(result.failed , 0 )
def __lowerCAmelCase ( self : Tuple )-> List[str]:
snake_case__ : Optional[int] = Path("""src/transformers""" )
snake_case__ : Optional[Any] = """modeling"""
snake_case__ : Optional[Any] = [
"""modeling_ctrl.py""",
"""modeling_tf_ctrl.py""",
]
self.analyze_directory(lowerCamelCase , identifier=lowerCamelCase , ignore_files=lowerCamelCase )
def __lowerCAmelCase ( self : List[str] )-> Union[str, Any]:
snake_case__ : Optional[Any] = Path("""src/transformers""" )
snake_case__ : Any = """tokenization"""
self.analyze_directory(lowerCamelCase , identifier=lowerCamelCase )
def __lowerCAmelCase ( self : Dict )-> Dict:
snake_case__ : Any = Path("""src/transformers""" )
snake_case__ : List[Any] = """configuration"""
self.analyze_directory(lowerCamelCase , identifier=lowerCamelCase )
def __lowerCAmelCase ( self : Dict )-> Tuple:
snake_case__ : int = Path("""src/transformers""" )
snake_case__ : int = ["""configuration""", """modeling""", """tokenization"""]
self.analyze_directory(lowerCamelCase , n_identifier=lowerCamelCase )
def __lowerCAmelCase ( self : Union[str, Any] )-> Tuple:
snake_case__ : List[Any] = Path("""docs/source""" )
snake_case__ : Optional[int] = ["""favicon.ico"""]
self.analyze_directory(lowerCamelCase , ignore_files=lowerCamelCase , only_modules=lowerCamelCase )
| 172
| 1
|
"""simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
_lowerCAmelCase = logging.getLogger(__name__)
class UpperCamelCase :
def __init__( self :List[Any] ) ->Any:
lowercase : List[str] = False
def __snake_case ( self :Union[str, Any] , __magic_name__ :List[str] , __magic_name__ :Tuple , __magic_name__ :Optional[Any] , __magic_name__ :str ) ->List[Any]:
if not self.initialized:
lowercase : Tuple = RagRetriever(
__snake_case , question_encoder_tokenizer=__snake_case , generator_tokenizer=__snake_case , index=__snake_case , init_retrieval=__snake_case , )
lowercase : Tuple = True
def __snake_case ( self :Dict ) ->Any:
self.retriever.index.init_index()
def __snake_case ( self :Optional[int] , __magic_name__ :str , __magic_name__ :Any ) ->Optional[int]:
lowercase : Optional[int] = self.retriever._main_retrieve(__snake_case , __snake_case )
return doc_ids, retrieved_doc_embeds
class UpperCamelCase (__snake_case ):
def __init__( self :Optional[int] , __magic_name__ :Any , __magic_name__ :Dict , __magic_name__ :Tuple , __magic_name__ :Any , __magic_name__ :Dict=None ) ->Optional[int]:
if index is not None and index.is_initialized() and len(__snake_case ) > 0:
raise ValueError(
"""When using Ray for distributed fine-tuning, """
"""you\'ll need to provide the paths instead, """
"""as the dataset and the index are loaded """
"""separately. More info in examples/rag/use_own_knowledge_dataset.py """ )
super().__init__(
__snake_case , question_encoder_tokenizer=__snake_case , generator_tokenizer=__snake_case , index=__snake_case , init_retrieval=__snake_case , )
lowercase : List[Any] = retrieval_workers
if len(self.retrieval_workers ) > 0:
ray.get(
[
worker.create_rag_retriever.remote(__snake_case , __snake_case , __snake_case , __snake_case )
for worker in self.retrieval_workers
] )
def __snake_case ( self :Optional[Any] ) ->Optional[Any]:
logger.info("""initializing retrieval""" )
if len(self.retrieval_workers ) > 0:
ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
else:
# Non-distributed training. Load index into this same process.
self.index.init_index()
def __snake_case ( self :str , __magic_name__ :Dict , __magic_name__ :List[str] ) ->Optional[int]:
if len(self.retrieval_workers ) > 0:
# Select a random retrieval actor.
lowercase : Tuple = self.retrieval_workers[random.randint(0 , len(self.retrieval_workers ) - 1 )]
lowercase : Union[str, Any] = ray.get(random_worker.retrieve.remote(__snake_case , __snake_case ) )
else:
lowercase : str = self._main_retrieve(__snake_case , __snake_case )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__snake_case )
@classmethod
def __snake_case ( cls :str , __magic_name__ :Any , __magic_name__ :Tuple=None , **__magic_name__ :Optional[int] ) ->List[Any]:
return super(__snake_case , cls ).get_tokenizers(__snake_case , __snake_case , **__snake_case )
@classmethod
def __snake_case ( cls :Dict , __magic_name__ :Tuple , __magic_name__ :Optional[int] , __magic_name__ :str=None , **__magic_name__ :Optional[int] ) ->Any:
lowercase : Tuple = kwargs.pop("""config""" , __snake_case ) or RagConfig.from_pretrained(__snake_case , **__snake_case )
lowercase : Any = RagTokenizer.from_pretrained(__snake_case , config=__snake_case )
lowercase : Union[str, Any] = rag_tokenizer.question_encoder
lowercase : Any = rag_tokenizer.generator
if indexed_dataset is not None:
lowercase : Dict = '''custom'''
lowercase : Optional[Any] = CustomHFIndex(config.retrieval_vector_size , __snake_case )
else:
lowercase : str = cls._build_index(__snake_case )
return cls(
__snake_case , question_encoder_tokenizer=__snake_case , generator_tokenizer=__snake_case , retrieval_workers=__snake_case , index=__snake_case , )
| 264
|
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
__UpperCAmelCase : List[Any] = pytest.mark.integration
@require_faiss
class lowerCamelCase ( SCREAMING_SNAKE_CASE ):
def snake_case_ ( self : Tuple ) -> Tuple:
_a : Tuple = Dataset.from_dict({'''filename''': ['''my_name-train''' + '''_''' + str(__snake_case ) for x in np.arange(30 ).tolist()]} )
return dset
def snake_case_ ( self : Optional[Any] ) -> str:
import faiss
_a : Dataset = self._create_dummy_dataset()
_a : Optional[Any] = dset.map(
lambda __snake_case , __snake_case : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=__snake_case , keep_in_memory=__snake_case )
_a : List[Any] = dset.add_faiss_index('''vecs''' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT )
_a , _a : Union[str, Any] = dset.get_nearest_examples('''vecs''' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['''filename'''][0] , '''my_name-train_29''' )
dset.drop_index('''vecs''' )
def snake_case_ ( self : Optional[Any] ) -> str:
import faiss
_a : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='''vecs''' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT , )
_a , _a : int = dset.get_nearest_examples('''vecs''' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['''filename'''][0] , '''my_name-train_29''' )
def snake_case_ ( self : List[Any] ) -> List[str]:
import faiss
_a : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='''vecs''' , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__snake_case ) as tmp_file:
dset.save_faiss_index('''vecs''' , tmp_file.name )
dset.load_faiss_index('''vecs2''' , tmp_file.name )
os.unlink(tmp_file.name )
_a , _a : Dict = dset.get_nearest_examples('''vecs2''' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['''filename'''][0] , '''my_name-train_29''' )
def snake_case_ ( self : Dict ) -> int:
_a : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='''vecs''' )
dset.drop_index('''vecs''' )
self.assertRaises(__snake_case , partial(dset.get_nearest_examples , '''vecs2''' , np.ones(5 , dtype=np.floataa ) ) )
def snake_case_ ( self : List[str] ) -> Dict:
from elasticsearch import Elasticsearch
_a : Dataset = self._create_dummy_dataset()
with patch('''elasticsearch.Elasticsearch.search''' ) as mocked_search, patch(
'''elasticsearch.client.IndicesClient.create''' ) as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk''' ) as mocked_bulk:
_a : int = {'''acknowledged''': True}
mocked_bulk.return_value([(True, None)] * 30 )
_a : Dict = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 29}]}}
_a : List[Any] = Elasticsearch()
dset.add_elasticsearch_index('''filename''' , es_client=__snake_case )
_a , _a : Any = dset.get_nearest_examples('''filename''' , '''my_name-train_29''' )
self.assertEqual(examples['''filename'''][0] , '''my_name-train_29''' )
@require_faiss
class lowerCamelCase ( SCREAMING_SNAKE_CASE ):
def snake_case_ ( self : str ) -> Any:
import faiss
_a : List[Any] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 10 )
# single query
_a : Union[str, Any] = np.zeros(5 , dtype=np.floataa )
_a : Optional[Any] = 1
_a , _a : Optional[int] = index.search(__snake_case )
self.assertRaises(__snake_case , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
_a : Optional[int] = np.eye(5 , dtype=np.floataa )[::-1]
_a , _a : Any = index.search_batch(__snake_case )
self.assertRaises(__snake_case , index.search_batch , queries[0] )
_a : Dict = [scores[0] for scores in total_scores]
_a : Tuple = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__snake_case ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , __snake_case )
def snake_case_ ( self : List[str] ) -> int:
import faiss
_a : List[str] = FaissIndex(string_factory='''Flat''' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
_a : Union[str, Any] = FaissIndex(string_factory='''LSH''' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(__snake_case ):
_a : Optional[Any] = FaissIndex(string_factory='''Flat''' , custom_index=faiss.IndexFlat(5 ) )
def snake_case_ ( self : Union[str, Any] ) -> Union[str, Any]:
import faiss
_a : Tuple = faiss.IndexFlat(5 )
_a : Optional[Any] = FaissIndex(custom_index=__snake_case )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def snake_case_ ( self : Union[str, Any] ) -> Tuple:
import faiss
_a : List[str] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__snake_case ) as tmp_file:
index.save(tmp_file.name )
_a : int = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
_a : List[Any] = np.zeros(5 , dtype=np.floataa )
_a : List[Any] = 1
_a , _a : List[str] = index.search(__snake_case )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def lowerCamelCase_ ( UpperCamelCase_ ):
import faiss
_a : List[Any] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
_a : Optional[int] = '''index.faiss'''
_a : List[Any] = f"""mock://{index_name}"""
index.save(UpperCamelCase_ , storage_options=mockfs.storage_options )
_a : str = FaissIndex.load(UpperCamelCase_ , storage_options=mockfs.storage_options )
_a : Union[str, Any] = np.zeros(5 , dtype=np.floataa )
_a : Dict = 1
_a , _a : List[Any] = index.search(UpperCamelCase_ )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class lowerCamelCase ( SCREAMING_SNAKE_CASE ):
def snake_case_ ( self : Any ) -> str:
from elasticsearch import Elasticsearch
with patch('''elasticsearch.Elasticsearch.search''' ) as mocked_search, patch(
'''elasticsearch.client.IndicesClient.create''' ) as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk''' ) as mocked_bulk:
_a : List[Any] = Elasticsearch()
_a : int = {'''acknowledged''': True}
_a : Tuple = ElasticSearchIndex(es_client=__snake_case )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(['''foo''', '''bar''', '''foobar'''] )
# single query
_a : Any = '''foo'''
_a : Union[str, Any] = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
_a , _a : Union[str, Any] = index.search(__snake_case )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
_a : List[str] = '''foo'''
_a : Optional[int] = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
_a , _a : str = index.search(__snake_case , request_timeout=30 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
_a : Union[str, Any] = ['''foo''', '''bar''', '''foobar''']
_a : int = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
_a , _a : Any = index.search_batch(__snake_case )
_a : Optional[Any] = [scores[0] for scores in total_scores]
_a : List[str] = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__snake_case ) , 0 )
self.assertListEqual([1, 1, 1] , __snake_case )
# batched queries with timeout
_a : Any = ['''foo''', '''bar''', '''foobar''']
_a : List[str] = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
_a , _a : List[str] = index.search_batch(__snake_case , request_timeout=30 )
_a : Optional[Any] = [scores[0] for scores in total_scores]
_a : str = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__snake_case ) , 0 )
self.assertListEqual([1, 1, 1] , __snake_case )
| 471
| 0
|
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class UpperCamelCase_ ( __UpperCamelCase ):
"""simple docstring"""
A = 42
A = None
def UpperCamelCase__ ( _A: Dict , _A: Optional[Any]=0.999 , _A: Union[str, Any]="cosine" , ):
'''simple docstring'''
if alpha_transform_type == "cosine":
def alpha_bar_fn(_A: str ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(_A: int ):
return math.exp(t * -12.0 )
else:
raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
__lowerCamelCase = []
for i in range(_A ):
__lowerCamelCase = i / num_diffusion_timesteps
__lowerCamelCase = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(_A ) / alpha_bar_fn(_A ) , _A ) )
return torch.tensor(_A , dtype=torch.floataa )
class UpperCamelCase_ ( __UpperCamelCase ,__UpperCamelCase ):
"""simple docstring"""
A = 1
@register_to_config
def __init__( self , UpperCAmelCase = 1_0_0_0 , UpperCAmelCase = 0.00_01 , UpperCAmelCase = 0.02 , UpperCAmelCase = "linear" , UpperCAmelCase = None , UpperCAmelCase = True , UpperCAmelCase = True , UpperCAmelCase = 0 , UpperCAmelCase = "epsilon" , UpperCAmelCase = 1.0 , **UpperCAmelCase , ):
if kwargs.get("""set_alpha_to_one""" , UpperCAmelCase ) is not None:
__lowerCamelCase = (
"""The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
)
deprecate("""set_alpha_to_one""" , """1.0.0""" , UpperCAmelCase , standard_warn=UpperCAmelCase )
__lowerCamelCase = kwargs["""set_alpha_to_one"""]
if trained_betas is not None:
__lowerCamelCase = torch.tensor(UpperCAmelCase , dtype=torch.floataa )
elif beta_schedule == "linear":
__lowerCamelCase = torch.linspace(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
__lowerCamelCase = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , UpperCAmelCase , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
__lowerCamelCase = betas_for_alpha_bar(UpperCAmelCase )
else:
raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )
__lowerCamelCase = 1.0 - self.betas
__lowerCamelCase = torch.cumprod(self.alphas , dim=0 )
# At every step in inverted ddim, we are looking into the next alphas_cumprod
# For the final step, there is no next alphas_cumprod, and the index is out of bounds
# `set_alpha_to_zero` decides whether we set this parameter simply to zero
# in this case, self.step() just output the predicted noise
# or whether we use the final alpha of the "non-previous" one.
__lowerCamelCase = torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
__lowerCamelCase = 1.0
# setable values
__lowerCamelCase = None
__lowerCamelCase = torch.from_numpy(np.arange(0 , UpperCAmelCase ).copy().astype(np.intaa ) )
def lowerCamelCase_ ( self , UpperCAmelCase , UpperCAmelCase = None ):
return sample
def lowerCamelCase_ ( self , UpperCAmelCase , UpperCAmelCase = None ):
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
f'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
f''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
f''' maximal {self.config.num_train_timesteps} timesteps.''' )
__lowerCamelCase = num_inference_steps
__lowerCamelCase = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
__lowerCamelCase = (np.arange(0 , UpperCAmelCase ) * step_ratio).round().copy().astype(np.intaa )
__lowerCamelCase = torch.from_numpy(UpperCAmelCase ).to(UpperCAmelCase )
self.timesteps += self.config.steps_offset
def lowerCamelCase_ ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 0.0 , UpperCAmelCase = False , UpperCAmelCase = None , UpperCAmelCase = True , ):
# 1. get previous step value (=t+1)
__lowerCamelCase = timestep + self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
# change original implementation to exactly match noise levels for analogous forward process
__lowerCamelCase = self.alphas_cumprod[timestep]
__lowerCamelCase = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
else self.final_alpha_cumprod
)
__lowerCamelCase = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
__lowerCamelCase = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
__lowerCamelCase = model_output
elif self.config.prediction_type == "sample":
__lowerCamelCase = model_output
__lowerCamelCase = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
__lowerCamelCase = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
__lowerCamelCase = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
""" `v_prediction`""" )
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
__lowerCamelCase = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
__lowerCamelCase = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon
# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
__lowerCamelCase = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=UpperCAmelCase , pred_original_sample=UpperCAmelCase )
def __len__( self ):
return self.config.num_train_timesteps
| 712
|
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
_a : Union[str, Any] = 1.0_54_57_18_17e-34 # unit of ℏ : J * s
_a : Optional[Any] = 3e8 # unit of c : m * s^-1
def UpperCamelCase__ ( _A: float , _A: float , _A: float ):
'''simple docstring'''
if (force, area, distance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if force < 0:
raise ValueError("""Magnitude of force can not be negative""" )
if distance < 0:
raise ValueError("""Distance can not be negative""" )
if area < 0:
raise ValueError("""Area can not be negative""" )
if force == 0:
__lowerCamelCase = (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
240 * (distance) ** 4
)
return {"force": force}
elif area == 0:
__lowerCamelCase = (240 * force * (distance) ** 4) / (
REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
)
return {"area": area}
elif distance == 0:
__lowerCamelCase = (
(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
) ** (1 / 4)
return {"distance": distance}
raise ValueError("""One and only one argument must be 0""" )
# Run doctest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 571
| 0
|
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a : Dict = logging.get_logger(__name__)
a : Union[str, Any] = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
# See all BART models at https://huggingface.co/models?filter=bart
a : Optional[int] = {
"vocab_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
},
"merges_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
},
}
a : int = {
"facebook/bart-base": 1_024,
"facebook/bart-large": 1_024,
"facebook/bart-large-mnli": 1_024,
"facebook/bart-large-cnn": 1_024,
"facebook/bart-large-xsum": 1_024,
"yjernite/bart_eli5": 1_024,
}
@lru_cache()
def lowerCamelCase__ ( ):
__UpperCAmelCase : List[Any] = (
list(range(ord("""!""" ) , ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" ) , ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" ) , ord("""ÿ""" ) + 1 ) )
)
__UpperCAmelCase : Tuple = bs[:]
__UpperCAmelCase : int = 0
for b in range(2**8 ):
if b not in bs:
bs.append(__lowerCamelCase )
cs.append(2**8 + n )
n += 1
__UpperCAmelCase : Dict = [chr(__lowerCamelCase ) for n in cs]
return dict(zip(__lowerCamelCase , __lowerCamelCase ) )
def lowerCamelCase__ ( __lowerCamelCase : int ):
__UpperCAmelCase : List[str] = set()
__UpperCAmelCase : List[str] = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__UpperCAmelCase : str = char
return pairs
class a ( lowercase__ ):
"""simple docstring"""
a : Any = VOCAB_FILES_NAMES
a : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
a : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a : List[str] = ['input_ids', 'attention_mask']
def __init__( self : List[str] , __lowercase : Tuple , __lowercase : List[Any] , __lowercase : Union[str, Any]="replace" , __lowercase : List[Any]="<s>" , __lowercase : List[str]="</s>" , __lowercase : int="</s>" , __lowercase : str="<s>" , __lowercase : Union[str, Any]="<unk>" , __lowercase : Tuple="<pad>" , __lowercase : Tuple="<mask>" , __lowercase : Optional[int]=False , **__lowercase : int , ) -> Any:
__UpperCAmelCase : List[Any] = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else bos_token
__UpperCAmelCase : Optional[Any] = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else eos_token
__UpperCAmelCase : Union[str, Any] = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else sep_token
__UpperCAmelCase : List[Any] = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else cls_token
__UpperCAmelCase : Dict = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else unk_token
__UpperCAmelCase : List[str] = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
__UpperCAmelCase : int = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else mask_token
super().__init__(
errors=__lowercase , bos_token=__lowercase , eos_token=__lowercase , unk_token=__lowercase , sep_token=__lowercase , cls_token=__lowercase , pad_token=__lowercase , mask_token=__lowercase , add_prefix_space=__lowercase , **__lowercase , )
with open(__lowercase , encoding="""utf-8""" ) as vocab_handle:
__UpperCAmelCase : str = json.load(__lowercase )
__UpperCAmelCase : Optional[Any] = {v: k for k, v in self.encoder.items()}
__UpperCAmelCase : Tuple = errors # how to handle errors in decoding
__UpperCAmelCase : Optional[int] = bytes_to_unicode()
__UpperCAmelCase : Optional[int] = {v: k for k, v in self.byte_encoder.items()}
with open(__lowercase , encoding="""utf-8""" ) as merges_handle:
__UpperCAmelCase : Union[str, Any] = merges_handle.read().split("""\n""" )[1:-1]
__UpperCAmelCase : List[str] = [tuple(merge.split() ) for merge in bpe_merges]
__UpperCAmelCase : List[str] = dict(zip(__lowercase , range(len(__lowercase ) ) ) )
__UpperCAmelCase : Optional[Any] = {}
__UpperCAmelCase : Optional[Any] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
__UpperCAmelCase : str = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""" )
@property
def UpperCAmelCase ( self : Any ) -> List[str]:
return len(self.encoder )
def UpperCAmelCase ( self : Dict ) -> int:
return dict(self.encoder , **self.added_tokens_encoder )
def UpperCAmelCase ( self : Any , __lowercase : Union[str, Any] ) -> Optional[int]:
if token in self.cache:
return self.cache[token]
__UpperCAmelCase : Tuple = tuple(__lowercase )
__UpperCAmelCase : Optional[Any] = get_pairs(__lowercase )
if not pairs:
return token
while True:
__UpperCAmelCase : Tuple = min(__lowercase , key=lambda __lowercase : self.bpe_ranks.get(__lowercase , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
__UpperCAmelCase , __UpperCAmelCase : List[str] = bigram
__UpperCAmelCase : List[str] = []
__UpperCAmelCase : Optional[int] = 0
while i < len(__lowercase ):
try:
__UpperCAmelCase : List[str] = word.index(__lowercase , __lowercase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__UpperCAmelCase : Tuple = j
if word[i] == first and i < len(__lowercase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__UpperCAmelCase : Dict = tuple(__lowercase )
__UpperCAmelCase : Union[str, Any] = new_word
if len(__lowercase ) == 1:
break
else:
__UpperCAmelCase : int = get_pairs(__lowercase )
__UpperCAmelCase : Union[str, Any] = """ """.join(__lowercase )
__UpperCAmelCase : int = word
return word
def UpperCAmelCase ( self : Optional[Any] , __lowercase : int ) -> List[str]:
__UpperCAmelCase : List[Any] = []
for token in re.findall(self.pat , __lowercase ):
__UpperCAmelCase : Dict = """""".join(
self.byte_encoder[b] for b in token.encode("""utf-8""" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowercase ).split(""" """ ) )
return bpe_tokens
def UpperCAmelCase ( self : Optional[Any] , __lowercase : Optional[int] ) -> str:
return self.encoder.get(__lowercase , self.encoder.get(self.unk_token ) )
def UpperCAmelCase ( self : Any , __lowercase : Any ) -> Optional[Any]:
return self.decoder.get(__lowercase )
def UpperCAmelCase ( self : str , __lowercase : Any ) -> List[str]:
__UpperCAmelCase : List[Any] = """""".join(__lowercase )
__UpperCAmelCase : str = bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8""" , errors=self.errors )
return text
def UpperCAmelCase ( self : Union[str, Any] , __lowercase : str , __lowercase : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__lowercase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__UpperCAmelCase : Union[str, Any] = os.path.join(
__lowercase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
__UpperCAmelCase : Optional[int] = os.path.join(
__lowercase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(__lowercase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__lowercase , ensure_ascii=__lowercase ) + """\n""" )
__UpperCAmelCase : Tuple = 0
with open(__lowercase , """w""" , encoding="""utf-8""" ) as writer:
writer.write("""#version: 0.2\n""" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __lowercase : kv[1] ):
if index != token_index:
logger.warning(
f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
""" Please check that the tokenizer is not corrupted!""" )
__UpperCAmelCase : Union[str, Any] = token_index
writer.write(""" """.join(__lowercase ) + """\n""" )
index += 1
return vocab_file, merge_file
def UpperCAmelCase ( self : Optional[int] , __lowercase : List[int] , __lowercase : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__UpperCAmelCase : List[str] = [self.cls_token_id]
__UpperCAmelCase : Optional[Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCAmelCase ( self : Dict , __lowercase : List[int] , __lowercase : Optional[List[int]] = None , __lowercase : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowercase , token_ids_a=__lowercase , already_has_special_tokens=__lowercase )
if token_ids_a is None:
return [1] + ([0] * len(__lowercase )) + [1]
return [1] + ([0] * len(__lowercase )) + [1, 1] + ([0] * len(__lowercase )) + [1]
def UpperCAmelCase ( self : int , __lowercase : List[int] , __lowercase : Optional[List[int]] = None ) -> List[int]:
__UpperCAmelCase : Optional[int] = [self.sep_token_id]
__UpperCAmelCase : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCAmelCase ( self : List[str] , __lowercase : Optional[Any] , __lowercase : Any=False , **__lowercase : List[str] ) -> Union[str, Any]:
__UpperCAmelCase : Optional[int] = kwargs.pop("""add_prefix_space""" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__lowercase ) > 0 and not text[0].isspace()):
__UpperCAmelCase : List[Any] = """ """ + text
return (text, kwargs)
| 63
|
from __future__ import annotations
import math
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : bool , __lowerCamelCase : list[int] , __lowerCamelCase : float ):
if depth < 0:
raise ValueError("""Depth cannot be less than 0""" )
if len(__lowerCamelCase ) == 0:
raise ValueError("""Scores cannot be empty""" )
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1 , node_index * 2 , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) , minimax(depth + 1 , node_index * 2 + 1 , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) , )
return min(
minimax(depth + 1 , node_index * 2 , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) , minimax(depth + 1 , node_index * 2 + 1 , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) , )
def lowerCamelCase__ ( ):
__UpperCAmelCase : Tuple = [90, 23, 6, 33, 21, 65, 123, 34423]
__UpperCAmelCase : str = math.log(len(__lowerCamelCase ) , 2 )
print("""Optimal value : """ , end="""""" )
print(minimax(0 , 0 , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 63
| 1
|
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
a__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
a__ = ''' \"""
Output class for the scheduler\'s step function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample (x_{0}) based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
\"""
prev_sample: torch.FloatTensor
pred_original_sample: Optional[torch.FloatTensor] = None
'''
class __magic_name__( unittest.TestCase ):
def __lowerCAmelCase( self : Optional[int] ):
'''simple docstring'''
snake_case__ = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , """schedulers/""" ) )
snake_case__ = self.diffusers_dir
shutil.copy(
os.path.join(__UpperCamelCase , """src/diffusers/schedulers/scheduling_ddpm.py""" ) , os.path.join(self.diffusers_dir , """schedulers/scheduling_ddpm.py""" ) , )
def __lowerCAmelCase( self : List[Any] ):
'''simple docstring'''
snake_case__ = """src/diffusers"""
shutil.rmtree(self.diffusers_dir )
def __lowerCAmelCase( self : int , __UpperCamelCase : Optional[Any] , __UpperCamelCase : List[str] , __UpperCamelCase : List[Any] , __UpperCamelCase : List[Any]=None ):
'''simple docstring'''
snake_case__ = comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
if overwrite_result is not None:
snake_case__ = comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
snake_case__ = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_1_9 )
snake_case__ = black.format_str(__UpperCamelCase , mode=__UpperCamelCase )
snake_case__ = os.path.join(self.diffusers_dir , """new_code.py""" )
with open(__UpperCamelCase , """w""" , newline="""\n""" ) as f:
f.write(__UpperCamelCase )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(__UpperCamelCase ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=__UpperCamelCase )
with open(__UpperCamelCase , """r""" ) as f:
self.assertTrue(f.read() , __UpperCamelCase )
def __lowerCAmelCase( self : List[str] ):
'''simple docstring'''
snake_case__ = check_copies.find_code_in_diffusers("""schedulers.scheduling_ddpm.DDPMSchedulerOutput""" )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def __lowerCAmelCase( self : Tuple ):
'''simple docstring'''
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput""" , """DDPMSchedulerOutput""" , REFERENCE_CODE + """\n""" , )
# With no empty line at the end
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput""" , """DDPMSchedulerOutput""" , __UpperCamelCase , )
# Copy consistency with rename
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test""" , """TestSchedulerOutput""" , re.sub("""DDPM""" , """Test""" , __UpperCamelCase ) , )
# Copy consistency with a really long name
snake_case__ = """TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"""
self.check_copy_consistency(
f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" , f"""{long_class_name}SchedulerOutput""" , re.sub("""Bert""" , __UpperCamelCase , __UpperCamelCase ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test""" , """TestSchedulerOutput""" , __UpperCamelCase , overwrite_result=re.sub("""DDPM""" , """Test""" , __UpperCamelCase ) , )
| 704
|
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"
from pathlib import Path
import json
import tempfile
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
a__ = '''tiny-wmt19-en-ru'''
# Build
# borrowed from a test
a__ = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''w</w>''',
'''r</w>''',
'''t</w>''',
'''lo''',
'''low''',
'''er</w>''',
'''low</w>''',
'''lowest</w>''',
'''newer</w>''',
'''wider</w>''',
'''<unk>''',
]
a__ = dict(zip(vocab, range(len(vocab))))
a__ = ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']
with tempfile.TemporaryDirectory() as tmpdirname:
a__ = Path(tmpdirname)
a__ = build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
a__ = build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
a__ = build_dir / VOCAB_FILES_NAMES['''merges_file''']
with open(src_vocab_file, '''w''') as fp:
fp.write(json.dumps(vocab_tokens))
with open(tgt_vocab_file, '''w''') as fp:
fp.write(json.dumps(vocab_tokens))
with open(merges_file, '''w''') as fp:
fp.write('''\n'''.join(merges))
a__ = FSMTTokenizer(
langs=['''en''', '''ru'''],
src_vocab_size=len(vocab),
tgt_vocab_size=len(vocab),
src_vocab_file=src_vocab_file,
tgt_vocab_file=tgt_vocab_file,
merges_file=merges_file,
)
a__ = FSMTConfig(
langs=['''ru''', '''en'''],
src_vocab_size=1_000,
tgt_vocab_size=1_000,
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
a__ = FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")
# Test
a__ = tokenizer(['''Making tiny model'''], return_tensors='''pt''')
a__ = tiny_model(**batch)
print('''test output:''', len(outputs.logits[0]))
# Save
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F"Generated {mname_tiny}")
# Upload
# transformers-cli upload tiny-wmt19-en-ru
| 566
| 0
|
import os
from datetime import datetime as dt
from github import Github
__a :Dict = [
'good first issue',
'good second issue',
'good difficult issue',
'enhancement',
'new pipeline/model',
'new scheduler',
'wip',
]
def __snake_case ( ):
"""simple docstring"""
A_ = Github(os.environ["GITHUB_TOKEN"] )
A_ = g.get_repo("huggingface/diffusers" )
A_ = repo.get_issues(state="open" )
for issue in open_issues:
A_ = sorted(issue.get_comments() ,key=lambda __UpperCamelCase : i.created_at ,reverse=__UpperCamelCase )
A_ = comments[0] if len(__UpperCamelCase ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state="closed" )
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state="open" )
issue.remove_from_labels("stale" )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
"This issue has been automatically marked as stale because it has not had "
"recent activity. If you think this still needs to be addressed "
"please comment on this thread.\n\nPlease note that issues that do not follow the "
"[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) "
"are likely to be ignored." )
issue.add_to_labels("stale" )
if __name__ == "__main__":
main()
| 86
|
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
SCREAMING_SNAKE_CASE__ = False
class a_ ( unittest.TestCase ):
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def A__ ( self ) -> List[str]:
"""simple docstring"""
return 12
@property
def A__ ( self ) -> int:
"""simple docstring"""
return 12
@property
def A__ ( self ) -> int:
"""simple docstring"""
return 32
@property
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
UpperCamelCase = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=3 , num_vq_embeddings=self.num_embed , vq_embed_dim=3 , )
return model
@property
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
return tokenizer
@property
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
torch.manual_seed(0 )
UpperCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModel(_SCREAMING_SNAKE_CASE )
@property
def A__ ( self ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
UpperCamelCase = 12
UpperCamelCase = 12
UpperCamelCase = {
"""attention_bias""": True,
"""cross_attention_dim""": 32,
"""attention_head_dim""": height * width,
"""num_attention_heads""": 1,
"""num_vector_embeds""": self.num_embed,
"""num_embeds_ada_norm""": self.num_embeds_ada_norm,
"""norm_num_groups""": 32,
"""sample_size""": width,
"""activation_fn""": """geglu-approximate""",
}
UpperCamelCase = TransformeraDModel(**_SCREAMING_SNAKE_CASE )
return model
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = """cpu"""
UpperCamelCase = self.dummy_vqvae
UpperCamelCase = self.dummy_text_encoder
UpperCamelCase = self.dummy_tokenizer
UpperCamelCase = self.dummy_transformer
UpperCamelCase = VQDiffusionScheduler(self.num_embed )
UpperCamelCase = LearnedClassifierFreeSamplingEmbeddings(learnable=_SCREAMING_SNAKE_CASE )
UpperCamelCase = VQDiffusionPipeline(
vqvae=_SCREAMING_SNAKE_CASE , text_encoder=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE , transformer=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE , learned_classifier_free_sampling_embeddings=_SCREAMING_SNAKE_CASE , )
UpperCamelCase = pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
UpperCamelCase = """teddy bear playing in the pool"""
UpperCamelCase = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(0 )
UpperCamelCase = pipe([prompt] , generator=_SCREAMING_SNAKE_CASE , num_inference_steps=2 , output_type="""np""" )
UpperCamelCase = output.images
UpperCamelCase = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(0 )
UpperCamelCase = pipe(
[prompt] , generator=_SCREAMING_SNAKE_CASE , output_type="""np""" , return_dict=_SCREAMING_SNAKE_CASE , num_inference_steps=2 )[0]
UpperCamelCase = image[0, -3:, -3:, -1]
UpperCamelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
UpperCamelCase = np.array([0.6_5_5_1, 0.6_1_6_8, 0.5_0_0_8, 0.5_6_7_6, 0.5_6_5_9, 0.4_2_9_5, 0.6_0_7_3, 0.5_5_9_9, 0.4_9_9_2] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def A__ ( self ) -> Dict:
"""simple docstring"""
UpperCamelCase = """cpu"""
UpperCamelCase = self.dummy_vqvae
UpperCamelCase = self.dummy_text_encoder
UpperCamelCase = self.dummy_tokenizer
UpperCamelCase = self.dummy_transformer
UpperCamelCase = VQDiffusionScheduler(self.num_embed )
UpperCamelCase = LearnedClassifierFreeSamplingEmbeddings(
learnable=_SCREAMING_SNAKE_CASE , hidden_size=self.text_embedder_hidden_size , length=tokenizer.model_max_length )
UpperCamelCase = VQDiffusionPipeline(
vqvae=_SCREAMING_SNAKE_CASE , text_encoder=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE , transformer=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE , learned_classifier_free_sampling_embeddings=_SCREAMING_SNAKE_CASE , )
UpperCamelCase = pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
UpperCamelCase = """teddy bear playing in the pool"""
UpperCamelCase = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(0 )
UpperCamelCase = pipe([prompt] , generator=_SCREAMING_SNAKE_CASE , num_inference_steps=2 , output_type="""np""" )
UpperCamelCase = output.images
UpperCamelCase = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(0 )
UpperCamelCase = pipe(
[prompt] , generator=_SCREAMING_SNAKE_CASE , output_type="""np""" , return_dict=_SCREAMING_SNAKE_CASE , num_inference_steps=2 )[0]
UpperCamelCase = image[0, -3:, -3:, -1]
UpperCamelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
UpperCamelCase = np.array([0.6_6_9_3, 0.6_0_7_5, 0.4_9_5_9, 0.5_7_0_1, 0.5_5_8_3, 0.4_3_3_3, 0.6_1_7_1, 0.5_6_8_4, 0.4_9_8_8] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class a_ ( unittest.TestCase ):
def A__ ( self ) -> List[Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy""" )
UpperCamelCase = VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq""" )
UpperCamelCase = pipeline.to(_SCREAMING_SNAKE_CASE )
pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
# requires GPU generator for gumbel softmax
# don't use GPU generator in tests though
UpperCamelCase = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(0 )
UpperCamelCase = pipeline(
"""teddy bear playing in the pool""" , num_images_per_prompt=1 , generator=_SCREAMING_SNAKE_CASE , output_type="""np""" , )
UpperCamelCase = output.images[0]
assert image.shape == (256, 256, 3)
assert np.abs(expected_image - image ).max() < 2.0
| 301
| 0
|
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
lowercase__ = {
'''Acehnese Arabic''': '''ace_Arab''',
'''Acehnese Latin''': '''ace_Latn''',
'''Mesopotamian Arabic''': '''acm_Arab''',
'''Ta\'izzi-Adeni Arabic''': '''acq_Arab''',
'''Tunisian Arabic''': '''aeb_Arab''',
'''Afrikaans''': '''afr_Latn''',
'''South Levantine Arabic''': '''ajp_Arab''',
'''Akan''': '''aka_Latn''',
'''Amharic''': '''amh_Ethi''',
'''North Levantine Arabic''': '''apc_Arab''',
'''Modern Standard Arabic''': '''arb_Arab''',
'''Modern Standard Arabic Romanized''': '''arb_Latn''',
'''Najdi Arabic''': '''ars_Arab''',
'''Moroccan Arabic''': '''ary_Arab''',
'''Egyptian Arabic''': '''arz_Arab''',
'''Assamese''': '''asm_Beng''',
'''Asturian''': '''ast_Latn''',
'''Awadhi''': '''awa_Deva''',
'''Central Aymara''': '''ayr_Latn''',
'''South Azerbaijani''': '''azb_Arab''',
'''North Azerbaijani''': '''azj_Latn''',
'''Bashkir''': '''bak_Cyrl''',
'''Bambara''': '''bam_Latn''',
'''Balinese''': '''ban_Latn''',
'''Belarusian''': '''bel_Cyrl''',
'''Bemba''': '''bem_Latn''',
'''Bengali''': '''ben_Beng''',
'''Bhojpuri''': '''bho_Deva''',
'''Banjar Arabic''': '''bjn_Arab''',
'''Banjar Latin''': '''bjn_Latn''',
'''Standard Tibetan''': '''bod_Tibt''',
'''Bosnian''': '''bos_Latn''',
'''Buginese''': '''bug_Latn''',
'''Bulgarian''': '''bul_Cyrl''',
'''Catalan''': '''cat_Latn''',
'''Cebuano''': '''ceb_Latn''',
'''Czech''': '''ces_Latn''',
'''Chokwe''': '''cjk_Latn''',
'''Central Kurdish''': '''ckb_Arab''',
'''Crimean Tatar''': '''crh_Latn''',
'''Welsh''': '''cym_Latn''',
'''Danish''': '''dan_Latn''',
'''German''': '''deu_Latn''',
'''Southwestern Dinka''': '''dik_Latn''',
'''Dyula''': '''dyu_Latn''',
'''Dzongkha''': '''dzo_Tibt''',
'''Greek''': '''ell_Grek''',
'''English''': '''eng_Latn''',
'''Esperanto''': '''epo_Latn''',
'''Estonian''': '''est_Latn''',
'''Basque''': '''eus_Latn''',
'''Ewe''': '''ewe_Latn''',
'''Faroese''': '''fao_Latn''',
'''Fijian''': '''fij_Latn''',
'''Finnish''': '''fin_Latn''',
'''Fon''': '''fon_Latn''',
'''French''': '''fra_Latn''',
'''Friulian''': '''fur_Latn''',
'''Nigerian Fulfulde''': '''fuv_Latn''',
'''Scottish Gaelic''': '''gla_Latn''',
'''Irish''': '''gle_Latn''',
'''Galician''': '''glg_Latn''',
'''Guarani''': '''grn_Latn''',
'''Gujarati''': '''guj_Gujr''',
'''Haitian Creole''': '''hat_Latn''',
'''Hausa''': '''hau_Latn''',
'''Hebrew''': '''heb_Hebr''',
'''Hindi''': '''hin_Deva''',
'''Chhattisgarhi''': '''hne_Deva''',
'''Croatian''': '''hrv_Latn''',
'''Hungarian''': '''hun_Latn''',
'''Armenian''': '''hye_Armn''',
'''Igbo''': '''ibo_Latn''',
'''Ilocano''': '''ilo_Latn''',
'''Indonesian''': '''ind_Latn''',
'''Icelandic''': '''isl_Latn''',
'''Italian''': '''ita_Latn''',
'''Javanese''': '''jav_Latn''',
'''Japanese''': '''jpn_Jpan''',
'''Kabyle''': '''kab_Latn''',
'''Jingpho''': '''kac_Latn''',
'''Kamba''': '''kam_Latn''',
'''Kannada''': '''kan_Knda''',
'''Kashmiri Arabic''': '''kas_Arab''',
'''Kashmiri Devanagari''': '''kas_Deva''',
'''Georgian''': '''kat_Geor''',
'''Central Kanuri Arabic''': '''knc_Arab''',
'''Central Kanuri Latin''': '''knc_Latn''',
'''Kazakh''': '''kaz_Cyrl''',
'''Kabiyè''': '''kbp_Latn''',
'''Kabuverdianu''': '''kea_Latn''',
'''Khmer''': '''khm_Khmr''',
'''Kikuyu''': '''kik_Latn''',
'''Kinyarwanda''': '''kin_Latn''',
'''Kyrgyz''': '''kir_Cyrl''',
'''Kimbundu''': '''kmb_Latn''',
'''Northern Kurdish''': '''kmr_Latn''',
'''Kikongo''': '''kon_Latn''',
'''Korean''': '''kor_Hang''',
'''Lao''': '''lao_Laoo''',
'''Ligurian''': '''lij_Latn''',
'''Limburgish''': '''lim_Latn''',
'''Lingala''': '''lin_Latn''',
'''Lithuanian''': '''lit_Latn''',
'''Lombard''': '''lmo_Latn''',
'''Latgalian''': '''ltg_Latn''',
'''Luxembourgish''': '''ltz_Latn''',
'''Luba-Kasai''': '''lua_Latn''',
'''Ganda''': '''lug_Latn''',
'''Luo''': '''luo_Latn''',
'''Mizo''': '''lus_Latn''',
'''Standard Latvian''': '''lvs_Latn''',
'''Magahi''': '''mag_Deva''',
'''Maithili''': '''mai_Deva''',
'''Malayalam''': '''mal_Mlym''',
'''Marathi''': '''mar_Deva''',
'''Minangkabau Arabic ''': '''min_Arab''',
'''Minangkabau Latin''': '''min_Latn''',
'''Macedonian''': '''mkd_Cyrl''',
'''Plateau Malagasy''': '''plt_Latn''',
'''Maltese''': '''mlt_Latn''',
'''Meitei Bengali''': '''mni_Beng''',
'''Halh Mongolian''': '''khk_Cyrl''',
'''Mossi''': '''mos_Latn''',
'''Maori''': '''mri_Latn''',
'''Burmese''': '''mya_Mymr''',
'''Dutch''': '''nld_Latn''',
'''Norwegian Nynorsk''': '''nno_Latn''',
'''Norwegian Bokmål''': '''nob_Latn''',
'''Nepali''': '''npi_Deva''',
'''Northern Sotho''': '''nso_Latn''',
'''Nuer''': '''nus_Latn''',
'''Nyanja''': '''nya_Latn''',
'''Occitan''': '''oci_Latn''',
'''West Central Oromo''': '''gaz_Latn''',
'''Odia''': '''ory_Orya''',
'''Pangasinan''': '''pag_Latn''',
'''Eastern Panjabi''': '''pan_Guru''',
'''Papiamento''': '''pap_Latn''',
'''Western Persian''': '''pes_Arab''',
'''Polish''': '''pol_Latn''',
'''Portuguese''': '''por_Latn''',
'''Dari''': '''prs_Arab''',
'''Southern Pashto''': '''pbt_Arab''',
'''Ayacucho Quechua''': '''quy_Latn''',
'''Romanian''': '''ron_Latn''',
'''Rundi''': '''run_Latn''',
'''Russian''': '''rus_Cyrl''',
'''Sango''': '''sag_Latn''',
'''Sanskrit''': '''san_Deva''',
'''Santali''': '''sat_Olck''',
'''Sicilian''': '''scn_Latn''',
'''Shan''': '''shn_Mymr''',
'''Sinhala''': '''sin_Sinh''',
'''Slovak''': '''slk_Latn''',
'''Slovenian''': '''slv_Latn''',
'''Samoan''': '''smo_Latn''',
'''Shona''': '''sna_Latn''',
'''Sindhi''': '''snd_Arab''',
'''Somali''': '''som_Latn''',
'''Southern Sotho''': '''sot_Latn''',
'''Spanish''': '''spa_Latn''',
'''Tosk Albanian''': '''als_Latn''',
'''Sardinian''': '''srd_Latn''',
'''Serbian''': '''srp_Cyrl''',
'''Swati''': '''ssw_Latn''',
'''Sundanese''': '''sun_Latn''',
'''Swedish''': '''swe_Latn''',
'''Swahili''': '''swh_Latn''',
'''Silesian''': '''szl_Latn''',
'''Tamil''': '''tam_Taml''',
'''Tatar''': '''tat_Cyrl''',
'''Telugu''': '''tel_Telu''',
'''Tajik''': '''tgk_Cyrl''',
'''Tagalog''': '''tgl_Latn''',
'''Thai''': '''tha_Thai''',
'''Tigrinya''': '''tir_Ethi''',
'''Tamasheq Latin''': '''taq_Latn''',
'''Tamasheq Tifinagh''': '''taq_Tfng''',
'''Tok Pisin''': '''tpi_Latn''',
'''Tswana''': '''tsn_Latn''',
'''Tsonga''': '''tso_Latn''',
'''Turkmen''': '''tuk_Latn''',
'''Tumbuka''': '''tum_Latn''',
'''Turkish''': '''tur_Latn''',
'''Twi''': '''twi_Latn''',
'''Central Atlas Tamazight''': '''tzm_Tfng''',
'''Uyghur''': '''uig_Arab''',
'''Ukrainian''': '''ukr_Cyrl''',
'''Umbundu''': '''umb_Latn''',
'''Urdu''': '''urd_Arab''',
'''Northern Uzbek''': '''uzn_Latn''',
'''Venetian''': '''vec_Latn''',
'''Vietnamese''': '''vie_Latn''',
'''Waray''': '''war_Latn''',
'''Wolof''': '''wol_Latn''',
'''Xhosa''': '''xho_Latn''',
'''Eastern Yiddish''': '''ydd_Hebr''',
'''Yoruba''': '''yor_Latn''',
'''Yue Chinese''': '''yue_Hant''',
'''Chinese Simplified''': '''zho_Hans''',
'''Chinese Traditional''': '''zho_Hant''',
'''Standard Malay''': '''zsm_Latn''',
'''Zulu''': '''zul_Latn''',
}
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
snake_case = """facebook/nllb-200-distilled-600M"""
snake_case = (
"""This is a tool that translates text from a language to another. It takes three inputs: `text`, which should """
"""be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, """
"""which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in """
"""plain English, such as 'Romanian', or 'Albanian'. It returns the text translated in `tgt_lang`."""
)
snake_case = """translator"""
snake_case = AutoTokenizer
snake_case = AutoModelForSeqaSeqLM
snake_case = LANGUAGE_CODES
snake_case = ["""text""", """text""", """text"""]
snake_case = ["""text"""]
def _lowercase ( self , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
if src_lang not in self.lang_to_code:
raise ValueError(f'''{src_lang} is not a supported language.''' )
if tgt_lang not in self.lang_to_code:
raise ValueError(f'''{tgt_lang} is not a supported language.''' )
snake_case_ = self.lang_to_code[src_lang]
snake_case_ = self.lang_to_code[tgt_lang]
return self.pre_processor._build_translation_inputs(
_A , return_tensors="pt" , src_lang=_A , tgt_lang=_A )
def _lowercase ( self , UpperCAmelCase_ ):
return self.model.generate(**_A )
def _lowercase ( self , UpperCAmelCase_ ):
return self.post_processor.decode(outputs[0].tolist() , skip_special_tokens=_A )
| 714
|
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
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 (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self , UpperCAmelCase_ , UpperCAmelCase_=13 , UpperCAmelCase_=7 , UpperCAmelCase_=True , UpperCAmelCase_=True , UpperCAmelCase_=False , UpperCAmelCase_=True , UpperCAmelCase_=99 , UpperCAmelCase_=32 , UpperCAmelCase_=5 , UpperCAmelCase_=4 , UpperCAmelCase_=37 , UpperCAmelCase_="gelu" , UpperCAmelCase_=0.1 , UpperCAmelCase_=0.1 , UpperCAmelCase_=5_12 , UpperCAmelCase_=16 , UpperCAmelCase_=2 , UpperCAmelCase_=0.02 , UpperCAmelCase_=3 , UpperCAmelCase_=4 , UpperCAmelCase_=None , ):
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_input_mask
snake_case_ = use_token_type_ids
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = type_vocab_size
snake_case_ = type_sequence_label_size
snake_case_ = initializer_range
snake_case_ = num_labels
snake_case_ = num_choices
snake_case_ = scope
def _lowercase ( self ):
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = None
if self.use_input_mask:
snake_case_ = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ = None
snake_case_ = None
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowercase ( self ):
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def _lowercase ( self , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
snake_case_ = DistilBertModel(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
snake_case_ = model(UpperCAmelCase_ , UpperCAmelCase_ )
snake_case_ = model(UpperCAmelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
snake_case_ = DistilBertForMaskedLM(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
snake_case_ = model(UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , labels=UpperCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase ( self , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
snake_case_ = DistilBertForQuestionAnswering(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
snake_case_ = model(
UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , start_positions=UpperCAmelCase_ , end_positions=UpperCAmelCase_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _lowercase ( self , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
snake_case_ = self.num_labels
snake_case_ = DistilBertForSequenceClassification(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
snake_case_ = model(UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , labels=UpperCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowercase ( self , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
snake_case_ = self.num_labels
snake_case_ = DistilBertForTokenClassification(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
snake_case_ = model(UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , labels=UpperCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowercase ( self , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
snake_case_ = self.num_choices
snake_case_ = DistilBertForMultipleChoice(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
snake_case_ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = model(
UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , labels=UpperCAmelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _lowercase ( self ):
snake_case_ = self.prepare_config_and_inputs()
((snake_case_) , (snake_case_) , (snake_case_) , (snake_case_) , (snake_case_) , (snake_case_)) = config_and_inputs
snake_case_ = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
snake_case = (
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
snake_case = (
{
"""feature-extraction""": DistilBertModel,
"""fill-mask""": DistilBertForMaskedLM,
"""question-answering""": DistilBertForQuestionAnswering,
"""text-classification""": DistilBertForSequenceClassification,
"""token-classification""": DistilBertForTokenClassification,
"""zero-shot""": DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case = True
snake_case = True
snake_case = True
snake_case = True
def _lowercase ( self ):
snake_case_ = DistilBertModelTester(self )
snake_case_ = ConfigTester(self , config_class=UpperCAmelCase_ , dim=37 )
def _lowercase ( self ):
self.config_tester.run_common_tests()
def _lowercase ( self ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*UpperCAmelCase_ )
def _lowercase ( self ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*UpperCAmelCase_ )
def _lowercase ( self ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*UpperCAmelCase_ )
def _lowercase ( self ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*UpperCAmelCase_ )
def _lowercase ( self ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*UpperCAmelCase_ )
def _lowercase ( self ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*UpperCAmelCase_ )
@slow
def _lowercase ( self ):
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = DistilBertModel.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
@slow
@require_torch_gpu
def _lowercase ( self ):
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
snake_case_ = True
snake_case_ = model_class(config=UpperCAmelCase_ )
snake_case_ = self._prepare_for_class(UpperCAmelCase_ , UpperCAmelCase_ )
snake_case_ = torch.jit.trace(
UpperCAmelCase_ , (inputs_dict["input_ids"].to("cpu" ), inputs_dict["attention_mask"].to("cpu" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(UpperCAmelCase_ , os.path.join(UpperCAmelCase_ , "traced_model.pt" ) )
snake_case_ = torch.jit.load(os.path.join(UpperCAmelCase_ , "traced_model.pt" ) , map_location=UpperCAmelCase_ )
loaded(inputs_dict["input_ids"].to(UpperCAmelCase_ ) , inputs_dict["attention_mask"].to(UpperCAmelCase_ ) )
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@slow
def _lowercase ( self ):
snake_case_ = DistilBertModel.from_pretrained("distilbert-base-uncased" )
snake_case_ = torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
snake_case_ = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
snake_case_ = model(UpperCAmelCase_ , attention_mask=UpperCAmelCase_ )[0]
snake_case_ = torch.Size((1, 11, 7_68) )
self.assertEqual(output.shape , UpperCAmelCase_ )
snake_case_ = torch.tensor(
[[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , UpperCAmelCase_ , atol=1e-4 ) )
| 420
| 0
|
'''simple docstring'''
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
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : int = {"vocab_file": "spm_char.model"}
SCREAMING_SNAKE_CASE : Tuple = {
"vocab_file": {
"microsoft/speecht5_asr": "https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model",
"microsoft/speecht5_tts": "https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model",
"microsoft/speecht5_vc": "https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model",
}
}
SCREAMING_SNAKE_CASE : Dict = {
"microsoft/speecht5_asr": 1024,
"microsoft/speecht5_tts": 1024,
"microsoft/speecht5_vc": 1024,
}
class snake_case ( lowercase_ ):
"""simple docstring"""
_a = VOCAB_FILES_NAMES
_a = PRETRAINED_VOCAB_FILES_MAP
_a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a = ["""input_ids""", """attention_mask"""]
def __init__( self, _lowercase, _lowercase="<s>", _lowercase="</s>", _lowercase="<unk>", _lowercase="<pad>", _lowercase = None, **_lowercase, ) -> None:
SCREAMING_SNAKE_CASE_ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_lowercase, eos_token=_lowercase, unk_token=_lowercase, pad_token=_lowercase, sp_model_kwargs=self.sp_model_kwargs, **_lowercase, )
SCREAMING_SNAKE_CASE_ = vocab_file
SCREAMING_SNAKE_CASE_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowercase )
@property
def a__ ( self ) -> List[Any]:
return self.sp_model.get_piece_size()
def a__ ( self ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = {self.convert_ids_to_tokens(_lowercase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = self.__dict__.copy()
SCREAMING_SNAKE_CASE_ = None
return state
def __setstate__( self, _lowercase ) -> str:
SCREAMING_SNAKE_CASE_ = d
# for backward compatibility
if not hasattr(self, 'sp_model_kwargs' ):
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def a__ ( self, _lowercase ) -> List[str]:
return self.sp_model.encode(_lowercase, out_type=_lowercase )
def a__ ( self, _lowercase ) -> List[str]:
return self.sp_model.piece_to_id(_lowercase )
def a__ ( self, _lowercase ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = self.sp_model.IdToPiece(_lowercase )
return token
def a__ ( self, _lowercase ) -> List[str]:
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = ''
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(_lowercase ) + token
SCREAMING_SNAKE_CASE_ = []
else:
current_sub_tokens.append(_lowercase )
out_string += self.sp_model.decode(_lowercase )
return out_string.strip()
def a__ ( self, _lowercase, _lowercase=None ) -> List[int]:
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def a__ ( self, _lowercase, _lowercase = None, _lowercase = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowercase, token_ids_a=_lowercase, already_has_special_tokens=_lowercase )
SCREAMING_SNAKE_CASE_ = [1]
if token_ids_a is None:
return ([0] * len(_lowercase )) + suffix_ones
return ([0] * len(_lowercase )) + ([0] * len(_lowercase )) + suffix_ones
def a__ ( self, _lowercase, _lowercase = None ) -> Tuple[str]:
if not os.path.isdir(_lowercase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
SCREAMING_SNAKE_CASE_ = os.path.join(
_lowercase, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowercase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file, _lowercase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowercase, 'wb' ) as fi:
SCREAMING_SNAKE_CASE_ = self.sp_model.serialized_model_proto()
fi.write(_lowercase )
return (out_vocab_file,)
| 294
|
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE : Dict = logging.get_logger()
def _UpperCamelCase ( lowerCAmelCase__: int ,lowerCAmelCase__: str ,lowerCAmelCase__: LevitConfig ,lowerCAmelCase__: Path ,lowerCAmelCase__: bool = True ) -> Optional[int]:
print(F"""Converting {name}...""" )
with torch.no_grad():
if hidden_sizes == 128:
if name[-1] == "S":
SCREAMING_SNAKE_CASE_ = timm.create_model('levit_128s' ,pretrained=lowerCAmelCase__ )
else:
SCREAMING_SNAKE_CASE_ = timm.create_model('levit_128' ,pretrained=lowerCAmelCase__ )
if hidden_sizes == 192:
SCREAMING_SNAKE_CASE_ = timm.create_model('levit_192' ,pretrained=lowerCAmelCase__ )
if hidden_sizes == 256:
SCREAMING_SNAKE_CASE_ = timm.create_model('levit_256' ,pretrained=lowerCAmelCase__ )
if hidden_sizes == 384:
SCREAMING_SNAKE_CASE_ = timm.create_model('levit_384' ,pretrained=lowerCAmelCase__ )
from_model.eval()
SCREAMING_SNAKE_CASE_ = LevitForImageClassificationWithTeacher(lowerCAmelCase__ ).eval()
SCREAMING_SNAKE_CASE_ = OrderedDict()
SCREAMING_SNAKE_CASE_ = from_model.state_dict()
SCREAMING_SNAKE_CASE_ = list(from_model.state_dict().keys() )
SCREAMING_SNAKE_CASE_ = list(our_model.state_dict().keys() )
print(len(lowerCAmelCase__ ) ,len(lowerCAmelCase__ ) )
for i in range(len(lowerCAmelCase__ ) ):
SCREAMING_SNAKE_CASE_ = weights[og_keys[i]]
our_model.load_state_dict(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ = torch.randn((2, 3, 224, 224) )
SCREAMING_SNAKE_CASE_ = from_model(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ = our_model(lowerCAmelCase__ ).logits
assert torch.allclose(lowerCAmelCase__ ,lowerCAmelCase__ ), "The model logits don't match the original one."
SCREAMING_SNAKE_CASE_ = name
print(lowerCAmelCase__ )
if push_to_hub:
our_model.save_pretrained(save_directory / checkpoint_name )
SCREAMING_SNAKE_CASE_ = LevitImageProcessor()
image_processor.save_pretrained(save_directory / checkpoint_name )
print(F"""Pushed {checkpoint_name}""" )
def _UpperCamelCase ( lowerCAmelCase__: Path ,lowerCAmelCase__: str = None ,lowerCAmelCase__: bool = True ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = 'imagenet-1k-id2label.json'
SCREAMING_SNAKE_CASE_ = 1000
SCREAMING_SNAKE_CASE_ = (1, num_labels)
SCREAMING_SNAKE_CASE_ = 'huggingface/label-files'
SCREAMING_SNAKE_CASE_ = num_labels
SCREAMING_SNAKE_CASE_ = json.load(open(hf_hub_download(lowerCAmelCase__ ,lowerCAmelCase__ ,repo_type='dataset' ) ,'r' ) )
SCREAMING_SNAKE_CASE_ = {int(lowerCAmelCase__ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE_ = idalabel
SCREAMING_SNAKE_CASE_ = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE_ = partial(lowerCAmelCase__ ,num_labels=lowerCAmelCase__ ,idalabel=lowerCAmelCase__ ,labelaid=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ = {
'levit-128S': 128,
'levit-128': 128,
'levit-192': 192,
'levit-256': 256,
'levit-384': 384,
}
SCREAMING_SNAKE_CASE_ = {
'levit-128S': ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] ,num_attention_heads=[4, 6, 8] ,depths=[2, 3, 4] ,key_dim=[16, 16, 16] ,drop_path_rate=0 ,),
'levit-128': ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] ,num_attention_heads=[4, 8, 12] ,depths=[4, 4, 4] ,key_dim=[16, 16, 16] ,drop_path_rate=0 ,),
'levit-192': ImageNetPreTrainedConfig(
hidden_sizes=[192, 288, 384] ,num_attention_heads=[3, 5, 6] ,depths=[4, 4, 4] ,key_dim=[32, 32, 32] ,drop_path_rate=0 ,),
'levit-256': ImageNetPreTrainedConfig(
hidden_sizes=[256, 384, 512] ,num_attention_heads=[4, 6, 8] ,depths=[4, 4, 4] ,key_dim=[32, 32, 32] ,drop_path_rate=0 ,),
'levit-384': ImageNetPreTrainedConfig(
hidden_sizes=[384, 512, 768] ,num_attention_heads=[6, 9, 12] ,depths=[4, 4, 4] ,key_dim=[32, 32, 32] ,drop_path_rate=0.1 ,),
}
if model_name:
convert_weight_and_push(
names_to_hidden_sizes[model_name] ,lowerCAmelCase__ ,names_to_config[model_name] ,lowerCAmelCase__ ,lowerCAmelCase__ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(names_to_hidden_sizes[model_name] ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
return config, expected_shape
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="levit-dump-folder/",
type=Path,
required=False,
help="Path to the output PyTorch model directory.",
)
parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
parser.add_argument(
"--no-push_to_hub",
dest="push_to_hub",
action="store_false",
help="Do not push model and image processor to the hub",
)
SCREAMING_SNAKE_CASE : Optional[Any] = parser.parse_args()
SCREAMING_SNAKE_CASE : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 294
| 1
|
"""simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
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_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 torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class _SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=13 , lowerCamelCase__=10 , lowerCamelCase__=3 , lowerCamelCase__=2 , lowerCamelCase__=2 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=32 , lowerCamelCase__=5 , lowerCamelCase__=4 , lowerCamelCase__=37 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=10 , lowerCamelCase__=0.02 , lowerCamelCase__="divided_space_time" , lowerCamelCase__=None , ) -> Any:
lowercase__ : Union[str, Any] = parent
lowercase__ : Tuple = batch_size
lowercase__ : List[str] = image_size
lowercase__ : Optional[Any] = num_channels
lowercase__ : Any = patch_size
lowercase__ : Union[str, Any] = num_frames
lowercase__ : Tuple = is_training
lowercase__ : List[str] = use_labels
lowercase__ : Any = hidden_size
lowercase__ : Tuple = num_hidden_layers
lowercase__ : Optional[int] = num_attention_heads
lowercase__ : str = intermediate_size
lowercase__ : str = hidden_act
lowercase__ : Dict = hidden_dropout_prob
lowercase__ : Optional[Any] = attention_probs_dropout_prob
lowercase__ : Tuple = attention_type
lowercase__ : Optional[Any] = initializer_range
lowercase__ : str = scope
lowercase__ : int = num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
lowercase__ : Any = (image_size // patch_size) ** 2
lowercase__ : Union[str, Any] = (num_frames) * self.num_patches_per_frame + 1
def UpperCAmelCase__( self ) -> Union[str, Any]:
lowercase__ : Optional[Any] = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
lowercase__ : Tuple = None
if self.use_labels:
lowercase__ : int = ids_tensor([self.batch_size] , self.num_labels )
lowercase__ : int = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase__( self ) -> List[Any]:
lowercase__ : List[str] = TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , 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 , initializer_range=self.initializer_range , attention_type=self.attention_type , )
lowercase__ : int = self.num_labels
return config
def UpperCAmelCase__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Dict:
lowercase__ : Tuple = TimesformerModel(config=A_ )
model.to(A_ )
model.eval()
lowercase__ : str = model(A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> str:
lowercase__ : Any = TimesformerForVideoClassification(A_ )
model.to(A_ )
model.eval()
lowercase__ : List[Any] = model(A_ )
# verify the logits shape
lowercase__ : int = torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , A_ )
def UpperCAmelCase__( self ) -> Optional[int]:
lowercase__ : str = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : str = config_and_inputs
lowercase__ : Union[str, Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class _SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
_a : Optional[int] = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
_a : Dict = (
{"feature-extraction": TimesformerModel, "video-classification": TimesformerForVideoClassification}
if is_torch_available()
else {}
)
_a : Optional[Any] = False
_a : int = False
_a : Any = False
_a : Any = False
def UpperCAmelCase__( self ) -> List[str]:
lowercase__ : Optional[Any] = TimesformerModelTester(self )
lowercase__ : Any = ConfigTester(
self , config_class=A_ , has_text_modality=A_ , hidden_size=37 )
def UpperCAmelCase__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False ) -> Any:
lowercase__ : Dict = copy.deepcopy(A_ )
if return_labels:
if model_class in get_values(A_ ):
lowercase__ : Tuple = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=A_ )
return inputs_dict
def UpperCAmelCase__( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
@unittest.skip(reason="""TimeSformer does not use inputs_embeds""" )
def UpperCAmelCase__( self ) -> str:
pass
def UpperCAmelCase__( self ) -> Optional[Any]:
lowercase__ , lowercase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = model_class(A_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A_ , nn.Linear ) )
def UpperCAmelCase__( self ) -> Optional[int]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Union[str, Any] = model_class(A_ )
lowercase__ : List[str] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Tuple = [*signature.parameters.keys()]
lowercase__ : str = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , A_ )
def UpperCAmelCase__( self ) -> List[Any]:
lowercase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A_ )
def UpperCAmelCase__( self ) -> Optional[int]:
lowercase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*A_ )
@slow
def UpperCAmelCase__( self ) -> Optional[int]:
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : int = TimesformerModel.from_pretrained(A_ )
self.assertIsNotNone(A_ )
def UpperCAmelCase__( self ) -> Optional[int]:
if not self.has_attentions:
pass
else:
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Any = True
for model_class in self.all_model_classes:
lowercase__ : Optional[Any] = self.model_tester.seq_length
lowercase__ : Optional[Any] = self.model_tester.num_frames
lowercase__ : Union[str, Any] = True
lowercase__ : List[Any] = False
lowercase__ : Dict = True
lowercase__ : int = model_class(A_ )
model.to(A_ )
model.eval()
with torch.no_grad():
lowercase__ : int = model(**self._prepare_for_class(A_ , A_ ) )
lowercase__ : List[str] = outputs.attentions
self.assertEqual(len(A_ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase__ : Dict = True
lowercase__ : Optional[Any] = model_class(A_ )
model.to(A_ )
model.eval()
with torch.no_grad():
lowercase__ : Any = model(**self._prepare_for_class(A_ , A_ ) )
lowercase__ : int = outputs.attentions
self.assertEqual(len(A_ ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
lowercase__ : Optional[Any] = len(A_ )
# Check attention is always last and order is fine
lowercase__ : str = True
lowercase__ : List[str] = True
lowercase__ : Any = model_class(A_ )
model.to(A_ )
model.eval()
with torch.no_grad():
lowercase__ : int = model(**self._prepare_for_class(A_ , A_ ) )
self.assertEqual(out_len + 1 , len(A_ ) )
lowercase__ : List[Any] = outputs.attentions
self.assertEqual(len(A_ ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def UpperCAmelCase__( self ) -> Dict:
def check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowercase__ : Optional[int] = model_class(A_ )
model.to(A_ )
model.eval()
with torch.no_grad():
lowercase__ : List[Any] = model(**self._prepare_for_class(A_ , A_ ) )
lowercase__ : str = outputs.hidden_states
lowercase__ : Dict = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(A_ ) , A_ )
lowercase__ : Any = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
lowercase__ , lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : str = True
check_hidden_states_output(A_ , A_ , A_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ : Union[str, Any] = True
check_hidden_states_output(A_ , A_ , A_ )
def snake_case_ ( ):
lowercase__ : str = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename="""eating_spaghetti.npy""" , repo_type="""dataset""" )
lowercase__ : Optional[Any] = np.load(lowerCamelCase__ )
return list(lowerCamelCase__ )
@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def UpperCAmelCase__( self ) -> List[Any]:
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def UpperCAmelCase__( self ) -> str:
lowercase__ : Optional[int] = TimesformerForVideoClassification.from_pretrained("""facebook/timesformer-base-finetuned-k400""" ).to(
A_ )
lowercase__ : Dict = self.default_image_processor
lowercase__ : Optional[int] = prepare_video()
lowercase__ : Tuple = image_processor(video[:8] , return_tensors="""pt""" ).to(A_ )
# forward pass
with torch.no_grad():
lowercase__ : Optional[Any] = model(**A_ )
# verify the logits
lowercase__ : str = torch.Size((1, 400) )
self.assertEqual(outputs.logits.shape , A_ )
lowercase__ : List[str] = torch.tensor([-0.3016, -0.7713, -0.4205] ).to(A_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , A_ , atol=1E-4 ) )
| 703
|
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class _SCREAMING_SNAKE_CASE :
"""simple docstring"""
_a : int
_a : Node | None = None
_a : Node | None = None
def _lowerCamelCase ( ):
lowercase__ : List[str] = Node(1 )
lowercase__ : List[str] = Node(2 )
lowercase__ : Any = Node(3 )
lowercase__ : str = Node(4 )
lowercase__ : List[str] = Node(5 )
return tree
def _lowerCamelCase ( lowerCamelCase__ : Node | None ):
return [root.data, *preorder(root.left ), *preorder(root.right )] if root else []
def _lowerCamelCase ( lowerCamelCase__ : Node | None ):
return postorder(root.left ) + postorder(root.right ) + [root.data] if root else []
def _lowerCamelCase ( lowerCamelCase__ : Node | None ):
return [*inorder(root.left ), root.data, *inorder(root.right )] if root else []
def _lowerCamelCase ( lowerCamelCase__ : Node | None ):
return (max(height(root.left ) , height(root.right ) ) + 1) if root else 0
def _lowerCamelCase ( lowerCamelCase__ : Node | None ):
lowercase__ : list[Any] = []
if root is None:
return output
lowercase__ : Optional[int] = deque([root] )
while process_queue:
lowercase__ : Tuple = process_queue.popleft()
output.append(node.data )
if node.left:
process_queue.append(node.left )
if node.right:
process_queue.append(node.right )
return output
def _lowerCamelCase ( lowerCamelCase__ : Node | None , lowerCamelCase__ : int ):
lowercase__ : list[Any] = []
def populate_output(lowerCamelCase__ : Node | None , lowerCamelCase__ : int ) -> None:
if not root:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.left , level - 1 )
populate_output(root.right , level - 1 )
populate_output(lowerCamelCase__ , lowerCamelCase__ )
return output
def _lowerCamelCase ( lowerCamelCase__ : Node | None , lowerCamelCase__ : int ):
lowercase__ : list[Any] = []
def populate_output(lowerCamelCase__ : Node | None , lowerCamelCase__ : int ) -> None:
if root is None:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.right , level - 1 )
populate_output(root.left , level - 1 )
populate_output(lowerCamelCase__ , lowerCamelCase__ )
return output
def _lowerCamelCase ( lowerCamelCase__ : Node | None ):
if root is None:
return []
lowercase__ : list[Sequence[Node | None]] = []
lowercase__ : Dict = 0
lowercase__ : Union[str, Any] = height(lowerCamelCase__ )
for h in range(1 , height_tree + 1 ):
if not flag:
output.append(get_nodes_from_left_to_right(lowerCamelCase__ , lowerCamelCase__ ) )
lowercase__ : Union[str, Any] = 1
else:
output.append(get_nodes_from_right_to_left(lowerCamelCase__ , lowerCamelCase__ ) )
lowercase__ : Any = 0
return output
def _lowerCamelCase ( ): # Main function for testing.
lowercase__ : List[Any] = make_tree()
print(f'''In-order Traversal: {inorder(lowerCamelCase__ )}''' )
print(f'''Pre-order Traversal: {preorder(lowerCamelCase__ )}''' )
print(f'''Post-order Traversal: {postorder(lowerCamelCase__ )}''' , """\n""" )
print(f'''Height of Tree: {height(lowerCamelCase__ )}''' , """\n""" )
print("""Complete Level Order Traversal: """ )
print(level_order(lowerCamelCase__ ) , """\n""" )
print("""Level-wise order Traversal: """ )
for level in range(1 , height(lowerCamelCase__ ) + 1 ):
print(f'''Level {level}:''' , get_nodes_from_left_to_right(lowerCamelCase__ , level=lowerCamelCase__ ) )
print("""\nZigZag order Traversal: """ )
print(zigzag(lowerCamelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 128
| 0
|
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 UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = BlipImageProcessor()
__SCREAMING_SNAKE_CASE = GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model' )
__SCREAMING_SNAKE_CASE = BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert' )
__SCREAMING_SNAKE_CASE = InstructBlipProcessor(_A , _A , _A )
processor.save_pretrained(self.tmpdirname )
def _A ( self , **_A ):
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **_A ).tokenizer
def _A ( self , **_A ):
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **_A ).image_processor
def _A ( self , **_A ):
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **_A ).qformer_tokenizer
def _A ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__SCREAMING_SNAKE_CASE = [Image.fromarray(np.moveaxis(_A , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = InstructBlipProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() , qformer_tokenizer=self.get_qformer_tokenizer() , )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
__SCREAMING_SNAKE_CASE = self.get_image_processor(do_normalize=_A , padding_value=1.0 )
__SCREAMING_SNAKE_CASE = InstructBlipProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=_A , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _A )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _A )
self.assertIsInstance(processor.qformer_tokenizer , _A )
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.get_image_processor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = self.get_qformer_tokenizer()
__SCREAMING_SNAKE_CASE = InstructBlipProcessor(
tokenizer=_A , image_processor=_A , qformer_tokenizer=_A )
__SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE = image_processor(_A , return_tensors='np' )
__SCREAMING_SNAKE_CASE = processor(images=_A , 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 _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.get_image_processor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = self.get_qformer_tokenizer()
__SCREAMING_SNAKE_CASE = InstructBlipProcessor(
tokenizer=_A , image_processor=_A , qformer_tokenizer=_A )
__SCREAMING_SNAKE_CASE = 'lower newer'
__SCREAMING_SNAKE_CASE = processor(text=_A )
__SCREAMING_SNAKE_CASE = tokenizer(_A , return_token_type_ids=_A )
__SCREAMING_SNAKE_CASE = qformer_tokenizer(_A , return_token_type_ids=_A )
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 _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.get_image_processor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = self.get_qformer_tokenizer()
__SCREAMING_SNAKE_CASE = InstructBlipProcessor(
tokenizer=_A , image_processor=_A , qformer_tokenizer=_A )
__SCREAMING_SNAKE_CASE = 'lower newer'
__SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE = processor(text=_A , images=_A )
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(_A ):
processor()
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.get_image_processor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = self.get_qformer_tokenizer()
__SCREAMING_SNAKE_CASE = InstructBlipProcessor(
tokenizer=_A , image_processor=_A , qformer_tokenizer=_A )
__SCREAMING_SNAKE_CASE = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__SCREAMING_SNAKE_CASE = processor.batch_decode(_A )
__SCREAMING_SNAKE_CASE = tokenizer.batch_decode(_A )
self.assertListEqual(_A , _A )
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.get_image_processor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = self.get_qformer_tokenizer()
__SCREAMING_SNAKE_CASE = InstructBlipProcessor(
tokenizer=_A , image_processor=_A , qformer_tokenizer=_A )
__SCREAMING_SNAKE_CASE = 'lower newer'
__SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE = processor(text=_A , images=_A )
self.assertListEqual(
list(inputs.keys() ) , ['input_ids', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values'] , )
| 148
|
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : Optional[int] = '''MCTCTFeatureExtractor'''
UpperCamelCase__ : Union[str, Any] = '''AutoTokenizer'''
def __init__( self , _A , _A ):
'''simple docstring'''
super().__init__(_A , _A )
__SCREAMING_SNAKE_CASE = self.feature_extractor
__SCREAMING_SNAKE_CASE = False
def __call__( self , *_A , **_A ):
'''simple docstring'''
if self._in_target_context_manager:
return self.current_processor(*_A , **_A )
if "raw_speech" in kwargs:
warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.' )
__SCREAMING_SNAKE_CASE = kwargs.pop('raw_speech' )
else:
__SCREAMING_SNAKE_CASE = kwargs.pop('audio' , _A )
__SCREAMING_SNAKE_CASE = kwargs.pop('sampling_rate' , _A )
__SCREAMING_SNAKE_CASE = kwargs.pop('text' , _A )
if len(_A ) > 0:
__SCREAMING_SNAKE_CASE = args[0]
__SCREAMING_SNAKE_CASE = args[1:]
if audio is None and text is None:
raise ValueError('You need to specify either an `audio` or `text` input to process.' )
if audio is not None:
__SCREAMING_SNAKE_CASE = self.feature_extractor(_A , *_A , sampling_rate=_A , **_A )
if text is not None:
__SCREAMING_SNAKE_CASE = self.tokenizer(_A , **_A )
if text is None:
return inputs
elif audio is None:
return encodings
else:
__SCREAMING_SNAKE_CASE = encodings['input_ids']
return inputs
def _A ( self , *_A , **_A ):
'''simple docstring'''
return self.tokenizer.batch_decode(*_A , **_A )
def _A ( self , *_A , **_A ):
'''simple docstring'''
if self._in_target_context_manager:
return self.current_processor.pad(*_A , **_A )
__SCREAMING_SNAKE_CASE = kwargs.pop('input_features' , _A )
__SCREAMING_SNAKE_CASE = kwargs.pop('labels' , _A )
if len(_A ) > 0:
__SCREAMING_SNAKE_CASE = args[0]
__SCREAMING_SNAKE_CASE = args[1:]
if input_features is not None:
__SCREAMING_SNAKE_CASE = self.feature_extractor.pad(_A , *_A , **_A )
if labels is not None:
__SCREAMING_SNAKE_CASE = self.tokenizer.pad(_A , **_A )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
__SCREAMING_SNAKE_CASE = labels['input_ids']
return input_features
def _A ( self , *_A , **_A ):
'''simple docstring'''
return self.tokenizer.decode(*_A , **_A )
@contextmanager
def _A ( self ):
'''simple docstring'''
warnings.warn(
'`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
'your audio inputs, or in a separate call.' )
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = self.tokenizer
yield
__SCREAMING_SNAKE_CASE = self.feature_extractor
__SCREAMING_SNAKE_CASE = False
| 148
| 1
|
from __future__ import annotations
def lowerCamelCase ( _UpperCamelCase : Union[str, Any] ) -> bool:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = len(SCREAMING_SNAKE_CASE_ )
# We need to create solution object to save path.
__UpperCAmelCase : int = [[0 for _ in range(SCREAMING_SNAKE_CASE_ )] for _ in range(SCREAMING_SNAKE_CASE_ )]
__UpperCAmelCase : str = run_maze(SCREAMING_SNAKE_CASE_ , 0 , 0 , SCREAMING_SNAKE_CASE_ )
if solved:
print("""\n""".join(str(SCREAMING_SNAKE_CASE_ ) for row in solutions ) )
else:
print("""No solution exists!""" )
return solved
def lowerCamelCase ( _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Optional[int] , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Optional[int] ) -> bool:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = len(SCREAMING_SNAKE_CASE_ )
# Final check point.
if i == j == (size - 1):
__UpperCAmelCase : Optional[int] = 1
return True
__UpperCAmelCase : List[str] = (not i < 0) and (not j < 0) # Check lower bounds
__UpperCAmelCase : Union[str, Any] = (i < size) and (j < size) # Check upper bounds
if lower_flag and upper_flag:
# check for already visited and block points.
__UpperCAmelCase : str = (not solutions[i][j]) and (not maze[i][j])
if block_flag:
# check visited
__UpperCAmelCase : Tuple = 1
# check for directions
if (
run_maze(SCREAMING_SNAKE_CASE_ , i + 1 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
or run_maze(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , j + 1 , SCREAMING_SNAKE_CASE_ )
or run_maze(SCREAMING_SNAKE_CASE_ , i - 1 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
or run_maze(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , j - 1 , SCREAMING_SNAKE_CASE_ )
):
return True
__UpperCAmelCase : Union[str, Any] = 0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 700
|
"""simple docstring"""
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
UpperCAmelCase : Tuple = {
'E': 12.70,
'T': 9.06,
'A': 8.17,
'O': 7.51,
'I': 6.97,
'N': 6.75,
'S': 6.33,
'H': 6.09,
'R': 5.99,
'D': 4.25,
'L': 4.03,
'C': 2.78,
'U': 2.76,
'M': 2.41,
'W': 2.36,
'F': 2.23,
'G': 2.02,
'Y': 1.97,
'P': 1.93,
'B': 1.29,
'V': 0.98,
'K': 0.77,
'J': 0.15,
'X': 0.15,
'Q': 0.10,
'Z': 0.07,
}
UpperCAmelCase : Dict = 'ETAOINSHRDLCUMWFGYPBVKJXQZ'
UpperCAmelCase : List[str] = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def lowerCamelCase ( _UpperCamelCase : str ) -> dict[str, int]:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = {letter: 0 for letter in string.ascii_uppercase}
for letter in message.upper():
if letter in LETTERS:
letter_count[letter] += 1
return letter_count
def lowerCamelCase ( _UpperCamelCase : tuple ) -> str:
'''simple docstring'''
return x[0]
def lowerCamelCase ( _UpperCamelCase : str ) -> str:
'''simple docstring'''
__UpperCAmelCase : int = get_letter_count(_UpperCamelCase )
__UpperCAmelCase : dict[int, list[str]] = {
freq: [] for letter, freq in letter_to_freq.items()
}
for letter in LETTERS:
freq_to_letter[letter_to_freq[letter]].append(_UpperCamelCase )
__UpperCAmelCase : dict[int, str] = {}
for freq in freq_to_letter:
freq_to_letter[freq].sort(key=ETAOIN.find , reverse=_UpperCamelCase )
__UpperCAmelCase : Any = """""".join(freq_to_letter[freq] )
__UpperCAmelCase : Any = list(freq_to_letter_str.items() )
freq_pairs.sort(key=_UpperCamelCase , reverse=_UpperCamelCase )
__UpperCAmelCase : list[str] = [freq_pair[1] for freq_pair in freq_pairs]
return "".join(_UpperCamelCase )
def lowerCamelCase ( _UpperCamelCase : str ) -> int:
'''simple docstring'''
__UpperCAmelCase : List[str] = get_frequency_order(_UpperCamelCase )
__UpperCAmelCase : Optional[int] = 0
for common_letter in ETAOIN[:6]:
if common_letter in freq_order[:6]:
match_score += 1
for uncommon_letter in ETAOIN[-6:]:
if uncommon_letter in freq_order[-6:]:
match_score += 1
return match_score
if __name__ == "__main__":
import doctest
doctest.testmod()
| 299
| 0
|
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
class __lowercase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a : Union[str, Any] = "deta"
a : Tuple = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__(self ,_lowerCamelCase=None ,_lowerCamelCase=900 ,_lowerCamelCase=2048 ,_lowerCamelCase=6 ,_lowerCamelCase=2048 ,_lowerCamelCase=8 ,_lowerCamelCase=6 ,_lowerCamelCase=1024 ,_lowerCamelCase=8 ,_lowerCamelCase=0.0 ,_lowerCamelCase=True ,_lowerCamelCase="relu" ,_lowerCamelCase=256 ,_lowerCamelCase=0.1 ,_lowerCamelCase=0.0 ,_lowerCamelCase=0.0 ,_lowerCamelCase=0.0_2 ,_lowerCamelCase=1.0 ,_lowerCamelCase=True ,_lowerCamelCase=False ,_lowerCamelCase="sine" ,_lowerCamelCase=5 ,_lowerCamelCase=4 ,_lowerCamelCase=4 ,_lowerCamelCase=True ,_lowerCamelCase=300 ,_lowerCamelCase=True ,_lowerCamelCase=True ,_lowerCamelCase=1 ,_lowerCamelCase=5 ,_lowerCamelCase=2 ,_lowerCamelCase=1 ,_lowerCamelCase=1 ,_lowerCamelCase=5 ,_lowerCamelCase=2 ,_lowerCamelCase=0.1 ,_lowerCamelCase=0.2_5 ,**_lowerCamelCase ,) -> Dict:
'''simple docstring'''
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
__lowercase = CONFIG_MAPPING["resnet"](out_features=['''stage2''', '''stage3''', '''stage4'''] )
else:
if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ):
__lowercase = backbone_config.pop('''model_type''' )
__lowercase = CONFIG_MAPPING[backbone_model_type]
__lowercase = config_class.from_dict(_SCREAMING_SNAKE_CASE )
__lowercase = backbone_config
__lowercase = num_queries
__lowercase = max_position_embeddings
__lowercase = d_model
__lowercase = encoder_ffn_dim
__lowercase = encoder_layers
__lowercase = encoder_attention_heads
__lowercase = decoder_ffn_dim
__lowercase = decoder_layers
__lowercase = decoder_attention_heads
__lowercase = dropout
__lowercase = attention_dropout
__lowercase = activation_dropout
__lowercase = activation_function
__lowercase = init_std
__lowercase = init_xavier_std
__lowercase = encoder_layerdrop
__lowercase = auxiliary_loss
__lowercase = position_embedding_type
# deformable attributes
__lowercase = num_feature_levels
__lowercase = encoder_n_points
__lowercase = decoder_n_points
__lowercase = two_stage
__lowercase = two_stage_num_proposals
__lowercase = with_box_refine
__lowercase = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError('''If two_stage is True, with_box_refine must be True.''' )
# Hungarian matcher
__lowercase = class_cost
__lowercase = bbox_cost
__lowercase = giou_cost
# Loss coefficients
__lowercase = mask_loss_coefficient
__lowercase = dice_loss_coefficient
__lowercase = bbox_loss_coefficient
__lowercase = giou_loss_coefficient
__lowercase = eos_coefficient
__lowercase = focal_alpha
super().__init__(is_encoder_decoder=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
@property
def _UpperCAmelCase (self ) -> int:
'''simple docstring'''
return self.encoder_attention_heads
@property
def _UpperCAmelCase (self ) -> int:
'''simple docstring'''
return self.d_model
def _UpperCAmelCase (self ) -> List[Any]:
'''simple docstring'''
__lowercase = copy.deepcopy(self.__dict__ )
__lowercase = self.backbone_config.to_dict()
__lowercase = self.__class__.model_type
return output
| 502
|
"""simple docstring"""
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
BertPooler,
BertPreTrainedModel,
)
def _lowercase ( __snake_case ) -> Dict:
__lowerCAmelCase : Optional[int] = torch.exp(__snake_case )
__lowerCAmelCase : int = torch.sum(__snake_case ,dim=1 ) # sum of exp(x_i)
__lowerCAmelCase : Optional[Any] = torch.sum(x * exp_x ,dim=1 ) # sum of x_i * exp(x_i)
return torch.log(__snake_case ) - B / A
class A__ ( nn.Module ):
'''simple docstring'''
def __init__( self: Any , _SCREAMING_SNAKE_CASE: List[Any]) -> List[str]:
"""simple docstring"""
super().__init__()
__lowerCAmelCase : Any = config.output_attentions
__lowerCAmelCase : Optional[Any] = config.output_hidden_states
__lowerCAmelCase : Tuple = nn.ModuleList([BertLayer(_SCREAMING_SNAKE_CASE) for _ in range(config.num_hidden_layers)])
__lowerCAmelCase : int = nn.ModuleList([BertHighway(_SCREAMING_SNAKE_CASE) for _ in range(config.num_hidden_layers)])
__lowerCAmelCase : List[str] = [-1 for _ in range(config.num_hidden_layers)]
def _SCREAMING_SNAKE_CASE ( self: List[Any] , _SCREAMING_SNAKE_CASE: List[str]) -> Optional[int]:
"""simple docstring"""
if (type(_SCREAMING_SNAKE_CASE) is float) or (type(_SCREAMING_SNAKE_CASE) is int):
for i in range(len(self.early_exit_entropy)):
__lowerCAmelCase : List[Any] = x
else:
__lowerCAmelCase : str = x
def _SCREAMING_SNAKE_CASE ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: Tuple) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = pooler.state_dict()
for highway in self.highway:
for name, param in highway.pooler.state_dict().items():
param.copy_(loaded_model[name])
def _SCREAMING_SNAKE_CASE ( self: int , _SCREAMING_SNAKE_CASE: List[str] , _SCREAMING_SNAKE_CASE: Tuple=None , _SCREAMING_SNAKE_CASE: List[str]=None , _SCREAMING_SNAKE_CASE: List[Any]=None , _SCREAMING_SNAKE_CASE: Tuple=None , ) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : Tuple = ()
__lowerCAmelCase : Tuple = ()
__lowerCAmelCase : Optional[Any] = ()
for i, layer_module in enumerate(self.layer):
if self.output_hidden_states:
__lowerCAmelCase : Tuple = all_hidden_states + (hidden_states,)
__lowerCAmelCase : Any = layer_module(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , head_mask[i] , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
__lowerCAmelCase : str = layer_outputs[0]
if self.output_attentions:
__lowerCAmelCase : List[Any] = all_attentions + (layer_outputs[1],)
__lowerCAmelCase : Optional[int] = (hidden_states,)
if self.output_hidden_states:
__lowerCAmelCase : Tuple = current_outputs + (all_hidden_states,)
if self.output_attentions:
__lowerCAmelCase : List[str] = current_outputs + (all_attentions,)
__lowerCAmelCase : List[str] = self.highway[i](_SCREAMING_SNAKE_CASE)
# logits, pooled_output
if not self.training:
__lowerCAmelCase : Union[str, Any] = highway_exit[0]
__lowerCAmelCase : Dict = entropy(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Dict = highway_exit + (highway_entropy,) # logits, hidden_states(?), entropy
__lowerCAmelCase : Dict = all_highway_exits + (highway_exit,)
if highway_entropy < self.early_exit_entropy[i]:
__lowerCAmelCase : Any = (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
raise HighwayException(_SCREAMING_SNAKE_CASE , i + 1)
else:
__lowerCAmelCase : Optional[Any] = all_highway_exits + (highway_exit,)
# Add last layer
if self.output_hidden_states:
__lowerCAmelCase : Union[str, Any] = all_hidden_states + (hidden_states,)
__lowerCAmelCase : str = (hidden_states,)
if self.output_hidden_states:
__lowerCAmelCase : Union[str, Any] = outputs + (all_hidden_states,)
if self.output_attentions:
__lowerCAmelCase : Optional[int] = outputs + (all_attentions,)
__lowerCAmelCase : List[str] = outputs + (all_highway_exits,)
return outputs # last-layer hidden state, (all hidden states), (all attentions), all highway exits
@add_start_docstrings(
'The Bert Model transformer with early exiting (DeeBERT). ' , __SCREAMING_SNAKE_CASE , )
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self: Dict , _SCREAMING_SNAKE_CASE: Tuple) -> Union[str, Any]:
"""simple docstring"""
super().__init__(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Tuple = config
__lowerCAmelCase : str = BertEmbeddings(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[int] = DeeBertEncoder(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[Any] = BertPooler(_SCREAMING_SNAKE_CASE)
self.init_weights()
def _SCREAMING_SNAKE_CASE ( self: List[Any]) -> Any:
"""simple docstring"""
self.encoder.init_highway_pooler(self.pooler)
def _SCREAMING_SNAKE_CASE ( self: str) -> str:
"""simple docstring"""
return self.embeddings.word_embeddings
def _SCREAMING_SNAKE_CASE ( self: Optional[int] , _SCREAMING_SNAKE_CASE: List[Any]) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : Dict = value
def _SCREAMING_SNAKE_CASE ( self: List[str] , _SCREAMING_SNAKE_CASE: Tuple) -> Optional[int]:
"""simple docstring"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(_SCREAMING_SNAKE_CASE)
@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE)
def _SCREAMING_SNAKE_CASE ( self: Optional[Any] , _SCREAMING_SNAKE_CASE: Optional[Any]=None , _SCREAMING_SNAKE_CASE: Optional[int]=None , _SCREAMING_SNAKE_CASE: Optional[int]=None , _SCREAMING_SNAKE_CASE: Optional[Any]=None , _SCREAMING_SNAKE_CASE: Any=None , _SCREAMING_SNAKE_CASE: int=None , _SCREAMING_SNAKE_CASE: Union[str, Any]=None , _SCREAMING_SNAKE_CASE: List[Any]=None , ) -> Dict:
"""simple docstring"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
__lowerCAmelCase : Tuple = input_ids.size()
elif inputs_embeds is not None:
__lowerCAmelCase : List[str] = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
__lowerCAmelCase : Any = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
__lowerCAmelCase : Union[str, Any] = torch.ones(_SCREAMING_SNAKE_CASE , device=_SCREAMING_SNAKE_CASE)
if encoder_attention_mask is None:
__lowerCAmelCase : Tuple = torch.ones(_SCREAMING_SNAKE_CASE , device=_SCREAMING_SNAKE_CASE)
if token_type_ids is None:
__lowerCAmelCase : Union[str, Any] = torch.zeros(_SCREAMING_SNAKE_CASE , dtype=torch.long , device=_SCREAMING_SNAKE_CASE)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
__lowerCAmelCase : torch.Tensor = self.get_extended_attention_mask(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if encoder_attention_mask.dim() == 3:
__lowerCAmelCase : Union[str, Any] = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.dim() == 2:
__lowerCAmelCase : Optional[Any] = encoder_attention_mask[:, None, None, :]
__lowerCAmelCase : List[str] = encoder_extended_attention_mask.to(
dtype=next(self.parameters()).dtype) # fp16 compatibility
__lowerCAmelCase : Union[str, Any] = (1.0 - encoder_extended_attention_mask) * -1_0000.0
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
__lowerCAmelCase : Union[str, Any] = self.get_head_mask(_SCREAMING_SNAKE_CASE , self.config.num_hidden_layers)
__lowerCAmelCase : Union[str, Any] = self.embeddings(
input_ids=_SCREAMING_SNAKE_CASE , position_ids=_SCREAMING_SNAKE_CASE , token_type_ids=_SCREAMING_SNAKE_CASE , inputs_embeds=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[Any] = self.encoder(
_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , head_mask=_SCREAMING_SNAKE_CASE , encoder_hidden_states=_SCREAMING_SNAKE_CASE , encoder_attention_mask=_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : List[str] = encoder_outputs[0]
__lowerCAmelCase : Union[str, Any] = self.pooler(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Dict = (
sequence_output,
pooled_output,
) + encoder_outputs[
1:
] # add hidden_states and attentions if they are here
return outputs # sequence_output, pooled_output, (hidden_states), (attentions), highway exits
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self: Optional[Any] , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: Dict) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : List[Any] = message
__lowerCAmelCase : Union[str, Any] = exit_layer # start from 1!
class A__ ( nn.Module ):
'''simple docstring'''
def __init__( self: Dict , _SCREAMING_SNAKE_CASE: Dict) -> str:
"""simple docstring"""
super().__init__()
__lowerCAmelCase : Optional[int] = BertPooler(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[int] = nn.Dropout(config.hidden_dropout_prob)
__lowerCAmelCase : Optional[Any] = nn.Linear(config.hidden_size , config.num_labels)
def _SCREAMING_SNAKE_CASE ( self: int , _SCREAMING_SNAKE_CASE: str) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : Dict = encoder_outputs[0]
__lowerCAmelCase : Union[str, Any] = self.pooler(_SCREAMING_SNAKE_CASE)
# "return" pooler_output
# BertModel
__lowerCAmelCase : str = (pooler_input, pooler_output) + encoder_outputs[1:]
# "return" bmodel_output
# Dropout and classification
__lowerCAmelCase : Tuple = bmodel_output[1]
__lowerCAmelCase : int = self.dropout(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Union[str, Any] = self.classifier(_SCREAMING_SNAKE_CASE)
return logits, pooled_output
@add_start_docstrings(
'Bert Model (with early exiting - DeeBERT) with a classifier on top,\n also takes care of multi-layer training. ' , __SCREAMING_SNAKE_CASE , )
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self: Optional[Any] , _SCREAMING_SNAKE_CASE: Union[str, Any]) -> Dict:
"""simple docstring"""
super().__init__(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[int] = config.num_labels
__lowerCAmelCase : Tuple = config.num_hidden_layers
__lowerCAmelCase : Optional[Any] = DeeBertModel(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[Any] = nn.Dropout(config.hidden_dropout_prob)
__lowerCAmelCase : Dict = nn.Linear(config.hidden_size , self.config.num_labels)
self.init_weights()
@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE)
def _SCREAMING_SNAKE_CASE ( self: Dict , _SCREAMING_SNAKE_CASE: List[str]=None , _SCREAMING_SNAKE_CASE: Any=None , _SCREAMING_SNAKE_CASE: Dict=None , _SCREAMING_SNAKE_CASE: Optional[Any]=None , _SCREAMING_SNAKE_CASE: Dict=None , _SCREAMING_SNAKE_CASE: Union[str, Any]=None , _SCREAMING_SNAKE_CASE: List[Any]=None , _SCREAMING_SNAKE_CASE: Optional[Any]=-1 , _SCREAMING_SNAKE_CASE: str=False , ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : str = self.num_layers
try:
__lowerCAmelCase : Optional[int] = self.bert(
_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , token_type_ids=_SCREAMING_SNAKE_CASE , position_ids=_SCREAMING_SNAKE_CASE , head_mask=_SCREAMING_SNAKE_CASE , inputs_embeds=_SCREAMING_SNAKE_CASE , )
# sequence_output, pooled_output, (hidden_states), (attentions), highway exits
__lowerCAmelCase : Tuple = outputs[1]
__lowerCAmelCase : Union[str, Any] = self.dropout(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[str] = self.classifier(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Union[str, Any] = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
__lowerCAmelCase : str = e.message
__lowerCAmelCase : Optional[Any] = e.exit_layer
__lowerCAmelCase : Optional[Any] = outputs[0]
if not self.training:
__lowerCAmelCase : int = entropy(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : str = []
__lowerCAmelCase : Union[str, Any] = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
__lowerCAmelCase : Optional[int] = MSELoss()
__lowerCAmelCase : List[Any] = loss_fct(logits.view(-1) , labels.view(-1))
else:
__lowerCAmelCase : Optional[int] = CrossEntropyLoss()
__lowerCAmelCase : Tuple = loss_fct(logits.view(-1 , self.num_labels) , labels.view(-1))
# work with highway exits
__lowerCAmelCase : List[str] = []
for highway_exit in outputs[-1]:
__lowerCAmelCase : List[Any] = highway_exit[0]
if not self.training:
highway_logits_all.append(_SCREAMING_SNAKE_CASE)
highway_entropy.append(highway_exit[2])
if self.num_labels == 1:
# We are doing regression
__lowerCAmelCase : Optional[int] = MSELoss()
__lowerCAmelCase : str = loss_fct(highway_logits.view(-1) , labels.view(-1))
else:
__lowerCAmelCase : int = CrossEntropyLoss()
__lowerCAmelCase : Optional[Any] = loss_fct(highway_logits.view(-1 , self.num_labels) , labels.view(-1))
highway_losses.append(_SCREAMING_SNAKE_CASE)
if train_highway:
__lowerCAmelCase : List[Any] = (sum(highway_losses[:-1]),) + outputs
# exclude the final highway, of course
else:
__lowerCAmelCase : int = (loss,) + outputs
if not self.training:
__lowerCAmelCase : Any = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
__lowerCAmelCase : Any = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), (highway_exits)
| 293
| 0
|
"""simple docstring"""
from random import randint, random
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = False , SCREAMING_SNAKE_CASE = False , SCREAMING_SNAKE_CASE = 5 , ) -> list:
"""simple docstring"""
__snake_case = [[-1] * number_of_cells] # Create a highway without any car
__snake_case = 0
__snake_case = max(SCREAMING_SNAKE_CASE , 0 )
while i < number_of_cells:
__snake_case = (
randint(0 , SCREAMING_SNAKE_CASE ) if random_speed else initial_speed
) # Place the cars
i += (
randint(1 , max_speed * 2 ) if random_frequency else frequency
) # Arbitrary number, may need tuning
return highway
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
__snake_case = 0
__snake_case = highway_now[car_index + 1 :]
for cell in range(len(SCREAMING_SNAKE_CASE ) ): # May need a better name for this
if cells[cell] != -1: # If the cell is not empty then
return distance # we have the distance we wanted
distance += 1
# Here if the car is near the end of the highway
return distance + get_distance(SCREAMING_SNAKE_CASE , -1 )
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> list:
"""simple docstring"""
__snake_case = len(SCREAMING_SNAKE_CASE )
# Beforce calculations, the highway is empty
__snake_case = [-1] * number_of_cells
for car_index in range(SCREAMING_SNAKE_CASE ):
if highway_now[car_index] != -1:
# Add 1 to the current speed of the car and cap the speed
__snake_case = min(highway_now[car_index] + 1 , SCREAMING_SNAKE_CASE )
# Number of empty cell before the next car
__snake_case = get_distance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) - 1
# We can't have the car causing an accident
__snake_case = min(next_highway[car_index] , SCREAMING_SNAKE_CASE )
if random() < probability:
# Randomly, a driver will slow down
__snake_case = max(next_highway[car_index] - 1 , 0 )
return next_highway
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> list:
"""simple docstring"""
__snake_case = len(highway[0] )
for i in range(SCREAMING_SNAKE_CASE ):
__snake_case = update(highway[i] , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__snake_case = [-1] * number_of_cells
for car_index in range(SCREAMING_SNAKE_CASE ):
__snake_case = next_speeds_calculated[car_index]
if speed != -1:
# Change the position based on the speed (with % to create the loop)
__snake_case = (car_index + speed) % number_of_cells
# Commit the change of position
__snake_case = speed
highway.append(SCREAMING_SNAKE_CASE )
return highway
if __name__ == "__main__":
import doctest
doctest.testmod()
| 700
|
"""simple docstring"""
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
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 (
MODEL_FOR_PRETRAINING_MAPPING,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
)
class __magic_name__ :
def __init__( self : Optional[int] , snake_case_ : List[str] , snake_case_ : str=13 , snake_case_ : Optional[int]=7 , snake_case_ : Optional[Any]=True , snake_case_ : Union[str, Any]=True , snake_case_ : Tuple=True , snake_case_ : int=True , snake_case_ : int=99 , snake_case_ : Optional[int]=64 , snake_case_ : Dict=32 , snake_case_ : Dict=5 , snake_case_ : List[str]=4 , snake_case_ : List[Any]=37 , snake_case_ : Optional[Any]="gelu" , snake_case_ : Optional[Any]=0.1 , snake_case_ : List[Any]=0.1 , snake_case_ : Union[str, Any]=512 , snake_case_ : int=16 , snake_case_ : List[str]=2 , snake_case_ : Union[str, Any]=0.02 , snake_case_ : Union[str, Any]=3 , snake_case_ : str=4 , snake_case_ : int=None , ):
__snake_case = parent
__snake_case = batch_size
__snake_case = seq_length
__snake_case = is_training
__snake_case = use_input_mask
__snake_case = use_token_type_ids
__snake_case = use_labels
__snake_case = vocab_size
__snake_case = hidden_size
__snake_case = embedding_size
__snake_case = num_hidden_layers
__snake_case = num_attention_heads
__snake_case = intermediate_size
__snake_case = hidden_act
__snake_case = hidden_dropout_prob
__snake_case = attention_probs_dropout_prob
__snake_case = max_position_embeddings
__snake_case = type_vocab_size
__snake_case = type_sequence_label_size
__snake_case = initializer_range
__snake_case = num_labels
__snake_case = num_choices
__snake_case = scope
def lowerCAmelCase ( self : Union[str, Any] ):
__snake_case = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__snake_case = None
if self.use_input_mask:
__snake_case = random_attention_mask([self.batch_size, self.seq_length] )
__snake_case = None
if self.use_token_type_ids:
__snake_case = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__snake_case = None
__snake_case = None
__snake_case = None
if self.use_labels:
__snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__snake_case = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__snake_case = ids_tensor([self.batch_size] , self.num_choices )
__snake_case = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCAmelCase ( self : Union[str, Any] ):
return MobileBertConfig(
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 , embedding_size=self.embedding_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 , )
def lowerCAmelCase ( self : Optional[Any] , snake_case_ : Any , snake_case_ : Optional[Any] , snake_case_ : Tuple , snake_case_ : Any , snake_case_ : str , snake_case_ : Dict , snake_case_ : List[str] ):
__snake_case = MobileBertModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
__snake_case = model(snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ )
__snake_case = model(snake_case_ , token_type_ids=snake_case_ )
__snake_case = model(snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def lowerCAmelCase ( self : List[str] , snake_case_ : List[Any] , snake_case_ : Any , snake_case_ : Optional[int] , snake_case_ : int , snake_case_ : int , snake_case_ : str , snake_case_ : str ):
__snake_case = MobileBertForMaskedLM(config=snake_case_ )
model.to(snake_case_ )
model.eval()
__snake_case = model(snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase ( self : Optional[Any] , snake_case_ : Optional[int] , snake_case_ : Union[str, Any] , snake_case_ : List[str] , snake_case_ : int , snake_case_ : int , snake_case_ : int , snake_case_ : Any ):
__snake_case = MobileBertForNextSentencePrediction(config=snake_case_ )
model.to(snake_case_ )
model.eval()
__snake_case = model(
snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def lowerCAmelCase ( self : Optional[int] , snake_case_ : List[str] , snake_case_ : List[str] , snake_case_ : str , snake_case_ : Optional[Any] , snake_case_ : List[str] , snake_case_ : List[str] , snake_case_ : Dict ):
__snake_case = MobileBertForPreTraining(config=snake_case_ )
model.to(snake_case_ )
model.eval()
__snake_case = model(
snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ , next_sentence_label=snake_case_ , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def lowerCAmelCase ( self : Any , snake_case_ : List[str] , snake_case_ : int , snake_case_ : Any , snake_case_ : Union[str, Any] , snake_case_ : Dict , snake_case_ : int , snake_case_ : int ):
__snake_case = MobileBertForQuestionAnswering(config=snake_case_ )
model.to(snake_case_ )
model.eval()
__snake_case = model(
snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , start_positions=snake_case_ , end_positions=snake_case_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCAmelCase ( self : str , snake_case_ : Tuple , snake_case_ : int , snake_case_ : str , snake_case_ : Dict , snake_case_ : Optional[int] , snake_case_ : str , snake_case_ : List[str] ):
__snake_case = self.num_labels
__snake_case = MobileBertForSequenceClassification(snake_case_ )
model.to(snake_case_ )
model.eval()
__snake_case = model(snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase ( self : Optional[Any] , snake_case_ : Tuple , snake_case_ : str , snake_case_ : Union[str, Any] , snake_case_ : Tuple , snake_case_ : Optional[int] , snake_case_ : Tuple , snake_case_ : List[Any] ):
__snake_case = self.num_labels
__snake_case = MobileBertForTokenClassification(config=snake_case_ )
model.to(snake_case_ )
model.eval()
__snake_case = model(snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCAmelCase ( self : Tuple , snake_case_ : Union[str, Any] , snake_case_ : Tuple , snake_case_ : Optional[Any] , snake_case_ : Any , snake_case_ : Any , snake_case_ : Dict , snake_case_ : Union[str, Any] ):
__snake_case = self.num_choices
__snake_case = MobileBertForMultipleChoice(config=snake_case_ )
model.to(snake_case_ )
model.eval()
__snake_case = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__snake_case = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__snake_case = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__snake_case = model(
snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCAmelCase ( self : List[Any] ):
__snake_case = self.prepare_config_and_inputs()
(
(
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) ,
) = config_and_inputs
__snake_case = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class __magic_name__ ( lowercase__ , lowercase__ , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Dict = (
(
MobileBertModel,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
)
if is_torch_available()
else ()
)
_SCREAMING_SNAKE_CASE : Optional[int] = (
{
'feature-extraction': MobileBertModel,
'fill-mask': MobileBertForMaskedLM,
'question-answering': MobileBertForQuestionAnswering,
'text-classification': MobileBertForSequenceClassification,
'token-classification': MobileBertForTokenClassification,
'zero-shot': MobileBertForSequenceClassification,
}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE : int = True
def lowerCAmelCase ( self : List[str] , snake_case_ : List[str] , snake_case_ : str , snake_case_ : str=False ):
__snake_case = super()._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
if return_labels:
if model_class in get_values(snake_case_ ):
__snake_case = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=snake_case_ )
__snake_case = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=snake_case_ )
return inputs_dict
def lowerCAmelCase ( self : Optional[Any] ):
__snake_case = MobileBertModelTester(self )
__snake_case = ConfigTester(self , config_class=snake_case_ , hidden_size=37 )
def lowerCAmelCase ( self : List[Any] ):
self.config_tester.run_common_tests()
def lowerCAmelCase ( self : Tuple ):
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*snake_case_ )
def lowerCAmelCase ( self : Optional[Any] ):
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case_ )
def lowerCAmelCase ( self : Tuple ):
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case_ )
def lowerCAmelCase ( self : Any ):
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case_ )
def lowerCAmelCase ( self : Any ):
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case_ )
def lowerCAmelCase ( self : List[str] ):
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*snake_case_ )
def lowerCAmelCase ( self : List[Any] ):
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case_ )
def lowerCAmelCase ( self : str ):
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case_ )
def __UpperCamelCase ( SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
return torch.tensor(
SCREAMING_SNAKE_CASE , dtype=torch.long , device=SCREAMING_SNAKE_CASE , )
_SCREAMING_SNAKE_CASE = 1E-3
@require_torch
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( unittest.TestCase ):
@slow
def lowerCAmelCase ( self : Tuple ):
__snake_case = MobileBertModel.from_pretrained("google/mobilebert-uncased" ).to(snake_case_ )
__snake_case = _long_tensor([[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]] )
with torch.no_grad():
__snake_case = model(snake_case_ )[0]
__snake_case = torch.Size((1, 9, 512) )
self.assertEqual(output.shape , snake_case_ )
__snake_case = torch.tensor(
[
[
[-2.4_73_65_26e07, 8.2_69_16_56e04, 1.6_52_18_38e05],
[-5.7_54_17_04e-01, 3.9_05_60_22e00, 4.4_01_15_07e00],
[2.6_04_73_59e00, 1.5_67_76_52e00, -1.7_32_41_88e-01],
]
] , device=snake_case_ , )
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
__snake_case = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
__snake_case = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )
self.assertTrue(lower_bound and upper_bound )
| 614
| 0
|
'''simple docstring'''
def snake_case_ (UpperCamelCase : list[int] ):
'''simple docstring'''
if not numbers:
return 0
if not isinstance(UpperCamelCase , (list, tuple) ) or not all(
isinstance(UpperCamelCase , UpperCamelCase ) for number in numbers ):
raise ValueError('''numbers must be an iterable of integers''' )
_a = _a = _a = numbers[0]
for i in range(1 , len(UpperCamelCase ) ):
# update the maximum and minimum subarray products
_a = numbers[i]
if number < 0:
_a , _a = min_till_now, max_till_now
_a = max(UpperCamelCase , max_till_now * number )
_a = min(UpperCamelCase , min_till_now * number )
# update the maximum product found till now
_a = max(UpperCamelCase , UpperCamelCase )
return max_prod
| 22
|
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ = logging.get_logger(__name__)
def _lowerCamelCase ( __a ):
SCREAMING_SNAKE_CASE_ = '''huggingface/label-files'''
SCREAMING_SNAKE_CASE_ = '''imagenet-1k-id2label.json'''
SCREAMING_SNAKE_CASE_ = json.load(open(hf_hub_download(__a, __a, repo_type='''dataset''' ), '''r''' ) )
SCREAMING_SNAKE_CASE_ = {int(__a ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE_ = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE_ = '''std_conv''' if '''bit''' in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
SCREAMING_SNAKE_CASE_ = BitConfig(
conv_layer=__a, num_labels=1_000, idalabel=__a, labelaid=__a, )
return config
def _lowerCamelCase ( __a ):
if "stem.conv" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''stem.conv''', '''bit.embedder.convolution''' )
if "blocks" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''blocks''', '''layers''' )
if "head.fc" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''head.fc''', '''classifier.1''' )
if name.startswith('''norm''' ):
SCREAMING_SNAKE_CASE_ = '''bit.''' + name
if "bit" not in name and "classifier" not in name:
SCREAMING_SNAKE_CASE_ = '''bit.encoder.''' + name
return name
def _lowerCamelCase ( ):
SCREAMING_SNAKE_CASE_ = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
SCREAMING_SNAKE_CASE_ = Image.open(requests.get(__a, stream=__a ).raw )
return im
@torch.no_grad()
def _lowerCamelCase ( __a, __a, __a=False ):
SCREAMING_SNAKE_CASE_ = get_config(__a )
# load original model from timm
SCREAMING_SNAKE_CASE_ = create_model(__a, pretrained=__a )
timm_model.eval()
# load state_dict of original model
SCREAMING_SNAKE_CASE_ = timm_model.state_dict()
for key in state_dict.copy().keys():
SCREAMING_SNAKE_CASE_ = state_dict.pop(__a )
SCREAMING_SNAKE_CASE_ = val.squeeze() if '''head''' in key else val
# load HuggingFace model
SCREAMING_SNAKE_CASE_ = BitForImageClassification(__a )
model.eval()
model.load_state_dict(__a )
# create image processor
SCREAMING_SNAKE_CASE_ = create_transform(**resolve_data_config({}, model=__a ) )
SCREAMING_SNAKE_CASE_ = transform.transforms
SCREAMING_SNAKE_CASE_ = {
'''bilinear''': PILImageResampling.BILINEAR,
'''bicubic''': PILImageResampling.BICUBIC,
'''nearest''': PILImageResampling.NEAREST,
}
SCREAMING_SNAKE_CASE_ = BitImageProcessor(
do_resize=__a, size={'''shortest_edge''': timm_transforms[0].size}, resample=pillow_resamplings[timm_transforms[0].interpolation.value], do_center_crop=__a, crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}, do_normalize=__a, image_mean=timm_transforms[-1].mean.tolist(), image_std=timm_transforms[-1].std.tolist(), )
SCREAMING_SNAKE_CASE_ = prepare_img()
SCREAMING_SNAKE_CASE_ = transform(__a ).unsqueeze(0 )
SCREAMING_SNAKE_CASE_ = processor(__a, return_tensors='''pt''' ).pixel_values
# verify pixel values
assert torch.allclose(__a, __a )
# verify logits
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(__a )
SCREAMING_SNAKE_CASE_ = outputs.logits
print('''Logits:''', logits[0, :3] )
print('''Predicted class:''', model.config.idalabel[logits.argmax(-1 ).item()] )
SCREAMING_SNAKE_CASE_ = timm_model(__a )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(__a, outputs.logits, atol=1E-3 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
Path(__a ).mkdir(exist_ok=__a )
print(F'Saving model {model_name} and processor to {pytorch_dump_folder_path}' )
model.save_pretrained(__a )
processor.save_pretrained(__a )
if push_to_hub:
print(F'Pushing model {model_name} and processor to the hub' )
model.push_to_hub(F'ybelkada/{model_name}' )
processor.push_to_hub(F'ybelkada/{model_name}' )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='resnetv2_50x1_bitm',
type=str,
help='Name of the BiT timm model you\'d like to convert.',
)
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 to push the model to the hub.',
)
lowerCAmelCase__ = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 626
| 0
|
'''simple docstring'''
from math import isclose, sqrt
def UpperCAmelCase ( UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : float):
lowerCamelCase : Union[str, Any] = point_y / 4 / point_x
lowerCamelCase : List[Any] = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
lowerCamelCase : Tuple = (1 - normal_gradient * normal_gradient) / (
1 + normal_gradient * normal_gradient
)
lowerCamelCase : int = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)
# to find the next point, solve the simultaeneous equations:
# y^2 + 4x^2 = 100
# y - b = m * (x - a)
# ==> A x^2 + B x + C = 0
lowerCamelCase : Union[str, Any] = outgoing_gradient**2 + 4
lowerCamelCase : str = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
lowerCamelCase : Optional[int] = (point_y - outgoing_gradient * point_x) ** 2 - 1_00
lowerCamelCase : str = (
-linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term)
) / (2 * quadratic_term)
lowerCamelCase : Tuple = (
-linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term)
) / (2 * quadratic_term)
# two solutions, one of which is our input point
lowerCamelCase : Dict = x_minus if isclose(UpperCAmelCase__ , UpperCAmelCase__) else x_plus
lowerCamelCase : List[Any] = point_y + outgoing_gradient * (next_x - point_x)
return next_x, next_y, outgoing_gradient
def UpperCAmelCase ( UpperCAmelCase__ : float = 1.4 , UpperCAmelCase__ : float = -9.6):
lowerCamelCase : int = 0
lowerCamelCase : float = first_x_coord
lowerCamelCase : float = first_y_coord
lowerCamelCase : float = (10.1 - point_y) / (0.0 - point_x)
while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0):
lowerCamelCase : Optional[int] = next_point(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__)
num_reflections += 1
return num_reflections
if __name__ == "__main__":
print(f"""{solution() = }""")
| 707
|
'''simple docstring'''
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
A = {
'<': operator.lt,
'<=': operator.le,
'==': operator.eq,
'!=': operator.ne,
'>=': operator.ge,
'>': operator.gt,
}
def UpperCAmelCase ( UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Optional[int]):
if got_ver is None or want_ver is None:
raise ValueError(
F'''Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider'''
F''' reinstalling {pkg}.''')
if not ops[op](version.parse(UpperCAmelCase__) , version.parse(UpperCAmelCase__)):
raise ImportError(
F'''{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}''')
def UpperCAmelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[str] = None):
lowerCamelCase : List[Any] = F'''\n{hint}''' if hint is not None else ''
# non-versioned check
if re.match(R'^[\w_\-\d]+$' , UpperCAmelCase__):
lowerCamelCase , lowerCamelCase , lowerCamelCase : Optional[Any] = requirement, None, None
else:
lowerCamelCase : Optional[Any] = re.findall(R'^([^!=<>\s]+)([\s!=<>]{1,2}.+)' , UpperCAmelCase__)
if not match:
raise ValueError(
'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'
F''' got {requirement}''')
lowerCamelCase , lowerCamelCase : Dict = match[0]
lowerCamelCase : Dict = want_full.split(',') # there could be multiple requirements
lowerCamelCase : Union[str, Any] = {}
for w in want_range:
lowerCamelCase : int = re.findall(R'^([\s!=<>]{1,2})(.+)' , UpperCAmelCase__)
if not match:
raise ValueError(
'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'
F''' but got {requirement}''')
lowerCamelCase , lowerCamelCase : List[Any] = match[0]
lowerCamelCase : Optional[int] = want_ver
if op not in ops:
raise ValueError(F'''{requirement}: need one of {list(ops.keys())}, but got {op}''')
# special case
if pkg == "python":
lowerCamelCase : Optional[int] = '.'.join([str(UpperCAmelCase__) for x in sys.version_info[:3]])
for op, want_ver in wanted.items():
_compare_versions(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__)
return
# check if any version is installed
try:
lowerCamelCase : Any = importlib.metadata.version(UpperCAmelCase__)
except importlib.metadata.PackageNotFoundError:
raise importlib.metadata.PackageNotFoundError(
F'''The \'{requirement}\' distribution was not found and is required by this application. {hint}''')
# check that the right version is installed if version number or a range was provided
if want_ver is not None:
for op, want_ver in wanted.items():
_compare_versions(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__)
def UpperCAmelCase ( UpperCAmelCase__ : str):
lowerCamelCase : List[str] = 'Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'
return require_version(UpperCAmelCase__ , UpperCAmelCase__)
| 449
| 0
|
"""simple docstring"""
from __future__ import annotations
from math import pi, sqrt
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> tuple:
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
raise ValueError('''Capacitance cannot be 0 or negative''' )
else:
return (
"Resonant frequency",
float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 103
|
from __future__ import annotations
import math
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> list[int]:
if num <= 0:
_lowercase : List[str] = F"""{num}: Invalid input, please enter a positive integer."""
raise ValueError(SCREAMING_SNAKE_CASE )
_lowercase : Union[str, Any] = [True] * (num + 1)
_lowercase : Union[str, Any] = []
_lowercase : Dict = 2
_lowercase : Union[str, Any] = int(math.sqrt(SCREAMING_SNAKE_CASE ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(SCREAMING_SNAKE_CASE )
# Set multiples of start be False
for i in range(start * start , num + 1 , SCREAMING_SNAKE_CASE ):
if sieve[i] is True:
_lowercase : str = False
start += 1
for j in range(end + 1 , num + 1 ):
if sieve[j] is True:
prime.append(SCREAMING_SNAKE_CASE )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input("Enter a positive integer: ").strip())))
| 66
| 0
|
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __snake_case ) -> int:
for i in range(1 ,len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 ,len(__snake_case ) ):
matrix[i][0] += matrix[i - 1][0]
# updating the path cost for current position
for i in range(1 ,len(__snake_case ) ):
for j in range(1 ,len(matrix[0] ) ):
matrix[i][j] += min(matrix[i - 1][j] ,matrix[i][j - 1] )
return matrix[-1][-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 719
|
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import SpeechaTextFeatureExtractor
__snake_case : Union[str, Any] = random.Random()
def _lowercase ( __snake_case ,__snake_case=1.0 ,__snake_case=None ,__snake_case=None ) -> List[Any]:
if rng is None:
__lowerCAmelCase : Dict = global_rng
__lowerCAmelCase : Tuple = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class A__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self: Optional[Any] , _SCREAMING_SNAKE_CASE: Any , _SCREAMING_SNAKE_CASE: List[Any]=7 , _SCREAMING_SNAKE_CASE: int=400 , _SCREAMING_SNAKE_CASE: List[str]=2000 , _SCREAMING_SNAKE_CASE: Optional[Any]=24 , _SCREAMING_SNAKE_CASE: Dict=24 , _SCREAMING_SNAKE_CASE: Optional[int]=0.0 , _SCREAMING_SNAKE_CASE: Any=1_6000 , _SCREAMING_SNAKE_CASE: int=True , _SCREAMING_SNAKE_CASE: Union[str, Any]=True , ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : Any = parent
__lowerCAmelCase : str = batch_size
__lowerCAmelCase : List[str] = min_seq_length
__lowerCAmelCase : Any = max_seq_length
__lowerCAmelCase : Dict = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
__lowerCAmelCase : Union[str, Any] = feature_size
__lowerCAmelCase : int = num_mel_bins
__lowerCAmelCase : Optional[Any] = padding_value
__lowerCAmelCase : List[Any] = sampling_rate
__lowerCAmelCase : Dict = return_attention_mask
__lowerCAmelCase : int = do_normalize
def _SCREAMING_SNAKE_CASE ( self: Any) -> Optional[int]:
"""simple docstring"""
return {
"feature_size": self.feature_size,
"num_mel_bins": self.num_mel_bins,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def _SCREAMING_SNAKE_CASE ( self: Tuple , _SCREAMING_SNAKE_CASE: Tuple=False , _SCREAMING_SNAKE_CASE: int=False) -> Optional[int]:
"""simple docstring"""
def _flatten(_SCREAMING_SNAKE_CASE: Optional[Any]):
return list(itertools.chain(*_SCREAMING_SNAKE_CASE))
if equal_length:
__lowerCAmelCase : Tuple = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
else:
# make sure that inputs increase in size
__lowerCAmelCase : List[Any] = [
floats_list((x, self.feature_size))
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff)
]
if numpify:
__lowerCAmelCase : Any = [np.asarray(_SCREAMING_SNAKE_CASE) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class A__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = SpeechaTextFeatureExtractor if is_speech_available() else None
def _SCREAMING_SNAKE_CASE ( self: List[Any]) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : Tuple = SpeechaTextFeatureExtractionTester(self)
def _SCREAMING_SNAKE_CASE ( self: Optional[Any] , _SCREAMING_SNAKE_CASE: Optional[Any]) -> str:
"""simple docstring"""
self.assertTrue(np.all(np.mean(_SCREAMING_SNAKE_CASE , axis=0) < 1e-3))
self.assertTrue(np.all(np.abs(np.var(_SCREAMING_SNAKE_CASE , axis=0) - 1) < 1e-3))
def _SCREAMING_SNAKE_CASE ( self: Union[str, Any]) -> int:
"""simple docstring"""
__lowerCAmelCase : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
# create three inputs of length 800, 1000, and 1200
__lowerCAmelCase : Tuple = [floats_list((1, x))[0] for x in range(800 , 1400 , 200)]
__lowerCAmelCase : Optional[int] = [np.asarray(_SCREAMING_SNAKE_CASE) for speech_input in speech_inputs]
# Test feature size
__lowerCAmelCase : Optional[int] = feature_extractor(_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , return_tensors="np").input_features
self.assertTrue(input_features.ndim == 3)
self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size)
# Test not batched input
__lowerCAmelCase : Optional[Any] = feature_extractor(speech_inputs[0] , return_tensors="np").input_features
__lowerCAmelCase : Any = feature_extractor(np_speech_inputs[0] , return_tensors="np").input_features
self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1e-3))
# Test batched
__lowerCAmelCase : Tuple = feature_extractor(_SCREAMING_SNAKE_CASE , return_tensors="np").input_features
__lowerCAmelCase : Tuple = feature_extractor(_SCREAMING_SNAKE_CASE , return_tensors="np").input_features
for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE):
self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1e-3))
# Test 2-D numpy arrays are batched.
__lowerCAmelCase : List[str] = [floats_list((1, x))[0] for x in (800, 800, 800)]
__lowerCAmelCase : Dict = np.asarray(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[int] = feature_extractor(_SCREAMING_SNAKE_CASE , return_tensors="np").input_features
__lowerCAmelCase : Optional[Any] = feature_extractor(_SCREAMING_SNAKE_CASE , return_tensors="np").input_features
for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE):
self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1e-3))
def _SCREAMING_SNAKE_CASE ( self: Union[str, Any]) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
__lowerCAmelCase : Tuple = [floats_list((1, x))[0] for x in range(800 , 1400 , 200)]
__lowerCAmelCase : Optional[Any] = ["longest", "max_length", "do_not_pad"]
__lowerCAmelCase : str = [None, 16, None]
for max_length, padding in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE):
__lowerCAmelCase : str = feature_extractor(
_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[Any] = inputs.input_features
__lowerCAmelCase : Optional[Any] = inputs.attention_mask
__lowerCAmelCase : Tuple = [np.sum(_SCREAMING_SNAKE_CASE) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]])
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]])
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]])
def _SCREAMING_SNAKE_CASE ( self: Any) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
__lowerCAmelCase : Any = [floats_list((1, x))[0] for x in range(800 , 1400 , 200)]
__lowerCAmelCase : Dict = ["longest", "max_length", "do_not_pad"]
__lowerCAmelCase : List[Any] = [None, 16, None]
for max_length, padding in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE):
__lowerCAmelCase : List[str] = feature_extractor(
_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , return_tensors="np" , return_attention_mask=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[str] = inputs.input_features
__lowerCAmelCase : Dict = inputs.attention_mask
__lowerCAmelCase : Dict = [np.sum(_SCREAMING_SNAKE_CASE) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]])
self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6)
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]])
self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6)
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]])
def _SCREAMING_SNAKE_CASE ( self: str) -> Any:
"""simple docstring"""
__lowerCAmelCase : str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
__lowerCAmelCase : str = [floats_list((1, x))[0] for x in range(800 , 1400 , 200)]
__lowerCAmelCase : str = feature_extractor(
_SCREAMING_SNAKE_CASE , padding="max_length" , max_length=4 , truncation=_SCREAMING_SNAKE_CASE , return_tensors="np" , return_attention_mask=_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : List[str] = inputs.input_features
__lowerCAmelCase : Dict = inputs.attention_mask
__lowerCAmelCase : Any = np.sum(attention_mask == 1 , axis=1)
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
self._check_zero_mean_unit_variance(input_features[1])
self._check_zero_mean_unit_variance(input_features[2])
def _SCREAMING_SNAKE_CASE ( self: int) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
__lowerCAmelCase : List[Any] = [floats_list((1, x))[0] for x in range(800 , 1400 , 200)]
__lowerCAmelCase : Any = feature_extractor(
_SCREAMING_SNAKE_CASE , padding="longest" , max_length=4 , truncation=_SCREAMING_SNAKE_CASE , return_tensors="np" , return_attention_mask=_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : List[str] = inputs.input_features
__lowerCAmelCase : Optional[Any] = inputs.attention_mask
__lowerCAmelCase : Optional[Any] = np.sum(attention_mask == 1 , axis=1)
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]])
self._check_zero_mean_unit_variance(input_features[2])
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape , (3, 4, 24))
__lowerCAmelCase : List[str] = [floats_list((1, x))[0] for x in range(800 , 1400 , 200)]
__lowerCAmelCase : List[str] = feature_extractor(
_SCREAMING_SNAKE_CASE , padding="longest" , max_length=16 , truncation=_SCREAMING_SNAKE_CASE , return_tensors="np" , return_attention_mask=_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : Optional[Any] = inputs.input_features
__lowerCAmelCase : Optional[int] = inputs.attention_mask
__lowerCAmelCase : str = np.sum(attention_mask == 1 , axis=1)
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]])
self._check_zero_mean_unit_variance(input_features[2])
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape , (3, 6, 24))
def _SCREAMING_SNAKE_CASE ( self: Any) -> Dict:
"""simple docstring"""
import torch
__lowerCAmelCase : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
__lowerCAmelCase : List[str] = np.random.rand(100 , 32).astype(np.floataa)
__lowerCAmelCase : List[Any] = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
__lowerCAmelCase : Any = feature_extractor.pad([{"input_features": inputs}] , return_tensors="np")
self.assertTrue(np_processed.input_features.dtype == np.floataa)
__lowerCAmelCase : List[Any] = feature_extractor.pad([{"input_features": inputs}] , return_tensors="pt")
self.assertTrue(pt_processed.input_features.dtype == torch.floataa)
def _SCREAMING_SNAKE_CASE ( self: str , _SCREAMING_SNAKE_CASE: List[Any]) -> Optional[Any]:
"""simple docstring"""
from datasets import load_dataset
__lowerCAmelCase : Any = load_dataset("hf-internal-testing/librispeech_asr_dummy" , "clean" , split="validation")
# automatic decoding with librispeech
__lowerCAmelCase : List[Any] = ds.sort("id").select(range(_SCREAMING_SNAKE_CASE))[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def _SCREAMING_SNAKE_CASE ( self: List[str]) -> Any:
"""simple docstring"""
__lowerCAmelCase : str = np.array([
-1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
-1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
-1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
])
# fmt: on
__lowerCAmelCase : str = self._load_datasamples(1)
__lowerCAmelCase : Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
__lowerCAmelCase : List[str] = feature_extractor(_SCREAMING_SNAKE_CASE , return_tensors="pt").input_features
self.assertEquals(input_features.shape , (1, 584, 24))
self.assertTrue(np.allclose(input_features[0, 0, :30] , _SCREAMING_SNAKE_CASE , atol=1e-4))
| 615
| 0
|
"""simple docstring"""
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
UpperCAmelCase_ : int = logging.getLogger(__name__)
def _lowerCAmelCase(a : Optional[int] , a : Any ) -> List[Any]:
return (preds == labels).mean()
@dataclass
class __UpperCAmelCase :
'''simple docstring'''
lowercase : str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
lowercase : Optional[str] = field(
default=_lowerCamelCase, metadata={"help": "Pretrained config name or path if not the same as model_name"} )
lowercase : Optional[str] = field(
default=_lowerCamelCase, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
lowercase : Optional[str] = field(
default=_lowerCamelCase, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, )
@dataclass
class __UpperCAmelCase :
'''simple docstring'''
lowercase : str = field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys() )} )
lowercase : str = field(metadata={"help": "Should contain the data files for the task."} )
lowercase : int = 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."
)
}, )
lowercase : bool = field(
default=_lowerCamelCase, metadata={"help": "Overwrite the cached training and evaluation sets"} )
def _lowerCAmelCase() -> Tuple:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_SCREAMING_SNAKE_CASE =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =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''' , a )
# Set seed
set_seed(training_args.seed )
try:
_SCREAMING_SNAKE_CASE =processors[data_args.task_name]()
_SCREAMING_SNAKE_CASE =processor.get_labels()
_SCREAMING_SNAKE_CASE =len(a )
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.
_SCREAMING_SNAKE_CASE =AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=a , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
_SCREAMING_SNAKE_CASE =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 , )
_SCREAMING_SNAKE_CASE =AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=a , cache_dir=model_args.cache_dir , )
# Get datasets
_SCREAMING_SNAKE_CASE =(
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=a , 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
)
_SCREAMING_SNAKE_CASE =(
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=a , 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(a : EvalPrediction ) -> Dict:
_SCREAMING_SNAKE_CASE =np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(a , p.label_ids )}
# Data collator
_SCREAMING_SNAKE_CASE =DataCollatorWithPadding(a , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
_SCREAMING_SNAKE_CASE =Trainer(
model=a , args=a , train_dataset=a , eval_dataset=a , compute_metrics=a , data_collator=a , )
# 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
_SCREAMING_SNAKE_CASE ={}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
_SCREAMING_SNAKE_CASE =trainer.evaluate()
_SCREAMING_SNAKE_CASE =os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_master():
with open(a , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , a , a )
writer.write('''%s = %s\n''' % (key, value) )
results.update(a )
return results
def _lowerCAmelCase(a : List[str] ) -> Union[str, Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 255
|
"""simple docstring"""
from __future__ import annotations
import numpy as np
def _lowerCAmelCase(a : list[float] ) -> Any:
return np.maximum(0 , a )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 255
| 1
|
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
UpperCAmelCase_ = sys.version_info >= (3, 10)
def SCREAMING_SNAKE_CASE ( a_ : List[Any]=None , a_ : Optional[int]=None ):
return field(default_factory=lambda: default , metadata=a_ )
@dataclass
class __lowercase :
_a = 42
_a = 42
_a = 42
_a = 42
@dataclass
class __lowercase :
_a = 42
_a = field(default="""toto""" , metadata={"""help""": """help message"""} )
@dataclass
class __lowercase :
_a = False
_a = True
_a = None
class __lowercase ( __magic_name__ ):
_a = """titi"""
_a = """toto"""
class __lowercase ( __magic_name__ ):
_a = """titi"""
_a = """toto"""
_a = 42
@dataclass
class __lowercase :
_a = "toto"
def UpperCamelCase__ ( self ) -> Any:
__a = BasicEnum(self.foo )
@dataclass
class __lowercase :
_a = "toto"
def UpperCamelCase__ ( self ) -> Union[str, Any]:
__a = MixedTypeEnum(self.foo )
@dataclass
class __lowercase :
_a = None
_a = field(default=__magic_name__ , metadata={"""help""": """help message"""} )
_a = None
_a = list_field(default=[] )
_a = list_field(default=[] )
@dataclass
class __lowercase :
_a = list_field(default=[] )
_a = list_field(default=[1, 2, 3] )
_a = list_field(default=["""Hallo""", """Bonjour""", """Hello"""] )
_a = list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class __lowercase :
_a = field()
_a = field()
_a = field()
def UpperCamelCase__ ( self ) -> Tuple:
__a = BasicEnum(self.required_enum )
@dataclass
class __lowercase :
_a = 42
_a = field()
_a = None
_a = field(default="""toto""" , metadata={"""help""": """help message"""} )
_a = list_field(default=["""Hallo""", """Bonjour""", """Hello"""] )
if is_python_no_less_than_3_10:
@dataclass
class __lowercase :
_a = False
_a = True
_a = None
@dataclass
class __lowercase :
_a = None
_a = field(default=__magic_name__ , metadata={"""help""": """help message"""} )
_a = None
_a = list_field(default=[] )
_a = list_field(default=[] )
class __lowercase ( unittest.TestCase ):
def UpperCamelCase__ ( self , UpperCamelCase , UpperCamelCase ) -> Any:
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
__a = {k: v for k, v in vars(UpperCamelCase ).items() if k != 'container'}
__a = {k: v for k, v in vars(UpperCamelCase ).items() if k != 'container'}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get('choices' , UpperCamelCase ) and yy.get('choices' , UpperCamelCase ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx['type'](UpperCamelCase ) , yy['type'](UpperCamelCase ) )
del xx["type"], yy["type"]
self.assertEqual(UpperCamelCase , UpperCamelCase )
def UpperCamelCase__ ( self ) -> Tuple:
__a = HfArgumentParser(UpperCamelCase )
__a = argparse.ArgumentParser()
expected.add_argument('--foo' , type=UpperCamelCase , required=UpperCamelCase )
expected.add_argument('--bar' , type=UpperCamelCase , required=UpperCamelCase )
expected.add_argument('--baz' , type=UpperCamelCase , required=UpperCamelCase )
expected.add_argument('--flag' , type=UpperCamelCase , default=UpperCamelCase , const=UpperCamelCase , nargs='?' )
self.argparsersEqual(UpperCamelCase , UpperCamelCase )
__a = ['--foo', '1', '--baz', 'quux', '--bar', '0.5']
((__a) , ) = parser.parse_args_into_dataclasses(UpperCamelCase , look_for_args_file=UpperCamelCase )
self.assertFalse(example.flag )
def UpperCamelCase__ ( self ) -> Optional[int]:
__a = HfArgumentParser(UpperCamelCase )
__a = argparse.ArgumentParser()
expected.add_argument('--foo' , default=42 , type=UpperCamelCase )
expected.add_argument('--baz' , default='toto' , type=UpperCamelCase , help='help message' )
self.argparsersEqual(UpperCamelCase , UpperCamelCase )
def UpperCamelCase__ ( self ) -> List[Any]:
__a = argparse.ArgumentParser()
expected.add_argument('--foo' , type=UpperCamelCase , default=UpperCamelCase , const=UpperCamelCase , nargs='?' )
expected.add_argument('--baz' , type=UpperCamelCase , default=UpperCamelCase , const=UpperCamelCase , nargs='?' )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument('--no_baz' , action='store_false' , default=UpperCamelCase , dest='baz' )
expected.add_argument('--opt' , type=UpperCamelCase , default=UpperCamelCase )
__a = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(UpperCamelCase )
for dataclass_type in dataclass_types:
__a = HfArgumentParser(UpperCamelCase )
self.argparsersEqual(UpperCamelCase , UpperCamelCase )
__a = parser.parse_args([] )
self.assertEqual(UpperCamelCase , Namespace(foo=UpperCamelCase , baz=UpperCamelCase , opt=UpperCamelCase ) )
__a = parser.parse_args(['--foo', '--no_baz'] )
self.assertEqual(UpperCamelCase , Namespace(foo=UpperCamelCase , baz=UpperCamelCase , opt=UpperCamelCase ) )
__a = parser.parse_args(['--foo', '--baz'] )
self.assertEqual(UpperCamelCase , Namespace(foo=UpperCamelCase , baz=UpperCamelCase , opt=UpperCamelCase ) )
__a = parser.parse_args(['--foo', 'True', '--baz', 'True', '--opt', 'True'] )
self.assertEqual(UpperCamelCase , Namespace(foo=UpperCamelCase , baz=UpperCamelCase , opt=UpperCamelCase ) )
__a = parser.parse_args(['--foo', 'False', '--baz', 'False', '--opt', 'False'] )
self.assertEqual(UpperCamelCase , Namespace(foo=UpperCamelCase , baz=UpperCamelCase , opt=UpperCamelCase ) )
def UpperCamelCase__ ( self ) -> List[Any]:
__a = HfArgumentParser(UpperCamelCase )
__a = argparse.ArgumentParser()
expected.add_argument(
'--foo' , default='toto' , choices=['titi', 'toto', 42] , type=make_choice_type_function(['titi', 'toto', 42] ) , )
self.argparsersEqual(UpperCamelCase , UpperCamelCase )
__a = parser.parse_args([] )
self.assertEqual(args.foo , 'toto' )
__a = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
__a = parser.parse_args(['--foo', 'titi'] )
self.assertEqual(args.foo , 'titi' )
__a = parser.parse_args_into_dataclasses(['--foo', 'titi'] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
__a = parser.parse_args(['--foo', '42'] )
self.assertEqual(args.foo , 42 )
__a = parser.parse_args_into_dataclasses(['--foo', '42'] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def UpperCamelCase__ ( self ) -> Optional[int]:
@dataclass
class __lowercase :
_a = "toto"
__a = HfArgumentParser(UpperCamelCase )
__a = argparse.ArgumentParser()
expected.add_argument(
'--foo' , default='toto' , choices=('titi', 'toto', 42) , type=make_choice_type_function(['titi', 'toto', 42] ) , )
self.argparsersEqual(UpperCamelCase , UpperCamelCase )
__a = parser.parse_args([] )
self.assertEqual(args.foo , 'toto' )
__a = parser.parse_args(['--foo', 'titi'] )
self.assertEqual(args.foo , 'titi' )
__a = parser.parse_args(['--foo', '42'] )
self.assertEqual(args.foo , 42 )
def UpperCamelCase__ ( self ) -> Optional[Any]:
__a = HfArgumentParser(UpperCamelCase )
__a = argparse.ArgumentParser()
expected.add_argument('--foo_int' , nargs='+' , default=[] , type=UpperCamelCase )
expected.add_argument('--bar_int' , nargs='+' , default=[1, 2, 3] , type=UpperCamelCase )
expected.add_argument('--foo_str' , nargs='+' , default=['Hallo', 'Bonjour', 'Hello'] , type=UpperCamelCase )
expected.add_argument('--foo_float' , nargs='+' , default=[0.1, 0.2, 0.3] , type=UpperCamelCase )
self.argparsersEqual(UpperCamelCase , UpperCamelCase )
__a = parser.parse_args([] )
self.assertEqual(
UpperCamelCase , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=['Hallo', 'Bonjour', 'Hello'] , foo_float=[0.1, 0.2, 0.3] ) , )
__a = parser.parse_args('--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'.split() )
self.assertEqual(UpperCamelCase , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=['a', 'b', 'c'] , foo_float=[0.1, 0.7] ) )
def UpperCamelCase__ ( self ) -> Union[str, Any]:
__a = argparse.ArgumentParser()
expected.add_argument('--foo' , default=UpperCamelCase , type=UpperCamelCase )
expected.add_argument('--bar' , default=UpperCamelCase , type=UpperCamelCase , help='help message' )
expected.add_argument('--baz' , default=UpperCamelCase , type=UpperCamelCase )
expected.add_argument('--ces' , nargs='+' , default=[] , type=UpperCamelCase )
expected.add_argument('--des' , nargs='+' , default=[] , type=UpperCamelCase )
__a = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(UpperCamelCase )
for dataclass_type in dataclass_types:
__a = HfArgumentParser(UpperCamelCase )
self.argparsersEqual(UpperCamelCase , UpperCamelCase )
__a = parser.parse_args([] )
self.assertEqual(UpperCamelCase , Namespace(foo=UpperCamelCase , bar=UpperCamelCase , baz=UpperCamelCase , ces=[] , des=[] ) )
__a = parser.parse_args('--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'.split() )
self.assertEqual(UpperCamelCase , Namespace(foo=12 , bar=3.14 , baz='42' , ces=['a', 'b', 'c'] , des=[1, 2, 3] ) )
def UpperCamelCase__ ( self ) -> Dict:
__a = HfArgumentParser(UpperCamelCase )
__a = argparse.ArgumentParser()
expected.add_argument('--required_list' , nargs='+' , type=UpperCamelCase , required=UpperCamelCase )
expected.add_argument('--required_str' , type=UpperCamelCase , required=UpperCamelCase )
expected.add_argument(
'--required_enum' , type=make_choice_type_function(['titi', 'toto'] ) , choices=['titi', 'toto'] , required=UpperCamelCase , )
self.argparsersEqual(UpperCamelCase , UpperCamelCase )
def UpperCamelCase__ ( self ) -> Optional[Any]:
__a = HfArgumentParser(UpperCamelCase )
__a = argparse.ArgumentParser()
expected.add_argument('--foo' , type=UpperCamelCase , required=UpperCamelCase )
expected.add_argument(
'--required_enum' , type=make_choice_type_function(['titi', 'toto'] ) , choices=['titi', 'toto'] , required=UpperCamelCase , )
expected.add_argument('--opt' , type=UpperCamelCase , default=UpperCamelCase )
expected.add_argument('--baz' , default='toto' , type=UpperCamelCase , help='help message' )
expected.add_argument('--foo_str' , nargs='+' , default=['Hallo', 'Bonjour', 'Hello'] , type=UpperCamelCase )
self.argparsersEqual(UpperCamelCase , UpperCamelCase )
def UpperCamelCase__ ( self ) -> Any:
__a = HfArgumentParser(UpperCamelCase )
__a = {
'foo': 12,
'bar': 3.14,
'baz': '42',
'flag': True,
}
__a = parser.parse_dict(UpperCamelCase )[0]
__a = BasicExample(**UpperCamelCase )
self.assertEqual(UpperCamelCase , UpperCamelCase )
def UpperCamelCase__ ( self ) -> Any:
__a = HfArgumentParser(UpperCamelCase )
__a = {
'foo': 12,
'bar': 3.14,
'baz': '42',
'flag': True,
'extra': 42,
}
self.assertRaises(UpperCamelCase , parser.parse_dict , UpperCamelCase , allow_extra_keys=UpperCamelCase )
def UpperCamelCase__ ( self ) -> Any:
__a = HfArgumentParser(UpperCamelCase )
__a = {
'foo': 12,
'bar': 3.14,
'baz': '42',
'flag': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
__a = os.path.join(UpperCamelCase , 'temp_json' )
os.mkdir(UpperCamelCase )
with open(temp_local_path + '.json' , 'w+' ) as f:
json.dump(UpperCamelCase , UpperCamelCase )
__a = parser.parse_yaml_file(Path(temp_local_path + '.json' ) )[0]
__a = BasicExample(**UpperCamelCase )
self.assertEqual(UpperCamelCase , UpperCamelCase )
def UpperCamelCase__ ( self ) -> Any:
__a = HfArgumentParser(UpperCamelCase )
__a = {
'foo': 12,
'bar': 3.14,
'baz': '42',
'flag': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
__a = os.path.join(UpperCamelCase , 'temp_yaml' )
os.mkdir(UpperCamelCase )
with open(temp_local_path + '.yaml' , 'w+' ) as f:
yaml.dump(UpperCamelCase , UpperCamelCase )
__a = parser.parse_yaml_file(Path(temp_local_path + '.yaml' ) )[0]
__a = BasicExample(**UpperCamelCase )
self.assertEqual(UpperCamelCase , UpperCamelCase )
def UpperCamelCase__ ( self ) -> Optional[Any]:
__a = HfArgumentParser(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
| 490
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ = {
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
"tokenization_roformer": ["RoFormerTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = ["RoFormerTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
"ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"RoFormerForCausalLM",
"RoFormerForMaskedLM",
"RoFormerForMultipleChoice",
"RoFormerForQuestionAnswering",
"RoFormerForSequenceClassification",
"RoFormerForTokenClassification",
"RoFormerLayer",
"RoFormerModel",
"RoFormerPreTrainedModel",
"load_tf_weights_in_roformer",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
"TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRoFormerForCausalLM",
"TFRoFormerForMaskedLM",
"TFRoFormerForMultipleChoice",
"TFRoFormerForQuestionAnswering",
"TFRoFormerForSequenceClassification",
"TFRoFormerForTokenClassification",
"TFRoFormerLayer",
"TFRoFormerModel",
"TFRoFormerPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
"FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"FlaxRoFormerForMaskedLM",
"FlaxRoFormerForMultipleChoice",
"FlaxRoFormerForQuestionAnswering",
"FlaxRoFormerForSequenceClassification",
"FlaxRoFormerForTokenClassification",
"FlaxRoFormerModel",
"FlaxRoFormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
from .tokenization_roformer import RoFormerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roformer_fast import RoFormerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roformer import (
ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
RoFormerForCausalLM,
RoFormerForMaskedLM,
RoFormerForMultipleChoice,
RoFormerForQuestionAnswering,
RoFormerForSequenceClassification,
RoFormerForTokenClassification,
RoFormerLayer,
RoFormerModel,
RoFormerPreTrainedModel,
load_tf_weights_in_roformer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roformer import (
TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerLayer,
TFRoFormerModel,
TFRoFormerPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roformer import (
FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
FlaxRoFormerPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 490
| 1
|
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __magic_name__ ( lowercase__ , lowercase__ , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Optional[int] = IFInpaintingPipeline
_SCREAMING_SNAKE_CASE : int = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
_SCREAMING_SNAKE_CASE : Dict = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
_SCREAMING_SNAKE_CASE : Union[str, Any] = PipelineTesterMixin.required_optional_params - {'latents'}
def lowerCAmelCase ( self : Tuple ):
return self._get_dummy_components()
def lowerCAmelCase ( self : List[str] , snake_case_ : Tuple , snake_case_ : Optional[Any]=0 ):
if str(snake_case_ ).startswith("mps" ):
__snake_case = torch.manual_seed(snake_case_ )
else:
__snake_case = torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
__snake_case = floats_tensor((1, 3, 32, 32) , rng=random.Random(snake_case_ ) ).to(snake_case_ )
__snake_case = floats_tensor((1, 3, 32, 32) , rng=random.Random(snake_case_ ) ).to(snake_case_ )
__snake_case = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def lowerCAmelCase ( self : str ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def lowerCAmelCase ( self : Optional[Any] ):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda" , reason="float16 requires CUDA" )
def lowerCAmelCase ( self : List[str] ):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def lowerCAmelCase ( self : str ):
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def lowerCAmelCase ( self : Tuple ):
self._test_save_load_local()
def lowerCAmelCase ( self : List[Any] ):
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 163
|
"""simple docstring"""
def __UpperCamelCase ( SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
__snake_case = ""
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 ( SCREAMING_SNAKE_CASE ) -> dict[str, str]:
"""simple docstring"""
__snake_case = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
__snake_case = remove_duplicates(key.upper() )
__snake_case = len(SCREAMING_SNAKE_CASE )
# First fill cipher with key characters
__snake_case = {alphabet[i]: char for i, char in enumerate(SCREAMING_SNAKE_CASE )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(SCREAMING_SNAKE_CASE ) , 26 ):
__snake_case = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
__snake_case = alphabet[i - offset]
__snake_case = char
return cipher_alphabet
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
return "".join(cipher_map.get(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) for ch in message.upper() )
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
__snake_case = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) for ch in message.upper() )
def __UpperCamelCase ( ) -> None:
"""simple docstring"""
__snake_case = input("Enter message to encode or decode: " ).strip()
__snake_case = input("Enter keyword: " ).strip()
__snake_case = input("Encipher or decipher? E/D:" ).strip()[0].lower()
try:
__snake_case = {"e": encipher, "d": decipher}[option]
except KeyError:
raise KeyError("invalid input option" )
__snake_case = create_cipher_map(SCREAMING_SNAKE_CASE )
print(func(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 163
| 1
|
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class A__ ( unittest.TestCase ):
@slow
def lowercase ( self ) -> Optional[int]:
"""simple docstring"""
__magic_name__ : int = FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small''' )
__magic_name__ : List[str] = AutoTokenizer.from_pretrained('''google/mt5-small''' )
__magic_name__ : str = tokenizer('''Hello there''' , return_tensors='''np''' ).input_ids
__magic_name__ : Dict = tokenizer('''Hi I am''' , return_tensors='''np''' ).input_ids
__magic_name__ : Optional[Any] = shift_tokens_right(__lowerCAmelCase , model.config.pad_token_id , model.config.decoder_start_token_id )
__magic_name__ : Dict = model(__lowerCAmelCase , decoder_input_ids=__lowerCAmelCase ).logits
__magic_name__ : Union[str, Any] = optax.softmax_cross_entropy(__lowerCAmelCase , onehot(__lowerCAmelCase , logits.shape[-1] ) ).mean()
__magic_name__ : int = -(labels.shape[-1] * loss.item())
__magic_name__ : int = -8_4.9_1_2_7
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 707
|
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions
import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate
lowercase_ = trt.Logger(trt.Logger.WARNING)
lowercase_ = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)
lowercase_ = logging.getLogger(__name__)
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--onnx_model_path''',
default=None,
type=str,
required=True,
help='''Path to ONNX model: ''',
)
parser.add_argument(
'''--output_dir''',
default=None,
type=str,
required=True,
help='''The output directory where the model checkpoints and predictions will be written.''',
)
# Other parameters
parser.add_argument(
'''--tokenizer_name''',
default='''''',
type=str,
required=True,
help='''Pretrained tokenizer name or path if not the same as model_name''',
)
parser.add_argument(
'''--version_2_with_negative''',
action='''store_true''',
help='''If true, the SQuAD examples contain some that do not have an answer.''',
)
parser.add_argument(
'''--null_score_diff_threshold''',
type=float,
default=0.0,
help='''If null_score - best_non_null is greater than the threshold predict null.''',
)
parser.add_argument(
'''--max_seq_length''',
default=384,
type=int,
help=(
'''The maximum total input sequence length after WordPiece tokenization. Sequences '''
'''longer than this will be truncated, and sequences shorter than this will be padded.'''
),
)
parser.add_argument(
'''--doc_stride''',
default=128,
type=int,
help='''When splitting up a long document into chunks, how much stride to take between chunks.''',
)
parser.add_argument('''--per_device_eval_batch_size''', default=8, type=int, help='''Batch size per GPU/CPU for evaluation.''')
parser.add_argument(
'''--n_best_size''',
default=20,
type=int,
help='''The total number of n-best predictions to generate in the nbest_predictions.json output file.''',
)
parser.add_argument(
'''--max_answer_length''',
default=30,
type=int,
help=(
'''The maximum length of an answer that can be generated. This is needed because the start '''
'''and end predictions are not conditioned on one another.'''
),
)
parser.add_argument('''--seed''', type=int, default=42, help='''random seed for initialization''')
parser.add_argument(
'''--dataset_name''',
type=str,
default=None,
required=True,
help='''The name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--dataset_config_name''',
type=str,
default=None,
help='''The configuration name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--preprocessing_num_workers''', type=int, default=4, help='''A csv or a json file containing the training data.'''
)
parser.add_argument('''--overwrite_cache''', action='''store_true''', help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
'''--fp16''',
action='''store_true''',
help='''Whether to use 16-bit (mixed) precision instead of 32-bit''',
)
parser.add_argument(
'''--int8''',
action='''store_true''',
help='''Whether to use INT8''',
)
lowercase_ = parser.parse_args()
if args.tokenizer_name:
lowercase_ = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
raise ValueError(
'''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
'''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''
)
logger.info('''Training/evaluation parameters %s''', args)
lowercase_ = args.per_device_eval_batch_size
lowercase_ = (args.eval_batch_size, args.max_seq_length)
# TRT Engine properties
lowercase_ = True
lowercase_ = '''temp_engine/bert-fp32.engine'''
if args.fpaa:
lowercase_ = '''temp_engine/bert-fp16.engine'''
if args.inta:
lowercase_ = '''temp_engine/bert-int8.engine'''
# import ONNX file
if not os.path.exists('''temp_engine'''):
os.makedirs('''temp_engine''')
lowercase_ = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
network, TRT_LOGGER
) as parser:
with open(args.onnx_model_path, '''rb''') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
# Query input names and shapes from parsed TensorRT network
lowercase_ = [network.get_input(i) for i in range(network.num_inputs)]
lowercase_ = [_input.name for _input in network_inputs] # ex: ["actual_input1"]
with builder.create_builder_config() as config:
lowercase_ = 1 << 50
if STRICT_TYPES:
config.set_flag(trt.BuilderFlag.STRICT_TYPES)
if args.fpaa:
config.set_flag(trt.BuilderFlag.FPaa)
if args.inta:
config.set_flag(trt.BuilderFlag.INTa)
lowercase_ = builder.create_optimization_profile()
config.add_optimization_profile(profile)
for i in range(len(input_names)):
profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
lowercase_ = builder.build_engine(network, config)
# serialize_engine and store in file (can be directly loaded and deserialized):
with open(engine_name, '''wb''') as f:
f.write(engine.serialize())
def lowerCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase ) ->Optional[Any]:
"""simple docstring"""
__magic_name__ : Union[str, Any] = np.asarray(inputs['''input_ids'''], dtype=np.intaa )
__magic_name__ : Optional[int] = np.asarray(inputs['''attention_mask'''], dtype=np.intaa )
__magic_name__ : Tuple = np.asarray(inputs['''token_type_ids'''], dtype=np.intaa )
# Copy inputs
cuda.memcpy_htod_async(d_inputs[0], input_ids.ravel(), UpperCAmelCase )
cuda.memcpy_htod_async(d_inputs[1], attention_mask.ravel(), UpperCAmelCase )
cuda.memcpy_htod_async(d_inputs[2], token_type_ids.ravel(), UpperCAmelCase )
# start time
__magic_name__ : Optional[int] = time.time()
# Run inference
context.execute_async(
bindings=[int(UpperCAmelCase ) for d_inp in d_inputs] + [int(UpperCAmelCase ), int(UpperCAmelCase )], stream_handle=stream.handle )
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )
cuda.memcpy_dtoh_async(UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )
# Synchronize the stream and take time
stream.synchronize()
# end time
__magic_name__ : str = time.time()
__magic_name__ : Any = end_time - start_time
__magic_name__ : Tuple = (h_outputa, h_outputa)
# print(outputs)
return outputs, infer_time
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
lowercase_ = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
lowercase_ = load_dataset(args.dataset_name, args.dataset_config_name)
else:
raise ValueError('''Evaluation requires a dataset name''')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.
lowercase_ = raw_datasets['''validation'''].column_names
lowercase_ = '''question''' if '''question''' in column_names else column_names[0]
lowercase_ = '''context''' if '''context''' in column_names else column_names[1]
lowercase_ = '''answers''' if '''answers''' in column_names else column_names[2]
# Padding side determines if we do (question|context) or (context|question).
lowercase_ = tokenizer.padding_side == '''right'''
if args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"
f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
)
lowercase_ = min(args.max_seq_length, tokenizer.model_max_length)
def lowerCAmelCase ( UpperCAmelCase ) ->int:
"""simple docstring"""
__magic_name__ : Optional[int] = [q.lstrip() for q in examples[question_column_name]]
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
__magic_name__ : List[str] = tokenizer(
examples[question_column_name if pad_on_right else context_column_name], examples[context_column_name if pad_on_right else question_column_name], truncation='''only_second''' if pad_on_right else '''only_first''', max_length=UpperCAmelCase, stride=args.doc_stride, return_overflowing_tokens=UpperCAmelCase, return_offsets_mapping=UpperCAmelCase, padding='''max_length''', )
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
__magic_name__ : str = tokenized_examples.pop('''overflow_to_sample_mapping''' )
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
__magic_name__ : str = []
for i in range(len(tokenized_examples['''input_ids'''] ) ):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
__magic_name__ : Dict = tokenized_examples.sequence_ids(UpperCAmelCase )
__magic_name__ : Optional[int] = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
__magic_name__ : int = sample_mapping[i]
tokenized_examples["example_id"].append(examples['''id'''][sample_index] )
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
__magic_name__ : List[Any] = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples['''offset_mapping'''][i] )
]
return tokenized_examples
lowercase_ = raw_datasets['''validation''']
# Validation Feature Creation
lowercase_ = eval_examples.map(
prepare_validation_features,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc='''Running tokenizer on validation dataset''',
)
lowercase_ = default_data_collator
lowercase_ = eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
lowercase_ = DataLoader(
eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def lowerCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase="eval" ) ->List[str]:
"""simple docstring"""
__magic_name__ : List[str] = postprocess_qa_predictions(
examples=UpperCAmelCase, features=UpperCAmelCase, predictions=UpperCAmelCase, version_2_with_negative=args.version_2_with_negative, n_best_size=args.n_best_size, max_answer_length=args.max_answer_length, null_score_diff_threshold=args.null_score_diff_threshold, output_dir=args.output_dir, prefix=UpperCAmelCase, )
# Format the result to the format the metric expects.
if args.version_2_with_negative:
__magic_name__ : str = [
{'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
]
else:
__magic_name__ : int = [{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]
__magic_name__ : Optional[int] = [{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=UpperCAmelCase, label_ids=UpperCAmelCase )
lowercase_ = load_metric('''squad_v2''' if args.version_2_with_negative else '''squad''')
# Evaluation!
logger.info('''Loading ONNX model %s for evaluation''', args.onnx_model_path)
with open(engine_name, '''rb''') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
f.read()
) as engine, engine.create_execution_context() as context:
# setup for TRT inferrence
for i in range(len(input_names)):
context.set_binding_shape(i, INPUT_SHAPE)
assert context.all_binding_shapes_specified
def lowerCAmelCase ( UpperCAmelCase ) ->Optional[Any]:
"""simple docstring"""
return trt.volume(engine.get_binding_shape(UpperCAmelCase ) ) * engine.get_binding_dtype(UpperCAmelCase ).itemsize
# Allocate device memory for inputs and outputs.
lowercase_ = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]
# Allocate output buffer
lowercase_ = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
lowercase_ = cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
lowercase_ = cuda.mem_alloc(h_outputa.nbytes)
lowercase_ = cuda.mem_alloc(h_outputa.nbytes)
# Create a stream in which to copy inputs/outputs and run inference.
lowercase_ = cuda.Stream()
# Evaluation
logger.info('''***** Running Evaluation *****''')
logger.info(f" Num examples = {len(eval_dataset)}")
logger.info(f" Batch size = {args.per_device_eval_batch_size}")
lowercase_ = 0.0
lowercase_ = 0
lowercase_ = timeit.default_timer()
lowercase_ = None
for step, batch in enumerate(eval_dataloader):
lowercase_, lowercase_ = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
total_time += infer_time
niter += 1
lowercase_, lowercase_ = outputs
lowercase_ = torch.tensor(start_logits)
lowercase_ = torch.tensor(end_logits)
# necessary to pad predictions and labels for being gathered
lowercase_ = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
lowercase_ = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)
lowercase_ = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
lowercase_ = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)
if all_preds is not None:
lowercase_ = nested_truncate(all_preds, len(eval_dataset))
lowercase_ = timeit.default_timer() - start_time
logger.info(''' Evaluation done in total %f secs (%f sec per example)''', evalTime, evalTime / len(eval_dataset))
# Inference time from TRT
logger.info('''Average Inference Time = {:.3f} ms'''.format(total_time * 1000 / niter))
logger.info('''Total Inference Time = {:.3f} ms'''.format(total_time * 1000))
logger.info('''Total Number of Inference = %d''', niter)
lowercase_ = post_processing_function(eval_examples, eval_dataset, all_preds)
lowercase_ = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f"Evaluation metrics: {eval_metric}")
| 336
| 0
|
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class _a ( unittest.TestCase ):
"""simple docstring"""
def _UpperCAmelCase ( self ) -> Optional[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def _UpperCAmelCase ( self ) -> int:
torch.manual_seed(0 )
UpperCamelCase_ = UNetaDModel(
sample_size=(32, 64) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('AttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'AttnUpBlock2D') , )
return model
@property
def _UpperCAmelCase ( self ) -> str:
torch.manual_seed(0 )
UpperCamelCase_ = UNetaDConditionModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D') , cross_attention_dim=10 , )
return model
@property
def _UpperCAmelCase ( self ) -> Dict:
torch.manual_seed(0 )
UpperCamelCase_ = AutoencoderKL(
sample_size=(128, 64) , in_channels=1 , out_channels=1 , latent_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') , up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') , )
UpperCamelCase_ = UNetaDModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('AttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'AttnUpBlock2D') , )
return vqvae, unet
@slow
def _UpperCAmelCase ( self ) -> Optional[Any]:
UpperCamelCase_ = 'cpu' # ensure determinism for the device-dependent torch.Generator
UpperCamelCase_ = Mel(
x_res=self.dummy_unet.config.sample_size[1] , y_res=self.dummy_unet.config.sample_size[0] , )
UpperCamelCase_ = DDPMScheduler()
UpperCamelCase_ = AudioDiffusionPipeline(vqvae=_UpperCAmelCase , unet=self.dummy_unet , mel=_UpperCAmelCase , scheduler=_UpperCAmelCase )
UpperCamelCase_ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
UpperCamelCase_ = torch.Generator(device=_UpperCAmelCase ).manual_seed(42 )
UpperCamelCase_ = pipe(generator=_UpperCAmelCase , steps=4 )
UpperCamelCase_ = output.audios[0]
UpperCamelCase_ = output.images[0]
UpperCamelCase_ = torch.Generator(device=_UpperCAmelCase ).manual_seed(42 )
UpperCamelCase_ = pipe(generator=_UpperCAmelCase , steps=4 , return_dict=_UpperCAmelCase )
UpperCamelCase_ = output[0][0]
assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
assert (
image.height == self.dummy_unet.config.sample_size[0]
and image.width == self.dummy_unet.config.sample_size[1]
)
UpperCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
UpperCamelCase_ = np.frombuffer(image_from_tuple.tobytes() , dtype='uint8' )[:10]
UpperCamelCase_ = np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() == 0
UpperCamelCase_ = Mel(
x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1] , y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0] , )
UpperCamelCase_ = DDIMScheduler()
UpperCamelCase_ = self.dummy_vqvae_and_unet
UpperCamelCase_ = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=dummy_vqvae_and_unet[1] , mel=_UpperCAmelCase , scheduler=_UpperCAmelCase )
UpperCamelCase_ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
np.random.seed(0 )
UpperCamelCase_ = np.random.uniform(-1 , 1 , ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,) )
UpperCamelCase_ = torch.Generator(device=_UpperCAmelCase ).manual_seed(42 )
UpperCamelCase_ = pipe(raw_audio=_UpperCAmelCase , generator=_UpperCAmelCase , start_step=5 , steps=10 )
UpperCamelCase_ = output.images[0]
assert (
image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
)
UpperCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
UpperCamelCase_ = np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
UpperCamelCase_ = self.dummy_unet_condition
UpperCamelCase_ = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=_UpperCAmelCase , mel=_UpperCAmelCase , scheduler=_UpperCAmelCase )
UpperCamelCase_ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
np.random.seed(0 )
UpperCamelCase_ = torch.rand((1, 1, 10) )
UpperCamelCase_ = pipe(generator=_UpperCAmelCase , encoding=_UpperCAmelCase )
UpperCamelCase_ = output.images[0]
UpperCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
UpperCamelCase_ = np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
@slow
@require_torch_gpu
class _a ( unittest.TestCase ):
"""simple docstring"""
def _UpperCAmelCase ( self ) -> Union[str, Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _UpperCAmelCase ( self ) -> List[str]:
UpperCamelCase_ = torch_device
UpperCamelCase_ = DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256' )
UpperCamelCase_ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
UpperCamelCase_ = torch.Generator(device=_UpperCAmelCase ).manual_seed(42 )
UpperCamelCase_ = pipe(generator=_UpperCAmelCase )
UpperCamelCase_ = output.audios[0]
UpperCamelCase_ = output.images[0]
assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
UpperCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
UpperCamelCase_ = np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
| 23
|
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __lowercase ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(_a ):
requests.request('''GET''' , '''https://huggingface.co''' )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request('''GET''' , '''https://huggingface.co''' , timeout=1.0 )
@pytest.mark.integration
def __lowercase ( ):
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request('''GET''' , '''https://huggingface.co''' )
def __lowercase ( ):
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(_a ):
http_head('''https://huggingface.co''' )
| 123
| 0
|
'''simple docstring'''
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def A ( A_ : int ):
return EnvironmentCommand()
class a ( __magic_name__ ):
@staticmethod
def __snake_case ( SCREAMING_SNAKE_CASE_ : ArgumentParser ):
snake_case : Optional[int] = parser.add_parser('''env''' )
download_parser.set_defaults(func=SCREAMING_SNAKE_CASE_ )
def __snake_case ( self : List[str] ):
snake_case : Tuple = huggingface_hub.__version__
snake_case : List[Any] = '''not installed'''
snake_case : Optional[Any] = '''NA'''
if is_torch_available():
import torch
snake_case : str = torch.__version__
snake_case : Any = torch.cuda.is_available()
snake_case : Tuple = '''not installed'''
if is_transformers_available():
import transformers
snake_case : Dict = transformers.__version__
snake_case : Optional[Any] = '''not installed'''
if is_accelerate_available():
import accelerate
snake_case : List[Any] = accelerate.__version__
snake_case : Optional[int] = '''not installed'''
if is_xformers_available():
import xformers
snake_case : List[Any] = xformers.__version__
snake_case : str = {
'''`diffusers` version''': version,
'''Platform''': platform.platform(),
'''Python version''': platform.python_version(),
'''PyTorch version (GPU?)''': F"""{pt_version} ({pt_cuda_available})""",
'''Huggingface_hub version''': hub_version,
'''Transformers version''': transformers_version,
'''Accelerate version''': accelerate_version,
'''xFormers version''': xformers_version,
'''Using GPU in script?''': '''<fill in>''',
'''Using distributed or parallel set-up in script?''': '''<fill in>''',
}
print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n''' )
print(self.format_dict(SCREAMING_SNAKE_CASE_ ) )
return info
@staticmethod
def __snake_case ( SCREAMING_SNAKE_CASE_ : str ):
return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()] ) + "\n"
| 712
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {
"configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
"tokenization_convbert": ["ConvBertTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = ["ConvBertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
"CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"ConvBertForMaskedLM",
"ConvBertForMultipleChoice",
"ConvBertForQuestionAnswering",
"ConvBertForSequenceClassification",
"ConvBertForTokenClassification",
"ConvBertLayer",
"ConvBertModel",
"ConvBertPreTrainedModel",
"load_tf_weights_in_convbert",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
"TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFConvBertForMaskedLM",
"TFConvBertForMultipleChoice",
"TFConvBertForQuestionAnswering",
"TFConvBertForSequenceClassification",
"TFConvBertForTokenClassification",
"TFConvBertLayer",
"TFConvBertModel",
"TFConvBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
from .tokenization_convbert import ConvBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_convbert_fast import ConvBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convbert import (
CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvBertForMaskedLM,
ConvBertForMultipleChoice,
ConvBertForQuestionAnswering,
ConvBertForSequenceClassification,
ConvBertForTokenClassification,
ConvBertLayer,
ConvBertModel,
ConvBertPreTrainedModel,
load_tf_weights_in_convbert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convbert import (
TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertLayer,
TFConvBertModel,
TFConvBertPreTrainedModel,
)
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 555
| 0
|
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def __lowercase ( snake_case, snake_case ):
"""simple docstring"""
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
__magic_name__ :Optional[Any] = flax_key_tuple[:-1] + ('''weight''',)
__magic_name__ :Tuple = torch.permute(snake_case, (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(snake_case ):
# linear layer
__magic_name__ :Dict = flax_key_tuple[:-1] + ('''weight''',)
__magic_name__ :Union[str, Any] = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
__magic_name__ :List[str] = flax_key_tuple[:-1] + ('''weight''',)
return flax_key_tuple, flax_tensor
def __lowercase ( snake_case, snake_case, snake_case ):
"""simple docstring"""
if "metadata" in layer:
__magic_name__ :List[str] = layer.split('''metadata''' )
__magic_name__ :int = ''''''.join(split_layer[0] )[:-1]
__magic_name__ :Optional[Any] = [tuple(('''metadata''' + split_layer[1]).split('''/''' ) )]
elif "kvstore" in layer:
__magic_name__ :Union[str, Any] = layer.split('''kvstore''' )
__magic_name__ :int = ''''''.join(split_layer[0] )[:-1]
__magic_name__ :List[str] = [tuple(('''kvstore''' + split_layer[1]).split('''/''' ) )]
else:
__magic_name__ :Dict = layer.split('''/''' )
__magic_name__ :Union[str, Any] = '''/'''.join(split_layer[:-1] )
__magic_name__ :Dict = (split_layer[-1],)
if "kvstore/path" in layer:
__magic_name__ :Optional[Any] = f'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
elif "kvstore/driver" in layer:
__magic_name__ :Optional[Any] = '''file'''
else:
__magic_name__ :Union[str, Any] = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def __lowercase ( snake_case, snake_case ):
"""simple docstring"""
__magic_name__ :Tuple = rename_keys(snake_case )
__magic_name__ :List[str] = {}
for k, v in current_block.items():
__magic_name__ :Union[str, Any] = v
__magic_name__ :List[str] = new_current_block
torch.save(snake_case, snake_case )
def __lowercase ( snake_case, snake_case, snake_case, snake_case, snake_case = WEIGHTS_NAME ):
"""simple docstring"""
__magic_name__ :Union[str, Any] = convert_file_size_to_int(snake_case )
__magic_name__ :Union[str, Any] = []
__magic_name__ :Optional[Any] = {}
__magic_name__ :Optional[int] = 0
__magic_name__ :Optional[int] = 0
os.makedirs(snake_case, exist_ok=snake_case )
with gfile.GFile(switch_checkpoint_path + '''/checkpoint''', '''rb''' ) as fp:
__magic_name__ :List[Any] = serialization.msgpack_restore(fp.read() )['''optimizer''']['''target''']
__magic_name__ :List[Any] = flatten_dict(snake_case, sep='''/''' )
__magic_name__ :Any = {}
for layer in checkpoint_info.keys():
__magic_name__ , __magic_name__ , __magic_name__ :Optional[Any] = get_key_and_tensorstore_dict(
snake_case, snake_case, snake_case )
if curr_real_layer_name in all_layers:
__magic_name__ :str = content
else:
__magic_name__ :Union[str, Any] = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
__magic_name__ :Any = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
__magic_name__ :str = torch.tensor(snake_case )
__magic_name__ :List[str] = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
__magic_name__ , __magic_name__ :Optional[Any] = rename_base_flax_keys(tuple(key.split('''/''' ) ), snake_case )
__magic_name__ :Optional[Any] = '''/'''.join(snake_case )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
__magic_name__ :Union[str, Any] = os.path.join(
snake_case, weights_name.replace('''.bin''', f'''-{len(snake_case )+1:05d}-of-???.bin''' ) )
rename_and_save_block(snake_case, snake_case )
sharded_state_dicts.append(current_block.keys() )
del current_block
__magic_name__ :Union[str, Any] = {}
__magic_name__ :List[str] = 0
__magic_name__ :int = raw_weights.to(getattr(snake_case, snake_case ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
__magic_name__ :int = os.path.join(snake_case, weights_name.replace('''.bin''', f'''-{len(snake_case )+1:05d}-of-???.bin''' ) )
rename_and_save_block(snake_case, snake_case )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(snake_case ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
__magic_name__ :Union[str, Any] = {}
__magic_name__ :Union[str, Any] = {}
for idx, shard in enumerate(snake_case ):
__magic_name__ :Union[str, Any] = weights_name.replace(
'''.bin''', f'''-{idx+1:05d}-of-{len(snake_case ):05d}.bin''' ) # len(sharded_state_dicts):05d}
__magic_name__ :Dict = os.path.join(snake_case, weights_name.replace('''.bin''', f'''-{idx+1:05d}-of-???.bin''' ) )
os.rename(snake_case, os.path.join(snake_case, snake_case ) )
__magic_name__ :str = shard
for key in shard:
__magic_name__ :List[str] = shard_file
# Add the metadata
__magic_name__ :List[Any] = {'''total_size''': total_size}
__magic_name__ :int = {'''metadata''': metadata, '''weight_map''': weight_map}
with open(os.path.join(snake_case, snake_case ), '''w''', encoding='''utf-8''' ) as f:
__magic_name__ :Any = json.dumps(snake_case, indent=2, sort_keys=snake_case ) + '''\n'''
f.write(snake_case )
return metadata, index
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--switch_t5x_checkpoint_path""",
default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
type=str,
required=False,
help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
)
parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
type=str,
required=False,
help="""Path to the output pytorch model.""",
)
SCREAMING_SNAKE_CASE__ : Optional[int] = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def __lowercase ( ):
"""simple docstring"""
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
__magic_name__ :int = SwitchTransformersConfig.from_pretrained('''google/switch-base-8''' )
config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted''' )
__magic_name__ :List[Any] = SwitchTransformersForConditionalGeneration.from_pretrained(
'''/home/arthur_huggingface_co/transformers/switch_converted''', device_map='''auto''' )
__magic_name__ :int = TaTokenizer.from_pretrained('''t5-small''' )
__magic_name__ :List[Any] = '''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''
__magic_name__ :Optional[Any] = tokenizer(snake_case, return_tensors='''pt''' ).input_ids
__magic_name__ :Any = model.generate(snake_case, decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) )
| 0
|
"""simple docstring"""
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
snake_case : Optional[Any] = """."""
# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
snake_case : List[str] = [
"""Assert""",
"""AssignVariableOp""",
"""EmptyTensorList""",
"""MergeV2Checkpoints""",
"""ReadVariableOp""",
"""ResourceGather""",
"""RestoreV2""",
"""SaveV2""",
"""ShardedFilename""",
"""StatefulPartitionedCall""",
"""StaticRegexFullMatch""",
"""VarHandleOp""",
]
def A ( __snake_case: Optional[Any] , __snake_case: Tuple , __snake_case: Any ) -> int:
"""simple docstring"""
__magic_name__ = SavedModel()
__magic_name__ = []
with open(os.path.join(__snake_case , 'utils' , 'tf_ops' , 'onnx.json' ) ) as f:
__magic_name__ = json.load(__snake_case )['opsets']
for i in range(1 , opset + 1 ):
onnx_ops.extend(onnx_opsets[str(__snake_case )] )
with open(__snake_case , 'rb' ) as f:
saved_model.ParseFromString(f.read() )
__magic_name__ = set()
# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
for meta_graph in saved_model.meta_graphs:
# Add operations in the graph definition
model_op_names.update(node.op for node in meta_graph.graph_def.node )
# Go through the functions in the graph definition
for func in meta_graph.graph_def.library.function:
# Add operations in each function
model_op_names.update(node.op for node in func.node_def )
# Convert to list, sorted if you want
__magic_name__ = sorted(__snake_case )
__magic_name__ = []
for op in model_op_names:
if op not in onnx_ops and op not in INTERNAL_OPS:
incompatible_ops.append(__snake_case )
if strict and len(__snake_case ) > 0:
raise Exception(F"""Found the following incompatible ops for the opset {opset}:\n""" + incompatible_ops )
elif len(__snake_case ) > 0:
print(F"""Found the following incompatible ops for the opset {opset}:""" )
print(*__snake_case , sep='\n' )
else:
print(F"""The saved model {saved_model_path} can properly be converted with ONNX.""" )
if __name__ == "__main__":
snake_case : Optional[Any] = argparse.ArgumentParser()
parser.add_argument("""--saved_model_path""", help="""Path of the saved model to check (the .pb file).""")
parser.add_argument(
"""--opset""", default=1_2, type=int, help="""The ONNX opset against which the model has to be tested."""
)
parser.add_argument(
"""--framework""", choices=["""onnx"""], default="""onnx""", help="""Frameworks against which to test the saved model."""
)
parser.add_argument(
"""--strict""", action="""store_true""", help="""Whether make the checking strict (raise errors) or not (raise warnings)"""
)
snake_case : Any = parser.parse_args()
if args.framework == "onnx":
onnx_compliancy(args.saved_model_path, args.strict, args.opset)
| 545
| 0
|
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from transformers.modeling_outputs import BaseModelOutput
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
lowerCAmelCase__ = logging.get_logger(__name__)
@add_end_docstrings(_lowercase )
class lowercase ( _lowercase ):
"""simple docstring"""
def __init__( self , **__snake_case):
super().__init__(**__snake_case)
if self.framework == "tf":
raise ValueError(f'''The {self.__class__} is only available in PyTorch.''')
requires_backends(self , 'vision')
self.check_model_type(__snake_case)
def __call__( self , __snake_case , __snake_case = None , **__snake_case , ):
if "text_queries" in kwargs:
_UpperCamelCase : List[Any] = kwargs.pop('text_queries')
if isinstance(__snake_case , (str, Image.Image)):
_UpperCamelCase : Union[str, Any] = {'image': image, 'candidate_labels': candidate_labels}
else:
_UpperCamelCase : int = image
_UpperCamelCase : Tuple = super().__call__(__snake_case , **__snake_case)
return results
def A__ ( self , **__snake_case):
_UpperCamelCase : int = {}
if "threshold" in kwargs:
_UpperCamelCase : Tuple = kwargs['threshold']
if "top_k" in kwargs:
_UpperCamelCase : Optional[Any] = kwargs['top_k']
return {}, {}, postprocess_params
def A__ ( self , __snake_case):
_UpperCamelCase : Dict = load_image(inputs['image'])
_UpperCamelCase : Dict = inputs['candidate_labels']
if isinstance(__snake_case , __snake_case):
_UpperCamelCase : Tuple = candidate_labels.split(',')
_UpperCamelCase : str = torch.tensor([[image.height, image.width]] , dtype=torch.intaa)
for i, candidate_label in enumerate(__snake_case):
_UpperCamelCase : Optional[Any] = self.tokenizer(__snake_case , return_tensors=self.framework)
_UpperCamelCase : int = self.image_processor(__snake_case , return_tensors=self.framework)
yield {
"is_last": i == len(__snake_case) - 1,
"target_size": target_size,
"candidate_label": candidate_label,
**text_inputs,
**image_features,
}
def A__ ( self , __snake_case):
_UpperCamelCase : Any = model_inputs.pop('target_size')
_UpperCamelCase : str = model_inputs.pop('candidate_label')
_UpperCamelCase : List[str] = model_inputs.pop('is_last')
_UpperCamelCase : Any = self.model(**__snake_case)
_UpperCamelCase : List[str] = {'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
return model_outputs
def A__ ( self , __snake_case , __snake_case=0.1 , __snake_case=None):
_UpperCamelCase : Union[str, Any] = []
for model_output in model_outputs:
_UpperCamelCase : Union[str, Any] = model_output['candidate_label']
_UpperCamelCase : Any = BaseModelOutput(__snake_case)
_UpperCamelCase : Union[str, Any] = self.image_processor.post_process_object_detection(
outputs=__snake_case , threshold=__snake_case , target_sizes=model_output['target_size'])[0]
for index in outputs["scores"].nonzero():
_UpperCamelCase : Optional[Any] = outputs['scores'][index].item()
_UpperCamelCase : int = self._get_bounding_box(outputs['boxes'][index][0])
_UpperCamelCase : Optional[int] = {'score': score, 'label': label, 'box': box}
results.append(__snake_case)
_UpperCamelCase : Any = sorted(__snake_case , key=lambda __snake_case: x["score"] , reverse=__snake_case)
if top_k:
_UpperCamelCase : str = results[:top_k]
return results
def A__ ( self , __snake_case):
if self.framework != "pt":
raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.')
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase : List[Any] = box.int().tolist()
_UpperCamelCase : str = {
'xmin': xmin,
'ymin': ymin,
'xmax': xmax,
'ymax': ymax,
}
return bbox
| 648
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
"""bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
"""bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
"""bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
"""bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
"""bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
"""bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
"""bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
"""bert-large-uncased-whole-word-masking""": (
"""https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
),
"""bert-large-cased-whole-word-masking""": (
"""https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
),
"""bert-large-uncased-whole-word-masking-finetuned-squad""": (
"""https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
),
"""bert-large-cased-whole-word-masking-finetuned-squad""": (
"""https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
),
"""bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
"""bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
"""bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
"""cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
"""cl-tohoku/bert-base-japanese-whole-word-masking""": (
"""https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
),
"""cl-tohoku/bert-base-japanese-char""": (
"""https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
),
"""cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
"""https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
),
"""TurkuNLP/bert-base-finnish-cased-v1""": (
"""https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
),
"""TurkuNLP/bert-base-finnish-uncased-v1""": (
"""https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
),
"""wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
# See all BERT models at https://huggingface.co/models?filter=bert
}
class lowercase ( _lowercase ):
"""simple docstring"""
a__ = "bert"
def __init__( self , __snake_case=3_05_22 , __snake_case=7_68 , __snake_case=12 , __snake_case=12 , __snake_case=30_72 , __snake_case="gelu" , __snake_case=0.1 , __snake_case=0.1 , __snake_case=5_12 , __snake_case=2 , __snake_case=0.0_2 , __snake_case=1e-12 , __snake_case=0 , __snake_case="absolute" , __snake_case=True , __snake_case=None , **__snake_case , ):
super().__init__(pad_token_id=__snake_case , **__snake_case)
_UpperCamelCase : int = vocab_size
_UpperCamelCase : Optional[Any] = hidden_size
_UpperCamelCase : Optional[Any] = num_hidden_layers
_UpperCamelCase : List[str] = num_attention_heads
_UpperCamelCase : int = hidden_act
_UpperCamelCase : Optional[Any] = intermediate_size
_UpperCamelCase : Union[str, Any] = hidden_dropout_prob
_UpperCamelCase : Tuple = attention_probs_dropout_prob
_UpperCamelCase : Optional[int] = max_position_embeddings
_UpperCamelCase : str = type_vocab_size
_UpperCamelCase : Optional[Any] = initializer_range
_UpperCamelCase : List[str] = layer_norm_eps
_UpperCamelCase : Any = position_embedding_type
_UpperCamelCase : Any = use_cache
_UpperCamelCase : Any = classifier_dropout
class lowercase ( _lowercase ):
"""simple docstring"""
@property
def A__ ( self):
if self.task == "multiple-choice":
_UpperCamelCase : Union[str, Any] = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_UpperCamelCase : Optional[Any] = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
])
| 648
| 1
|
"""simple docstring"""
def _snake_case ( snake_case__ : int = 1000 ):
A = 2**power
A = 0
while n:
A , A = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 91
|
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _A ( A__ ):
"""simple docstring"""
__lowercase = FileLock(str(tmpdir / '''foo.lock''' ) )
__lowercase = FileLock(str(tmpdir / '''foo.lock''' ) )
__lowercase = 0.0_1
with locka.acquire():
with pytest.raises(A__ ):
__lowercase = time.time()
locka.acquire(A__ )
assert time.time() - _start > timeout
def _A ( A__ ):
"""simple docstring"""
__lowercase = '''a''' * 1000 + '''.lock'''
__lowercase = FileLock(str(tmpdir / filename ) )
assert locka._lock_file.endswith('''.lock''' )
assert not locka._lock_file.endswith(A__ )
assert len(os.path.basename(locka._lock_file ) ) <= 255
__lowercase = FileLock(tmpdir / filename )
with locka.acquire():
with pytest.raises(A__ ):
locka.acquire(0 )
| 41
| 0
|
"""simple docstring"""
def lowerCamelCase_(__SCREAMING_SNAKE_CASE )-> str:
_SCREAMING_SNAKE_CASE : Union[str, Any] = 0
# if input_string is "aba" than new_input_string become "a|b|a"
_SCREAMING_SNAKE_CASE : List[str] = """"""
_SCREAMING_SNAKE_CASE : Dict = """"""
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(__SCREAMING_SNAKE_CASE ) - 1]:
new_input_string += i + "|"
# append last character
new_input_string += input_string[-1]
# we will store the starting and ending of previous furthest ending palindromic
# substring
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Any = 0, 0
# length[i] shows the length of palindromic substring with center i
_SCREAMING_SNAKE_CASE : str = [1 for i in range(len(__SCREAMING_SNAKE_CASE ) )]
# for each character in new_string find corresponding palindromic string
_SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for j in range(len(__SCREAMING_SNAKE_CASE ) ):
_SCREAMING_SNAKE_CASE : str = 1 if j > r else min(length[l + r - j] // 2 , r - j + 1 )
while (
j - k >= 0
and j + k < len(__SCREAMING_SNAKE_CASE )
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
_SCREAMING_SNAKE_CASE : List[Any] = 2 * k - 1
# does this string is ending after the previously explored end (that is r) ?
# if yes the update the new r to the last index of this
if j + k - 1 > r:
_SCREAMING_SNAKE_CASE : List[str] = j - k + 1 # noqa: E741
_SCREAMING_SNAKE_CASE : str = j + k - 1
# update max_length and start position
if max_length < length[j]:
_SCREAMING_SNAKE_CASE : Optional[int] = length[j]
_SCREAMING_SNAKE_CASE : Any = j
# create that string
_SCREAMING_SNAKE_CASE : Dict = new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
for i in s:
if i != "|":
output_string += i
return output_string
if __name__ == "__main__":
import doctest
doctest.testmod()
| 635
|
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoImageProcessor
@require_torch
@require_vision
class _snake_case ( unittest.TestCase ):
"""simple docstring"""
@slow
def _lowerCAmelCase ( self : str):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Any = AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""")
_SCREAMING_SNAKE_CASE : str = AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""")
model.to(_A)
from datasets import load_dataset
_SCREAMING_SNAKE_CASE : Any = load_dataset("""nielsr/rvlcdip-demo""")
_SCREAMING_SNAKE_CASE : Any = dataset["""train"""][0]["""image"""].convert("""RGB""")
_SCREAMING_SNAKE_CASE : str = image_processor(_A , return_tensors="""pt""").to(_A)
# forward pass
with torch.no_grad():
_SCREAMING_SNAKE_CASE : Any = model(**_A)
_SCREAMING_SNAKE_CASE : List[Any] = outputs.logits
_SCREAMING_SNAKE_CASE : List[str] = torch.Size((1, 1_6))
self.assertEqual(logits.shape , _A)
_SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor(
[-0.4_158, -0.4_092, -0.4_347] , device=_A , dtype=torch.float , )
self.assertTrue(torch.allclose(logits[0, :3] , _A , atol=1e-4))
| 635
| 1
|
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _lowercase ( __snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ) -> int:
# Load configuration defined in the metadata file
with open(__snake_case ) as metadata_file:
__lowerCAmelCase : Optional[Any] = json.load(__snake_case )
__lowerCAmelCase : Optional[int] = LukeConfig(use_entity_aware_attention=__snake_case ,**metadata["model_config"] )
# Load in the weights from the checkpoint_path
__lowerCAmelCase : int = torch.load(__snake_case ,map_location="cpu" )["module"]
# Load the entity vocab file
__lowerCAmelCase : Optional[int] = load_original_entity_vocab(__snake_case )
# add an entry for [MASK2]
__lowerCAmelCase : str = max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
__lowerCAmelCase : List[Any] = XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"] )
# Add special tokens to the token vocabulary for downstream tasks
__lowerCAmelCase : Any = AddedToken("<ent>" ,lstrip=__snake_case ,rstrip=__snake_case )
__lowerCAmelCase : Optional[int] = AddedToken("<ent2>" ,lstrip=__snake_case ,rstrip=__snake_case )
tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(F"""Saving tokenizer to {pytorch_dump_folder_path}""" )
tokenizer.save_pretrained(__snake_case )
with open(os.path.join(__snake_case ,"tokenizer_config.json" ) ,"r" ) as f:
__lowerCAmelCase : Optional[int] = json.load(__snake_case )
__lowerCAmelCase : Tuple = "MLukeTokenizer"
with open(os.path.join(__snake_case ,"tokenizer_config.json" ) ,"w" ) as f:
json.dump(__snake_case ,__snake_case )
with open(os.path.join(__snake_case ,MLukeTokenizer.vocab_files_names["entity_vocab_file"] ) ,"w" ) as f:
json.dump(__snake_case ,__snake_case )
__lowerCAmelCase : Tuple = MLukeTokenizer.from_pretrained(__snake_case )
# Initialize the embeddings of the special tokens
__lowerCAmelCase : List[Any] = tokenizer.convert_tokens_to_ids(["@"] )[0]
__lowerCAmelCase : Optional[Any] = tokenizer.convert_tokens_to_ids(["#"] )[0]
__lowerCAmelCase : str = state_dict["embeddings.word_embeddings.weight"]
__lowerCAmelCase : Tuple = word_emb[ent_init_index].unsqueeze(0 )
__lowerCAmelCase : Any = word_emb[enta_init_index].unsqueeze(0 )
__lowerCAmelCase : str = torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
__lowerCAmelCase : str = state_dict[bias_name]
__lowerCAmelCase : Any = decoder_bias[ent_init_index].unsqueeze(0 )
__lowerCAmelCase : List[Any] = decoder_bias[enta_init_index].unsqueeze(0 )
__lowerCAmelCase : Optional[int] = torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
__lowerCAmelCase : Optional[int] = F"""encoder.layer.{layer_index}.attention.self."""
__lowerCAmelCase : Tuple = state_dict[prefix + matrix_name]
__lowerCAmelCase : List[str] = state_dict[prefix + matrix_name]
__lowerCAmelCase : Dict = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
__lowerCAmelCase : List[Any] = state_dict["entity_embeddings.entity_embeddings.weight"]
__lowerCAmelCase : int = entity_emb[entity_vocab["[MASK]"]].unsqueeze(0 )
__lowerCAmelCase : Any = torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
__lowerCAmelCase : Dict = state_dict["entity_predictions.bias"]
__lowerCAmelCase : Dict = entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0 )
__lowerCAmelCase : Optional[Any] = torch.cat([entity_prediction_bias, entity_mask_bias] )
__lowerCAmelCase : Union[str, Any] = LukeForMaskedLM(config=__snake_case ).eval()
state_dict.pop("entity_predictions.decoder.weight" )
state_dict.pop("lm_head.decoder.weight" )
state_dict.pop("lm_head.decoder.bias" )
__lowerCAmelCase : Dict = OrderedDict()
for key, value in state_dict.items():
if not (key.startswith("lm_head" ) or key.startswith("entity_predictions" )):
__lowerCAmelCase : Optional[int] = state_dict[key]
else:
__lowerCAmelCase : int = state_dict[key]
__lowerCAmelCase , __lowerCAmelCase : int = model.load_state_dict(__snake_case ,strict=__snake_case )
if set(__snake_case ) != {"luke.embeddings.position_ids"}:
raise ValueError(F"""Unexpected unexpected_keys: {unexpected_keys}""" )
if set(__snake_case ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(F"""Unexpected missing_keys: {missing_keys}""" )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
__lowerCAmelCase : Any = MLukeTokenizer.from_pretrained(__snake_case ,task="entity_classification" )
__lowerCAmelCase : Union[str, Any] = "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
__lowerCAmelCase : Optional[Any] = (0, 9)
__lowerCAmelCase : Any = tokenizer(__snake_case ,entity_spans=[span] ,return_tensors="pt" )
__lowerCAmelCase : Tuple = model(**__snake_case )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
__lowerCAmelCase : Dict = torch.Size((1, 33, 768) )
__lowerCAmelCase : str = torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
F"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}""" )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] ,__snake_case ,atol=1e-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
__lowerCAmelCase : Dict = torch.Size((1, 1, 768) )
__lowerCAmelCase : Tuple = torch.tensor([[-0.1482, 0.0609, 0.0322]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
F"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
F""" {expected_shape}""" )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] ,__snake_case ,atol=1e-4 ):
raise ValueError
# Verify masked word/entity prediction
__lowerCAmelCase : int = MLukeTokenizer.from_pretrained(__snake_case )
__lowerCAmelCase : List[str] = "Tokyo is the capital of <mask>."
__lowerCAmelCase : List[Any] = (24, 30)
__lowerCAmelCase : Tuple = tokenizer(__snake_case ,entity_spans=[span] ,return_tensors="pt" )
__lowerCAmelCase : Any = model(**__snake_case )
__lowerCAmelCase : List[Any] = encoding["input_ids"][0].tolist()
__lowerCAmelCase : List[str] = input_ids.index(tokenizer.convert_tokens_to_ids("<mask>" ) )
__lowerCAmelCase : Optional[int] = outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(__snake_case )
__lowerCAmelCase : int = outputs.entity_logits[0][0].argmax().item()
__lowerCAmelCase : int = [
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith("en:" )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print("Saving PyTorch model to {}".format(__snake_case ) )
model.save_pretrained(__snake_case )
def _lowercase ( __snake_case ) -> List[str]:
__lowerCAmelCase : Optional[int] = ["[MASK]", "[PAD]", "[UNK]"]
__lowerCAmelCase : List[Any] = [json.loads(__snake_case ) for line in open(__snake_case )]
__lowerCAmelCase : Dict = {}
for entry in data:
__lowerCAmelCase : List[Any] = entry["id"]
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
__lowerCAmelCase : List[str] = entity_id
break
__lowerCAmelCase : Tuple = F"""{language}:{entity_name}"""
__lowerCAmelCase : Union[str, Any] = entity_id
return new_mapping
if __name__ == "__main__":
__snake_case : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
parser.add_argument(
'--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
)
parser.add_argument(
'--entity_vocab_path',
default=None,
type=str,
help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
)
parser.add_argument(
'--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
)
__snake_case : List[Any] = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 293
|
"""simple docstring"""
from math import factorial
class A__ :
'''simple docstring'''
def __init__( self: str , _SCREAMING_SNAKE_CASE: Tuple , _SCREAMING_SNAKE_CASE: Any) -> str:
"""simple docstring"""
__lowerCAmelCase : str = real
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE):
__lowerCAmelCase : Optional[int] = [1] * rank
else:
__lowerCAmelCase : Optional[Any] = rank
def __repr__( self: Optional[int]) -> List[str]:
"""simple docstring"""
return (
F"""{self.real}+"""
F"""{'+'.join(str(_SCREAMING_SNAKE_CASE)+'E'+str(n+1)for n,dual in enumerate(self.duals))}"""
)
def _SCREAMING_SNAKE_CASE ( self: List[str]) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : List[str] = self.duals.copy()
while cur[-1] == 0:
cur.pop(-1)
return Dual(self.real , _SCREAMING_SNAKE_CASE)
def __add__( self: Optional[int] , _SCREAMING_SNAKE_CASE: Tuple) -> Union[str, Any]:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE):
return Dual(self.real + other , self.duals)
__lowerCAmelCase : int = self.duals.copy()
__lowerCAmelCase : List[Any] = other.duals.copy()
if len(_SCREAMING_SNAKE_CASE) > len(_SCREAMING_SNAKE_CASE):
o_dual.extend([1] * (len(_SCREAMING_SNAKE_CASE) - len(_SCREAMING_SNAKE_CASE)))
elif len(_SCREAMING_SNAKE_CASE) < len(_SCREAMING_SNAKE_CASE):
s_dual.extend([1] * (len(_SCREAMING_SNAKE_CASE) - len(_SCREAMING_SNAKE_CASE)))
__lowerCAmelCase : Tuple = []
for i in range(len(_SCREAMING_SNAKE_CASE)):
new_duals.append(s_dual[i] + o_dual[i])
return Dual(self.real + other.real , _SCREAMING_SNAKE_CASE)
SCREAMING_SNAKE_CASE = __add__
def __sub__( self: Dict , _SCREAMING_SNAKE_CASE: str) -> Optional[Any]:
"""simple docstring"""
return self + other * -1
def __mul__( self: str , _SCREAMING_SNAKE_CASE: Tuple) -> Optional[int]:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE):
__lowerCAmelCase : List[Any] = []
for i in self.duals:
new_duals.append(i * other)
return Dual(self.real * other , _SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[str] = [0] * (len(self.duals) + len(other.duals) + 1)
for i, item in enumerate(self.duals):
for j, jtem in enumerate(other.duals):
new_duals[i + j + 1] += item * jtem
for k in range(len(self.duals)):
new_duals[k] += self.duals[k] * other.real
for index in range(len(other.duals)):
new_duals[index] += other.duals[index] * self.real
return Dual(self.real * other.real , _SCREAMING_SNAKE_CASE)
SCREAMING_SNAKE_CASE = __mul__
def __truediv__( self: Any , _SCREAMING_SNAKE_CASE: str) -> Dict:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE):
__lowerCAmelCase : List[str] = []
for i in self.duals:
new_duals.append(i / other)
return Dual(self.real / other , _SCREAMING_SNAKE_CASE)
raise ValueError
def __floordiv__( self: List[Any] , _SCREAMING_SNAKE_CASE: Tuple) -> str:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE):
__lowerCAmelCase : str = []
for i in self.duals:
new_duals.append(i // other)
return Dual(self.real // other , _SCREAMING_SNAKE_CASE)
raise ValueError
def __pow__( self: Optional[Any] , _SCREAMING_SNAKE_CASE: Tuple) -> List[Any]:
"""simple docstring"""
if n < 0 or isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE):
raise ValueError("power must be a positive integer")
if n == 0:
return 1
if n == 1:
return self
__lowerCAmelCase : List[Any] = self
for _ in range(n - 1):
x *= self
return x
def _lowercase ( __snake_case ,__snake_case ,__snake_case ) -> Any:
if not callable(__snake_case ):
raise ValueError("differentiate() requires a function as input for func" )
if not isinstance(__snake_case ,(float, int) ):
raise ValueError("differentiate() requires a float as input for position" )
if not isinstance(__snake_case ,__snake_case ):
raise ValueError("differentiate() requires an int as input for order" )
__lowerCAmelCase : Any = Dual(__snake_case ,1 )
__lowerCAmelCase : Tuple = func(__snake_case )
if order == 0:
return result.real
return result.duals[order - 1] * factorial(__snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
def _lowercase ( __snake_case ) -> Tuple:
return y**2 * y**4
print(differentiate(f, 9, 2))
| 293
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
A = {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = ['GPTNeoXTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
'GPTNeoXForCausalLM',
'GPTNeoXForQuestionAnswering',
'GPTNeoXForSequenceClassification',
'GPTNeoXForTokenClassification',
'GPTNeoXLayer',
'GPTNeoXModel',
'GPTNeoXPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox import (
GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXLayer,
GPTNeoXModel,
GPTNeoXPreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 709
|
'''simple docstring'''
import numpy as np
def UpperCAmelCase ( UpperCAmelCase__ : np.array):
return 1 / (1 + np.exp(-vector))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 449
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ : List[str] = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVisionConfig',
],
'processing_clipseg': ['CLIPSegProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Optional[Any] = [
'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
'CLIPSegModel',
'CLIPSegPreTrainedModel',
'CLIPSegTextModel',
'CLIPSegVisionModel',
'CLIPSegForImageSegmentation',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
lowercase__ : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 98
|
class lowerCamelCase_ :
def __init__( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Tuple:
"""simple docstring"""
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = graph
self._normalize_graph(lowerCamelCase_ , lowerCamelCase_ )
_UpperCamelCase = len(lowerCamelCase_ )
_UpperCamelCase = None
def lowercase ( self , lowerCamelCase_ , lowerCamelCase_ ) -> str:
"""simple docstring"""
if sources is int:
_UpperCamelCase = [sources]
if sinks is int:
_UpperCamelCase = [sinks]
if len(lowerCamelCase_ ) == 0 or len(lowerCamelCase_ ) == 0:
return
_UpperCamelCase = sources[0]
_UpperCamelCase = sinks[0]
# make fake vertex if there are more
# than one source or sink
if len(lowerCamelCase_ ) > 1 or len(lowerCamelCase_ ) > 1:
_UpperCamelCase = 0
for i in sources:
max_input_flow += sum(self.graph[i] )
_UpperCamelCase = len(self.graph ) + 1
for room in self.graph:
room.insert(0 , 0 )
self.graph.insert(0 , [0] * size )
for i in sources:
_UpperCamelCase = max_input_flow
_UpperCamelCase = 0
_UpperCamelCase = len(self.graph ) + 1
for room in self.graph:
room.append(0 )
self.graph.append([0] * size )
for i in sinks:
_UpperCamelCase = max_input_flow
_UpperCamelCase = size - 1
def lowercase ( self ) -> Union[str, Any]:
"""simple docstring"""
if self.maximum_flow_algorithm is None:
raise Exception("You need to set maximum flow algorithm before." )
if self.source_index is None or self.sink_index is None:
return 0
self.maximum_flow_algorithm.execute()
return self.maximum_flow_algorithm.getMaximumFlow()
def lowercase ( self , lowerCamelCase_ ) -> List[str]:
"""simple docstring"""
_UpperCamelCase = algorithm(self )
class lowerCamelCase_ :
def __init__( self , lowerCamelCase_ ) -> Tuple:
"""simple docstring"""
_UpperCamelCase = flow_network
_UpperCamelCase = flow_network.verticesCount
_UpperCamelCase = flow_network.sourceIndex
_UpperCamelCase = flow_network.sinkIndex
# it's just a reference, so you shouldn't change
# it in your algorithms, use deep copy before doing that
_UpperCamelCase = flow_network.graph
_UpperCamelCase = False
def lowercase ( self ) -> Optional[Any]:
"""simple docstring"""
if not self.executed:
self._algorithm()
_UpperCamelCase = True
def lowercase ( self ) -> List[str]:
"""simple docstring"""
pass
class lowerCamelCase_ ( lowercase ):
def __init__( self , lowerCamelCase_ ) -> Dict:
"""simple docstring"""
super().__init__(lowerCamelCase_ )
# use this to save your result
_UpperCamelCase = -1
def lowercase ( self ) -> List[str]:
"""simple docstring"""
if not self.executed:
raise Exception("You should execute algorithm before using its result!" )
return self.maximum_flow
class lowerCamelCase_ ( lowercase ):
def __init__( self , lowerCamelCase_ ) -> List[str]:
"""simple docstring"""
super().__init__(lowerCamelCase_ )
_UpperCamelCase = [[0] * self.verticies_count for i in range(self.verticies_count )]
_UpperCamelCase = [0] * self.verticies_count
_UpperCamelCase = [0] * self.verticies_count
def lowercase ( self ) -> int:
"""simple docstring"""
_UpperCamelCase = self.verticies_count
# push some substance to graph
for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ):
self.preflow[self.source_index][nextvertex_index] += bandwidth
self.preflow[nextvertex_index][self.source_index] -= bandwidth
self.excesses[nextvertex_index] += bandwidth
# Relabel-to-front selection rule
_UpperCamelCase = [
i
for i in range(self.verticies_count )
if i != self.source_index and i != self.sink_index
]
# move through list
_UpperCamelCase = 0
while i < len(lowerCamelCase_ ):
_UpperCamelCase = vertices_list[i]
_UpperCamelCase = self.heights[vertex_index]
self.process_vertex(lowerCamelCase_ )
if self.heights[vertex_index] > previous_height:
# if it was relabeled, swap elements
# and start from 0 index
vertices_list.insert(0 , vertices_list.pop(lowerCamelCase_ ) )
_UpperCamelCase = 0
else:
i += 1
_UpperCamelCase = sum(self.preflow[self.source_index] )
def lowercase ( self , lowerCamelCase_ ) -> int:
"""simple docstring"""
while self.excesses[vertex_index] > 0:
for neighbour_index in range(self.verticies_count ):
# if it's neighbour and current vertex is higher
if (
self.graph[vertex_index][neighbour_index]
- self.preflow[vertex_index][neighbour_index]
> 0
and self.heights[vertex_index] > self.heights[neighbour_index]
):
self.push(lowerCamelCase_ , lowerCamelCase_ )
self.relabel(lowerCamelCase_ )
def lowercase ( self , lowerCamelCase_ , lowerCamelCase_ ) -> Dict:
"""simple docstring"""
_UpperCamelCase = min(
self.excesses[from_index] , self.graph[from_index][to_index] - self.preflow[from_index][to_index] , )
self.preflow[from_index][to_index] += preflow_delta
self.preflow[to_index][from_index] -= preflow_delta
self.excesses[from_index] -= preflow_delta
self.excesses[to_index] += preflow_delta
def lowercase ( self , lowerCamelCase_ ) -> Tuple:
"""simple docstring"""
_UpperCamelCase = None
for to_index in range(self.verticies_count ):
if (
self.graph[vertex_index][to_index]
- self.preflow[vertex_index][to_index]
> 0
) and (min_height is None or self.heights[to_index] < min_height):
_UpperCamelCase = self.heights[to_index]
if min_height is not None:
_UpperCamelCase = min_height + 1
if __name__ == "__main__":
__lowerCAmelCase = [0]
__lowerCAmelCase = [3]
# graph = [
# [0, 0, 4, 6, 0, 0],
# [0, 0, 5, 2, 0, 0],
# [0, 0, 0, 0, 4, 4],
# [0, 0, 0, 0, 6, 6],
# [0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0],
# ]
__lowerCAmelCase = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]
# prepare our network
__lowerCAmelCase = FlowNetwork(graph, entrances, exits)
# set algorithm
flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
# and calculate
__lowerCAmelCase = flow_network.find_maximum_flow()
print(F'''maximum flow is {maximum_flow}''')
| 147
| 0
|
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
_lowercase : Optional[int] = logging.get_logger(__name__)
@dataclass
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = [
'''no_inference''',
'''no_cuda''',
'''no_tpu''',
'''no_speed''',
'''no_memory''',
'''no_env_print''',
'''no_multi_process''',
]
def __init__( self : Optional[Any] , **lowercase_ : Tuple ):
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
lowercase_ : Dict = deprecated_arg[3:]
lowercase_ : Dict = not kwargs.pop(lowercase_ )
logger.warning(
f'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
f''' {positive_arg}={kwargs[positive_arg]}''' )
lowercase_ : Optional[Any] = kwargs.pop("""tpu_name""" , self.tpu_name )
lowercase_ : Optional[int] = kwargs.pop("""device_idx""" , self.device_idx )
lowercase_ : List[Any] = kwargs.pop("""eager_mode""" , self.eager_mode )
lowercase_ : Dict = kwargs.pop("""use_xla""" , self.use_xla )
super().__init__(**lowercase_ )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Name of TPU'''}, )
UpperCamelCase__ = field(
default=0, metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''}, )
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''Benchmark models in eager model.'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={
'''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
}, )
@cached_property
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
requires_backends(self , ["""tf"""] )
lowercase_ : Tuple = None
if self.tpu:
try:
if self.tpu_name:
lowercase_ : Optional[int] = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
lowercase_ : Union[str, Any] = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
lowercase_ : Optional[int] = None
return tpu
@cached_property
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
requires_backends(self , ["""tf"""] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
lowercase_ : str = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx] , """GPU""" )
lowercase_ : Union[str, Any] = tf.distribute.OneDeviceStrategy(device=f'''/gpu:{self.device_idx}''' )
else:
tf.config.set_visible_devices([] , """GPU""" ) # disable GPU
lowercase_ : Dict = tf.distribute.OneDeviceStrategy(device=f'''/cpu:{self.device_idx}''' )
return strategy
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
requires_backends(self , ["""tf"""] )
return self._setup_tpu is not None
@property
def SCREAMING_SNAKE_CASE_ ( self : Any ):
requires_backends(self , ["""tf"""] )
return self._setup_strategy
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
requires_backends(self , ["""tf"""] )
return tf.config.list_physical_devices("""GPU""" )
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
requires_backends(self , ["""tf"""] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
return self.n_gpu > 0
| 30
|
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_lowercase : Optional[Any] = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm", action="store_true", help="Enable DPMSolver or not")
parser.add_argument("--steps", default=None, type=int, help="Num inference steps")
_lowercase : Dict = parser.parse_args()
_lowercase : Dict = "cpu"
_lowercase : str = "a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"
_lowercase : Any = "path-to-your-trained-model"
_lowercase : str = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
_lowercase : Optional[int] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
_lowercase : Any = pipe.to(device)
# to channels last
_lowercase : Union[str, Any] = pipe.unet.to(memory_format=torch.channels_last)
_lowercase : List[Any] = pipe.vae.to(memory_format=torch.channels_last)
_lowercase : Union[str, Any] = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
_lowercase : Tuple = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
_lowercase : int = torch.randn(2, 4, 64, 64)
_lowercase : int = torch.rand(1) * 999
_lowercase : Union[str, Any] = torch.randn(2, 77, 768)
_lowercase : Optional[Any] = (sample, timestep, encoder_hidden_status)
try:
_lowercase : int = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
_lowercase : Optional[int] = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_lowercase : List[Any] = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_lowercase : str = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
_lowercase : int = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
_lowercase : int = 666
_lowercase : Any = torch.Generator(device).manual_seed(seed)
_lowercase : int = {"generator": generator}
if args.steps is not None:
_lowercase : Optional[int] = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
_lowercase : List[Any] = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save("generated.png")
| 30
| 1
|
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
a_ = {
'/attention/': '/0/SelfAttention/',
'/self_attention/': '/0/SelfAttention/',
'/encoder_decoder_attention/': '/1/EncDecAttention/',
'value': 'v',
'query': 'q',
'key': 'k',
'out': 'o',
'pre_self_attention_layer_norm': '0/layer_norm',
'pre_cross_attention_layer_norm': '1/layer_norm',
'pre_attention_layer_norm': '0/layer_norm', # previously 1, but seems wrong
'token_embedder': 'shared',
'encoder_norm': 'final_layer_norm',
'decoder_norm': 'final_layer_norm',
'relpos_bias/rel_embedding': 'block/0/layer/0/SelfAttention/relative_attention_bias/weight',
'router/router_weights/w/': 'router/classifier/',
'roer/roer_weights/w/': 'router/classifier/',
'logits_dense': 'lm_head',
}
def _a( UpperCamelCase__ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any =list(s_dict.keys() )
for key in keys:
SCREAMING_SNAKE_CASE__ : Dict =R'''.*/layers_(\d+)'''
SCREAMING_SNAKE_CASE__ : int =key
if re.match(UpperCamelCase__, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : List[Any] =re.sub(R'''layers_(\d+)''', R'''block/\1/layer''', UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Any =R'''(encoder|decoder)\/'''
if re.match(UpperCamelCase__, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Dict =re.match(UpperCamelCase__, UpperCamelCase__ ).groups()
if groups[0] == "encoder":
SCREAMING_SNAKE_CASE__ : Dict =re.sub(R'''/mlp/''', R'''/1/mlp/''', UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[str] =re.sub(R'''/pre_mlp_layer_norm/''', R'''/1/layer_norm/''', UpperCamelCase__ )
elif groups[0] == "decoder":
SCREAMING_SNAKE_CASE__ : Any =re.sub(R'''/mlp/''', R'''/2/mlp/''', UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] =re.sub(R'''/pre_mlp_layer_norm/''', R'''/2/layer_norm/''', UpperCamelCase__ )
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
SCREAMING_SNAKE_CASE__ : Optional[int] =new_key.replace(UpperCamelCase__, UpperCamelCase__ )
print(f"{key} -> {new_key}" )
SCREAMING_SNAKE_CASE__ : Dict =s_dict.pop(UpperCamelCase__ )
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
SCREAMING_SNAKE_CASE__ : str =s_dict[
'''encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
SCREAMING_SNAKE_CASE__ : Dict =s_dict[
'''decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys() ):
if "expert" in key:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =s_dict[key].shape[0]
SCREAMING_SNAKE_CASE__ : List[str] =s_dict[key]
for idx in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Optional[int] =expert_weihts[idx]
print(f"{key} -> {key.replace('expert/', 'nested fstring' )}" )
s_dict.pop(UpperCamelCase__ )
return s_dict
a_ = {
'NUM_ENCODER_LAYERS': 'num_layers',
'NUM_DECODER_LAYERS': 'num_decoder_layers',
'NUM_HEADS': 'num_heads',
'HEAD_DIM': 'd_kv',
'EMBED_DIM': 'd_model',
'MLP_DIM': 'd_ff',
'NUM_SELECTED_EXPERTS': 'num_selected_experts',
'NUM_ENCODER_SPARSE_LAYERS': 'num_sparse_encoder_layers',
'NUM_DECODER_SPARSE_LAYERS': 'num_sparse_decoder_layers',
'dense.MlpBlock.activations': 'feed_forward_proj',
}
def _a( UpperCamelCase__ : Optional[Any], UpperCamelCase__ : Optional[int] ):
'''simple docstring'''
import regex as re
with open(UpperCamelCase__, '''r''' ) as f:
SCREAMING_SNAKE_CASE__ : Optional[Any] =f.read()
SCREAMING_SNAKE_CASE__ : Optional[int] =re.findall(R'''(.*) = ([0-9.]*)''', UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] ={}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
SCREAMING_SNAKE_CASE__ : Union[str, Any] =float(UpperCamelCase__ ) if '''.''' in value else int(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Dict =re.findall(R'''(.*activations) = \(\'(.*)\',\)''', UpperCamelCase__ )[0]
SCREAMING_SNAKE_CASE__ : Optional[Any] =str(activation[1] )
SCREAMING_SNAKE_CASE__ : str =num_experts
SCREAMING_SNAKE_CASE__ : Dict =SwitchTransformersConfig(**UpperCamelCase__ )
return config
def _a( UpperCamelCase__ : str, UpperCamelCase__ : int, UpperCamelCase__ : Any=None, UpperCamelCase__ : int="./", UpperCamelCase__ : List[str]=8 ):
'''simple docstring'''
print(f"Loading flax weights from : {flax_checkpoint_path}" )
SCREAMING_SNAKE_CASE__ : Any =checkpoints.load_tax_checkpoint(UpperCamelCase__ )
if gin_file is not None:
SCREAMING_SNAKE_CASE__ : Optional[int] =convert_gin_to_config(UpperCamelCase__, UpperCamelCase__ )
else:
SCREAMING_SNAKE_CASE__ : Tuple =SwitchTransformersConfig.from_pretrained(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[str] =SwitchTransformersForConditionalGeneration(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : str =flax_params['''target''']
SCREAMING_SNAKE_CASE__ : List[Any] =flatten_dict(UpperCamelCase__, sep='''/''' )
SCREAMING_SNAKE_CASE__ : Dict =rename_keys(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[str] =unflatten_dict(UpperCamelCase__, sep='''/''' )
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(UpperCamelCase__, UpperCamelCase__ )
print(f"Save PyTorch model to {pytorch_dump_path}" )
pt_model.save_pretrained(UpperCamelCase__ )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--switch_t5x_checkpoint_path',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'
' model architecture. If not provided, a `gin_file` has to be provided.'
),
)
parser.add_argument(
'--gin_file',
default=None,
type=str,
required=False,
help='Path to the gin config file. If not provided, a `config_file` has to be passed ',
)
parser.add_argument(
'--config_name', default=None, type=str, required=False, help='Config name of SwitchTransformers model.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output pytorch model.'
)
parser.add_argument('--num_experts', default=8, type=int, required=False, help='Number of experts')
a_ = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 296
|
'''simple docstring'''
def _a( UpperCamelCase__ : int = 1_0, UpperCamelCase__ : int = 2_2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str =range(1, UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[str] =range(1, UpperCamelCase__ )
return sum(
1 for power in powers for base in bases if len(str(base**power ) ) == power )
if __name__ == "__main__":
print(F'''{solution(1_0, 2_2) = }''')
| 296
| 1
|
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCAmelCase__ = abspath(join(dirname(dirname(__file__)), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def lowerCAmelCase__ ( UpperCAmelCase ):
"""simple docstring"""
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(UpperCAmelCase )
def lowerCAmelCase__ ( UpperCAmelCase ):
"""simple docstring"""
from diffusers.utils.testing_utils import pytest_terminal_summary_main
snake_case__ : Optional[Any] = terminalreporter.config.getoption("""--make-reports""" )
if make_reports:
pytest_terminal_summary_main(UpperCAmelCase , id=UpperCAmelCase )
| 713
|
'''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class _A :
'''simple docstring'''
@property
def __lowerCAmelCase ( self : List[str] )-> Any:
return self.get_dummy_input()
@property
def __lowerCAmelCase ( self : int )-> Optional[Any]:
if self.block_type == "down":
return (4, 32, 16, 16)
elif self.block_type == "mid":
return (4, 32, 32, 32)
elif self.block_type == "up":
return (4, 32, 64, 64)
raise ValueError(F"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'.""" )
def __lowerCAmelCase ( self : Any , lowerCamelCase : Union[str, Any]=True , lowerCamelCase : List[str]=False , lowerCamelCase : int=False , lowerCamelCase : str=False , )-> Optional[int]:
snake_case__ : Dict = 4
snake_case__ : Optional[int] = 32
snake_case__ : Tuple = (32, 32)
snake_case__ : List[str] = torch.manual_seed(0 )
snake_case__ : List[str] = torch.device(lowerCamelCase )
snake_case__ : Optional[int] = (batch_size, num_channels) + sizes
snake_case__ : Tuple = randn_tensor(lowerCamelCase , generator=lowerCamelCase , device=lowerCamelCase )
snake_case__ : str = {"""hidden_states""": hidden_states}
if include_temb:
snake_case__ : Optional[int] = 128
snake_case__ : str = randn_tensor((batch_size, temb_channels) , generator=lowerCamelCase , device=lowerCamelCase )
if include_res_hidden_states_tuple:
snake_case__ : List[str] = torch.manual_seed(1 )
snake_case__ : Optional[Any] = (randn_tensor(lowerCamelCase , generator=lowerCamelCase , device=lowerCamelCase ),)
if include_encoder_hidden_states:
snake_case__ : List[Any] = floats_tensor((batch_size, 32, 32) ).to(lowerCamelCase )
if include_skip_sample:
snake_case__ : List[Any] = randn_tensor(((batch_size, 3) + sizes) , generator=lowerCamelCase , device=lowerCamelCase )
return dummy_input
def __lowerCAmelCase ( self : int )-> int:
snake_case__ : List[Any] = {
"""in_channels""": 32,
"""out_channels""": 32,
"""temb_channels""": 128,
}
if self.block_type == "up":
snake_case__ : Any = 32
if self.block_type == "mid":
init_dict.pop("""out_channels""" )
snake_case__ : int = self.dummy_input
return init_dict, inputs_dict
def __lowerCAmelCase ( self : int , lowerCamelCase : List[Any] )-> Optional[Any]:
snake_case__ , snake_case__ : Union[str, Any] = self.prepare_init_args_and_inputs_for_common()
snake_case__ : int = self.block_class(**lowerCamelCase )
unet_block.to(lowerCamelCase )
unet_block.eval()
with torch.no_grad():
snake_case__ : str = unet_block(**lowerCamelCase )
if isinstance(lowerCamelCase , lowerCamelCase ):
snake_case__ : int = output[0]
self.assertEqual(output.shape , self.output_shape )
snake_case__ : Union[str, Any] = output[0, -1, -3:, -3:]
snake_case__ : Dict = torch.tensor(lowerCamelCase ).to(lowerCamelCase )
assert torch_all_close(output_slice.flatten() , lowerCamelCase , atol=5e-3 )
@unittest.skipIf(torch_device == """mps""" , """Training is not supported in mps""" )
def __lowerCAmelCase ( self : List[Any] )-> List[Any]:
snake_case__ , snake_case__ : List[Any] = self.prepare_init_args_and_inputs_for_common()
snake_case__ : Dict = self.block_class(**lowerCamelCase )
model.to(lowerCamelCase )
model.train()
snake_case__ : Union[str, Any] = model(**lowerCamelCase )
if isinstance(lowerCamelCase , lowerCamelCase ):
snake_case__ : Tuple = output[0]
snake_case__ : List[str] = torch.device(lowerCamelCase )
snake_case__ : Optional[Any] = randn_tensor(output.shape , device=lowerCamelCase )
snake_case__ : Tuple = torch.nn.functional.mse_loss(lowerCamelCase , lowerCamelCase )
loss.backward()
| 172
| 0
|
"""simple docstring"""
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class A_ :
def __init__( self: Optional[int] ,__lowerCAmelCase: List[str] ,__lowerCAmelCase: int=2 ,__lowerCAmelCase: Dict=8 ,__lowerCAmelCase: List[Any]=True ,__lowerCAmelCase: List[str]=True ,__lowerCAmelCase: List[Any]=True ,__lowerCAmelCase: Dict=True ,__lowerCAmelCase: Any=99 ,__lowerCAmelCase: int=16 ,__lowerCAmelCase: List[Any]=5 ,__lowerCAmelCase: List[str]=2 ,__lowerCAmelCase: str=36 ,__lowerCAmelCase: List[str]="gelu" ,__lowerCAmelCase: Any=0.0 ,__lowerCAmelCase: List[str]=0.0 ,__lowerCAmelCase: Any=512 ,__lowerCAmelCase: Union[str, Any]=16 ,__lowerCAmelCase: str=2 ,__lowerCAmelCase: str=0.02 ,__lowerCAmelCase: Dict=3 ,__lowerCAmelCase: List[str]=4 ,__lowerCAmelCase: Tuple=None ,):
'''simple docstring'''
_lowerCamelCase : Tuple = parent
_lowerCamelCase : Union[str, Any] = batch_size
_lowerCamelCase : Optional[Any] = seq_length
_lowerCamelCase : int = is_training
_lowerCamelCase : List[Any] = use_input_mask
_lowerCamelCase : Dict = use_token_type_ids
_lowerCamelCase : Tuple = use_labels
_lowerCamelCase : List[str] = vocab_size
_lowerCamelCase : List[str] = hidden_size
_lowerCamelCase : List[Any] = num_hidden_layers
_lowerCamelCase : List[str] = num_attention_heads
_lowerCamelCase : Dict = intermediate_size
_lowerCamelCase : List[str] = hidden_act
_lowerCamelCase : List[Any] = hidden_dropout_prob
_lowerCamelCase : Optional[int] = attention_probs_dropout_prob
_lowerCamelCase : List[str] = max_position_embeddings
_lowerCamelCase : Optional[Any] = type_vocab_size
_lowerCamelCase : Dict = type_sequence_label_size
_lowerCamelCase : Dict = initializer_range
_lowerCamelCase : List[Any] = num_labels
_lowerCamelCase : Any = num_choices
_lowerCamelCase : List[Any] = scope
def _lowercase ( self: List[Any] ):
'''simple docstring'''
_lowerCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
_lowerCamelCase : str = None
if self.use_input_mask:
_lowerCamelCase : Tuple = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCamelCase : Optional[int] = None
if self.use_token_type_ids:
_lowerCamelCase : Dict = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
_lowerCamelCase : Dict = None
_lowerCamelCase : Union[str, Any] = None
_lowerCamelCase : int = None
if self.use_labels:
_lowerCamelCase : Optional[int] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
_lowerCamelCase : Any = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
_lowerCamelCase : Dict = ids_tensor([self.batch_size] ,self.num_choices )
_lowerCamelCase : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowercase ( self: Union[str, Any] ):
'''simple docstring'''
return MraConfig(
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=__lowerCAmelCase ,initializer_range=self.initializer_range ,)
def _lowercase ( self: int ):
'''simple docstring'''
_lowerCamelCase : str = self.get_config()
_lowerCamelCase : Dict = 300
return config
def _lowercase ( self: Union[str, Any] ):
'''simple docstring'''
(
(
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
),
) : int = self.prepare_config_and_inputs()
_lowerCamelCase : Tuple = True
_lowerCamelCase : int = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
_lowerCamelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] ,vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def _lowercase ( self: List[Any] ,__lowerCAmelCase: Tuple ,__lowerCAmelCase: Optional[int] ,__lowerCAmelCase: str ,__lowerCAmelCase: Dict ,__lowerCAmelCase: int ,__lowerCAmelCase: Optional[Any] ,__lowerCAmelCase: Tuple ):
'''simple docstring'''
_lowerCamelCase : Optional[Any] = MraModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_lowerCamelCase : Optional[int] = model(__lowerCAmelCase ,attention_mask=__lowerCAmelCase ,token_type_ids=__lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = model(__lowerCAmelCase ,token_type_ids=__lowerCAmelCase )
_lowerCamelCase : List[Any] = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self: Union[str, Any] ,__lowerCAmelCase: Any ,__lowerCAmelCase: Union[str, Any] ,__lowerCAmelCase: Tuple ,__lowerCAmelCase: Tuple ,__lowerCAmelCase: int ,__lowerCAmelCase: Any ,__lowerCAmelCase: Optional[int] ,__lowerCAmelCase: Tuple ,__lowerCAmelCase: List[str] ,):
'''simple docstring'''
_lowerCamelCase : List[Any] = True
_lowerCamelCase : int = MraModel(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_lowerCamelCase : int = model(
__lowerCAmelCase ,attention_mask=__lowerCAmelCase ,token_type_ids=__lowerCAmelCase ,encoder_hidden_states=__lowerCAmelCase ,encoder_attention_mask=__lowerCAmelCase ,)
_lowerCamelCase : str = model(
__lowerCAmelCase ,attention_mask=__lowerCAmelCase ,token_type_ids=__lowerCAmelCase ,encoder_hidden_states=__lowerCAmelCase ,)
_lowerCamelCase : Optional[int] = model(__lowerCAmelCase ,attention_mask=__lowerCAmelCase ,token_type_ids=__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self: int ,__lowerCAmelCase: Any ,__lowerCAmelCase: Union[str, Any] ,__lowerCAmelCase: Union[str, Any] ,__lowerCAmelCase: Optional[Any] ,__lowerCAmelCase: Tuple ,__lowerCAmelCase: int ,__lowerCAmelCase: List[str] ):
'''simple docstring'''
_lowerCamelCase : Tuple = MraForMaskedLM(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_lowerCamelCase : Any = model(__lowerCAmelCase ,attention_mask=__lowerCAmelCase ,token_type_ids=__lowerCAmelCase ,labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase ( self: Union[str, Any] ,__lowerCAmelCase: Dict ,__lowerCAmelCase: int ,__lowerCAmelCase: List[str] ,__lowerCAmelCase: List[Any] ,__lowerCAmelCase: Optional[int] ,__lowerCAmelCase: List[Any] ,__lowerCAmelCase: Optional[int] ):
'''simple docstring'''
_lowerCamelCase : Dict = MraForQuestionAnswering(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_lowerCamelCase : int = model(
__lowerCAmelCase ,attention_mask=__lowerCAmelCase ,token_type_ids=__lowerCAmelCase ,start_positions=__lowerCAmelCase ,end_positions=__lowerCAmelCase ,)
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def _lowercase ( self: Tuple ,__lowerCAmelCase: Optional[int] ,__lowerCAmelCase: str ,__lowerCAmelCase: Union[str, Any] ,__lowerCAmelCase: Tuple ,__lowerCAmelCase: Optional[Any] ,__lowerCAmelCase: Union[str, Any] ,__lowerCAmelCase: Optional[int] ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = self.num_labels
_lowerCamelCase : Any = MraForSequenceClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_lowerCamelCase : str = model(__lowerCAmelCase ,attention_mask=__lowerCAmelCase ,token_type_ids=__lowerCAmelCase ,labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def _lowercase ( self: str ,__lowerCAmelCase: Dict ,__lowerCAmelCase: Dict ,__lowerCAmelCase: int ,__lowerCAmelCase: List[Any] ,__lowerCAmelCase: Optional[int] ,__lowerCAmelCase: List[Any] ,__lowerCAmelCase: str ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = self.num_labels
_lowerCamelCase : int = MraForTokenClassification(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_lowerCamelCase : str = model(__lowerCAmelCase ,attention_mask=__lowerCAmelCase ,token_type_ids=__lowerCAmelCase ,labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def _lowercase ( self: Optional[int] ,__lowerCAmelCase: Optional[Any] ,__lowerCAmelCase: Any ,__lowerCAmelCase: Tuple ,__lowerCAmelCase: Tuple ,__lowerCAmelCase: Union[str, Any] ,__lowerCAmelCase: Union[str, Any] ,__lowerCAmelCase: Tuple ):
'''simple docstring'''
_lowerCamelCase : Optional[Any] = self.num_choices
_lowerCamelCase : Optional[Any] = MraForMultipleChoice(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_lowerCamelCase : Optional[Any] = input_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
_lowerCamelCase : List[str] = token_type_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
_lowerCamelCase : List[str] = input_mask.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
_lowerCamelCase : List[Any] = model(
__lowerCAmelCase ,attention_mask=__lowerCAmelCase ,token_type_ids=__lowerCAmelCase ,labels=__lowerCAmelCase ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def _lowercase ( self: Optional[Any] ):
'''simple docstring'''
_lowerCamelCase : Optional[Any] = self.prepare_config_and_inputs()
(
(
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
),
) : Any = config_and_inputs
_lowerCamelCase : Union[str, Any] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class A_ ( _a , unittest.TestCase ):
lowerCAmelCase__ = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = ()
def _lowercase ( self: Dict ):
'''simple docstring'''
_lowerCamelCase : str = MraModelTester(self )
_lowerCamelCase : List[str] = ConfigTester(self ,config_class=__lowerCAmelCase ,hidden_size=37 )
def _lowercase ( self: Union[str, Any] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def _lowercase ( self: Dict ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def _lowercase ( self: Union[str, Any] ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_lowerCamelCase : Dict = type
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def _lowercase ( self: Union[str, Any] ):
'''simple docstring'''
_lowerCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase )
def _lowercase ( self: int ):
'''simple docstring'''
_lowerCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__lowerCAmelCase )
def _lowercase ( self: Tuple ):
'''simple docstring'''
_lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase )
def _lowercase ( self: Dict ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase )
def _lowercase ( self: List[Any] ):
'''simple docstring'''
_lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase )
@slow
def _lowercase ( self: Tuple ):
'''simple docstring'''
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Dict = MraModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
@unittest.skip(reason="MRA does not output attentions" )
def _lowercase ( self: str ):
'''simple docstring'''
return
@require_torch
class A_ ( unittest.TestCase ):
@slow
def _lowercase ( self: Any ):
'''simple docstring'''
_lowerCamelCase : Dict = MraModel.from_pretrained("uw-madison/mra-base-512-4" )
_lowerCamelCase : List[Any] = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
_lowerCamelCase : List[str] = model(__lowerCAmelCase )[0]
_lowerCamelCase : str = torch.Size((1, 256, 768) )
self.assertEqual(output.shape ,__lowerCAmelCase )
_lowerCamelCase : List[Any] = torch.tensor(
[[[-0.01_40, 0.08_30, -0.03_81], [0.15_46, 0.14_02, 0.02_20], [0.11_62, 0.08_51, 0.01_65]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] ,__lowerCAmelCase ,atol=1e-4 ) )
@slow
def _lowercase ( self: Optional[int] ):
'''simple docstring'''
_lowerCamelCase : List[Any] = MraForMaskedLM.from_pretrained("uw-madison/mra-base-512-4" )
_lowerCamelCase : List[str] = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
_lowerCamelCase : Dict = model(__lowerCAmelCase )[0]
_lowerCamelCase : Optional[Any] = 50_265
_lowerCamelCase : Dict = torch.Size((1, 256, vocab_size) )
self.assertEqual(output.shape ,__lowerCAmelCase )
_lowerCamelCase : Optional[int] = torch.tensor(
[[[9.25_95, -3.60_38, 11.88_19], [9.38_69, -3.26_93, 11.09_56], [11.85_24, -3.49_38, 13.12_10]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] ,__lowerCAmelCase ,atol=1e-4 ) )
@slow
def _lowercase ( self: Dict ):
'''simple docstring'''
_lowerCamelCase : Any = MraForMaskedLM.from_pretrained("uw-madison/mra-base-4096-8-d3" )
_lowerCamelCase : str = torch.arange(4_096 ).unsqueeze(0 )
with torch.no_grad():
_lowerCamelCase : Any = model(__lowerCAmelCase )[0]
_lowerCamelCase : Any = 50_265
_lowerCamelCase : Dict = torch.Size((1, 4_096, vocab_size) )
self.assertEqual(output.shape ,__lowerCAmelCase )
_lowerCamelCase : int = torch.tensor(
[[[5.47_89, -2.35_64, 7.50_64], [7.90_67, -1.33_69, 9.96_68], [9.07_12, -1.81_06, 7.03_80]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] ,__lowerCAmelCase ,atol=1e-4 ) )
| 46
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCAmelCase : List[str] = {
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'''
),
# See all Vivit models at https://huggingface.co/models?filter=vivit
}
class A_ ( _a ):
lowerCAmelCase__ = 'vivit'
def __init__( self: List[Any] ,__lowerCAmelCase: int=224 ,__lowerCAmelCase: Any=32 ,__lowerCAmelCase: str=[2, 16, 16] ,__lowerCAmelCase: Optional[Any]=3 ,__lowerCAmelCase: List[str]=768 ,__lowerCAmelCase: List[Any]=12 ,__lowerCAmelCase: Optional[int]=12 ,__lowerCAmelCase: Optional[Any]=3_072 ,__lowerCAmelCase: Any="gelu_fast" ,__lowerCAmelCase: Tuple=0.0 ,__lowerCAmelCase: Any=0.0 ,__lowerCAmelCase: Union[str, Any]=0.02 ,__lowerCAmelCase: List[str]=1e-06 ,__lowerCAmelCase: Optional[Any]=True ,**__lowerCAmelCase: Optional[int] ,):
'''simple docstring'''
_lowerCamelCase : Any = hidden_size
_lowerCamelCase : List[str] = num_hidden_layers
_lowerCamelCase : Union[str, Any] = num_attention_heads
_lowerCamelCase : Any = intermediate_size
_lowerCamelCase : Tuple = hidden_act
_lowerCamelCase : Tuple = hidden_dropout_prob
_lowerCamelCase : Optional[Any] = attention_probs_dropout_prob
_lowerCamelCase : List[Any] = initializer_range
_lowerCamelCase : int = layer_norm_eps
_lowerCamelCase : Tuple = image_size
_lowerCamelCase : Dict = num_frames
_lowerCamelCase : Optional[int] = tubelet_size
_lowerCamelCase : int = num_channels
_lowerCamelCase : List[str] = qkv_bias
super().__init__(**__lowerCAmelCase )
| 46
| 1
|
import numpy as np
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = int(np.ceil((x_end - xa) / h ) )
SCREAMING_SNAKE_CASE_ = np.zeros((n + 1,) )
SCREAMING_SNAKE_CASE_ = ya
SCREAMING_SNAKE_CASE_ = xa
for k in range(__lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = f(__lowerCamelCase, y[k] )
SCREAMING_SNAKE_CASE_ = f(x + 0.5 * h, y[k] + 0.5 * h * ka )
SCREAMING_SNAKE_CASE_ = f(x + 0.5 * h, y[k] + 0.5 * h * ka )
SCREAMING_SNAKE_CASE_ = f(x + h, y[k] + h * ka )
SCREAMING_SNAKE_CASE_ = y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
x += h
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 597
|
import socket
def A__ ( ):
SCREAMING_SNAKE_CASE_ = socket.socket(socket.AF_INET, socket.SOCK_STREAM )
SCREAMING_SNAKE_CASE_ = socket.gethostname()
SCREAMING_SNAKE_CASE_ = 1_23_12
sock.connect((host, port) )
sock.send(B'''Hello server!''' )
with open('''Received_file''', '''wb''' ) as out_file:
print('''File opened''' )
print('''Receiving data...''' )
while True:
SCREAMING_SNAKE_CASE_ = sock.recv(10_24 )
if not data:
break
out_file.write(__lowerCamelCase )
print('''Successfully received the file''' )
sock.close()
print('''Connection closed''' )
if __name__ == "__main__":
main()
| 597
| 1
|
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
UpperCamelCase__ = logging.get_logger(__name__)
class a :
def __init__( self , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_=None , UpperCamelCase_=None ):
if not conversation_id:
UpperCAmelCase__ : Any = uuid.uuida()
if past_user_inputs is None:
UpperCAmelCase__ : Union[str, Any] = []
if generated_responses is None:
UpperCAmelCase__ : List[str] = []
UpperCAmelCase__ : uuid.UUID = conversation_id
UpperCAmelCase__ : List[str] = past_user_inputs
UpperCAmelCase__ : List[str] = generated_responses
UpperCAmelCase__ : Optional[str] = text
def __eq__( self , UpperCamelCase_ ):
if not isinstance(__lowercase , __lowercase ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def __snake_case ( self , UpperCamelCase_ , UpperCamelCase_ = False ):
if self.new_user_input:
if overwrite:
logger.warning(
F'''User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten '''
F'''with: \"{text}\".''' )
UpperCAmelCase__ : Optional[int] = text
else:
logger.warning(
F'''User input added while unprocessed input was existing: \"{self.new_user_input}\" new input '''
F'''ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input''' )
else:
UpperCAmelCase__ : Any = text
def __snake_case ( self ):
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
UpperCAmelCase__ : int = None
def __snake_case ( self , UpperCamelCase_ ):
self.generated_responses.append(__lowercase )
def __snake_case ( self ):
for user_input, generated_response in zip(self.past_user_inputs , self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__( self ):
UpperCAmelCase__ : List[str] = F'''Conversation id: {self.uuid} \n'''
for is_user, text in self.iter_texts():
UpperCAmelCase__ : Union[str, Any] = '''user''' if is_user else '''bot'''
output += F'''{name} >> {text} \n'''
return output
@add_end_docstrings(
__lowerCamelCase , r"""
min_length_for_response (`int`, *optional*, defaults to 32):
The minimum length (in number of tokens) for a response.
minimum_tokens (`int`, *optional*, defaults to 10):
The minimum length of tokens to leave for a response.
""" , )
class a ( __lowerCamelCase ):
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ):
super().__init__(*__lowercase , **__lowercase )
if self.tokenizer.pad_token_id is None:
UpperCAmelCase__ : int = self.tokenizer.eos_token
def __snake_case ( self , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_=None , **UpperCamelCase_ ):
UpperCAmelCase__ : Any = {}
UpperCAmelCase__ : Any = {}
UpperCAmelCase__ : Tuple = {}
if min_length_for_response is not None:
UpperCAmelCase__ : Dict = min_length_for_response
if minimum_tokens is not None:
UpperCAmelCase__ : Dict = minimum_tokens
if "max_length" in generate_kwargs:
UpperCAmelCase__ : Any = generate_kwargs['''max_length''']
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
UpperCAmelCase__ : List[Any] = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(__lowercase )
return preprocess_params, forward_params, postprocess_params
def __call__( self , UpperCamelCase_ , UpperCamelCase_=0 , **UpperCamelCase_ ):
UpperCAmelCase__ : Optional[int] = super().__call__(__lowercase , num_workers=__lowercase , **__lowercase )
if isinstance(__lowercase , __lowercase ) and len(__lowercase ) == 1:
return outputs[0]
return outputs
def __snake_case ( self , UpperCamelCase_ , UpperCamelCase_=32 ):
if not isinstance(__lowercase , __lowercase ):
raise ValueError('ConversationalPipeline, expects Conversation as inputs' )
if conversation.new_user_input is None:
raise ValueError(
F'''Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. '''
'Add user inputs with the conversation\'s `add_user_input` method' )
if hasattr(self.tokenizer , '_build_conversation_input_ids' ):
UpperCAmelCase__ : Tuple = self.tokenizer._build_conversation_input_ids(__lowercase )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
UpperCAmelCase__ : Tuple = self._legacy_parse_and_tokenize(__lowercase )
if self.framework == "pt":
UpperCAmelCase__ : str = torch.LongTensor([input_ids] )
elif self.framework == "tf":
UpperCAmelCase__ : Tuple = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def __snake_case ( self , UpperCamelCase_ , UpperCamelCase_=10 , **UpperCamelCase_ ):
UpperCAmelCase__ : List[Any] = generate_kwargs.get('max_length' , self.model.config.max_length )
UpperCAmelCase__ : Dict = model_inputs['''input_ids'''].shape[1]
if max_length - minimum_tokens < n:
logger.warning(F'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})''' )
UpperCAmelCase__ : List[Any] = max_length - minimum_tokens
UpperCAmelCase__ : Tuple = model_inputs['''input_ids'''][:, -trim:]
if "attention_mask" in model_inputs:
UpperCAmelCase__ : Union[str, Any] = model_inputs['''attention_mask'''][:, -trim:]
UpperCAmelCase__ : List[Any] = model_inputs.pop('conversation' )
UpperCAmelCase__ : List[str] = max_length
UpperCAmelCase__ : str = self.model.generate(**__lowercase , **__lowercase )
if self.model.config.is_encoder_decoder:
UpperCAmelCase__ : Dict = 1
else:
UpperCAmelCase__ : str = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def __snake_case ( self , UpperCamelCase_ , UpperCamelCase_=True ):
UpperCAmelCase__ : Any = model_outputs['''output_ids''']
UpperCAmelCase__ : Tuple = self.tokenizer.decode(
output_ids[0] , skip_special_tokens=__lowercase , clean_up_tokenization_spaces=__lowercase , )
UpperCAmelCase__ : Optional[Any] = model_outputs['''conversation''']
conversation.mark_processed()
conversation.append_response(__lowercase )
return conversation
def __snake_case ( self , UpperCamelCase_ ):
UpperCAmelCase__ : Any = self.tokenizer.eos_token_id
UpperCAmelCase__ : List[str] = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(__lowercase , add_special_tokens=__lowercase ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(__lowercase , add_special_tokens=__lowercase ) )
if len(__lowercase ) > self.tokenizer.model_max_length:
UpperCAmelCase__ : Tuple = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 110
|
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class a ( unittest.TestCase ):
def __init__( self :List[str] ,__lowercase :Any ,__lowercase :Optional[Any]=1_3 ,__lowercase :Optional[Any]=7 ,__lowercase :Union[str, Any]=True ,__lowercase :Optional[int]=True ,__lowercase :Dict=True ,__lowercase :int=True ,__lowercase :List[str]=9_9 ,__lowercase :Optional[Any]=3_2 ,__lowercase :Dict=5 ,__lowercase :List[str]=4 ,__lowercase :Dict=3_7 ,__lowercase :Dict="gelu" ,__lowercase :Any=0.1 ,__lowercase :Any=0.1 ,__lowercase :int=5_1_2 ,__lowercase :List[str]=1_6 ,__lowercase :List[Any]=2 ,__lowercase :List[str]=0.02 ,__lowercase :Optional[int]=4 ,):
snake_case__ : Union[str, Any] = parent
snake_case__ : List[Any] = batch_size
snake_case__ : Optional[int] = seq_length
snake_case__ : Optional[Any] = is_training
snake_case__ : Optional[Any] = use_attention_mask
snake_case__ : Tuple = use_token_type_ids
snake_case__ : str = use_labels
snake_case__ : Union[str, Any] = vocab_size
snake_case__ : List[Any] = hidden_size
snake_case__ : List[str] = num_hidden_layers
snake_case__ : Optional[Any] = num_attention_heads
snake_case__ : Optional[int] = intermediate_size
snake_case__ : Dict = hidden_act
snake_case__ : str = hidden_dropout_prob
snake_case__ : str = attention_probs_dropout_prob
snake_case__ : List[Any] = max_position_embeddings
snake_case__ : List[str] = type_vocab_size
snake_case__ : Union[str, Any] = type_sequence_label_size
snake_case__ : Tuple = initializer_range
snake_case__ : Tuple = num_choices
def __lowerCamelCase ( self :Union[str, Any] ):
snake_case__ : Dict = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
snake_case__ : Optional[Any] = None
if self.use_attention_mask:
snake_case__ : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case__ : str = None
if self.use_token_type_ids:
snake_case__ : Dict = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
snake_case__ : Any = AlbertConfig(
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=__lowercase ,initializer_range=self.initializer_range ,)
return config, input_ids, token_type_ids, attention_mask
def __lowerCamelCase ( self :Any ):
snake_case__ : Any = self.prepare_config_and_inputs()
snake_case__ , snake_case__ , snake_case__ , snake_case__ : Any = config_and_inputs
snake_case__ : Dict = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
@require_flax
class a ( __lowerCamelCase , unittest.TestCase ):
__lowerCAmelCase : List[str] = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __lowerCamelCase ( self :Optional[Any] ):
snake_case__ : Optional[int] = FlaxAlbertModelTester(self )
@slow
def __lowerCamelCase ( self :List[str] ):
for model_class_name in self.all_model_classes:
snake_case__ : Any = model_class_name.from_pretrained('''albert-base-v2''' )
snake_case__ : str = model(np.ones((1, 1) ) )
self.assertIsNotNone(__lowercase )
@require_flax
class a ( unittest.TestCase ):
@slow
def __lowerCamelCase ( self :str ):
snake_case__ : str = FlaxAlbertModel.from_pretrained('''albert-base-v2''' )
snake_case__ : int = np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
snake_case__ : Optional[Any] = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
snake_case__ : List[str] = model(__lowercase ,attention_mask=__lowercase )[0]
snake_case__ : Optional[Any] = (1, 1_1, 7_6_8)
self.assertEqual(output.shape ,__lowercase )
snake_case__ : List[str] = np.array(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] ,__lowercase ,atol=1e-4 ) )
| 252
| 0
|
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
_lowerCamelCase : Dict = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}
class __snake_case (_a ):
lowerCAmelCase__ = "xlnet"
lowerCAmelCase__ = ["mems"]
lowerCAmelCase__ = {
"n_token": "vocab_size", # Backward compatibility
"hidden_size": "d_model",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : Any , _UpperCAmelCase : Dict=3_2000 , _UpperCAmelCase : Optional[Any]=1024 , _UpperCAmelCase : Optional[Any]=24 , _UpperCAmelCase : str=16 , _UpperCAmelCase : Dict=4096 , _UpperCAmelCase : Any="gelu" , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : Dict="bi" , _UpperCAmelCase : List[Any]=0.02 , _UpperCAmelCase : Optional[Any]=1E-12 , _UpperCAmelCase : Dict=0.1 , _UpperCAmelCase : int=512 , _UpperCAmelCase : Tuple=None , _UpperCAmelCase : List[Any]=True , _UpperCAmelCase : Dict=False , _UpperCAmelCase : Any=False , _UpperCAmelCase : str=-1 , _UpperCAmelCase : Optional[Any]=False , _UpperCAmelCase : str="last" , _UpperCAmelCase : Dict=True , _UpperCAmelCase : Dict="tanh" , _UpperCAmelCase : int=0.1 , _UpperCAmelCase : Tuple=5 , _UpperCAmelCase : int=5 , _UpperCAmelCase : Any=5 , _UpperCAmelCase : Optional[int]=1 , _UpperCAmelCase : Any=2 , **_UpperCAmelCase : List[Any] , ) -> Optional[int]:
'''simple docstring'''
_lowerCAmelCase : Any = vocab_size
_lowerCAmelCase : Dict = d_model
_lowerCAmelCase : Tuple = n_layer
_lowerCAmelCase : Any = n_head
if d_model % n_head != 0:
raise ValueError(f"'d_model % n_head' ({d_model % n_head}) should be equal to 0" )
if "d_head" in kwargs:
if kwargs["d_head"] != d_model // n_head:
raise ValueError(
f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})" )
_lowerCAmelCase : Optional[Any] = d_model // n_head
_lowerCAmelCase : Union[str, Any] = ff_activation
_lowerCAmelCase : str = d_inner
_lowerCAmelCase : Tuple = untie_r
_lowerCAmelCase : Tuple = attn_type
_lowerCAmelCase : Tuple = initializer_range
_lowerCAmelCase : Union[str, Any] = layer_norm_eps
_lowerCAmelCase : Any = dropout
_lowerCAmelCase : Dict = mem_len
_lowerCAmelCase : Optional[Any] = reuse_len
_lowerCAmelCase : List[Any] = bi_data
_lowerCAmelCase : str = clamp_len
_lowerCAmelCase : Union[str, Any] = same_length
_lowerCAmelCase : Tuple = summary_type
_lowerCAmelCase : str = summary_use_proj
_lowerCAmelCase : int = summary_activation
_lowerCAmelCase : Optional[Any] = summary_last_dropout
_lowerCAmelCase : int = start_n_top
_lowerCAmelCase : Tuple = end_n_top
_lowerCAmelCase : Optional[Any] = bos_token_id
_lowerCAmelCase : int = pad_token_id
_lowerCAmelCase : Tuple = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
"""The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"""
""" instead.""" , _UpperCAmelCase , )
_lowerCAmelCase : Union[str, Any] = kwargs["""use_cache"""]
_lowerCAmelCase : Optional[Any] = use_mems_eval
_lowerCAmelCase : Tuple = use_mems_train
super().__init__(pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
@property
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> int:
'''simple docstring'''
logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit." )
return -1
@max_position_embeddings.setter
def SCREAMING_SNAKE_CASE ( self : str , _UpperCAmelCase : str ) -> List[Any]:
'''simple docstring'''
raise NotImplementedError(
f"The model {self.model_type} is one of the few models that has no sequence length limit." )
| 196
|
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_lowerCamelCase : Any = 1_6
_lowerCamelCase : Tuple = 3_2
def _UpperCAmelCase (UpperCamelCase_ : Accelerator , UpperCamelCase_ : DatasetDict , UpperCamelCase_ : List[int] , UpperCamelCase_ : List[int] , UpperCamelCase_ : int = 16 ):
'''simple docstring'''
_lowerCAmelCase : Any = AutoTokenizer.from_pretrained("""bert-base-cased""" )
_lowerCAmelCase : int = DatasetDict(
{
"""train""": dataset["""train"""].select(UpperCamelCase_ ),
"""validation""": dataset["""train"""].select(UpperCamelCase_ ),
"""test""": dataset["""validation"""],
} )
def tokenize_function(UpperCamelCase_ : str ):
# max_length=None => use the model max length (it's actually the default)
_lowerCAmelCase : List[Any] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=UpperCamelCase_ , max_length=UpperCamelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_lowerCAmelCase : List[str] = datasets.map(
UpperCamelCase_ , batched=UpperCamelCase_ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_lowerCAmelCase : Optional[Any] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(UpperCamelCase_ : Optional[Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_lowerCAmelCase : List[str] = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_lowerCAmelCase : Optional[Any] = 16
elif accelerator.mixed_precision != "no":
_lowerCAmelCase : int = 8
else:
_lowerCAmelCase : Dict = None
return tokenizer.pad(
UpperCamelCase_ , padding="""longest""" , max_length=UpperCamelCase_ , pad_to_multiple_of=UpperCamelCase_ , return_tensors="""pt""" , )
# Instantiate dataloaders.
_lowerCAmelCase : Optional[Any] = DataLoader(
tokenized_datasets["""train"""] , shuffle=UpperCamelCase_ , collate_fn=UpperCamelCase_ , batch_size=UpperCamelCase_ )
_lowerCAmelCase : Tuple = DataLoader(
tokenized_datasets["""validation"""] , shuffle=UpperCamelCase_ , collate_fn=UpperCamelCase_ , batch_size=UpperCamelCase_ )
_lowerCAmelCase : List[str] = DataLoader(
tokenized_datasets["""test"""] , shuffle=UpperCamelCase_ , collate_fn=UpperCamelCase_ , batch_size=UpperCamelCase_ )
return train_dataloader, eval_dataloader, test_dataloader
def _UpperCAmelCase (UpperCamelCase_ : Any , UpperCamelCase_ : Optional[int] ):
'''simple docstring'''
# New Code #
_lowerCAmelCase : Dict = []
# Download the dataset
_lowerCAmelCase : Optional[int] = load_dataset("""glue""" , """mrpc""" )
# Create our splits
_lowerCAmelCase : Any = StratifiedKFold(n_splits=int(args.num_folds ) )
# Initialize accelerator
_lowerCAmelCase : int = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_lowerCAmelCase : List[str] = config["""lr"""]
_lowerCAmelCase : Any = int(config["""num_epochs"""] )
_lowerCAmelCase : Dict = int(config["""seed"""] )
_lowerCAmelCase : Union[str, Any] = int(config["""batch_size"""] )
_lowerCAmelCase : Optional[Any] = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
_lowerCAmelCase : Optional[Any] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_lowerCAmelCase : Optional[Any] = batch_size // MAX_GPU_BATCH_SIZE
_lowerCAmelCase : int = MAX_GPU_BATCH_SIZE
set_seed(UpperCamelCase_ )
# New Code #
# Create our folds:
_lowerCAmelCase : Optional[int] = kfold.split(np.zeros(datasets["""train"""].num_rows ) , datasets["""train"""]["""label"""] )
_lowerCAmelCase : Any = []
# Iterate over them
for i, (train_idxs, valid_idxs) in enumerate(UpperCamelCase_ ):
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : List[str] = get_fold_dataloaders(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_lowerCAmelCase : int = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=UpperCamelCase_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_lowerCAmelCase : Union[str, Any] = model.to(accelerator.device )
# Instantiate optimizer
_lowerCAmelCase : Any = AdamW(params=model.parameters() , lr=UpperCamelCase_ )
# Instantiate scheduler
_lowerCAmelCase : Tuple = get_linear_schedule_with_warmup(
optimizer=UpperCamelCase_ , num_warmup_steps=100 , num_training_steps=(len(UpperCamelCase_ ) * num_epochs) // gradient_accumulation_steps , )
# 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 : List[str] = accelerator.prepare(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# Now we train the model
for epoch in range(UpperCamelCase_ ):
model.train()
for step, batch in enumerate(UpperCamelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_lowerCAmelCase : Tuple = model(**UpperCamelCase_ )
_lowerCAmelCase : Optional[int] = outputs.loss
_lowerCAmelCase : Optional[int] = loss / gradient_accumulation_steps
accelerator.backward(UpperCamelCase_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(UpperCamelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_lowerCAmelCase : List[Any] = model(**UpperCamelCase_ )
_lowerCAmelCase : List[Any] = outputs.logits.argmax(dim=-1 )
_lowerCAmelCase , _lowerCAmelCase : Any = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=UpperCamelCase_ , references=UpperCamelCase_ , )
_lowerCAmelCase : List[Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"epoch {epoch}:" , UpperCamelCase_ )
# New Code #
# We also run predictions on the test set at the very end
_lowerCAmelCase : Dict = []
for step, batch in enumerate(UpperCamelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_lowerCAmelCase : Dict = model(**UpperCamelCase_ )
_lowerCAmelCase : List[Any] = outputs.logits
_lowerCAmelCase , _lowerCAmelCase : Optional[int] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
fold_predictions.append(predictions.cpu() )
if i == 0:
# We need all of the test predictions
test_references.append(references.cpu() )
# Use accelerator.print to print only on the main process.
test_predictions.append(torch.cat(UpperCamelCase_ , dim=0 ) )
# We now need to release all our memory and get rid of the current model, optimizer, etc
accelerator.free_memory()
# New Code #
# Finally we check the accuracy of our folded results:
_lowerCAmelCase : Tuple = torch.cat(UpperCamelCase_ , dim=0 )
_lowerCAmelCase : Tuple = torch.stack(UpperCamelCase_ , dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
_lowerCAmelCase : Union[str, Any] = metric.compute(predictions=UpperCamelCase_ , references=UpperCamelCase_ )
accelerator.print("""Average test metrics from all folds:""" , UpperCamelCase_ )
def _UpperCAmelCase ():
'''simple docstring'''
_lowerCAmelCase : Optional[int] = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=UpperCamelCase_ , default=UpperCamelCase_ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
# New Code #
parser.add_argument("""--num_folds""" , type=UpperCamelCase_ , default=3 , help="""The number of splits to perform across the dataset""" )
_lowerCAmelCase : Optional[Any] = parser.parse_args()
_lowerCAmelCase : List[Any] = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(UpperCamelCase_ , UpperCamelCase_ )
if __name__ == "__main__":
main()
| 196
| 1
|
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self : str ) -> Dict:
a_ : Union[str, Any] = inspect.getfile(accelerate.test_utils )
a_ : str = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_script.py'''] )
a_ : Any = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_distributed_data_loop.py'''] )
a_ : Tuple = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_ops.py'''] )
@require_multi_gpu
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Tuple:
print(f'Found {torch.cuda.device_count()} devices.' )
a_ : List[str] = ['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__SCREAMING_SNAKE_CASE , env=os.environ.copy() )
@require_multi_gpu
def SCREAMING_SNAKE_CASE ( self : int ) -> Optional[Any]:
print(f'Found {torch.cuda.device_count()} devices.' )
a_ : List[Any] = ['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', self.operation_file_path]
print(f'Command: {cmd}' )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__SCREAMING_SNAKE_CASE , env=os.environ.copy() )
@require_multi_gpu
def SCREAMING_SNAKE_CASE ( self : str ) -> Optional[int]:
a_ : Optional[int] = ['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__ )]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__SCREAMING_SNAKE_CASE , env=os.environ.copy() )
@require_multi_gpu
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Optional[Any]:
print(f'Found {torch.cuda.device_count()} devices, using 2 devices only' )
a_ : Optional[Any] = ['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', self.data_loop_file_path]
with patch_environment(omp_num_threads=1 , cuda_visible_devices='''0,1''' ):
execute_subprocess_async(__SCREAMING_SNAKE_CASE , env=os.environ.copy() )
if __name__ == "__main__":
__lowerCAmelCase = Accelerator()
__lowerCAmelCase = (accelerator.state.process_index + 2, 10)
__lowerCAmelCase = torch.randint(0, 10, shape).to(accelerator.device)
__lowerCAmelCase = ''
__lowerCAmelCase = accelerator.pad_across_processes(tensor)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
error_msg += "Padding was not done with the right value (0)."
__lowerCAmelCase = accelerator.pad_across_processes(tensor, pad_first=True)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
__lowerCAmelCase = accelerator.state.num_processes - accelerator.state.process_index - 1
if not torch.equal(tensora[index:], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[:index] == 0):
error_msg += "Padding was not done with the right value (0)."
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 466
|
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"
# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
__lowerCAmelCase = 'facebook/wmt19-en-de'
__lowerCAmelCase = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__lowerCAmelCase = FSMTConfig.from_pretrained(mname)
config.update(
dict(
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
)
__lowerCAmelCase = FSMTForConditionalGeneration(config)
print(F"""num of params {tiny_model.num_parameters()}""")
# Test
__lowerCAmelCase = tokenizer(['Making tiny model'], return_tensors='pt')
__lowerCAmelCase = tiny_model(**batch)
print('test output:', len(outputs.logits[0]))
# Save
__lowerCAmelCase = 'tiny-wmt19-en-de'
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F"""Generated {mname_tiny}""")
# Upload
# transformers-cli upload tiny-wmt19-en-de
| 466
| 1
|
"""simple docstring"""
from math import pow, sqrt
def _snake_case ( *lowercase__ ):
_lowerCamelCase : Dict = len(lowercase__ ) > 0 and all(value > 0.0 for value in values )
return result
def _snake_case ( lowercase__ , lowercase__ ):
return (
round(sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(lowercase__ , lowercase__ )
else ValueError('Input Error: Molar mass values must greater than 0.' )
)
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
return (
round(effusion_rate * sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(lowercase__ , lowercase__ , lowercase__ )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
return (
round(effusion_rate / sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(lowercase__ , lowercase__ , lowercase__ )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
return (
round(molar_mass / pow(effusion_rate_a / effusion_rate_a , 2 ) , 6 )
if validate(lowercase__ , lowercase__ , lowercase__ )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
return (
round(pow(effusion_rate_a / effusion_rate_a , 2 ) / molar_mass , 6 )
if validate(lowercase__ , lowercase__ , lowercase__ )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
| 712
|
"""simple docstring"""
def _snake_case ( lowercase__ ):
_lowerCamelCase : Optional[int] = int(lowercase__ )
if decimal in (0, 1): # Exit cases for the recursion
return str(lowercase__ )
_lowerCamelCase, _lowerCamelCase : Dict = divmod(lowercase__ , 2 )
return binary_recursive(lowercase__ ) + str(lowercase__ )
def _snake_case ( lowercase__ ):
_lowerCamelCase : List[Any] = str(lowercase__ ).strip()
if not number:
raise ValueError('No input value was provided' )
_lowerCamelCase : str = '-' if number.startswith('-' ) else ''
_lowerCamelCase : Union[str, Any] = number.lstrip('-' )
if not number.isnumeric():
raise ValueError('Input value is not an integer' )
return f'''{negative}0b{binary_recursive(int(lowercase__ ) )}'''
if __name__ == "__main__":
from doctest import testmod
testmod()
| 492
| 0
|
import math
import sys
def _SCREAMING_SNAKE_CASE ( snake_case ) -> int:
if number != int(snake_case ):
raise ValueError("""the value of input must be a natural number""" )
if number < 0:
raise ValueError("""the value of input must not be a negative number""" )
if number == 0:
return 1
_UpperCAmelCase = [-1] * (number + 1)
_UpperCAmelCase = 0
for i in range(1 , number + 1 ):
_UpperCAmelCase = sys.maxsize
_UpperCAmelCase = int(math.sqrt(snake_case ) )
for j in range(1 , root + 1 ):
_UpperCAmelCase = 1 + answers[i - (j**2)]
_UpperCAmelCase = min(snake_case , snake_case )
_UpperCAmelCase = answer
return answers[number]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 518
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = [
"NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST",
"NllbMoeForConditionalGeneration",
"NllbMoeModel",
"NllbMoePreTrainedModel",
"NllbMoeTop2Router",
"NllbMoeSparseMLP",
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
a = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 518
| 1
|
"""simple docstring"""
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowerCamelCase = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n"
__lowerCamelCase = "\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"
__lowerCamelCase = "\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n 'google_bleu': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.4\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowercase ( datasets.Metric ):
def lowerCAmelCase__ ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ),
'''references''': datasets.Sequence(
datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ) , id='''references''' ),
} ) , )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = 1 , UpperCamelCase_ = 4 , ):
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=UpperCamelCase_ , hypotheses=UpperCamelCase_ , min_len=UpperCamelCase_ , max_len=UpperCamelCase_ )
}
| 713
|
"""simple docstring"""
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowercase ( __UpperCAmelCase , unittest.TestCase ):
_lowerCamelCase = FunnelTokenizer
_lowerCamelCase = FunnelTokenizerFast
_lowerCamelCase = True
_lowerCamelCase = True
def lowerCAmelCase__ ( self ):
super().setUp()
__magic_name__ = [
'''<unk>''',
'''<cls>''',
'''<sep>''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
__magic_name__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def lowerCAmelCase__ ( self , **UpperCamelCase_ ):
return FunnelTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase_ )
def lowerCAmelCase__ ( self , **UpperCamelCase_ ):
return FunnelTokenizerFast.from_pretrained(self.tmpdirname , **UpperCamelCase_ )
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
__magic_name__ = '''UNwant\u00E9d,running'''
__magic_name__ = '''unwanted, running'''
return input_text, output_text
def lowerCAmelCase__ ( self ):
__magic_name__ = self.tokenizer_class(self.vocab_file )
__magic_name__ = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(UpperCamelCase_ , ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_ ) , [7, 4, 5, 10, 8, 9] )
def lowerCAmelCase__ ( self ):
__magic_name__ = self.get_tokenizers(do_lower_case=UpperCamelCase_ )
for tokenizer in tokenizers:
__magic_name__ = tokenizer('''UNwant\u00E9d,running''' )
__magic_name__ = len(inputs['''input_ids'''] ) - 1
self.assertListEqual(inputs['''token_type_ids'''] , [2] + [0] * sentence_len )
__magic_name__ = tokenizer('''UNwant\u00E9d,running''' , '''UNwant\u00E9d,running''' )
self.assertListEqual(inputs['''token_type_ids'''] , [2] + [0] * sentence_len + [1] * sentence_len )
| 190
| 0
|
'''simple docstring'''
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
SeqaSeqDataset,
calculate_bleu,
calculate_rouge,
chunks,
lmap,
load_json,
parse_numeric_n_bool_cl_kwargs,
save_json,
use_task_specific_params,
write_txt_file,
)
lowerCAmelCase : Union[str, Any] =getLogger(__name__)
def UpperCAmelCase_ ( __lowerCamelCase : Union[str, Any] ,__lowerCamelCase : Dict ,__lowerCamelCase : int ,__lowerCamelCase : Union[str, Any] = 8 ,__lowerCamelCase : Union[str, Any] = 10_24 ,__lowerCamelCase : Optional[Any]="val" ,__lowerCamelCase : int=None ,__lowerCamelCase : Dict=False ,__lowerCamelCase : int="summarization" ,__lowerCamelCase : Tuple=None ,__lowerCamelCase : str=1 ,__lowerCamelCase : Dict = None ,__lowerCamelCase : Tuple="" ,**__lowerCamelCase : Union[str, Any] ,):
lowercase_ :Any = str(a__ )
assert local_rank is not None
torch.distributed.init_process_group(backend="nccl" ,rank=a__ )
lowercase_ :Tuple = Path(a__ )
lowercase_ :Optional[Any] = save_dir.joinpath(F'rank_{local_rank}_output.json' )
torch.cuda.set_device(a__ )
lowercase_ :List[str] = AutoModelForSeqaSeqLM.from_pretrained(a__ ).cuda()
if fpaa:
lowercase_ :Optional[Any] = model.half()
# determine if we need to increase num_beams
use_task_specific_params(a__ ,a__ ) # update config with task specific params
lowercase_ :List[str] = generate_kwargs.pop("num_beams" ,model.config.num_beams ) # AttributeError risk?
if num_return_sequences > num_beams:
lowercase_ :Tuple = num_return_sequences
lowercase_ :List[Any] = AutoTokenizer.from_pretrained(a__ )
logger.info(F'Inferred tokenizer type: {tokenizer.__class__}' ) # if this is wrong, check config.model_type.
if max_source_length is None:
lowercase_ :str = tokenizer.model_max_length
if prefix is None:
lowercase_ :str = prefix or getattr(model.config ,"prefix" ,"" ) or """"""
lowercase_ :Optional[int] = SeqaSeqDataset(
a__ ,a__ ,a__ ,max_target_length=10_24 ,type_path=a__ ,n_obs=a__ ,prefix=a__ ,**a__ ,)
# I set shuffle=True for a more accurate progress bar.
# If all the longest samples are first, the prog bar estimate is too high at the beginning.
lowercase_ :Optional[int] = ds.make_sortish_sampler(a__ ,distributed=a__ ,add_extra_examples=a__ ,shuffle=a__ )
lowercase_ :Dict = DataLoader(a__ ,sampler=a__ ,batch_size=a__ ,collate_fn=ds.collate_fn )
lowercase_ :Dict = []
for batch in tqdm(a__ ):
lowercase_ :Any = model.generate(
input_ids=batch["input_ids"].to(model.device ) ,attention_mask=batch["attention_mask"].to(model.device ) ,num_return_sequences=a__ ,num_beams=a__ ,**a__ ,)
lowercase_ :List[str] = tokenizer.batch_decode(a__ ,skip_special_tokens=a__ ,clean_up_tokenization_spaces=a__ )
lowercase_ :Union[str, Any] = batch["""ids"""]
if num_return_sequences > 1:
lowercase_ :int = chunks(a__ ,a__ ) # batch size chunks, each of size num_return_seq
for i, pred in enumerate(a__ ):
results.append({"pred": pred, "id": ids[i].item()} )
save_json(a__ ,a__ )
return results, sampler.num_replicas
def UpperCAmelCase_ ( ):
lowercase_ :int = argparse.ArgumentParser(
epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate" )
parser.add_argument("--data_dir" ,type=a__ ,help="like cnn_dm/test.source" )
parser.add_argument(
"--model_name" ,type=a__ ,help="like facebook/bart-large-cnn,t5-base, etc." ,default="sshleifer/distilbart-xsum-12-3" ,)
parser.add_argument("--save_dir" ,type=a__ ,help="where to save" ,default="tmp_gen" )
parser.add_argument("--max_source_length" ,type=a__ ,default=a__ )
parser.add_argument(
"--type_path" ,type=a__ ,default="test" ,help="which subset to evaluate typically train/val/test" )
parser.add_argument("--task" ,type=a__ ,default="summarization" ,help="used for task_specific_params + metrics" )
parser.add_argument("--bs" ,type=a__ ,default=8 ,required=a__ ,help="batch size" )
parser.add_argument(
"--local_rank" ,type=a__ ,default=-1 ,required=a__ ,help="should be passed by distributed.launch" )
parser.add_argument(
"--n_obs" ,type=a__ ,default=a__ ,required=a__ ,help="How many observations. Defaults to all." )
parser.add_argument(
"--num_return_sequences" ,type=a__ ,default=1 ,required=a__ ,help="How many sequences to return" )
parser.add_argument(
"--sync_timeout" ,type=a__ ,default=6_00 ,required=a__ ,help="How long should master process wait for other processes to finish." ,)
parser.add_argument("--src_lang" ,type=a__ ,default=a__ ,required=a__ )
parser.add_argument("--tgt_lang" ,type=a__ ,default=a__ ,required=a__ )
parser.add_argument(
"--prefix" ,type=a__ ,required=a__ ,default=a__ ,help="will be added to the begininng of src examples" )
parser.add_argument("--fp16" ,action="store_true" )
parser.add_argument("--debug" ,action="store_true" )
lowercase_ :Union[str, Any] = time.time()
lowercase_ :Optional[Any] = parser.parse_known_args()
lowercase_ :int = parse_numeric_n_bool_cl_kwargs(a__ )
if generate_kwargs and args.local_rank <= 0:
print(F'parsed the following generate kwargs: {generate_kwargs}' )
lowercase_ :str = Path(args.save_dir + "_tmp" )
Path(a__ ).mkdir(exist_ok=a__ ) # this handles locking.
lowercase_ :str = list(json_save_dir.glob("rank_*.json" ) )
if intermediate_files:
raise ValueError(F'Found files at {json_save_dir} please move or remove them.' )
# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
lowercase_ :Dict = {}
if args.src_lang is not None:
lowercase_ :Dict = args.src_lang
if args.tgt_lang is not None:
lowercase_ :str = args.tgt_lang
Path(args.save_dir ).mkdir(exist_ok=a__ )
lowercase_ :Tuple = eval_data_dir(
args.data_dir ,a__ ,args.model_name ,type_path=args.type_path ,bs=args.bs ,fpaa=args.fpaa ,task=args.task ,local_rank=args.local_rank ,n_obs=args.n_obs ,max_source_length=args.max_source_length ,num_return_sequences=args.num_return_sequences ,prefix=args.prefix ,dataset_kwargs=a__ ,**a__ ,)
if args.local_rank <= 0:
lowercase_ :List[str] = Path(args.save_dir )
save_dir.mkdir(exist_ok=a__ )
lowercase_ :Tuple = gather_results_from_each_node(a__ ,a__ ,args.sync_timeout )
lowercase_ :str = combine_partial_results(a__ )
if args.num_return_sequences > 1:
lowercase_ :str = save_dir.joinpath("pseudolabel_results.json" )
print(F'Saving aggregated results at {save_path}, intermediate in {json_save_dir}/' )
save_json(a__ ,a__ )
return
lowercase_ :Any = Path(args.data_dir ).joinpath(args.type_path + ".target" )
with open(a__ ) as f:
lowercase_ :Optional[int] = [x.rstrip() for x in f.readlines()][: len(a__ )]
# Calculate metrics, save metrics, and save _generations.txt
lowercase_ :Any = """translation""" in args.task
lowercase_ :Optional[int] = calculate_bleu if calc_bleu else calculate_rouge
lowercase_ :Union[str, Any] = """bleu""" if calc_bleu else """rouge"""
lowercase_ :Dict = score_fn(a__ ,a__ )
lowercase_ :Optional[Any] = len(a__ )
lowercase_ :Tuple = time.time() - start_time
lowercase_ :Optional[int] = round(runtime / metrics["n_obs"] ,4 )
lowercase_ :List[str] = num_replicas
# TODO(@stas00): add whatever metadata to metrics
lowercase_ :int = save_dir.joinpath(F'{args.type_path}_{metric_name}.json' )
save_json(a__ ,a__ ,indent=a__ )
print(a__ )
write_txt_file(a__ ,save_dir.joinpath(F'{args.type_path}_generations.txt' ) )
if args.debug:
write_txt_file(a__ ,save_dir.joinpath(F'{args.type_path}.target' ) )
else:
shutil.rmtree(a__ )
def UpperCAmelCase_ ( __lowerCamelCase : Union[str, Any] ):
lowercase_ :Union[str, Any] = []
for partial_result in partial_results:
records.extend(a__ )
lowercase_ :Any = sorted(a__ ,key=lambda __lowerCamelCase : x["id"] )
lowercase_ :Optional[int] = [x["""pred"""] for x in records]
return preds
def UpperCAmelCase_ ( __lowerCamelCase : List[str] ,__lowerCamelCase : Dict ,__lowerCamelCase : Optional[int] ):
lowercase_ :List[Any] = time.time()
logger.info("waiting for all nodes to finish" )
lowercase_ :Optional[int] = None
while (time.time() - start_wait) < timeout:
lowercase_ :Union[str, Any] = list(save_dir.glob("rank_*.json" ) )
if len(a__ ) < num_replicas:
continue
try:
# make sure all json files are fully saved
lowercase_ :Tuple = lmap(a__ ,a__ )
return json_data
except JSONDecodeError:
continue
else:
raise TimeoutError("Rank 0 gave up on waiting for other processes" )
# Unreachable
if __name__ == "__main__":
# Usage for MT:
run_generate()
| 172
|
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@require_tf
class A__(a_ ):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> List[str]:
a_ : List[Any] = SMALL_MODEL_IDENTIFIER
a_ : Optional[int] = """pt"""
a_ : Union[str, Any] = """tf"""
def UpperCamelCase__ ( self , _lowercase ) -> str:
a_ : Union[str, Any] = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(_lowercase )
def UpperCamelCase__ ( self , _lowercase ) -> List[str]:
a_ : int = TFAutoModel.from_pretrained(self.test_model , from_pt=_lowercase )
model_tf.save_pretrained(_lowercase )
def UpperCamelCase__ ( self ) -> Optional[Any]:
a_ : str = """mock_framework"""
# Framework provided - return whatever the user provides
a_ : Optional[int] = FeaturesManager.determine_framework(self.test_model , _lowercase )
self.assertEqual(_lowercase , _lowercase )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(_lowercase )
a_ : Optional[int] = FeaturesManager.determine_framework(_lowercase , _lowercase )
self.assertEqual(_lowercase , _lowercase )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(_lowercase )
a_ : List[str] = FeaturesManager.determine_framework(_lowercase , _lowercase )
self.assertEqual(_lowercase , _lowercase )
def UpperCamelCase__ ( self ) -> Union[str, Any]:
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(_lowercase )
a_ : Union[str, Any] = FeaturesManager.determine_framework(_lowercase )
self.assertEqual(_lowercase , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(_lowercase )
a_ : Union[str, Any] = FeaturesManager.determine_framework(_lowercase )
self.assertEqual(_lowercase , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(_lowercase ):
a_ : Union[str, Any] = FeaturesManager.determine_framework(_lowercase )
def UpperCamelCase__ ( self ) -> List[Any]:
a_ : int = MagicMock(return_value=_lowercase )
with patch("""transformers.onnx.features.is_tf_available""" , _lowercase ):
a_ : Union[str, Any] = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_lowercase , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
a_ : Dict = MagicMock(return_value=_lowercase )
with patch("""transformers.onnx.features.is_torch_available""" , _lowercase ):
a_ : Union[str, Any] = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_lowercase , self.framework_tf )
# Both in environment -> use PyTorch
a_ : Optional[Any] = MagicMock(return_value=_lowercase )
a_ : List[Any] = MagicMock(return_value=_lowercase )
with patch("""transformers.onnx.features.is_tf_available""" , _lowercase ), patch(
"""transformers.onnx.features.is_torch_available""" , _lowercase ):
a_ : Any = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_lowercase , self.framework_pt )
# Both not in environment -> raise error
a_ : List[str] = MagicMock(return_value=_lowercase )
a_ : Optional[Any] = MagicMock(return_value=_lowercase )
with patch("""transformers.onnx.features.is_tf_available""" , _lowercase ), patch(
"""transformers.onnx.features.is_torch_available""" , _lowercase ):
with self.assertRaises(_lowercase ):
a_ : Dict = FeaturesManager.determine_framework(self.test_model )
| 540
| 0
|
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCAmelCase = [
"word_embeddings_layernorm.weight",
"word_embeddings_layernorm.bias",
"input_layernorm.weight",
"input_layernorm.bias",
"post_attention_layernorm.weight",
"post_attention_layernorm.bias",
"self_attention.dense.bias",
"mlp.dense_4h_to_h.bias",
"ln_f.weight",
"ln_f.bias",
]
_UpperCAmelCase = [
"mlp.dense_4h_to_h.weight",
"self_attention.dense.weight",
]
def _lowerCamelCase ( _a , _a ):
"""simple docstring"""
_lowerCamelCase = {
'''word_embeddings.weight''': '''word_embeddings.weight''',
'''word_embeddings.norm.weight''': '''word_embeddings_layernorm.weight''',
'''word_embeddings.norm.bias''': '''word_embeddings_layernorm.bias''',
'''weight''': '''ln_f.weight''',
'''bias''': '''ln_f.bias''',
}
if key in layer_rename_map:
return layer_rename_map[key]
# Handle transformer blocks
_lowerCamelCase = int(re.match(R'''.*layer_(\d*).*''' , _a )[1] )
layer_number -= 3
return F'''h.{layer_number}.''' + key
def _lowerCamelCase ( _a ):
"""simple docstring"""
if dtype == torch.bool:
return 1 / 8
_lowerCamelCase = re.search(R'''[^\d](\d+)$''' , str(_a ) )
if bit_search is None:
raise ValueError(F'''`dtype` is not a valid dtype: {dtype}.''' )
_lowerCamelCase = int(bit_search.groups()[0] )
return bit_size // 8
def _lowerCamelCase ( _a , _a , _a , _a , _a ):
"""simple docstring"""
if bloom_config_file == "":
_lowerCamelCase = BloomConfig()
else:
_lowerCamelCase = BloomConfig.from_json_file(_a )
if shard_model:
_lowerCamelCase = os.listdir(_a )
_lowerCamelCase = sorted(filter(lambda _a : s.startswith('''layer''' ) and "model_00" in s , _a ) )
_lowerCamelCase = {'''weight_map''': {}, '''metadata''': {}}
_lowerCamelCase = 0
_lowerCamelCase = None
_lowerCamelCase = BloomConfig()
for j, file in enumerate(_a ):
print('''Processing file: {}'''.format(_a ) )
_lowerCamelCase = None
for i in range(_a ):
# load all TP files
_lowerCamelCase = file.replace('''model_00''' , F'''model_0{i}''' )
_lowerCamelCase = torch.load(os.path.join(_a , _a ) , map_location='''cpu''' )
# Rename keys in the transformers names
_lowerCamelCase = list(temp.keys() )
for key in keys:
_lowerCamelCase = temp.pop(_a )
if tensors is None:
_lowerCamelCase = temp
else:
for key in tensors.keys():
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
_lowerCamelCase = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
_lowerCamelCase = torch.cat([tensors[key], temp[key]] , dim=_a )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
_lowerCamelCase = tensors[key] / pretraining_tp
torch.save(
_a , os.path.join(
_a , '''pytorch_model_{}-of-{}.bin'''.format(str(j + 1 ).zfill(5 ) , str(len(_a ) ).zfill(5 ) ) , ) , )
for key in tensors.keys():
_lowerCamelCase = tensors[key]
total_size += value.numel() * get_dtype_size(value.dtype )
if key not in index_dict["weight_map"]:
_lowerCamelCase = '''pytorch_model_{}-of-{}.bin'''.format(
str(j + 1 ).zfill(5 ) , str(len(_a ) ).zfill(5 ) )
_lowerCamelCase = BloomConfig()
_lowerCamelCase = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
_lowerCamelCase = total_size
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
with open(os.path.join(_a , WEIGHTS_NAME + '''.index.json''' ) , '''w''' , encoding='''utf-8''' ) as f:
_lowerCamelCase = json.dumps(_a , indent=2 , sort_keys=_a ) + '''\n'''
f.write(_a )
else:
_lowerCamelCase = BloomModel(_a )
_lowerCamelCase = os.listdir(_a )
_lowerCamelCase = sorted(filter(lambda _a : s.startswith('''layer''' ) and "model_00" in s , _a ) )
_lowerCamelCase = None
for i, file in enumerate(_a ):
_lowerCamelCase = None
for i in range(_a ):
# load all TP files
_lowerCamelCase = file.replace('''model_00''' , F'''model_0{i}''' )
_lowerCamelCase = torch.load(os.path.join(_a , _a ) , map_location='''cpu''' )
# Rename keys in the transformers names
_lowerCamelCase = list(temp.keys() )
for key in keys:
_lowerCamelCase = temp.pop(_a )
if tensors is None:
_lowerCamelCase = temp
else:
for key in tensors.keys():
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
_lowerCamelCase = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
_lowerCamelCase = torch.cat([tensors[key], temp[key]] , dim=_a )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
_lowerCamelCase = tensors[key] / pretraining_tp
_lowerCamelCase = model.load_state_dict(_a , strict=_a )
assert not other_keys.unexpected_keys, F'''The keys {other_keys.unexpected_keys} are unexpected'''
if missing_keys is None:
_lowerCamelCase = set(other_keys.missing_keys )
else:
_lowerCamelCase = missing_keys.intersection(set(other_keys.missing_keys ) )
assert not missing_keys, F'''The keys {missing_keys} are missing'''
# Save pytorch-model
os.makedirs(_a , exist_ok=_a )
_lowerCamelCase = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
_lowerCamelCase = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
print(F'''Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}''' )
if config.torch_dtype is not None:
_lowerCamelCase = model.to(config.torch_dtype )
torch.save(model.state_dict() , _a )
print(F'''Save configuration file to {pytorch_config_dump_path}''' )
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--bloom_checkpoint_path",
default=None,
type=str,
required=True,
help="Path to the Megatron-LM checkpoint path.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--bloom_config_file",
default="",
type=str,
help=(
"An optional config json file corresponding to the pre-trained model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--shard_model",
action="store_true",
help="An optional setting to shard the output model \nThis enables sharding the converted checkpoint",
)
parser.add_argument(
"--pretraining_tp",
default=4,
type=int,
help="Pretraining TP rank that has been used when training the model in Megatron-LM \n",
)
_UpperCAmelCase = parser.parse_args()
convert_bloom_checkpoint_to_pytorch(
args.bloom_checkpoint_path,
args.bloom_config_file,
args.pytorch_dump_folder_path,
args.shard_model,
args.pretraining_tp,
)
| 297
|
import heapq
def _lowerCamelCase ( _a ):
"""simple docstring"""
_lowerCamelCase = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(_a , [-1 * len(_a ), (key, value)] )
# chosen_vertices = set of chosen vertices
_lowerCamelCase = set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
_lowerCamelCase = heapq.heappop(_a )[1][0]
chosen_vertices.add(_a )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
_lowerCamelCase = elem[1][1].index(_a )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(_a )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
_UpperCAmelCase = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(F'Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}')
| 297
| 1
|
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
_UpperCAmelCase = """platform"""
import jax
import jax.numpy as jnp
from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
shift_tokens_right,
)
def __magic_name__ ( lowercase , lowercase , lowercase=None , lowercase=None , lowercase=None , lowercase=None , lowercase=None , lowercase=None , ):
if attention_mask is None:
SCREAMING_SNAKE_CASE_: int =np.where(input_ids != config.pad_token_id , 1 , 0 )
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE_: Optional[int] =np.where(decoder_input_ids != config.pad_token_id , 1 , 0 )
if head_mask is None:
SCREAMING_SNAKE_CASE_: Dict =np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
SCREAMING_SNAKE_CASE_: int =np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
SCREAMING_SNAKE_CASE_: Any =np.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class a :
def __init__( self : List[Any] , lowerCAmelCase : Optional[int] , lowerCAmelCase : Optional[int]=13 , lowerCAmelCase : Dict=7 , lowerCAmelCase : Union[str, Any]=True , lowerCAmelCase : Optional[Any]=False , lowerCAmelCase : str=99 , lowerCAmelCase : int=16 , lowerCAmelCase : Any=2 , lowerCAmelCase : List[str]=4 , lowerCAmelCase : int=4 , lowerCAmelCase : Any="gelu" , lowerCAmelCase : str=0.1 , lowerCAmelCase : Optional[int]=0.1 , lowerCAmelCase : Optional[int]=32 , lowerCAmelCase : int=2 , lowerCAmelCase : Tuple=1 , lowerCAmelCase : Optional[int]=0 , lowerCAmelCase : str=0.0_2 , ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: str =parent
SCREAMING_SNAKE_CASE_: List[Any] =batch_size
SCREAMING_SNAKE_CASE_: Any =seq_length
SCREAMING_SNAKE_CASE_: str =is_training
SCREAMING_SNAKE_CASE_: Dict =use_labels
SCREAMING_SNAKE_CASE_: Optional[Any] =vocab_size
SCREAMING_SNAKE_CASE_: List[str] =hidden_size
SCREAMING_SNAKE_CASE_: Dict =num_hidden_layers
SCREAMING_SNAKE_CASE_: Any =num_attention_heads
SCREAMING_SNAKE_CASE_: Dict =intermediate_size
SCREAMING_SNAKE_CASE_: List[Any] =hidden_act
SCREAMING_SNAKE_CASE_: List[str] =hidden_dropout_prob
SCREAMING_SNAKE_CASE_: Any =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_: List[str] =max_position_embeddings
SCREAMING_SNAKE_CASE_: Union[str, Any] =eos_token_id
SCREAMING_SNAKE_CASE_: List[str] =pad_token_id
SCREAMING_SNAKE_CASE_: Optional[Any] =bos_token_id
SCREAMING_SNAKE_CASE_: Tuple =initializer_range
def lowerCamelCase__ ( self : Any ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: int =np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) , 3 , self.vocab_size )
SCREAMING_SNAKE_CASE_: Any =np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa )) , -1 )
SCREAMING_SNAKE_CASE_: List[str] =shift_tokens_right(lowerCAmelCase , 1 , 2 )
SCREAMING_SNAKE_CASE_: Union[str, Any] =BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=lowerCAmelCase , )
SCREAMING_SNAKE_CASE_: List[str] =prepare_blenderbot_inputs_dict(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
return config, inputs_dict
def lowerCamelCase__ ( self : int ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: List[Any] =self.prepare_config_and_inputs()
return config, inputs_dict
def lowerCamelCase__ ( self : Optional[int] , lowerCAmelCase : Dict , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : Tuple ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: List[Any] =20
SCREAMING_SNAKE_CASE_: int =model_class_name(lowerCAmelCase )
SCREAMING_SNAKE_CASE_: int =model.encode(inputs_dict["""input_ids"""] )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Any =(
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
SCREAMING_SNAKE_CASE_: Dict =model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase , lowerCAmelCase )
SCREAMING_SNAKE_CASE_: int =jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="""i4""" )
SCREAMING_SNAKE_CASE_: Union[str, Any] =jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
SCREAMING_SNAKE_CASE_: Optional[int] =model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase , decoder_attention_mask=lowerCAmelCase , past_key_values=lowerCAmelCase , decoder_position_ids=lowerCAmelCase , )
SCREAMING_SNAKE_CASE_: Union[str, Any] =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""" )
SCREAMING_SNAKE_CASE_: Optional[Any] =model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase , decoder_attention_mask=lowerCAmelCase , past_key_values=outputs_cache.past_key_values , decoder_position_ids=lowerCAmelCase , )
SCREAMING_SNAKE_CASE_: Any =model.decode(lowerCAmelCase , lowerCAmelCase )
SCREAMING_SNAKE_CASE_: Tuple =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 lowerCamelCase__ ( self : str , lowerCAmelCase : Optional[int] , lowerCAmelCase : int , lowerCAmelCase : Tuple ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: Union[str, Any] =20
SCREAMING_SNAKE_CASE_: Union[str, Any] =model_class_name(lowerCAmelCase )
SCREAMING_SNAKE_CASE_: str =model.encode(inputs_dict["""input_ids"""] )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Dict =(
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
SCREAMING_SNAKE_CASE_: int =jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
SCREAMING_SNAKE_CASE_: Optional[Any] =model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase , lowerCAmelCase )
SCREAMING_SNAKE_CASE_: Optional[Any] =jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
SCREAMING_SNAKE_CASE_: Dict =model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase , decoder_attention_mask=lowerCAmelCase , past_key_values=lowerCAmelCase , decoder_position_ids=lowerCAmelCase , )
SCREAMING_SNAKE_CASE_: List[Any] =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""" )
SCREAMING_SNAKE_CASE_: Optional[Any] =model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=lowerCAmelCase , decoder_position_ids=lowerCAmelCase , )
SCREAMING_SNAKE_CASE_: Union[str, Any] =model.decode(lowerCAmelCase , lowerCAmelCase , decoder_attention_mask=lowerCAmelCase )
SCREAMING_SNAKE_CASE_: List[str] =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 a ( unittest.TestCase ):
UpperCamelCase : Any = 9_9
def lowerCamelCase__ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: List[str] =np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
] , dtype=np.intaa , )
SCREAMING_SNAKE_CASE_: Dict =input_ids.shape[0]
SCREAMING_SNAKE_CASE_: int =BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def lowerCamelCase__ ( self : List[str] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Dict =self._get_config_and_data()
SCREAMING_SNAKE_CASE_: Union[str, Any] =FlaxBlenderbotSmallForConditionalGeneration(lowerCAmelCase )
SCREAMING_SNAKE_CASE_: List[Any] =lm_model(input_ids=lowerCAmelCase )
SCREAMING_SNAKE_CASE_: int =(batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs["""logits"""].shape , lowerCAmelCase )
def lowerCamelCase__ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: Any =BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , )
SCREAMING_SNAKE_CASE_: Optional[int] =FlaxBlenderbotSmallForConditionalGeneration(lowerCAmelCase )
SCREAMING_SNAKE_CASE_: List[str] =np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa )
SCREAMING_SNAKE_CASE_: List[Any] =np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa )
SCREAMING_SNAKE_CASE_: Optional[int] =lm_model(input_ids=lowerCAmelCase , decoder_input_ids=lowerCAmelCase )
SCREAMING_SNAKE_CASE_: Optional[Any] =(*summary.shape, config.vocab_size)
self.assertEqual(outputs["""logits"""].shape , lowerCAmelCase )
def lowerCamelCase__ ( self : Any ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: List[Any] =np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa )
SCREAMING_SNAKE_CASE_: List[str] =shift_tokens_right(lowerCAmelCase , 1 , 2 )
SCREAMING_SNAKE_CASE_: int =np.equal(lowerCAmelCase , 1 ).astype(np.floataa ).sum()
SCREAMING_SNAKE_CASE_: Union[str, Any] =np.equal(lowerCAmelCase , 1 ).astype(np.floataa ).sum()
self.assertEqual(shifted.shape , input_ids.shape )
self.assertEqual(lowerCAmelCase , n_pad_before - 1 )
self.assertTrue(np.equal(shifted[:, 0] , 2 ).all() )
@require_flax
class a ( UpperCAmelCase__ , unittest.TestCase , UpperCAmelCase__ ):
UpperCamelCase : Tuple = True
UpperCamelCase : int = (
(
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallForConditionalGeneration,
)
if is_flax_available()
else ()
)
UpperCamelCase : Union[str, Any] = (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()
def lowerCamelCase__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: Optional[int] =FlaxBlenderbotSmallModelTester(self )
def lowerCamelCase__ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[int] =self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
def lowerCamelCase__ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: List[str] =self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
def lowerCamelCase__ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
SCREAMING_SNAKE_CASE_: Optional[Any] =self._prepare_for_class(lowerCAmelCase , lowerCAmelCase )
SCREAMING_SNAKE_CASE_: Tuple =model_class(lowerCAmelCase )
@jax.jit
def encode_jitted(lowerCAmelCase : Any , lowerCAmelCase : Any=None , **lowerCAmelCase : Dict ):
return model.encode(input_ids=lowerCAmelCase , attention_mask=lowerCAmelCase )
with self.subTest("""JIT Enabled""" ):
SCREAMING_SNAKE_CASE_: Tuple =encode_jitted(**lowerCAmelCase ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
SCREAMING_SNAKE_CASE_: Union[str, Any] =encode_jitted(**lowerCAmelCase ).to_tuple()
self.assertEqual(len(lowerCAmelCase ) , len(lowerCAmelCase ) )
for jitted_output, output in zip(lowerCAmelCase , lowerCAmelCase ):
self.assertEqual(jitted_output.shape , output.shape )
def lowerCamelCase__ ( self : Dict ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: str =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
SCREAMING_SNAKE_CASE_: str =model_class(lowerCAmelCase )
SCREAMING_SNAKE_CASE_: Optional[int] =model.encode(inputs_dict["""input_ids"""] , inputs_dict["""attention_mask"""] )
SCREAMING_SNAKE_CASE_: List[str] ={
"""decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
"""decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
"""encoder_outputs""": encoder_outputs,
}
@jax.jit
def decode_jitted(lowerCAmelCase : Dict , lowerCAmelCase : str , lowerCAmelCase : Optional[Any] ):
return model.decode(
decoder_input_ids=lowerCAmelCase , decoder_attention_mask=lowerCAmelCase , encoder_outputs=lowerCAmelCase , )
with self.subTest("""JIT Enabled""" ):
SCREAMING_SNAKE_CASE_: List[str] =decode_jitted(**lowerCAmelCase ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
SCREAMING_SNAKE_CASE_: str =decode_jitted(**lowerCAmelCase ).to_tuple()
self.assertEqual(len(lowerCAmelCase ) , len(lowerCAmelCase ) )
for jitted_output, output in zip(lowerCAmelCase , lowerCAmelCase ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def lowerCamelCase__ ( self : str ) -> List[str]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE_: List[str] =model_class_name.from_pretrained("""facebook/blenderbot_small-90M""" )
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
SCREAMING_SNAKE_CASE_: Union[str, Any] =np.ones((1, 1) ) * model.config.eos_token_id
SCREAMING_SNAKE_CASE_: List[Any] =model(lowerCAmelCase )
self.assertIsNotNone(lowerCAmelCase )
| 409
|
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class a ( UpperCAmelCase__ ):
UpperCamelCase : Any = 'Speech2TextFeatureExtractor'
UpperCamelCase : Optional[Any] = 'Speech2TextTokenizer'
def __init__( self : Optional[Any] , lowerCAmelCase : List[str] , lowerCAmelCase : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(lowerCAmelCase , lowerCAmelCase )
SCREAMING_SNAKE_CASE_: Optional[int] =self.feature_extractor
SCREAMING_SNAKE_CASE_: List[Any] =False
def __call__( self : Dict , *lowerCAmelCase : str , **lowerCAmelCase : str ) -> str:
'''simple docstring'''
if self._in_target_context_manager:
return self.current_processor(*lowerCAmelCase , **lowerCAmelCase )
if "raw_speech" in kwargs:
warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.""" )
SCREAMING_SNAKE_CASE_: Tuple =kwargs.pop("""raw_speech""" )
else:
SCREAMING_SNAKE_CASE_: int =kwargs.pop("""audio""" , lowerCAmelCase )
SCREAMING_SNAKE_CASE_: Optional[int] =kwargs.pop("""sampling_rate""" , lowerCAmelCase )
SCREAMING_SNAKE_CASE_: Tuple =kwargs.pop("""text""" , lowerCAmelCase )
if len(lowerCAmelCase ) > 0:
SCREAMING_SNAKE_CASE_: List[str] =args[0]
SCREAMING_SNAKE_CASE_: List[str] =args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if audio is not None:
SCREAMING_SNAKE_CASE_: Optional[int] =self.feature_extractor(lowerCAmelCase , *lowerCAmelCase , sampling_rate=lowerCAmelCase , **lowerCAmelCase )
if text is not None:
SCREAMING_SNAKE_CASE_: Union[str, Any] =self.tokenizer(lowerCAmelCase , **lowerCAmelCase )
if text is None:
return inputs
elif audio is None:
return encodings
else:
SCREAMING_SNAKE_CASE_: Any =encodings["""input_ids"""]
return inputs
def lowerCamelCase__ ( self : Any , *lowerCAmelCase : Any , **lowerCAmelCase : Tuple ) -> Any:
'''simple docstring'''
return self.tokenizer.batch_decode(*lowerCAmelCase , **lowerCAmelCase )
def lowerCamelCase__ ( self : Tuple , *lowerCAmelCase : Any , **lowerCAmelCase : int ) -> Any:
'''simple docstring'''
return self.tokenizer.decode(*lowerCAmelCase , **lowerCAmelCase )
@contextmanager
def lowerCamelCase__ ( self : Tuple ) -> Tuple:
'''simple docstring'''
warnings.warn(
"""`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
"""labels by using the argument `text` of the regular `__call__` method (either in the same call as """
"""your audio inputs, or in a separate call.""" )
SCREAMING_SNAKE_CASE_: Optional[Any] =True
SCREAMING_SNAKE_CASE_: Dict =self.tokenizer
yield
SCREAMING_SNAKE_CASE_: int =self.feature_extractor
SCREAMING_SNAKE_CASE_: str =False
| 409
| 1
|
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCamelCase :Dict = get_tests_dir('''fixtures/spiece.model''')
@require_sentencepiece
@require_tokenizers
class _lowerCAmelCase ( __UpperCAmelCase , unittest.TestCase ):
__SCREAMING_SNAKE_CASE : Any = AlbertTokenizer
__SCREAMING_SNAKE_CASE : Tuple = AlbertTokenizerFast
__SCREAMING_SNAKE_CASE : int = True
__SCREAMING_SNAKE_CASE : str = True
__SCREAMING_SNAKE_CASE : Union[str, Any] = True
def _a (self ):
super().setUp()
# We have a SentencePiece fixture for testing
A_ : Optional[int] = AlbertTokenizer(lowercase )
tokenizer.save_pretrained(self.tmpdirname )
def _a (self , lowercase ):
A_ : Dict = """this is a test"""
A_ : Optional[int] = """this is a test"""
return input_text, output_text
def _a (self ):
A_ : Optional[int] = """<pad>"""
A_ : int = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase ) , lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase ) , lowercase )
def _a (self ):
A_ : Optional[Any] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<pad>""" )
self.assertEqual(vocab_keys[1] , """<unk>""" )
self.assertEqual(vocab_keys[-1] , """▁eloquent""" )
self.assertEqual(len(lowercase ) , 30000 )
def _a (self ):
self.assertEqual(self.get_tokenizer().vocab_size , 30000 )
def _a (self ):
if not self.test_rust_tokenizer:
return
A_ : Tuple = self.get_tokenizer()
A_ : Optional[int] = self.get_rust_tokenizer()
A_ : Tuple = """I was born in 92000, and this is falsé."""
A_ : Tuple = tokenizer.tokenize(lowercase )
A_ : str = rust_tokenizer.tokenize(lowercase )
self.assertListEqual(lowercase , lowercase )
A_ : Tuple = tokenizer.encode(lowercase , add_special_tokens=lowercase )
A_ : Optional[Any] = rust_tokenizer.encode(lowercase , add_special_tokens=lowercase )
self.assertListEqual(lowercase , lowercase )
A_ : Optional[Any] = self.get_rust_tokenizer()
A_ : Optional[int] = tokenizer.encode(lowercase )
A_ : List[Any] = rust_tokenizer.encode(lowercase )
self.assertListEqual(lowercase , lowercase )
def _a (self ):
A_ : Tuple = AlbertTokenizer(lowercase , keep_accents=lowercase )
A_ : int = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(lowercase , ["""▁this""", """▁is""", """▁a""", """▁test"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase ) , [48, 25, 21, 1289] )
A_ : List[str] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
lowercase , ["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""] )
A_ : Any = tokenizer.convert_tokens_to_ids(lowercase )
self.assertListEqual(lowercase , [31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9] )
A_ : Optional[Any] = tokenizer.convert_ids_to_tokens(lowercase )
self.assertListEqual(
lowercase , ["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""] , )
def _a (self ):
A_ : int = AlbertTokenizer(lowercase )
A_ : Optional[int] = tokenizer.encode("""sequence builders""" )
A_ : Optional[int] = tokenizer.encode("""multi-sequence build""" )
A_ : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(lowercase )
A_ : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(lowercase , lowercase )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
@slow
def _a (self ):
A_ : int = {"""attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """input_ids""": [[2, 21970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 12051, 18, 17, 7103, 2153, 673, 8, 3515, 18684, 8, 4461, 6, 1927, 297, 8, 12060, 2607, 18, 13, 5, 4461, 15, 10538, 38, 8, 135, 15, 822, 58, 15, 993, 10363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 10641, 6, 29, 84, 2512, 2430, 782, 18684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 11712, 15, 7103, 2153, 673, 17, 24883, 9990, 9, 3], [2, 11502, 25, 1006, 20, 782, 8, 11809, 855, 1732, 19393, 18667, 37, 367, 21018, 69, 1854, 34, 11860, 19124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2231, 886, 2385, 17659, 84, 14, 16792, 1952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowercase , model_name="""albert-base-v2""" , revision="""6b6560eaf5ff2e250b00c50f380c5389a9c2d82e""" , )
| 716
|
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def a ( ):
'''simple docstring'''
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(lowerCamelCase__ ):
requests.request("""GET""" , """https://huggingface.co""" )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request("""GET""" , """https://huggingface.co""" , timeout=1.0 )
@pytest.mark.integration
def a ( ):
'''simple docstring'''
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request("""GET""" , """https://huggingface.co""" )
def a ( ):
'''simple docstring'''
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(lowerCamelCase__ ):
http_head("""https://huggingface.co""" )
| 686
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase_ : Union[str, Any] = {
'''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
'''tokenization_tapas''': ['''TapasTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : Optional[int] = [
'''TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TapasForMaskedLM''',
'''TapasForQuestionAnswering''',
'''TapasForSequenceClassification''',
'''TapasModel''',
'''TapasPreTrainedModel''',
'''load_tf_weights_in_tapas''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : Dict = [
'''TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFTapasForMaskedLM''',
'''TFTapasForQuestionAnswering''',
'''TFTapasForSequenceClassification''',
'''TFTapasModel''',
'''TFTapasPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
from .tokenization_tapas import TapasTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tapas import (
TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasPreTrainedModel,
load_tf_weights_in_tapas,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_tapas import (
TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTapasForMaskedLM,
TFTapasForQuestionAnswering,
TFTapasForSequenceClassification,
TFTapasModel,
TFTapasPreTrainedModel,
)
else:
import sys
lowercase_ : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 572
|
"""simple docstring"""
def _lowerCAmelCase ( ) -> int:
return [
a * b * (1_0_0_0 - a - b)
for a in range(1, 9_9_9 )
for b in range(lowerCamelCase__, 9_9_9 )
if (a * a + b * b == (1_0_0_0 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F'{solution() = }')
| 572
| 1
|
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
lowerCamelCase__ = {
'''linear''': PIL.Image.Resampling.BILINEAR,
'''bilinear''': PIL.Image.Resampling.BILINEAR,
'''bicubic''': PIL.Image.Resampling.BICUBIC,
'''lanczos''': PIL.Image.Resampling.LANCZOS,
'''nearest''': PIL.Image.Resampling.NEAREST,
}
else:
lowerCamelCase__ = {
'''linear''': PIL.Image.LINEAR,
'''bilinear''': PIL.Image.BILINEAR,
'''bicubic''': PIL.Image.BICUBIC,
'''lanczos''': PIL.Image.LANCZOS,
'''nearest''': PIL.Image.NEAREST,
}
def lowercase_ ( SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
snake_case__ : List[str] =(images / 2 + 0.5).clamp(0 , 1 )
snake_case__ : str =images.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
snake_case__ : Optional[int] =numpy_to_pil(snake_case__ )
return images
def lowercase_ ( SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
if images.ndim == 3:
snake_case__ : Any =images[None, ...]
snake_case__ : List[str] =(images * 2_55).round().astype('''uint8''' )
if images.shape[-1] == 1:
# special case for grayscale (single channel) images
snake_case__ : Optional[int] =[Image.fromarray(image.squeeze() , mode='''L''' ) for image in images]
else:
snake_case__ : List[Any] =[Image.fromarray(snake_case__ ) for image in images]
return pil_images
| 700
|
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 ( __UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ =42
lowerCAmelCase__ =jnp.floataa
lowerCAmelCase__ =True
def UpperCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
super().setup()
snake_case__ : int =nn.Dense(5 , dtype=self.dtype )
def __call__( self , *__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
snake_case__ : int =super().__call__(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
snake_case__ : Union[str, Any] =self.cls(outputs[2] )
return outputs[:2] + (cls_out,)
class _lowerCAmelCase ( __UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ =FlaxBigBirdForNaturalQuestionsModule
def lowercase_ ( SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
def cross_entropy(SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Optional[Any]=None ):
snake_case__ : Optional[Any] =logits.shape[-1]
snake_case__ : List[str] =(labels[..., None] == jnp.arange(SCREAMING_SNAKE_CASE )[None]).astype('''f4''' )
snake_case__ : str =jax.nn.log_softmax(SCREAMING_SNAKE_CASE , axis=-1 )
snake_case__ : Any =-jnp.sum(labels * logits , axis=-1 )
if reduction is not None:
snake_case__ : Tuple =reduction(SCREAMING_SNAKE_CASE )
return loss
snake_case__ : List[Any] =partial(SCREAMING_SNAKE_CASE , reduction=jnp.mean )
snake_case__ : Optional[int] =cross_entropy(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
snake_case__ : Any =cross_entropy(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
snake_case__ : int =cross_entropy(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class _lowerCAmelCase :
"""simple docstring"""
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__ =0.0_0_9_5
lowerCAmelCase__ ="bigbird-roberta-natural-questions"
lowerCAmelCase__ ="training-expt"
lowerCAmelCase__ ="data/nq-training.jsonl"
lowerCAmelCase__ ="data/nq-validation.jsonl"
def UpperCAmelCase ( self ) -> Any:
"""simple docstring"""
os.makedirs(self.base_dir , exist_ok=__SCREAMING_SNAKE_CASE )
snake_case__ : int =os.path.join(self.base_dir , self.save_dir )
snake_case__ : Union[str, Any] =self.batch_size_per_device * jax.device_count()
@dataclass
class _lowerCAmelCase :
"""simple docstring"""
lowerCAmelCase__ =42
lowerCAmelCase__ =4_096 # no dynamic padding on TPUs
def __call__( self , __SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : Dict =self.collate_fn(__SCREAMING_SNAKE_CASE )
snake_case__ : str =jax.tree_util.tree_map(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return batch
def UpperCAmelCase ( self , __SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
snake_case__, snake_case__ : Tuple =self.fetch_inputs(features['''input_ids'''] )
snake_case__ : str ={
'''input_ids''': jnp.array(__SCREAMING_SNAKE_CASE , dtype=jnp.intaa ),
'''attention_mask''': jnp.array(__SCREAMING_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 UpperCAmelCase ( self , __SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
snake_case__ : List[Any] =[self._fetch_inputs(__SCREAMING_SNAKE_CASE ) for ids in input_ids]
return zip(*__SCREAMING_SNAKE_CASE )
def UpperCAmelCase ( self , __SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : Optional[Any] =[1 for _ in range(len(__SCREAMING_SNAKE_CASE ) )]
while len(__SCREAMING_SNAKE_CASE ) < self.max_length:
input_ids.append(self.pad_id )
attention_mask.append(0 )
return input_ids, attention_mask
def lowercase_ ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Optional[int]=None ):
"""simple docstring"""
if seed is not None:
snake_case__ : Union[str, Any] =dataset.shuffle(seed=SCREAMING_SNAKE_CASE )
for i in range(len(SCREAMING_SNAKE_CASE ) // batch_size ):
snake_case__ : Any =dataset[i * batch_size : (i + 1) * batch_size]
yield dict(SCREAMING_SNAKE_CASE )
@partial(jax.pmap , axis_name='''batch''' )
def lowercase_ ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Union[str, Any] , **SCREAMING_SNAKE_CASE : Optional[Any] ):
"""simple docstring"""
def loss_fn(SCREAMING_SNAKE_CASE : int ):
snake_case__ : Any =model_inputs.pop('''start_labels''' )
snake_case__ : List[Any] =model_inputs.pop('''end_labels''' )
snake_case__ : List[Any] =model_inputs.pop('''pooled_labels''' )
snake_case__ : str =state.apply_fn(**SCREAMING_SNAKE_CASE , params=SCREAMING_SNAKE_CASE , dropout_rng=SCREAMING_SNAKE_CASE , train=SCREAMING_SNAKE_CASE )
snake_case__, snake_case__, snake_case__ : int =outputs
return state.loss_fn(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , )
snake_case__, snake_case__ : Optional[Any] =jax.random.split(SCREAMING_SNAKE_CASE )
snake_case__ : int =jax.value_and_grad(SCREAMING_SNAKE_CASE )
snake_case__, snake_case__ : int =grad_fn(state.params )
snake_case__ : List[str] =jax.lax.pmean({'''loss''': loss} , axis_name='''batch''' )
snake_case__ : List[str] =jax.lax.pmean(SCREAMING_SNAKE_CASE , '''batch''' )
snake_case__ : Optional[Any] =state.apply_gradients(grads=SCREAMING_SNAKE_CASE )
return state, metrics, new_drp_rng
@partial(jax.pmap , axis_name='''batch''' )
def lowercase_ ( SCREAMING_SNAKE_CASE : Optional[Any] , **SCREAMING_SNAKE_CASE : List[str] ):
"""simple docstring"""
snake_case__ : int =model_inputs.pop('''start_labels''' )
snake_case__ : Tuple =model_inputs.pop('''end_labels''' )
snake_case__ : Optional[int] =model_inputs.pop('''pooled_labels''' )
snake_case__ : Any =state.apply_fn(**SCREAMING_SNAKE_CASE , params=state.params , train=SCREAMING_SNAKE_CASE )
snake_case__, snake_case__, snake_case__ : Optional[Any] =outputs
snake_case__ : Optional[int] =state.loss_fn(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
snake_case__ : int =jax.lax.pmean({'''loss''': loss} , axis_name='''batch''' )
return metrics
class _lowerCAmelCase ( train_state.TrainState ):
"""simple docstring"""
lowerCAmelCase__ =struct.field(pytree_node=__UpperCamelCase )
@dataclass
class _lowerCAmelCase :
"""simple docstring"""
lowerCAmelCase__ =42
lowerCAmelCase__ =42
lowerCAmelCase__ =42
lowerCAmelCase__ =42
lowerCAmelCase__ =42
lowerCAmelCase__ =42
lowerCAmelCase__ =None
def UpperCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=None ) -> Tuple:
"""simple docstring"""
snake_case__ : List[Any] =model.params
snake_case__ : str =TrainState.create(
apply_fn=model.__call__ , params=__SCREAMING_SNAKE_CASE , tx=__SCREAMING_SNAKE_CASE , loss_fn=__SCREAMING_SNAKE_CASE , )
if ckpt_dir is not None:
snake_case__, snake_case__, snake_case__, snake_case__, snake_case__ : int =restore_checkpoint(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
snake_case__ : Any ={
'''lr''': args.lr,
'''init_lr''': args.init_lr,
'''warmup_steps''': args.warmup_steps,
'''num_train_steps''': num_train_steps,
'''weight_decay''': args.weight_decay,
}
snake_case__, snake_case__ : Union[str, Any] =build_tx(**__SCREAMING_SNAKE_CASE )
snake_case__ : str =train_state.TrainState(
step=__SCREAMING_SNAKE_CASE , apply_fn=model.__call__ , params=__SCREAMING_SNAKE_CASE , tx=__SCREAMING_SNAKE_CASE , opt_state=__SCREAMING_SNAKE_CASE , )
snake_case__ : Optional[int] =args
snake_case__ : Union[str, Any] =data_collator
snake_case__ : Any =lr
snake_case__ : Tuple =params
snake_case__ : Tuple =jax_utils.replicate(__SCREAMING_SNAKE_CASE )
return state
def UpperCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
snake_case__ : Union[str, Any] =self.args
snake_case__ : List[str] =len(__SCREAMING_SNAKE_CASE ) // args.batch_size
snake_case__ : str =jax.random.PRNGKey(0 )
snake_case__ : List[Any] =jax.random.split(__SCREAMING_SNAKE_CASE , jax.device_count() )
for epoch in range(args.max_epochs ):
snake_case__ : Optional[Any] =jnp.array(0 , dtype=jnp.floataa )
snake_case__ : int =get_batched_dataset(__SCREAMING_SNAKE_CASE , args.batch_size , seed=__SCREAMING_SNAKE_CASE )
snake_case__ : Any =0
for batch in tqdm(__SCREAMING_SNAKE_CASE , total=__SCREAMING_SNAKE_CASE , desc=f'''Running EPOCH-{epoch}''' ):
snake_case__ : Tuple =self.data_collator(__SCREAMING_SNAKE_CASE )
snake_case__, snake_case__, snake_case__ : int =self.train_step_fn(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
running_loss += jax_utils.unreplicate(metrics['''loss'''] )
i += 1
if i % args.logging_steps == 0:
snake_case__ : List[str] =jax_utils.unreplicate(state.step )
snake_case__ : Optional[int] =running_loss.item() / i
snake_case__ : Dict =self.scheduler_fn(state_step - 1 )
snake_case__ : Optional[int] =self.evaluate(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
snake_case__ : Optional[int] ={
'''step''': state_step.item(),
'''eval_loss''': eval_loss.item(),
'''tr_loss''': tr_loss,
'''lr''': lr.item(),
}
tqdm.write(str(__SCREAMING_SNAKE_CASE ) )
self.logger.log(__SCREAMING_SNAKE_CASE , commit=__SCREAMING_SNAKE_CASE )
if i % args.save_steps == 0:
self.save_checkpoint(args.save_dir + f'''-e{epoch}-s{i}''' , state=__SCREAMING_SNAKE_CASE )
def UpperCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
snake_case__ : int =get_batched_dataset(__SCREAMING_SNAKE_CASE , self.args.batch_size )
snake_case__ : Any =len(__SCREAMING_SNAKE_CASE ) // self.args.batch_size
snake_case__ : List[Any] =jnp.array(0 , dtype=jnp.floataa )
snake_case__ : Optional[Any] =0
for batch in tqdm(__SCREAMING_SNAKE_CASE , total=__SCREAMING_SNAKE_CASE , desc='''Evaluating ... ''' ):
snake_case__ : Optional[int] =self.data_collator(__SCREAMING_SNAKE_CASE )
snake_case__ : Optional[int] =self.val_step_fn(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
running_loss += jax_utils.unreplicate(metrics['''loss'''] )
i += 1
return running_loss / i
def UpperCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Dict:
"""simple docstring"""
snake_case__ : Tuple =jax_utils.unreplicate(__SCREAMING_SNAKE_CASE )
print(f'''SAVING CHECKPOINT IN {save_dir}''' , end=''' ... ''' )
self.model_save_fn(__SCREAMING_SNAKE_CASE , params=state.params )
with open(os.path.join(__SCREAMING_SNAKE_CASE , '''opt_state.msgpack''' ) , '''wb''' ) as f:
f.write(to_bytes(state.opt_state ) )
joblib.dump(self.args , os.path.join(__SCREAMING_SNAKE_CASE , '''args.joblib''' ) )
joblib.dump(self.data_collator , os.path.join(__SCREAMING_SNAKE_CASE , '''data_collator.joblib''' ) )
with open(os.path.join(__SCREAMING_SNAKE_CASE , '''training_state.json''' ) , '''w''' ) as f:
json.dump({'''step''': state.step.item()} , __SCREAMING_SNAKE_CASE )
print('''DONE''' )
def lowercase_ ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
print(F'''RESTORING CHECKPOINT FROM {save_dir}''' , end=''' ... ''' )
with open(os.path.join(SCREAMING_SNAKE_CASE , '''flax_model.msgpack''' ) , '''rb''' ) as f:
snake_case__ : Optional[Any] =from_bytes(state.params , f.read() )
with open(os.path.join(SCREAMING_SNAKE_CASE , '''opt_state.msgpack''' ) , '''rb''' ) as f:
snake_case__ : Optional[Any] =from_bytes(state.opt_state , f.read() )
snake_case__ : Dict =joblib.load(os.path.join(SCREAMING_SNAKE_CASE , '''args.joblib''' ) )
snake_case__ : Union[str, Any] =joblib.load(os.path.join(SCREAMING_SNAKE_CASE , '''data_collator.joblib''' ) )
with open(os.path.join(SCREAMING_SNAKE_CASE , '''training_state.json''' ) , '''r''' ) as f:
snake_case__ : Tuple =json.load(SCREAMING_SNAKE_CASE )
snake_case__ : Tuple =training_state['''step''']
print('''DONE''' )
return params, opt_state, step, args, data_collator
def lowercase_ ( SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Optional[Any] ):
"""simple docstring"""
snake_case__ : Tuple =num_train_steps - warmup_steps
snake_case__ : Union[str, Any] =optax.linear_schedule(init_value=SCREAMING_SNAKE_CASE , end_value=SCREAMING_SNAKE_CASE , transition_steps=SCREAMING_SNAKE_CASE )
snake_case__ : Dict =optax.linear_schedule(init_value=SCREAMING_SNAKE_CASE , end_value=1E-7 , transition_steps=SCREAMING_SNAKE_CASE )
snake_case__ : int =optax.join_schedules(schedules=[warmup_fn, decay_fn] , boundaries=[warmup_steps] )
return lr
def lowercase_ ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
def weight_decay_mask(SCREAMING_SNAKE_CASE : Tuple ):
snake_case__ : Any =traverse_util.flatten_dict(SCREAMING_SNAKE_CASE )
snake_case__ : Optional[int] ={k: (v[-1] != '''bias''' and v[-2:] != ('''LayerNorm''', '''scale''')) for k, v in params.items()}
return traverse_util.unflatten_dict(SCREAMING_SNAKE_CASE )
snake_case__ : Any =scheduler_fn(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
snake_case__ : List[str] =optax.adamw(learning_rate=SCREAMING_SNAKE_CASE , weight_decay=SCREAMING_SNAKE_CASE , mask=SCREAMING_SNAKE_CASE )
return tx, lr
| 408
| 0
|
def _a ( __lowercase ) -> bool:
"""simple docstring"""
__UpperCamelCase = 0
for ch in input_str:
__UpperCamelCase = ord(__lowercase )
__UpperCamelCase = pow(2 , __lowercase )
# If we already turned on bit for current character's unicode
if bitmap >> ch_unicode & 1 == 1:
return False
bitmap |= ch_bit_index_on
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 383
|
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
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 TFViTForImageClassification, TFViTModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowerCAmelCase_ :
"""simple docstring"""
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=30 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=37 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=10 , _SCREAMING_SNAKE_CASE=0.0_2 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=None , ) -> Any:
__UpperCamelCase = parent
__UpperCamelCase = batch_size
__UpperCamelCase = image_size
__UpperCamelCase = patch_size
__UpperCamelCase = num_channels
__UpperCamelCase = is_training
__UpperCamelCase = use_labels
__UpperCamelCase = hidden_size
__UpperCamelCase = num_hidden_layers
__UpperCamelCase = num_attention_heads
__UpperCamelCase = intermediate_size
__UpperCamelCase = hidden_act
__UpperCamelCase = hidden_dropout_prob
__UpperCamelCase = attention_probs_dropout_prob
__UpperCamelCase = type_sequence_label_size
__UpperCamelCase = initializer_range
__UpperCamelCase = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
__UpperCamelCase = (image_size // patch_size) ** 2
__UpperCamelCase = num_patches + 1
def __lowercase( self ) -> Optional[Any]:
__UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__UpperCamelCase = None
if self.use_labels:
__UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCamelCase = self.get_config()
return config, pixel_values, labels
def __lowercase( self ) -> str:
return ViTConfig(
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=_SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , )
def __lowercase( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
__UpperCamelCase = TFViTModel(config=_SCREAMING_SNAKE_CASE )
__UpperCamelCase = model(_SCREAMING_SNAKE_CASE , training=_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# Test with an image with different size than the one specified in config.
__UpperCamelCase = self.image_size // 2
__UpperCamelCase = pixel_values[:, :, :image_size, :image_size]
__UpperCamelCase = model(_SCREAMING_SNAKE_CASE , interpolate_pos_encoding=_SCREAMING_SNAKE_CASE , training=_SCREAMING_SNAKE_CASE )
__UpperCamelCase = (image_size // self.patch_size) ** 2 + 1
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, seq_length, self.hidden_size) )
def __lowercase( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
__UpperCamelCase = self.type_sequence_label_size
__UpperCamelCase = TFViTForImageClassification(_SCREAMING_SNAKE_CASE )
__UpperCamelCase = model(_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE , training=_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# Test with an image with different size than the one specified in config.
__UpperCamelCase = self.image_size // 2
__UpperCamelCase = pixel_values[:, :, :image_size, :image_size]
__UpperCamelCase = model(_SCREAMING_SNAKE_CASE , interpolate_pos_encoding=_SCREAMING_SNAKE_CASE , training=_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__UpperCamelCase = 1
__UpperCamelCase = TFViTForImageClassification(_SCREAMING_SNAKE_CASE )
__UpperCamelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__UpperCamelCase = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __lowercase( self ) -> Dict:
__UpperCamelCase = self.prepare_config_and_inputs()
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase = config_and_inputs
__UpperCamelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_tf
class lowerCAmelCase_ ( _lowercase , _lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
UpperCAmelCase__ = (
{"feature-extraction": TFViTModel, "image-classification": TFViTForImageClassification}
if is_tf_available()
else {}
)
UpperCAmelCase__ = False
UpperCAmelCase__ = False
UpperCAmelCase__ = False
def __lowercase( self ) -> List[str]:
__UpperCamelCase = TFViTModelTester(self )
__UpperCamelCase = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , has_text_modality=_SCREAMING_SNAKE_CASE , hidden_size=37 )
def __lowercase( self ) -> int:
self.config_tester.run_common_tests()
@unittest.skip(reason='ViT does not use inputs_embeds' )
def __lowercase( self ) -> str:
pass
@unittest.skip(reason='ViT does not use inputs_embeds' )
def __lowercase( self ) -> Tuple:
pass
def __lowercase( self ) -> int:
__UpperCamelCase , __UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCamelCase = model_class(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
__UpperCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE , tf.keras.layers.Layer ) )
def __lowercase( self ) -> Dict:
__UpperCamelCase , __UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCamelCase = model_class(_SCREAMING_SNAKE_CASE )
__UpperCamelCase = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__UpperCamelCase = [*signature.parameters.keys()]
__UpperCamelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , _SCREAMING_SNAKE_CASE )
def __lowercase( self ) -> str:
__UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )
def __lowercase( self ) -> str:
__UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE )
@slow
def __lowercase( self ) -> List[Any]:
__UpperCamelCase = TFViTModel.from_pretrained('google/vit-base-patch16-224' )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
def _a ( ) -> Optional[Any]:
"""simple docstring"""
__UpperCamelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
@require_vision
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowercase( self ) -> List[Any]:
return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224' ) if is_vision_available() else None
@slow
def __lowercase( self ) -> Dict:
__UpperCamelCase = TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224' )
__UpperCamelCase = self.default_image_processor
__UpperCamelCase = prepare_img()
__UpperCamelCase = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors='tf' )
# forward pass
__UpperCamelCase = model(**_SCREAMING_SNAKE_CASE )
# verify the logits
__UpperCamelCase = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape , _SCREAMING_SNAKE_CASE )
__UpperCamelCase = tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6] )
tf.debugging.assert_near(outputs.logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1e-4 )
| 383
| 1
|
"""simple docstring"""
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
UpperCAmelCase__ : Any = logging.get_logger(__name__)
UpperCAmelCase__ : Dict = {
'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_ (a__ ):
"""simple docstring"""
__UpperCamelCase : List[str] = '''marian'''
__UpperCamelCase : str = ['''past_key_values''']
__UpperCamelCase : Optional[Any] = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__(self , SCREAMING_SNAKE_CASE__=5_81_01 , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=10_24 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=40_96 , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=40_96 , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=10_24 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=5_81_00 , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=5_81_00 , SCREAMING_SNAKE_CASE__=0 , SCREAMING_SNAKE_CASE__=0 , SCREAMING_SNAKE_CASE__=True , **SCREAMING_SNAKE_CASE__ , ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = vocab_size
SCREAMING_SNAKE_CASE__ : List[Any] = decoder_vocab_size or vocab_size
SCREAMING_SNAKE_CASE__ : Dict = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d_model
SCREAMING_SNAKE_CASE__ : int = encoder_ffn_dim
SCREAMING_SNAKE_CASE__ : Optional[Any] = encoder_layers
SCREAMING_SNAKE_CASE__ : int = encoder_attention_heads
SCREAMING_SNAKE_CASE__ : List[str] = decoder_ffn_dim
SCREAMING_SNAKE_CASE__ : List[str] = decoder_layers
SCREAMING_SNAKE_CASE__ : Optional[int] = decoder_attention_heads
SCREAMING_SNAKE_CASE__ : Any = dropout
SCREAMING_SNAKE_CASE__ : str = attention_dropout
SCREAMING_SNAKE_CASE__ : Dict = activation_dropout
SCREAMING_SNAKE_CASE__ : Tuple = activation_function
SCREAMING_SNAKE_CASE__ : List[str] = init_std
SCREAMING_SNAKE_CASE__ : Optional[Any] = encoder_layerdrop
SCREAMING_SNAKE_CASE__ : Tuple = decoder_layerdrop
SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_cache
SCREAMING_SNAKE_CASE__ : str = encoder_layers
SCREAMING_SNAKE_CASE__ : Optional[Any] = scale_embedding # scale factor will be sqrt(d_model) if True
SCREAMING_SNAKE_CASE__ : Tuple = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE__ , eos_token_id=SCREAMING_SNAKE_CASE__ , is_encoder_decoder=SCREAMING_SNAKE_CASE__ , decoder_start_token_id=SCREAMING_SNAKE_CASE__ , forced_eos_token_id=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
class lowerCAmelCase_ (a__ ):
"""simple docstring"""
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def __magic_name__ (self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE__ : Any = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
SCREAMING_SNAKE_CASE__ : str = {0: """batch"""}
SCREAMING_SNAKE_CASE__ : Any = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
SCREAMING_SNAKE_CASE__ : List[Any] = {0: """batch""", 1: """decoder_sequence"""}
SCREAMING_SNAKE_CASE__ : Tuple = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE__ , direction="""inputs""" )
elif self.task == "causal-lm":
# TODO: figure this case out.
SCREAMING_SNAKE_CASE__ : Optional[Any] = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
SCREAMING_SNAKE_CASE__ : Dict = self.num_layers
for i in range(SCREAMING_SNAKE_CASE__ ):
SCREAMING_SNAKE_CASE__ : List[Any] = {0: """batch""", 2: """past_sequence + sequence"""}
SCREAMING_SNAKE_CASE__ : Optional[Any] = {0: """batch""", 2: """past_sequence + sequence"""}
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = 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 __magic_name__ (self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = super().outputs
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = super(SCREAMING_SNAKE_CASE__ , self ).outputs
if self.use_past:
SCREAMING_SNAKE_CASE__ : List[Any] = self.num_layers
for i in range(SCREAMING_SNAKE_CASE__ ):
SCREAMING_SNAKE_CASE__ : Any = {0: """batch""", 2: """past_sequence + sequence"""}
SCREAMING_SNAKE_CASE__ : List[str] = {0: """batch""", 2: """past_sequence + sequence"""}
return common_outputs
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = -1 , SCREAMING_SNAKE_CASE__ = -1 , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = None , ) -> Mapping[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self._generate_dummy_inputs_for_encoder_and_decoder(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Generate decoder inputs
SCREAMING_SNAKE_CASE__ : Optional[int] = seq_length if not self.use_past else 1
SCREAMING_SNAKE_CASE__ : Optional[int] = self._generate_dummy_inputs_for_encoder_and_decoder(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Tuple = {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
SCREAMING_SNAKE_CASE__ : int = dict(**SCREAMING_SNAKE_CASE__ , **SCREAMING_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
SCREAMING_SNAKE_CASE__ : Union[str, Any] = common_inputs["""input_ids"""].shape
SCREAMING_SNAKE_CASE__ : str = common_inputs["""decoder_input_ids"""].shape[1]
SCREAMING_SNAKE_CASE__ : int = self.num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = decoder_seq_length + 3
SCREAMING_SNAKE_CASE__ : Optional[Any] = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat(
[common_inputs["""decoder_attention_mask"""], torch.ones(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )] , dim=1 )
SCREAMING_SNAKE_CASE__ : Any = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.num_layers
SCREAMING_SNAKE_CASE__ : Any = min(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : List[Any] = max(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) - min_num_layers
SCREAMING_SNAKE_CASE__ : Optional[Any] = """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""
for _ in range(SCREAMING_SNAKE_CASE__ ):
common_inputs["past_key_values"].append(
(
torch.zeros(SCREAMING_SNAKE_CASE__ ),
torch.zeros(SCREAMING_SNAKE_CASE__ ),
torch.zeros(SCREAMING_SNAKE_CASE__ ),
torch.zeros(SCREAMING_SNAKE_CASE__ ),
) )
# TODO: test this.
SCREAMING_SNAKE_CASE__ : Dict = encoder_shape if remaining_side_name == """encoder""" else decoder_shape
for _ in range(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
common_inputs["past_key_values"].append((torch.zeros(SCREAMING_SNAKE_CASE__ ), torch.zeros(SCREAMING_SNAKE_CASE__ )) )
return common_inputs
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = -1 , SCREAMING_SNAKE_CASE__ = -1 , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = None , ) -> Mapping[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self._generate_dummy_inputs_for_encoder_and_decoder(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_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
SCREAMING_SNAKE_CASE__ : Any = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
SCREAMING_SNAKE_CASE__ : Any = seqlen + 2
SCREAMING_SNAKE_CASE__ : List[Any] = self.num_layers
SCREAMING_SNAKE_CASE__ : int = self.num_attention_heads
SCREAMING_SNAKE_CASE__ : int = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
SCREAMING_SNAKE_CASE__ : List[str] = common_inputs["""attention_mask"""].dtype
SCREAMING_SNAKE_CASE__ : Dict = torch.cat(
[common_inputs["""attention_mask"""], torch.ones(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , dtype=SCREAMING_SNAKE_CASE__ )] , dim=1 )
SCREAMING_SNAKE_CASE__ : List[str] = [
(torch.zeros(SCREAMING_SNAKE_CASE__ ), torch.zeros(SCREAMING_SNAKE_CASE__ )) for _ in range(SCREAMING_SNAKE_CASE__ )
]
return common_inputs
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = -1 , SCREAMING_SNAKE_CASE__ = -1 , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = None , ) -> Mapping[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = compute_effective_axis_dimension(
SCREAMING_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
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = compute_effective_axis_dimension(
SCREAMING_SNAKE_CASE__ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=SCREAMING_SNAKE_CASE__ )
# Generate dummy inputs according to compute batch and sequence
SCREAMING_SNAKE_CASE__ : Optional[int] = [""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
SCREAMING_SNAKE_CASE__ : Optional[int] = dict(tokenizer(SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ ) )
return common_inputs
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = -1 , SCREAMING_SNAKE_CASE__ = -1 , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = None , ) -> Mapping[str, Any]:
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE__ : Tuple = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
SCREAMING_SNAKE_CASE__ , batch_size=SCREAMING_SNAKE_CASE__ , seq_length=SCREAMING_SNAKE_CASE__ , is_pair=SCREAMING_SNAKE_CASE__ , framework=SCREAMING_SNAKE_CASE__ )
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = self._generate_dummy_inputs_for_causal_lm(
SCREAMING_SNAKE_CASE__ , batch_size=SCREAMING_SNAKE_CASE__ , seq_length=SCREAMING_SNAKE_CASE__ , is_pair=SCREAMING_SNAKE_CASE__ , framework=SCREAMING_SNAKE_CASE__ )
return common_inputs
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Dict:
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE__ : Optional[int] = super()._flatten_past_key_values_(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
else:
SCREAMING_SNAKE_CASE__ : List[str] = super(SCREAMING_SNAKE_CASE__ , self )._flatten_past_key_values_(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
@property
def __magic_name__ (self ) -> float:
"""simple docstring"""
return 1E-4
| 704
|
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_MODEL_FOR_PRETRAINING_MAPPING,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertModel,
)
@require_tf
class lowerCAmelCase_ (a__ , a__ , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase : Any = (
(
TFMobileBertModel,
TFMobileBertForMaskedLM,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertForMultipleChoice,
)
if is_tf_available()
else ()
)
__UpperCamelCase : Tuple = (
{
'''feature-extraction''': TFMobileBertModel,
'''fill-mask''': TFMobileBertForMaskedLM,
'''question-answering''': TFMobileBertForQuestionAnswering,
'''text-classification''': TFMobileBertForSequenceClassification,
'''token-classification''': TFMobileBertForTokenClassification,
'''zero-shot''': TFMobileBertForSequenceClassification,
}
if is_tf_available()
else {}
)
__UpperCamelCase : Optional[int] = False
__UpperCamelCase : str = False
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = super()._prepare_for_class(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , return_labels=SCREAMING_SNAKE_CASE__ )
if return_labels:
if model_class in get_values(SCREAMING_SNAKE_CASE__ ):
SCREAMING_SNAKE_CASE__ : List[str] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
return inputs_dict
class lowerCAmelCase_ (a__ ):
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=13 , SCREAMING_SNAKE_CASE__=7 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=99 , SCREAMING_SNAKE_CASE__=32 , SCREAMING_SNAKE_CASE__=32 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=37 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=5_12 , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=None , ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = parent
SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE__ : List[Any] = seq_length
SCREAMING_SNAKE_CASE__ : Any = is_training
SCREAMING_SNAKE_CASE__ : List[str] = use_input_mask
SCREAMING_SNAKE_CASE__ : List[Any] = use_token_type_ids
SCREAMING_SNAKE_CASE__ : List[Any] = use_labels
SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_size
SCREAMING_SNAKE_CASE__ : int = hidden_size
SCREAMING_SNAKE_CASE__ : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : List[str] = num_attention_heads
SCREAMING_SNAKE_CASE__ : List[str] = intermediate_size
SCREAMING_SNAKE_CASE__ : List[Any] = hidden_act
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Dict = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Dict = type_vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : str = initializer_range
SCREAMING_SNAKE_CASE__ : str = num_labels
SCREAMING_SNAKE_CASE__ : Dict = num_choices
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scope
SCREAMING_SNAKE_CASE__ : Dict = embedding_size
def __magic_name__ (self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : List[Any] = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : int = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : List[str] = None
SCREAMING_SNAKE_CASE__ : List[str] = None
SCREAMING_SNAKE_CASE__ : str = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ : str = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE__ : Optional[Any] = MobileBertConfig(
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 , initializer_range=self.initializer_range , embedding_size=self.embedding_size , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = TFMobileBertModel(config=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : int = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
SCREAMING_SNAKE_CASE__ : List[Any] = model(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Dict = [input_ids, input_mask]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : List[str] = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = TFMobileBertForMaskedLM(config=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
SCREAMING_SNAKE_CASE__ : List[str] = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = TFMobileBertForNextSentencePrediction(config=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Any = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
SCREAMING_SNAKE_CASE__ : List[str] = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = TFMobileBertForPreTraining(config=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : List[Any] = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(
result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.num_labels
SCREAMING_SNAKE_CASE__ : List[Any] = TFMobileBertForSequenceClassification(config=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : str = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.num_choices
SCREAMING_SNAKE_CASE__ : List[Any] = TFMobileBertForMultipleChoice(config=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Any = tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE__ , 1 ) , (1, self.num_choices, 1) )
SCREAMING_SNAKE_CASE__ : Tuple = tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE__ , 1 ) , (1, self.num_choices, 1) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE__ , 1 ) , (1, self.num_choices, 1) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
SCREAMING_SNAKE_CASE__ : Optional[int] = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.num_labels
SCREAMING_SNAKE_CASE__ : Tuple = TFMobileBertForTokenClassification(config=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
SCREAMING_SNAKE_CASE__ : List[str] = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = TFMobileBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Dict = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
SCREAMING_SNAKE_CASE__ : str = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __magic_name__ (self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : List[Any] = config_and_inputs
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
def __magic_name__ (self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = TFMobileBertModelTest.TFMobileBertModelTester(self )
SCREAMING_SNAKE_CASE__ : str = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE__ , hidden_size=37 )
def __magic_name__ (self ) -> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def __magic_name__ (self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*SCREAMING_SNAKE_CASE__ )
@slow
def __magic_name__ (self ) -> List[str]:
"""simple docstring"""
for model_name in ["google/mobilebert-uncased"]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = TFMobileBertModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
@require_tf
class lowerCAmelCase_ (unittest.TestCase ):
"""simple docstring"""
@slow
def __magic_name__ (self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = TFMobileBertForPreTraining.from_pretrained("""google/mobilebert-uncased""" )
SCREAMING_SNAKE_CASE__ : List[Any] = tf.constant([[0, 1, 2, 3, 4, 5]] )
SCREAMING_SNAKE_CASE__ : Dict = model(SCREAMING_SNAKE_CASE__ )[0]
SCREAMING_SNAKE_CASE__ : Optional[Any] = [1, 6, 3_05_22]
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : List[Any] = tf.constant(
[
[
[-4.5919547, -9.248295, -9.645256],
[-6.7306175, -6.440284, -6.6052837],
[-7.2743506, -6.7847915, -6.024673],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , SCREAMING_SNAKE_CASE__ , atol=1E-4 )
| 545
| 0
|
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
a : Union[str, Any] = (3, 9, -11, 0, 7, 5, 1, -1)
a : Tuple = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : Node | None
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self , snake_case ):
'''simple docstring'''
UpperCAmelCase : Node | None = None
for i in sorted(snake_case , reverse=snake_case ):
UpperCAmelCase : List[str] = Node(snake_case , self.head )
def __iter__( self ):
'''simple docstring'''
UpperCAmelCase : Any = self.head
while node:
yield node.data
UpperCAmelCase : Dict = node.next_node
def __len__( self ):
'''simple docstring'''
return sum(1 for _ in self )
def __str__( self ):
'''simple docstring'''
return " -> ".join([str(snake_case ) for node in self] )
def lowercase ( __magic_name__ , __magic_name__ ):
'''simple docstring'''
return SortedLinkedList(list(__magic_name__ ) + list(__magic_name__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
a : Union[str, Any] = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 679
|
'''simple docstring'''
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_soundfile_availble():
import soundfile as sf
if is_vision_available():
from PIL import Image
def lowercase ( __magic_name__="" ):
'''simple docstring'''
UpperCAmelCase : Dict = tempfile.mkdtemp()
return os.path.join(__magic_name__ , str(uuid.uuida() ) + suffix )
@require_soundfile
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Any = torch.rand(1_2 , dtype=torch.floataa ) - 0.5
UpperCAmelCase : int = AgentAudio(snake_case )
UpperCAmelCase : str = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(snake_case , agent_type.to_raw() , atol=1e-4 ) )
del agent_type
# Ensure the path remains even after the object deletion
self.assertTrue(os.path.exists(snake_case ) )
# Ensure that the file contains the same value as the original tensor
UpperCAmelCase , UpperCAmelCase : str = sf.read(snake_case )
self.assertTrue(torch.allclose(snake_case , torch.tensor(snake_case ) , atol=1e-4 ) )
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = torch.rand(1_2 , dtype=torch.floataa ) - 0.5
UpperCAmelCase : Any = get_new_path(suffix=".wav" )
sf.write(snake_case , snake_case , 1_6_0_0_0 )
UpperCAmelCase : Optional[Any] = AgentAudio(snake_case )
self.assertTrue(torch.allclose(snake_case , agent_type.to_raw() , atol=1e-4 ) )
self.assertEqual(agent_type.to_string() , snake_case )
@require_vision
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Optional[Any] = torch.randint(0 , 2_5_6 , (6_4, 6_4, 3) )
UpperCAmelCase : Tuple = AgentImage(snake_case )
UpperCAmelCase : Tuple = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(snake_case , agent_type._tensor , atol=1e-4 ) )
self.assertIsInstance(agent_type.to_raw() , Image.Image )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(snake_case ) )
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Optional[Any] = Path(get_tests_dir("fixtures/tests_samples/COCO" ) ) / "000000039769.png"
UpperCAmelCase : Any = Image.open(snake_case )
UpperCAmelCase : List[str] = AgentImage(snake_case )
self.assertTrue(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(snake_case ) )
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = Path(get_tests_dir("fixtures/tests_samples/COCO" ) ) / "000000039769.png"
UpperCAmelCase : Dict = Image.open(snake_case )
UpperCAmelCase : int = AgentImage(snake_case )
self.assertFalse(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(snake_case ) )
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Any = "Hey!"
UpperCAmelCase : Tuple = AgentText(snake_case )
self.assertEqual(snake_case , agent_type.to_string() )
self.assertEqual(snake_case , agent_type.to_raw() )
self.assertEqual(snake_case , snake_case )
| 679
| 1
|
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
_UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = ['input_features', 'attention_mask']
def __init__( self , snake_case_=80 , snake_case_=1_60_00 , snake_case_=0.0 , snake_case_=10 , snake_case_=25 , snake_case_="hamming_window" , snake_case_=3_27_68.0 , snake_case_=0.97 , snake_case_=1.0 , snake_case_=True , snake_case_=True , snake_case_=False , **snake_case_ , ):
super().__init__(feature_size=snake_case_ , sampling_rate=snake_case_ , padding_value=snake_case_ , **snake_case_ )
lowercase =feature_size
lowercase =sampling_rate
lowercase =padding_value
lowercase =hop_length
lowercase =win_length
lowercase =frame_signal_scale
lowercase =preemphasis_coeff
lowercase =mel_floor
lowercase =normalize_means
lowercase =normalize_vars
lowercase =win_function
lowercase =return_attention_mask
lowercase =win_length * sampling_rate // 10_00
lowercase =hop_length * sampling_rate // 10_00
lowercase =optimal_fft_length(self.sample_size )
lowercase =(self.n_fft // 2) + 1
def _A( self , snake_case_ ):
if self.win_function == "hamming_window":
lowercase =window_function(window_length=self.sample_size , name=self.win_function , periodic=snake_case_ )
else:
lowercase =window_function(window_length=self.sample_size , name=self.win_function )
lowercase =mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.feature_size , min_frequency=0.0 , max_frequency=self.sampling_rate / 2.0 , sampling_rate=self.sampling_rate , )
lowercase =spectrogram(
one_waveform * self.frame_signal_scale , window=snake_case_ , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , center=snake_case_ , preemphasis=self.preemphasis_coeff , mel_filters=snake_case_ , mel_floor=self.mel_floor , log_mel='''log''' , )
return msfc_features.T
def _A( self , snake_case_ , snake_case_ , snake_case_ ):
# make sure we normalize float32 arrays
if self.normalize_means:
lowercase =x[:input_length].mean(axis=0 )
lowercase =np.subtract(snake_case_ , snake_case_ )
if self.normalize_vars:
lowercase =x[:input_length].std(axis=0 )
lowercase =np.divide(snake_case_ , snake_case_ )
if input_length < x.shape[0]:
lowercase =padding_value
# make sure array is in float32
lowercase =x.astype(np.floataa )
return x
def _A( self , snake_case_ , snake_case_ = None ):
lowercase =attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(snake_case_ , snake_case_ , self.padding_value ) for x, n in zip(snake_case_ , snake_case_ )]
def __call__( self , snake_case_ , snake_case_ = False , snake_case_ = None , snake_case_ = False , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , **snake_case_ , ):
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
f' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
f' {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
'''It is strongly recommended to pass the ``sampling_rate`` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
lowercase =isinstance(snake_case_ , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'Only mono-channel audio is supported for input to {self}' )
lowercase =is_batched_numpy or (
isinstance(snake_case_ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
lowercase =[np.asarray(snake_case_ , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(snake_case_ , np.ndarray ):
lowercase =np.asarray(snake_case_ , dtype=np.floataa )
elif isinstance(snake_case_ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
lowercase =raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
lowercase =[raw_speech]
# extract fbank features
lowercase =[self._extract_mfsc_features(snake_case_ ) for one_waveform in raw_speech]
# convert into correct format for padding
lowercase =BatchFeature({'''input_features''': features} )
lowercase =self.pad(
snake_case_ , padding=snake_case_ , max_length=snake_case_ , truncation=snake_case_ , pad_to_multiple_of=snake_case_ , return_attention_mask=snake_case_ , **snake_case_ , )
# make sure list is in array format
lowercase =padded_inputs.get('''input_features''' )
if isinstance(input_features[0] , snake_case_ ):
lowercase =[np.asarray(snake_case_ , dtype=np.floataa ) for feature in input_features]
lowercase =padded_inputs.get('''attention_mask''' )
if attention_mask is not None:
lowercase =[np.asarray(snake_case_ , dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
lowercase =(
np.array(snake_case_ , dtype=np.intaa )
if self._get_padding_strategies(snake_case_ , max_length=snake_case_ ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
lowercase =self.normalize(
padded_inputs['''input_features'''] , attention_mask=snake_case_ )
if return_tensors is not None:
lowercase =padded_inputs.convert_to_tensors(snake_case_ )
return padded_inputs
| 702
|
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : List[str] = logging.get_logger(__name__)
_UpperCAmelCase : str = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
),
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'pix2struct_text_model'
UpperCamelCase__ = ['past_key_values']
UpperCamelCase__ = {
'hidden_size': 'hidden_size',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self , snake_case_=5_02_44 , snake_case_=7_68 , snake_case_=64 , snake_case_=20_48 , snake_case_=12 , snake_case_=12 , snake_case_=32 , snake_case_=1_28 , snake_case_=0.1 , snake_case_=1E-6 , snake_case_=1.0 , snake_case_="gelu_new" , snake_case_=0 , snake_case_=False , snake_case_=0 , snake_case_=1 , snake_case_=False , snake_case_=True , **snake_case_ , ):
lowercase =vocab_size
lowercase =hidden_size
lowercase =d_kv
lowercase =d_ff
lowercase =num_layers
lowercase =num_heads
lowercase =relative_attention_num_buckets
lowercase =relative_attention_max_distance
lowercase =dropout_rate
lowercase =layer_norm_epsilon
lowercase =initializer_factor
lowercase =use_cache
lowercase =eos_token_id
lowercase =decoder_start_token_id
# for backwards compatibility
lowercase =dense_act_fn
super().__init__(
pad_token_id=snake_case_ , eos_token_id=snake_case_ , decoder_start_token_id=snake_case_ , tie_word_embeddings=snake_case_ , is_decoder=snake_case_ , **snake_case_ , )
@classmethod
def _A( cls , snake_case_ , **snake_case_ ):
cls._set_token_in_kwargs(snake_case_ )
lowercase , lowercase =cls.get_config_dict(snake_case_ , **snake_case_ )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get('''model_type''' ) == "pix2struct":
lowercase =config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case_ , **snake_case_ )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'pix2struct_vision_model'
def __init__( self , snake_case_=7_68 , snake_case_=7_68 , snake_case_=20_48 , snake_case_=64 , snake_case_=12 , snake_case_=12 , snake_case_="gelu_new" , snake_case_=1E-6 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=1E-10 , snake_case_=1.0 , snake_case_=40_96 , snake_case_=32 , snake_case_=1_28 , **snake_case_ , ):
super().__init__(**snake_case_ )
lowercase =hidden_size
lowercase =patch_embed_hidden_size
lowercase =d_ff
lowercase =dropout_rate
lowercase =num_hidden_layers
lowercase =num_attention_heads
lowercase =initializer_range
lowercase =initializer_factor
lowercase =attention_dropout
lowercase =layer_norm_eps
lowercase =dense_act_fn
lowercase =seq_len
lowercase =relative_attention_num_buckets
lowercase =relative_attention_max_distance
lowercase =d_kv
@classmethod
def _A( cls , snake_case_ , **snake_case_ ):
cls._set_token_in_kwargs(snake_case_ )
lowercase , lowercase =cls.get_config_dict(snake_case_ , **snake_case_ )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get('''model_type''' ) == "pix2struct":
lowercase =config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case_ , **snake_case_ )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'pix2struct'
UpperCamelCase__ = True
def __init__( self , snake_case_=None , snake_case_=None , snake_case_=1.0 , snake_case_=0.02 , snake_case_=False , snake_case_=False , snake_case_=True , **snake_case_ , ):
super().__init__(tie_word_embeddings=snake_case_ , is_encoder_decoder=snake_case_ , **snake_case_ )
if text_config is None:
lowercase ={}
logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.''' )
if vision_config is None:
lowercase ={}
logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.''' )
lowercase =PixaStructTextConfig(**snake_case_ )
lowercase =PixaStructVisionConfig(**snake_case_ )
lowercase =self.text_config.decoder_start_token_id
lowercase =self.text_config.pad_token_id
lowercase =self.text_config.eos_token_id
lowercase =initializer_factor
lowercase =initializer_range
lowercase =self.initializer_range
lowercase =self.initializer_range
lowercase =is_vqa
@classmethod
def _A( cls , snake_case_ , snake_case_ , **snake_case_ ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **snake_case_ )
def _A( self ):
lowercase =copy.deepcopy(self.__dict__ )
lowercase =self.text_config.to_dict()
lowercase =self.vision_config.to_dict()
lowercase =self.__class__.model_type
return output
| 145
| 0
|
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class a_ ( UpperCamelCase__ ):
lowerCamelCase__ : Optional[int] = 'Speech2TextFeatureExtractor'
lowerCamelCase__ : Dict = 'Speech2TextTokenizer'
def __init__( self , UpperCAmelCase , UpperCAmelCase ):
super().__init__(UpperCAmelCase , UpperCAmelCase )
a_ = self.feature_extractor
a_ = False
def __call__( self , *UpperCAmelCase , **UpperCAmelCase ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*UpperCAmelCase , **UpperCAmelCase )
if "raw_speech" in kwargs:
warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.""" )
a_ = kwargs.pop("""raw_speech""" )
else:
a_ = kwargs.pop("""audio""" , UpperCAmelCase )
a_ = kwargs.pop("""sampling_rate""" , UpperCAmelCase )
a_ = kwargs.pop("""text""" , UpperCAmelCase )
if len(UpperCAmelCase ) > 0:
a_ = args[0]
a_ = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if audio is not None:
a_ = self.feature_extractor(UpperCAmelCase , *UpperCAmelCase , sampling_rate=UpperCAmelCase , **UpperCAmelCase )
if text is not None:
a_ = self.tokenizer(UpperCAmelCase , **UpperCAmelCase )
if text is None:
return inputs
elif audio is None:
return encodings
else:
a_ = encodings["""input_ids"""]
return inputs
def lowerCAmelCase__ ( self , *UpperCAmelCase , **UpperCAmelCase ):
return self.tokenizer.batch_decode(*UpperCAmelCase , **UpperCAmelCase )
def lowerCAmelCase__ ( self , *UpperCAmelCase , **UpperCAmelCase ):
return self.tokenizer.decode(*UpperCAmelCase , **UpperCAmelCase )
@contextmanager
def lowerCAmelCase__ ( self ):
warnings.warn(
"""`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
"""labels by using the argument `text` of the regular `__call__` method (either in the same call as """
"""your audio inputs, or in a separate call.""" )
a_ = True
a_ = self.tokenizer
yield
a_ = self.feature_extractor
a_ = False
| 263
|
'''simple docstring'''
import requests
lowercase__ ='' # <-- Put your OpenWeatherMap appid here!
lowercase__ ='https://api.openweathermap.org/data/2.5/'
def UpperCamelCase_ ( A__ = "Chicago" , A__ = APPID ):
return requests.get(URL_BASE + """weather""" , params=locals() ).json()
def UpperCamelCase_ ( A__ = "Kolkata, India" , A__ = APPID ):
return requests.get(URL_BASE + """forecast""" , params=locals() ).json()
def UpperCamelCase_ ( A__ = 55.68 , A__ = 12.57 , A__ = APPID ):
return requests.get(URL_BASE + """onecall""" , params=locals() ).json()
if __name__ == "__main__":
from pprint import pprint
while True:
lowercase__ =input('Enter a location:').strip()
if location:
pprint(current_weather(location))
else:
break
| 263
| 1
|
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class __snake_case :
'''simple docstring'''
def _a ( self ):
torch.manual_seed(0 )
a__ = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
a__ = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
a__ = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
"""ResnetDownsampleBlock2D""",
"""SimpleCrossAttnDownBlock2D""",
] , mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" , up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="""text""" , addition_embed_type_num_heads=2 , cross_attention_norm="""group_norm""" , resnet_time_scale_shift="""scale_shift""" , act_fn="""gelu""" , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
a__ = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_001 , beta_end=0.02 , thresholding=a_ , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="""epsilon""" , variance_type="""learned_range""" , )
torch.manual_seed(0 )
a__ = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def _a ( self ):
torch.manual_seed(0 )
a__ = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
a__ = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
a__ = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
"""ResnetDownsampleBlock2D""",
"""SimpleCrossAttnDownBlock2D""",
] , mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" , up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="""text""" , addition_embed_type_num_heads=2 , cross_attention_norm="""group_norm""" , resnet_time_scale_shift="""scale_shift""" , act_fn="""gelu""" , class_embed_type="""timestep""" , mid_block_scale_factor=1.414 , time_embedding_act_fn="""gelu""" , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
a__ = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_001 , beta_end=0.02 , thresholding=a_ , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="""epsilon""" , variance_type="""learned_range""" , )
torch.manual_seed(0 )
a__ = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_001 , beta_end=0.02 , )
torch.manual_seed(0 )
a__ = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def _a ( self ):
a__ = self.get_dummy_components()
a__ = self.pipeline_class(**a_ )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
a__ = self.get_dummy_inputs(a_ )
a__ = inputs["""prompt"""]
a__ = inputs["""generator"""]
a__ = inputs["""num_inference_steps"""]
a__ = inputs["""output_type"""]
if "image" in inputs:
a__ = inputs["""image"""]
else:
a__ = None
if "mask_image" in inputs:
a__ = inputs["""mask_image"""]
else:
a__ = None
if "original_image" in inputs:
a__ = inputs["""original_image"""]
else:
a__ = None
a__ , a__ = pipe.encode_prompt(a_ )
# inputs with prompt converted to embeddings
a__ = {
"""prompt_embeds""": prompt_embeds,
"""negative_prompt_embeds""": negative_prompt_embeds,
"""generator""": generator,
"""num_inference_steps""": num_inference_steps,
"""output_type""": output_type,
}
if image is not None:
a__ = image
if mask_image is not None:
a__ = mask_image
if original_image is not None:
a__ = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(a_ , a_ , a_ )
a__ = pipe(**a_ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(a_ )
a__ = self.pipeline_class.from_pretrained(a_ )
pipe_loaded.to(a_ )
pipe_loaded.set_progress_bar_config(disable=a_ )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(a_ , a_ ) is None , F'''`{optional_component}` did not stay set to None after loading.''' , )
a__ = self.get_dummy_inputs(a_ )
a__ = inputs["""generator"""]
a__ = inputs["""num_inference_steps"""]
a__ = inputs["""output_type"""]
# inputs with prompt converted to embeddings
a__ = {
"""prompt_embeds""": prompt_embeds,
"""negative_prompt_embeds""": negative_prompt_embeds,
"""generator""": generator,
"""num_inference_steps""": num_inference_steps,
"""output_type""": output_type,
}
if image is not None:
a__ = image
if mask_image is not None:
a__ = mask_image
if original_image is not None:
a__ = original_image
a__ = pipe_loaded(**a_ )[0]
a__ = np.abs(to_np(a_ ) - to_np(a_ ) ).max()
self.assertLess(a_ , 1E-4 )
def _a ( self ):
a__ = self.get_dummy_components()
a__ = self.pipeline_class(**a_ )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
a__ = self.get_dummy_inputs(a_ )
a__ = pipe(**a_ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(a_ )
a__ = self.pipeline_class.from_pretrained(a_ )
pipe_loaded.to(a_ )
pipe_loaded.set_progress_bar_config(disable=a_ )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
a__ = self.get_dummy_inputs(a_ )
a__ = pipe_loaded(**a_ )[0]
a__ = np.abs(to_np(a_ ) - to_np(a_ ) ).max()
self.assertLess(a_ , 1E-4 )
| 701
|
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available():
raise RuntimeError("""At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training""")
# TF training parameters
UpperCAmelCase = False
UpperCAmelCase = False
def A_ ( __a : Namespace ):
"""simple docstring"""
return TrainCommand(__a )
class __snake_case ( SCREAMING_SNAKE_CASE):
'''simple docstring'''
@staticmethod
def _a ( a_ ):
a__ = parser.add_parser("""train""" , help="""CLI tool to train a model on a task.""" )
train_parser.add_argument(
"""--train_data""" , type=a_ , required=a_ , help="""path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.""" , )
train_parser.add_argument(
"""--column_label""" , type=a_ , default=0 , help="""Column of the dataset csv file with example labels.""" )
train_parser.add_argument(
"""--column_text""" , type=a_ , default=1 , help="""Column of the dataset csv file with example texts.""" )
train_parser.add_argument(
"""--column_id""" , type=a_ , default=2 , help="""Column of the dataset csv file with example ids.""" )
train_parser.add_argument(
"""--skip_first_row""" , action="""store_true""" , help="""Skip the first row of the csv file (headers).""" )
train_parser.add_argument("""--validation_data""" , type=a_ , default="""""" , help="""path to validation dataset.""" )
train_parser.add_argument(
"""--validation_split""" , type=a_ , default=0.1 , help="""if validation dataset is not provided, fraction of train dataset to use as validation dataset.""" , )
train_parser.add_argument("""--output""" , type=a_ , default="""./""" , help="""path to saved the trained model.""" )
train_parser.add_argument(
"""--task""" , type=a_ , default="""text_classification""" , help="""Task to train the model on.""" )
train_parser.add_argument(
"""--model""" , type=a_ , default="""bert-base-uncased""" , help="""Model's name or path to stored model.""" )
train_parser.add_argument("""--train_batch_size""" , type=a_ , default=32 , help="""Batch size for training.""" )
train_parser.add_argument("""--valid_batch_size""" , type=a_ , default=64 , help="""Batch size for validation.""" )
train_parser.add_argument("""--learning_rate""" , type=a_ , default=3E-5 , help="""Learning rate.""" )
train_parser.add_argument("""--adam_epsilon""" , type=a_ , default=1E-0_8 , help="""Epsilon for Adam optimizer.""" )
train_parser.set_defaults(func=a_ )
def __init__( self , a_ ):
a__ = logging.get_logger("""transformers-cli/training""" )
a__ = """tf""" if is_tf_available() else """torch"""
os.makedirs(args.output , exist_ok=a_ )
a__ = args.output
a__ = args.column_label
a__ = args.column_text
a__ = args.column_id
self.logger.info(F'''Loading {args.task} pipeline for {args.model}''' )
if args.task == "text_classification":
a__ = TextClassificationPipeline.from_pretrained(args.model )
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(F'''Loading dataset from {args.train_data}''' )
a__ = Processor.create_from_csv(
args.train_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
a__ = None
if args.validation_data:
self.logger.info(F'''Loading validation dataset from {args.validation_data}''' )
a__ = Processor.create_from_csv(
args.validation_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
a__ = args.validation_split
a__ = args.train_batch_size
a__ = args.valid_batch_size
a__ = args.learning_rate
a__ = args.adam_epsilon
def _a ( self ):
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def _a ( self ):
raise NotImplementedError
def _a ( self ):
self.pipeline.fit(
self.train_dataset , validation_data=self.valid_dataset , validation_split=self.validation_split , learning_rate=self.learning_rate , adam_epsilon=self.adam_epsilon , train_batch_size=self.train_batch_size , valid_batch_size=self.valid_batch_size , )
# Save trained pipeline
self.pipeline.save_pretrained(self.output )
| 351
| 0
|
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
_lowerCamelCase = False
class snake_case ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :int ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : int = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' , torch_dtype=torch.floataa )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
__SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = pipe.dual_guided(
prompt='''first prompt''' , image=_lowerCamelCase , text_to_image_strength=0.7_5 , generator=_lowerCamelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''' , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = VersatileDiffusionPipeline.from_pretrained(_lowerCamelCase , torch_dtype=torch.floataa )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = generator.manual_seed(0 )
__SCREAMING_SNAKE_CASE : int = pipe.dual_guided(
prompt='''first prompt''' , image=_lowerCamelCase , text_to_image_strength=0.7_5 , generator=_lowerCamelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''' , ).images
assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Tuple = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' , torch_dtype=torch.floataa )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = '''cyberpunk 2077'''
__SCREAMING_SNAKE_CASE : Any = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
__SCREAMING_SNAKE_CASE : str = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE : Dict = pipe.dual_guided(
prompt=_lowerCamelCase , image=_lowerCamelCase , text_to_image_strength=0.7_5 , generator=_lowerCamelCase , guidance_scale=7.5 , num_inference_steps=5_0 , output_type='''numpy''' , ).images
__SCREAMING_SNAKE_CASE : List[Any] = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__SCREAMING_SNAKE_CASE : List[str] = np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
__SCREAMING_SNAKE_CASE : List[str] = '''A painting of a squirrel eating a burger '''
__SCREAMING_SNAKE_CASE : str = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE : Dict = pipe.text_to_image(
prompt=_lowerCamelCase , generator=_lowerCamelCase , guidance_scale=7.5 , num_inference_steps=5_0 , output_type='''numpy''' ).images
__SCREAMING_SNAKE_CASE : Any = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__SCREAMING_SNAKE_CASE : List[Any] = np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
__SCREAMING_SNAKE_CASE : str = pipe.image_variation(_lowerCamelCase , generator=_lowerCamelCase , output_type='''numpy''' ).images
__SCREAMING_SNAKE_CASE : List[Any] = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__SCREAMING_SNAKE_CASE : Tuple = np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
| 674
|
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case :
def __init__( self :Optional[Any] , _lowerCamelCase :int ):
__SCREAMING_SNAKE_CASE : int = num_of_nodes
__SCREAMING_SNAKE_CASE : list[list[int]] = []
__SCREAMING_SNAKE_CASE : dict[int, int] = {}
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :int , _lowerCamelCase :int , _lowerCamelCase :int ):
self.m_edges.append([u_node, v_node, weight] )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :int ):
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :int ):
if self.m_component[u_node] != u_node:
for k in self.m_component:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.find_component(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :list[int] , _lowerCamelCase :int , _lowerCamelCase :int ):
if component_size[u_node] <= component_size[v_node]:
__SCREAMING_SNAKE_CASE : List[Any] = v_node
component_size[v_node] += component_size[u_node]
self.set_component(_lowerCamelCase )
elif component_size[u_node] >= component_size[v_node]:
__SCREAMING_SNAKE_CASE : Dict = self.find_component(_lowerCamelCase )
component_size[u_node] += component_size[v_node]
self.set_component(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Optional[int] = []
__SCREAMING_SNAKE_CASE : str = 0
__SCREAMING_SNAKE_CASE : list[Any] = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
__SCREAMING_SNAKE_CASE : str = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = edge
__SCREAMING_SNAKE_CASE : Optional[Any] = self.m_component[u]
__SCREAMING_SNAKE_CASE : int = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
__SCREAMING_SNAKE_CASE : Optional[Any] = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(_lowerCamelCase , _lowerCamelCase ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = edge
__SCREAMING_SNAKE_CASE : Tuple = self.m_component[u]
__SCREAMING_SNAKE_CASE : int = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
print(f'''Added edge [{u} - {v}]\nAdded weight: {w}\n''' )
num_of_components -= 1
__SCREAMING_SNAKE_CASE : Optional[Any] = [-1] * self.m_num_of_nodes
print(f'''The total weight of the minimal spanning tree is: {mst_weight}''' )
def lowerCAmelCase_ ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674
| 1
|
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def _UpperCamelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase_ : Optional[int] = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decoder.version',
'decoder.output_projection.weight',
'_float_tensor',
'encoder.embed_positions._float_tensor',
'decoder.embed_positions._float_tensor',
]
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _UpperCamelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase_ : Optional[Any] = list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
lowercase_ : List[str] = s_dict.pop(SCREAMING_SNAKE_CASE_ )
elif "subsample" in key:
lowercase_ : List[Any] = s_dict.pop(SCREAMING_SNAKE_CASE_ )
def _UpperCamelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase_ ,lowercase_ : Any = emb.weight.shape
lowercase_ : Dict = nn.Linear(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , bias=SCREAMING_SNAKE_CASE_ )
lowercase_ : Any = emb.weight.data
return lin_layer
def _UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase_ : Any = torch.load(SCREAMING_SNAKE_CASE_ , map_location='cpu' )
lowercase_ : List[Any] = mam_aaa['args']
lowercase_ : List[Any] = mam_aaa['model']
lowercase_ : Optional[int] = state_dict['decoder.output_projection.weight']
remove_ignore_keys_(SCREAMING_SNAKE_CASE_ )
rename_keys(SCREAMING_SNAKE_CASE_ )
lowercase_ : Any = state_dict['decoder.embed_tokens.weight'].shape[0]
lowercase_ : str = args.share_decoder_input_output_embed
lowercase_ : Tuple = [int(SCREAMING_SNAKE_CASE_ ) for i in args.conv_kernel_sizes.split(',' )]
lowercase_ : Union[str, Any] = SpeechaTextConfig(
vocab_size=SCREAMING_SNAKE_CASE_ , max_source_positions=args.max_source_positions , max_target_positions=args.max_target_positions , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function='relu' , num_conv_layers=len(SCREAMING_SNAKE_CASE_ ) , conv_channels=args.conv_channels , conv_kernel_sizes=SCREAMING_SNAKE_CASE_ , input_feat_per_channel=args.input_feat_per_channel , input_channels=args.input_channels , tie_word_embeddings=SCREAMING_SNAKE_CASE_ , num_beams=5 , max_length=200 , use_cache=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=2 , early_stopping=SCREAMING_SNAKE_CASE_ , )
lowercase_ : List[Any] = SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE_ )
lowercase_ ,lowercase_ : List[Any] = model.model.load_state_dict(SCREAMING_SNAKE_CASE_ , strict=SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0 and not set(SCREAMING_SNAKE_CASE_ ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'
f''' but all the following weights are missing {missing}''' )
if tie_embeds:
lowercase_ : int = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
lowercase_ : List[str] = lm_head_weights
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
_A = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
_A = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 438
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
_A = logging.get_logger(__name__)
def _UpperCamelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase_ : List[Any] = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
lowercase_ : List[Any] = 128
elif "12-12" in model_name:
lowercase_ : Tuple = 12
lowercase_ : List[Any] = 12
elif "14-14" in model_name:
lowercase_ : List[str] = 14
lowercase_ : Optional[Any] = 14
elif "16-16" in model_name:
lowercase_ : Union[str, Any] = 16
lowercase_ : List[str] = 16
else:
raise ValueError('Model not supported' )
lowercase_ : Optional[Any] = 'huggingface/label-files'
if "speech-commands" in model_name:
lowercase_ : List[str] = 35
lowercase_ : int = 'speech-commands-v2-id2label.json'
else:
lowercase_ : Union[str, Any] = 527
lowercase_ : int = 'audioset-id2label.json'
lowercase_ : Union[str, Any] = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , repo_type='dataset' ) , 'r' ) )
lowercase_ : Union[str, Any] = {int(SCREAMING_SNAKE_CASE_ ): v for k, v in idalabel.items()}
lowercase_ : Optional[int] = idalabel
lowercase_ : Optional[int] = {v: k for k, v in idalabel.items()}
return config
def _UpperCamelCase ( SCREAMING_SNAKE_CASE_ ):
if "module.v" in name:
lowercase_ : Dict = name.replace('module.v' , 'audio_spectrogram_transformer' )
if "cls_token" in name:
lowercase_ : Optional[Any] = name.replace('cls_token' , 'embeddings.cls_token' )
if "dist_token" in name:
lowercase_ : Any = name.replace('dist_token' , 'embeddings.distillation_token' )
if "pos_embed" in name:
lowercase_ : List[str] = name.replace('pos_embed' , 'embeddings.position_embeddings' )
if "patch_embed.proj" in name:
lowercase_ : int = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
# transformer blocks
if "blocks" in name:
lowercase_ : Optional[Any] = name.replace('blocks' , 'encoder.layer' )
if "attn.proj" in name:
lowercase_ : Optional[int] = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name:
lowercase_ : Dict = name.replace('attn' , 'attention.self' )
if "norm1" in name:
lowercase_ : int = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
lowercase_ : Optional[int] = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
lowercase_ : Optional[int] = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
lowercase_ : int = name.replace('mlp.fc2' , 'output.dense' )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
lowercase_ : int = name.replace('audio_spectrogram_transformer.norm' , 'audio_spectrogram_transformer.layernorm' )
# classifier head
if "module.mlp_head.0" in name:
lowercase_ : Dict = name.replace('module.mlp_head.0' , 'classifier.layernorm' )
if "module.mlp_head.1" in name:
lowercase_ : List[Any] = name.replace('module.mlp_head.1' , 'classifier.dense' )
return name
def _UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
for key in orig_state_dict.copy().keys():
lowercase_ : List[str] = orig_state_dict.pop(SCREAMING_SNAKE_CASE_ )
if "qkv" in key:
lowercase_ : List[str] = key.split('.' )
lowercase_ : int = int(key_split[3] )
lowercase_ : Tuple = config.hidden_size
if "weight" in key:
lowercase_ : Tuple = val[:dim, :]
lowercase_ : Union[str, Any] = val[dim : dim * 2, :]
lowercase_ : Optional[int] = val[-dim:, :]
else:
lowercase_ : Optional[Any] = val[:dim]
lowercase_ : Any = val[dim : dim * 2]
lowercase_ : Tuple = val[-dim:]
else:
lowercase_ : Optional[Any] = val
return orig_state_dict
def _UpperCamelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase_ : List[Any] = [
'module.v.head.weight',
'module.v.head.bias',
'module.v.head_dist.weight',
'module.v.head_dist.bias',
]
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@torch.no_grad()
def _UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ):
lowercase_ : Dict = get_audio_spectrogram_transformer_config(SCREAMING_SNAKE_CASE_ )
lowercase_ : Optional[int] = {
'ast-finetuned-audioset-10-10-0.4593': (
'https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'
),
'ast-finetuned-audioset-10-10-0.450': (
'https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'
),
'ast-finetuned-audioset-10-10-0.448': (
'https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'
),
'ast-finetuned-audioset-10-10-0.448-v2': (
'https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'
),
'ast-finetuned-audioset-12-12-0.447': (
'https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'
),
'ast-finetuned-audioset-14-14-0.443': (
'https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'
),
'ast-finetuned-audioset-16-16-0.442': (
'https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'
),
'ast-finetuned-speech-commands-v2': (
'https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'
),
}
# load original state_dict
lowercase_ : Dict = model_name_to_url[model_name]
lowercase_ : Optional[Any] = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_ , map_location='cpu' )
# remove some keys
remove_keys(SCREAMING_SNAKE_CASE_ )
# rename some keys
lowercase_ : str = convert_state_dict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# load 🤗 model
lowercase_ : Optional[Any] = ASTForAudioClassification(SCREAMING_SNAKE_CASE_ )
model.eval()
model.load_state_dict(SCREAMING_SNAKE_CASE_ )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
lowercase_ : Tuple = -4.267_7393 if 'speech-commands' not in model_name else -6.84_5978
lowercase_ : str = 4.568_9974 if 'speech-commands' not in model_name else 5.565_4526
lowercase_ : str = 1_024 if 'speech-commands' not in model_name else 128
lowercase_ : Dict = ASTFeatureExtractor(mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ )
if "speech-commands" in model_name:
lowercase_ : Optional[Any] = load_dataset('speech_commands' , 'v0.02' , split='validation' )
lowercase_ : Any = dataset[0]['audio']['array']
else:
lowercase_ : Any = hf_hub_download(
repo_id='nielsr/audio-spectogram-transformer-checkpoint' , filename='sample_audio.flac' , repo_type='dataset' , )
lowercase_ ,lowercase_ : Union[str, Any] = torchaudio.load(SCREAMING_SNAKE_CASE_ )
lowercase_ : str = waveform.squeeze().numpy()
lowercase_ : str = feature_extractor(SCREAMING_SNAKE_CASE_ , sampling_rate=16_000 , return_tensors='pt' )
# forward pass
lowercase_ : Tuple = model(**SCREAMING_SNAKE_CASE_ )
lowercase_ : Tuple = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
lowercase_ : int = torch.tensor([-0.8760, -7.0042, -8.6602] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
lowercase_ : Optional[int] = torch.tensor([-1.1986, -7.0903, -8.2718] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
lowercase_ : Optional[Any] = torch.tensor([-2.6128, -8.0080, -9.4344] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
lowercase_ : List[str] = torch.tensor([-1.5080, -7.4534, -8.8917] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
lowercase_ : List[str] = torch.tensor([-0.5050, -6.5833, -8.0843] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
lowercase_ : Any = torch.tensor([-0.3826, -7.0336, -8.2413] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
lowercase_ : List[str] = torch.tensor([-1.2113, -6.9101, -8.3470] )
elif model_name == "ast-finetuned-speech-commands-v2":
lowercase_ : Optional[Any] = torch.tensor([6.1589, -8.0566, -8.7984] )
else:
raise ValueError('Unknown model name' )
if not torch.allclose(logits[0, :3] , SCREAMING_SNAKE_CASE_ , atol=1e-4 ):
raise ValueError('Logits don\'t match' )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
Path(SCREAMING_SNAKE_CASE_ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )
print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
print(f'''Saving feature extractor to {pytorch_dump_folder_path}''' )
feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_ )
if push_to_hub:
print('Pushing model and feature extractor to the hub...' )
model.push_to_hub(f'''MIT/{model_name}''' )
feature_extractor.push_to_hub(f'''MIT/{model_name}''' )
if __name__ == "__main__":
_A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='ast-finetuned-audioset-10-10-0.4593',
type=str,
help='Name of the Audio Spectrogram Transformer model you\'d like to convert.',
)
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_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 438
| 1
|
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser(
description=(
"Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"
" Distillation"
)
)
parser.add_argument("--model_type", default="bert", choices=["bert"])
parser.add_argument("--model_name", default="bert-base-uncased", type=str)
parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_bert-base-uncased_0247911.pth", type=str)
parser.add_argument("--vocab_transform", action="store_true")
lowercase_ = parser.parse_args()
if args.model_type == "bert":
lowercase_ = BertForMaskedLM.from_pretrained(args.model_name)
lowercase_ = "bert"
else:
raise ValueError("args.model_type should be \"bert\".")
lowercase_ = model.state_dict()
lowercase_ = {}
for w in ["word_embeddings", "position_embeddings"]:
lowercase_ = state_dict[f"""{prefix}.embeddings.{w}.weight"""]
for w in ["weight", "bias"]:
lowercase_ = state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]
lowercase_ = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
lowercase_ = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
]
lowercase_ = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
]
lowercase_ = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
]
lowercase_ = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
]
lowercase_ = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
]
lowercase_ = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
]
lowercase_ = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
]
lowercase_ = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
]
std_idx += 1
lowercase_ = state_dict["cls.predictions.decoder.weight"]
lowercase_ = state_dict["cls.predictions.bias"]
if args.vocab_transform:
for w in ["weight", "bias"]:
lowercase_ = state_dict[f"""cls.predictions.transform.dense.{w}"""]
lowercase_ = state_dict[f"""cls.predictions.transform.LayerNorm.{w}"""]
print(f"""N layers selected for distillation: {std_idx}""")
print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")
print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
torch.save(compressed_sd, args.dump_checkpoint)
| 470
|
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class UpperCAmelCase_ (lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : List[Any] , a_ : str = "▁" , a_ : bool = True , a_ : Union[str, AddedToken] = "<unk>" , a_ : Union[str, AddedToken] = "</s>" , a_ : Union[str, AddedToken] = "<pad>" , )-> List[Any]:
"""simple docstring"""
UpperCAmelCase_ : Dict = {
"""pad""": {"""id""": 0, """token""": pad_token},
"""eos""": {"""id""": 1, """token""": eos_token},
"""unk""": {"""id""": 2, """token""": unk_token},
}
UpperCAmelCase_ : Dict = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
UpperCAmelCase_ : Optional[int] = token_dict["""token"""]
UpperCAmelCase_ : Tuple = Tokenizer(Unigram() )
UpperCAmelCase_ : Optional[Any] = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(""" {2,}""" ) , """ """ ),
normalizers.Lowercase(),
] )
UpperCAmelCase_ : Any = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=a_ , add_prefix_space=a_ ),
pre_tokenizers.Digits(individual_digits=a_ ),
pre_tokenizers.Punctuation(),
] )
UpperCAmelCase_ : str = decoders.Metaspace(replacement=a_ , add_prefix_space=a_ )
UpperCAmelCase_ : List[Any] = TemplateProcessing(
single=f'''$A {self.special_tokens['eos']['token']}''' , special_tokens=[(self.special_tokens["""eos"""]["""token"""], self.special_tokens["""eos"""]["""id"""])] , )
UpperCAmelCase_ : Dict = {
"""model""": """SentencePieceUnigram""",
"""replacement""": replacement,
"""add_prefix_space""": add_prefix_space,
}
super().__init__(a_ , a_ )
def a ( self : int , a_ : Union[str, List[str]] , a_ : int = 80_00 , a_ : bool = True , )-> int:
"""simple docstring"""
UpperCAmelCase_ : int = trainers.UnigramTrainer(
vocab_size=a_ , special_tokens=self.special_tokens_list , show_progress=a_ , )
if isinstance(a_ , a_ ):
UpperCAmelCase_ : str = [files]
self._tokenizer.train(a_ , trainer=a_ )
self.add_unk_id()
def a ( self : List[Any] , a_ : Union[Iterator[str], Iterator[Iterator[str]]] , a_ : int = 80_00 , a_ : bool = True , )-> Any:
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = trainers.UnigramTrainer(
vocab_size=a_ , special_tokens=self.special_tokens_list , show_progress=a_ , )
self._tokenizer.train_from_iterator(a_ , trainer=a_ )
self.add_unk_id()
def a ( self : Dict )-> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ : int = json.loads(self._tokenizer.to_str() )
UpperCAmelCase_ : Any = self.special_tokens["""unk"""]["""id"""]
UpperCAmelCase_ : Tuple = Tokenizer.from_str(json.dumps(a_ ) )
| 470
| 1
|
import unittest
from knapsack import greedy_knapsack as kp
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def _a ( self : str ):
"""simple docstring"""
A__ = [10, 20, 30, 40, 50, 60]
A__ = [2, 4, 6, 8, 10, 12]
A__ = 1_00
self.assertEqual(kp.calc_profit(_snake_case , _snake_case , _snake_case ) , 2_10 )
def _a ( self : Any ):
"""simple docstring"""
self.assertRaisesRegex(_snake_case , 'max_weight must greater than zero.' )
def _a ( self : str ):
"""simple docstring"""
self.assertRaisesRegex(_snake_case , 'Weight can not be negative.' )
def _a ( self : Optional[Any] ):
"""simple docstring"""
self.assertRaisesRegex(_snake_case , 'Profit can not be negative.' )
def _a ( self : Dict ):
"""simple docstring"""
self.assertRaisesRegex(_snake_case , 'max_weight must greater than zero.' )
def _a ( self : List[str] ):
"""simple docstring"""
self.assertRaisesRegex(
_snake_case , 'The length of profit and weight must be same.' )
if __name__ == "__main__":
unittest.main()
| 52
|
import math
import random
def A ( __UpperCamelCase , __UpperCamelCase = False ) -> float:
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
SCREAMING_SNAKE_CASE__ = 0.02
def A ( __UpperCamelCase , __UpperCamelCase ) -> float:
A__ = float(2 * (random.randint(1 , 100 )) - 1 )
for _ in range(__UpperCamelCase ):
# Forward propagation
A__ = sigmoid_function(INITIAL_VALUE * weight )
# How much did we miss?
A__ = (expected / 100) - layer_a
# Error delta
A__ = layer_1_error * sigmoid_function(__UpperCamelCase , __UpperCamelCase )
# Update weight
weight += INITIAL_VALUE * layer_1_delta
return layer_a * 100
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE__ = int(input('''Expected value: '''))
SCREAMING_SNAKE_CASE__ = int(input('''Number of propagations: '''))
print(forward_propagation(expected, number_propagations))
| 52
| 1
|
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
)
# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
parser.add_argument(
'--original_config_file',
default=None,
type=str,
help='The YAML config file corresponding to the original architecture.',
)
parser.add_argument(
'--num_in_channels',
default=None,
type=int,
help='The number of input channels. If `None` number of input channels will be automatically inferred.',
)
parser.add_argument(
'--scheduler_type',
default='pndm',
type=str,
help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
)
parser.add_argument(
'--pipeline_type',
default=None,
type=str,
help=(
'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
'. If `None` pipeline will be automatically inferred.'
),
)
parser.add_argument(
'--image_size',
default=None,
type=int,
help=(
'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
' Base. Use 768 for Stable Diffusion v2.'
),
)
parser.add_argument(
'--prediction_type',
default=None,
type=str,
help=(
'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
),
)
parser.add_argument(
'--extract_ema',
action='store_true',
help=(
'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
),
)
parser.add_argument(
'--upcast_attention',
action='store_true',
help=(
'Whether the attention computation should always be upcasted. This is necessary when running stable'
' diffusion 2.1.'
),
)
parser.add_argument(
'--from_safetensors',
action='store_true',
help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
)
parser.add_argument(
'--to_safetensors',
action='store_true',
help='Whether to store pipeline in safetensors format or not.',
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
parser.add_argument(
'--stable_unclip',
type=str,
default=None,
required=False,
help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
)
parser.add_argument(
'--stable_unclip_prior',
type=str,
default=None,
required=False,
help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
)
parser.add_argument(
'--clip_stats_path',
type=str,
help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
required=False,
)
parser.add_argument(
'--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
)
parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
parser.add_argument(
'--vae_path',
type=str,
default=None,
required=False,
help='Set to a path, hub id to an already converted vae to not convert it again.',
)
UpperCAmelCase = parser.parse_args()
UpperCAmelCase = download_from_original_stable_diffusion_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
prediction_type=args.prediction_type,
model_type=args.pipeline_type,
extract_ema=args.extract_ema,
scheduler_type=args.scheduler_type,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
stable_unclip=args.stable_unclip,
stable_unclip_prior=args.stable_unclip_prior,
clip_stats_path=args.clip_stats_path,
controlnet=args.controlnet,
vae_path=args.vae_path,
)
if args.half:
pipe.to(torch_dtype=torch.floataa)
if args.controlnet:
# only save the controlnet model
pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
else:
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 433
|
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}
class __snake_case( _lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : int = "git_vision_model"
def __init__( self , A_=768 , A_=3072 , A_=12 , A_=12 , A_=3 , A_=224 , A_=16 , A_="quick_gelu" , A_=1e-5 , A_=0.0 , A_=0.0_2 , **A_ , ) -> Dict:
super().__init__(**A_ )
lowerCAmelCase = hidden_size
lowerCAmelCase = intermediate_size
lowerCAmelCase = num_hidden_layers
lowerCAmelCase = num_attention_heads
lowerCAmelCase = num_channels
lowerCAmelCase = patch_size
lowerCAmelCase = image_size
lowerCAmelCase = initializer_range
lowerCAmelCase = attention_dropout
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = hidden_act
@classmethod
def __snake_case ( cls , A_ , **A_ ) -> "PretrainedConfig":
cls._set_token_in_kwargs(A_ )
lowerCAmelCase, lowerCAmelCase = cls.get_config_dict(A_ , **A_ )
# get the vision config dict if we are loading from GITConfig
if config_dict.get("""model_type""" ) == "git":
lowerCAmelCase = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(A_ , **A_ )
class __snake_case( _lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : str = "git"
def __init__( self , A_=None , A_=3_0522 , A_=768 , A_=6 , A_=12 , A_=3072 , A_="gelu" , A_=0.1 , A_=0.1 , A_=1024 , A_=0.0_2 , A_=1e-12 , A_=0 , A_="absolute" , A_=True , A_=False , A_=101 , A_=102 , A_=None , **A_ , ) -> Tuple:
super().__init__(bos_token_id=A_ , eos_token_id=A_ , pad_token_id=A_ , **A_ )
if vision_config is None:
lowerCAmelCase = {}
logger.info("""vision_config is None. initializing the GitVisionConfig with default values.""" )
lowerCAmelCase = GitVisionConfig(**A_ )
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 = initializer_range
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = position_embedding_type
lowerCAmelCase = use_cache
lowerCAmelCase = tie_word_embeddings
lowerCAmelCase = num_image_with_embedding
lowerCAmelCase = bos_token_id
lowerCAmelCase = eos_token_id
def __snake_case ( self ) -> List[Any]:
lowerCAmelCase = copy.deepcopy(self.__dict__ )
lowerCAmelCase = self.vision_config.to_dict()
lowerCAmelCase = self.__class__.model_type
return output
| 433
| 1
|
'''simple docstring'''
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
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
lowerCAmelCase : Dict = '''src/transformers'''
# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase : List[str] = direct_transformers_import(PATH_TO_TRANSFORMERS)
lowerCAmelCase : Optional[int] = transformers.models.auto.configuration_auto.CONFIG_MAPPING
lowerCAmelCase : List[Any] = {
# used to compute the property `self.chunk_length`
'''EncodecConfig''': ['''overlap'''],
# used as `self.bert_model = BertModel(config, ...)`
'''DPRConfig''': True,
# not used in modeling files, but it's an important information
'''FSMTConfig''': ['''langs'''],
# used internally in the configuration class file
'''GPTNeoConfig''': ['''attention_types'''],
# used internally in the configuration class file
'''EsmConfig''': ['''is_folding_model'''],
# used during training (despite we don't have training script for these models yet)
'''Mask2FormerConfig''': ['''ignore_value'''],
# `ignore_value` used during training (despite we don't have training script for these models yet)
# `norm` used in conversion script (despite not using in the modeling file)
'''OneFormerConfig''': ['''ignore_value''', '''norm'''],
# used during preprocessing and collation, see `collating_graphormer.py`
'''GraphormerConfig''': ['''spatial_pos_max'''],
# used internally in the configuration class file
'''T5Config''': ['''feed_forward_proj'''],
# used internally in the configuration class file
# `tokenizer_class` get default value `T5Tokenizer` intentionally
'''MT5Config''': ['''feed_forward_proj''', '''tokenizer_class'''],
'''UMT5Config''': ['''feed_forward_proj''', '''tokenizer_class'''],
# used internally in the configuration class file
'''LongT5Config''': ['''feed_forward_proj'''],
# used internally in the configuration class file
'''SwitchTransformersConfig''': ['''feed_forward_proj'''],
# having default values other than `1e-5` - we can't fix them without breaking
'''BioGptConfig''': ['''layer_norm_eps'''],
# having default values other than `1e-5` - we can't fix them without breaking
'''GLPNConfig''': ['''layer_norm_eps'''],
# having default values other than `1e-5` - we can't fix them without breaking
'''SegformerConfig''': ['''layer_norm_eps'''],
# having default values other than `1e-5` - we can't fix them without breaking
'''CvtConfig''': ['''layer_norm_eps'''],
# having default values other than `1e-5` - we can't fix them without breaking
'''PerceiverConfig''': ['''layer_norm_eps'''],
# used internally to calculate the feature size
'''InformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
# used internally to calculate the feature size
'''TimeSeriesTransformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
# used internally to calculate the feature size
'''AutoformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
# used internally to calculate `mlp_dim`
'''SamVisionConfig''': ['''mlp_ratio'''],
# For (head) training, but so far not implemented
'''ClapAudioConfig''': ['''num_classes'''],
# Not used, but providing useful information to users
'''SpeechT5HifiGanConfig''': ['''sampling_rate'''],
}
# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
{
'''CLIPSegConfig''': True,
'''DeformableDetrConfig''': True,
'''DetaConfig''': True,
'''DinatConfig''': True,
'''DonutSwinConfig''': True,
'''EfficientFormerConfig''': True,
'''FSMTConfig''': True,
'''JukeboxConfig''': True,
'''LayoutLMv2Config''': True,
'''MaskFormerSwinConfig''': True,
'''MT5Config''': True,
'''NatConfig''': True,
'''OneFormerConfig''': True,
'''PerceiverConfig''': True,
'''RagConfig''': True,
'''SpeechT5Config''': True,
'''SwinConfig''': True,
'''Swin2SRConfig''': True,
'''Swinv2Config''': True,
'''SwitchTransformersConfig''': True,
'''TableTransformerConfig''': True,
'''TapasConfig''': True,
'''TransfoXLConfig''': True,
'''UniSpeechConfig''': True,
'''UniSpeechSatConfig''': True,
'''WavLMConfig''': True,
'''WhisperConfig''': True,
# TODO: @Arthur (for `alignment_head` and `alignment_layer`)
'''JukeboxPriorConfig''': True,
# TODO: @Younes (for `is_decoder`)
'''Pix2StructTextConfig''': True,
}
)
def __lowerCAmelCase ( lowerCamelCase : int , lowerCamelCase : Dict , lowerCamelCase : Union[str, Any] , lowerCamelCase : str ):
'''simple docstring'''
__lowerCAmelCase = False
for attribute in attributes:
for modeling_source in source_strings:
# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
if (
f'''config.{attribute}''' in modeling_source
or f'''getattr(config, "{attribute}"''' in modeling_source
or f'''getattr(self.config, "{attribute}"''' in modeling_source
):
__lowerCAmelCase = True
# Deal with multi-line cases
elif (
re.search(
rf'''getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"''' , lowerCamelCase , )
is not None
):
__lowerCAmelCase = True
# `SequenceSummary` is called with `SequenceSummary(config)`
elif attribute in [
"summary_type",
"summary_use_proj",
"summary_activation",
"summary_last_dropout",
"summary_proj_to_labels",
"summary_first_dropout",
]:
if "SequenceSummary" in modeling_source:
__lowerCAmelCase = True
if attribute_used:
break
if attribute_used:
break
# common and important attributes, even if they do not always appear in the modeling files
__lowerCAmelCase = [
"bos_index",
"eos_index",
"pad_index",
"unk_index",
"mask_index",
"image_size",
"use_cache",
"out_features",
"out_indices",
]
__lowerCAmelCase = ["encoder_no_repeat_ngram_size"]
# Special cases to be allowed
__lowerCAmelCase = True
if not attribute_used:
__lowerCAmelCase = False
for attribute in attributes:
# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
if attribute in ["is_encoder_decoder"] and default_value is True:
__lowerCAmelCase = True
elif attribute in ["tie_word_embeddings"] and default_value is False:
__lowerCAmelCase = True
# Allow cases without checking the default value in the configuration class
elif attribute in attributes_to_allow + attributes_used_in_generation:
__lowerCAmelCase = True
elif attribute.endswith("_token_id" ):
__lowerCAmelCase = True
# configuration class specific cases
if not case_allowed:
__lowerCAmelCase = SPECIAL_CASES_TO_ALLOW.get(config_class.__name__ , [] )
__lowerCAmelCase = allowed_cases is True or attribute in allowed_cases
return attribute_used or case_allowed
def __lowerCAmelCase ( lowerCamelCase : int ):
'''simple docstring'''
__lowerCAmelCase = dict(inspect.signature(config_class.__init__ ).parameters )
__lowerCAmelCase = [x for x in list(signature.keys() ) if x not in ["self", "kwargs"]]
__lowerCAmelCase = [signature[param].default for param in parameter_names]
# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
# as one variant is used, the test should pass
__lowerCAmelCase = {}
if len(config_class.attribute_map ) > 0:
__lowerCAmelCase = {v: k for k, v in config_class.attribute_map.items()}
# Get the path to modeling source files
__lowerCAmelCase = inspect.getsourcefile(lowerCamelCase )
__lowerCAmelCase = os.path.dirname(lowerCamelCase )
# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
__lowerCAmelCase = [os.path.join(lowerCamelCase , lowerCamelCase ) for fn in os.listdir(lowerCamelCase ) if fn.startswith("modeling_" )]
# Get the source code strings
__lowerCAmelCase = []
for path in modeling_paths:
if os.path.isfile(lowerCamelCase ):
with open(lowerCamelCase ) as fp:
modeling_sources.append(fp.read() )
__lowerCAmelCase = []
for config_param, default_value in zip(lowerCamelCase , lowerCamelCase ):
# `attributes` here is all the variant names for `config_param`
__lowerCAmelCase = [config_param]
# some configuration classes have non-empty `attribute_map`, and both names could be used in the
# corresponding modeling files. As long as one of them appears, it is fine.
if config_param in reversed_attribute_map:
attributes.append(reversed_attribute_map[config_param] )
if not check_attribute_being_used(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
unused_attributes.append(attributes[0] )
return sorted(lowerCamelCase )
def __lowerCAmelCase ( ):
'''simple docstring'''
__lowerCAmelCase = {}
for _config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in _config_class.__module__:
continue
# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
__lowerCAmelCase = [
cls
for name, cls in inspect.getmembers(
inspect.getmodule(_config_class ) , lambda lowerCamelCase : inspect.isclass(lowerCamelCase )
and issubclass(lowerCamelCase , lowerCamelCase )
and inspect.getmodule(lowerCamelCase ) == inspect.getmodule(_config_class ) , )
]
for config_class in config_classes_in_module:
__lowerCAmelCase = check_config_attributes_being_used(lowerCamelCase )
if len(lowerCamelCase ) > 0:
__lowerCAmelCase = unused_attributes
if len(lowerCamelCase ) > 0:
__lowerCAmelCase = "The following configuration classes contain unused attributes in the corresponding modeling files:\n"
for name, attributes in configs_with_unused_attributes.items():
error += f'''{name}: {attributes}\n'''
raise ValueError(lowerCamelCase )
if __name__ == "__main__":
check_config_attributes()
| 39
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase : Optional[int] = {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'''
),
'''facebook/dpr-question_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'''
),
'''facebook/dpr-reader-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'''
),
'''facebook/dpr-ctx_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'''
),
'''facebook/dpr-question_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'''
),
'''facebook/dpr-reader-multiset-base''': (
'''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'''
),
}
class UpperCAmelCase__ ( UpperCamelCase__ ):
a : Optional[Any] = """dpr"""
def __init__( self , UpperCamelCase=3_0522 , UpperCamelCase=768 , UpperCamelCase=12 , UpperCamelCase=12 , UpperCamelCase=3072 , UpperCamelCase="gelu" , UpperCamelCase=0.1 , UpperCamelCase=0.1 , UpperCamelCase=512 , UpperCamelCase=2 , UpperCamelCase=0.02 , UpperCamelCase=1E-12 , UpperCamelCase=0 , UpperCamelCase="absolute" , UpperCamelCase = 0 , **UpperCamelCase , ) -> Tuple:
super().__init__(pad_token_id=UpperCamelCase , **UpperCamelCase )
__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_size
__lowerCAmelCase = initializer_range
__lowerCAmelCase = layer_norm_eps
__lowerCAmelCase = projection_dim
__lowerCAmelCase = position_embedding_type
| 39
| 1
|
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def _snake_case ( lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
if (
(cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
or (cp >= 0X3_4_0_0 and cp <= 0X4_D_B_F) #
or (cp >= 0X2_0_0_0_0 and cp <= 0X2_A_6_D_F) #
or (cp >= 0X2_A_7_0_0 and cp <= 0X2_B_7_3_F) #
or (cp >= 0X2_B_7_4_0 and cp <= 0X2_B_8_1_F) #
or (cp >= 0X2_B_8_2_0 and cp <= 0X2_C_E_A_F) #
or (cp >= 0XF_9_0_0 and cp <= 0XF_A_F_F)
or (cp >= 0X2_F_8_0_0 and cp <= 0X2_F_A_1_F) #
): #
return True
return False
def _snake_case ( lowerCAmelCase : str ):
"""simple docstring"""
for char in word:
SCREAMING_SNAKE_CASE_ : str = ord(lowerCAmelCase )
if not _is_chinese_char(lowerCAmelCase ):
return 0
return 1
def _snake_case ( lowerCAmelCase : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = set()
for token in tokens:
SCREAMING_SNAKE_CASE_ : Optional[Any] = len(lowerCAmelCase ) > 1 and is_chinese(lowerCAmelCase )
if chinese_word:
word_set.add(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Any = list(lowerCAmelCase )
return word_list
def _snake_case ( lowerCAmelCase : List[str] , lowerCAmelCase : set() ):
"""simple docstring"""
if not chinese_word_set:
return bert_tokens
SCREAMING_SNAKE_CASE_ : Union[str, Any] = max([len(lowerCAmelCase ) for w in chinese_word_set] )
SCREAMING_SNAKE_CASE_ : List[str] = bert_tokens
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : str = 0, len(lowerCAmelCase )
while start < end:
SCREAMING_SNAKE_CASE_ : Any = True
if is_chinese(bert_word[start] ):
SCREAMING_SNAKE_CASE_ : str = min(end - start , lowerCAmelCase )
for i in range(lowerCAmelCase , 1 , -1 ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = "".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
SCREAMING_SNAKE_CASE_ : Dict = "##" + bert_word[j]
SCREAMING_SNAKE_CASE_ : Optional[Any] = start + i
SCREAMING_SNAKE_CASE_ : Optional[int] = False
break
if single_word:
start += 1
return bert_word
def _snake_case ( lowerCAmelCase : List[str] , lowerCAmelCase : LTP , lowerCAmelCase : BertTokenizer ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = []
for i in range(0 , len(lowerCAmelCase ) , 1_0_0 ):
SCREAMING_SNAKE_CASE_ : str = ltp_tokenizer.pipeline(lines[i : i + 1_0_0] , tasks=["cws"] ).cws
SCREAMING_SNAKE_CASE_ : Optional[int] = [get_chinese_word(lowerCAmelCase ) for r in res]
ltp_res.extend(lowerCAmelCase )
assert len(lowerCAmelCase ) == len(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Tuple = []
for i in range(0 , len(lowerCAmelCase ) , 1_0_0 ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = bert_tokenizer(lines[i : i + 1_0_0] , add_special_tokens=lowerCAmelCase , truncation=lowerCAmelCase , max_length=5_1_2 )
bert_res.extend(res["input_ids"] )
assert len(lowerCAmelCase ) == len(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : int = []
for input_ids, chinese_word in zip(lowerCAmelCase , lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ : Optional[int] = []
for id in input_ids:
SCREAMING_SNAKE_CASE_ : List[Any] = bert_tokenizer._convert_id_to_token(lowerCAmelCase )
input_tokens.append(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : List[Any] = add_sub_symbol(lowerCAmelCase , lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : str = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(lowerCAmelCase ):
if token[:2] == "##":
SCREAMING_SNAKE_CASE_ : str = token[2:]
# save chinese tokens' pos
if len(lowerCAmelCase ) == 1 and _is_chinese_char(ord(lowerCAmelCase ) ):
ref_id.append(lowerCAmelCase )
ref_ids.append(lowerCAmelCase )
assert len(lowerCAmelCase ) == len(lowerCAmelCase )
return ref_ids
def _snake_case ( lowerCAmelCase : str ):
"""simple docstring"""
with open(args.file_name , "r" , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE_ : str = f.readlines()
SCREAMING_SNAKE_CASE_ : Optional[Any] = [line.strip() for line in data if len(lowerCAmelCase ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
SCREAMING_SNAKE_CASE_ : Dict = LTP(args.ltp ) # faster in GPU device
SCREAMING_SNAKE_CASE_ : int = BertTokenizer.from_pretrained(args.bert )
SCREAMING_SNAKE_CASE_ : int = prepare_ref(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
with open(args.save_path , "w" , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE_ : Optional[int] = [json.dumps(lowerCAmelCase ) + "\n" for ref in ref_ids]
f.writelines(lowerCAmelCase )
if __name__ == "__main__":
__lowerCamelCase : List[str] = argparse.ArgumentParser(description='''prepare_chinese_ref''')
parser.add_argument(
'''--file_name''',
required=False,
type=str,
default='''./resources/chinese-demo.txt''',
help='''file need process, same as training data in lm''',
)
parser.add_argument(
'''--ltp''',
required=False,
type=str,
default='''./resources/ltp''',
help='''resources for LTP tokenizer, usually a path''',
)
parser.add_argument(
'''--bert''',
required=False,
type=str,
default='''./resources/robert''',
help='''resources for Bert tokenizer''',
)
parser.add_argument(
'''--save_path''',
required=False,
type=str,
default='''./resources/ref.txt''',
help='''path to save res''',
)
__lowerCamelCase : List[Any] = parser.parse_args()
main(args)
| 216
|
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
__lowerCamelCase : Dict = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class a__ ( datasets.BuilderConfig ):
A = None
def _snake_case ( lowerCAmelCase : "pyspark.sql.DataFrame" , lowerCAmelCase : List[int] , ):
"""simple docstring"""
import pyspark
def generate_fn():
SCREAMING_SNAKE_CASE_ : Optional[Any] = df.select("*" , pyspark.sql.functions.spark_partition_id().alias("part_id" ) )
for partition_id in partition_order:
SCREAMING_SNAKE_CASE_ : Optional[Any] = df_with_partition_id.select("*" ).where(f'part_id = {partition_id}' ).drop("part_id" )
SCREAMING_SNAKE_CASE_ : List[str] = partition_df.collect()
SCREAMING_SNAKE_CASE_ : Tuple = 0
for row in rows:
yield f'{partition_id}_{row_id}', row.asDict()
row_id += 1
return generate_fn
class a__ ( _BaseExamplesIterable ):
def __init__( self : Union[str, Any],_A : "pyspark.sql.DataFrame",_A : Any=None,):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = df
SCREAMING_SNAKE_CASE_ : Tuple = partition_order or range(self.df.rdd.getNumPartitions() )
SCREAMING_SNAKE_CASE_ : Optional[Any] = _generate_iterable_examples(self.df,self.partition_order )
def __iter__( self : Union[str, Any] ):
"""simple docstring"""
yield from self.generate_examples_fn()
def __UpperCamelCase ( self : int,_A : np.random.Generator ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(_A )
return SparkExamplesIterable(self.df,partition_order=_A )
def __UpperCamelCase ( self : str,_A : int,_A : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.split_shard_indices_by_worker(_A,_A )
return SparkExamplesIterable(self.df,partition_order=_A )
@property
def __UpperCamelCase ( self : str ):
"""simple docstring"""
return len(self.partition_order )
class a__ ( datasets.DatasetBuilder ):
A = SparkConfig
def __init__( self : List[str],_A : "pyspark.sql.DataFrame",_A : str = None,_A : str = None,**_A : Optional[Any],):
"""simple docstring"""
import pyspark
SCREAMING_SNAKE_CASE_ : Union[str, Any] = pyspark.sql.SparkSession.builder.getOrCreate()
SCREAMING_SNAKE_CASE_ : Optional[int] = df
SCREAMING_SNAKE_CASE_ : Dict = working_dir
super().__init__(
cache_dir=_A,config_name=str(self.df.semanticHash() ),**_A,)
def __UpperCamelCase ( self : int ):
"""simple docstring"""
def create_cache_and_write_probe(_A : int ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir,exist_ok=_A )
SCREAMING_SNAKE_CASE_ : int = os.path.join(self._cache_dir,"fs_test" + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(_A,"a" )
return [probe_file]
if self._spark.conf.get("spark.master","" ).startswith("local" ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
SCREAMING_SNAKE_CASE_ : Optional[Any] = (
self._spark.sparkContext.parallelize(range(1 ),1 ).mapPartitions(_A ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
"When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir" )
def __UpperCamelCase ( self : List[str] ):
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def __UpperCamelCase ( self : Tuple,_A : datasets.download.download_manager.DownloadManager ):
"""simple docstring"""
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def __UpperCamelCase ( self : Union[str, Any],_A : List[Any] ):
"""simple docstring"""
import pyspark
def get_arrow_batch_size(_A : Optional[Any] ):
for batch in it:
yield pa.RecordBatch.from_pydict({"batch_bytes": [batch.nbytes]} )
SCREAMING_SNAKE_CASE_ : Optional[int] = self.df.count()
SCREAMING_SNAKE_CASE_ : str = df_num_rows if df_num_rows <= 100 else 100
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
SCREAMING_SNAKE_CASE_ : List[str] = (
self.df.limit(_A )
.repartition(1 )
.mapInArrow(_A,"batch_bytes: long" )
.agg(pyspark.sql.functions.sum("batch_bytes" ).alias("sample_bytes" ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
SCREAMING_SNAKE_CASE_ : str = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
SCREAMING_SNAKE_CASE_ : int = min(_A,int(approx_total_size / max_shard_size ) )
SCREAMING_SNAKE_CASE_ : List[Any] = self.df.repartition(_A )
def __UpperCamelCase ( self : Any,_A : str,_A : str,_A : int,):
"""simple docstring"""
import pyspark
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ParquetWriter if file_format == "parquet" else ArrowWriter
SCREAMING_SNAKE_CASE_ : Tuple = os.path.join(self._working_dir,os.path.basename(_A ) ) if self._working_dir else fpath
SCREAMING_SNAKE_CASE_ : Tuple = file_format == "parquet"
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
SCREAMING_SNAKE_CASE_ : Dict = self.config.features
SCREAMING_SNAKE_CASE_ : Optional[int] = self._writer_batch_size
SCREAMING_SNAKE_CASE_ : Tuple = self._fs.storage_options
def write_arrow(_A : Optional[int] ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
SCREAMING_SNAKE_CASE_ : Any = pyspark.TaskContext().taskAttemptId()
SCREAMING_SNAKE_CASE_ : List[Any] = next(_A,_A )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]],names=["task_id", "num_examples", "num_bytes"],)
SCREAMING_SNAKE_CASE_ : Optional[int] = 0
SCREAMING_SNAKE_CASE_ : Tuple = writer_class(
features=_A,path=working_fpath.replace("SSSSS",F'{shard_id:05d}' ).replace("TTTTT",F'{task_id:05d}' ),writer_batch_size=_A,storage_options=_A,embed_local_files=_A,)
SCREAMING_SNAKE_CASE_ : Dict = pa.Table.from_batches([first_batch] )
writer.write_table(_A )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Union[str, Any] = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]],names=["task_id", "num_examples", "num_bytes"],)
shard_id += 1
SCREAMING_SNAKE_CASE_ : List[str] = writer_class(
features=writer._features,path=working_fpath.replace("SSSSS",F'{shard_id:05d}' ).replace("TTTTT",F'{task_id:05d}' ),writer_batch_size=_A,storage_options=_A,embed_local_files=_A,)
SCREAMING_SNAKE_CASE_ : List[Any] = pa.Table.from_batches([batch] )
writer.write_table(_A )
if writer._num_bytes > 0:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : str = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]],names=["task_id", "num_examples", "num_bytes"],)
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(_A ) ):
SCREAMING_SNAKE_CASE_ : List[str] = os.path.join(os.path.dirname(_A ),os.path.basename(_A ) )
shutil.move(_A,_A )
SCREAMING_SNAKE_CASE_ : Any = (
self.df.mapInArrow(_A,"task_id: long, num_examples: long, num_bytes: long" )
.groupBy("task_id" )
.agg(
pyspark.sql.functions.sum("num_examples" ).alias("total_num_examples" ),pyspark.sql.functions.sum("num_bytes" ).alias("total_num_bytes" ),pyspark.sql.functions.count("num_bytes" ).alias("num_shards" ),pyspark.sql.functions.collect_list("num_examples" ).alias("shard_lengths" ),)
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def __UpperCamelCase ( self : Optional[int],_A : "datasets.SplitGenerator",_A : str = "arrow",_A : Optional[Union[str, int]] = None,_A : Optional[int] = None,**_A : Union[str, Any],):
"""simple docstring"""
self._validate_cache_dir()
SCREAMING_SNAKE_CASE_ : int = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(_A )
SCREAMING_SNAKE_CASE_ : List[Any] = not is_remote_filesystem(self._fs )
SCREAMING_SNAKE_CASE_ : int = os.path.join if is_local else posixpath.join
SCREAMING_SNAKE_CASE_ : str = "-TTTTT-SSSSS-of-NNNNN"
SCREAMING_SNAKE_CASE_ : Any = F'{self.name}-{split_generator.name}{SUFFIX}.{file_format}'
SCREAMING_SNAKE_CASE_ : Dict = path_join(self._output_dir,_A )
SCREAMING_SNAKE_CASE_ : Optional[Any] = 0
SCREAMING_SNAKE_CASE_ : Optional[int] = 0
SCREAMING_SNAKE_CASE_ : List[str] = 0
SCREAMING_SNAKE_CASE_ : List[Any] = []
SCREAMING_SNAKE_CASE_ : Any = []
for task_id, content in self._prepare_split_single(_A,_A,_A ):
(
(
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) ,
) : Union[str, Any] = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(_A )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = total_num_examples
SCREAMING_SNAKE_CASE_ : Optional[int] = total_num_bytes
# should rename everything at the end
logger.debug(F'Renaming {total_shards} shards.' )
if total_shards > 1:
SCREAMING_SNAKE_CASE_ : int = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
SCREAMING_SNAKE_CASE_ : List[Any] = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
_A : int,_A : int,_A : int,):
rename(
_A,fpath.replace("SSSSS",F'{shard_id:05d}' ).replace("TTTTT",F'{task_id:05d}' ),fpath.replace("TTTTT-SSSSS",F'{global_shard_id:05d}' ).replace("NNNNN",F'{total_shards:05d}' ),)
SCREAMING_SNAKE_CASE_ : List[Any] = []
SCREAMING_SNAKE_CASE_ : List[str] = 0
for i in range(len(_A ) ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Any = task_id_and_num_shards[i]
for shard_id in range(_A ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(_A,len(_A ) ).map(lambda _A : _rename_shard(*_A ) ).collect()
else:
# don't use any pattern
SCREAMING_SNAKE_CASE_ : Any = 0
SCREAMING_SNAKE_CASE_ : Optional[int] = task_id_and_num_shards[0][0]
self._rename(
fpath.replace("SSSSS",F'{shard_id:05d}' ).replace("TTTTT",F'{task_id:05d}' ),fpath.replace(_A,"" ),)
def __UpperCamelCase ( self : List[str],_A : "datasets.SplitGenerator",):
"""simple docstring"""
return SparkExamplesIterable(self.df )
| 216
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Dict = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
"tokenization_deberta": ["DebertaTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : int = ["DebertaTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : Tuple = [
"DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"DebertaForMaskedLM",
"DebertaForQuestionAnswering",
"DebertaForSequenceClassification",
"DebertaForTokenClassification",
"DebertaModel",
"DebertaPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : int = [
"TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFDebertaForMaskedLM",
"TFDebertaForQuestionAnswering",
"TFDebertaForSequenceClassification",
"TFDebertaForTokenClassification",
"TFDebertaModel",
"TFDebertaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ : List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 509
|
"""simple docstring"""
from graphs.minimum_spanning_tree_kruskal import kruskal
def A_ ( ) -> List[str]:
a : List[Any] = 9
a : List[Any] = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
a : int = kruskal(UpperCAmelCase__ , UpperCAmelCase__ )
a : List[Any] = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
assert sorted(UpperCAmelCase__ ) == sorted(UpperCAmelCase__ )
| 509
| 1
|
from __future__ import annotations
class A_ :
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = order
# a_{0} ... a_{k}
lowercase = [1.0] + [0.0] * order
# b_{0} ... b_{k}
lowercase = [1.0] + [0.0] * order
# x[n-1] ... x[n-k]
lowercase = [0.0] * self.order
# y[n-1] ... y[n-k]
lowercase = [0.0] * self.order
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
if len(snake_case ) < self.order:
lowercase = [1.0, *a_coeffs]
if len(snake_case ) != self.order + 1:
lowercase = (
F'''Expected a_coeffs to have {self.order + 1} elements '''
F'''for {self.order}-order filter, got {len(snake_case )}'''
)
raise ValueError(snake_case )
if len(snake_case ) != self.order + 1:
lowercase = (
F'''Expected b_coeffs to have {self.order + 1} elements '''
F'''for {self.order}-order filter, got {len(snake_case )}'''
)
raise ValueError(snake_case )
lowercase = a_coeffs
lowercase = b_coeffs
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = 0.0
# Start at index 1 and do index 0 at the end.
for i in range(1 , self.order + 1 ):
result += (
self.b_coeffs[i] * self.input_history[i - 1]
- self.a_coeffs[i] * self.output_history[i - 1]
)
lowercase = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]
lowercase = self.input_history[:-1]
lowercase = self.output_history[:-1]
lowercase = sample
lowercase = result
return result
| 84
|
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
from .pipeline_kandinsky import KandinskyPipeline
from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
from .text_encoder import MultilingualCLIP
| 440
| 0
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
"""umberto-commoncrawl-cased-v1""": (
"""https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
),
"""umberto-wikipedia-uncased-v1""": (
"""https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
),
}
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
__lowerCamelCase : Optional[Any] ='camembert'
def __init__( self : Union[str, Any] , __lowercase : List[str]=30522 , __lowercase : List[str]=768 , __lowercase : Union[str, Any]=12 , __lowercase : List[str]=12 , __lowercase : str=3072 , __lowercase : Optional[int]="gelu" , __lowercase : List[Any]=0.1 , __lowercase : List[Any]=0.1 , __lowercase : List[str]=512 , __lowercase : Optional[Any]=2 , __lowercase : Tuple=0.02 , __lowercase : List[str]=1E-12 , __lowercase : int=1 , __lowercase : List[str]=0 , __lowercase : int=2 , __lowercase : str="absolute" , __lowercase : Optional[Any]=True , __lowercase : Any=None , **__lowercase : Tuple , ):
'''simple docstring'''
super().__init__(pad_token_id=__lowercase , bos_token_id=__lowercase , eos_token_id=__lowercase , **__lowercase )
__a = vocab_size
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = hidden_act
__a = intermediate_size
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = max_position_embeddings
__a = type_vocab_size
__a = initializer_range
__a = layer_norm_eps
__a = position_embedding_type
__a = use_cache
__a = classifier_dropout
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
@property
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
if self.task == "multiple-choice":
__a = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
__a = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 547
|
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 SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
__lowerCamelCase : torch.FloatTensor
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ , lowerCamelCase__ ):
@register_to_config
def __init__( self : Tuple , __lowercase : int = 16 , __lowercase : int = 88 , __lowercase : Optional[int] = None , __lowercase : Optional[int] = None , __lowercase : int = 1 , __lowercase : float = 0.0 , __lowercase : int = 32 , __lowercase : Optional[int] = None , __lowercase : bool = False , __lowercase : Optional[int] = None , __lowercase : str = "geglu" , __lowercase : bool = True , __lowercase : bool = True , ):
'''simple docstring'''
super().__init__()
__a = num_attention_heads
__a = attention_head_dim
__a = num_attention_heads * attention_head_dim
__a = in_channels
__a = torch.nn.GroupNorm(num_groups=__lowercase , num_channels=__lowercase , eps=1E-6 , affine=__lowercase )
__a = nn.Linear(__lowercase , __lowercase )
# 3. Define transformers blocks
__a = nn.ModuleList(
[
BasicTransformerBlock(
__lowercase , __lowercase , __lowercase , dropout=__lowercase , cross_attention_dim=__lowercase , activation_fn=__lowercase , attention_bias=__lowercase , double_self_attention=__lowercase , norm_elementwise_affine=__lowercase , )
for d in range(__lowercase )
] )
__a = nn.Linear(__lowercase , __lowercase )
def UpperCamelCase_ ( self : Any , __lowercase : Optional[Any] , __lowercase : str=None , __lowercase : List[Any]=None , __lowercase : List[Any]=None , __lowercase : int=1 , __lowercase : Union[str, Any]=None , __lowercase : bool = True , ):
'''simple docstring'''
__a , __a , __a , __a = hidden_states.shape
__a = batch_frames // num_frames
__a = hidden_states
__a = hidden_states[None, :].reshape(__lowercase , __lowercase , __lowercase , __lowercase , __lowercase )
__a = hidden_states.permute(0 , 2 , 1 , 3 , 4 )
__a = self.norm(__lowercase )
__a = hidden_states.permute(0 , 3 , 4 , 2 , 1 ).reshape(batch_size * height * width , __lowercase , __lowercase )
__a = self.proj_in(__lowercase )
# 2. Blocks
for block in self.transformer_blocks:
__a = block(
__lowercase , encoder_hidden_states=__lowercase , timestep=__lowercase , cross_attention_kwargs=__lowercase , class_labels=__lowercase , )
# 3. Output
__a = self.proj_out(__lowercase )
__a = (
hidden_states[None, None, :]
.reshape(__lowercase , __lowercase , __lowercase , __lowercase , __lowercase )
.permute(0 , 3 , 4 , 1 , 2 )
.contiguous()
)
__a = hidden_states.reshape(__lowercase , __lowercase , __lowercase , __lowercase )
__a = hidden_states + residual
if not return_dict:
return (output,)
return TransformerTemporalModelOutput(sample=__lowercase )
| 547
| 1
|
from __future__ import annotations
def UpperCAmelCase_ ( __UpperCAmelCase : list[int] , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : int ) -> None:
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = array[indexa], array[indexa]
def UpperCAmelCase_ ( __UpperCAmelCase : list[int] , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : int ) -> None:
if length > 1:
SCREAMING_SNAKE_CASE_ = int(length / 2 )
for i in range(__UpperCAmelCase , low + middle ):
comp_and_swap(__UpperCAmelCase , __UpperCAmelCase , i + middle , __UpperCAmelCase )
bitonic_merge(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
bitonic_merge(__UpperCAmelCase , low + middle , __UpperCAmelCase , __UpperCAmelCase )
def UpperCAmelCase_ ( __UpperCAmelCase : list[int] , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : int ) -> None:
if length > 1:
SCREAMING_SNAKE_CASE_ = int(length / 2 )
bitonic_sort(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , 1 )
bitonic_sort(__UpperCAmelCase , low + middle , __UpperCAmelCase , 0 )
bitonic_merge(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
if __name__ == "__main__":
lowerCamelCase__ : Optional[Any] = input('Enter numbers separated by a comma:\n').strip()
lowerCamelCase__ : Tuple = [int(item.strip()) for item in user_input.split(',')]
bitonic_sort(unsorted, 0, len(unsorted), 1)
print('\nSorted array in ascending order is: ', end='')
print(*unsorted, sep=', ')
bitonic_merge(unsorted, 0, len(unsorted), 0)
print('Sorted array in descending order is: ', end='')
print(*unsorted, sep=', ')
| 31
|
'''simple docstring'''
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
lowerCamelCase : Optional[Any] = False
try:
lowerCamelCase : Union[str, Any] = _is_package_available("google.colab")
except ModuleNotFoundError:
pass
@input.register
class A__ :
def __init__( self : Tuple , _a : str = None , _a : list = [] ) -> str:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =choices
_SCREAMING_SNAKE_CASE =prompt
if sys.platform == "win32":
_SCREAMING_SNAKE_CASE ='*'
else:
_SCREAMING_SNAKE_CASE ='➔ '
def A ( self : Dict , _a : Union[str, Any] , _a : str = "" ) -> Dict:
'''simple docstring'''
if sys.platform != "win32":
writeColor(self.choices[index] , 32 , _a )
else:
forceWrite(self.choices[index] , _a )
def A ( self : str , _a : int ) -> int:
'''simple docstring'''
if index == self.position:
forceWrite(f" {self.arrow_char} " )
self.write_choice(_a )
else:
forceWrite(f" {self.choices[index]}" )
reset_cursor()
def A ( self : Tuple , _a : Direction , _a : int = 1 ) -> Union[str, Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.position
if direction == Direction.DOWN:
if self.position + 1 >= len(self.choices ):
return
self.position += num_spaces
else:
if self.position - 1 < 0:
return
self.position -= num_spaces
clear_line()
self.print_choice(_a )
move_cursor(_a , direction.name )
self.print_choice(self.position )
@input.mark(KEYMAP['up'] )
def A ( self : int ) -> Optional[Any]:
'''simple docstring'''
self.move_direction(Direction.UP )
@input.mark(KEYMAP['down'] )
def A ( self : Any ) -> List[Any]:
'''simple docstring'''
self.move_direction(Direction.DOWN )
@input.mark(KEYMAP['newline'] )
def A ( self : Any ) -> List[Any]:
'''simple docstring'''
move_cursor(len(self.choices ) - self.position , 'DOWN' )
return self.position
@input.mark(KEYMAP['interrupt'] )
def A ( self : Union[str, Any] ) -> str:
'''simple docstring'''
move_cursor(len(self.choices ) - self.position , 'DOWN' )
raise KeyboardInterrupt
@input.mark_multiple(*[KEYMAP[str(_a )] for number in range(10 )] )
def A ( self : Tuple ) -> List[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =int(chr(self.current_selection ) )
_SCREAMING_SNAKE_CASE =index - self.position
if index == self.position:
return
if index < len(self.choices ):
if self.position > index:
self.move_direction(Direction.UP , -movement )
elif self.position < index:
self.move_direction(Direction.DOWN , _a )
else:
return
else:
return
def A ( self : str , _a : int = 0 ) -> Optional[Any]:
'''simple docstring'''
if self.prompt:
linebreak()
forceWrite(self.prompt , '\n' )
if in_colab:
forceWrite('Please input a choice index (starting from 0), and press enter' , '\n' )
else:
forceWrite('Please select a choice using the arrow or number keys, and selecting with enter' , '\n' )
_SCREAMING_SNAKE_CASE =default_choice
for i in range(len(self.choices ) ):
self.print_choice(_a )
forceWrite('\n' )
move_cursor(len(self.choices ) - self.position , 'UP' )
with cursor.hide():
while True:
if in_colab:
try:
_SCREAMING_SNAKE_CASE =int(builtins.input() )
except ValueError:
_SCREAMING_SNAKE_CASE =default_choice
else:
_SCREAMING_SNAKE_CASE =self.handle_input()
if choice is not None:
reset_cursor()
for _ in range(len(self.choices ) + 1 ):
move_cursor(1 , 'UP' )
clear_line()
self.write_choice(_a , '\n' )
return choice
| 405
| 0
|
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : str ):
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
A__ = 1
A__ = 1
while repunit:
A__ = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def _snake_case ( UpperCAmelCase_ : Dict = 100_0000 ):
A__ = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(_A ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f"""{solution() = }""")
| 719
|
"""simple docstring"""
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_ : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ : Any = ['model.decoder.embed_positions.weights']
def _snake_case ( UpperCAmelCase_ : Optional[int] ):
if "emb" in name:
A__ = name.replace("""emb""" , """model.decoder.embed_tokens""" )
if "transformer" in name:
A__ = name.replace("""transformer""" , """model.decoder""" )
if "cross_attention" in name:
A__ = name.replace("""cross_attention""" , """encoder_attn""" )
if "linear1" in name:
A__ = name.replace("""linear1""" , """fc1""" )
if "linear2" in name:
A__ = name.replace("""linear2""" , """fc2""" )
if "norm1" in name:
A__ = name.replace("""norm1""" , """self_attn_layer_norm""" )
if "norm_cross" in name:
A__ = name.replace("""norm_cross""" , """encoder_attn_layer_norm""" )
if "norm2" in name:
A__ = name.replace("""norm2""" , """final_layer_norm""" )
if "out_norm" in name:
A__ = name.replace("""out_norm""" , """model.decoder.layer_norm""" )
if "linears" in name:
A__ = name.replace("""linears""" , """lm_heads""" )
if "condition_provider.conditioners.description.output_proj" in name:
A__ = name.replace("""condition_provider.conditioners.description.output_proj""" , """enc_to_dec_proj""" )
return name
def _snake_case ( UpperCAmelCase_ : OrderedDict , UpperCAmelCase_ : int ):
A__ = list(state_dict.keys() )
A__ = {}
for key in keys:
A__ = state_dict.pop(UpperCAmelCase_ )
A__ = rename_keys(UpperCAmelCase_ )
if "in_proj_weight" in key:
# split fused qkv proj
A__ = val[:hidden_size, :]
A__ = val[hidden_size : 2 * hidden_size, :]
A__ = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
A__ = val
else:
A__ = val
return state_dict, enc_dec_proj_state_dict
def _snake_case ( UpperCAmelCase_ : str ):
if checkpoint == "small":
# default config values
A__ = 1024
A__ = 24
A__ = 16
elif checkpoint == "medium":
A__ = 1536
A__ = 48
A__ = 24
elif checkpoint == "large":
A__ = 2048
A__ = 48
A__ = 32
else:
raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}.""" )
A__ = MusicgenDecoderConfig(
hidden_size=UpperCAmelCase_ , ffn_dim=hidden_size * 4 , num_hidden_layers=UpperCAmelCase_ , num_attention_heads=UpperCAmelCase_ , )
return config
@torch.no_grad()
def _snake_case ( UpperCAmelCase_ : int , UpperCAmelCase_ : List[Any]=None , UpperCAmelCase_ : Tuple=None , UpperCAmelCase_ : Any="cpu" ):
A__ = MusicGen.get_pretrained(UpperCAmelCase_ , device=UpperCAmelCase_ )
A__ = decoder_config_from_checkpoint(UpperCAmelCase_ )
A__ = fairseq_model.lm.state_dict()
A__ , A__ = rename_state_dict(
UpperCAmelCase_ , hidden_size=decoder_config.hidden_size )
A__ = TaEncoderModel.from_pretrained("""t5-base""" )
A__ = EncodecModel.from_pretrained("""facebook/encodec_32khz""" )
A__ = MusicgenForCausalLM(UpperCAmelCase_ ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
A__ , A__ = decoder.load_state_dict(UpperCAmelCase_ , strict=UpperCAmelCase_ )
for key in missing_keys.copy():
if key.startswith(("""text_encoder""", """audio_encoder""") ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(UpperCAmelCase_ )
if len(UpperCAmelCase_ ) > 0:
raise ValueError(F"""Missing key(s) in state_dict: {missing_keys}""" )
if len(UpperCAmelCase_ ) > 0:
raise ValueError(F"""Unexpected key(s) in state_dict: {unexpected_keys}""" )
# init the composite model
A__ = MusicgenForConditionalGeneration(text_encoder=UpperCAmelCase_ , audio_encoder=UpperCAmelCase_ , decoder=UpperCAmelCase_ )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(UpperCAmelCase_ )
# check we can do a forward pass
A__ = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
A__ = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
A__ = model(input_ids=UpperCAmelCase_ , decoder_input_ids=UpperCAmelCase_ ).logits
if logits.shape != (8, 1, 2048):
raise ValueError("""Incorrect shape for logits""" )
# now construct the processor
A__ = AutoTokenizer.from_pretrained("""t5-base""" )
A__ = AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz""" , padding_side="""left""" )
A__ = MusicgenProcessor(feature_extractor=UpperCAmelCase_ , tokenizer=UpperCAmelCase_ )
# set the appropriate bos/pad token ids
A__ = 2048
A__ = 2048
# set other default generation config params
A__ = int(30 * audio_encoder.config.frame_rate )
A__ = True
A__ = 3.0
if pytorch_dump_folder is not None:
Path(UpperCAmelCase_ ).mkdir(exist_ok=UpperCAmelCase_ )
logger.info(F"""Saving model {checkpoint} to {pytorch_dump_folder}""" )
model.save_pretrained(UpperCAmelCase_ )
processor.save_pretrained(UpperCAmelCase_ )
if repo_id:
logger.info(F"""Pushing model {checkpoint} to {repo_id}""" )
model.push_to_hub(UpperCAmelCase_ )
processor.push_to_hub(UpperCAmelCase_ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint',
default='small',
type=str,
help='Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.',
)
parser.add_argument(
'--pytorch_dump_folder',
required=True,
default=None,
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
)
parser.add_argument(
'--device', default='cpu', type=str, help='Torch device to run the conversion, either cpu or cuda.'
)
SCREAMING_SNAKE_CASE_ : Tuple = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 500
| 0
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}
class lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
_A = 'focalnet'
def __init__( self , _A=224 , _A=4 , _A=3 , _A=96 , _A=False , _A=[192, 384, 768, 768] , _A=[2, 2, 6, 2] , _A=[2, 2, 2, 2] , _A=[3, 3, 3, 3] , _A="gelu" , _A=4.0 , _A=0.0 , _A=0.1 , _A=False , _A=1E-4 , _A=False , _A=False , _A=False , _A=0.02 , _A=1E-5 , _A=32 , _A=None , _A=None , **_A , ) -> List[str]:
super().__init__(**_A )
__a : Tuple = image_size
__a : str = patch_size
__a : List[Any] = num_channels
__a : Union[str, Any] = embed_dim
__a : Dict = use_conv_embed
__a : List[str] = hidden_sizes
__a : Optional[int] = depths
__a : List[str] = focal_levels
__a : str = focal_windows
__a : List[str] = hidden_act
__a : int = mlp_ratio
__a : Optional[int] = hidden_dropout_prob
__a : Optional[Any] = drop_path_rate
__a : Union[str, Any] = use_layerscale
__a : Optional[int] = layerscale_value
__a : List[Any] = use_post_layernorm
__a : str = use_post_layernorm_in_modulation
__a : str = normalize_modulator
__a : int = initializer_range
__a : int = layer_norm_eps
__a : str = encoder_stride
__a : Optional[int] = ['stem'] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
__a , __a : Any = get_aligned_output_features_output_indices(
out_features=_A , out_indices=_A , stage_names=self.stage_names )
| 597
|
'''simple docstring'''
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.utils import logging
SCREAMING_SNAKE_CASE_ = ["bart.large", "bart.large.mnli", "bart.large.cnn", "bart_xsum/model.pt"]
SCREAMING_SNAKE_CASE_ = {"bart.large": BartModel, "bart.large.mnli": BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse("0.9.0"):
raise Exception("requires fairseq >= 0.9.0")
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = " Hello world! cécé herlolip"
SCREAMING_SNAKE_CASE_ = [
("model.classification_heads.mnli.dense.weight", "classification_head.dense.weight"),
("model.classification_heads.mnli.dense.bias", "classification_head.dense.bias"),
("model.classification_heads.mnli.out_proj.weight", "classification_head.out_proj.weight"),
("model.classification_heads.mnli.out_proj.bias", "classification_head.out_proj.bias"),
]
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE__ ):
__a : Dict = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decoder.version',
'_float_tensor',
]
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
__a : Dict = dct.pop(SCREAMING_SNAKE_CASE__ )
__a : Dict = val
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE__ ):
__a : Dict = torch.load(SCREAMING_SNAKE_CASE__ , map_location='cpu' )
__a : Dict = torch.hub.load('pytorch/fairseq' , 'bart.large.cnn' ).eval()
hub_interface.model.load_state_dict(sd['model'] )
return hub_interface
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE__ ):
__a , __a : Dict = emb.weight.shape
__a : Optional[Any] = nn.Linear(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , bias=SCREAMING_SNAKE_CASE__ )
__a : List[Any] = emb.weight.data
return lin_layer
@torch.no_grad()
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ):
if not os.path.exists(SCREAMING_SNAKE_CASE__ ):
__a : Tuple = torch.hub.load('pytorch/fairseq' , SCREAMING_SNAKE_CASE__ ).eval()
else:
__a : Optional[int] = load_xsum_checkpoint(SCREAMING_SNAKE_CASE__ )
bart.model.upgrade_state_dict(bart.model.state_dict() )
if hf_checkpoint_name is None:
__a : List[str] = checkpoint_path.replace('.' , '-' )
__a : Optional[Any] = BartConfig.from_pretrained(SCREAMING_SNAKE_CASE__ )
__a : Union[str, Any] = bart.encode(SCREAMING_SNAKE_CASE__ ).unsqueeze(0 )
__a : List[str] = BartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ ).encode(SCREAMING_SNAKE_CASE__ , return_tensors='pt' ).unsqueeze(0 )
if not torch.eq(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).all():
raise ValueError(
f'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}''' )
if checkpoint_path == "bart.large.mnli":
__a : List[Any] = bart.state_dict()
remove_ignore_keys_(SCREAMING_SNAKE_CASE__ )
__a : str = state_dict['model.decoder.embed_tokens.weight']
for src, dest in mnli_rename_keys:
rename_key(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__a : Dict = BartForSequenceClassification(SCREAMING_SNAKE_CASE__ ).eval()
model.load_state_dict(SCREAMING_SNAKE_CASE__ )
__a : Any = bart.predict('mnli' , SCREAMING_SNAKE_CASE__ , return_logits=SCREAMING_SNAKE_CASE__ )
__a : Optional[Any] = model(SCREAMING_SNAKE_CASE__ )[0] # logits
else: # no classification heads to worry about
__a : Dict = bart.model.state_dict()
remove_ignore_keys_(SCREAMING_SNAKE_CASE__ )
__a : Optional[Any] = state_dict['decoder.embed_tokens.weight']
__a : List[Any] = bart.extract_features(SCREAMING_SNAKE_CASE__ )
if hf_checkpoint_name == "facebook/bart-large":
__a : Dict = BartModel(SCREAMING_SNAKE_CASE__ ).eval()
model.load_state_dict(SCREAMING_SNAKE_CASE__ )
__a : str = model(SCREAMING_SNAKE_CASE__ ).model[0]
else:
__a : Optional[Any] = BartForConditionalGeneration(SCREAMING_SNAKE_CASE__ ).eval() # an existing summarization ckpt
model.model.load_state_dict(SCREAMING_SNAKE_CASE__ )
if hasattr(SCREAMING_SNAKE_CASE__ , 'lm_head' ):
__a : Optional[int] = make_linear_from_emb(model.model.shared )
__a : List[Any] = model.model(SCREAMING_SNAKE_CASE__ )[0]
# Check results
if fairseq_output.shape != new_model_outputs.shape:
raise ValueError(
f'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}''' )
if (fairseq_output != new_model_outputs).any().item():
raise ValueError('Some values in `fairseq_output` are different from `new_model_outputs`' )
Path(SCREAMING_SNAKE_CASE__ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__ )
model.save_pretrained(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
)
parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument(
"--hf_config", default=None, type=str, help="Which huggingface architecture to use: bart-large-xsum"
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)
| 597
| 1
|
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
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 MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
from transformers.models.mobilevitva.modeling_mobilevitva import (
MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
make_divisible,
)
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def __magic_name__ ( self : Optional[Any] ):
'''simple docstring'''
snake_case__ : str = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(snake_case_ , '''width_multiplier''' ) )
class a :
"""simple docstring"""
def __init__( self : List[str] , snake_case_ : Optional[int] , snake_case_ : Dict=1_3 , snake_case_ : Any=6_4 , snake_case_ : Dict=2 , snake_case_ : Optional[int]=3 , snake_case_ : str="swish" , snake_case_ : str=3 , snake_case_ : Union[str, Any]=3_2 , snake_case_ : Optional[Any]=0.1 , snake_case_ : Any=0.0_2 , snake_case_ : int=True , snake_case_ : Tuple=True , snake_case_ : Dict=1_0 , snake_case_ : Optional[int]=None , snake_case_ : str=0.2_5 , snake_case_ : List[Any]=0.0 , snake_case_ : Optional[Any]=0.0 , ):
'''simple docstring'''
snake_case__ : List[Any] = parent
snake_case__ : Dict = batch_size
snake_case__ : Dict = image_size
snake_case__ : Tuple = patch_size
snake_case__ : Tuple = num_channels
snake_case__ : Tuple = make_divisible(5_1_2 * width_multiplier , divisor=8 )
snake_case__ : Optional[int] = hidden_act
snake_case__ : int = conv_kernel_size
snake_case__ : Optional[int] = output_stride
snake_case__ : List[Any] = classifier_dropout_prob
snake_case__ : int = use_labels
snake_case__ : Optional[Any] = is_training
snake_case__ : int = num_labels
snake_case__ : str = initializer_range
snake_case__ : Dict = scope
snake_case__ : Tuple = width_multiplier
snake_case__ : Optional[Any] = ffn_dropout
snake_case__ : Dict = attn_dropout
def __magic_name__ ( self : str ):
'''simple docstring'''
snake_case__ : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case__ : List[str] = None
snake_case__ : Union[str, Any] = None
if self.use_labels:
snake_case__ : Tuple = ids_tensor([self.batch_size] , self.num_labels )
snake_case__ : Optional[Any] = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
snake_case__ : Optional[int] = self.get_config()
return config, pixel_values, labels, pixel_labels
def __magic_name__ ( self : List[str] ):
'''simple docstring'''
return MobileViTVaConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , width_multiplier=self.width_multiplier , ffn_dropout=self.ffn_dropout_prob , attn_dropout=self.attn_dropout_prob , )
def __magic_name__ ( self : Optional[Any] , snake_case_ : Optional[int] , snake_case_ : Optional[int] , snake_case_ : Optional[Any] , snake_case_ : Optional[Any] ):
'''simple docstring'''
snake_case__ : Optional[Any] = MobileViTVaModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
snake_case__ : Dict = model(snake_case_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __magic_name__ ( self : Union[str, Any] , snake_case_ : Optional[int] , snake_case_ : List[Any] , snake_case_ : Tuple , snake_case_ : Optional[Any] ):
'''simple docstring'''
snake_case__ : Optional[int] = self.num_labels
snake_case__ : Optional[Any] = MobileViTVaForImageClassification(snake_case_ )
model.to(snake_case_ )
model.eval()
snake_case__ : int = model(snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __magic_name__ ( self : int , snake_case_ : List[Any] , snake_case_ : Dict , snake_case_ : Any , snake_case_ : List[str] ):
'''simple docstring'''
snake_case__ : Dict = self.num_labels
snake_case__ : Any = MobileViTVaForSemanticSegmentation(snake_case_ )
model.to(snake_case_ )
model.eval()
snake_case__ : str = model(snake_case_ )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
snake_case__ : Optional[int] = model(snake_case_ , labels=snake_case_ )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __magic_name__ ( self : str ):
'''simple docstring'''
snake_case__ : Dict = self.prepare_config_and_inputs()
snake_case__ : List[str] = config_and_inputs
snake_case__ : List[str] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = (
(MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
if is_torch_available()
else ()
)
__UpperCAmelCase = (
{
"""feature-extraction""": MobileViTVaModel,
"""image-classification""": MobileViTVaForImageClassification,
"""image-segmentation""": MobileViTVaForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__UpperCAmelCase = False
__UpperCAmelCase = False
__UpperCAmelCase = False
__UpperCAmelCase = False
def __magic_name__ ( self : int ):
'''simple docstring'''
snake_case__ : str = MobileViTVaModelTester(self )
snake_case__ : Union[str, Any] = MobileViTVaConfigTester(self , config_class=snake_case_ , has_text_modality=snake_case_ )
def __magic_name__ ( self : List[str] ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''MobileViTV2 does not use inputs_embeds''' )
def __magic_name__ ( self : Optional[int] ):
'''simple docstring'''
pass
@unittest.skip(reason='''MobileViTV2 does not support input and output embeddings''' )
def __magic_name__ ( self : Optional[int] ):
'''simple docstring'''
pass
@unittest.skip(reason='''MobileViTV2 does not output attentions''' )
def __magic_name__ ( self : int ):
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(reason='''Got `CUDA error: misaligned address` for tests after this one being run.''' )
def __magic_name__ ( self : int ):
'''simple docstring'''
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __magic_name__ ( self : Optional[Any] ):
'''simple docstring'''
pass
def __magic_name__ ( self : Optional[Any] ):
'''simple docstring'''
snake_case__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case__ : Optional[int] = model_class(snake_case_ )
snake_case__ : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case__ : Any = [*signature.parameters.keys()]
snake_case__ : Optional[int] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , snake_case_ )
def __magic_name__ ( self : List[str] ):
'''simple docstring'''
snake_case__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
def __magic_name__ ( self : List[str] ):
'''simple docstring'''
def check_hidden_states_output(snake_case_ : Tuple , snake_case_ : Optional[Any] , snake_case_ : Tuple ):
snake_case__ : Optional[Any] = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
snake_case__ : Tuple = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
snake_case__ : Union[str, Any] = outputs.hidden_states
snake_case__ : Any = 5
self.assertEqual(len(snake_case_ ) , snake_case_ )
# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
snake_case__ : Dict = 2
for i in range(len(snake_case_ ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
divisor *= 2
self.assertEqual(self.model_tester.output_stride , divisor // 2 )
snake_case__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case__ : List[str] = 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"]
snake_case__ : int = True
check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ )
def __magic_name__ ( self : int ):
'''simple docstring'''
snake_case__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case_ )
def __magic_name__ ( self : Any ):
'''simple docstring'''
snake_case__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*snake_case_ )
@slow
def __magic_name__ ( self : List[Any] ):
'''simple docstring'''
for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case__ : Any = MobileViTVaModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def _a ( ):
"""simple docstring"""
snake_case__ : Optional[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class a ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __magic_name__ ( self : Optional[int] ):
'''simple docstring'''
return (
MobileViTImageProcessor.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256''' )
if is_vision_available()
else None
)
@slow
def __magic_name__ ( self : Tuple ):
'''simple docstring'''
snake_case__ : Tuple = MobileViTVaForImageClassification.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256''' ).to(
snake_case_ )
snake_case__ : Any = self.default_image_processor
snake_case__ : Tuple = prepare_img()
snake_case__ : str = image_processor(images=snake_case_ , return_tensors='''pt''' ).to(snake_case_ )
# forward pass
with torch.no_grad():
snake_case__ : Any = model(**snake_case_ )
# verify the logits
snake_case__ : Dict = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , snake_case_ )
snake_case__ : Tuple = torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case_ , atol=1e-4 ) )
@slow
def __magic_name__ ( self : Tuple ):
'''simple docstring'''
snake_case__ : List[Any] = MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''' )
snake_case__ : Any = model.to(snake_case_ )
snake_case__ : Tuple = MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''' )
snake_case__ : str = prepare_img()
snake_case__ : Union[str, Any] = image_processor(images=snake_case_ , return_tensors='''pt''' ).to(snake_case_ )
# forward pass
with torch.no_grad():
snake_case__ : Union[str, Any] = model(**snake_case_ )
snake_case__ : Tuple = outputs.logits
# verify the logits
snake_case__ : Optional[Any] = torch.Size((1, 2_1, 3_2, 3_2) )
self.assertEqual(logits.shape , snake_case_ )
snake_case__ : List[Any] = torch.tensor(
[
[[7.0_8_6_3, 7.1_5_2_5, 6.8_2_0_1], [6.6_9_3_1, 6.8_7_7_0, 6.8_9_3_3], [6.2_9_7_8, 7.0_3_6_6, 6.9_6_3_6]],
[[-3.7_1_3_4, -3.6_7_1_2, -3.6_6_7_5], [-3.5_8_2_5, -3.3_5_4_9, -3.4_7_7_7], [-3.3_4_3_5, -3.3_9_7_9, -3.2_8_5_7]],
[[-2.9_3_2_9, -2.8_0_0_3, -2.7_3_6_9], [-3.0_5_6_4, -2.4_7_8_0, -2.0_2_0_7], [-2.6_8_8_9, -1.9_2_9_8, -1.7_6_4_0]],
] , device=snake_case_ , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , snake_case_ , atol=1e-4 ) )
@slow
def __magic_name__ ( self : List[Any] ):
'''simple docstring'''
snake_case__ : Optional[int] = MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''' )
snake_case__ : List[str] = model.to(snake_case_ )
snake_case__ : Optional[int] = MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''' )
snake_case__ : str = prepare_img()
snake_case__ : str = image_processor(images=snake_case_ , return_tensors='''pt''' ).to(snake_case_ )
# forward pass
with torch.no_grad():
snake_case__ : Any = model(**snake_case_ )
snake_case__ : str = outputs.logits.detach().cpu()
snake_case__ : int = image_processor.post_process_semantic_segmentation(outputs=snake_case_ , target_sizes=[(5_0, 6_0)] )
snake_case__ : int = torch.Size((5_0, 6_0) )
self.assertEqual(segmentation[0].shape , snake_case_ )
snake_case__ : str = image_processor.post_process_semantic_segmentation(outputs=snake_case_ )
snake_case__ : Any = torch.Size((3_2, 3_2) )
self.assertEqual(segmentation[0].shape , snake_case_ )
| 717
|
'''simple docstring'''
from collections.abc import Sequence
def _a ( __lowerCAmelCase : Sequence[int] | None = None ):
"""simple docstring"""
if nums is None or not nums:
raise ValueError('''Input sequence should not be empty''' )
snake_case__ : Optional[Any] = nums[0]
for i in range(1 , len(__lowerCAmelCase ) ):
snake_case__ : Tuple = nums[i]
snake_case__ : Optional[int] = max(__lowerCAmelCase , ans + num , __lowerCAmelCase )
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
lowerCAmelCase__ : str = int(input("""Enter number of elements : """).strip())
lowerCAmelCase__ : Dict = list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
print(max_subsequence_sum(array))
| 502
| 0
|
import sys
lowerCamelCase : int = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def _SCREAMING_SNAKE_CASE ( lowercase : str = N ):
'''simple docstring'''
lowerCamelCase_ = -sys.maxsize - 1
for i in range(len(lowercase ) - 12 ):
lowerCamelCase_ = 1
for j in range(13 ):
product *= int(n[i + j] )
if product > largest_product:
lowerCamelCase_ = product
return largest_product
if __name__ == "__main__":
print(F"""{solution() = }""")
| 70
|
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
lowerCamelCase : int = False
class A( unittest.TestCase ):
'''simple docstring'''
def a__ ( self : int , A_ : Dict=32 ) -> Any:
"""simple docstring"""
set_seed(0 )
lowerCamelCase_ = UNetaDModel(sample_size=A_ , in_channels=3 , out_channels=3 )
lowerCamelCase_ = torch.optim.SGD(model.parameters() , lr=0.0001 )
return model, optimizer
@slow
def a__ ( self : int ) -> str:
"""simple docstring"""
lowerCamelCase_ = 'cpu' # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
lowerCamelCase_ = DDPMScheduler(
num_train_timesteps=1000 , beta_start=0.0001 , beta_end=0.02 , beta_schedule='linear' , clip_sample=A_ , )
lowerCamelCase_ = DDIMScheduler(
num_train_timesteps=1000 , beta_start=0.0001 , beta_end=0.02 , beta_schedule='linear' , clip_sample=A_ , )
assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps
# shared batches for DDPM and DDIM
set_seed(0 )
lowerCamelCase_ = [torch.randn((4, 3, 32, 32) ).clip(-1 , 1 ).to(A_ ) for _ in range(4 )]
lowerCamelCase_ = [torch.randn((4, 3, 32, 32) ).to(A_ ) for _ in range(4 )]
lowerCamelCase_ = [torch.randint(0 , 1000 , (4,) ).long().to(A_ ) for _ in range(4 )]
# train with a DDPM scheduler
lowerCamelCase_ , lowerCamelCase_ = self.get_model_optimizer(resolution=32 )
model.train().to(A_ )
for i in range(4 ):
optimizer.zero_grad()
lowerCamelCase_ = ddpm_scheduler.add_noise(clean_images[i] , noise[i] , timesteps[i] )
lowerCamelCase_ = model(A_ , timesteps[i] ).sample
lowerCamelCase_ = torch.nn.functional.mse_loss(A_ , noise[i] )
loss.backward()
optimizer.step()
del model, optimizer
# recreate the model and optimizer, and retry with DDIM
lowerCamelCase_ , lowerCamelCase_ = self.get_model_optimizer(resolution=32 )
model.train().to(A_ )
for i in range(4 ):
optimizer.zero_grad()
lowerCamelCase_ = ddim_scheduler.add_noise(clean_images[i] , noise[i] , timesteps[i] )
lowerCamelCase_ = model(A_ , timesteps[i] ).sample
lowerCamelCase_ = torch.nn.functional.mse_loss(A_ , noise[i] )
loss.backward()
optimizer.step()
del model, optimizer
self.assertTrue(torch.allclose(A_ , A_ , atol=1E-5 ) )
self.assertTrue(torch.allclose(A_ , A_ , atol=1E-5 ) )
| 70
| 1
|
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : Any = {
"post_extract_proj": "feature_projection.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.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
for attribute in key.split(""".""" ):
__magic_name__ : List[str] =getattr(lowerCamelCase , lowerCamelCase )
if weight_type is not None:
__magic_name__ : Optional[int] =getattr(lowerCamelCase , lowerCamelCase ).shape
else:
__magic_name__ : Tuple =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":
__magic_name__ : Any =value
elif weight_type == "weight_g":
__magic_name__ : Optional[Any] =value
elif weight_type == "weight_v":
__magic_name__ : int =value
elif weight_type == "bias":
__magic_name__ : int =value
else:
__magic_name__ : int =value
logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : Any =[]
__magic_name__ : Optional[int] =fairseq_model.state_dict()
__magic_name__ : Optional[Any] =hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
__magic_name__ : int =False
if "conv_layers" in name:
load_conv_layer(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , hf_model.config.feat_extract_norm == """group""" , )
__magic_name__ : Optional[int] =True
else:
for key, mapped_key in MAPPING.items():
__magic_name__ : List[str] ="""hubert.""" + 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] and not is_finetuned):
__magic_name__ : Optional[int] =True
if "*" in mapped_key:
__magic_name__ : Any =name.split(lowerCamelCase )[0].split(""".""" )[-2]
__magic_name__ : Optional[int] =mapped_key.replace("""*""" , lowerCamelCase )
if "weight_g" in name:
__magic_name__ : Optional[int] ="""weight_g"""
elif "weight_v" in name:
__magic_name__ : int ="""weight_v"""
elif "weight" in name:
__magic_name__ : Optional[Any] ="""weight"""
elif "bias" in name:
__magic_name__ : List[Any] ="""bias"""
else:
__magic_name__ : Union[str, Any] =None
set_recursively(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
continue
if not is_used:
unused_weights.append(lowerCamelCase )
logger.warning(F"Unused weights: {unused_weights}" )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : Any =full_name.split("""conv_layers.""" )[-1]
__magic_name__ : Any =name.split(""".""" )
__magic_name__ : Any =int(items[0] )
__magic_name__ : Union[str, Any] =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."
)
__magic_name__ : Any =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."
)
__magic_name__ : Union[str, Any] =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."
)
__magic_name__ : Optional[int] =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."
)
__magic_name__ : Dict =value
logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
else:
unused_weights.append(lowerCamelCase )
@torch.no_grad()
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=True ):
if config_path is not None:
__magic_name__ : List[Any] =HubertConfig.from_pretrained(lowerCamelCase )
else:
__magic_name__ : int =HubertConfig()
if is_finetuned:
if dict_path:
__magic_name__ : List[str] =Dictionary.load(lowerCamelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
__magic_name__ : Union[str, Any] =target_dict.pad_index
__magic_name__ : int =target_dict.bos_index
__magic_name__ : Dict =target_dict.eos_index
__magic_name__ : str =len(target_dict.symbols )
__magic_name__ : List[Any] =os.path.join(lowerCamelCase , """vocab.json""" )
if not os.path.isdir(lowerCamelCase ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowerCamelCase ) )
return
os.makedirs(lowerCamelCase , exist_ok=lowerCamelCase )
with open(lowerCamelCase , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(target_dict.indices , lowerCamelCase )
__magic_name__ : Any =WavaVecaCTCTokenizer(
lowerCamelCase , 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=lowerCamelCase , )
__magic_name__ : List[Any] =True if config.feat_extract_norm == """layer""" else False
__magic_name__ : Optional[Any] =WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=lowerCamelCase , return_attention_mask=lowerCamelCase , )
__magic_name__ : Optional[Any] =WavaVecaProcessor(feature_extractor=lowerCamelCase , tokenizer=lowerCamelCase )
processor.save_pretrained(lowerCamelCase )
__magic_name__ : Tuple =HubertForCTC(lowerCamelCase )
else:
__magic_name__ : Union[str, Any] =HubertModel(lowerCamelCase )
if is_finetuned:
__magic_name__ , __magic_name__ , __magic_name__ : Dict =fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
__magic_name__ , __magic_name__ , __magic_name__ : Any =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
__magic_name__ : str =model[0].eval()
recursively_load_weights(lowerCamelCase , lowerCamelCase , lowerCamelCase )
hf_wavavec.save_pretrained(lowerCamelCase )
if __name__ == "__main__":
UpperCAmelCase_ : Dict = 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(
"--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
UpperCAmelCase_ : Union[str, Any] = parser.parse_args()
convert_hubert_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 367
|
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceClassification,
TFTrainer,
TFTrainingArguments,
)
from transformers.utils import logging as hf_logging
hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = None , ):
__magic_name__ : Optional[int] ={}
if train_file is not None:
__magic_name__ : Optional[int] =[train_file]
if eval_file is not None:
__magic_name__ : Any =[eval_file]
if test_file is not None:
__magic_name__ : int =[test_file]
__magic_name__ : Any =datasets.load_dataset("""csv""" , data_files=lowerCamelCase )
__magic_name__ : Optional[Any] =list(ds[list(files.keys() )[0]].features.keys() )
__magic_name__ : Optional[Any] =features_name.pop(lowerCamelCase )
__magic_name__ : str =list(set(ds[list(files.keys() )[0]][label_name] ) )
__magic_name__ : Union[str, Any] ={label: i for i, label in enumerate(lowerCamelCase )}
__magic_name__ : Dict =tokenizer.model_input_names
__magic_name__ : Any ={}
if len(lowerCamelCase ) == 1:
for k in files.keys():
__magic_name__ : Dict =ds[k].map(
lambda lowerCamelCase : tokenizer.batch_encode_plus(
example[features_name[0]] , truncation=lowerCamelCase , max_length=lowerCamelCase , padding="""max_length""" ) , batched=lowerCamelCase , )
elif len(lowerCamelCase ) == 2:
for k in files.keys():
__magic_name__ : Optional[Any] =ds[k].map(
lambda lowerCamelCase : tokenizer.batch_encode_plus(
(example[features_name[0]], example[features_name[1]]) , truncation=lowerCamelCase , max_length=lowerCamelCase , padding="""max_length""" , ) , batched=lowerCamelCase , )
def gen_train():
for ex in transformed_ds[datasets.Split.TRAIN]:
__magic_name__ : Any ={k: v for k, v in ex.items() if k in input_names}
__magic_name__ : Any =labelaid[ex[label_name]]
yield (d, label)
def gen_val():
for ex in transformed_ds[datasets.Split.VALIDATION]:
__magic_name__ : Dict ={k: v for k, v in ex.items() if k in input_names}
__magic_name__ : str =labelaid[ex[label_name]]
yield (d, label)
def gen_test():
for ex in transformed_ds[datasets.Split.TEST]:
__magic_name__ : Union[str, Any] ={k: v for k, v in ex.items() if k in input_names}
__magic_name__ : Optional[int] =labelaid[ex[label_name]]
yield (d, label)
__magic_name__ : Union[str, Any] =(
tf.data.Dataset.from_generator(
lowerCamelCase , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.TRAIN in transformed_ds
else None
)
if train_ds is not None:
__magic_name__ : Optional[Any] =train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )
__magic_name__ : Optional[Any] =(
tf.data.Dataset.from_generator(
lowerCamelCase , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.VALIDATION in transformed_ds
else None
)
if val_ds is not None:
__magic_name__ : Any =val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) )
__magic_name__ : Any =(
tf.data.Dataset.from_generator(
lowerCamelCase , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.TEST in transformed_ds
else None
)
if test_ds is not None:
__magic_name__ : Optional[int] =test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )
return train_ds, val_ds, test_ds, labelaid
UpperCAmelCase_ : int = logging.getLogger(__name__)
@dataclass
class __A :
UpperCamelCase = field(metadata={"""help""": """Which column contains the label"""} )
UpperCamelCase = field(default=UpperCamelCase__ , metadata={"""help""": """The path of the training file"""} )
UpperCamelCase = field(default=UpperCamelCase__ , metadata={"""help""": """The path of the development file"""} )
UpperCamelCase = field(default=UpperCamelCase__ , metadata={"""help""": """The path of the test file"""} )
UpperCamelCase = 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."""
)
} , )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
@dataclass
class __A :
UpperCamelCase = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
UpperCamelCase = field(default=UpperCamelCase__ , metadata={"""help""": """Set this flag to use fast tokenization."""} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
def lowerCAmelCase_ ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
__magic_name__ : List[Any] =HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
__magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] =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 , )
logger.info(
F"n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1 )}, "
F"16-bits training: {training_args.fpaa}" )
logger.info(F"Training/evaluation parameters {training_args}" )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__magic_name__ : Dict =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 , )
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Optional[Any] =get_tfds(
train_file=data_args.train_file , eval_file=data_args.dev_file , test_file=data_args.test_file , tokenizer=lowerCamelCase , label_column_id=data_args.label_column_id , max_seq_length=data_args.max_seq_length , )
__magic_name__ : Any =AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=len(lowerCamelCase ) , labelaid=lowerCamelCase , idalabel={id: label for label, id in labelaid.items()} , finetuning_task="""text-classification""" , cache_dir=model_args.cache_dir , )
with training_args.strategy.scope():
__magic_name__ : Any =TFAutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_pt=bool(""".bin""" in model_args.model_name_or_path ) , config=lowerCamelCase , cache_dir=model_args.cache_dir , )
def compute_metrics(lowerCamelCase ) -> Dict:
__magic_name__ : Tuple =np.argmax(p.predictions , axis=1 )
return {"acc": (preds == p.label_ids).mean()}
# Initialize our Trainer
__magic_name__ : int =TFTrainer(
model=lowerCamelCase , args=lowerCamelCase , train_dataset=lowerCamelCase , eval_dataset=lowerCamelCase , compute_metrics=lowerCamelCase , )
# Training
if training_args.do_train:
trainer.train()
trainer.save_model()
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
__magic_name__ : List[str] ={}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
__magic_name__ : List[str] =trainer.evaluate()
__magic_name__ : Optional[Any] =os.path.join(training_args.output_dir , """eval_results.txt""" )
with open(lowerCamelCase , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key, value in result.items():
logger.info(F" {key} = {value}" )
writer.write(F"{key} = {value}\n" )
results.update(lowerCamelCase )
return results
if __name__ == "__main__":
main()
| 367
| 1
|
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
lowerCamelCase__ = {
'tokenizer_file': {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
},
}
lowerCamelCase__ = {
'gpt-neox-20b': 20_48,
}
class UpperCamelCase ( snake_case__ ):
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = ["""input_ids""", """attention_mask"""]
def __init__( self : Tuple ,_lowerCAmelCase : Dict=None ,_lowerCAmelCase : Union[str, Any]=None ,_lowerCAmelCase : List[str]=None ,_lowerCAmelCase : Any="<|endoftext|>" ,_lowerCAmelCase : Dict="<|endoftext|>" ,_lowerCAmelCase : Union[str, Any]="<|endoftext|>" ,_lowerCAmelCase : int=False ,**_lowerCAmelCase : List[str] ,):
"""simple docstring"""
super().__init__(
_lowerCAmelCase ,_lowerCAmelCase ,tokenizer_file=_lowerCAmelCase ,unk_token=_lowerCAmelCase ,bos_token=_lowerCAmelCase ,eos_token=_lowerCAmelCase ,add_prefix_space=_lowerCAmelCase ,**_lowerCAmelCase ,)
__snake_case = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" ,_lowerCAmelCase ) != add_prefix_space:
__snake_case = getattr(_lowerCAmelCase ,pre_tok_state.pop("type" ) )
__snake_case = add_prefix_space
__snake_case = pre_tok_class(**_lowerCAmelCase )
__snake_case = add_prefix_space
def UpperCamelCase_ ( self : int ,_lowerCAmelCase : str ,_lowerCAmelCase : Optional[str] = None ):
"""simple docstring"""
__snake_case = self._tokenizer.model.save(_lowerCAmelCase ,name=_lowerCAmelCase )
return tuple(_lowerCAmelCase )
def UpperCamelCase_ ( self : str ,_lowerCAmelCase : "Conversation" ):
"""simple docstring"""
__snake_case = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(_lowerCAmelCase ,add_special_tokens=_lowerCAmelCase ) + [self.eos_token_id] )
if len(_lowerCAmelCase ) > self.model_max_length:
__snake_case = input_ids[-self.model_max_length :]
return input_ids
| 524
|
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase__ = logging.get_logger(__name__)
def _lowerCamelCase( __snake_case , __snake_case ) -> Dict:
__snake_case = []
for i in range(encoder_config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(f"""encoder.deit.blocks.{i}.norm1.weight""", f"""encoder.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((f"""encoder.deit.blocks.{i}.norm1.bias""", f"""encoder.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append(
(f"""encoder.deit.blocks.{i}.attn.proj.weight""", f"""encoder.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append(
(f"""encoder.deit.blocks.{i}.attn.proj.bias""", f"""encoder.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append(
(f"""encoder.deit.blocks.{i}.norm2.weight""", f"""encoder.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((f"""encoder.deit.blocks.{i}.norm2.bias""", f"""encoder.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append(
(f"""encoder.deit.blocks.{i}.mlp.fc1.weight""", f"""encoder.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append(
(f"""encoder.deit.blocks.{i}.mlp.fc1.bias""", f"""encoder.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append(
(f"""encoder.deit.blocks.{i}.mlp.fc2.weight""", f"""encoder.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((f"""encoder.deit.blocks.{i}.mlp.fc2.bias""", f"""encoder.encoder.layer.{i}.output.dense.bias""") )
# cls token, position embeddings and patch embeddings of encoder
rename_keys.extend(
[
("encoder.deit.cls_token", "encoder.embeddings.cls_token"),
("encoder.deit.pos_embed", "encoder.embeddings.position_embeddings"),
("encoder.deit.patch_embed.proj.weight", "encoder.embeddings.patch_embeddings.projection.weight"),
("encoder.deit.patch_embed.proj.bias", "encoder.embeddings.patch_embeddings.projection.bias"),
("encoder.deit.norm.weight", "encoder.layernorm.weight"),
("encoder.deit.norm.bias", "encoder.layernorm.bias"),
] )
return rename_keys
def _lowerCamelCase( __snake_case , __snake_case ) -> Dict:
for i in range(encoder_config.num_hidden_layers ):
# queries, keys and values (only weights, no biases)
__snake_case = state_dict.pop(f"""encoder.deit.blocks.{i}.attn.qkv.weight""" )
__snake_case = in_proj_weight[
: encoder_config.hidden_size, :
]
__snake_case = in_proj_weight[
encoder_config.hidden_size : encoder_config.hidden_size * 2, :
]
__snake_case = in_proj_weight[
-encoder_config.hidden_size :, :
]
def _lowerCamelCase( __snake_case , __snake_case , __snake_case ) -> List[Any]:
__snake_case = dct.pop(__snake_case )
__snake_case = val
def _lowerCamelCase( __snake_case ) -> str:
if "handwritten" in checkpoint_url:
__snake_case = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg" # industry
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" #
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
__snake_case = "https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"
__snake_case = Image.open(requests.get(__snake_case , stream=__snake_case ).raw ).convert("RGB" )
return im
@torch.no_grad()
def _lowerCamelCase( __snake_case , __snake_case ) -> int:
__snake_case = ViTConfig(image_size=384 , qkv_bias=__snake_case )
__snake_case = TrOCRConfig()
# size of the architecture
if "base" in checkpoint_url:
__snake_case = 768
elif "large" in checkpoint_url:
# use ViT-large encoder
__snake_case = 1024
__snake_case = 4096
__snake_case = 24
__snake_case = 16
__snake_case = 1024
else:
raise ValueError("Should either find 'base' or 'large' in checkpoint URL" )
# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
__snake_case = False
__snake_case = "relu"
__snake_case = 1024
__snake_case = True
__snake_case = False
__snake_case = False
# load HuggingFace model
__snake_case = ViTModel(__snake_case , add_pooling_layer=__snake_case )
__snake_case = TrOCRForCausalLM(__snake_case )
__snake_case = VisionEncoderDecoderModel(encoder=__snake_case , decoder=__snake_case )
model.eval()
# load state_dict of original model, rename some keys
__snake_case = torch.hub.load_state_dict_from_url(__snake_case , map_location="cpu" , check_hash=__snake_case )["model"]
__snake_case = create_rename_keys(__snake_case , __snake_case )
for src, dest in rename_keys:
rename_key(__snake_case , __snake_case , __snake_case )
read_in_q_k_v(__snake_case , __snake_case )
# remove parameters we don't need
del state_dict["encoder.deit.head.weight"]
del state_dict["encoder.deit.head.bias"]
del state_dict["decoder.version"]
# add prefix to decoder keys
for key, val in state_dict.copy().items():
__snake_case = state_dict.pop(__snake_case )
if key.startswith("decoder" ) and "output_projection" not in key:
__snake_case = val
else:
__snake_case = val
# load state dict
model.load_state_dict(__snake_case )
# Check outputs on an image
__snake_case = ViTImageProcessor(size=encoder_config.image_size )
__snake_case = RobertaTokenizer.from_pretrained("roberta-large" )
__snake_case = TrOCRProcessor(__snake_case , __snake_case )
__snake_case = processor(images=prepare_img(__snake_case ) , return_tensors="pt" ).pixel_values
# verify logits
__snake_case = torch.tensor([[model.config.decoder.decoder_start_token_id]] )
__snake_case = model(pixel_values=__snake_case , decoder_input_ids=__snake_case )
__snake_case = outputs.logits
__snake_case = torch.Size([1, 1, 5_0265] )
if "trocr-base-handwritten" in checkpoint_url:
__snake_case = torch.tensor(
[-1.4_5_0_2, -4.6_6_8_3, -0.5_3_4_7, -2.9_2_9_1, 9.1_4_3_5, -3.0_5_7_1, 8.9_7_6_4, 1.7_5_6_0, 8.7_3_5_8, -1.5_3_1_1] )
elif "trocr-large-handwritten" in checkpoint_url:
__snake_case = torch.tensor(
[-2.6_4_3_7, -1.3_1_2_9, -2.2_5_9_6, -5.3_4_5_5, 6.3_5_3_9, 1.7_6_0_4, 5.4_9_9_1, 1.4_7_0_2, 5.6_1_1_3, 2.0_1_7_0] )
elif "trocr-base-printed" in checkpoint_url:
__snake_case = torch.tensor(
[-5.6_8_1_6, -5.8_3_8_8, 1.1_3_9_8, -6.9_0_3_4, 6.8_5_0_5, -2.4_3_9_3, 1.2_2_8_4, -1.0_2_3_2, -1.9_6_6_1, -3.9_2_1_0] )
elif "trocr-large-printed" in checkpoint_url:
__snake_case = torch.tensor(
[-6.0_1_6_2, -7.0_9_5_9, 4.4_1_5_5, -5.1_0_6_3, 7.0_4_6_8, -3.1_6_3_1, 2.6_4_6_6, -0.3_0_8_1, -0.8_1_0_6, -1.7_5_3_5] )
if "stage1" not in checkpoint_url:
assert logits.shape == expected_shape, "Shape of logits not as expected"
assert torch.allclose(logits[0, 0, :10] , __snake_case , atol=1e-3 ), "First elements of logits not as expected"
Path(__snake_case ).mkdir(exist_ok=__snake_case )
print(f"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(__snake_case )
print(f"""Saving processor to {pytorch_dump_folder_path}""" )
processor.save_pretrained(__snake_case )
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_url',
default='https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt',
type=str,
help='URL 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.'
)
lowerCamelCase__ = parser.parse_args()
convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 524
| 1
|
"""simple docstring"""
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowerCamelCase = (EulerDiscreteScheduler,)
_lowerCamelCase = 10
def UpperCamelCase__ ( self ,**lowerCamelCase_ ) -> Tuple:
A = {
"""num_train_timesteps""": 1_1_0_0,
"""beta_start""": 0.00_01,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
}
config.update(**lowerCamelCase_ )
return config
def UpperCamelCase__ ( self ) -> int:
for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=lowerCamelCase_ )
def UpperCamelCase__ ( self ) -> List[str]:
for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01] ,[0.00_02, 0.0_02, 0.02] ):
self.check_over_configs(beta_start=lowerCamelCase_ ,beta_end=lowerCamelCase_ )
def UpperCamelCase__ ( self ) -> Union[str, Any]:
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=lowerCamelCase_ )
def UpperCamelCase__ ( self ) -> str:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCamelCase_ )
def UpperCamelCase__ ( self ) -> Tuple:
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**lowerCamelCase_ )
scheduler.set_timesteps(self.num_inference_steps )
A = torch.manual_seed(0 )
A = self.dummy_model()
A = self.dummy_sample_deter * scheduler.init_noise_sigma
A = sample.to(lowerCamelCase_ )
for i, t in enumerate(scheduler.timesteps ):
A = scheduler.scale_model_input(lowerCamelCase_ ,lowerCamelCase_ )
A = model(lowerCamelCase_ ,lowerCamelCase_ )
A = scheduler.step(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,generator=lowerCamelCase_ )
A = output.prev_sample
A = torch.sum(torch.abs(lowerCamelCase_ ) )
A = torch.mean(torch.abs(lowerCamelCase_ ) )
assert abs(result_sum.item() - 10.08_07 ) < 1E-2
assert abs(result_mean.item() - 0.01_31 ) < 1E-3
def UpperCamelCase__ ( self ) -> List[str]:
A = self.scheduler_classes[0]
A = self.get_scheduler_config(prediction_type="""v_prediction""" )
A = scheduler_class(**lowerCamelCase_ )
scheduler.set_timesteps(self.num_inference_steps )
A = torch.manual_seed(0 )
A = self.dummy_model()
A = self.dummy_sample_deter * scheduler.init_noise_sigma
A = sample.to(lowerCamelCase_ )
for i, t in enumerate(scheduler.timesteps ):
A = scheduler.scale_model_input(lowerCamelCase_ ,lowerCamelCase_ )
A = model(lowerCamelCase_ ,lowerCamelCase_ )
A = scheduler.step(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,generator=lowerCamelCase_ )
A = output.prev_sample
A = torch.sum(torch.abs(lowerCamelCase_ ) )
A = torch.mean(torch.abs(lowerCamelCase_ ) )
assert abs(result_sum.item() - 0.00_02 ) < 1E-2
assert abs(result_mean.item() - 2.2676E-06 ) < 1E-3
def UpperCamelCase__ ( self ) -> Dict:
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**lowerCamelCase_ )
scheduler.set_timesteps(self.num_inference_steps ,device=lowerCamelCase_ )
A = torch.manual_seed(0 )
A = self.dummy_model()
A = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
A = sample.to(lowerCamelCase_ )
for t in scheduler.timesteps:
A = scheduler.scale_model_input(lowerCamelCase_ ,lowerCamelCase_ )
A = model(lowerCamelCase_ ,lowerCamelCase_ )
A = scheduler.step(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,generator=lowerCamelCase_ )
A = output.prev_sample
A = torch.sum(torch.abs(lowerCamelCase_ ) )
A = torch.mean(torch.abs(lowerCamelCase_ ) )
assert abs(result_sum.item() - 10.08_07 ) < 1E-2
assert abs(result_mean.item() - 0.01_31 ) < 1E-3
def UpperCamelCase__ ( self ) -> int:
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**lowerCamelCase_ ,use_karras_sigmas=lowerCamelCase_ )
scheduler.set_timesteps(self.num_inference_steps ,device=lowerCamelCase_ )
A = torch.manual_seed(0 )
A = self.dummy_model()
A = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
A = sample.to(lowerCamelCase_ )
for t in scheduler.timesteps:
A = scheduler.scale_model_input(lowerCamelCase_ ,lowerCamelCase_ )
A = model(lowerCamelCase_ ,lowerCamelCase_ )
A = scheduler.step(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,generator=lowerCamelCase_ )
A = output.prev_sample
A = torch.sum(torch.abs(lowerCamelCase_ ) )
A = torch.mean(torch.abs(lowerCamelCase_ ) )
assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9 ) < 1E-2
assert abs(result_mean.item() - 0.1_62_13_93_26_33_39_99_63 ) < 1E-3
| 719
|
"""simple docstring"""
UpperCAmelCase =256
# Modulus to hash a string
UpperCAmelCase =1_000_003
def _A ( _a : str , _a : str ):
"""simple docstring"""
A = len(_a )
A = len(_a )
if p_len > t_len:
return False
A = 0
A = 0
A = 1
# Calculating the hash of pattern and substring of text
for i in range(_a ):
A = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
A = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
A = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
A = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def _A ( ):
"""simple docstring"""
A = """abc1abc12"""
A = """alskfjaldsabc1abc1abc12k23adsfabcabc"""
A = """alskfjaldsk23adsfabcabc"""
assert rabin_karp(_a , _a ) and not rabin_karp(_a , _a )
# Test 2)
A = """ABABX"""
A = """ABABZABABYABABX"""
assert rabin_karp(_a , _a )
# Test 3)
A = """AAAB"""
A = """ABAAAAAB"""
assert rabin_karp(_a , _a )
# Test 4)
A = """abcdabcy"""
A = """abcxabcdabxabcdabcdabcy"""
assert rabin_karp(_a , _a )
# Test 5)
A = """Lü"""
A = """Lüsai"""
assert rabin_karp(_a , _a )
A = """Lue"""
assert not rabin_karp(_a , _a )
print("""Success.""" )
if __name__ == "__main__":
test_rabin_karp()
| 255
| 0
|
import math
def a ( snake_case__: list , snake_case__: int ):
'''simple docstring'''
lowercase_ = len(snake_case__ )
lowercase_ = int(math.floor(math.sqrt(snake_case__ ) ) )
lowercase_ = 0
while arr[min(snake_case__ , snake_case__ ) - 1] < x:
lowercase_ = step
step += int(math.floor(math.sqrt(snake_case__ ) ) )
if prev >= n:
return -1
while arr[prev] < x:
lowercase_ = prev + 1
if prev == min(snake_case__ , snake_case__ ):
return -1
if arr[prev] == x:
return prev
return -1
if __name__ == "__main__":
__a = input('Enter numbers separated by a comma:\n').strip()
__a = [int(item) for item in user_input.split(',')]
__a = int(input('Enter the number to be searched:\n'))
__a = jump_search(arr, x)
if res == -1:
print('Number not found!')
else:
print(f"Number {x} is at index {res}")
| 97
|
"""simple docstring"""
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__ :
'''simple docstring'''
def __init__( self : Any , lowercase_ : str , lowercase_ : Dict=13 , lowercase_ : Dict=32 , lowercase_ : Any=3 , lowercase_ : Any=4 , lowercase_ : int=[10, 20, 30, 40] , lowercase_ : Union[str, Any]=[2, 2, 3, 2] , lowercase_ : Optional[int]=True , lowercase_ : Union[str, Any]=True , lowercase_ : Dict=37 , lowercase_ : Optional[Any]="gelu" , lowercase_ : Union[str, Any]=10 , lowercase_ : Optional[int]=0.02 , lowercase_ : Tuple=["stage2", "stage3", "stage4"] , lowercase_ : Optional[Any]=[2, 3, 4] , lowercase_ : Optional[int]=None , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : int = parent
SCREAMING_SNAKE_CASE_ : Any = batch_size
SCREAMING_SNAKE_CASE_ : List[Any] = image_size
SCREAMING_SNAKE_CASE_ : Union[str, Any] = num_channels
SCREAMING_SNAKE_CASE_ : List[Any] = num_stages
SCREAMING_SNAKE_CASE_ : List[str] = hidden_sizes
SCREAMING_SNAKE_CASE_ : Optional[int] = depths
SCREAMING_SNAKE_CASE_ : str = is_training
SCREAMING_SNAKE_CASE_ : int = use_labels
SCREAMING_SNAKE_CASE_ : Dict = intermediate_size
SCREAMING_SNAKE_CASE_ : Optional[int] = hidden_act
SCREAMING_SNAKE_CASE_ : Optional[Any] = num_labels
SCREAMING_SNAKE_CASE_ : List[Any] = initializer_range
SCREAMING_SNAKE_CASE_ : Dict = out_features
SCREAMING_SNAKE_CASE_ : List[str] = out_indices
SCREAMING_SNAKE_CASE_ : int = scope
def _SCREAMING_SNAKE_CASE ( self : Tuple):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
SCREAMING_SNAKE_CASE_ : Any = None
if self.use_labels:
SCREAMING_SNAKE_CASE_ : Optional[Any] = ids_tensor([self.batch_size] , self.num_labels)
SCREAMING_SNAKE_CASE_ : Optional[Any] = self.get_config()
return config, pixel_values, labels
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any]):
'''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=lowercase_ , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def _SCREAMING_SNAKE_CASE ( self : str , lowercase_ : Union[str, Any] , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : int = ConvNextModel(config=lowercase_)
model.to(lowercase_)
model.eval()
SCREAMING_SNAKE_CASE_ : Union[str, Any] = model(lowercase_)
# 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 _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowercase_ : str , lowercase_ : Dict , lowercase_ : str):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : int = ConvNextForImageClassification(lowercase_)
model.to(lowercase_)
model.eval()
SCREAMING_SNAKE_CASE_ : List[Any] = model(lowercase_ , labels=lowercase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def _SCREAMING_SNAKE_CASE ( self : Tuple , lowercase_ : Union[str, Any] , lowercase_ : Any , lowercase_ : Dict):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : int = ConvNextBackbone(config=lowercase_)
model.to(lowercase_)
model.eval()
SCREAMING_SNAKE_CASE_ : Any = model(lowercase_)
# 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
SCREAMING_SNAKE_CASE_ : Optional[Any] = None
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ConvNextBackbone(config=lowercase_)
model.to(lowercase_)
model.eval()
SCREAMING_SNAKE_CASE_ : Union[str, Any] = model(lowercase_)
# 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 _SCREAMING_SNAKE_CASE ( self : int):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[Any] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : str = config_and_inputs
SCREAMING_SNAKE_CASE_ : Dict = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowerCAmelCase__ ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = (
(
ConvNextModel,
ConvNextForImageClassification,
ConvNextBackbone,
)
if is_torch_available()
else ()
)
__UpperCamelCase = (
{"feature-extraction": ConvNextModel, "image-classification": ConvNextForImageClassification}
if is_torch_available()
else {}
)
__UpperCamelCase = True
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
def _SCREAMING_SNAKE_CASE ( self : Any):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[str] = ConvNextModelTester(self)
SCREAMING_SNAKE_CASE_ : int = ConfigTester(self , config_class=lowercase_ , has_text_modality=lowercase_ , hidden_size=37)
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any]):
'''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 _SCREAMING_SNAKE_CASE ( self : List[str]):
'''simple docstring'''
return
@unittest.skip(reason='''ConvNext does not use inputs_embeds''')
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any]):
'''simple docstring'''
pass
@unittest.skip(reason='''ConvNext does not support input and output embeddings''')
def _SCREAMING_SNAKE_CASE ( self : Any):
'''simple docstring'''
pass
@unittest.skip(reason='''ConvNext does not use feedforward chunking''')
def _SCREAMING_SNAKE_CASE ( self : int):
'''simple docstring'''
pass
def _SCREAMING_SNAKE_CASE ( self : List[Any]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ : Dict = model_class(lowercase_)
SCREAMING_SNAKE_CASE_ : int = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE_ : List[str] = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE_ : int = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowercase_)
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_)
def _SCREAMING_SNAKE_CASE ( self : str):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*lowercase_)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any]):
'''simple docstring'''
def check_hidden_states_output(lowercase_ : List[Any] , lowercase_ : List[Any] , lowercase_ : str):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = model_class(lowercase_)
model.to(lowercase_)
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE_ : List[Any] = model(**self._prepare_for_class(lowercase_ , lowercase_))
SCREAMING_SNAKE_CASE_ : Tuple = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
SCREAMING_SNAKE_CASE_ : Tuple = self.model_tester.num_stages
self.assertEqual(len(lowercase_) , 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] , )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ : Tuple = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE_ : str = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase_)
@slow
def _SCREAMING_SNAKE_CASE ( self : Optional[Any]):
'''simple docstring'''
for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE_ : int = ConvNextModel.from_pretrained(lowercase_)
self.assertIsNotNone(lowercase_)
def _A () -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _SCREAMING_SNAKE_CASE ( self : List[str]):
'''simple docstring'''
return AutoImageProcessor.from_pretrained('''facebook/convnext-tiny-224''') if is_vision_available() else None
@slow
def _SCREAMING_SNAKE_CASE ( self : int):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Any = ConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224''').to(lowercase_)
SCREAMING_SNAKE_CASE_ : str = self.default_image_processor
SCREAMING_SNAKE_CASE_ : Union[str, Any] = prepare_img()
SCREAMING_SNAKE_CASE_ : List[Any] = image_processor(images=lowercase_ , return_tensors='''pt''').to(lowercase_)
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ : str = model(**lowercase_)
# verify the logits
SCREAMING_SNAKE_CASE_ : List[Any] = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape , lowercase_)
SCREAMING_SNAKE_CASE_ : int = torch.tensor([-0.02_60, -0.47_39, 0.19_11]).to(lowercase_)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowercase_ , atol=1e-4))
@require_torch
class lowerCAmelCase__ ( unittest.TestCase , UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = (ConvNextBackbone,) if is_torch_available() else ()
__UpperCamelCase = ConvNextConfig
__UpperCamelCase = False
def _SCREAMING_SNAKE_CASE ( self : Optional[int]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : str = ConvNextModelTester(self)
| 512
| 0
|
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 381
|
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class lowerCAmelCase__( __lowercase ):
'''simple docstring'''
__snake_case = 42
class lowerCAmelCase__( __lowercase , __lowercase ):
'''simple docstring'''
@register_to_config
def __init__( self , __lowerCamelCase = 3 , __lowerCamelCase = 3 , __lowerCamelCase = ("DownEncoderBlock2D",) , __lowerCamelCase = ("UpDecoderBlock2D",) , __lowerCamelCase = (6_4,) , __lowerCamelCase = 1 , __lowerCamelCase = "silu" , __lowerCamelCase = 3 , __lowerCamelCase = 3_2 , __lowerCamelCase = 2_5_6 , __lowerCamelCase = 3_2 , __lowerCamelCase = None , __lowerCamelCase = 0.1_8215 , __lowerCamelCase = "group" , ) -> List[str]:
super().__init__()
# pass init params to Encoder
_SCREAMING_SNAKE_CASE : Tuple = Encoder(
in_channels=__lowerCamelCase , out_channels=__lowerCamelCase , down_block_types=__lowerCamelCase , block_out_channels=__lowerCamelCase , layers_per_block=__lowerCamelCase , act_fn=__lowerCamelCase , norm_num_groups=__lowerCamelCase , double_z=__lowerCamelCase , )
_SCREAMING_SNAKE_CASE : Dict = vq_embed_dim if vq_embed_dim is not None else latent_channels
_SCREAMING_SNAKE_CASE : str = nn.Convad(__lowerCamelCase , __lowerCamelCase , 1 )
_SCREAMING_SNAKE_CASE : Tuple = VectorQuantizer(__lowerCamelCase , __lowerCamelCase , beta=0.25 , remap=__lowerCamelCase , sane_index_shape=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : int = nn.Convad(__lowerCamelCase , __lowerCamelCase , 1 )
# pass init params to Decoder
_SCREAMING_SNAKE_CASE : Dict = Decoder(
in_channels=__lowerCamelCase , out_channels=__lowerCamelCase , up_block_types=__lowerCamelCase , block_out_channels=__lowerCamelCase , layers_per_block=__lowerCamelCase , act_fn=__lowerCamelCase , norm_num_groups=__lowerCamelCase , norm_type=__lowerCamelCase , )
@apply_forward_hook
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase = True ) -> VQEncoderOutput:
_SCREAMING_SNAKE_CASE : Optional[int] = self.encoder(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Any = self.quant_conv(__lowerCamelCase )
if not return_dict:
return (h,)
return VQEncoderOutput(latents=__lowerCamelCase )
@apply_forward_hook
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase = False , __lowerCamelCase = True ) -> Union[DecoderOutput, torch.FloatTensor]:
# also go through quantization layer
if not force_not_quantize:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Optional[int] = self.quantize(__lowerCamelCase )
else:
_SCREAMING_SNAKE_CASE : Dict = h
_SCREAMING_SNAKE_CASE : str = self.post_quant_conv(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Dict = self.decoder(__lowerCamelCase , quant if self.config.norm_type == "spatial" else None )
if not return_dict:
return (dec,)
return DecoderOutput(sample=__lowerCamelCase )
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase = True ) -> Union[DecoderOutput, torch.FloatTensor]:
_SCREAMING_SNAKE_CASE : List[Any] = sample
_SCREAMING_SNAKE_CASE : List[str] = self.encode(__lowerCamelCase ).latents
_SCREAMING_SNAKE_CASE : List[str] = self.decode(__lowerCamelCase ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=__lowerCamelCase )
| 381
| 1
|
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class __magic_name__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self :Any , snake_case :Optional[int] ):
'''simple docstring'''
A_ : str = parent
def SCREAMING_SNAKE_CASE ( self :Any ):
'''simple docstring'''
return {}
def __snake_case ( ) -> int:
A_ : Tuple = "<HTML>\n\n <HEAD>\n <TITLE>sample document</TITLE>\n </HEAD>\n\n <BODY BGCOLOR=\"FFFFFF\">\n <HR>\n <a href=\"http://google.com\">Goog</a>\n <H1>This is one header</H1>\n <H2>This is a another Header</H2>\n <P>Travel from\n <P>\n <B>SFO to JFK</B>\n <BR>\n <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n <HR>\n <div style=\"color:#0000FF\">\n <h3>Traveler <b> name </b> is\n <p> John Doe </p>\n </div>"
A_ : int = "\n <!DOCTYPE html>\n <html>\n <body>\n\n <h1>My First Heading</h1>\n <p>My first paragraph.</p>\n\n </body>\n </html>\n "
return [html_string_a, html_string_a]
@require_bsa
class __magic_name__ ( lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase = MarkupLMFeatureExtractor if is_bsa_available() else None
def SCREAMING_SNAKE_CASE ( self :List[Any] ):
'''simple docstring'''
A_ : Optional[int] = MarkupLMFeatureExtractionTester(self )
@property
def SCREAMING_SNAKE_CASE ( self :str ):
'''simple docstring'''
return self.feature_extract_tester.prepare_feat_extract_dict()
def SCREAMING_SNAKE_CASE ( self :Union[str, Any] ):
'''simple docstring'''
A_ : Optional[int] = self.feature_extraction_class()
# Test not batched input
A_ : str = get_html_strings()[0]
A_ : Tuple = feature_extractor(snake_case )
# fmt: off
A_ : Any = [["sample document", "Goog", "This is one header", "This is a another Header", "Travel from", "SFO to JFK", "on May 2, 2015 at 2:00 pm. For details go to confirm.com", "Traveler", "name", "is", "John Doe"]]
A_ : Tuple = [["/html/head/title", "/html/body/a", "/html/body/h1", "/html/body/h2", "/html/body/p", "/html/body/p/p/b[1]", "/html/body/p/p/b[2]/i", "/html/body/p/p/div/h3", "/html/body/p/p/div/h3/b", "/html/body/p/p/div/h3", "/html/body/p/p/div/h3/p"]]
# fmt: on
self.assertEqual(encoding.nodes , snake_case )
self.assertEqual(encoding.xpaths , snake_case )
# Test batched
A_ : Tuple = get_html_strings()
A_ : List[str] = feature_extractor(snake_case )
# fmt: off
A_ : str = expected_nodes + [["My First Heading", "My first paragraph."]]
A_ : Tuple = expected_xpaths + [["/html/body/h1", "/html/body/p"]]
self.assertEqual(len(encoding.nodes ) , 2 )
self.assertEqual(len(encoding.xpaths ) , 2 )
self.assertEqual(encoding.nodes , snake_case )
self.assertEqual(encoding.xpaths , snake_case )
| 454
|
def __snake_case ( _lowerCAmelCase : int = 1 , _lowerCAmelCase : int = 1000 ) -> int:
A_ : Optional[int] = 1
A_ : int = 0
for divide_by_number in range(_lowerCAmelCase , digit + 1 ):
A_ : list[int] = []
A_ : Union[str, Any] = numerator
for _ in range(1 , digit + 1 ):
if now_divide in has_been_divided:
if longest_list_length < len(_lowerCAmelCase ):
A_ : Optional[Any] = len(_lowerCAmelCase )
A_ : Union[str, Any] = divide_by_number
else:
has_been_divided.append(_lowerCAmelCase )
A_ : Dict = now_divide * 10 % divide_by_number
return the_digit
# Tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| 454
| 1
|
"""simple docstring"""
def __A ( a_ :int) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('''The given input must be positive''')
# get the generated string sequence
__a : int = gray_code_sequence_string(a_)
#
# convert them to integers
for i in range(len(a_)):
__a : Union[str, Any] = int(sequence[i] , 2)
return sequence
def __A ( a_ :int) -> list:
# The approach is a recursive one
# Base case achieved when either n = 0 or n=1
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
__a : Optional[Any] = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
__a : Any = gray_code_sequence_string(bit_count - 1)
__a : Optional[int] = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2):
__a : List[Any] = '''0''' + smaller_sequence[i]
sequence.append(a_)
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2)):
__a : int = '''1''' + smaller_sequence[i]
sequence.append(a_)
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 716
|
"""simple docstring"""
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 __lowercase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
__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 _lowerCamelCase ( self ):
torch.manual_seed(0 )
__a : Optional[Any] = 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 , )
__a : List[str] = PNDMScheduler(skip_prk_steps=_UpperCAmelCase )
torch.manual_seed(0 )
__a : int = 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 )
__a : str = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
__a : Optional[int] = CLIPTextModel(_UpperCAmelCase )
__a : str = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
__a : Union[str, Any] = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase=0 ):
__a : Any = floats_tensor((1, 3, 32, 32) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
__a : Optional[Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__a : Tuple = Image.fromarray(np.uinta(_UpperCAmelCase ) ).convert('''RGB''' )
if str(_UpperCAmelCase ).startswith('''mps''' ):
__a : Union[str, Any] = torch.manual_seed(_UpperCAmelCase )
else:
__a : Union[str, Any] = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
__a : Union[str, Any] = {
'''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 _lowerCamelCase ( self ):
__a : List[Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__a : Dict = self.get_dummy_components()
__a : Any = StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase )
__a : int = sd_pipe.to(_UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase )
__a : Union[str, Any] = self.get_dummy_inputs(_UpperCAmelCase )
__a : str = sd_pipe(**_UpperCAmelCase ).images
__a : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__a : Optional[int] = np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _lowerCamelCase ( self ):
__a : Dict = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__a : Optional[Any] = self.get_dummy_components()
__a : Dict = StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase )
__a : List[Any] = sd_pipe.to(_UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase )
__a : Dict = self.get_dummy_inputs(_UpperCAmelCase )
__a : Union[str, Any] = '''french fries'''
__a : str = sd_pipe(**_UpperCAmelCase , negative_prompt=_UpperCAmelCase )
__a : Dict = output.images
__a : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__a : Tuple = np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _lowerCamelCase ( self ):
__a : Union[str, Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__a : Dict = self.get_dummy_components()
__a : str = StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase )
__a : Optional[int] = sd_pipe.to(_UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase )
__a : Union[str, Any] = self.get_dummy_inputs(_UpperCAmelCase )
__a : List[str] = [inputs['''prompt''']] * 2
__a : Optional[Any] = np.array(inputs['''image'''] ).astype(np.floataa ) / 2_5_5.0
__a : Optional[Any] = torch.from_numpy(_UpperCAmelCase ).unsqueeze(0 ).to(_UpperCAmelCase )
__a : Tuple = image / 2 + 0.5
__a : str = image.permute(0 , 3 , 1 , 2 )
__a : List[str] = image.repeat(2 , 1 , 1 , 1 )
__a : int = sd_pipe(**_UpperCAmelCase ).images
__a : Optional[Any] = image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
__a : List[str] = np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _lowerCamelCase ( self ):
__a : Dict = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__a : List[str] = self.get_dummy_components()
__a : Union[str, Any] = EulerAncestralDiscreteScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='''scaled_linear''' )
__a : str = StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase )
__a : List[str] = sd_pipe.to(_UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase )
__a : Dict = self.get_dummy_inputs(_UpperCAmelCase )
__a : Any = sd_pipe(**_UpperCAmelCase ).images
__a : Dict = image[0, -3:, -3:, -1]
__a : Optional[int] = [round(_UpperCAmelCase , 4 ) for x in image_slice.flatten().tolist()]
print(''','''.join([str(_UpperCAmelCase ) for x in slice] ) )
assert image.shape == (1, 32, 32, 3)
__a : int = np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _lowerCamelCase ( self ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def _lowerCamelCase ( self ):
__a : Any = self.get_dummy_components()
__a : Optional[Any] = StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase )
__a : Any = VaeImageProcessor(do_resize=_UpperCAmelCase , do_normalize=_UpperCAmelCase )
__a : Dict = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
__a : str = pipe(**self.get_dummy_inputs_by_type(_UpperCAmelCase , input_image_type='''pt''' ) )[0]
__a : List[Any] = components['''vae''']
__a : List[Any] = self.get_dummy_inputs_by_type(_UpperCAmelCase , input_image_type='''pt''' )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
__a : Optional[Any] = vae.encode(inputs[image_param] ).latent_dist.mode()
__a : str = pipe(**_UpperCAmelCase )[0]
__a : Union[str, Any] = np.abs(out - out_latents_inputs ).max()
self.assertLess(_UpperCAmelCase , 1e-4 , '''passing latents as image input generate different result from passing image''' )
@slow
@require_torch_gpu
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
def _lowerCamelCase ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCamelCase ( self , _UpperCAmelCase=0 ):
__a : List[str] = torch.manual_seed(_UpperCAmelCase )
__a : str = load_image(
'''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg''' )
__a : List[str] = {
'''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 _lowerCamelCase ( self ):
__a : List[str] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
pipe.enable_attention_slicing()
__a : str = self.get_inputs()
__a : Optional[Any] = pipe(**_UpperCAmelCase ).images
__a : List[str] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
__a : Tuple = np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def _lowerCamelCase ( self ):
__a : Tuple = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=_UpperCAmelCase )
__a : Optional[int] = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
pipe.enable_attention_slicing()
__a : List[str] = self.get_inputs()
__a : Optional[int] = pipe(**_UpperCAmelCase ).images
__a : Optional[Any] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
__a : Tuple = np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def _lowerCamelCase ( self ):
__a : List[Any] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=_UpperCAmelCase )
__a : Optional[int] = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
pipe.enable_attention_slicing()
__a : List[str] = self.get_inputs()
__a : str = pipe(**_UpperCAmelCase ).images
__a : Optional[int] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
__a : Any = np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def _lowerCamelCase ( self ):
__a : Dict = 0
def callback_fn(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> None:
__a : Optional[Any] = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
__a : Optional[int] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
__a : int = latents[0, -3:, -3:, -1]
__a : int = np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
__a : Union[str, Any] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
__a : List[str] = latents[0, -3:, -3:, -1]
__a : Tuple = np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
__a : Union[str, Any] = False
__a : Optional[int] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=_UpperCAmelCase , torch_dtype=torch.floataa )
__a : Optional[int] = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
pipe.enable_attention_slicing()
__a : str = self.get_inputs()
pipe(**_UpperCAmelCase , callback=_UpperCAmelCase , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def _lowerCamelCase ( self ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__a : Any = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=_UpperCAmelCase , torch_dtype=torch.floataa )
__a : Optional[int] = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
__a : List[Any] = self.get_inputs()
__a : Tuple = pipe(**_UpperCAmelCase )
__a : List[str] = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
def _lowerCamelCase ( self ):
__a : List[Any] = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
__a : str = inputs['''image'''].resize((504, 504) )
__a : Tuple = '''timbrooks/instruct-pix2pix'''
__a : Any = StableDiffusionInstructPixaPixPipeline.from_pretrained(
_UpperCAmelCase , safety_checker=_UpperCAmelCase , )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
pipe.enable_attention_slicing()
__a : List[Any] = pipe(**_UpperCAmelCase )
__a : int = output.images[0]
__a : Optional[int] = image[255:258, 383:386, -1]
assert image.shape == (504, 504, 3)
__a : Union[str, Any] = np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 101
| 0
|
def lowerCAmelCase__ ( a__: int = 1_0_0_0 ) -> int:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = 1, 1
_UpperCAmelCase = 2
while True:
_UpperCAmelCase = 0
_UpperCAmelCase = fa + fa
_UpperCAmelCase , _UpperCAmelCase = fa, f
index += 1
for _ in str(a__ ):
i += 1
if i == n:
break
return index
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 618
|
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
skip_first_batches,
)
class __a ( UpperCAmelCase ):
def __init__( self , _SCREAMING_SNAKE_CASE=0.01 , _SCREAMING_SNAKE_CASE=1000 ) -> str:
"""simple docstring"""
_UpperCAmelCase = p_stop
_UpperCAmelCase = max_length
def __iter__( self ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = 0
_UpperCAmelCase = False
while not stop and count < self.max_length:
yield count
count += 1
_UpperCAmelCase = random.random() < self.p_stop
class __a ( unittest.TestCase ):
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=True ) -> int:
"""simple docstring"""
_UpperCAmelCase = [
BatchSamplerShard(_SCREAMING_SNAKE_CASE , 2 , _SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE , even_batches=_SCREAMING_SNAKE_CASE )
for i in range(2 )
]
_UpperCAmelCase = [list(_SCREAMING_SNAKE_CASE ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(_SCREAMING_SNAKE_CASE ) for shard in batch_sampler_shards] , [len(_SCREAMING_SNAKE_CASE ) for e in expected] )
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=_SCREAMING_SNAKE_CASE )
# Expected shouldn't change
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
_UpperCAmelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
_UpperCAmelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
_UpperCAmelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Check the shards when the dataset is very small.
_UpperCAmelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [[], []]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE )
# Expected shouldn't change
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE )
# Check the shards when the dataset is not a round multiple of batch size.
_UpperCAmelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
_UpperCAmelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE )
# Check the shards when the dataset is very small.
_UpperCAmelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [[], []]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , even_batches=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=_SCREAMING_SNAKE_CASE )
# Expected shouldn't change
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , even_batches=_SCREAMING_SNAKE_CASE )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
_UpperCAmelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , even_batches=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , even_batches=_SCREAMING_SNAKE_CASE )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
_UpperCAmelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , even_batches=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , even_batches=_SCREAMING_SNAKE_CASE )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
_UpperCAmelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , even_batches=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , even_batches=_SCREAMING_SNAKE_CASE )
# Check the shards when the dataset is very small.
_UpperCAmelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [[[0, 1]], []]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , even_batches=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [[], []]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , even_batches=_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE , even_batches=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE )
# Expected shouldn't change
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE , even_batches=_SCREAMING_SNAKE_CASE )
# Check the shards when the dataset is not a round multiple of batch size.
_UpperCAmelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE , even_batches=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE , even_batches=_SCREAMING_SNAKE_CASE )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
_UpperCAmelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE , even_batches=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE , even_batches=_SCREAMING_SNAKE_CASE )
# Check the shards when the dataset is very small.
_UpperCAmelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [[[0, 1]], []]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE , even_batches=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [[], []]
self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE , even_batches=_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
_UpperCAmelCase = [BatchSamplerShard(_SCREAMING_SNAKE_CASE , 2 , _SCREAMING_SNAKE_CASE , even_batches=_SCREAMING_SNAKE_CASE ) for i in range(2 )]
self.assertEqual(len(batch_sampler_shards[0] ) , 3 )
self.assertEqual(len(batch_sampler_shards[1] ) , 2 )
self.assertListEqual(list(batch_sampler_shards[0] ) , [[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
self.assertListEqual(list(batch_sampler_shards[1] ) , [[3, 4], [9, 10, 11]] )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=False ) -> Dict:
"""simple docstring"""
random.seed(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = list(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
IterableDatasetShard(
_SCREAMING_SNAKE_CASE , batch_size=_SCREAMING_SNAKE_CASE , drop_last=_SCREAMING_SNAKE_CASE , num_processes=_SCREAMING_SNAKE_CASE , process_index=_SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE , )
for i in range(_SCREAMING_SNAKE_CASE )
]
_UpperCAmelCase = []
for iterable_dataset_shard in iterable_dataset_shards:
# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
random.seed(_SCREAMING_SNAKE_CASE )
iterable_dataset_lists.append(list(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = batch_size // num_processes if split_batches else batch_size
# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
_UpperCAmelCase = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , len(_SCREAMING_SNAKE_CASE ) )
self.assertTrue(len(_SCREAMING_SNAKE_CASE ) % shard_batch_size == 0 )
_UpperCAmelCase = []
for idx in range(0 , len(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(_SCREAMING_SNAKE_CASE ) < len(_SCREAMING_SNAKE_CASE ):
reference += reference
self.assertListEqual(_SCREAMING_SNAKE_CASE , reference[: len(_SCREAMING_SNAKE_CASE )] )
def UpperCAmelCase__ ( self ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = 42
_UpperCAmelCase = RandomIterableDataset()
self.check_iterable_dataset_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE )
self.check_iterable_dataset_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE )
self.check_iterable_dataset_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE )
self.check_iterable_dataset_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE )
# Edge case with a very small dataset
_UpperCAmelCase = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE )
self.check_iterable_dataset_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE )
self.check_iterable_dataset_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE )
self.check_iterable_dataset_shards(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE , split_batches=_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> str:
"""simple docstring"""
_UpperCAmelCase = BatchSampler(range(16 ) , batch_size=4 , drop_last=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = SkipBatchSampler(_SCREAMING_SNAKE_CASE , 2 )
self.assertListEqual(list(_SCREAMING_SNAKE_CASE ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def UpperCAmelCase__ ( self ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = SkipDataLoader(list(range(16 ) ) , batch_size=4 , skip_batches=2 )
self.assertListEqual([t.tolist() for t in dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def UpperCAmelCase__ ( self ) -> int:
"""simple docstring"""
_UpperCAmelCase = DataLoader(list(range(16 ) ) , batch_size=4 )
_UpperCAmelCase = skip_first_batches(_SCREAMING_SNAKE_CASE , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def UpperCAmelCase__ ( self ) -> int:
"""simple docstring"""
_UpperCAmelCase = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(_SCREAMING_SNAKE_CASE ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(_SCREAMING_SNAKE_CASE ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def UpperCAmelCase__ ( self ) -> Any:
"""simple docstring"""
Accelerator()
_UpperCAmelCase = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(_SCREAMING_SNAKE_CASE ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(_SCREAMING_SNAKE_CASE ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 618
| 1
|
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def A__ ( SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
monkeypatch.setattr('''datasets.utils.deprecation_utils._emitted_deprecation_warnings''' , set() )
@pytest.fixture
def A__ ( SCREAMING_SNAKE_CASE_ ) -> List[Any]:
class snake_case_ :
def __init__( self , __lowercase ) -> Tuple:
lowerCamelCase : Tuple =metric_id
class snake_case_ :
lowerCamelCase :List[Any] = [MetricMock(__SCREAMING_SNAKE_CASE) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]
def __lowercase ( self ) -> List[str]:
return self._metrics
monkeypatch.setattr('''datasets.inspect.huggingface_hub''' , HfhMock() )
@pytest.mark.parametrize(
'''func, args''' , [(load_metric, ('''metrics/mse''',)), (list_metrics, ()), (inspect_metric, ('''metrics/mse''', '''tmp_path'''))] )
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
if "tmp_path" in args:
lowerCamelCase : Any =tuple(arg if arg != '''tmp_path''' else tmp_path for arg in args )
with pytest.warns(a_ , match='''https://huggingface.co/docs/evaluate''' ):
func(*a_ )
| 705
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'''facebook/timesformer''': '''https://huggingface.co/facebook/timesformer/resolve/main/config.json''',
}
class snake_case_ ( _A):
lowerCamelCase :Union[str, Any] = "timesformer"
def __init__( self , __lowercase=2_2_4 , __lowercase=1_6 , __lowercase=3 , __lowercase=8 , __lowercase=7_6_8 , __lowercase=1_2 , __lowercase=1_2 , __lowercase=3_0_7_2 , __lowercase="gelu" , __lowercase=0.0 , __lowercase=0.0 , __lowercase=0.0_2 , __lowercase=1e-6 , __lowercase=True , __lowercase="divided_space_time" , __lowercase=0 , **__lowercase , ) -> List[Any]:
super().__init__(**__lowercase )
lowerCamelCase : int =image_size
lowerCamelCase : List[str] =patch_size
lowerCamelCase : Union[str, Any] =num_channels
lowerCamelCase : str =num_frames
lowerCamelCase : Dict =hidden_size
lowerCamelCase : int =num_hidden_layers
lowerCamelCase : Dict =num_attention_heads
lowerCamelCase : Dict =intermediate_size
lowerCamelCase : Union[str, Any] =hidden_act
lowerCamelCase : str =hidden_dropout_prob
lowerCamelCase : Union[str, Any] =attention_probs_dropout_prob
lowerCamelCase : List[Any] =initializer_range
lowerCamelCase : List[Any] =layer_norm_eps
lowerCamelCase : List[str] =qkv_bias
lowerCamelCase : Tuple =attention_type
lowerCamelCase : List[Any] =drop_path_rate
| 262
| 0
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Optional[int] = """data2vec-text"""
def __init__( self , snake_case=3_0522 , 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=512 , snake_case=2 , snake_case=0.02 , snake_case=1E-12 , snake_case=1 , snake_case=0 , snake_case=2 , snake_case="absolute" , snake_case=True , snake_case=None , **snake_case , ):
super().__init__(pad_token_id=snake_case , bos_token_id=snake_case , eos_token_id=snake_case , **snake_case )
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = hidden_act
lowercase = intermediate_size
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = type_vocab_size
lowercase = initializer_range
lowercase = layer_norm_eps
lowercase = position_embedding_type
lowercase = use_cache
lowercase = classifier_dropout
class A_ ( __lowerCamelCase ):
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE__ ( self ):
if self.task == "multiple-choice":
lowercase = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
lowercase = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 84
|
'''simple docstring'''
from math import sqrt
def UpperCAmelCase_ ( lowerCAmelCase_ ):
"""simple docstring"""
lowercase = 0
for i in range(1 , int(sqrt(lowerCAmelCase_ ) + 1 ) ):
if n % i == 0 and i != sqrt(lowerCAmelCase_ ):
total += i + n // i
elif i == sqrt(lowerCAmelCase_ ):
total += i
return total - n
def UpperCAmelCase_ ( lowerCAmelCase_ = 1_0000 ):
"""simple docstring"""
lowercase = sum(
i
for i in range(1 , lowerCAmelCase_ )
if sum_of_divisors(sum_of_divisors(lowerCAmelCase_ ) ) == i and sum_of_divisors(lowerCAmelCase_ ) != i )
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 310
| 0
|
from __future__ import annotations
import queue
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : str , lowerCamelCase_ : Optional[Any] ):
_lowerCAmelCase =data
_lowerCAmelCase =None
_lowerCAmelCase =None
def snake_case_ ( ):
'''simple docstring'''
print("""\n********Press N to stop entering at any point of time********\n""" )
_lowerCAmelCase =input("""Enter the value of the root node: """ ).strip().lower()
_lowerCAmelCase =queue.Queue()
_lowerCAmelCase =TreeNode(int(lowercase__ ) )
q.put(lowercase__ )
while not q.empty():
_lowerCAmelCase =q.get()
_lowerCAmelCase =f"Enter the left node of {node_found.data}: "
_lowerCAmelCase =input(lowercase__ ).strip().lower() or """n"""
if check == "n":
return tree_node
_lowerCAmelCase =TreeNode(int(lowercase__ ) )
_lowerCAmelCase =left_node
q.put(lowercase__ )
_lowerCAmelCase =f"Enter the right node of {node_found.data}: "
_lowerCAmelCase =input(lowercase__ ).strip().lower() or """n"""
if check == "n":
return tree_node
_lowerCAmelCase =TreeNode(int(lowercase__ ) )
_lowerCAmelCase =right_node
q.put(lowercase__ )
raise
def snake_case_ ( lowercase__ : TreeNode ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ) or not node:
return
print(node.data , end=""",""" )
pre_order(node.left )
pre_order(node.right )
def snake_case_ ( lowercase__ : TreeNode ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ) or not node:
return
in_order(node.left )
print(node.data , end=""",""" )
in_order(node.right )
def snake_case_ ( lowercase__ : TreeNode ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ) or not node:
return
post_order(node.left )
post_order(node.right )
print(node.data , end=""",""" )
def snake_case_ ( lowercase__ : TreeNode ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ) or not node:
return
_lowerCAmelCase =queue.Queue()
q.put(lowercase__ )
while not q.empty():
_lowerCAmelCase =q.get()
print(node_dequeued.data , end=""",""" )
if node_dequeued.left:
q.put(node_dequeued.left )
if node_dequeued.right:
q.put(node_dequeued.right )
def snake_case_ ( lowercase__ : TreeNode ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ) or not node:
return
_lowerCAmelCase =queue.Queue()
q.put(lowercase__ )
while not q.empty():
_lowerCAmelCase =[]
while not q.empty():
_lowerCAmelCase =q.get()
print(node_dequeued.data , end=""",""" )
if node_dequeued.left:
list_.append(node_dequeued.left )
if node_dequeued.right:
list_.append(node_dequeued.right )
print()
for node in list_:
q.put(lowercase__ )
def snake_case_ ( lowercase__ : TreeNode ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ) or not node:
return
_lowerCAmelCase =[]
_lowerCAmelCase =node
while n or stack:
while n: # start from root node, find its left child
print(n.data , end=""",""" )
stack.append(lowercase__ )
_lowerCAmelCase =n.left
# end of while means current node doesn't have left child
_lowerCAmelCase =stack.pop()
# start to traverse its right child
_lowerCAmelCase =n.right
def snake_case_ ( lowercase__ : TreeNode ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ) or not node:
return
_lowerCAmelCase =[]
_lowerCAmelCase =node
while n or stack:
while n:
stack.append(lowercase__ )
_lowerCAmelCase =n.left
_lowerCAmelCase =stack.pop()
print(n.data , end=""",""" )
_lowerCAmelCase =n.right
def snake_case_ ( lowercase__ : TreeNode ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ) or not node:
return
_lowerCAmelCase , _lowerCAmelCase =[], []
_lowerCAmelCase =node
stacka.append(lowercase__ )
while stacka: # to find the reversed order of post order, store it in stack2
_lowerCAmelCase =stacka.pop()
if n.left:
stacka.append(n.left )
if n.right:
stacka.append(n.right )
stacka.append(lowercase__ )
while stacka: # pop up from stack2 will be the post order
print(stacka.pop().data , end=""",""" )
def snake_case_ ( lowercase__ : str = "" , lowercase__ : List[str]=50 , lowercase__ : int="*" ):
'''simple docstring'''
if not s:
return "\n" + width * char
_lowerCAmelCase , _lowerCAmelCase =divmod(width - len(lowercase__ ) - 2 , 2 )
return f"{left * char} {s} {(left + extra) * char}"
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt('''Binary Tree Traversals'''))
__SCREAMING_SNAKE_CASE : TreeNode = build_tree()
print(prompt('''Pre Order Traversal'''))
pre_order(node)
print(prompt() + '''\n''')
print(prompt('''In Order Traversal'''))
in_order(node)
print(prompt() + '''\n''')
print(prompt('''Post Order Traversal'''))
post_order(node)
print(prompt() + '''\n''')
print(prompt('''Level Order Traversal'''))
level_order(node)
print(prompt() + '''\n''')
print(prompt('''Actual Level Order Traversal'''))
level_order_actual(node)
print('''*''' * 50 + '''\n''')
print(prompt('''Pre Order Traversal - Iteration Version'''))
pre_order_iter(node)
print(prompt() + '''\n''')
print(prompt('''In Order Traversal - Iteration Version'''))
in_order_iter(node)
print(prompt() + '''\n''')
print(prompt('''Post Order Traversal - Iteration Version'''))
post_order_iter(node)
print(prompt())
| 712
|
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
# TODO Update this
__SCREAMING_SNAKE_CASE : int = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class __lowerCamelCase ( lowerCamelCase_ ):
"""simple docstring"""
a_: Any = """esm"""
def __init__( self : Dict , lowerCamelCase_ : Any=None , lowerCamelCase_ : List[Any]=None , lowerCamelCase_ : Dict=None , lowerCamelCase_ : Tuple=768 , lowerCamelCase_ : List[str]=12 , lowerCamelCase_ : List[Any]=12 , lowerCamelCase_ : Optional[Any]=3072 , lowerCamelCase_ : str=0.1 , lowerCamelCase_ : int=0.1 , lowerCamelCase_ : List[Any]=1026 , lowerCamelCase_ : List[str]=0.02 , lowerCamelCase_ : str=1e-12 , lowerCamelCase_ : int="absolute" , lowerCamelCase_ : Dict=True , lowerCamelCase_ : Optional[int]=None , lowerCamelCase_ : Any=False , lowerCamelCase_ : Dict=False , lowerCamelCase_ : Any=None , lowerCamelCase_ : Union[str, Any]=None , **lowerCamelCase_ : Union[str, Any] , ):
super().__init__(pad_token_id=lowerCamelCase_ , mask_token_id=lowerCamelCase_ , **lowerCamelCase_ )
_lowerCAmelCase =vocab_size
_lowerCAmelCase =hidden_size
_lowerCAmelCase =num_hidden_layers
_lowerCAmelCase =num_attention_heads
_lowerCAmelCase =intermediate_size
_lowerCAmelCase =hidden_dropout_prob
_lowerCAmelCase =attention_probs_dropout_prob
_lowerCAmelCase =max_position_embeddings
_lowerCAmelCase =initializer_range
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =position_embedding_type
_lowerCAmelCase =use_cache
_lowerCAmelCase =emb_layer_norm_before
_lowerCAmelCase =token_dropout
_lowerCAmelCase =is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info("""No esmfold_config supplied for folding model, using default values.""" )
_lowerCAmelCase =EsmFoldConfig()
elif isinstance(lowerCamelCase_ , lowerCamelCase_ ):
_lowerCAmelCase =EsmFoldConfig(**lowerCamelCase_ )
_lowerCAmelCase =esmfold_config
if vocab_list is None:
logger.warning("""No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!""" )
_lowerCAmelCase =get_default_vocab_list()
else:
_lowerCAmelCase =vocab_list
else:
_lowerCAmelCase =None
_lowerCAmelCase =None
if self.esmfold_config is not None and getattr(self.esmfold_config , """use_esm_attn_map""" , lowerCamelCase_ ):
raise ValueError("""The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!""" )
def lowerCAmelCase__ ( self : Tuple ):
_lowerCAmelCase =super().to_dict()
if isinstance(self.esmfold_config , lowerCamelCase_ ):
_lowerCAmelCase =self.esmfold_config.to_dict()
return output
@dataclass
class __lowerCamelCase :
"""simple docstring"""
a_: str = None
a_: bool = True
a_: bool = False
a_: bool = False
a_: bool = False
a_: float = 0
a_: bool = True
a_: bool = False
a_: int = 1_28
a_: "TrunkConfig" = None
def lowerCAmelCase__ ( self : str ):
if self.trunk is None:
_lowerCAmelCase =TrunkConfig()
elif isinstance(self.trunk , lowerCamelCase_ ):
_lowerCAmelCase =TrunkConfig(**self.trunk )
def lowerCAmelCase__ ( self : str ):
_lowerCAmelCase =asdict(self )
_lowerCAmelCase =self.trunk.to_dict()
return output
@dataclass
class __lowerCamelCase :
"""simple docstring"""
a_: int = 48
a_: int = 10_24
a_: int = 1_28
a_: int = 32
a_: int = 32
a_: int = 32
a_: float = 0
a_: float = 0
a_: bool = False
a_: int = 4
a_: Optional[int] = 1_28
a_: "StructureModuleConfig" = None
def lowerCAmelCase__ ( self : Optional[Any] ):
if self.structure_module is None:
_lowerCAmelCase =StructureModuleConfig()
elif isinstance(self.structure_module , lowerCamelCase_ ):
_lowerCAmelCase =StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(F"`max_recycles` should be positive, got {self.max_recycles}." )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
"""`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"""
F" {self.sequence_state_dim} and {self.sequence_state_dim}." )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
"""`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"""
F" {self.pairwise_state_dim} and {self.pairwise_state_dim}." )
_lowerCAmelCase =self.sequence_state_dim // self.sequence_head_width
_lowerCAmelCase =self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
"""`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"""
F" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}." )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
"""`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"""
F" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}." )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}." )
if self.dropout >= 0.4:
raise ValueError(F"`dropout` should not be greater than 0.4, got {self.dropout}." )
def lowerCAmelCase__ ( self : Any ):
_lowerCAmelCase =asdict(self )
_lowerCAmelCase =self.structure_module.to_dict()
return output
@dataclass
class __lowerCamelCase :
"""simple docstring"""
a_: int = 3_84
a_: int = 1_28
a_: int = 16
a_: int = 1_28
a_: int = 12
a_: int = 4
a_: int = 8
a_: float = 0.1
a_: int = 8
a_: int = 1
a_: int = 2
a_: int = 7
a_: int = 10
a_: float = 1e-8
a_: float = 1e5
def lowerCAmelCase__ ( self : int ):
return asdict(self )
def snake_case_ ( ):
'''simple docstring'''
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 149
| 0
|
"""simple docstring"""
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
a = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
)
parser.add_argument(
'--original_config_file',
type=str,
required=True,
help='The YAML config file corresponding to the original architecture.',
)
parser.add_argument(
'--num_in_channels',
default=None,
type=int,
help='The number of input channels. If `None` number of input channels will be automatically inferred.',
)
parser.add_argument(
'--image_size',
default=5_1_2,
type=int,
help=(
'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
' Base. Use 768 for Stable Diffusion v2.'
),
)
parser.add_argument(
'--extract_ema',
action='store_true',
help=(
'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
),
)
parser.add_argument(
'--upcast_attention',
action='store_true',
help=(
'Whether the attention computation should always be upcasted. This is necessary when running stable'
' diffusion 2.1.'
),
)
parser.add_argument(
'--from_safetensors',
action='store_true',
help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
)
parser.add_argument(
'--to_safetensors',
action='store_true',
help='Whether to store pipeline in safetensors format or not.',
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
def lowercase (snake_case__ : Tuple ) -> List[str]:
'''simple docstring'''
if string == "True":
return True
elif string == "False":
return False
else:
raise ValueError(f'''could not parse string as bool {string}''' )
parser.add_argument(
'--use_linear_projection', help='Override for use linear projection', required=False, type=parse_bool
)
parser.add_argument('--cross_attention_dim', help='Override for cross attention_dim', required=False, type=int)
a = parser.parse_args()
a = download_controlnet_from_original_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
extract_ema=args.extract_ema,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
use_linear_projection=args.use_linear_projection,
cross_attention_dim=args.cross_attention_dim,
)
controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 169
|
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
__magic_name__ =get_tests_dir('''fixtures/test_sentencepiece.model''')
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
__magic_name__ =250004
__magic_name__ =250020
@require_sentencepiece
@require_tokenizers
class _A ( __UpperCamelCase , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Any =MBartTokenizer
SCREAMING_SNAKE_CASE_ : Any =MBartTokenizerFast
SCREAMING_SNAKE_CASE_ : Optional[int] =True
SCREAMING_SNAKE_CASE_ : Optional[Any] =True
def _a (self ) -> Optional[Any]:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
UpperCamelCase__ = MBartTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ )
tokenizer.save_pretrained(self.tmpdirname )
def _a (self ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ = MBartTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
UpperCamelCase__ = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
SCREAMING_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''',
'''.''',
] , )
UpperCamelCase__ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_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 ^
] , )
UpperCamelCase__ = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_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 _a (self ) -> Dict:
'''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
UpperCamelCase__ = (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})" ):
UpperCamelCase__ = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = tempfile.mkdtemp()
UpperCamelCase__ = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = tokenizer_p.save_pretrained(SCREAMING_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 ) )
UpperCamelCase__ = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Checks everything loads correctly in the same way
UpperCamelCase__ = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_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(SCREAMING_SNAKE_CASE_ )
# Save tokenizer rust, legacy_format=True
UpperCamelCase__ = tempfile.mkdtemp()
UpperCamelCase__ = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ , legacy_format=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it save with the same files
self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Checks everything loads correctly in the same way
UpperCamelCase__ = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
# Save tokenizer rust, legacy_format=False
UpperCamelCase__ = tempfile.mkdtemp()
UpperCamelCase__ = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ , legacy_format=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = tokenizer_p.save_pretrained(SCREAMING_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
UpperCamelCase__ = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
@require_torch
@require_sentencepiece
@require_tokenizers
class _A ( unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] ="facebook/mbart-large-en-ro"
SCREAMING_SNAKE_CASE_ : Any =[
" 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.",
]
SCREAMING_SNAKE_CASE_ : Union[str, Any] =[
"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.",
]
SCREAMING_SNAKE_CASE_ : str =[82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]
@classmethod
def _a (cls ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''en_XX''' , tgt_lang='''ro_RO''' )
UpperCamelCase__ = 1
return cls
def _a (self ) -> Dict:
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR'''] , 25_0001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN'''] , 25_0004 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO'''] , 25_0020 )
def _a (self ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , SCREAMING_SNAKE_CASE_ )
def _a (self ) -> Dict:
'''simple docstring'''
self.assertIn(SCREAMING_SNAKE_CASE_ , self.tokenizer.all_special_ids )
UpperCamelCase__ = [RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
UpperCamelCase__ = self.tokenizer.decode(SCREAMING_SNAKE_CASE_ , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertNotIn(self.tokenizer.eos_token , SCREAMING_SNAKE_CASE_ )
def _a (self ) -> Any:
'''simple docstring'''
UpperCamelCase__ = ['''this is gunna be a long sentence ''' * 20]
assert isinstance(src_text[0] , SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = 10
UpperCamelCase__ = self.tokenizer(SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , SCREAMING_SNAKE_CASE_ )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
def _a (self ) -> Optional[int]:
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR'''] ) , [25_0026, 25_0001] )
def _a (self ) -> Any:
'''simple docstring'''
UpperCamelCase__ = tempfile.mkdtemp()
UpperCamelCase__ = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = MBartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , SCREAMING_SNAKE_CASE_ )
@require_torch
def _a (self ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' )
UpperCamelCase__ = 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 _a (self ) -> Dict:
'''simple docstring'''
UpperCamelCase__ = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=len(self.expected_src_tokens ) , return_tensors='''pt''' , )
UpperCamelCase__ = shift_tokens_right(batch['''labels'''] , self.tokenizer.pad_token_id )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
UpperCamelCase__ = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , SCREAMING_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 _a (self ) -> str:
'''simple docstring'''
UpperCamelCase__ = self.tokenizer(self.src_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=3 , return_tensors='''pt''' )
UpperCamelCase__ = self.tokenizer(
text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=10 , return_tensors='''pt''' )
UpperCamelCase__ = targets['''input_ids''']
UpperCamelCase__ = shift_tokens_right(SCREAMING_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 _a (self ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ = self.tokenizer._build_translation_inputs(
'''A test''' , return_tensors='''pt''' , src_lang='''en_XX''' , tgt_lang='''ar_AR''' )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) , {
# A, test, EOS, en_XX
'''input_ids''': [[62, 3034, 2, 25_0004]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 25_0001,
} , )
| 415
| 0
|
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def UpperCamelCase (lowercase_: Optional[int] , lowercase_: str , lowercase_: Any , lowercase_: List[Any] , lowercase_: Tuple ) -> Any:
# load base model
A__ : List[str] = StableDiffusionPipeline.from_pretrained(lowercase_ , torch_dtype=torch.floataa )
# load LoRA weight from .safetensors
A__ : Dict = load_file(lowercase_ )
A__ : List[Any] = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
A__ : Optional[Any] = key.split(""".""" )[0].split(LORA_PREFIX_TEXT_ENCODER + """_""" )[-1].split("""_""" )
A__ : str = pipeline.text_encoder
else:
A__ : Any = key.split(""".""" )[0].split(LORA_PREFIX_UNET + """_""" )[-1].split("""_""" )
A__ : Union[str, Any] = pipeline.unet
# find the target layer
A__ : List[str] = layer_infos.pop(0 )
while len(lowercase_ ) > -1:
try:
A__ : Optional[Any] = curr_layer.__getattr__(lowercase_ )
if len(lowercase_ ) > 0:
A__ : List[str] = layer_infos.pop(0 )
elif len(lowercase_ ) == 0:
break
except Exception:
if len(lowercase_ ) > 0:
temp_name += "_" + layer_infos.pop(0 )
else:
A__ : Dict = layer_infos.pop(0 )
A__ : str = []
if "lora_down" in key:
pair_keys.append(key.replace("""lora_down""" , """lora_up""" ) )
pair_keys.append(lowercase_ )
else:
pair_keys.append(lowercase_ )
pair_keys.append(key.replace("""lora_up""" , """lora_down""" ) )
# update weight
if len(state_dict[pair_keys[0]].shape ) == 4:
A__ : List[str] = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
A__ : Optional[int] = state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(lowercase_ , lowercase_ ).unsqueeze(2 ).unsqueeze(3 )
else:
A__ : str = state_dict[pair_keys[0]].to(torch.floataa )
A__ : List[Any] = state_dict[pair_keys[1]].to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(lowercase_ , lowercase_ )
# update visited list
for item in pair_keys:
visited.append(lowercase_ )
return pipeline
if __name__ == "__main__":
A_ : Dict = argparse.ArgumentParser()
parser.add_argument(
'--base_model_path', default=None, type=str, required=True, help='Path to the base model in diffusers format.'
)
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--lora_prefix_unet', default='lora_unet', type=str, help='The prefix of UNet weight in safetensors'
)
parser.add_argument(
'--lora_prefix_text_encoder',
default='lora_te',
type=str,
help='The prefix of text encoder weight in safetensors',
)
parser.add_argument('--alpha', default=0.75, type=float, help='The merging ratio in W = W0 + alpha * deltaW')
parser.add_argument(
'--to_safetensors', action='store_true', help='Whether to store pipeline in safetensors format or not.'
)
parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
A_ : str = parser.parse_args()
A_ : int = args.base_model_path
A_ : Any = args.checkpoint_path
A_ : Optional[int] = args.dump_path
A_ : Dict = args.lora_prefix_unet
A_ : Optional[int] = args.lora_prefix_text_encoder
A_ : List[str] = args.alpha
A_ : int = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
A_ : int = pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 64
|
def UpperCamelCase (lowercase_: str , lowercase_: str ) -> bool:
A__ : Union[str, Any] = len(lowercase_ )
A__ : List[Any] = len(lowercase_ )
A__ : List[Any] = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
A__ : str = True
for i in range(lowercase_ ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
A__ : int = True
if a[i].islower():
A__ : Dict = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 64
| 1
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.