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import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
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_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __lowerCamelCase (_a , _a , unittest.TestCase ):
_lowercase = IFImgaImgSuperResolutionPipeline
_lowercase = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
_lowercase = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""} )
_lowercase = PipelineTesterMixin.required_optional_params - {"""latents"""}
def snake_case_ ( self: Any ):
'''simple docstring'''
return self._get_superresolution_dummy_components()
def snake_case_ ( self: List[str],A_: int,A_: Union[str, Any]=0 ):
'''simple docstring'''
if str(A_ ).startswith('mps' ):
__UpperCamelCase = torch.manual_seed(A_ )
else:
__UpperCamelCase = torch.Generator(device=A_ ).manual_seed(A_ )
__UpperCamelCase = floats_tensor((1, 3, 32, 32),rng=random.Random(A_ ) ).to(A_ )
__UpperCamelCase = floats_tensor((1, 3, 16, 16),rng=random.Random(A_ ) ).to(A_ )
__UpperCamelCase = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'original_image': original_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 snake_case_ ( self: int ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def snake_case_ ( self: Optional[int] ):
'''simple docstring'''
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda',reason='float16 requires CUDA' )
def snake_case_ ( self: str ):
'''simple docstring'''
super().test_save_load_floataa(expected_max_diff=1E-1 )
def snake_case_ ( self: Any ):
'''simple docstring'''
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def snake_case_ ( self: int ):
'''simple docstring'''
self._test_save_load_local()
def snake_case_ ( self: Union[str, Any] ):
'''simple docstring'''
self._test_inference_batch_single_identical(
expected_max_diff=1E-2,)
| 1
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : Optional[int] = logging.get_logger(__name__)
__lowercase : Optional[int] = {
'''studio-ousia/luke-base''': '''https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json''',
'''studio-ousia/luke-large''': '''https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json''',
}
class __lowercase ( _lowercase ):
lowerCamelCase : List[str] = "luke"
def __init__(self , A=5_0_2_6_7 , A=5_0_0_0_0_0 , A=7_6_8 , A=2_5_6 , A=1_2 , A=1_2 , A=3_0_7_2 , A="gelu" , A=0.1 , A=0.1 , A=5_1_2 , A=2 , A=0.02 , A=1E-12 , A=True , A=None , A=1 , A=0 , A=2 , **A , ):
super().__init__(pad_token_id=A , bos_token_id=A , eos_token_id=A , **A )
lowerCamelCase_ : Dict = vocab_size
lowerCamelCase_ : List[str] = entity_vocab_size
lowerCamelCase_ : Dict = hidden_size
lowerCamelCase_ : str = entity_emb_size
lowerCamelCase_ : List[str] = num_hidden_layers
lowerCamelCase_ : List[Any] = num_attention_heads
lowerCamelCase_ : int = hidden_act
lowerCamelCase_ : List[str] = intermediate_size
lowerCamelCase_ : Tuple = hidden_dropout_prob
lowerCamelCase_ : Optional[Any] = attention_probs_dropout_prob
lowerCamelCase_ : Any = max_position_embeddings
lowerCamelCase_ : Any = type_vocab_size
lowerCamelCase_ : List[str] = initializer_range
lowerCamelCase_ : Any = layer_norm_eps
lowerCamelCase_ : Union[str, Any] = use_entity_aware_attention
lowerCamelCase_ : Optional[Any] = classifier_dropout
| 422
| 0
|
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def lowerCAmelCase__ ( _UpperCamelCase : str , _UpperCamelCase : dict ) -> str:
"""simple docstring"""
snake_case = BeautifulSoup(requests.get(_UpperCamelCase , params=_UpperCamelCase ).content , 'html.parser' )
snake_case = soup.find('div' , attrs={'class': 'gs_ri'} )
snake_case = div.find('div' , attrs={'class': 'gs_fl'} ).find_all('a' )
return anchors[2].get_text()
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = {
"title": (
"Precisely geometry controlled microsupercapacitors for ultrahigh areal "
"capacitance, volumetric capacitance, and energy density"
),
"journal": "Chem. Mater.",
"volume": 30,
"pages": "3979-3990",
"year": 2_018,
"hl": "en",
}
print(get_citation("https://scholar.google.com/scholar_lookup", params=params))
| 711
|
"""simple docstring"""
import os
import string
import sys
SCREAMING_SNAKE_CASE__ = 1 << 8
SCREAMING_SNAKE_CASE__ = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW_KEY_FLAG,
"mod_int": 91,
"undefined": sys.maxsize,
"interrupt": 3,
"insert": 50,
"delete": 51,
"pg_up": 53,
"pg_down": 54,
}
SCREAMING_SNAKE_CASE__ = KEYMAP["up"]
SCREAMING_SNAKE_CASE__ = KEYMAP["left"]
if sys.platform == "win32":
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = {
b"\xe0H": KEYMAP["up"] - ARROW_KEY_FLAG,
b"\x00H": KEYMAP["up"] - ARROW_KEY_FLAG,
b"\xe0P": KEYMAP["down"] - ARROW_KEY_FLAG,
b"\x00P": KEYMAP["down"] - ARROW_KEY_FLAG,
b"\xe0M": KEYMAP["right"] - ARROW_KEY_FLAG,
b"\x00M": KEYMAP["right"] - ARROW_KEY_FLAG,
b"\xe0K": KEYMAP["left"] - ARROW_KEY_FLAG,
b"\x00K": KEYMAP["left"] - ARROW_KEY_FLAG,
}
for i in range(10):
SCREAMING_SNAKE_CASE__ = ord(str(i))
def lowerCAmelCase__ ( ) -> List[Any]:
"""simple docstring"""
if os.name == "nt":
import msvcrt
snake_case = 'mbcs'
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(_UpperCamelCase ) == 0:
# Read the keystroke
snake_case = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
snake_case = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
snake_case = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP['mod_int'] ) )
WIN_CH_BUFFER.append(_UpperCamelCase )
if ord(_UpperCamelCase ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(1_2_6 ) )
snake_case = chr(KEYMAP['esc'] )
except KeyError:
snake_case = cha[1]
else:
snake_case = ch.decode(_UpperCamelCase )
else:
snake_case = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
snake_case = sys.stdin.fileno()
snake_case = termios.tcgetattr(_UpperCamelCase )
try:
tty.setraw(_UpperCamelCase )
snake_case = sys.stdin.read(1 )
finally:
termios.tcsetattr(_UpperCamelCase , termios.TCSADRAIN , _UpperCamelCase )
return ch
def lowerCAmelCase__ ( ) -> Union[str, Any]:
"""simple docstring"""
snake_case = get_raw_chars()
if ord(_UpperCamelCase ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(_UpperCamelCase ) == KEYMAP["esc"]:
snake_case = get_raw_chars()
if ord(_UpperCamelCase ) == KEYMAP["mod_int"]:
snake_case = get_raw_chars()
if ord(_UpperCamelCase ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(_UpperCamelCase ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(_UpperCamelCase ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 104
| 0
|
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class _lowerCamelCase :
def __init__( self : Dict , UpperCamelCase : int , UpperCamelCase : Any=99 , UpperCamelCase : Union[str, Any]=13 , UpperCamelCase : Optional[Any]=7 , UpperCamelCase : List[str]=9 , UpperCamelCase : List[str]=True , UpperCamelCase : Optional[Any]=True , UpperCamelCase : List[str]=False , UpperCamelCase : str=32 , UpperCamelCase : str=5 , UpperCamelCase : Dict=4 , UpperCamelCase : Any=37 , UpperCamelCase : Any=8 , UpperCamelCase : List[str]=0.1 , UpperCamelCase : Tuple=0.002 , UpperCamelCase : Optional[Any]=1 , UpperCamelCase : List[str]=0 , UpperCamelCase : Dict=0 , UpperCamelCase : Any=None , UpperCamelCase : Tuple=None , ) -> Any:
"""simple docstring"""
lowerCAmelCase__ : Tuple = parent
lowerCAmelCase__ : Union[str, Any] = batch_size
lowerCAmelCase__ : int = encoder_seq_length
lowerCAmelCase__ : Any = decoder_seq_length
# For common tests
lowerCAmelCase__ : int = self.decoder_seq_length
lowerCAmelCase__ : Any = is_training
lowerCAmelCase__ : Tuple = use_attention_mask
lowerCAmelCase__ : Optional[int] = use_labels
lowerCAmelCase__ : Tuple = vocab_size
lowerCAmelCase__ : int = hidden_size
lowerCAmelCase__ : Any = num_hidden_layers
lowerCAmelCase__ : List[str] = num_attention_heads
lowerCAmelCase__ : Dict = d_ff
lowerCAmelCase__ : Union[str, Any] = relative_attention_num_buckets
lowerCAmelCase__ : str = dropout_rate
lowerCAmelCase__ : Tuple = initializer_factor
lowerCAmelCase__ : Dict = eos_token_id
lowerCAmelCase__ : Optional[int] = pad_token_id
lowerCAmelCase__ : Dict = decoder_start_token_id
lowerCAmelCase__ : Union[str, Any] = None
lowerCAmelCase__ : Tuple = decoder_layers
def _lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
return TaConfig.from_pretrained("""google/umt5-base""" )
def _lowerCAmelCase ( self : List[str] , UpperCamelCase : Tuple , UpperCamelCase : Any , UpperCamelCase : int , UpperCamelCase : List[str]=None , UpperCamelCase : Optional[int]=None , UpperCamelCase : Any=None , UpperCamelCase : str=None , UpperCamelCase : str=None , ) -> int:
"""simple docstring"""
if attention_mask is None:
lowerCAmelCase__ : List[str] = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
lowerCAmelCase__ : Dict = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
lowerCAmelCase__ : Union[str, Any] = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=UpperCamelCase )
if decoder_head_mask is None:
lowerCAmelCase__ : Optional[int] = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=UpperCamelCase )
if cross_attn_head_mask is None:
lowerCAmelCase__ : List[str] = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=UpperCamelCase )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def _lowerCAmelCase ( self : Optional[Any] ) -> Any:
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
lowerCAmelCase__ : List[Any] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
lowerCAmelCase__ : Tuple = input_ids.clamp(self.pad_token_id + 1 )
lowerCAmelCase__ : str = decoder_input_ids.clamp(self.pad_token_id + 1 )
lowerCAmelCase__ : Optional[int] = self.get_config()
lowerCAmelCase__ : Optional[int] = config.num_attention_heads
lowerCAmelCase__ : Optional[Any] = self.prepare_inputs_dict(UpperCamelCase , UpperCamelCase , UpperCamelCase )
return config, input_dict
def _lowerCAmelCase ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ : int = self.prepare_config_and_inputs()
return config, inputs_dict
def _lowerCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
return TaConfig(
vocab_size=1_66 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _lowerCAmelCase ( self : int ) -> Tuple:
"""simple docstring"""
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _lowerCAmelCase ( self : List[str] , UpperCamelCase : Any , UpperCamelCase : List[str] , UpperCamelCase : Any , UpperCamelCase : Optional[Any] , UpperCamelCase : Dict , UpperCamelCase : Any , ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase__ : Tuple = UMTaModel(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
lowerCAmelCase__ : Union[str, Any] = model(
input_ids=UpperCamelCase , decoder_input_ids=UpperCamelCase , attention_mask=UpperCamelCase , decoder_attention_mask=UpperCamelCase , )
lowerCAmelCase__ : List[Any] = model(input_ids=UpperCamelCase , decoder_input_ids=UpperCamelCase )
lowerCAmelCase__ : Dict = result.last_hidden_state
lowerCAmelCase__ : List[Any] = result.past_key_values
lowerCAmelCase__ : Any = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(UpperCamelCase ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def _lowerCAmelCase ( self : Any , UpperCamelCase : Any , UpperCamelCase : Any , UpperCamelCase : int , UpperCamelCase : List[str] , UpperCamelCase : Union[str, Any] , UpperCamelCase : Optional[int] , ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ : Optional[int] = UMTaModel(config=UpperCamelCase ).get_decoder().to(UpperCamelCase ).eval()
# first forward pass
lowerCAmelCase__ : str = model(UpperCamelCase , use_cache=UpperCamelCase )
lowerCAmelCase__ : Dict = model(UpperCamelCase )
lowerCAmelCase__ : str = model(UpperCamelCase , use_cache=UpperCamelCase )
self.parent.assertTrue(len(UpperCamelCase ) == len(UpperCamelCase ) )
self.parent.assertTrue(len(UpperCamelCase ) == len(UpperCamelCase ) + 1 )
lowerCAmelCase__ , lowerCAmelCase__ : int = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
lowerCAmelCase__ : Optional[int] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
lowerCAmelCase__ : List[str] = torch.cat([input_ids, next_tokens] , dim=-1 )
lowerCAmelCase__ : Dict = model(UpperCamelCase )["""last_hidden_state"""]
lowerCAmelCase__ : List[str] = model(UpperCamelCase , past_key_values=UpperCamelCase )["""last_hidden_state"""]
# select random slice
lowerCAmelCase__ : Any = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowerCAmelCase__ : int = output_from_no_past[:, -1, random_slice_idx].detach()
lowerCAmelCase__ : Union[str, Any] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(UpperCamelCase , UpperCamelCase , atol=1E-3 ) )
def _lowerCAmelCase ( self : List[str] , UpperCamelCase : List[Any] , UpperCamelCase : Any , ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ : int = UMTaModel(config=UpperCamelCase ).to(UpperCamelCase ).half().eval()
lowerCAmelCase__ : List[str] = model(**UpperCamelCase )["""last_hidden_state"""]
self.parent.assertFalse(torch.isnan(UpperCamelCase ).any().item() )
@require_torch
class _lowerCamelCase ( a_ , a_ , a_ , unittest.TestCase ):
_lowerCamelCase :Tuple = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
_lowerCamelCase :Union[str, Any] = (UMTaForConditionalGeneration,) if is_torch_available() else ()
_lowerCamelCase :Dict = (
{
"conversational": UMTaForConditionalGeneration,
"feature-extraction": UMTaModel,
"summarization": UMTaForConditionalGeneration,
"text2text-generation": UMTaForConditionalGeneration,
"translation": UMTaForConditionalGeneration,
"question-answering": UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
_lowerCamelCase :Any = True
_lowerCamelCase :List[Any] = False
_lowerCamelCase :int = False
_lowerCamelCase :str = True
_lowerCamelCase :Dict = True
# The small UMT5 model needs higher percentages for CPU/MP tests
_lowerCamelCase :List[Any] = [0.8, 0.9]
def _lowerCAmelCase ( self : str ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ : str = UMTaModelTester(self )
@unittest.skip("""Test has a segmentation fault on torch 1.8.0""" )
def _lowerCAmelCase ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ : List[str] = self.model_tester.prepare_config_and_inputs()
lowerCAmelCase__ : Optional[int] = UMTaModel(config_and_inputs[0] ).to(UpperCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
UpperCamelCase , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f"""{tmpdirname}/t5_test.onnx""" , export_params=UpperCamelCase , opset_version=9 , input_names=["""input_ids""", """decoder_input_ids"""] , )
@unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*UpperCamelCase )
def _lowerCAmelCase ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ : Any = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
lowerCAmelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
lowerCAmelCase__ : List[Any] = config_and_inputs[0]
lowerCAmelCase__ : Optional[Any] = UMTaForConditionalGeneration(UpperCamelCase ).eval()
model.to(UpperCamelCase )
lowerCAmelCase__ : int = {
"""head_mask""": torch.zeros(config.num_layers , config.num_heads , device=UpperCamelCase ),
"""decoder_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=UpperCamelCase ),
"""cross_attn_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=UpperCamelCase ),
}
for attn_name, (name, mask) in zip(UpperCamelCase , head_masking.items() ):
lowerCAmelCase__ : List[Any] = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
lowerCAmelCase__ : Any = torch.ones(
config.num_decoder_layers , config.num_heads , device=UpperCamelCase )
lowerCAmelCase__ : Optional[int] = model.generate(
config_and_inputs[1]["""input_ids"""] , num_beams=1 , max_length=3 , output_attentions=UpperCamelCase , return_dict_in_generate=UpperCamelCase , **UpperCamelCase , )
# We check the state of decoder_attentions and cross_attentions just from the last step
lowerCAmelCase__ : List[Any] = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" )
def _lowerCAmelCase ( self : Any ) -> int:
"""simple docstring"""
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowerCamelCase ( unittest.TestCase ):
@slow
@unittest.skip(
"""Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" )
def _lowerCAmelCase ( self : int ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ : str = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" , return_dict=UpperCamelCase ).to(UpperCamelCase )
lowerCAmelCase__ : Optional[Any] = AutoTokenizer.from_pretrained("""google/umt5-small""" , use_fast=UpperCamelCase , legacy=UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = [
"""Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
"""No se como puedo <extra_id_0>.""",
"""This is the reason why we <extra_id_0> them.""",
"""The <extra_id_0> walks in <extra_id_1>, seats""",
"""A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
]
lowerCAmelCase__ : Optional[Any] = tokenizer(UpperCamelCase , return_tensors="""pt""" , padding=UpperCamelCase ).input_ids
# fmt: off
lowerCAmelCase__ : Tuple = torch.tensor(
[
[ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
] )
# fmt: on
torch.testing.assert_allclose(UpperCamelCase , UpperCamelCase )
lowerCAmelCase__ : List[str] = model.generate(input_ids.to(UpperCamelCase ) )
lowerCAmelCase__ : Optional[Any] = [
"""<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
"""<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
]
lowerCAmelCase__ : int = tokenizer.batch_decode(UpperCamelCase )
self.assertEqual(UpperCamelCase , UpperCamelCase )
| 299
|
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser
_A = logging.getLogger(__name__)
torch.set_grad_enabled(False)
_A = """cuda""" if torch.cuda.is_available() else """cpu"""
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase=100 , __UpperCAmelCase=" " ) -> List[str]:
lowerCAmelCase__ : str = text.split(__UpperCAmelCase )
return [character.join(text[i : i + n] ).strip() for i in range(0 , len(__UpperCAmelCase ) , __UpperCAmelCase )]
def lowercase_ ( __UpperCAmelCase ) -> dict:
lowerCAmelCase__ , lowerCAmelCase__ : List[str] = [], []
for title, text in zip(documents["""title"""] , documents["""text"""] ):
if text is not None:
for passage in split_text(__UpperCAmelCase ):
titles.append(title if title is not None else """""" )
texts.append(__UpperCAmelCase )
return {"title": titles, "text": texts}
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> dict:
lowerCAmelCase__ : str = ctx_tokenizer(
documents["""title"""] , documents["""text"""] , truncation=__UpperCAmelCase , padding="""longest""" , return_tensors="""pt""" )["""input_ids"""]
lowerCAmelCase__ : Tuple = ctx_encoder(input_ids.to(device=__UpperCAmelCase ) , return_dict=__UpperCAmelCase ).pooler_output
return {"embeddings": embeddings.detach().cpu().numpy()}
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ) -> Tuple:
######################################
logger.info("""Step 1 - Create the dataset""" )
######################################
# The dataset needed for RAG must have three columns:
# - title (string): title of the document
# - text (string): text of a passage of the document
# - embeddings (array of dimension d): DPR representation of the passage
# Let's say you have documents in tab-separated csv files with columns "title" and "text"
assert os.path.isfile(rag_example_args.csv_path ), "Please provide a valid path to a csv file"
# You can load a Dataset object this way
lowerCAmelCase__ : Dict = load_dataset(
"""csv""" , data_files=[rag_example_args.csv_path] , split="""train""" , delimiter="""\t""" , column_names=["""title""", """text"""] )
# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files
# Then split the documents into passages of 100 words
lowerCAmelCase__ : Dict = dataset.map(__UpperCAmelCase , batched=__UpperCAmelCase , num_proc=processing_args.num_proc )
# And compute the embeddings
lowerCAmelCase__ : Any = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=__UpperCAmelCase )
lowerCAmelCase__ : Any = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
lowerCAmelCase__ : List[Any] = Features(
{"""text""": Value("""string""" ), """title""": Value("""string""" ), """embeddings""": Sequence(Value("""float32""" ) )} ) # optional, save as float32 instead of float64 to save space
lowerCAmelCase__ : Optional[Any] = dataset.map(
partial(__UpperCAmelCase , ctx_encoder=__UpperCAmelCase , ctx_tokenizer=__UpperCAmelCase ) , batched=__UpperCAmelCase , batch_size=processing_args.batch_size , features=__UpperCAmelCase , )
# And finally save your dataset
lowerCAmelCase__ : List[Any] = os.path.join(rag_example_args.output_dir , """my_knowledge_dataset""" )
dataset.save_to_disk(__UpperCAmelCase )
# from datasets import load_from_disk
# dataset = load_from_disk(passages_path) # to reload the dataset
######################################
logger.info("""Step 2 - Index the dataset""" )
######################################
# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
lowerCAmelCase__ : Tuple = faiss.IndexHNSWFlat(index_hnsw_args.d , index_hnsw_args.m , faiss.METRIC_INNER_PRODUCT )
dataset.add_faiss_index("""embeddings""" , custom_index=__UpperCAmelCase )
# And save the index
lowerCAmelCase__ : Union[str, Any] = os.path.join(rag_example_args.output_dir , """my_knowledge_dataset_hnsw_index.faiss""" )
dataset.get_index("""embeddings""" ).save(__UpperCAmelCase )
# dataset.load_faiss_index("embeddings", index_path) # to reload the index
@dataclass
class _lowerCamelCase :
_lowerCamelCase :str = field(
default=str(Path(a_ ).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv" ) , metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"} , )
_lowerCamelCase :Optional[str] = field(
default=a_ , metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."} , )
_lowerCamelCase :str = field(
default="facebook/rag-sequence-nq" , metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"} , )
_lowerCamelCase :str = field(
default="facebook/dpr-ctx_encoder-multiset-base" , metadata={
"help": (
"The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or"
" 'facebook/dpr-ctx_encoder-multiset-base'"
)
} , )
_lowerCamelCase :Optional[str] = field(
default=str(Path(a_ ).parent / "test_run" / "dummy-kb" ) , metadata={"help": "Path to a directory where the dataset passages and the index will be saved"} , )
@dataclass
class _lowerCamelCase :
_lowerCamelCase :Optional[int] = field(
default=a_ , metadata={
"help": "The number of processes to use to split the documents into passages. Default is single process."
} , )
_lowerCamelCase :int = field(
default=16 , metadata={
"help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
} , )
@dataclass
class _lowerCamelCase :
_lowerCamelCase :int = field(
default=768 , metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."} , )
_lowerCamelCase :int = field(
default=128 , metadata={
"help": (
"The number of bi-directional links created for every new element during the HNSW index construction."
)
} , )
if __name__ == "__main__":
logging.basicConfig(level=logging.WARNING)
logger.setLevel(logging.INFO)
_A = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
_A , _A , _A = parser.parse_args_into_dataclasses()
with TemporaryDirectory() as tmp_dir:
_A = rag_example_args.output_dir or tmp_dir
main(rag_example_args, processing_args, index_hnsw_args)
| 299
| 1
|
# Copyright 2021 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 packaging import version
from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
copy_func,
replace_return_docstrings,
)
from .generic import (
ContextManagers,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
TensorType,
add_model_info_to_auto_map,
cached_property,
can_return_loss,
expand_dims,
find_labels,
flatten_dict,
infer_framework,
is_jax_tensor,
is_numpy_array,
is_tensor,
is_tf_symbolic_tensor,
is_tf_tensor,
is_torch_device,
is_torch_dtype,
is_torch_tensor,
reshape,
squeeze,
strtobool,
tensor_size,
to_numpy,
to_py_obj,
transpose,
working_or_temp_dir,
)
from .hub import (
CLOUDFRONT_DISTRIB_PREFIX,
DISABLE_TELEMETRY,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
EntryNotFoundError,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
cached_file,
default_cache_path,
define_sagemaker_information,
download_url,
extract_commit_hash,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
has_file,
http_user_agent,
is_offline_mode,
is_remote_url,
move_cache,
send_example_telemetry,
try_to_load_from_cache,
)
from .import_utils import (
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
TORCH_FX_REQUIRED_VERSION,
USE_JAX,
USE_TF,
USE_TORCH,
DummyObject,
OptionalDependencyNotAvailable,
_LazyModule,
ccl_version,
direct_transformers_import,
get_torch_version,
is_accelerate_available,
is_apex_available,
is_bitsandbytes_available,
is_bsa_available,
is_coloredlogs_available,
is_cython_available,
is_datasets_available,
is_decord_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_jieba_available,
is_jumanpp_available,
is_kenlm_available,
is_keras_nlp_available,
is_librosa_available,
is_natten_available,
is_ninja_available,
is_onnx_available,
is_openai_available,
is_optimum_available,
is_pandas_available,
is_peft_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytest_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sacremoses_available,
is_safetensors_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_sudachi_available,
is_tensorflow_probability_available,
is_tensorflow_text_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_bfaa_cpu_available,
is_torch_bfaa_gpu_available,
is_torch_compile_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_neuroncore_available,
is_torch_tensorrt_fx_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_torchdistx_available,
is_torchdynamo_available,
is_torchvision_available,
is_training_run_on_sagemaker,
is_vision_available,
requires_backends,
torch_only_method,
)
__UpperCAmelCase = """pytorch_model.bin"""
__UpperCAmelCase = """pytorch_model.bin.index.json"""
__UpperCAmelCase = """adapter_config.json"""
__UpperCAmelCase = """adapter_model.bin"""
__UpperCAmelCase = """adapter_model.safetensors"""
__UpperCAmelCase = """tf_model.h5"""
__UpperCAmelCase = """tf_model.h5.index.json"""
__UpperCAmelCase = """model.ckpt"""
__UpperCAmelCase = """flax_model.msgpack"""
__UpperCAmelCase = """flax_model.msgpack.index.json"""
__UpperCAmelCase = """model.safetensors"""
__UpperCAmelCase = """model.safetensors.index.json"""
__UpperCAmelCase = """config.json"""
__UpperCAmelCase = """preprocessor_config.json"""
__UpperCAmelCase = FEATURE_EXTRACTOR_NAME
__UpperCAmelCase = """generation_config.json"""
__UpperCAmelCase = """modelcard.json"""
__UpperCAmelCase = """▁"""
__UpperCAmelCase = SENTENCEPIECE_UNDERLINE # Kept for backward compatibility
__UpperCAmelCase = [
[[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2 # Needs to have 0s and 1s only since XLM uses it for langs too.
__UpperCAmelCase = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
__UpperCAmelCase = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
def snake_case_ (__A : List[Any] ) -> Any:
if version.parse(__A ) < version.parse(__A ):
if "dev" in min_version:
__lowerCAmelCase : Tuple = (
"""This example requires a source install from HuggingFace Transformers (see """
"""`https://huggingface.co/docs/transformers/installation#install-from-source`),"""
)
else:
__lowerCAmelCase : Optional[int] = f'''This example requires a minimum version of {min_version},'''
error_message += f''' but the version found is {__version__}.\n'''
raise ImportError(
error_message
+ """Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other """
"""versions of HuggingFace Transformers.""" )
| 218
|
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""vocab_file""": """vocab.txt""",
"""merges_file""": """bpe.codes""",
}
__UpperCAmelCase = {
"""vocab_file""": {
"""vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt""",
"""vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt""",
},
"""merges_file""": {
"""vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes""",
"""vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes""",
},
}
__UpperCAmelCase = {
"""vinai/phobert-base""": 256,
"""vinai/phobert-large""": 256,
}
def snake_case_ (__A : List[str] ) -> Optional[int]:
__lowerCAmelCase : List[Any] = set()
__lowerCAmelCase : int = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__lowerCAmelCase : int = char
__lowerCAmelCase : str = set(__A )
return pairs
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
lowerCamelCase : Tuple =VOCAB_FILES_NAMES
lowerCamelCase : str =PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase : Optional[Any] =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : Dict , lowerCAmelCase : List[Any] , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : Optional[int]="<s>" , lowerCAmelCase : List[str]="</s>" , lowerCAmelCase : Dict="</s>" , lowerCAmelCase : Dict="<s>" , lowerCAmelCase : Optional[Any]="<unk>" , lowerCAmelCase : int="<pad>" , lowerCAmelCase : Dict="<mask>" , **lowerCAmelCase : int , ) -> Dict:
"""simple docstring"""
super().__init__(
bos_token=lowerCAmelCase , eos_token=lowerCAmelCase , unk_token=lowerCAmelCase , sep_token=lowerCAmelCase , cls_token=lowerCAmelCase , pad_token=lowerCAmelCase , mask_token=lowerCAmelCase , **lowerCAmelCase , )
__lowerCAmelCase : int = vocab_file
__lowerCAmelCase : int = merges_file
__lowerCAmelCase : Union[str, Any] = {}
__lowerCAmelCase : Optional[int] = 0
__lowerCAmelCase : List[Any] = 1
__lowerCAmelCase : List[Any] = 2
__lowerCAmelCase : List[str] = 3
self.add_from_file(lowerCAmelCase )
__lowerCAmelCase : List[Any] = {v: k for k, v in self.encoder.items()}
with open(lowerCAmelCase , encoding="""utf-8""" ) as merges_handle:
__lowerCAmelCase : Dict = merges_handle.read().split("""\n""" )[:-1]
__lowerCAmelCase : Any = [tuple(merge.split()[:-1] ) for merge in merges]
__lowerCAmelCase : Dict = dict(zip(lowerCAmelCase , range(len(lowerCAmelCase ) ) ) )
__lowerCAmelCase : int = {}
def SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase : List[int] , lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__lowerCAmelCase : str = [self.cls_token_id]
__lowerCAmelCase : Optional[int] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE ( self : Tuple , lowerCAmelCase : List[int] , lowerCAmelCase : Optional[List[int]] = None , lowerCAmelCase : bool = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCAmelCase , token_ids_a=lowerCAmelCase , already_has_special_tokens=lowerCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(lowerCAmelCase )) + [1]
return [1] + ([0] * len(lowerCAmelCase )) + [1, 1] + ([0] * len(lowerCAmelCase )) + [1]
def SCREAMING_SNAKE_CASE ( self : Any , lowerCAmelCase : List[int] , lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
__lowerCAmelCase : str = [self.sep_token_id]
__lowerCAmelCase : int = [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]
@property
def SCREAMING_SNAKE_CASE ( self : int ) -> Optional[int]:
"""simple docstring"""
return len(self.encoder )
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def SCREAMING_SNAKE_CASE ( self : Dict , lowerCAmelCase : Optional[Any] ) -> int:
"""simple docstring"""
if token in self.cache:
return self.cache[token]
__lowerCAmelCase : Union[str, Any] = tuple(lowerCAmelCase )
__lowerCAmelCase : List[str] = tuple(list(word[:-1] ) + [word[-1] + """</w>"""] )
__lowerCAmelCase : Optional[Any] = get_pairs(lowerCAmelCase )
if not pairs:
return token
while True:
__lowerCAmelCase : Optional[Any] = min(lowerCAmelCase , key=lambda lowerCAmelCase : self.bpe_ranks.get(lowerCAmelCase , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
__lowerCAmelCase ,__lowerCAmelCase : int = bigram
__lowerCAmelCase : Optional[int] = []
__lowerCAmelCase : List[Any] = 0
while i < len(lowerCAmelCase ):
try:
__lowerCAmelCase : Tuple = word.index(lowerCAmelCase , lowerCAmelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__lowerCAmelCase : Any = j
if word[i] == first and i < len(lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__lowerCAmelCase : Dict = tuple(lowerCAmelCase )
__lowerCAmelCase : List[Any] = new_word
if len(lowerCAmelCase ) == 1:
break
else:
__lowerCAmelCase : Dict = get_pairs(lowerCAmelCase )
__lowerCAmelCase : List[str] = """@@ """.join(lowerCAmelCase )
__lowerCAmelCase : Any = word[:-4]
__lowerCAmelCase : int = word
return word
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , lowerCAmelCase : Tuple ) -> str:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = []
__lowerCAmelCase : str = re.findall(r"""\S+\n?""" , lowerCAmelCase )
for token in words:
split_tokens.extend(list(self.bpe(lowerCAmelCase ).split(""" """ ) ) )
return split_tokens
def SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase : int ) -> Any:
"""simple docstring"""
return self.encoder.get(lowerCAmelCase , self.encoder.get(self.unk_token ) )
def SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase : str ) -> Optional[int]:
"""simple docstring"""
return self.decoder.get(lowerCAmelCase , self.unk_token )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowerCAmelCase : Any ) -> Any:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = """ """.join(lowerCAmelCase ).replace("""@@ """ , """""" ).strip()
return out_string
def SCREAMING_SNAKE_CASE ( self : Tuple , lowerCAmelCase : str , lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__lowerCAmelCase : List[Any] = os.path.join(
lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
__lowerCAmelCase : Dict = os.path.join(
lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase ):
copyfile(self.vocab_file , lowerCAmelCase )
if os.path.abspath(self.merges_file ) != os.path.abspath(lowerCAmelCase ):
copyfile(self.merges_file , lowerCAmelCase )
return out_vocab_file, out_merge_file
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowerCAmelCase : Optional[int] ) -> List[str]:
"""simple docstring"""
if isinstance(lowerCAmelCase , lowerCAmelCase ):
try:
with open(lowerCAmelCase , """r""" , encoding="""utf-8""" ) as fd:
self.add_from_file(lowerCAmelCase )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset''' )
return
__lowerCAmelCase : Union[str, Any] = f.readlines()
for lineTmp in lines:
__lowerCAmelCase : Optional[int] = lineTmp.strip()
__lowerCAmelCase : str = line.rfind(""" """ )
if idx == -1:
raise ValueError("""Incorrect dictionary format, expected '<token> <cnt>'""" )
__lowerCAmelCase : int = line[:idx]
__lowerCAmelCase : List[Any] = len(self.encoder )
| 218
| 1
|
'''simple docstring'''
a : List[Any] = [0, 2, 4, 6, 8]
a : Any = [1, 3, 5, 7, 9]
def __UpperCAmelCase ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Union[str, Any] ) -> int:
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 10
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
__snake_case = 0
for digit in range(10 ):
__snake_case = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 10 , _lowerCAmelCase , _lowerCAmelCase )
return result
__snake_case = 0
for digita in range(10 ):
__snake_case = digita
if (remainder + digita) % 2 == 0:
__snake_case = ODD_DIGITS
else:
__snake_case = EVEN_DIGITS
for digita in other_parity_digits:
__snake_case = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 10 , _lowerCAmelCase , _lowerCAmelCase , )
return result
def __UpperCAmelCase ( _UpperCAmelCase : Union[str, Any] = 9 ) -> int:
__snake_case = 0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(_lowerCAmelCase , 0 , [0] * length , _lowerCAmelCase )
return result
if __name__ == "__main__":
print(F'''{solution() = }''')
| 69
|
# 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.
import re
from ..utils import cached_file
# docstyle-ignore
__lowerCAmelCase = "\nHuman: <<task>>\n\nAssistant: "
__lowerCAmelCase = "huggingface-tools/default-prompts"
__lowerCAmelCase = {"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase="run" ) -> Union[str, Any]:
if prompt_or_repo_id is None:
_UpperCAmelCase = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search("\\s" , _lowerCAmelCase ) is not None:
return prompt_or_repo_id
_UpperCAmelCase = cached_file(
_lowerCAmelCase , PROMPT_FILES[mode] , repo_type="dataset" , user_agent={"agent": agent_name} )
with open(_lowerCAmelCase , "r" , encoding="utf-8" ) as f:
return f.read()
| 684
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ = {
'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST',
'NezhaForNextSentencePrediction',
'NezhaForMaskedLM',
'NezhaForPreTraining',
'NezhaForMultipleChoice',
'NezhaForQuestionAnswering',
'NezhaForSequenceClassification',
'NezhaForTokenClassification',
'NezhaModel',
'NezhaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nezha import (
NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
NezhaPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 700
|
"""simple docstring"""
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
def get_matched_characters(__UpperCamelCase , __UpperCamelCase ) -> str:
__A = []
__A = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
__A = int(max(0 , i - limit ) )
__A = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(__UpperCamelCase )
__A = f'{_stra[0:_stra.index(__UpperCamelCase )]} {_stra[_stra.index(__UpperCamelCase ) + 1:]}'
return "".join(__UpperCamelCase )
# matching characters
__A = get_matched_characters(__UpperCamelCase , __UpperCamelCase )
__A = get_matched_characters(__UpperCamelCase , __UpperCamelCase )
__A = len(__UpperCamelCase )
# transposition
__A = (
len([(ca, ca) for ca, ca in zip(__UpperCamelCase , __UpperCamelCase ) if ca != ca] ) // 2
)
if not match_count:
__A = 0.0
else:
__A = (
1
/ 3
* (
match_count / len(__UpperCamelCase )
+ match_count / len(__UpperCamelCase )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
__A = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('hello', 'world'))
| 215
| 0
|
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class lowercase_ :
"""simple docstring"""
def __init__( self , _UpperCAmelCase = None ):
"""simple docstring"""
if components is None:
a_ = []
a_ = list(__UpperCamelCase )
def __len__( self ):
"""simple docstring"""
return len(self.__components )
def __str__( self ):
"""simple docstring"""
return "(" + ",".join(map(__UpperCamelCase , self.__components ) ) + ")"
def __add__( self , _UpperCAmelCase ):
"""simple docstring"""
a_ = len(self )
if size == len(__UpperCamelCase ):
a_ = [self.__components[i] + other.component(__UpperCamelCase ) for i in range(__UpperCamelCase )]
return Vector(__UpperCamelCase )
else:
raise Exception("""must have the same size""" )
def __sub__( self , _UpperCAmelCase ):
"""simple docstring"""
a_ = len(self )
if size == len(__UpperCamelCase ):
a_ = [self.__components[i] - other.component(__UpperCamelCase ) for i in range(__UpperCamelCase )]
return Vector(__UpperCamelCase )
else: # error case
raise Exception("""must have the same size""" )
@overload
def __mul__( self , _UpperCAmelCase ):
"""simple docstring"""
...
@overload
def __mul__( self , _UpperCAmelCase ):
"""simple docstring"""
...
def __mul__( self , _UpperCAmelCase ):
"""simple docstring"""
if isinstance(__UpperCamelCase , (float, int) ):
a_ = [c * other for c in self.__components]
return Vector(__UpperCamelCase )
elif isinstance(__UpperCamelCase , __UpperCamelCase ) and len(self ) == len(__UpperCamelCase ):
a_ = len(self )
a_ = [self.__components[i] * other.component(__UpperCamelCase ) for i in range(__UpperCamelCase )]
return sum(__UpperCamelCase )
else: # error case
raise Exception("""invalid operand!""" )
def lowercase__ ( self ):
"""simple docstring"""
return Vector(self.__components )
def lowercase__ ( self , _UpperCAmelCase ):
"""simple docstring"""
if isinstance(__UpperCamelCase , __UpperCamelCase ) and -len(self.__components ) <= i < len(self.__components ):
return self.__components[i]
else:
raise Exception("""index out of range""" )
def lowercase__ ( self , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
assert -len(self.__components ) <= pos < len(self.__components )
a_ = value
def lowercase__ ( self ):
"""simple docstring"""
if len(self.__components ) == 0:
raise Exception("""Vector is empty""" )
a_ = [c**2 for c in self.__components]
return math.sqrt(sum(__UpperCamelCase ) )
def lowercase__ ( self , _UpperCAmelCase , _UpperCAmelCase = False ):
"""simple docstring"""
a_ = self * other
a_ = self.euclidean_length() * other.euclidean_length()
if deg:
return math.degrees(math.acos(num / den ) )
else:
return math.acos(num / den )
def lowerCamelCase_ ( UpperCAmelCase__ ):
"""simple docstring"""
assert isinstance(UpperCamelCase__ , UpperCamelCase__ )
return Vector([0] * dimension )
def lowerCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
assert isinstance(UpperCamelCase__ , UpperCamelCase__ ) and (isinstance(UpperCamelCase__ , UpperCamelCase__ ))
a_ = [0] * dimension
a_ = 1
return Vector(UpperCamelCase__ )
def lowerCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
assert (
isinstance(UpperCamelCase__ , UpperCamelCase__ )
and isinstance(UpperCamelCase__ , UpperCamelCase__ )
and (isinstance(UpperCamelCase__ , (int, float) ))
)
return x * scalar + y
def lowerCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
random.seed(UpperCamelCase__ )
a_ = [random.randint(UpperCamelCase__ , UpperCamelCase__ ) for _ in range(UpperCamelCase__ )]
return Vector(UpperCamelCase__ )
class lowercase_ :
"""simple docstring"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
a_ = matrix
a_ = w
a_ = h
def __str__( self ):
"""simple docstring"""
a_ = ""
for i in range(self.__height ):
ans += "|"
for j in range(self.__width ):
if j < self.__width - 1:
ans += str(self.__matrix[i][j] ) + ","
else:
ans += str(self.__matrix[i][j] ) + "|\n"
return ans
def __add__( self , _UpperCAmelCase ):
"""simple docstring"""
if self.__width == other.width() and self.__height == other.height():
a_ = []
for i in range(self.__height ):
a_ = [
self.__matrix[i][j] + other.component(__UpperCamelCase , __UpperCamelCase )
for j in range(self.__width )
]
matrix.append(__UpperCamelCase )
return Matrix(__UpperCamelCase , self.__width , self.__height )
else:
raise Exception("""matrix must have the same dimension!""" )
def __sub__( self , _UpperCAmelCase ):
"""simple docstring"""
if self.__width == other.width() and self.__height == other.height():
a_ = []
for i in range(self.__height ):
a_ = [
self.__matrix[i][j] - other.component(__UpperCamelCase , __UpperCamelCase )
for j in range(self.__width )
]
matrix.append(__UpperCamelCase )
return Matrix(__UpperCamelCase , self.__width , self.__height )
else:
raise Exception("""matrices must have the same dimension!""" )
@overload
def __mul__( self , _UpperCAmelCase ):
"""simple docstring"""
...
@overload
def __mul__( self , _UpperCAmelCase ):
"""simple docstring"""
...
def __mul__( self , _UpperCAmelCase ):
"""simple docstring"""
if isinstance(__UpperCamelCase , __UpperCamelCase ): # matrix-vector
if len(__UpperCamelCase ) == self.__width:
a_ = zero_vector(self.__height )
for i in range(self.__height ):
a_ = [
self.__matrix[i][j] * other.component(__UpperCamelCase )
for j in range(self.__width )
]
ans.change_component(__UpperCamelCase , sum(__UpperCamelCase ) )
return ans
else:
raise Exception(
"""vector must have the same size as the """
"""number of columns of the matrix!""" )
elif isinstance(__UpperCamelCase , (int, float) ): # matrix-scalar
a_ = [
[self.__matrix[i][j] * other for j in range(self.__width )]
for i in range(self.__height )
]
return Matrix(__UpperCamelCase , self.__width , self.__height )
return None
def lowercase__ ( self ):
"""simple docstring"""
return self.__height
def lowercase__ ( self ):
"""simple docstring"""
return self.__width
def lowercase__ ( self , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
if 0 <= x < self.__height and 0 <= y < self.__width:
return self.__matrix[x][y]
else:
raise Exception("""change_component: indices out of bounds""" )
def lowercase__ ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
if 0 <= x < self.__height and 0 <= y < self.__width:
a_ = value
else:
raise Exception("""change_component: indices out of bounds""" )
def lowercase__ ( self , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
if self.__height != self.__width:
raise Exception("""Matrix is not square""" )
a_ = self.__matrix[:x] + self.__matrix[x + 1 :]
for i in range(len(__UpperCamelCase ) ):
a_ = minor[i][:y] + minor[i][y + 1 :]
return Matrix(__UpperCamelCase , self.__width - 1 , self.__height - 1 ).determinant()
def lowercase__ ( self , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
if self.__height != self.__width:
raise Exception("""Matrix is not square""" )
if 0 <= x < self.__height and 0 <= y < self.__width:
return (-1) ** (x + y) * self.minor(__UpperCamelCase , __UpperCamelCase )
else:
raise Exception("""Indices out of bounds""" )
def lowercase__ ( self ):
"""simple docstring"""
if self.__height != self.__width:
raise Exception("""Matrix is not square""" )
if self.__height < 1:
raise Exception("""Matrix has no element""" )
elif self.__height == 1:
return self.__matrix[0][0]
elif self.__height == 2:
return (
self.__matrix[0][0] * self.__matrix[1][1]
- self.__matrix[0][1] * self.__matrix[1][0]
)
else:
a_ = [
self.__matrix[0][y] * self.cofactor(0 , __UpperCamelCase ) for y in range(self.__width )
]
return sum(__UpperCamelCase )
def lowerCamelCase_ ( UpperCAmelCase__ ):
"""simple docstring"""
a_ = [[0] * n for _ in range(UpperCamelCase__ )]
return Matrix(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def lowerCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
random.seed(UpperCamelCase__ )
a_ = [
[random.randint(UpperCamelCase__ , UpperCamelCase__ ) for _ in range(UpperCamelCase__ )] for _ in range(UpperCamelCase__ )
]
return Matrix(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
| 483
|
'''simple docstring'''
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
return "\n".join(
F"""{number} * {i} = {number * i}""" for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 436
| 0
|
"""simple docstring"""
from math import ceil, sqrt
def UpperCAmelCase__ ( lowerCAmelCase__ :int = 1_0_0_0_0_0_0 ) -> int:
'''simple docstring'''
lowercase = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
lowercase = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
lowercase = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(F"""{solution() = }""")
| 197
|
"""simple docstring"""
from __future__ import annotations
import math
def UpperCAmelCase__ ( lowerCAmelCase__ :int ) -> list[int]:
'''simple docstring'''
if num <= 0:
lowercase = f'{num}: Invalid input, please enter a positive integer.'
raise ValueError(lowerCAmelCase__ )
lowercase = [True] * (num + 1)
lowercase = []
lowercase = 2
lowercase = int(math.sqrt(lowerCAmelCase__ ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(lowerCAmelCase__ )
# Set multiples of start be False
for i in range(start * start , num + 1 , lowerCAmelCase__ ):
if sieve[i] is True:
lowercase = False
start += 1
for j in range(end + 1 , num + 1 ):
if sieve[j] is True:
prime.append(lowerCAmelCase__ )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input("""Enter a positive integer: """).strip())))
| 197
| 1
|
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
_A : Optional[Any] = logging.get_logger(__name__)
@add_end_docstrings(a_ )
class a__ ( a_ ):
def __init__( self , *_a , **_a ):
super().__init__(*_a , **_a )
requires_backends(self , "vision" )
self.check_model_type(
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
if self.framework == "tf"
else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING )
def __magic_name__ ( self , _a=None ):
lowercase : List[Any] = {}
if top_k is not None:
lowercase : Dict = top_k
return {}, {}, postprocess_params
def __call__( self , _a , **_a ):
return super().__call__(_a , **_a )
def __magic_name__ ( self , _a ):
lowercase : int = load_image(_a )
lowercase : List[Any] = self.image_processor(images=_a , return_tensors=self.framework )
return model_inputs
def __magic_name__ ( self , _a ):
lowercase : Dict = self.model(**_a )
return model_outputs
def __magic_name__ ( self , _a , _a=5 ):
if top_k > self.model.config.num_labels:
lowercase : List[Any] = self.model.config.num_labels
if self.framework == "pt":
lowercase : Union[str, Any] = model_outputs.logits.softmax(-1 )[0]
lowercase , lowercase : Tuple = probs.topk(_a )
elif self.framework == "tf":
lowercase : Dict = stable_softmax(model_outputs.logits , axis=-1 )[0]
lowercase : str = tf.math.top_k(_a , k=_a )
lowercase , lowercase : str = topk.values.numpy(), topk.indices.numpy()
else:
raise ValueError(f"""Unsupported framework: {self.framework}""" )
lowercase : int = scores.tolist()
lowercase : List[str] = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_a , _a )]
| 361
|
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class a__ ( a_ ):
__lowerCAmelCase = (DDPMScheduler,)
def __magic_name__ ( self , **_a ):
lowercase : Dict = {
"num_train_timesteps": 1_000,
"beta_start": 0.0_0_0_1,
"beta_end": 0.0_2,
"beta_schedule": "linear",
"variance_type": "fixed_small",
"clip_sample": True,
}
config.update(**_a )
return config
def __magic_name__ ( self ):
for timesteps in [1, 5, 100, 1_000]:
self.check_over_configs(num_train_timesteps=_a )
def __magic_name__ ( self ):
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] , [0.0_0_2, 0.0_2, 0.2, 2] ):
self.check_over_configs(beta_start=_a , beta_end=_a )
def __magic_name__ ( self ):
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_a )
def __magic_name__ ( self ):
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_a )
def __magic_name__ ( self ):
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_a )
def __magic_name__ ( self ):
self.check_over_configs(thresholding=_a )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_a , prediction_type=_a , sample_max_value=_a , )
def __magic_name__ ( self ):
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_a )
def __magic_name__ ( self ):
for t in [0, 500, 999]:
self.check_over_forward(time_step=_a )
def __magic_name__ ( self ):
lowercase : Union[str, Any] = self.scheduler_classes[0]
lowercase : Any = self.get_scheduler_config()
lowercase : Dict = scheduler_class(**_a )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1E-5
def __magic_name__ ( self ):
lowercase : Optional[int] = self.scheduler_classes[0]
lowercase : Tuple = self.get_scheduler_config()
lowercase : Dict = scheduler_class(**_a )
lowercase : Dict = len(_a )
lowercase : str = self.dummy_model()
lowercase : Optional[int] = self.dummy_sample_deter
lowercase : List[Any] = torch.manual_seed(0 )
for t in reversed(range(_a ) ):
# 1. predict noise residual
lowercase : Optional[Any] = model(_a , _a )
# 2. predict previous mean of sample x_t-1
lowercase : str = scheduler.step(_a , _a , _a , generator=_a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
lowercase : List[str] = pred_prev_sample
lowercase : Dict = torch.sum(torch.abs(_a ) )
lowercase : List[Any] = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1E-2
assert abs(result_mean.item() - 0.3_3_7_2 ) < 1E-3
def __magic_name__ ( self ):
lowercase : Optional[int] = self.scheduler_classes[0]
lowercase : Any = self.get_scheduler_config(prediction_type="v_prediction" )
lowercase : int = scheduler_class(**_a )
lowercase : str = len(_a )
lowercase : Optional[int] = self.dummy_model()
lowercase : List[str] = self.dummy_sample_deter
lowercase : Optional[Any] = torch.manual_seed(0 )
for t in reversed(range(_a ) ):
# 1. predict noise residual
lowercase : Union[str, Any] = model(_a , _a )
# 2. predict previous mean of sample x_t-1
lowercase : Optional[Any] = scheduler.step(_a , _a , _a , generator=_a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
lowercase : Dict = pred_prev_sample
lowercase : str = torch.sum(torch.abs(_a ) )
lowercase : Tuple = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1E-2
assert abs(result_mean.item() - 0.2_6_3_1 ) < 1E-3
def __magic_name__ ( self ):
lowercase : List[Any] = self.scheduler_classes[0]
lowercase : Tuple = self.get_scheduler_config()
lowercase : Tuple = scheduler_class(**_a )
lowercase : List[str] = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_a )
lowercase : Union[str, Any] = scheduler.timesteps
for i, timestep in enumerate(_a ):
if i == len(_a ) - 1:
lowercase : Any = -1
else:
lowercase : Union[str, Any] = timesteps[i + 1]
lowercase : Optional[int] = scheduler.previous_timestep(_a )
lowercase : Union[str, Any] = prev_t.item()
self.assertEqual(_a , _a )
def __magic_name__ ( self ):
lowercase : str = self.scheduler_classes[0]
lowercase : List[str] = self.get_scheduler_config()
lowercase : List[Any] = scheduler_class(**_a )
lowercase : Optional[int] = [100, 87, 50, 51, 0]
with self.assertRaises(_a , msg="`custom_timesteps` must be in descending order." ):
scheduler.set_timesteps(timesteps=_a )
def __magic_name__ ( self ):
lowercase : Dict = self.scheduler_classes[0]
lowercase : Union[str, Any] = self.get_scheduler_config()
lowercase : Any = scheduler_class(**_a )
lowercase : int = [100, 87, 50, 1, 0]
lowercase : Any = len(_a )
with self.assertRaises(_a , msg="Can only pass one of `num_inference_steps` or `custom_timesteps`." ):
scheduler.set_timesteps(num_inference_steps=_a , timesteps=_a )
def __magic_name__ ( self ):
lowercase : str = self.scheduler_classes[0]
lowercase : Tuple = self.get_scheduler_config()
lowercase : Optional[int] = scheduler_class(**_a )
lowercase : List[str] = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_a , msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}" , ):
scheduler.set_timesteps(timesteps=_a )
| 361
| 1
|
"""simple docstring"""
import comet # From: unbabel-comet
import torch
import datasets
lowercase = datasets.logging.get_logger(__name__)
lowercase = """\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},
title = {Unbabel's Participation in the WMT20 Metrics Shared Task},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
month = {November},
year = {2020},
address = {Online},
publisher = {Association for Computational Linguistics},
pages = {909--918},
}
@inproceedings{rei-etal-2020-comet,
title = \"{COMET}: A Neural Framework for {MT} Evaluation\",
author = \"Rei, Ricardo and
Stewart, Craig and
Farinha, Ana C and
Lavie, Alon\",
booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",
month = nov,
year = \"2020\",
address = \"Online\",
publisher = \"Association for Computational Linguistics\",
url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",
pages = \"2685--2702\",
}
"""
lowercase = """\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.
See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
"""
lowercase = """
COMET score.
Args:
`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.
Returns:
`samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
`scores`: List of scores.
Examples:
>>> comet_metric = datasets.load_metric('comet')
>>> # comet_metric = load_metric('comet', 'wmt20-comet-da') # you can also choose which model to use
>>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]
>>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]
>>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]
>>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
>>> print([round(v, 2) for v in results[\"scores\"]])
[0.19, 0.92]
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class SCREAMING_SNAKE_CASE_ ( datasets.Metric):
'''simple docstring'''
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="https://unbabel.github.io/COMET/html/index.html" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"sources": datasets.Value("string" , id="sequence"),
"predictions": datasets.Value("string" , id="sequence"),
"references": datasets.Value("string" , id="sequence"),
}) , codebase_urls=["https://github.com/Unbabel/COMET"] , reference_urls=[
"https://github.com/Unbabel/COMET",
"https://www.aclweb.org/anthology/2020.emnlp-main.213/",
"http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6",
] , )
def UpperCAmelCase ( self , lowerCamelCase__) -> Optional[int]:
'''simple docstring'''
if self.config_name == "default":
snake_case__ : Tuple = comet.load_from_checkpoint(comet.download_model("wmt20-comet-da"))
else:
snake_case__ : str = comet.load_from_checkpoint(comet.download_model(self.config_name))
def UpperCAmelCase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None , lowerCamelCase__=False) -> Union[str, Any]:
'''simple docstring'''
if gpus is None:
snake_case__ : Dict = 1 if torch.cuda.is_available() else 0
snake_case__ : Any = {"src": sources, "mt": predictions, "ref": references}
snake_case__ : Tuple = [dict(zip(UpperCamelCase__ , UpperCamelCase__)) for t in zip(*data.values())]
snake_case__, snake_case__ : Union[str, Any] = self.scorer.predict(UpperCamelCase__ , gpus=UpperCamelCase__ , progress_bar=UpperCamelCase__)
return {"mean_score": mean_score, "scores": scores}
| 711
|
"""simple docstring"""
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
lowercase = """\
Text data.
Second line of data."""
lowercase = """file"""
@pytest.fixture(scope="session" )
def A__ ( _UpperCAmelCase : Union[str, Any] ) -> str:
'''simple docstring'''
snake_case__ : Any = tmp_path_factory.mktemp("data" ) / (FILE_PATH + ".zstd")
snake_case__ : Optional[int] = bytes(_UpperCAmelCase , "utf-8" )
with zstd.open(_UpperCAmelCase , "wb" ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture
def A__ ( _UpperCAmelCase : Dict ) -> int:
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir , _UpperCAmelCase ) , "w" ) as f:
f.write(_UpperCAmelCase )
return FILE_PATH
@pytest.mark.parametrize("compression_format" , ["gzip", "xz", "zstd"] )
def A__ ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : str , _UpperCAmelCase : int , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Dict ) -> List[str]:
'''simple docstring'''
snake_case__ : str = {"gzip": gz_file, "xz": xz_file, "zstd": zstd_path}
snake_case__ : List[str] = input_paths[compression_format]
snake_case__ : List[str] = tmp_path / "cache"
snake_case__ : Tuple = DownloadConfig(cache_dir=_UpperCAmelCase , extract_compressed_file=_UpperCAmelCase )
snake_case__ : Any = cached_path(_UpperCAmelCase , download_config=_UpperCAmelCase )
with open(_UpperCAmelCase ) as f:
snake_case__ : str = f.read()
with open(_UpperCAmelCase ) as f:
snake_case__ : List[Any] = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize("default_extracted" , [True, False] )
@pytest.mark.parametrize("default_cache_dir" , [True, False] )
def A__ ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Any , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Any , _UpperCAmelCase : str ) -> Dict:
'''simple docstring'''
snake_case__ : List[str] = "custom_cache"
snake_case__ : Any = "custom_extracted_dir"
snake_case__ : List[str] = tmp_path / "custom_extracted_path"
if default_extracted:
snake_case__ : Tuple = ("downloads" if default_cache_dir else custom_cache_dir, "extracted")
else:
monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_DIR" , _UpperCAmelCase )
monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_PATH" , str(_UpperCAmelCase ) )
snake_case__ : Optional[int] = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
snake_case__ : List[Any] = xz_file
snake_case__ : Union[str, Any] = (
DownloadConfig(extract_compressed_file=_UpperCAmelCase )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=_UpperCAmelCase )
)
snake_case__ : List[Any] = cached_path(_UpperCAmelCase , download_config=_UpperCAmelCase )
assert Path(_UpperCAmelCase ).parent.parts[-2:] == expected
def A__ ( _UpperCAmelCase : str ) -> Optional[Any]:
'''simple docstring'''
snake_case__ : List[str] = str(Path(_UpperCAmelCase ).resolve() )
assert cached_path(_UpperCAmelCase ) == text_file
# relative path
snake_case__ : List[str] = str(Path(_UpperCAmelCase ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(_UpperCAmelCase ) == text_file
def A__ ( _UpperCAmelCase : Tuple ) -> Optional[Any]:
'''simple docstring'''
snake_case__ : Optional[int] = str(tmp_path.resolve() / "__missing_file__.txt" )
with pytest.raises(_UpperCAmelCase ):
cached_path(_UpperCAmelCase )
# relative path
snake_case__ : Optional[int] = "./__missing_file__.txt"
with pytest.raises(_UpperCAmelCase ):
cached_path(_UpperCAmelCase )
def A__ ( _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
snake_case__ : int = get_from_cache(F"""tmp://{tmpfs_file}""" )
with open(_UpperCAmelCase ) as f:
snake_case__ : List[str] = f.read()
assert output_file_content == FILE_CONTENT
@patch("datasets.config.HF_DATASETS_OFFLINE" , _UpperCAmelCase )
def A__ ( ) -> Dict:
'''simple docstring'''
with pytest.raises(_UpperCAmelCase ):
cached_path("https://huggingface.co" )
@patch("datasets.config.HF_DATASETS_OFFLINE" , _UpperCAmelCase )
def A__ ( _UpperCAmelCase : Any ) -> Tuple:
'''simple docstring'''
snake_case__ : int = tmp_path_factory.mktemp("data" ) / "file.html"
with pytest.raises(_UpperCAmelCase ):
http_get("https://huggingface.co" , temp_file=_UpperCAmelCase )
with pytest.raises(_UpperCAmelCase ):
http_head("https://huggingface.co" )
@patch("datasets.config.HF_DATASETS_OFFLINE" , _UpperCAmelCase )
def A__ ( _UpperCAmelCase : Optional[int] ) -> Tuple:
'''simple docstring'''
snake_case__ : Optional[int] = tmp_path_factory.mktemp("data" ) / "file.html"
with pytest.raises(_UpperCAmelCase ):
ftp_get("ftp://huggingface.co" , temp_file=_UpperCAmelCase )
with pytest.raises(_UpperCAmelCase ):
ftp_head("ftp://huggingface.co" )
@patch("datasets.config.HF_DATASETS_OFFLINE" , _UpperCAmelCase )
def A__ ( _UpperCAmelCase : str ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ : Dict = tmp_path_factory.mktemp("data" ) / "file.html"
with pytest.raises(_UpperCAmelCase ):
fsspec_get("s3://huggingface.co" , temp_file=_UpperCAmelCase )
with pytest.raises(_UpperCAmelCase ):
fsspec_head("s3://huggingface.co" )
| 150
| 0
|
'''simple docstring'''
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
lowercase_ = 4
lowercase_ = 3
class __A ( A ):
'''simple docstring'''
pass
def lowerCAmelCase (__A):
"""simple docstring"""
for shard in shards:
for i in range(__A):
yield {"i": i, "shard": shard}
def lowerCAmelCase ():
"""simple docstring"""
_a = int(os.environ['''RANK'''])
_a = int(os.environ['''WORLD_SIZE'''])
_a = ArgumentParser()
parser.add_argument('''--streaming''' , type=__A)
parser.add_argument('''--local_rank''' , type=__A)
parser.add_argument('''--num_workers''' , type=__A , default=0)
_a = parser.parse_args()
_a = args.streaming
_a = args.num_workers
_a = {'''shards''': [F'''shard_{shard_idx}''' for shard_idx in range(__A)]}
_a = IterableDataset.from_generator(__A , gen_kwargs=__A)
if not streaming:
_a = Dataset.from_list(list(__A))
_a = split_dataset_by_node(__A , rank=__A , world_size=__A)
_a = torch.utils.data.DataLoader(__A , num_workers=__A)
_a = NUM_SHARDS * NUM_ITEMS_PER_SHARD
_a = full_size // world_size
expected_local_size += int(rank < (full_size % world_size))
_a = sum(1 for _ in dataloader)
if local_size != expected_local_size:
raise FailedTestError(F'''local_size {local_size} != expected_local_size {expected_local_size}''')
if __name__ == "__main__":
main()
| 11
|
"""simple docstring"""
def __lowercase ( snake_case_ : str ,snake_case_ : str ) ->float:
'''simple docstring'''
def get_matched_characters(snake_case_ : str ,snake_case_ : str ) -> str:
__A : Any = []
__A : Any = min(len(_stra ) ,len(_stra ) ) // 2
for i, l in enumerate(_stra ):
__A : Dict = int(max(0 ,i - limit ) )
__A : Tuple = int(min(i + limit + 1 ,len(_stra ) ) )
if l in _stra[left:right]:
matched.append(snake_case_ )
__A : Any = F"""{_stra[0:_stra.index(snake_case_ )]} {_stra[_stra.index(snake_case_ ) + 1:]}"""
return "".join(snake_case_ )
# matching characters
__A : int = get_matched_characters(snake_case_ ,snake_case_ )
__A : Tuple = get_matched_characters(snake_case_ ,snake_case_ )
__A : str = len(snake_case_ )
# transposition
__A : Dict = (
len([(ca, ca) for ca, ca in zip(snake_case_ ,snake_case_ ) if ca != ca] ) // 2
)
if not match_count:
__A : List[str] = 0.0
else:
__A : Tuple = (
1
/ 3
* (
match_count / len(snake_case_ )
+ match_count / len(snake_case_ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
__A : Tuple = 0
for ca, ca in zip(stra[:4] ,stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler("""hello""", """world"""))
| 177
| 0
|
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTConfig
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 transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class lowerCamelCase_ ( __a ):
def lowercase_ ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(_A , '''hidden_sizes''' ) )
self.parent.assertTrue(hasattr(_A , '''neck_hidden_sizes''' ) )
self.parent.assertTrue(hasattr(_A , '''num_attention_heads''' ) )
class lowerCamelCase_ :
def __init__( self : Dict , _A : List[Any] , _A : Any=13 , _A : int=32 , _A : Optional[int]=2 , _A : Any=3 , _A : str=640 , _A : Any=4 , _A : Tuple="silu" , _A : Any=3 , _A : str=32 , _A : List[str]=0.1 , _A : str=0.1 , _A : Optional[Any]=0.1 , _A : int=0.0_2 , _A : List[str]=True , _A : str=True , _A : Tuple=10 , _A : Any=None , ):
'''simple docstring'''
UpperCAmelCase__ : str = parent
UpperCAmelCase__ : Dict = batch_size
UpperCAmelCase__ : List[str] = image_size
UpperCAmelCase__ : Tuple = patch_size
UpperCAmelCase__ : Dict = num_channels
UpperCAmelCase__ : Any = last_hidden_size
UpperCAmelCase__ : int = num_attention_heads
UpperCAmelCase__ : Dict = hidden_act
UpperCAmelCase__ : Optional[int] = conv_kernel_size
UpperCAmelCase__ : Dict = output_stride
UpperCAmelCase__ : Optional[Any] = hidden_dropout_prob
UpperCAmelCase__ : Dict = attention_probs_dropout_prob
UpperCAmelCase__ : List[str] = classifier_dropout_prob
UpperCAmelCase__ : Optional[Any] = use_labels
UpperCAmelCase__ : Optional[int] = is_training
UpperCAmelCase__ : Optional[int] = num_labels
UpperCAmelCase__ : List[str] = initializer_range
UpperCAmelCase__ : Optional[Any] = scope
def lowercase_ ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ : Tuple = None
UpperCAmelCase__ : List[str] = None
if self.use_labels:
UpperCAmelCase__ : Any = ids_tensor([self.batch_size] , self.num_labels )
UpperCAmelCase__ : str = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
UpperCAmelCase__ : str = self.get_config()
return config, pixel_values, labels, pixel_labels
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
return MobileViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def lowercase_ ( self : int , _A : Any , _A : List[str] , _A : Any , _A : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = MobileViTModel(config=_A )
model.to(_A )
model.eval()
UpperCAmelCase__ : Union[str, Any] = model(_A )
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 lowercase_ ( self : List[Any] , _A : Union[str, Any] , _A : Union[str, Any] , _A : Tuple , _A : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = self.num_labels
UpperCAmelCase__ : Union[str, Any] = MobileViTForImageClassification(_A )
model.to(_A )
model.eval()
UpperCAmelCase__ : Union[str, Any] = model(_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowercase_ ( self : Optional[int] , _A : Optional[Any] , _A : str , _A : str , _A : str ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.num_labels
UpperCAmelCase__ : Any = MobileViTForSemanticSegmentation(_A )
model.to(_A )
model.eval()
UpperCAmelCase__ : Union[str, Any] = model(_A )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
UpperCAmelCase__ : List[str] = model(_A , labels=_A )
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 lowercase_ ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : str = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Tuple = config_and_inputs
UpperCAmelCase__ : Union[str, Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase_ ( __a , __a , unittest.TestCase ):
lowerCAmelCase__ = (
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
lowerCAmelCase__ = (
{
'feature-extraction': MobileViTModel,
'image-classification': MobileViTForImageClassification,
'image-segmentation': MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowercase_ ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = MobileViTModelTester(self )
UpperCAmelCase__ : str = MobileViTConfigTester(self , config_class=_A , has_text_modality=_A )
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''MobileViT does not use inputs_embeds''' )
def lowercase_ ( self : int ):
'''simple docstring'''
pass
@unittest.skip(reason='''MobileViT does not support input and output embeddings''' )
def lowercase_ ( self : Any ):
'''simple docstring'''
pass
@unittest.skip(reason='''MobileViT does not output attentions''' )
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
pass
def lowercase_ ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : Dict = model_class(_A )
UpperCAmelCase__ : Any = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ : Union[str, Any] = [*signature.parameters.keys()]
UpperCAmelCase__ : Any = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _A )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def lowercase_ ( self : Dict ):
'''simple docstring'''
pass
def lowercase_ ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def lowercase_ ( self : int ):
'''simple docstring'''
def check_hidden_states_output(_A : str , _A : Optional[Any] , _A : Any ):
UpperCAmelCase__ : List[str] = model_class(_A )
model.to(_A )
model.eval()
with torch.no_grad():
UpperCAmelCase__ : Tuple = model(**self._prepare_for_class(_A , _A ) )
UpperCAmelCase__ : List[Any] = outputs.hidden_states
UpperCAmelCase__ : str = 5
self.assertEqual(len(_A ) , _A )
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
UpperCAmelCase__ : int = 2
for i in range(len(_A ) ):
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 )
UpperCAmelCase__ , UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : Optional[Any] = True
check_hidden_states_output(_A , _A , _A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase__ : List[str] = True
check_hidden_states_output(_A , _A , _A )
def lowercase_ ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_A )
def lowercase_ ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*_A )
@slow
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ : Optional[int] = MobileViTModel.from_pretrained(_A )
self.assertIsNotNone(_A )
def a__ ( ) -> str:
UpperCAmelCase__ : str = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowerCamelCase_ ( unittest.TestCase ):
@cached_property
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
return MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small''' ) if is_vision_available() else None
@slow
def lowercase_ ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small''' ).to(_A )
UpperCAmelCase__ : Tuple = self.default_image_processor
UpperCAmelCase__ : int = prepare_img()
UpperCAmelCase__ : Optional[int] = image_processor(images=_A , return_tensors='''pt''' ).to(_A )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : Any = model(**_A )
# verify the logits
UpperCAmelCase__ : int = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , _A )
UpperCAmelCase__ : Optional[int] = torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3] ).to(_A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _A , atol=1e-4 ) )
@slow
def lowercase_ ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
UpperCAmelCase__ : Dict = model.to(_A )
UpperCAmelCase__ : str = MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
UpperCAmelCase__ : Any = prepare_img()
UpperCAmelCase__ : Any = image_processor(images=_A , return_tensors='''pt''' ).to(_A )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : Tuple = model(**_A )
UpperCAmelCase__ : Dict = outputs.logits
# verify the logits
UpperCAmelCase__ : Any = torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape , _A )
UpperCAmelCase__ : List[str] = torch.tensor(
[
[[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
[[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9_8_6_8, -9.7_1_3_2], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
[[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
] , device=_A , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , _A , atol=1e-4 ) )
@slow
def lowercase_ ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : int = MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
UpperCAmelCase__ : List[str] = model.to(_A )
UpperCAmelCase__ : Any = MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
UpperCAmelCase__ : List[Any] = prepare_img()
UpperCAmelCase__ : Dict = image_processor(images=_A , return_tensors='''pt''' ).to(_A )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : Tuple = model(**_A )
UpperCAmelCase__ : Optional[int] = outputs.logits.detach().cpu()
UpperCAmelCase__ : Union[str, Any] = image_processor.post_process_semantic_segmentation(outputs=_A , target_sizes=[(50, 60)] )
UpperCAmelCase__ : Any = torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape , _A )
UpperCAmelCase__ : List[Any] = image_processor.post_process_semantic_segmentation(outputs=_A )
UpperCAmelCase__ : Any = torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape , _A )
| 312
|
'''simple docstring'''
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
UpperCamelCase__ = subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
UpperCamelCase__ = subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode('''utf-8''').split()
UpperCamelCase__ = '''|'''.join(sys.argv[1:])
UpperCamelCase__ = re.compile(RF"""^({joined_dirs}).*?\.py$""")
UpperCamelCase__ = [x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')
| 312
| 1
|
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin
enable_full_determinism()
class A ( __lowercase , unittest.TestCase ):
_snake_case =PriorTransformer
_snake_case ='''hidden_states'''
@property
def lowerCAmelCase__ ( self: str ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase_ =4
UpperCAmelCase_ =8
UpperCAmelCase_ =7
UpperCAmelCase_ =floats_tensor((batch_size, embedding_dim) ).to(_lowerCAmelCase )
UpperCAmelCase_ =floats_tensor((batch_size, embedding_dim) ).to(_lowerCAmelCase )
UpperCAmelCase_ =floats_tensor((batch_size, num_embeddings, embedding_dim) ).to(_lowerCAmelCase )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def lowerCAmelCase__ ( self: Tuple , _lowerCAmelCase: Tuple=0 ) -> Tuple:
'''simple docstring'''
torch.manual_seed(_lowerCAmelCase )
UpperCAmelCase_ =4
UpperCAmelCase_ =8
UpperCAmelCase_ =7
UpperCAmelCase_ =torch.randn((batch_size, embedding_dim) ).to(_lowerCAmelCase )
UpperCAmelCase_ =torch.randn((batch_size, embedding_dim) ).to(_lowerCAmelCase )
UpperCAmelCase_ =torch.randn((batch_size, num_embeddings, embedding_dim) ).to(_lowerCAmelCase )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
@property
def lowerCAmelCase__ ( self: List[Any] ) -> str:
'''simple docstring'''
return (4, 8)
@property
def lowerCAmelCase__ ( self: Optional[int] ) -> Optional[int]:
'''simple docstring'''
return (4, 8)
def lowerCAmelCase__ ( self: Optional[Any] ) -> Tuple:
'''simple docstring'''
UpperCAmelCase_ ={
"num_attention_heads": 2,
"attention_head_dim": 4,
"num_layers": 2,
"embedding_dim": 8,
"num_embeddings": 7,
"additional_embeddings": 4,
}
UpperCAmelCase_ =self.dummy_input
return init_dict, inputs_dict
def lowerCAmelCase__ ( self: List[Any] ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ =PriorTransformer.from_pretrained(
"hf-internal-testing/prior-dummy" , output_loading_info=_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
self.assertEqual(len(loading_info["missing_keys"] ) , 0 )
model.to(_lowerCAmelCase )
UpperCAmelCase_ =model(**self.dummy_input )[0]
assert hidden_states is not None, "Make sure output is not None"
def lowerCAmelCase__ ( self: Any ) -> Any:
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ =self.prepare_init_args_and_inputs_for_common()
UpperCAmelCase_ =self.model_class(**_lowerCAmelCase )
UpperCAmelCase_ =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase_ =[*signature.parameters.keys()]
UpperCAmelCase_ =["hidden_states", "timestep"]
self.assertListEqual(arg_names[:2] , _lowerCAmelCase )
def lowerCAmelCase__ ( self: List[str] ) -> int:
'''simple docstring'''
UpperCAmelCase_ =PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy" )
UpperCAmelCase_ =model.to(_lowerCAmelCase )
if hasattr(_lowerCAmelCase , "set_default_attn_processor" ):
model.set_default_attn_processor()
UpperCAmelCase_ =self.get_dummy_seed_input()
with torch.no_grad():
UpperCAmelCase_ =model(**_lowerCAmelCase )[0]
UpperCAmelCase_ =output[0, :5].flatten().cpu()
print(_lowerCAmelCase )
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
UpperCAmelCase_ =torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39] )
self.assertTrue(torch_all_close(_lowerCAmelCase , _lowerCAmelCase , rtol=1e-2 ) )
@slow
class A ( unittest.TestCase ):
def lowerCAmelCase__ ( self: Dict , _lowerCAmelCase: str=1 , _lowerCAmelCase: Any=768 , _lowerCAmelCase: Any=77 , _lowerCAmelCase: Optional[int]=0 ) -> str:
'''simple docstring'''
torch.manual_seed(_lowerCAmelCase )
UpperCAmelCase_ =batch_size
UpperCAmelCase_ =embedding_dim
UpperCAmelCase_ =num_embeddings
UpperCAmelCase_ =torch.randn((batch_size, embedding_dim) ).to(_lowerCAmelCase )
UpperCAmelCase_ =torch.randn((batch_size, embedding_dim) ).to(_lowerCAmelCase )
UpperCAmelCase_ =torch.randn((batch_size, num_embeddings, embedding_dim) ).to(_lowerCAmelCase )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def lowerCAmelCase__ ( self: Optional[int] ) -> str:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@parameterized.expand(
[
# fmt: off
[13, [-0.58_61, 0.12_83, -0.09_31, 0.08_82, 0.44_76, 0.13_29, -0.04_98, 0.06_40]],
[37, [-0.49_13, 0.01_10, -0.04_83, 0.05_41, 0.49_54, -0.01_70, 0.03_54, 0.16_51]],
# fmt: on
] )
def lowerCAmelCase__ ( self: Tuple , _lowerCAmelCase: List[str] , _lowerCAmelCase: List[str] ) -> int:
'''simple docstring'''
UpperCAmelCase_ =PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior" , subfolder="prior" )
model.to(_lowerCAmelCase )
UpperCAmelCase_ =self.get_dummy_seed_input(seed=_lowerCAmelCase )
with torch.no_grad():
UpperCAmelCase_ =model(**_lowerCAmelCase )[0]
assert list(sample.shape ) == [1, 768]
UpperCAmelCase_ =sample[0, :8].flatten().cpu()
print(_lowerCAmelCase )
UpperCAmelCase_ =torch.tensor(_lowerCAmelCase )
assert torch_all_close(_lowerCAmelCase , _lowerCAmelCase , atol=1e-3 )
| 54
|
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def a__ ( lowercase__ , lowercase__ , lowercase__=1_0_2_4 , lowercase__=1_0_2_4 , lowercase__=False , **lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =AutoTokenizer.from_pretrained(lowercase__ )
UpperCAmelCase_ =SeqaSeqDataset(lowercase__ , lowercase__ , lowercase__ , lowercase__ , type_path="train" , **lowercase__ )
UpperCAmelCase_ =tok.pad_token_id
def get_lens(lowercase__ ):
UpperCAmelCase_ =tqdm(
DataLoader(lowercase__ , batch_size=5_1_2 , num_workers=8 , shuffle=lowercase__ , collate_fn=ds.collate_fn ) , desc=str(ds.len_file ) , )
UpperCAmelCase_ =[]
for batch in dl:
UpperCAmelCase_ =batch["input_ids"].ne(lowercase__ ).sum(1 ).tolist()
UpperCAmelCase_ =batch["labels"].ne(lowercase__ ).sum(1 ).tolist()
if consider_target:
for src, tgt in zip(lowercase__ , lowercase__ ):
max_lens.append(max(lowercase__ , lowercase__ ) )
else:
max_lens.extend(lowercase__ )
return max_lens
UpperCAmelCase_ =get_lens(lowercase__ )
UpperCAmelCase_ =SeqaSeqDataset(lowercase__ , lowercase__ , lowercase__ , lowercase__ , type_path="val" , **lowercase__ )
UpperCAmelCase_ =get_lens(lowercase__ )
pickle_save(lowercase__ , train_ds.len_file )
pickle_save(lowercase__ , val_ds.len_file )
if __name__ == "__main__":
fire.Fire(save_len_file)
| 54
| 1
|
def lowerCamelCase__ ( _lowerCamelCase ) ->int: # noqa: E741
_UpperCAmelCase =len(_lowerCamelCase )
_UpperCAmelCase =0
_UpperCAmelCase =[0] * n
_UpperCAmelCase =[False] * n
_UpperCAmelCase =[False] * n
def dfs(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if parent == root:
out_edge_count += 1
_UpperCAmelCase =True
_UpperCAmelCase =at
for to in l[at]:
if to == parent:
pass
elif not visited[to]:
_UpperCAmelCase =dfs(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
_UpperCAmelCase =min(low[at] , low[to] )
# AP found via bridge
if at < low[to]:
_UpperCAmelCase =True
# AP found via cycle
if at == low[to]:
_UpperCAmelCase =True
else:
_UpperCAmelCase =min(low[at] , _lowerCamelCase )
return out_edge_count
for i in range(_lowerCamelCase ):
if not visited[i]:
_UpperCAmelCase =0
_UpperCAmelCase =dfs(_lowerCamelCase , _lowerCamelCase , -1 , _lowerCamelCase )
_UpperCAmelCase =out_edge_count > 1
for x in range(len(_lowerCamelCase ) ):
if is_art[x] is True:
print(_lowerCamelCase )
# Adjacency list of graph
snake_case__ : List[str] = {
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
}
compute_ap(data)
| 717
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 592
| 0
|
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_A : str = get_tests_dir("""fixtures/spiece.model""")
@require_sentencepiece
@require_tokenizers
class __snake_case ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : List[str] = DebertaVaTokenizer
lowerCamelCase__ : Union[str, Any] = DebertaVaTokenizerFast
lowerCamelCase__ : List[Any] = True
lowerCamelCase__ : List[Any] = True
def lowercase_ ( self ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE__ = DebertaVaTokenizer(A_ , unk_token='''<unk>''' )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase_ ( self , A_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = '''this is a test'''
SCREAMING_SNAKE_CASE__ = '''this is a test'''
return input_text, output_text
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = '''<pad>'''
SCREAMING_SNAKE_CASE__ = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_ ) , A_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_ ) , A_ )
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<pad>''' )
self.assertEqual(vocab_keys[1] , '''<unk>''' )
self.assertEqual(vocab_keys[-1] , '''[PAD]''' )
self.assertEqual(len(A_ ) , 3_00_01 )
def lowercase_ ( self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 3_00_00 )
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = ''' \tHeLLo!how \n Are yoU? '''
SCREAMING_SNAKE_CASE__ = ['''▁hello''', '''!''', '''how''', '''▁are''', '''▁you''', '''?''']
# fmt: on
SCREAMING_SNAKE_CASE__ = DebertaVaTokenizer(A_ , do_lower_case=A_ )
SCREAMING_SNAKE_CASE__ = tokenizer.convert_ids_to_tokens(tokenizer.encode(A_ , add_special_tokens=A_ ) )
self.assertListEqual(A_ , A_ )
SCREAMING_SNAKE_CASE__ = DebertaVaTokenizerFast(A_ , do_lower_case=A_ )
SCREAMING_SNAKE_CASE__ = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(A_ , add_special_tokens=A_ ) )
self.assertListEqual(A_ , A_ )
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''' )
def lowercase_ ( self ):
'''simple docstring'''
pass
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''' )
def lowercase_ ( self ):
'''simple docstring'''
pass
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = '''I was born in 92000, and this is falsé.'''
SCREAMING_SNAKE_CASE__ = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
SCREAMING_SNAKE_CASE__ = DebertaVaTokenizer(A_ , split_by_punct=A_ )
SCREAMING_SNAKE_CASE__ = tokenizer.convert_ids_to_tokens(tokenizer.encode(A_ , add_special_tokens=A_ ) )
self.assertListEqual(A_ , A_ )
SCREAMING_SNAKE_CASE__ = DebertaVaTokenizerFast(A_ , split_by_punct=A_ )
SCREAMING_SNAKE_CASE__ = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(A_ , add_special_tokens=A_ ) )
self.assertListEqual(A_ , A_ )
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = '''I was born in 92000, and this is falsé.'''
SCREAMING_SNAKE_CASE__ = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
SCREAMING_SNAKE_CASE__ = DebertaVaTokenizer(A_ , do_lower_case=A_ , split_by_punct=A_ )
SCREAMING_SNAKE_CASE__ = tokenizer.convert_ids_to_tokens(tokenizer.encode(A_ , add_special_tokens=A_ ) )
self.assertListEqual(A_ , A_ )
SCREAMING_SNAKE_CASE__ = DebertaVaTokenizerFast(A_ , do_lower_case=A_ , split_by_punct=A_ )
SCREAMING_SNAKE_CASE__ = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(A_ , add_special_tokens=A_ ) )
self.assertListEqual(A_ , A_ )
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = '''I was born in 92000, and this is falsé.'''
SCREAMING_SNAKE_CASE__ = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
SCREAMING_SNAKE_CASE__ = DebertaVaTokenizer(A_ , do_lower_case=A_ , split_by_punct=A_ )
SCREAMING_SNAKE_CASE__ = tokenizer.convert_ids_to_tokens(tokenizer.encode(A_ , add_special_tokens=A_ ) )
self.assertListEqual(A_ , A_ )
SCREAMING_SNAKE_CASE__ = DebertaVaTokenizerFast(A_ , do_lower_case=A_ , split_by_punct=A_ )
SCREAMING_SNAKE_CASE__ = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(A_ , add_special_tokens=A_ ) )
self.assertListEqual(A_ , A_ )
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = '''I was born in 92000, and this is falsé.'''
SCREAMING_SNAKE_CASE__ = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
SCREAMING_SNAKE_CASE__ = DebertaVaTokenizer(A_ , do_lower_case=A_ , split_by_punct=A_ )
SCREAMING_SNAKE_CASE__ = tokenizer.convert_ids_to_tokens(tokenizer.encode(A_ , add_special_tokens=A_ ) )
self.assertListEqual(A_ , A_ )
SCREAMING_SNAKE_CASE__ = DebertaVaTokenizerFast(A_ , do_lower_case=A_ , split_by_punct=A_ )
SCREAMING_SNAKE_CASE__ = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(A_ , add_special_tokens=A_ ) )
self.assertListEqual(A_ , A_ )
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = ''' \tHeLLo!how \n Are yoU? '''
SCREAMING_SNAKE_CASE__ = ['''▁''', '''<unk>''', '''e''', '''<unk>''', '''o''', '''!''', '''how''', '''▁''', '''<unk>''', '''re''', '''▁yo''', '''<unk>''', '''?''']
# fmt: on
SCREAMING_SNAKE_CASE__ = DebertaVaTokenizer(A_ , do_lower_case=A_ , split_by_punct=A_ )
SCREAMING_SNAKE_CASE__ = tokenizer.convert_ids_to_tokens(tokenizer.encode(A_ , add_special_tokens=A_ ) )
self.assertListEqual(A_ , A_ )
SCREAMING_SNAKE_CASE__ = DebertaVaTokenizerFast(A_ , do_lower_case=A_ , split_by_punct=A_ )
SCREAMING_SNAKE_CASE__ = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(A_ , add_special_tokens=A_ ) )
self.assertListEqual(A_ , A_ )
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ = '''I was born in 92000, and this is falsé.'''
SCREAMING_SNAKE_CASE__ = tokenizer.convert_ids_to_tokens(tokenizer.encode(A_ , add_special_tokens=A_ ) )
SCREAMING_SNAKE_CASE__ = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(A_ , add_special_tokens=A_ ) )
self.assertListEqual(A_ , A_ )
SCREAMING_SNAKE_CASE__ = tokenizer.encode(A_ , add_special_tokens=A_ )
SCREAMING_SNAKE_CASE__ = rust_tokenizer.encode(A_ , add_special_tokens=A_ )
self.assertListEqual(A_ , A_ )
SCREAMING_SNAKE_CASE__ = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ = tokenizer.encode(A_ )
SCREAMING_SNAKE_CASE__ = rust_tokenizer.encode(A_ )
self.assertListEqual(A_ , A_ )
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = '''This is a test'''
SCREAMING_SNAKE_CASE__ = [13, 1, 43_98, 25, 21, 12_89]
SCREAMING_SNAKE_CASE__ = ['''▁''', '''T''', '''his''', '''▁is''', '''▁a''', '''▁test''']
SCREAMING_SNAKE_CASE__ = ['''▁''', '''<unk>''', '''his''', '''▁is''', '''▁a''', '''▁test''']
SCREAMING_SNAKE_CASE__ = DebertaVaTokenizer(A_ , keep_accents=A_ )
SCREAMING_SNAKE_CASE__ = DebertaVaTokenizerFast(A_ , keep_accents=A_ )
SCREAMING_SNAKE_CASE__ = tokenizer.encode(A_ , add_special_tokens=A_ )
self.assertListEqual(A_ , A_ )
SCREAMING_SNAKE_CASE__ = tokenizer.tokenize(A_ )
self.assertListEqual(A_ , A_ )
SCREAMING_SNAKE_CASE__ = tokenizer.convert_ids_to_tokens(A_ )
self.assertListEqual(A_ , A_ )
SCREAMING_SNAKE_CASE__ = rust_tokenizer.encode(A_ , add_special_tokens=A_ )
self.assertListEqual(A_ , A_ )
SCREAMING_SNAKE_CASE__ = rust_tokenizer.tokenize(A_ )
self.assertListEqual(A_ , A_ )
SCREAMING_SNAKE_CASE__ = rust_tokenizer.convert_ids_to_tokens(A_ )
self.assertListEqual(A_ , A_ )
# fmt: off
SCREAMING_SNAKE_CASE__ = '''I was born in 92000, and this is falsé.'''
SCREAMING_SNAKE_CASE__ = [13, 1, 23, 3_86, 19, 5_61, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9]
SCREAMING_SNAKE_CASE__ = ['''▁''', '''I''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''', ]
SCREAMING_SNAKE_CASE__ = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
SCREAMING_SNAKE_CASE__ = tokenizer.encode(A_ , add_special_tokens=A_ )
self.assertListEqual(A_ , A_ )
SCREAMING_SNAKE_CASE__ = tokenizer.tokenize(A_ )
self.assertListEqual(A_ , A_ )
SCREAMING_SNAKE_CASE__ = tokenizer.convert_ids_to_tokens(A_ )
self.assertListEqual(A_ , A_ )
SCREAMING_SNAKE_CASE__ = rust_tokenizer.encode(A_ , add_special_tokens=A_ )
self.assertListEqual(A_ , A_ )
SCREAMING_SNAKE_CASE__ = rust_tokenizer.tokenize(A_ )
self.assertListEqual(A_ , A_ )
SCREAMING_SNAKE_CASE__ = rust_tokenizer.convert_ids_to_tokens(A_ )
self.assertListEqual(A_ , A_ )
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = DebertaVaTokenizer(A_ )
SCREAMING_SNAKE_CASE__ = tokenizer.encode('''sequence builders''' )
SCREAMING_SNAKE_CASE__ = tokenizer.encode('''multi-sequence build''' )
SCREAMING_SNAKE_CASE__ = tokenizer.build_inputs_with_special_tokens(A_ )
SCREAMING_SNAKE_CASE__ = tokenizer.build_inputs_with_special_tokens(A_ , A_ )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , A_ )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , A_ , )
@slow
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = {'''input_ids''': [[1, 3_98_67, 36, 1_93_90, 4_86, 27, 3_50_52, 8_14_36, 18, 6_06_85, 12_25, 7, 3_50_52, 8_14_36, 18, 93_67, 1_68_99, 18, 1_59_37, 53, 5_94, 7_73, 18, 1_62_87, 3_04_65, 36, 1_59_37, 6, 4_11_39, 38, 3_69_79, 6_07_63, 1_91, 6, 3_41_32, 99, 6, 5_05_38, 3_90, 4_32_30, 6, 3_41_32, 27_79, 2_08_50, 14, 6_99, 10_72, 11_94, 36, 3_82, 1_09_01, 53, 7, 6_99, 10_72, 20_84, 36, 2_04_22, 6_30, 53, 19, 1_05, 30_49, 18_96, 10_53, 1_68_99, 15_06, 11, 3_79_78, 42_43, 7, 12_37, 3_18_69, 2_00, 1_65_66, 6_54, 6, 3_50_52, 8_14_36, 7, 5_56_30, 1_35_93, 4, 2], [1, 26, 1_50_11, 13, 6_67, 8, 10_53, 18, 2_36_11, 12_37, 7_23_56, 1_28_20, 34, 10_41_34, 12_09, 35, 1_33_13, 66_27, 21, 2_02, 3_47, 7, 1_64, 23_99, 11, 46, 44_85, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 5, 12_32, 28_64, 1_57_85, 1_49_51, 1_05, 5, 85_81, 12_50, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]], '''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=A_ , model_name='''microsoft/deberta-v2-xlarge''' , revision='''ad6e42c1532ddf3a15c39246b63f5559d558b670''' , )
| 100
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Optional[int] = """decision_transformer"""
_UpperCAmelCase : str = ["""past_key_values"""]
_UpperCAmelCase : Any = {
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , __magic_name__=1_7 , __magic_name__=4 , __magic_name__=1_2_8 , __magic_name__=4_0_9_6 , __magic_name__=True , __magic_name__=1 , __magic_name__=1_0_2_4 , __magic_name__=3 , __magic_name__=1 , __magic_name__=None , __magic_name__="relu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=1e-5 , __magic_name__=0.02 , __magic_name__=True , __magic_name__=True , __magic_name__=5_0_2_5_6 , __magic_name__=5_0_2_5_6 , __magic_name__=False , __magic_name__=False , **__magic_name__ , ):
lowerCamelCase : Optional[int] = state_dim
lowerCamelCase : int = act_dim
lowerCamelCase : int = hidden_size
lowerCamelCase : Union[str, Any] = max_ep_len
lowerCamelCase : Optional[int] = action_tanh
lowerCamelCase : Any = vocab_size
lowerCamelCase : List[str] = n_positions
lowerCamelCase : List[Any] = n_layer
lowerCamelCase : Dict = n_head
lowerCamelCase : Optional[Any] = n_inner
lowerCamelCase : Tuple = activation_function
lowerCamelCase : Tuple = resid_pdrop
lowerCamelCase : str = embd_pdrop
lowerCamelCase : Dict = attn_pdrop
lowerCamelCase : Tuple = layer_norm_epsilon
lowerCamelCase : Tuple = initializer_range
lowerCamelCase : Tuple = scale_attn_weights
lowerCamelCase : str = use_cache
lowerCamelCase : List[Any] = scale_attn_by_inverse_layer_idx
lowerCamelCase : List[str] = reorder_and_upcast_attn
lowerCamelCase : Optional[Any] = bos_token_id
lowerCamelCase : str = eos_token_id
super().__init__(bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ )
| 681
| 0
|
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
lowerCAmelCase__ = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"
}
def snake_case_ ( A_ : str = "dhaka", A_ : int = 5 ):
'''simple docstring'''
_lowerCamelCase : Optional[Any] = min(_SCREAMING_SNAKE_CASE, 50 ) # Prevent abuse!
_lowerCamelCase : Dict = {
'''q''': query,
'''tbm''': '''isch''',
'''hl''': '''en''',
'''ijn''': '''0''',
}
_lowerCamelCase : Optional[int] = requests.get('''https://www.google.com/search''', params=_SCREAMING_SNAKE_CASE, headers=_SCREAMING_SNAKE_CASE )
_lowerCamelCase : Tuple = BeautifulSoup(html.text, '''html.parser''' )
_lowerCamelCase : Any = ''''''.join(
re.findall(R'''AF_initDataCallback\(([^<]+)\);''', str(soup.select('''script''' ) ) ) )
_lowerCamelCase : Dict = json.dumps(_SCREAMING_SNAKE_CASE )
_lowerCamelCase : List[Any] = json.loads(_SCREAMING_SNAKE_CASE )
_lowerCamelCase : Dict = re.findall(
R'''\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",''', _SCREAMING_SNAKE_CASE, )
if not matched_google_image_data:
return 0
_lowerCamelCase : int = re.sub(
R'''\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]''', '''''', str(_SCREAMING_SNAKE_CASE ), )
_lowerCamelCase : List[Any] = re.findall(
R'''(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]''', _SCREAMING_SNAKE_CASE, )
for index, fixed_full_res_image in enumerate(_SCREAMING_SNAKE_CASE ):
if index >= max_images:
return index
_lowerCamelCase : Any = bytes(_SCREAMING_SNAKE_CASE, '''ascii''' ).decode(
'''unicode-escape''' )
_lowerCamelCase : Dict = bytes(_SCREAMING_SNAKE_CASE, '''ascii''' ).decode(
'''unicode-escape''' )
_lowerCamelCase : Any = urllib.request.build_opener()
_lowerCamelCase : int = [
(
'''User-Agent''',
'''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
''' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582''',
)
]
urllib.request.install_opener(_SCREAMING_SNAKE_CASE )
_lowerCamelCase : Optional[Any] = F'''query_{query.replace(" ", "_" )}'''
if not os.path.exists(_SCREAMING_SNAKE_CASE ):
os.makedirs(_SCREAMING_SNAKE_CASE )
urllib.request.urlretrieve( # noqa: S310
_SCREAMING_SNAKE_CASE, F'''{path_name}/original_size_img_{index}.jpg''' )
return index
if __name__ == "__main__":
try:
lowerCAmelCase__ = download_images_from_google_query(sys.argv[1])
print(F"""{image_count} images were downloaded to disk.""")
except IndexError:
print('''Please provide a search term.''')
raise
| 707
|
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
lowerCAmelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
lowerCAmelCase__ = '''
Examples:
```py
>>> import torch
>>> import numpy as np
>>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
>>> from transformers import pipeline
>>> from diffusers.utils import load_image
>>> def make_hint(image, depth_estimator):
... image = depth_estimator(image)["depth"]
... image = np.array(image)
... image = image[:, :, None]
... image = np.concatenate([image, image, image], axis=2)
... detected_map = torch.from_numpy(image).float() / 255.0
... hint = detected_map.permute(2, 0, 1)
... return hint
>>> depth_estimator = pipeline("depth-estimation")
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
... )
>>> pipe_prior = pipe_prior.to("cuda")
>>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> img = load_image(
... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
... "/kandinsky/cat.png"
... ).resize((768, 768))
>>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")
>>> prompt = "A robot, 4k photo"
>>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"
>>> generator = torch.Generator(device="cuda").manual_seed(43)
>>> image_emb, zero_image_emb = pipe_prior(
... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
... ).to_tuple()
>>> images = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... hint=hint,
... num_inference_steps=50,
... generator=generator,
... height=768,
... width=768,
... ).images
>>> images[0].save("robot_cat.png")
```
'''
def snake_case_ ( A_ : Optional[int], A_ : int, A_ : int=8 ):
'''simple docstring'''
_lowerCamelCase : Optional[Any] = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
_lowerCamelCase : Dict = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class __snake_case ( _lowercase):
def __init__( self : List[str] , __lowerCAmelCase : UNetaDConditionModel , __lowerCAmelCase : DDPMScheduler , __lowerCAmelCase : VQModel , ):
"""simple docstring"""
super().__init__()
self.register_modules(
unet=__lowerCAmelCase , scheduler=__lowerCAmelCase , movq=__lowerCAmelCase , )
_lowerCamelCase : str = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def SCREAMING_SNAKE_CASE ( self : List[str] , __lowerCAmelCase : int , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Any , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[Any] ):
"""simple docstring"""
if latents is None:
_lowerCamelCase : Optional[int] = randn_tensor(__lowerCAmelCase , generator=__lowerCAmelCase , device=__lowerCAmelCase , dtype=__lowerCAmelCase )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
_lowerCamelCase : Any = latents.to(__lowerCAmelCase )
_lowerCamelCase : Optional[Any] = latents * scheduler.init_noise_sigma
return latents
def SCREAMING_SNAKE_CASE ( self : Tuple , __lowerCAmelCase : Any=0 ):
"""simple docstring"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
_lowerCamelCase : Tuple = torch.device(f'''cuda:{gpu_id}''' )
_lowerCamelCase : Optional[int] = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(__lowerCAmelCase , __lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Optional[int] , __lowerCAmelCase : Tuple=0 ):
"""simple docstring"""
if is_accelerate_available() and is_accelerate_version('''>=''' , '''0.17.0.dev0''' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.''' )
_lowerCamelCase : Optional[int] = torch.device(f'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to('''cpu''' , silence_dtype_warnings=__lowerCAmelCase )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
_lowerCamelCase : str = None
for cpu_offloaded_model in [self.unet, self.movq]:
_lowerCamelCase , _lowerCamelCase : List[str] = cpu_offload_with_hook(__lowerCAmelCase , __lowerCAmelCase , prev_module_hook=__lowerCAmelCase )
# We'll offload the last model manually.
_lowerCamelCase : Dict = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def SCREAMING_SNAKE_CASE ( self : int ):
"""simple docstring"""
if not hasattr(self.unet , '''_hf_hook''' ):
return self.device
for module in self.unet.modules():
if (
hasattr(__lowerCAmelCase , '''_hf_hook''' )
and hasattr(module._hf_hook , '''execution_device''' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(__lowerCAmelCase )
def __call__( self : Optional[int] , __lowerCAmelCase : Union[torch.FloatTensor, List[torch.FloatTensor]] , __lowerCAmelCase : Union[torch.FloatTensor, List[torch.FloatTensor]] , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : int = 5_1_2 , __lowerCAmelCase : int = 5_1_2 , __lowerCAmelCase : int = 1_0_0 , __lowerCAmelCase : float = 4.0 , __lowerCAmelCase : int = 1 , __lowerCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __lowerCAmelCase : Optional[torch.FloatTensor] = None , __lowerCAmelCase : Optional[str] = "pil" , __lowerCAmelCase : bool = True , ):
"""simple docstring"""
_lowerCamelCase : int = self._execution_device
_lowerCamelCase : List[Any] = guidance_scale > 1.0
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
_lowerCamelCase : List[Any] = torch.cat(__lowerCAmelCase , dim=0 )
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
_lowerCamelCase : Dict = torch.cat(__lowerCAmelCase , dim=0 )
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
_lowerCamelCase : List[str] = torch.cat(__lowerCAmelCase , dim=0 )
_lowerCamelCase : Union[str, Any] = image_embeds.shape[0] * num_images_per_prompt
if do_classifier_free_guidance:
_lowerCamelCase : List[str] = image_embeds.repeat_interleave(__lowerCAmelCase , dim=0 )
_lowerCamelCase : Union[str, Any] = negative_image_embeds.repeat_interleave(__lowerCAmelCase , dim=0 )
_lowerCamelCase : int = hint.repeat_interleave(__lowerCAmelCase , dim=0 )
_lowerCamelCase : Dict = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=__lowerCAmelCase )
_lowerCamelCase : List[str] = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=__lowerCAmelCase )
self.scheduler.set_timesteps(__lowerCAmelCase , device=__lowerCAmelCase )
_lowerCamelCase : List[Any] = self.scheduler.timesteps
_lowerCamelCase : Tuple = self.movq.config.latent_channels
_lowerCamelCase , _lowerCamelCase : List[Any] = downscale_height_and_width(__lowerCAmelCase , __lowerCAmelCase , self.movq_scale_factor )
# create initial latent
_lowerCamelCase : Optional[int] = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , self.scheduler , )
for i, t in enumerate(self.progress_bar(__lowerCAmelCase ) ):
# expand the latents if we are doing classifier free guidance
_lowerCamelCase : List[str] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_lowerCamelCase : int = {'''image_embeds''': image_embeds, '''hint''': hint}
_lowerCamelCase : List[str] = self.unet(
sample=__lowerCAmelCase , timestep=__lowerCAmelCase , encoder_hidden_states=__lowerCAmelCase , added_cond_kwargs=__lowerCAmelCase , return_dict=__lowerCAmelCase , )[0]
if do_classifier_free_guidance:
_lowerCamelCase , _lowerCamelCase : str = noise_pred.split(latents.shape[1] , dim=1 )
_lowerCamelCase , _lowerCamelCase : Optional[Any] = noise_pred.chunk(2 )
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = variance_pred.chunk(2 )
_lowerCamelCase : int = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
_lowerCamelCase : int = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , '''variance_type''' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
_lowerCamelCase , _lowerCamelCase : Any = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
_lowerCamelCase : Any = self.scheduler.step(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase , )[0]
# post-processing
_lowerCamelCase : Optional[int] = self.movq.decode(__lowerCAmelCase , force_not_quantize=__lowerCAmelCase )['''sample''']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
_lowerCamelCase : Union[str, Any] = image * 0.5 + 0.5
_lowerCamelCase : List[Any] = image.clamp(0 , 1 )
_lowerCamelCase : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
_lowerCamelCase : Union[str, Any] = self.numpy_to_pil(__lowerCAmelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__lowerCAmelCase )
| 598
| 0
|
"""simple docstring"""
def __lowerCamelCase ( a_ : int = 60_08_51_47_51_43 ) -> Any:
try:
__SCREAMING_SNAKE_CASE :Optional[Any] = int(_lowerCAmelCase )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n must be greater than or equal to one.''' )
__SCREAMING_SNAKE_CASE :List[Any] = 2
__SCREAMING_SNAKE_CASE :int = 0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
__SCREAMING_SNAKE_CASE :int = i
while n % i == 0:
__SCREAMING_SNAKE_CASE :Optional[Any] = n // i
i += 1
return int(_lowerCAmelCase )
if __name__ == "__main__":
print(f'{solution() = }')
| 498
|
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ):
"""simple docstring"""
lowercase : Optional[torch.FloatTensor] = None
lowercase : torch.FloatTensor = None
lowercase : Optional[Tuple[torch.FloatTensor]] = None
lowercase : Optional[Tuple[torch.FloatTensor]] = None
class SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , __UpperCamelCase=1 , __UpperCamelCase=0 , __UpperCamelCase=2 , __UpperCamelCase=5_12 , __UpperCamelCase="cls" , __UpperCamelCase=False , __UpperCamelCase=True , **__UpperCamelCase , ) -> List[Any]:
'''simple docstring'''
super().__init__(pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
__UpperCamelCase : str = project_dim
__UpperCamelCase : Union[str, Any] = pooler_fn
__UpperCamelCase : List[Any] = learn_encoder
__UpperCamelCase : Union[str, Any] = use_attention_mask
class SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ):
"""simple docstring"""
lowercase : Dict = [R'pooler', R'logit_scale']
lowercase : Optional[int] = [R'position_ids', R'predictions.decoder.bias']
lowercase : str = 'roberta'
lowercase : int = RobertaSeriesConfig
def __init__( self , __UpperCamelCase ) -> List[str]:
'''simple docstring'''
super().__init__(__UpperCamelCase )
__UpperCamelCase : List[Any] = XLMRobertaModel(__UpperCamelCase )
__UpperCamelCase : int = nn.Linear(config.hidden_size , config.project_dim )
__UpperCamelCase : str = getattr(__UpperCamelCase , "has_pre_transformation" , __UpperCamelCase )
if self.has_pre_transformation:
__UpperCamelCase : int = nn.Linear(config.hidden_size , config.project_dim )
__UpperCamelCase : Optional[Any] = nn.LayerNorm(config.hidden_size , eps=config.layer_norm_eps )
self.post_init()
def __lowerCamelCase ( self , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , ) -> Dict:
'''simple docstring'''
__UpperCamelCase : Tuple = return_dict if return_dict is not None else self.config.use_return_dict
__UpperCamelCase : Optional[Any] = self.base_model(
input_ids=__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , position_ids=__UpperCamelCase , head_mask=__UpperCamelCase , inputs_embeds=__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , encoder_attention_mask=__UpperCamelCase , output_attentions=__UpperCamelCase , output_hidden_states=True if self.has_pre_transformation else output_hidden_states , return_dict=__UpperCamelCase , )
if self.has_pre_transformation:
__UpperCamelCase : Any = outputs["hidden_states"][-2]
__UpperCamelCase : Tuple = self.pre_LN(__UpperCamelCase )
__UpperCamelCase : str = self.transformation_pre(__UpperCamelCase )
return TransformationModelOutput(
projection_state=__UpperCamelCase , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
else:
__UpperCamelCase : int = self.transformation(outputs.last_hidden_state )
return TransformationModelOutput(
projection_state=__UpperCamelCase , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 327
| 0
|
"""simple docstring"""
import pytest
A_ = '''__dummy_dataset1__'''
A_ = '''
import json
import os
import datasets
REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}
class __DummyDataset1__(datasets.GeneratorBasedBuilder):
def _info(self):
features = datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-PER",
"I-PER",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC",
]
)
),
"langs": datasets.Sequence(datasets.Value("string")),
"spans": datasets.Sequence(datasets.Value("string")),
}
)
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager):
dl_path = dl_manager.download(URLS)
return [
datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
]
def _generate_examples(self, filepath):
with open(filepath, "r", encoding="utf-8") as f:
for i, line in enumerate(f):
yield i, json.loads(line)
'''
@pytest.fixture
def UpperCAmelCase__ ():
"""simple docstring"""
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def UpperCAmelCase__ ():
"""simple docstring"""
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : Union[str, Any] , snake_case__ : List[str] ):
"""simple docstring"""
_snake_case : List[str] = dataset_loading_script_name
_snake_case : Optional[Any] = tmp_path / """datasets""" / script_name
script_dir.mkdir(parents=snake_case__ )
_snake_case : Tuple = script_dir / F"{script_name}.py"
with open(snake_case__ , """w""" ) as f:
f.write(snake_case__ )
return str(snake_case__ )
| 710
|
"""simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase( __a ):
'''simple docstring'''
lowercase__ = (IPNDMScheduler,)
lowercase__ = (("num_inference_steps", 50),)
def UpperCamelCase_ ( self: Union[str, Any], **a_: Union[str, Any] ):
'''simple docstring'''
_snake_case : List[Any] = {"""num_train_timesteps""": 1_000}
config.update(**a_ )
return config
def UpperCamelCase_ ( self: Tuple, a_: Optional[int]=0, **a_: int ):
'''simple docstring'''
_snake_case : Optional[int] = dict(self.forward_default_kwargs )
_snake_case : Optional[Any] = kwargs.pop("""num_inference_steps""", a_ )
_snake_case : Optional[Any] = self.dummy_sample
_snake_case : Dict = 0.1 * sample
_snake_case : Any = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
_snake_case : int = self.get_scheduler_config(**a_ )
_snake_case : Dict = scheduler_class(**a_ )
scheduler.set_timesteps(a_ )
# copy over dummy past residuals
_snake_case : int = dummy_past_residuals[:]
if time_step is None:
_snake_case : Union[str, Any] = scheduler.timesteps[len(scheduler.timesteps ) // 2]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(a_ )
_snake_case : Tuple = scheduler_class.from_pretrained(a_ )
new_scheduler.set_timesteps(a_ )
# copy over dummy past residuals
_snake_case : Optional[Any] = dummy_past_residuals[:]
_snake_case : List[Any] = scheduler.step(a_, a_, a_, **a_ ).prev_sample
_snake_case : str = new_scheduler.step(a_, a_, a_, **a_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
_snake_case : Optional[Any] = scheduler.step(a_, a_, a_, **a_ ).prev_sample
_snake_case : Optional[int] = new_scheduler.step(a_, a_, a_, **a_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def UpperCamelCase_ ( self: List[str] ):
'''simple docstring'''
pass
def UpperCamelCase_ ( self: str, a_: Any=0, **a_: Tuple ):
'''simple docstring'''
_snake_case : str = dict(self.forward_default_kwargs )
_snake_case : List[Any] = kwargs.pop("""num_inference_steps""", a_ )
_snake_case : Optional[int] = self.dummy_sample
_snake_case : Tuple = 0.1 * sample
_snake_case : Optional[int] = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
_snake_case : Any = self.get_scheduler_config()
_snake_case : Tuple = scheduler_class(**a_ )
scheduler.set_timesteps(a_ )
# copy over dummy past residuals (must be after setting timesteps)
_snake_case : Union[str, Any] = dummy_past_residuals[:]
if time_step is None:
_snake_case : Tuple = scheduler.timesteps[len(scheduler.timesteps ) // 2]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(a_ )
_snake_case : List[str] = scheduler_class.from_pretrained(a_ )
# copy over dummy past residuals
new_scheduler.set_timesteps(a_ )
# copy over dummy past residual (must be after setting timesteps)
_snake_case : List[str] = dummy_past_residuals[:]
_snake_case : str = scheduler.step(a_, a_, a_, **a_ ).prev_sample
_snake_case : Any = new_scheduler.step(a_, a_, a_, **a_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
_snake_case : str = scheduler.step(a_, a_, a_, **a_ ).prev_sample
_snake_case : int = new_scheduler.step(a_, a_, a_, **a_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def UpperCamelCase_ ( self: List[Any], **a_: Optional[int] ):
'''simple docstring'''
_snake_case : Union[str, Any] = self.scheduler_classes[0]
_snake_case : Any = self.get_scheduler_config(**a_ )
_snake_case : List[Any] = scheduler_class(**a_ )
_snake_case : Union[str, Any] = 10
_snake_case : Union[str, Any] = self.dummy_model()
_snake_case : List[Any] = self.dummy_sample_deter
scheduler.set_timesteps(a_ )
for i, t in enumerate(scheduler.timesteps ):
_snake_case : Optional[Any] = model(a_, a_ )
_snake_case : Any = scheduler.step(a_, a_, a_ ).prev_sample
for i, t in enumerate(scheduler.timesteps ):
_snake_case : Union[str, Any] = model(a_, a_ )
_snake_case : Any = scheduler.step(a_, a_, a_ ).prev_sample
return sample
def UpperCamelCase_ ( self: Any ):
'''simple docstring'''
_snake_case : str = dict(self.forward_default_kwargs )
_snake_case : int = kwargs.pop("""num_inference_steps""", a_ )
for scheduler_class in self.scheduler_classes:
_snake_case : Union[str, Any] = self.get_scheduler_config()
_snake_case : Tuple = scheduler_class(**a_ )
_snake_case : Dict = self.dummy_sample
_snake_case : List[str] = 0.1 * sample
if num_inference_steps is not None and hasattr(a_, """set_timesteps""" ):
scheduler.set_timesteps(a_ )
elif num_inference_steps is not None and not hasattr(a_, """set_timesteps""" ):
_snake_case : Dict = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
_snake_case : Union[str, Any] = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
_snake_case : List[str] = dummy_past_residuals[:]
_snake_case : Optional[int] = scheduler.timesteps[5]
_snake_case : Optional[Any] = scheduler.timesteps[6]
_snake_case : str = scheduler.step(a_, a_, a_, **a_ ).prev_sample
_snake_case : List[str] = scheduler.step(a_, a_, a_, **a_ ).prev_sample
self.assertEqual(output_a.shape, sample.shape )
self.assertEqual(output_a.shape, output_a.shape )
_snake_case : Any = scheduler.step(a_, a_, a_, **a_ ).prev_sample
_snake_case : Any = scheduler.step(a_, a_, a_, **a_ ).prev_sample
self.assertEqual(output_a.shape, sample.shape )
self.assertEqual(output_a.shape, output_a.shape )
def UpperCamelCase_ ( self: Optional[Any] ):
'''simple docstring'''
for timesteps in [100, 1_000]:
self.check_over_configs(num_train_timesteps=a_, time_step=a_ )
def UpperCamelCase_ ( self: Any ):
'''simple docstring'''
for t, num_inference_steps in zip([1, 5, 10], [10, 50, 100] ):
self.check_over_forward(num_inference_steps=a_, time_step=a_ )
def UpperCamelCase_ ( self: Optional[Any] ):
'''simple docstring'''
_snake_case : Optional[int] = self.full_loop()
_snake_case : Optional[int] = torch.mean(torch.abs(a_ ) )
assert abs(result_mean.item() - 2_540_529 ) < 10
| 28
| 0
|
'''simple docstring'''
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
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 CvtForImageClassification, CvtModel
from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCAmelCase ( snake_case_ ):
def lowercase__ ( self : Tuple ) -> int:
_lowerCAmelCase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__snake_case , """embed_dim""" ) )
self.parent.assertTrue(hasattr(__snake_case , """num_heads""" ) )
class UpperCAmelCase :
def __init__( self : str , __snake_case : List[str] , __snake_case : Dict=13 , __snake_case : Dict=64 , __snake_case : List[str]=3 , __snake_case : str=[16, 48, 96] , __snake_case : Tuple=[1, 3, 6] , __snake_case : Optional[Any]=[1, 2, 10] , __snake_case : str=[7, 3, 3] , __snake_case : Optional[int]=[4, 2, 2] , __snake_case : int=[2, 1, 1] , __snake_case : str=[2, 2, 2] , __snake_case : Any=[False, False, True] , __snake_case : Optional[Any]=[0.0, 0.0, 0.0] , __snake_case : List[Any]=0.02 , __snake_case : Optional[Any]=1E-1_2 , __snake_case : List[Any]=True , __snake_case : List[str]=True , __snake_case : Any=2 , ) -> Union[str, Any]:
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = image_size
_lowerCAmelCase = patch_sizes
_lowerCAmelCase = patch_stride
_lowerCAmelCase = patch_padding
_lowerCAmelCase = is_training
_lowerCAmelCase = use_labels
_lowerCAmelCase = num_labels
_lowerCAmelCase = num_channels
_lowerCAmelCase = embed_dim
_lowerCAmelCase = num_heads
_lowerCAmelCase = stride_kv
_lowerCAmelCase = depth
_lowerCAmelCase = cls_token
_lowerCAmelCase = attention_drop_rate
_lowerCAmelCase = initializer_range
_lowerCAmelCase = layer_norm_eps
def lowercase__ ( self : Dict ) -> Optional[Any]:
_lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowerCAmelCase = None
if self.use_labels:
_lowerCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
_lowerCAmelCase = self.get_config()
return config, pixel_values, labels
def lowercase__ ( self : int ) -> List[Any]:
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def lowercase__ ( self : List[str] , __snake_case : Optional[int] , __snake_case : List[str] , __snake_case : int ) -> int:
_lowerCAmelCase = CvtModel(config=__snake_case )
model.to(__snake_case )
model.eval()
_lowerCAmelCase = model(__snake_case )
_lowerCAmelCase = (self.image_size, self.image_size)
_lowerCAmelCase , _lowerCAmelCase = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
_lowerCAmelCase = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
_lowerCAmelCase = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def lowercase__ ( self : Tuple , __snake_case : int , __snake_case : List[str] , __snake_case : Tuple ) -> Tuple:
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = CvtForImageClassification(__snake_case )
model.to(__snake_case )
model.eval()
_lowerCAmelCase = model(__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowercase__ ( self : Tuple ) -> int:
_lowerCAmelCase = self.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = config_and_inputs
_lowerCAmelCase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase ( snake_case_ , snake_case_ , unittest.TestCase ):
_lowercase: int = (CvtModel, CvtForImageClassification) if is_torch_available() else ()
_lowercase: Tuple = (
{'''feature-extraction''': CvtModel, '''image-classification''': CvtForImageClassification}
if is_torch_available()
else {}
)
_lowercase: Tuple = False
_lowercase: Union[str, Any] = False
_lowercase: Tuple = False
_lowercase: List[str] = False
_lowercase: Optional[Any] = False
def lowercase__ ( self : Dict ) -> Any:
_lowerCAmelCase = CvtModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=__snake_case , has_text_modality=__snake_case , hidden_size=37 )
def lowercase__ ( self : Any ) -> Dict:
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 lowercase__ ( self : int ) -> Any:
return
@unittest.skip(reason="""Cvt does not output attentions""" )
def lowercase__ ( self : Optional[Any] ) -> Union[str, Any]:
pass
@unittest.skip(reason="""Cvt does not use inputs_embeds""" )
def lowercase__ ( self : Union[str, Any] ) -> Optional[Any]:
pass
@unittest.skip(reason="""Cvt does not support input and output embeddings""" )
def lowercase__ ( self : List[Any] ) -> Optional[int]:
pass
def lowercase__ ( self : int ) -> Any:
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase = model_class(__snake_case )
_lowerCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCAmelCase = [*signature.parameters.keys()]
_lowerCAmelCase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __snake_case )
def lowercase__ ( self : str ) -> Any:
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__snake_case )
def lowercase__ ( self : List[Any] ) -> Optional[int]:
def check_hidden_states_output(__snake_case : Union[str, Any] , __snake_case : List[Any] , __snake_case : Optional[Any] ):
_lowerCAmelCase = model_class(__snake_case )
model.to(__snake_case )
model.eval()
with torch.no_grad():
_lowerCAmelCase = model(**self._prepare_for_class(__snake_case , __snake_case ) )
_lowerCAmelCase = outputs.hidden_states
_lowerCAmelCase = len(self.model_tester.depth )
self.assertEqual(len(__snake_case ) , __snake_case )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase = True
check_hidden_states_output(__snake_case , __snake_case , __snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCAmelCase = True
check_hidden_states_output(__snake_case , __snake_case , __snake_case )
def lowercase__ ( self : str ) -> List[Any]:
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__snake_case )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def lowercase__ ( self : Optional[int] ) -> Dict:
pass
@slow
def lowercase__ ( self : Union[str, Any] ) -> Any:
for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase = CvtModel.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class UpperCAmelCase ( unittest.TestCase ):
@cached_property
def lowercase__ ( self : List[str] ) -> Any:
return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def lowercase__ ( self : List[Any] ) -> Tuple:
_lowerCAmelCase = CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(__snake_case )
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(images=__snake_case , return_tensors="""pt""" ).to(__snake_case )
# forward pass
with torch.no_grad():
_lowerCAmelCase = model(**__snake_case )
# verify the logits
_lowerCAmelCase = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __snake_case )
_lowerCAmelCase = torch.tensor([0.92_85, 0.90_15, -0.31_50] ).to(__snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __snake_case , atol=1E-4 ) )
| 207
|
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
if "cls_token" in name:
_lowerCAmelCase = name.replace("""cls_token""" , """vit.embeddings.cls_token""" )
if "mask_token" in name:
_lowerCAmelCase = name.replace("""mask_token""" , """decoder.mask_token""" )
if "decoder_pos_embed" in name:
_lowerCAmelCase = name.replace("""decoder_pos_embed""" , """decoder.decoder_pos_embed""" )
if "pos_embed" in name and "decoder" not in name:
_lowerCAmelCase = name.replace("""pos_embed""" , """vit.embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
_lowerCAmelCase = name.replace("""patch_embed.proj""" , """vit.embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
_lowerCAmelCase = name.replace("""patch_embed.norm""" , """vit.embeddings.norm""" )
if "decoder_blocks" in name:
_lowerCAmelCase = name.replace("""decoder_blocks""" , """decoder.decoder_layers""" )
if "blocks" in name:
_lowerCAmelCase = name.replace("""blocks""" , """vit.encoder.layer""" )
if "attn.proj" in name:
_lowerCAmelCase = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
_lowerCAmelCase = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
_lowerCAmelCase = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
_lowerCAmelCase = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
_lowerCAmelCase = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
_lowerCAmelCase = name.replace("""mlp.fc2""" , """output.dense""" )
if "decoder_embed" in name:
_lowerCAmelCase = name.replace("""decoder_embed""" , """decoder.decoder_embed""" )
if "decoder_norm" in name:
_lowerCAmelCase = name.replace("""decoder_norm""" , """decoder.decoder_norm""" )
if "decoder_pred" in name:
_lowerCAmelCase = name.replace("""decoder_pred""" , """decoder.decoder_pred""" )
if "norm.weight" in name and "decoder" not in name:
_lowerCAmelCase = name.replace("""norm.weight""" , """vit.layernorm.weight""" )
if "norm.bias" in name and "decoder" not in name:
_lowerCAmelCase = name.replace("""norm.bias""" , """vit.layernorm.bias""" )
return name
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_lowerCAmelCase = orig_state_dict.pop(lowerCAmelCase )
if "qkv" in key:
_lowerCAmelCase = key.split(""".""" )
_lowerCAmelCase = int(key_split[1] )
if "decoder_blocks" in key:
_lowerCAmelCase = config.decoder_hidden_size
_lowerCAmelCase = """decoder.decoder_layers."""
if "weight" in key:
_lowerCAmelCase = val[:dim, :]
_lowerCAmelCase = val[dim : dim * 2, :]
_lowerCAmelCase = val[-dim:, :]
elif "bias" in key:
_lowerCAmelCase = val[:dim]
_lowerCAmelCase = val[dim : dim * 2]
_lowerCAmelCase = val[-dim:]
else:
_lowerCAmelCase = config.hidden_size
_lowerCAmelCase = """vit.encoder.layer."""
if "weight" in key:
_lowerCAmelCase = val[:dim, :]
_lowerCAmelCase = val[dim : dim * 2, :]
_lowerCAmelCase = val[-dim:, :]
elif "bias" in key:
_lowerCAmelCase = val[:dim]
_lowerCAmelCase = val[dim : dim * 2]
_lowerCAmelCase = val[-dim:]
else:
_lowerCAmelCase = val
return orig_state_dict
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = ViTMAEConfig()
if "large" in checkpoint_url:
_lowerCAmelCase = 10_24
_lowerCAmelCase = 40_96
_lowerCAmelCase = 24
_lowerCAmelCase = 16
elif "huge" in checkpoint_url:
_lowerCAmelCase = 14
_lowerCAmelCase = 12_80
_lowerCAmelCase = 51_20
_lowerCAmelCase = 32
_lowerCAmelCase = 16
_lowerCAmelCase = ViTMAEForPreTraining(lowerCAmelCase )
_lowerCAmelCase = torch.hub.load_state_dict_from_url(lowerCAmelCase , map_location="""cpu""" )["""model"""]
_lowerCAmelCase = ViTMAEImageProcessor(size=config.image_size )
_lowerCAmelCase = convert_state_dict(lowerCAmelCase , lowerCAmelCase )
model.load_state_dict(lowerCAmelCase )
model.eval()
_lowerCAmelCase = """https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""
_lowerCAmelCase = Image.open(requests.get(lowerCAmelCase , stream=lowerCAmelCase ).raw )
_lowerCAmelCase = ViTMAEImageProcessor(size=config.image_size )
_lowerCAmelCase = image_processor(images=lowerCAmelCase , return_tensors="""pt""" )
# forward pass
torch.manual_seed(2 )
_lowerCAmelCase = model(**lowerCAmelCase )
_lowerCAmelCase = outputs.logits
if "large" in checkpoint_url:
_lowerCAmelCase = torch.tensor(
[[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]] )
elif "huge" in checkpoint_url:
_lowerCAmelCase = torch.tensor(
[[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]] )
else:
_lowerCAmelCase = torch.tensor(
[[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]] )
# verify logits
assert torch.allclose(logits[0, :3, :3] , lowerCAmelCase , atol=1e-4 )
print(f"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(lowerCAmelCase )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(lowerCAmelCase )
if __name__ == "__main__":
A__ : Dict =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth''',
type=str,
help='''URL of the checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
A__ : List[Any] =parser.parse_args()
convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 207
| 1
|
'''simple docstring'''
def a ( UpperCamelCase_ : list , UpperCamelCase_ : list , UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : int ) -> int:
if index == number_of_items:
return 0
snake_case__ =0
snake_case__ =0
snake_case__ =knapsack(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , index + 1 )
if weights[index] <= max_weight:
snake_case__ =values[index] + knapsack(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , max_weight - weights[index] , index + 1 )
return max(UpperCamelCase_ , UpperCamelCase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 581
|
'''simple docstring'''
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin
enable_full_determinism()
class a__( snake_case__ , unittest.TestCase ):
a_ : str = PriorTransformer
a_ : Tuple = '''hidden_states'''
@property
def _lowercase ( self ) -> int:
snake_case__ =4
snake_case__ =8
snake_case__ =7
snake_case__ =floats_tensor((batch_size, embedding_dim) ).to(_UpperCAmelCase )
snake_case__ =floats_tensor((batch_size, embedding_dim) ).to(_UpperCAmelCase )
snake_case__ =floats_tensor((batch_size, num_embeddings, embedding_dim) ).to(_UpperCAmelCase )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def _lowercase ( self , _UpperCAmelCase=0 ) -> List[str]:
torch.manual_seed(_UpperCAmelCase )
snake_case__ =4
snake_case__ =8
snake_case__ =7
snake_case__ =torch.randn((batch_size, embedding_dim) ).to(_UpperCAmelCase )
snake_case__ =torch.randn((batch_size, embedding_dim) ).to(_UpperCAmelCase )
snake_case__ =torch.randn((batch_size, num_embeddings, embedding_dim) ).to(_UpperCAmelCase )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
@property
def _lowercase ( self ) -> str:
return (4, 8)
@property
def _lowercase ( self ) -> Any:
return (4, 8)
def _lowercase ( self ) -> Dict:
snake_case__ ={
'num_attention_heads': 2,
'attention_head_dim': 4,
'num_layers': 2,
'embedding_dim': 8,
'num_embeddings': 7,
'additional_embeddings': 4,
}
snake_case__ =self.dummy_input
return init_dict, inputs_dict
def _lowercase ( self ) -> List[Any]:
snake_case__ , snake_case__ =PriorTransformer.from_pretrained(
'hf-internal-testing/prior-dummy' , output_loading_info=_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
self.assertEqual(len(loading_info['missing_keys'] ) , 0 )
model.to(_UpperCAmelCase )
snake_case__ =model(**self.dummy_input )[0]
assert hidden_states is not None, "Make sure output is not None"
def _lowercase ( self ) -> Optional[Any]:
snake_case__ , snake_case__ =self.prepare_init_args_and_inputs_for_common()
snake_case__ =self.model_class(**_UpperCAmelCase )
snake_case__ =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case__ =[*signature.parameters.keys()]
snake_case__ =['hidden_states', 'timestep']
self.assertListEqual(arg_names[:2] , _UpperCAmelCase )
def _lowercase ( self ) -> str:
snake_case__ =PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy' )
snake_case__ =model.to(_UpperCAmelCase )
if hasattr(_UpperCAmelCase , 'set_default_attn_processor' ):
model.set_default_attn_processor()
snake_case__ =self.get_dummy_seed_input()
with torch.no_grad():
snake_case__ =model(**_UpperCAmelCase )[0]
snake_case__ =output[0, :5].flatten().cpu()
print(_UpperCAmelCase )
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
snake_case__ =torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239] )
self.assertTrue(torch_all_close(_UpperCAmelCase , _UpperCAmelCase , rtol=1E-2 ) )
@slow
class a__( unittest.TestCase ):
def _lowercase ( self , _UpperCAmelCase=1 , _UpperCAmelCase=768 , _UpperCAmelCase=77 , _UpperCAmelCase=0 ) -> Optional[Any]:
torch.manual_seed(_UpperCAmelCase )
snake_case__ =batch_size
snake_case__ =embedding_dim
snake_case__ =num_embeddings
snake_case__ =torch.randn((batch_size, embedding_dim) ).to(_UpperCAmelCase )
snake_case__ =torch.randn((batch_size, embedding_dim) ).to(_UpperCAmelCase )
snake_case__ =torch.randn((batch_size, num_embeddings, embedding_dim) ).to(_UpperCAmelCase )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def _lowercase ( self ) -> Dict:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@parameterized.expand(
[
# fmt: off
[13, [-0.5_861, 0.1_283, -0.0_931, 0.0_882, 0.4_476, 0.1_329, -0.0_498, 0.0_640]],
[37, [-0.4_913, 0.0_110, -0.0_483, 0.0_541, 0.4_954, -0.0_170, 0.0_354, 0.1_651]],
# fmt: on
] )
def _lowercase ( self , _UpperCAmelCase , _UpperCAmelCase ) -> Optional[int]:
snake_case__ =PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior' , subfolder='prior' )
model.to(_UpperCAmelCase )
snake_case__ =self.get_dummy_seed_input(seed=_UpperCAmelCase )
with torch.no_grad():
snake_case__ =model(**_UpperCAmelCase )[0]
assert list(sample.shape ) == [1, 768]
snake_case__ =sample[0, :8].flatten().cpu()
print(_UpperCAmelCase )
snake_case__ =torch.tensor(_UpperCAmelCase )
assert torch_all_close(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 )
| 581
| 1
|
def UpperCamelCase ( snake_case__ : list ) -> list:
UpperCamelCase : str = len(snake_case__ )
for _ in range(snake_case__ ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
UpperCamelCase , UpperCamelCase : Optional[Any] = arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
__UpperCAmelCase = list(range(10, 0, -1))
print(F"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")
| 40
|
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
__UpperCAmelCase = random.Random()
def UpperCamelCase ( snake_case__ : List[Any] , snake_case__ : str=1.0 , snake_case__ : int=None , snake_case__ : Union[str, Any]=None ) -> Any:
if rng is None:
UpperCamelCase : int = global_rng
UpperCamelCase : Union[str, Any] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class lowerCAmelCase_ ( unittest.TestCase ):
def __init__( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=7, SCREAMING_SNAKE_CASE_=400, SCREAMING_SNAKE_CASE_=2000, SCREAMING_SNAKE_CASE_=1, SCREAMING_SNAKE_CASE_=0.0, SCREAMING_SNAKE_CASE_=1_6000, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=True, ) -> List[str]:
UpperCamelCase : Dict = parent
UpperCamelCase : Dict = batch_size
UpperCamelCase : Any = min_seq_length
UpperCamelCase : Optional[int] = max_seq_length
UpperCamelCase : Optional[int] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
UpperCamelCase : Tuple = feature_size
UpperCamelCase : Any = padding_value
UpperCamelCase : Tuple = sampling_rate
UpperCamelCase : Optional[Any] = return_attention_mask
UpperCamelCase : Optional[Any] = do_normalize
def snake_case_ ( self ) -> Union[str, Any]:
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def snake_case_ ( self, SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=False ) -> Union[str, Any]:
def _flatten(SCREAMING_SNAKE_CASE_ ):
return list(itertools.chain(*SCREAMING_SNAKE_CASE_ ) )
if equal_length:
UpperCamelCase : List[str] = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
UpperCamelCase : Union[str, Any] = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff )
]
if numpify:
UpperCamelCase : str = [np.asarray(SCREAMING_SNAKE_CASE_ ) for x in speech_inputs]
return speech_inputs
class lowerCAmelCase_ ( a__ , unittest.TestCase ):
UpperCAmelCase__ : Any = WavaVecaFeatureExtractor
def snake_case_ ( self ) -> Union[str, Any]:
UpperCamelCase : Tuple = WavaVecaFeatureExtractionTester(self )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
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 snake_case_ ( self ) -> Optional[int]:
# Tests that all call wrap to encode_plus and batch_encode_plus
UpperCamelCase : Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
UpperCamelCase : Any = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
UpperCamelCase : Dict = [np.asarray(SCREAMING_SNAKE_CASE_ ) for speech_input in speech_inputs]
# Test not batched input
UpperCamelCase : List[Any] = feat_extract(speech_inputs[0], return_tensors='np' ).input_values
UpperCamelCase : Union[str, Any] = feat_extract(np_speech_inputs[0], return_tensors='np' ).input_values
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, atol=1e-3 ) )
# Test batched
UpperCamelCase : List[Any] = feat_extract(SCREAMING_SNAKE_CASE_, return_tensors='np' ).input_values
UpperCamelCase : int = feat_extract(SCREAMING_SNAKE_CASE_, return_tensors='np' ).input_values
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.
UpperCamelCase : Tuple = [floats_list((1, x) )[0] for x in (800, 800, 800)]
UpperCamelCase : Optional[int] = np.asarray(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = feat_extract(SCREAMING_SNAKE_CASE_, return_tensors='np' ).input_values
UpperCamelCase : Dict = feat_extract(SCREAMING_SNAKE_CASE_, return_tensors='np' ).input_values
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 snake_case_ ( self ) -> int:
UpperCamelCase : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase : Dict = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
UpperCamelCase : str = ['longest', 'max_length', 'do_not_pad']
UpperCamelCase : Any = [None, 1600, None]
for max_length, padding in zip(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[Any] = feat_extract(SCREAMING_SNAKE_CASE_, padding=SCREAMING_SNAKE_CASE_, max_length=SCREAMING_SNAKE_CASE_, return_tensors='np' )
UpperCamelCase : Tuple = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self.assertTrue(input_values[0][800:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self.assertTrue(input_values[0][1000:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def snake_case_ ( self ) -> Tuple:
UpperCamelCase : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase : Tuple = range(800, 1400, 200 )
UpperCamelCase : str = [floats_list((1, x) )[0] for x in lengths]
UpperCamelCase : int = ['longest', 'max_length', 'do_not_pad']
UpperCamelCase : List[str] = [None, 1600, None]
for max_length, padding in zip(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = feat_extract(SCREAMING_SNAKE_CASE_, max_length=SCREAMING_SNAKE_CASE_, padding=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def snake_case_ ( self ) -> Optional[Any]:
UpperCamelCase : Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase : Optional[int] = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
UpperCamelCase : int = feat_extract(
SCREAMING_SNAKE_CASE_, truncation=SCREAMING_SNAKE_CASE_, max_length=1000, padding='max_length', return_tensors='np' )
UpperCamelCase : Tuple = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def snake_case_ ( self ) -> List[Any]:
UpperCamelCase : List[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase : Union[str, Any] = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
UpperCamelCase : Any = feat_extract(
SCREAMING_SNAKE_CASE_, truncation=SCREAMING_SNAKE_CASE_, max_length=1000, padding='longest', return_tensors='np' )
UpperCamelCase : Dict = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1000) )
UpperCamelCase : str = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
UpperCamelCase : Any = feat_extract(
SCREAMING_SNAKE_CASE_, truncation=SCREAMING_SNAKE_CASE_, max_length=2000, padding='longest', return_tensors='np' )
UpperCamelCase : int = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1200) )
@require_torch
def snake_case_ ( self ) -> str:
import torch
UpperCamelCase : Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase : Dict = np.random.rand(100 ).astype(np.floataa )
UpperCamelCase : Dict = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
UpperCamelCase : Union[str, Any] = feature_extractor.pad([{'input_values': inputs}], return_tensors='np' )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
UpperCamelCase : Any = feature_extractor.pad([{'input_values': inputs}], return_tensors='pt' )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
@slow
@require_torch
def snake_case_ ( self ) -> Tuple:
# this test makes sure that models that are using
# group norm don't have their feature extractor return the
# attention_mask
for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
UpperCamelCase : int = WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_ )
# only "layer" feature extraction norm should make use of
# attention_mask
self.assertEqual(feat_extract.return_attention_mask, config.feat_extract_norm == 'layer' )
| 40
| 1
|
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
__UpperCAmelCase = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
__UpperCAmelCase = {
"""vocab_file""": {
"""google/electra-small-generator""": (
"""https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"""
),
"""google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt""",
"""google/electra-large-generator""": (
"""https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"""
),
"""google/electra-small-discriminator""": (
"""https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"""
),
"""google/electra-base-discriminator""": (
"""https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"""
),
"""google/electra-large-discriminator""": (
"""https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""google/electra-small-generator""": (
"""https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"""
),
"""google/electra-base-generator""": (
"""https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"""
),
"""google/electra-large-generator""": (
"""https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"""
),
"""google/electra-small-discriminator""": (
"""https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"""
),
"""google/electra-base-discriminator""": (
"""https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"""
),
"""google/electra-large-discriminator""": (
"""https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"""
),
},
}
__UpperCAmelCase = {
"""google/electra-small-generator""": 512,
"""google/electra-base-generator""": 512,
"""google/electra-large-generator""": 512,
"""google/electra-small-discriminator""": 512,
"""google/electra-base-discriminator""": 512,
"""google/electra-large-discriminator""": 512,
}
__UpperCAmelCase = {
"""google/electra-small-generator""": {"""do_lower_case""": True},
"""google/electra-base-generator""": {"""do_lower_case""": True},
"""google/electra-large-generator""": {"""do_lower_case""": True},
"""google/electra-small-discriminator""": {"""do_lower_case""": True},
"""google/electra-base-discriminator""": {"""do_lower_case""": True},
"""google/electra-large-discriminator""": {"""do_lower_case""": True},
}
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ = PRETRAINED_INIT_CONFIGURATION
SCREAMING_SNAKE_CASE__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ = ElectraTokenizer
def __init__( self : int , lowerCamelCase_ : List[Any]=None , lowerCamelCase_ : Union[str, Any]=None , lowerCamelCase_ : Optional[int]=True , lowerCamelCase_ : List[str]="[UNK]" , lowerCamelCase_ : int="[SEP]" , lowerCamelCase_ : List[Any]="[PAD]" , lowerCamelCase_ : Union[str, Any]="[CLS]" , lowerCamelCase_ : Union[str, Any]="[MASK]" , lowerCamelCase_ : Dict=True , lowerCamelCase_ : Any=None , **lowerCamelCase_ : Union[str, Any] , ):
'''simple docstring'''
super().__init__(
lowerCamelCase_ , tokenizer_file=lowerCamelCase_ , do_lower_case=lowerCamelCase_ , unk_token=lowerCamelCase_ , sep_token=lowerCamelCase_ , pad_token=lowerCamelCase_ , cls_token=lowerCamelCase_ , mask_token=lowerCamelCase_ , tokenize_chinese_chars=lowerCamelCase_ , strip_accents=lowerCamelCase_ , **lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : Union[str, Any] = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , lowerCamelCase_ ) != do_lower_case
or normalizer_state.get("""strip_accents""" , lowerCamelCase_ ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , lowerCamelCase_ ) != tokenize_chinese_chars
):
SCREAMING_SNAKE_CASE : int = getattr(lowerCamelCase_ , normalizer_state.pop("""type""" ) )
SCREAMING_SNAKE_CASE : List[Any] = do_lower_case
SCREAMING_SNAKE_CASE : int = strip_accents
SCREAMING_SNAKE_CASE : Dict = tokenize_chinese_chars
SCREAMING_SNAKE_CASE : Optional[Any] = normalizer_class(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = do_lower_case
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : str , lowerCamelCase_ : Dict=None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = [self.sep_token_id]
SCREAMING_SNAKE_CASE : Tuple = [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 ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCamelCase_ ( self : int , lowerCamelCase_ : str , lowerCamelCase_ : Optional[str] = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = self._tokenizer.model.save(lowerCamelCase_ , name=lowerCamelCase_ )
return tuple(lowerCamelCase_ )
| 709
|
'''simple docstring'''
import math
def __A ( lowerCamelCase_ ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowerCamelCase_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __A ( lowerCamelCase_ = 1_00_01 ):
"""simple docstring"""
try:
SCREAMING_SNAKE_CASE : Tuple = int(lowerCamelCase_ )
except (TypeError, ValueError):
raise TypeError("""Parameter nth must be int or castable to int.""" ) from None
if nth <= 0:
raise ValueError("""Parameter nth must be greater than or equal to one.""" )
SCREAMING_SNAKE_CASE : list[int] = []
SCREAMING_SNAKE_CASE : Dict = 2
while len(lowerCamelCase_ ) < nth:
if is_prime(lowerCamelCase_ ):
primes.append(lowerCamelCase_ )
num += 1
else:
num += 1
return primes[len(lowerCamelCase_ ) - 1]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 79
| 0
|
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __lowercase :
@staticmethod
def lowerCAmelCase_ ( *a__ , **a__ ) -> List[str]:
'''simple docstring'''
pass
@is_pipeline_test
@require_torch
@require_vision
class __lowercase ( unittest.TestCase ):
__magic_name__ : Optional[int] = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
def lowerCAmelCase_ ( self , a__ , a__ , a__ ) -> Tuple:
'''simple docstring'''
A_ = pipeline('''visual-question-answering''' , model='''hf-internal-testing/tiny-vilt-random-vqa''' )
A_ = [
{
'''image''': Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ),
'''question''': '''How many cats are there?''',
},
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''question''': '''How many cats are there?''',
},
]
return vqa_pipeline, examples
def lowerCAmelCase_ ( self , a__ , a__ ) -> str:
'''simple docstring'''
A_ = vqa_pipeline(a__ , top_k=1 )
self.assertEqual(
a__ , [
[{'''score''': ANY(a__ ), '''answer''': ANY(a__ )}],
[{'''score''': ANY(a__ ), '''answer''': ANY(a__ )}],
] , )
@require_torch
def lowerCAmelCase_ ( self ) -> Dict:
'''simple docstring'''
A_ = pipeline('''visual-question-answering''' , model='''hf-internal-testing/tiny-vilt-random-vqa''' )
A_ = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
A_ = '''How many cats are there?'''
A_ = vqa_pipeline(image=a__ , question='''How many cats are there?''' , top_k=2 )
self.assertEqual(
a__ , [{'''score''': ANY(a__ ), '''answer''': ANY(a__ )}, {'''score''': ANY(a__ ), '''answer''': ANY(a__ )}] )
A_ = vqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
a__ , [{'''score''': ANY(a__ ), '''answer''': ANY(a__ )}, {'''score''': ANY(a__ ), '''answer''': ANY(a__ )}] )
@slow
@require_torch
def lowerCAmelCase_ ( self ) -> List[str]:
'''simple docstring'''
A_ = pipeline('''visual-question-answering''' , model='''dandelin/vilt-b32-finetuned-vqa''' )
A_ = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
A_ = '''How many cats are there?'''
A_ = vqa_pipeline(image=a__ , question=a__ , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [{'''score''': 0.87_99, '''answer''': '''2'''}, {'''score''': 0.2_96, '''answer''': '''1'''}] )
A_ = vqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [{'''score''': 0.87_99, '''answer''': '''2'''}, {'''score''': 0.2_96, '''answer''': '''1'''}] )
A_ = vqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [[{'''score''': 0.87_99, '''answer''': '''2'''}, {'''score''': 0.2_96, '''answer''': '''1'''}]] * 2 , )
@require_tf
@unittest.skip('''Visual question answering not implemented in TF''' )
def lowerCAmelCase_ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
| 141
|
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class __lowercase ( A , unittest.TestCase ):
__magic_name__ : Any = FlaxAutoencoderKL
@property
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
A_ = 4
A_ = 3
A_ = (3_2, 3_2)
A_ = jax.random.PRNGKey(0 )
A_ = jax.random.uniform(a__ , ((batch_size, num_channels) + sizes) )
return {"sample": image, "prng_key": prng_key}
def lowerCAmelCase_ ( self ) -> Any:
'''simple docstring'''
A_ = {
'''block_out_channels''': [3_2, 6_4],
'''in_channels''': 3,
'''out_channels''': 3,
'''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
'''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
'''latent_channels''': 4,
}
A_ = self.dummy_input
return init_dict, inputs_dict
| 141
| 1
|
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
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,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEModel,
)
from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class __UpperCamelCase :
def __init__( self : Optional[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[int]=13 , _lowerCAmelCase : Dict=10 , _lowerCAmelCase : Optional[Any]=3 , _lowerCAmelCase : str=2 , _lowerCAmelCase : Tuple=2 , _lowerCAmelCase : Any=2 , _lowerCAmelCase : Any=True , _lowerCAmelCase : int=True , _lowerCAmelCase : List[str]=32 , _lowerCAmelCase : Any=5 , _lowerCAmelCase : int=4 , _lowerCAmelCase : int=37 , _lowerCAmelCase : List[str]="gelu" , _lowerCAmelCase : Dict=0.1 , _lowerCAmelCase : Dict=0.1 , _lowerCAmelCase : Union[str, Any]=10 , _lowerCAmelCase : str=0.02 , _lowerCAmelCase : List[Any]=0.9 , _lowerCAmelCase : List[Any]=None , ) -> Tuple:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = num_channels
__lowercase = patch_size
__lowercase = tubelet_size
__lowercase = num_frames
__lowercase = is_training
__lowercase = use_labels
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = type_sequence_label_size
__lowercase = initializer_range
__lowercase = mask_ratio
__lowercase = scope
# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
__lowercase = (image_size // patch_size) ** 2
__lowercase = (num_frames // tubelet_size) * self.num_patches_per_frame
# use this variable to define bool_masked_pos
__lowercase = int(mask_ratio * self.seq_length )
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__lowercase = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase = self.get_config()
return config, pixel_values, labels
def _a ( self : Optional[int] ) -> Any:
"""simple docstring"""
return VideoMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , tubelet_size=self.tubelet_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 , is_decoder=_lowerCAmelCase , initializer_range=self.initializer_range , )
def _a ( self : Optional[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = VideoMAEModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : Tuple , _lowerCAmelCase : int , _lowerCAmelCase : str , _lowerCAmelCase : Dict ) -> Optional[int]:
"""simple docstring"""
__lowercase = VideoMAEForPreTraining(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
__lowercase = torch.ones((self.num_masks,) )
__lowercase = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0 ) )] )
__lowercase = mask.expand(self.batch_size , -1 ).bool()
__lowercase = model(_lowerCAmelCase , _lowerCAmelCase )
# model only returns predictions for masked patches
__lowercase = mask.sum().item()
__lowercase = 3 * self.tubelet_size * self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_masked_patches, decoder_num_labels) )
def _a ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :str = (
(VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
)
__snake_case :Any = (
{'feature-extraction': VideoMAEModel, 'video-classification': VideoMAEForVideoClassification}
if is_torch_available()
else {}
)
__snake_case :List[Any] = False
__snake_case :List[Any] = False
__snake_case :List[Any] = False
__snake_case :Union[str, Any] = False
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
__lowercase = VideoMAEModelTester(self )
__lowercase = ConfigTester(self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=37 )
def _a ( self : Any , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Tuple=False ) -> int:
"""simple docstring"""
__lowercase = copy.deepcopy(_lowerCAmelCase )
if model_class == VideoMAEForPreTraining:
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
__lowercase = torch.ones((self.model_tester.num_masks,) )
__lowercase = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0 ) )] )
__lowercase = mask.expand(self.model_tester.batch_size , -1 ).bool()
__lowercase = bool_masked_pos.to(_lowerCAmelCase )
if return_labels:
if model_class in [
*get_values(_lowerCAmelCase ),
]:
__lowercase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_lowerCAmelCase )
return inputs_dict
def _a ( self : int ) -> int:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""VideoMAE does not use inputs_embeds""" )
def _a ( self : List[Any] ) -> Any:
"""simple docstring"""
pass
def _a ( self : Any ) -> List[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowercase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCAmelCase , nn.Linear ) )
def _a ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def _a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def _a ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*_lowerCAmelCase )
@slow
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = VideoMAEModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
def _a ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
if not self.has_attentions:
pass
else:
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = True
for model_class in self.all_model_classes:
__lowercase = self.model_tester.seq_length - self.model_tester.num_masks
__lowercase = (
num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
)
__lowercase = True
__lowercase = False
__lowercase = True
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
__lowercase = outputs.attentions
self.assertEqual(len(_lowerCAmelCase ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__lowercase = True
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
__lowercase = outputs.attentions
self.assertEqual(len(_lowerCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
__lowercase = len(_lowerCAmelCase )
# Check attention is always last and order is fine
__lowercase = True
__lowercase = True
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
self.assertEqual(out_len + 1 , len(_lowerCAmelCase ) )
__lowercase = outputs.attentions
self.assertEqual(len(_lowerCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
def _a ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
def check_hidden_states_output(_lowerCAmelCase : Dict , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[Any] ):
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
__lowercase = outputs.hidden_states
__lowercase = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase )
__lowercase = self.model_tester.seq_length - self.model_tester.num_masks
__lowercase = num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _a ( self : Optional[int] ) -> Any:
"""simple docstring"""
pass
def snake_case ( ):
'''simple docstring'''
__lowercase = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename="""eating_spaghetti.npy""" , repo_type="""dataset""" )
__lowercase = np.load(lowerCamelCase )
return list(lowerCamelCase )
@require_torch
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
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 _a ( self : List[Any] ) -> Any:
"""simple docstring"""
__lowercase = VideoMAEForVideoClassification.from_pretrained("""MCG-NJU/videomae-base-finetuned-kinetics""" ).to(
_lowerCAmelCase )
__lowercase = self.default_image_processor
__lowercase = prepare_video()
__lowercase = image_processor(_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
# forward pass
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase )
# verify the logits
__lowercase = torch.Size((1, 400) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
__lowercase = torch.tensor([0.3_669, -0.0_688, -0.2_421] ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1e-4 ) )
@slow
def _a ( self : List[Any] ) -> Dict:
"""simple docstring"""
__lowercase = VideoMAEForPreTraining.from_pretrained("""MCG-NJU/videomae-base-short""" ).to(_lowerCAmelCase )
__lowercase = self.default_image_processor
__lowercase = prepare_video()
__lowercase = image_processor(_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
# add boolean mask, indicating which patches to mask
__lowercase = hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos""" , filename="""bool_masked_pos.pt""" )
__lowercase = torch.load(_lowerCAmelCase )
# forward pass
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase )
# verify the logits
__lowercase = torch.Size([1, 1408, 1536] )
__lowercase = torch.tensor(
[[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]] , device=_lowerCAmelCase )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , _lowerCAmelCase , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `True`)
__lowercase = torch.tensor([0.5_142] , device=_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.loss , _lowerCAmelCase , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `False`)
__lowercase = VideoMAEForPreTraining.from_pretrained("""MCG-NJU/videomae-base-short""" , norm_pix_loss=_lowerCAmelCase ).to(
_lowerCAmelCase )
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase )
__lowercase = torch.tensor(torch.tensor([0.6_469] ) , device=_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.loss , _lowerCAmelCase , atol=1e-4 ) )
| 53
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__UpperCamelCase : Tuple = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SwiftFormerConfig""",
"""SwiftFormerOnnxConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Optional[Any] = [
"""SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SwiftFormerForImageClassification""",
"""SwiftFormerModel""",
"""SwiftFormerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_swiftformer import (
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
SwiftFormerConfig,
SwiftFormerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swiftformer import (
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
SwiftFormerForImageClassification,
SwiftFormerModel,
SwiftFormerPreTrainedModel,
)
else:
import sys
__UpperCamelCase : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 53
| 1
|
"""simple docstring"""
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __snake_case ( _lowercase ,_lowercase ,_lowercase ):
"""simple docstring"""
UpperCamelCase = AutoConfig.from_pretrained(_lowercase )
UpperCamelCase = FlaxAutoModelForSeqaSeqLM.from_config(config=_lowercase )
UpperCamelCase = checkpoints.load_tax_checkpoint(_lowercase )
UpperCamelCase = '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']
if config.model_type == "t5":
UpperCamelCase = '''SelfAttention'''
if config.model_type == "longt5" and config.encoder_attention_type == "local":
UpperCamelCase = '''LocalSelfAttention'''
elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
UpperCamelCase = '''TransientGlobalSelfAttention'''
else:
raise ValueError(
'''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
''' attribute with a value from [\'local\', \'transient-global].''' )
# Encoder
for layer_index in range(config.num_layers ):
UpperCamelCase = f'layers_{str(_lowercase )}'
# Self-Attention
UpperCamelCase = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
UpperCamelCase = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
UpperCamelCase = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
UpperCamelCase = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
UpperCamelCase = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']
# Layer Normalization
UpperCamelCase = tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']
if split_mlp_wi:
UpperCamelCase = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
UpperCamelCase = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
else:
UpperCamelCase = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
UpperCamelCase = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
# Layer Normalization
UpperCamelCase = tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
# Assigning
UpperCamelCase = flax_model.params['''encoder''']['''block'''][str(_lowercase )]['''layer''']
UpperCamelCase = tax_attention_key
UpperCamelCase = tax_attention_out
UpperCamelCase = tax_attention_query
UpperCamelCase = tax_attention_value
UpperCamelCase = tax_attention_layer_norm
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
UpperCamelCase = tax_global_layer_norm
if split_mlp_wi:
UpperCamelCase = tax_mlp_wi_a
UpperCamelCase = tax_mlp_wi_a
else:
UpperCamelCase = tax_mlp_wi
UpperCamelCase = tax_mlp_wo
UpperCamelCase = tax_mlp_layer_norm
UpperCamelCase = flax_model_encoder_layer_block
# Only for layer 0:
UpperCamelCase = tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
UpperCamelCase = tax_encoder_rel_embedding
# Side/global relative position_bias + layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
UpperCamelCase = tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
UpperCamelCase = tax_encoder_global_rel_embedding
# Assigning
UpperCamelCase = tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
UpperCamelCase = tax_encoder_norm
# Decoder
for layer_index in range(config.num_layers ):
UpperCamelCase = f'layers_{str(_lowercase )}'
# Self-Attention
UpperCamelCase = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
UpperCamelCase = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
UpperCamelCase = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
UpperCamelCase = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']
# Layer Normalization
UpperCamelCase = tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
'''scale'''
]
# Encoder-Decoder-Attention
UpperCamelCase = tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
UpperCamelCase = tax_enc_dec_attention_module['''key''']['''kernel''']
UpperCamelCase = tax_enc_dec_attention_module['''out''']['''kernel''']
UpperCamelCase = tax_enc_dec_attention_module['''query''']['''kernel''']
UpperCamelCase = tax_enc_dec_attention_module['''value''']['''kernel''']
# Layer Normalization
UpperCamelCase = tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']
# MLP
if split_mlp_wi:
UpperCamelCase = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
UpperCamelCase = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
else:
UpperCamelCase = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
UpperCamelCase = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
# Layer Normalization
UpperCamelCase = tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
# Assigning
UpperCamelCase = flax_model.params['''decoder''']['''block'''][str(_lowercase )]['''layer''']
UpperCamelCase = tax_attention_key
UpperCamelCase = tax_attention_out
UpperCamelCase = tax_attention_query
UpperCamelCase = tax_attention_value
UpperCamelCase = tax_pre_attention_layer_norm
UpperCamelCase = tax_enc_dec_attention_key
UpperCamelCase = tax_enc_dec_attention_out
UpperCamelCase = tax_enc_dec_attention_query
UpperCamelCase = tax_enc_dec_attention_value
UpperCamelCase = tax_cross_layer_norm
if split_mlp_wi:
UpperCamelCase = tax_mlp_wi_a
UpperCamelCase = tax_mlp_wi_a
else:
UpperCamelCase = tax_mlp_wi
UpperCamelCase = tax_mlp_wo
UpperCamelCase = txa_mlp_layer_norm
UpperCamelCase = flax_model_decoder_layer_block
# Decoder Normalization
UpperCamelCase = tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
UpperCamelCase = txa_decoder_norm
# Only for layer 0:
UpperCamelCase = tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
UpperCamelCase = tax_decoder_rel_embedding
# Token Embeddings
UpperCamelCase = tax_model['''target''']['''token_embedder''']['''embedding''']
UpperCamelCase = txa_token_embeddings
# LM Head (only in v1.1 and LongT5 checkpoints)
if "logits_dense" in tax_model["target"]["decoder"]:
UpperCamelCase = tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']
flax_model.save_pretrained(_lowercase )
print('''T5X Model was sucessfully converted!''' )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
)
parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
parser.add_argument(
'--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
| 34
|
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
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_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __A ( snake_case__ ,snake_case__ ,unittest.TestCase ):
'''simple docstring'''
a_ = IFImgaImgSuperResolutionPipeline
a_ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
a_ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''original_image'''} )
a_ = PipelineTesterMixin.required_optional_params - {'''latents'''}
def SCREAMING_SNAKE_CASE__ ( self ):
return self._get_superresolution_dummy_components()
def SCREAMING_SNAKE_CASE__ ( self , _snake_case , _snake_case=0 ):
if str(_snake_case ).startswith("mps" ):
_lowerCAmelCase : Any = torch.manual_seed(_snake_case )
else:
_lowerCAmelCase : Dict = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
_lowerCAmelCase : Dict = floats_tensor((1, 3, 32, 32) , rng=random.Random(_snake_case ) ).to(_snake_case )
_lowerCAmelCase : int = floats_tensor((1, 3, 16, 16) , rng=random.Random(_snake_case ) ).to(_snake_case )
_lowerCAmelCase : Optional[int] = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"original_image": original_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 SCREAMING_SNAKE_CASE__ ( self ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def SCREAMING_SNAKE_CASE__ ( self ):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda" , reason="float16 requires CUDA" )
def SCREAMING_SNAKE_CASE__ ( self ):
# 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 SCREAMING_SNAKE_CASE__ ( self ):
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def SCREAMING_SNAKE_CASE__ ( self ):
self._test_save_load_local()
def SCREAMING_SNAKE_CASE__ ( self ):
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 424
| 0
|
import warnings
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__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/config.json",
# See all BART models at https://huggingface.co/models?filter=bart
}
class _lowerCAmelCase ( _UpperCAmelCase ):
"""simple docstring"""
lowercase__ : int = """bart"""
lowercase__ : str = ["""past_key_values"""]
lowercase__ : Union[str, Any] = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : Optional[Any] , lowercase : List[str]=50_265 , lowercase : Any=1_024 , lowercase : Dict=12 , lowercase : Union[str, Any]=4_096 , lowercase : str=16 , lowercase : Optional[Any]=12 , lowercase : str=4_096 , lowercase : Optional[Any]=16 , lowercase : int=0.0 , lowercase : int=0.0 , lowercase : Tuple="gelu" , lowercase : Dict=1_024 , lowercase : Union[str, Any]=0.1 , lowercase : List[Any]=0.0 , lowercase : Dict=0.0 , lowercase : str=0.02 , lowercase : Any=0.0 , lowercase : Dict=False , lowercase : str=True , lowercase : Union[str, Any]=3 , lowercase : Optional[int]=1 , lowercase : Dict=0 , lowercase : Dict=2 , lowercase : Tuple=True , lowercase : str=2 , lowercase : Tuple=2 , **lowercase : Any , ) -> int:
"""simple docstring"""
__lowercase = vocab_size
__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 = encoder_layerdrop
__lowercase = decoder_layerdrop
__lowercase = classifier_dropout
__lowercase = use_cache
__lowercase = encoder_layers
__lowercase = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
num_labels=lowercase , pad_token_id=lowercase , bos_token_id=lowercase , eos_token_id=lowercase , is_encoder_decoder=lowercase , decoder_start_token_id=lowercase , forced_eos_token_id=lowercase , **lowercase , )
# ensure backward compatibility for BART CNN models
if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated""" , lowercase ):
__lowercase = self.bos_token_id
warnings.warn(
F"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
"""The config can simply be saved and uploaded again to be fixed.""" )
class _lowerCAmelCase ( _UpperCAmelCase ):
"""simple docstring"""
@property
def snake_case__ ( self : Optional[int] ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
__lowercase = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
__lowercase = {0: """batch"""}
__lowercase = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
__lowercase = {0: """batch""", 1: """decoder_sequence"""}
__lowercase = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(lowercase , direction="""inputs""" )
elif self.task == "causal-lm":
# TODO: figure this case out.
__lowercase = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
__lowercase , __lowercase = self.num_layers
for i in range(lowercase ):
__lowercase = {0: """batch""", 2: """past_sequence + sequence"""}
__lowercase = {0: """batch""", 2: """past_sequence + sequence"""}
else:
__lowercase = 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
def snake_case__ ( self : Dict ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
__lowercase = super().outputs
else:
__lowercase = super(lowercase , self ).outputs
if self.use_past:
__lowercase , __lowercase = self.num_layers
for i in range(lowercase ):
__lowercase = {0: """batch""", 2: """past_sequence + sequence"""}
__lowercase = {0: """batch""", 2: """past_sequence + sequence"""}
return common_outputs
def snake_case__ ( self : Tuple , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ) -> Mapping[str, Any]:
"""simple docstring"""
__lowercase = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase , lowercase , lowercase , lowercase , lowercase )
# Generate decoder inputs
__lowercase = seq_length if not self.use_past else 1
__lowercase = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase , lowercase , lowercase , lowercase , lowercase )
__lowercase = {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
__lowercase = dict(**lowercase , **lowercase )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
__lowercase , __lowercase = common_inputs["""input_ids"""].shape
__lowercase = common_inputs["""decoder_input_ids"""].shape[1]
__lowercase , __lowercase = self.num_attention_heads
__lowercase = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
__lowercase = decoder_seq_length + 3
__lowercase = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
__lowercase = torch.cat(
[common_inputs["""decoder_attention_mask"""], torch.ones(lowercase , lowercase )] , dim=1 )
__lowercase = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
__lowercase , __lowercase = self.num_layers
__lowercase = min(lowercase , lowercase )
__lowercase = max(lowercase , lowercase ) - min_num_layers
__lowercase = """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""
for _ in range(lowercase ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowercase ),
torch.zeros(lowercase ),
torch.zeros(lowercase ),
torch.zeros(lowercase ),
) )
# TODO: test this.
__lowercase = encoder_shape if remaining_side_name == """encoder""" else decoder_shape
for _ in range(lowercase , lowercase ):
common_inputs["past_key_values"].append((torch.zeros(lowercase ), torch.zeros(lowercase )) )
return common_inputs
def snake_case__ ( self : int , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ) -> Mapping[str, Any]:
"""simple docstring"""
__lowercase = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase , lowercase , lowercase , lowercase , lowercase )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
__lowercase , __lowercase = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
__lowercase = seqlen + 2
__lowercase , __lowercase = self.num_layers
__lowercase , __lowercase = self.num_attention_heads
__lowercase = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
__lowercase = common_inputs["""attention_mask"""].dtype
__lowercase = torch.cat(
[common_inputs["""attention_mask"""], torch.ones(lowercase , lowercase , dtype=lowercase )] , dim=1 )
__lowercase = [
(torch.zeros(lowercase ), torch.zeros(lowercase )) for _ in range(lowercase )
]
return common_inputs
def snake_case__ ( self : Any , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ) -> Mapping[str, Any]:
"""simple docstring"""
__lowercase = compute_effective_axis_dimension(
lowercase , 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
__lowercase = tokenizer.num_special_tokens_to_add(lowercase )
__lowercase = compute_effective_axis_dimension(
lowercase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowercase )
# Generate dummy inputs according to compute batch and sequence
__lowercase = [""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
__lowercase = dict(tokenizer(lowercase , return_tensors=lowercase ) )
return common_inputs
def snake_case__ ( self : Any , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ) -> Mapping[str, Any]:
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
__lowercase = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowercase , batch_size=lowercase , seq_length=lowercase , is_pair=lowercase , framework=lowercase )
elif self.task == "causal-lm":
__lowercase = self._generate_dummy_inputs_for_causal_lm(
lowercase , batch_size=lowercase , seq_length=lowercase , is_pair=lowercase , framework=lowercase )
else:
__lowercase = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase , batch_size=lowercase , seq_length=lowercase , is_pair=lowercase , framework=lowercase )
return common_inputs
def snake_case__ ( self : Union[str, Any] , lowercase : Any , lowercase : Optional[Any] , lowercase : Optional[int] , lowercase : int ) -> Union[str, Any]:
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
__lowercase = super()._flatten_past_key_values_(lowercase , lowercase , lowercase , lowercase )
else:
__lowercase = super(lowercase , self )._flatten_past_key_values_(
lowercase , lowercase , lowercase , lowercase )
| 634
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase__ = {
"configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
"tokenization_ctrl": ["CTRLTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
"CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
"CTRLForSequenceClassification",
"CTRLLMHeadModel",
"CTRLModel",
"CTRLPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
"TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFCTRLForSequenceClassification",
"TFCTRLLMHeadModel",
"TFCTRLModel",
"TFCTRLPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
from .tokenization_ctrl import CTRLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ctrl import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
CTRLPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_ctrl import (
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCTRLForSequenceClassification,
TFCTRLLMHeadModel,
TFCTRLModel,
TFCTRLPreTrainedModel,
)
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 634
| 1
|
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE: int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE: Tuple = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class lowercase_ (SCREAMING_SNAKE_CASE__ ):
lowerCAmelCase__ ="mvp"
lowerCAmelCase__ =["past_key_values"]
lowerCAmelCase__ ={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : int , snake_case__ : Union[str, Any]=5_02_67 , snake_case__ : Dict=10_24 , snake_case__ : int=12 , snake_case__ : Tuple=40_96 , snake_case__ : Optional[int]=16 , snake_case__ : List[Any]=12 , snake_case__ : Any=40_96 , snake_case__ : Tuple=16 , snake_case__ : Any=0.0 , snake_case__ : List[str]=0.0 , snake_case__ : List[Any]="gelu" , snake_case__ : List[str]=10_24 , snake_case__ : List[Any]=0.1 , snake_case__ : Any=0.0 , snake_case__ : Optional[Any]=0.0 , snake_case__ : Optional[int]=0.02 , snake_case__ : Tuple=0.0 , snake_case__ : Union[str, Any]=False , snake_case__ : Dict=True , snake_case__ : Optional[Any]=1 , snake_case__ : List[Any]=0 , snake_case__ : str=2 , snake_case__ : Any=True , snake_case__ : Tuple=2 , snake_case__ : Optional[int]=2 , snake_case__ : List[str]=False , snake_case__ : int=1_00 , snake_case__ : int=8_00 , **snake_case__ : Union[str, Any] , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = d_model
SCREAMING_SNAKE_CASE_ = encoder_ffn_dim
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = encoder_attention_heads
SCREAMING_SNAKE_CASE_ = decoder_ffn_dim
SCREAMING_SNAKE_CASE_ = decoder_layers
SCREAMING_SNAKE_CASE_ = decoder_attention_heads
SCREAMING_SNAKE_CASE_ = dropout
SCREAMING_SNAKE_CASE_ = attention_dropout
SCREAMING_SNAKE_CASE_ = activation_dropout
SCREAMING_SNAKE_CASE_ = activation_function
SCREAMING_SNAKE_CASE_ = init_std
SCREAMING_SNAKE_CASE_ = encoder_layerdrop
SCREAMING_SNAKE_CASE_ = decoder_layerdrop
SCREAMING_SNAKE_CASE_ = classifier_dropout
SCREAMING_SNAKE_CASE_ = use_cache
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = scale_embedding # scale factor will be sqrt(d_model) if True
SCREAMING_SNAKE_CASE_ = use_prompt
SCREAMING_SNAKE_CASE_ = prompt_length
SCREAMING_SNAKE_CASE_ = prompt_mid_dim
super().__init__(
pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , is_encoder_decoder=snake_case__ , decoder_start_token_id=snake_case__ , forced_eos_token_id=snake_case__ , **snake_case__ , )
if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated' , snake_case__ ):
SCREAMING_SNAKE_CASE_ = self.bos_token_id
warnings.warn(
f'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
'The config can simply be saved and uploaded again to be fixed.' )
| 360
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE: Optional[int] = {
'''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
'''tokenization_roformer''': ['''RoFormerTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE: Tuple = ['''RoFormerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE: Any = [
'''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:
SCREAMING_SNAKE_CASE: Dict = [
'''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:
SCREAMING_SNAKE_CASE: str = [
'''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
SCREAMING_SNAKE_CASE: Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 360
| 1
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class snake_case (metaclass=UpperCamelCase ):
lowerCAmelCase__ :str = ["torch", "scipy"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ) -> Tuple:
requires_backends(self ,["torch", "scipy"] )
@classmethod
def _a ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ) -> Any:
requires_backends(cls ,["torch", "scipy"] )
@classmethod
def _a ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ) -> Optional[Any]:
requires_backends(cls ,["torch", "scipy"] )
| 539
|
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snake_case (UpperCamelCase , unittest.TestCase ):
lowerCAmelCase__ :Optional[int] = CodeGenTokenizer
lowerCAmelCase__ :List[Any] = CodeGenTokenizerFast
lowerCAmelCase__ :str = True
lowerCAmelCase__ :Tuple = {"add_prefix_space": True}
lowerCAmelCase__ :Dict = False
def _a ( self ) -> Union[str, Any]:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowercase__ = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
lowercase__ = dict(zip(UpperCAmelCase_ ,range(len(UpperCAmelCase_ ) ) ) )
lowercase__ = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
lowercase__ = {"unk_token": "<unk>"}
lowercase__ = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"] )
lowercase__ = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file ,"w" ,encoding="utf-8" ) as fp:
fp.write(json.dumps(UpperCAmelCase_ ) + "\n" )
with open(self.merges_file ,"w" ,encoding="utf-8" ) as fp:
fp.write("\n".join(UpperCAmelCase_ ) )
def _a ( self ,**UpperCAmelCase_ ) -> int:
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def _a ( self ,**UpperCAmelCase_ ) -> Tuple:
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def _a ( self ,UpperCAmelCase_ ) -> List[Any]:
lowercase__ = "lower newer"
lowercase__ = "lower newer"
return input_text, output_text
def _a ( self ) -> Optional[int]:
lowercase__ = CodeGenTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map )
lowercase__ = "lower newer"
lowercase__ = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
lowercase__ = tokenizer.tokenize(UpperCAmelCase_ ,add_prefix_space=UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
lowercase__ = tokens + [tokenizer.unk_token]
lowercase__ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_ ) ,UpperCAmelCase_ )
def _a ( self ) -> int:
if not self.test_rust_tokenizer:
return
lowercase__ = self.get_tokenizer()
lowercase__ = self.get_rust_tokenizer(add_prefix_space=UpperCAmelCase_ )
lowercase__ = "lower newer"
# Testing tokenization
lowercase__ = tokenizer.tokenize(UpperCAmelCase_ ,add_prefix_space=UpperCAmelCase_ )
lowercase__ = rust_tokenizer.tokenize(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
# Testing conversion to ids without special tokens
lowercase__ = tokenizer.encode(UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,add_prefix_space=UpperCAmelCase_ )
lowercase__ = rust_tokenizer.encode(UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
# Testing conversion to ids with special tokens
lowercase__ = self.get_rust_tokenizer(add_prefix_space=UpperCAmelCase_ )
lowercase__ = tokenizer.encode(UpperCAmelCase_ ,add_prefix_space=UpperCAmelCase_ )
lowercase__ = rust_tokenizer.encode(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
# Testing the unknown token
lowercase__ = tokens + [rust_tokenizer.unk_token]
lowercase__ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(UpperCAmelCase_ ) ,UpperCAmelCase_ )
def _a ( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ) -> Any:
# It's very difficult to mix/test pretokenization with byte-level
# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def _a ( self ,UpperCAmelCase_=15 ) -> Union[str, Any]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase__ = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
# Simple input
lowercase__ = "This is a simple input"
lowercase__ = ["This is a simple input 1", "This is a simple input 2"]
lowercase__ = ("This is a simple input", "This is a pair")
lowercase__ = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(UpperCAmelCase_ ,tokenizer_r.encode ,UpperCAmelCase_ ,max_length=UpperCAmelCase_ ,padding="max_length" )
# Simple input
self.assertRaises(UpperCAmelCase_ ,tokenizer_r.encode_plus ,UpperCAmelCase_ ,max_length=UpperCAmelCase_ ,padding="max_length" )
# Simple input
self.assertRaises(
UpperCAmelCase_ ,tokenizer_r.batch_encode_plus ,UpperCAmelCase_ ,max_length=UpperCAmelCase_ ,padding="max_length" ,)
# Pair input
self.assertRaises(UpperCAmelCase_ ,tokenizer_r.encode ,UpperCAmelCase_ ,max_length=UpperCAmelCase_ ,padding="max_length" )
# Pair input
self.assertRaises(UpperCAmelCase_ ,tokenizer_r.encode_plus ,UpperCAmelCase_ ,max_length=UpperCAmelCase_ ,padding="max_length" )
# Pair input
self.assertRaises(
UpperCAmelCase_ ,tokenizer_r.batch_encode_plus ,UpperCAmelCase_ ,max_length=UpperCAmelCase_ ,padding="max_length" ,)
def _a ( self ) -> Optional[Any]:
lowercase__ = CodeGenTokenizer.from_pretrained(self.tmpdirname ,pad_token="<pad>" )
# Simple input
lowercase__ = "This is a simple input"
lowercase__ = ["This is a simple input looooooooong", "This is a simple input"]
lowercase__ = ("This is a simple input", "This is a pair")
lowercase__ = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
lowercase__ = tokenizer.pad_token_id
lowercase__ = tokenizer(UpperCAmelCase_ ,padding="max_length" ,max_length=30 ,return_tensors="np" )
lowercase__ = tokenizer(UpperCAmelCase_ ,padding=UpperCAmelCase_ ,truncate=UpperCAmelCase_ ,return_tensors="np" )
lowercase__ = tokenizer(*UpperCAmelCase_ ,padding="max_length" ,max_length=60 ,return_tensors="np" )
lowercase__ = tokenizer(UpperCAmelCase_ ,padding=UpperCAmelCase_ ,truncate=UpperCAmelCase_ ,return_tensors="np" )
# s
# test single string max_length padding
self.assertEqual(out_s["input_ids"].shape[-1] ,30 )
self.assertTrue(pad_token_id in out_s["input_ids"] )
self.assertTrue(0 in out_s["attention_mask"] )
# s2
# test automatic padding
self.assertEqual(out_sa["input_ids"].shape[-1] ,33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["input_ids"][0] )
self.assertFalse(0 in out_sa["attention_mask"][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["input_ids"][1] )
self.assertTrue(0 in out_sa["attention_mask"][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["input_ids"].shape[-1] ,60 )
self.assertTrue(pad_token_id in out_p["input_ids"] )
self.assertTrue(0 in out_p["attention_mask"] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["input_ids"].shape[-1] ,52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["input_ids"][0] )
self.assertFalse(0 in out_pa["attention_mask"][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["input_ids"][1] )
self.assertTrue(0 in out_pa["attention_mask"][1] )
def _a ( self ) -> List[str]:
lowercase__ = "$$$"
lowercase__ = CodeGenTokenizer.from_pretrained(self.tmpdirname ,bos_token=UpperCAmelCase_ ,add_bos_token=UpperCAmelCase_ )
lowercase__ = "This is a simple input"
lowercase__ = ["This is a simple input 1", "This is a simple input 2"]
lowercase__ = tokenizer.bos_token_id
lowercase__ = tokenizer(UpperCAmelCase_ )
lowercase__ = tokenizer(UpperCAmelCase_ )
self.assertEqual(out_s.input_ids[0] ,UpperCAmelCase_ )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
lowercase__ = tokenizer.decode(out_s.input_ids )
lowercase__ = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] ,UpperCAmelCase_ )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def _a ( self ) -> List[Any]:
lowercase__ = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono" )
lowercase__ = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"
lowercase__ = "\nif len_a > len_b: result = a\nelse: result = b"
lowercase__ = tokenizer.encode(UpperCAmelCase_ )
lowercase__ = ["^#", re.escape("<|endoftext|>" ), "^'''", "^\"\"\"", "\n\n\n"]
lowercase__ = tokenizer.decode(UpperCAmelCase_ ,truncate_before_pattern=UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
def _a ( self ) -> Any:
pass
| 539
| 1
|
'''simple docstring'''
from __future__ import annotations
from math import pi
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if inductance < 0:
raise ValueError("""Inductance cannot be negative""" )
if frequency < 0:
raise ValueError("""Frequency cannot be negative""" )
if reactance < 0:
raise ValueError("""Inductive reactance cannot be negative""" )
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 407
|
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
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
a__ = get_tests_dir('''fixtures/dummy-config.json''')
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self : str ) -> Dict:
"""simple docstring"""
__UpperCamelCase : Optional[Any] = 0
def lowerCamelCase__ ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
self.assertIsNotNone(transformers.models.auto.__spec__ )
self.assertIsNotNone(importlib.util.find_spec("""transformers.models.auto""" ) )
def lowerCamelCase__ ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
__UpperCamelCase : int = AutoConfig.from_pretrained("""bert-base-uncased""" )
self.assertIsInstance(lowerCAmelCase , lowerCAmelCase )
def lowerCamelCase__ ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__UpperCamelCase : Dict = AutoConfig.from_pretrained(lowerCAmelCase )
self.assertIsInstance(lowerCAmelCase , lowerCAmelCase )
def lowerCamelCase__ ( self : Any ) -> Optional[int]:
"""simple docstring"""
__UpperCamelCase : Optional[int] = AutoConfig.from_pretrained(lowerCAmelCase )
self.assertIsInstance(lowerCAmelCase , lowerCAmelCase )
def lowerCamelCase__ ( self : Tuple ) -> Any:
"""simple docstring"""
__UpperCamelCase : List[Any] = AutoConfig.for_model("""roberta""" )
self.assertIsInstance(lowerCAmelCase , lowerCAmelCase )
def lowerCamelCase__ ( self : int ) -> List[Any]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
# This model name contains bert and roberta, but roberta ends up being picked.
__UpperCamelCase : Union[str, Any] = os.path.join(lowerCAmelCase , """fake-roberta""" )
os.makedirs(lowerCAmelCase , exist_ok=lowerCAmelCase )
with open(os.path.join(lowerCAmelCase , """config.json""" ) , """w""" ) as f:
f.write(json.dumps({} ) )
__UpperCamelCase : Dict = AutoConfig.from_pretrained(lowerCAmelCase )
self.assertEqual(type(lowerCAmelCase ) , lowerCAmelCase )
def lowerCamelCase__ ( self : int ) -> str:
"""simple docstring"""
try:
AutoConfig.register("""custom""" , lowerCAmelCase )
# Wrong model type will raise an error
with self.assertRaises(lowerCAmelCase ):
AutoConfig.register("""model""" , lowerCAmelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowerCAmelCase ):
AutoConfig.register("""bert""" , lowerCAmelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
__UpperCamelCase : Optional[Any] = CustomConfig()
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCAmelCase )
__UpperCamelCase : List[str] = AutoConfig.from_pretrained(lowerCAmelCase )
self.assertIsInstance(lowerCAmelCase , lowerCAmelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
def lowerCamelCase__ ( self : int ) -> Optional[Any]:
"""simple docstring"""
with self.assertRaisesRegex(
lowerCAmelCase , """bert-base is not a local folder and is not a valid model identifier""" ):
__UpperCamelCase : Tuple = AutoConfig.from_pretrained("""bert-base""" )
def lowerCamelCase__ ( self : Dict ) -> Any:
"""simple docstring"""
with self.assertRaisesRegex(
lowerCAmelCase , R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
__UpperCamelCase : Optional[Any] = AutoConfig.from_pretrained(lowerCAmelCase , revision="""aaaaaa""" )
def lowerCamelCase__ ( self : str ) -> List[str]:
"""simple docstring"""
with self.assertRaisesRegex(
lowerCAmelCase , """hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.""" , ):
__UpperCamelCase : Union[str, Any] = AutoConfig.from_pretrained("""hf-internal-testing/no-config-test-repo""" )
def lowerCamelCase__ ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
with self.assertRaises(lowerCAmelCase ):
__UpperCamelCase : str = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowerCAmelCase ):
__UpperCamelCase : Tuple = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" , trust_remote_code=lowerCAmelCase )
__UpperCamelCase : List[str] = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" , trust_remote_code=lowerCAmelCase )
self.assertEqual(config.__class__.__name__ , """NewModelConfig""" )
# Test config can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCAmelCase )
__UpperCamelCase : str = AutoConfig.from_pretrained(lowerCAmelCase , trust_remote_code=lowerCAmelCase )
self.assertEqual(reloaded_config.__class__.__name__ , """NewModelConfig""" )
def lowerCamelCase__ ( self : Optional[int] ) -> str:
"""simple docstring"""
class SCREAMING_SNAKE_CASE_ ( _UpperCamelCase ):
"""simple docstring"""
__magic_name__ : int = 'new-model'
try:
AutoConfig.register("""new-model""" , lowerCAmelCase )
# If remote code is not set, the default is to use local
__UpperCamelCase : Tuple = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
self.assertEqual(config.__class__.__name__ , """NewModelConfigLocal""" )
# If remote code is disabled, we load the local one.
__UpperCamelCase : Any = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" , trust_remote_code=lowerCAmelCase )
self.assertEqual(config.__class__.__name__ , """NewModelConfigLocal""" )
# If remote is enabled, we load from the Hub
__UpperCamelCase : List[Any] = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" , trust_remote_code=lowerCAmelCase )
self.assertEqual(config.__class__.__name__ , """NewModelConfig""" )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
| 279
| 0
|
"""simple docstring"""
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ->Tuple:
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(SCREAMING_SNAKE_CASE_ , n - 1 , SCREAMING_SNAKE_CASE_ ) * a) % mod
else:
_lowerCamelCase : str = binary_exponentiation(SCREAMING_SNAKE_CASE_ , n / 2 , SCREAMING_SNAKE_CASE_ )
return (b * b) % mod
# a prime number
SCREAMING_SNAKE_CASE__ : str =701
SCREAMING_SNAKE_CASE__ : Any =10_0000_0000
SCREAMING_SNAKE_CASE__ : List[Any] =10
# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)
print((a / b) % p == (a * b ** (p - 2)) % p)
| 708
|
"""simple docstring"""
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ ) ->Any:
_lowerCamelCase : List[Any] = len(SCREAMING_SNAKE_CASE_ )
_lowerCamelCase : Optional[Any] = sum(SCREAMING_SNAKE_CASE_ )
_lowerCamelCase : Union[str, Any] = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
_lowerCamelCase : List[str] = True
for i in range(1 , s + 1 ):
_lowerCamelCase : Union[str, Any] = False
for i in range(1 , n + 1 ):
for j in range(1 , s + 1 ):
_lowerCamelCase : int = dp[i][j - 1]
if arr[i - 1] <= j:
_lowerCamelCase : Tuple = dp[i][j] or dp[i - 1][j - arr[i - 1]]
for j in range(int(s / 2 ) , -1 , -1 ):
if dp[n][j] is True:
_lowerCamelCase : List[str] = s - 2 * j
break
return diff
| 558
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {'''configuration_mbart''': ['''MBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MBartConfig''', '''MBartOnnxConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''MBartTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''MBartTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''MBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MBartForCausalLM''',
'''MBartForConditionalGeneration''',
'''MBartForQuestionAnswering''',
'''MBartForSequenceClassification''',
'''MBartModel''',
'''MBartPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''TFMBartForConditionalGeneration''',
'''TFMBartModel''',
'''TFMBartPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''FlaxMBartForConditionalGeneration''',
'''FlaxMBartForQuestionAnswering''',
'''FlaxMBartForSequenceClassification''',
'''FlaxMBartModel''',
'''FlaxMBartPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart import MBartTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart_fast import MBartTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mbart import (
MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
MBartForCausalLM,
MBartForConditionalGeneration,
MBartForQuestionAnswering,
MBartForSequenceClassification,
MBartModel,
MBartPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,
FlaxMBartForSequenceClassification,
FlaxMBartModel,
FlaxMBartPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 83
|
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class __snake_case ( _lowercase):
snake_case__ : torch.FloatTensor
snake_case__ : torch.FloatTensor
class __snake_case ( _lowercase , _lowercase):
snake_case__ : int = 1
@register_to_config
def __init__( self : str , __lowerCAmelCase : int = 2_0_0_0 , __lowerCAmelCase : float = 0.15 , __lowerCAmelCase : float = 0.01 , __lowerCAmelCase : float = 13_48.0 , __lowerCAmelCase : float = 1E-5 , __lowerCAmelCase : int = 1 , ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = sigma_max
# setable values
_lowerCamelCase : Dict = None
self.set_sigmas(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Any , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : Optional[int] = None ):
"""simple docstring"""
return sample
def SCREAMING_SNAKE_CASE ( self : Optional[int] , __lowerCAmelCase : int , __lowerCAmelCase : float = None , __lowerCAmelCase : Union[str, torch.device] = None ):
"""simple docstring"""
_lowerCamelCase : Tuple = sampling_eps if sampling_eps is not None else self.config.sampling_eps
_lowerCamelCase : Optional[int] = torch.linspace(1 , __lowerCAmelCase , __lowerCAmelCase , device=__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , __lowerCAmelCase : int , __lowerCAmelCase : float = None , __lowerCAmelCase : float = None , __lowerCAmelCase : float = None ):
"""simple docstring"""
_lowerCamelCase : List[str] = sigma_min if sigma_min is not None else self.config.sigma_min
_lowerCamelCase : int = sigma_max if sigma_max is not None else self.config.sigma_max
_lowerCamelCase : Any = sampling_eps if sampling_eps is not None else self.config.sampling_eps
if self.timesteps is None:
self.set_timesteps(__lowerCAmelCase , __lowerCAmelCase )
_lowerCamelCase : List[Any] = sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
_lowerCamelCase : Optional[int] = torch.exp(torch.linspace(math.log(__lowerCAmelCase ) , math.log(__lowerCAmelCase ) , __lowerCAmelCase ) )
_lowerCamelCase : Tuple = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps] )
def SCREAMING_SNAKE_CASE ( self : Tuple , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
return torch.where(
timesteps == 0 , torch.zeros_like(t.to(timesteps.device ) ) , self.discrete_sigmas[timesteps - 1].to(timesteps.device ) , )
def SCREAMING_SNAKE_CASE ( self : Optional[int] , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : int , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : Optional[torch.Generator] = None , __lowerCAmelCase : bool = True , ):
"""simple docstring"""
if self.timesteps is None:
raise ValueError(
'''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''' )
_lowerCamelCase : Tuple = timestep * torch.ones(
sample.shape[0] , device=sample.device ) # torch.repeat_interleave(timestep, sample.shape[0])
_lowerCamelCase : Dict = (timestep * (len(self.timesteps ) - 1)).long()
# mps requires indices to be in the same device, so we use cpu as is the default with cuda
_lowerCamelCase : Optional[int] = timesteps.to(self.discrete_sigmas.device )
_lowerCamelCase : Any = self.discrete_sigmas[timesteps].to(sample.device )
_lowerCamelCase : int = self.get_adjacent_sigma(__lowerCAmelCase , __lowerCAmelCase ).to(sample.device )
_lowerCamelCase : Any = torch.zeros_like(__lowerCAmelCase )
_lowerCamelCase : Any = (sigma**2 - adjacent_sigma**2) ** 0.5
# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
# also equation 47 shows the analog from SDE models to ancestral sampling methods
_lowerCamelCase : Union[str, Any] = diffusion.flatten()
while len(diffusion.shape ) < len(sample.shape ):
_lowerCamelCase : List[Any] = diffusion.unsqueeze(-1 )
_lowerCamelCase : int = drift - diffusion**2 * model_output
# equation 6: sample noise for the diffusion term of
_lowerCamelCase : List[str] = randn_tensor(
sample.shape , layout=sample.layout , generator=__lowerCAmelCase , device=sample.device , dtype=sample.dtype )
_lowerCamelCase : List[Any] = sample - drift # subtract because `dt` is a small negative timestep
# TODO is the variable diffusion the correct scaling term for the noise?
_lowerCamelCase : int = prev_sample_mean + diffusion * noise # add impact of diffusion field g
if not return_dict:
return (prev_sample, prev_sample_mean)
return SdeVeOutput(prev_sample=__lowerCAmelCase , prev_sample_mean=__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : List[Any] , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : Optional[torch.Generator] = None , __lowerCAmelCase : bool = True , ):
"""simple docstring"""
if self.timesteps is None:
raise ValueError(
'''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''' )
# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
# sample noise for correction
_lowerCamelCase : Union[str, Any] = randn_tensor(sample.shape , layout=sample.layout , generator=__lowerCAmelCase ).to(sample.device )
# compute step size from the model_output, the noise, and the snr
_lowerCamelCase : Union[str, Any] = torch.norm(model_output.reshape(model_output.shape[0] , -1 ) , dim=-1 ).mean()
_lowerCamelCase : Tuple = torch.norm(noise.reshape(noise.shape[0] , -1 ) , dim=-1 ).mean()
_lowerCamelCase : str = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
_lowerCamelCase : Tuple = step_size * torch.ones(sample.shape[0] ).to(sample.device )
# self.repeat_scalar(step_size, sample.shape[0])
# compute corrected sample: model_output term and noise term
_lowerCamelCase : Union[str, Any] = step_size.flatten()
while len(step_size.shape ) < len(sample.shape ):
_lowerCamelCase : str = step_size.unsqueeze(-1 )
_lowerCamelCase : Any = sample + step_size * model_output
_lowerCamelCase : int = prev_sample_mean + ((step_size * 2) ** 0.5) * noise
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Any , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : torch.FloatTensor , ):
"""simple docstring"""
_lowerCamelCase : Dict = timesteps.to(original_samples.device )
_lowerCamelCase : Union[str, Any] = self.discrete_sigmas.to(original_samples.device )[timesteps]
_lowerCamelCase : Union[str, Any] = (
noise * sigmas[:, None, None, None]
if noise is not None
else torch.randn_like(__lowerCAmelCase ) * sigmas[:, None, None, None]
)
_lowerCamelCase : int = noise + original_samples
return noisy_samples
def __len__( self : Optional[int] ):
"""simple docstring"""
return self.config.num_train_timesteps
| 83
| 1
|
'''simple docstring'''
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate
#
# 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 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__ = 16
lowerCAmelCase__ = 32
def lowerCAmelCase__ ( UpperCAmelCase , UpperCAmelCase = 16 ):
"""simple docstring"""
snake_case__ : int = AutoTokenizer.from_pretrained("""bert-base-cased""" )
snake_case__ : List[str] = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(UpperCAmelCase ):
# max_length=None => use the model max length (it's actually the default)
snake_case__ : 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():
snake_case__ : Optional[int] = 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
snake_case__ : str = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(UpperCAmelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
snake_case__ : Optional[int] = 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":
snake_case__ : Any = 16
elif accelerator.mixed_precision != "no":
snake_case__ : str = 8
else:
snake_case__ : Optional[int] = None
return tokenizer.pad(
UpperCAmelCase , padding="""longest""" , max_length=UpperCAmelCase , pad_to_multiple_of=UpperCAmelCase , return_tensors="""pt""" , )
# Instantiate dataloaders.
snake_case__ : Any = DataLoader(
tokenized_datasets["""train"""] , shuffle=UpperCAmelCase , collate_fn=UpperCAmelCase , batch_size=UpperCAmelCase , drop_last=UpperCAmelCase )
snake_case__ : int = DataLoader(
tokenized_datasets["""validation"""] , shuffle=UpperCAmelCase , collate_fn=UpperCAmelCase , batch_size=UpperCAmelCase , drop_last=(accelerator.mixed_precision == """fp8""") , )
return train_dataloader, eval_dataloader
def lowerCAmelCase__ ( UpperCAmelCase , UpperCAmelCase ):
"""simple docstring"""
snake_case__ : Any = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case__ : int = config["""lr"""]
snake_case__ : Tuple = int(config["""num_epochs"""] )
snake_case__ : int = int(config["""seed"""] )
snake_case__ : List[str] = int(config["""batch_size"""] )
snake_case__ : Optional[Any] = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
snake_case__ : Optional[int] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
snake_case__ : Any = batch_size // MAX_GPU_BATCH_SIZE
snake_case__ : str = MAX_GPU_BATCH_SIZE
set_seed(UpperCAmelCase )
snake_case__ , snake_case__ : str = get_dataloaders(UpperCAmelCase , UpperCAmelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
snake_case__ : Union[str, Any] = 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).
snake_case__ : int = model.to(accelerator.device )
# Instantiate optimizer
snake_case__ : List[str] = AdamW(params=model.parameters() , lr=UpperCAmelCase )
# Instantiate scheduler
snake_case__ : List[Any] = 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.
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ : 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 )
snake_case__ : Any = model(**UpperCAmelCase )
snake_case__ : int = outputs.loss
snake_case__ : Optional[Any] = 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():
snake_case__ : Dict = model(**UpperCAmelCase )
snake_case__ : Optional[Any] = outputs.logits.argmax(dim=-1 )
snake_case__ , snake_case__ : Optional[int] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=UpperCAmelCase , references=UpperCAmelCase , )
snake_case__ : int = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , UpperCAmelCase )
def lowerCAmelCase__ ( ):
"""simple docstring"""
snake_case__ : 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.""" )
snake_case__ : Union[str, Any] = parser.parse_args()
snake_case__ : int = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(UpperCAmelCase , UpperCAmelCase )
if __name__ == "__main__":
main()
| 172
|
'''simple docstring'''
import math
from datetime import datetime, timedelta
def lowerCAmelCase__ ( UpperCAmelCase ):
"""simple docstring"""
snake_case__ : List[str] = year % 19
snake_case__ : Optional[Any] = year % 4
snake_case__ : Optional[Any] = year % 7
snake_case__ : List[str] = math.floor(year / 100 )
snake_case__ : int = math.floor((13 + 8 * leap_day_inhibits) / 25 )
snake_case__ : Dict = leap_day_inhibits / 4
snake_case__ : Optional[int] = (
15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
) % 30
snake_case__ : Optional[int] = (4 + leap_day_inhibits - leap_day_reinstall_number) % 7
# days to be added to March 21
snake_case__ : Optional[Any] = (19 * metonic_cycle + secular_moon_shift) % 30
# PHM -> Paschal Full Moon
snake_case__ : Optional[int] = (
2 * julian_leap_year
+ 4 * non_leap_year
+ 6 * days_to_add
+ century_starting_point
) % 7
if days_to_add == 29 and days_from_phm_to_sunday == 6:
return datetime(UpperCAmelCase , 4 , 19 )
elif days_to_add == 28 and days_from_phm_to_sunday == 6:
return datetime(UpperCAmelCase , 4 , 18 )
else:
return datetime(UpperCAmelCase , 3 , 22 ) + timedelta(
days=int(days_to_add + days_from_phm_to_sunday ) )
if __name__ == "__main__":
for year in (19_94, 20_00, 20_10, 20_21, 20_23):
lowerCAmelCase__ = 'will be' if year > datetime.now().year else 'was'
print(f"""Easter in {year} {tense} {gauss_easter(year)}""")
| 172
| 1
|
from sklearn.metrics import fa_score
import datasets
SCREAMING_SNAKE_CASE :Optional[int] = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'
SCREAMING_SNAKE_CASE :int = '\nArgs:\n predictions (`list` of `int`): Predicted labels.\n references (`list` of `int`): Ground truth labels.\n labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.\n\n - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n Example 1-A simple binary example\n >>> f1_metric = datasets.load_metric("f1")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n >>> print(results)\n {\'f1\': 0.5}\n\n Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n >>> f1_metric = datasets.load_metric("f1")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n >>> print(round(results[\'f1\'], 2))\n 0.67\n\n Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n >>> f1_metric = datasets.load_metric("f1")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n >>> print(round(results[\'f1\'], 2))\n 0.35\n\n Example 4-A multiclass example, with different values for the `average` input.\n >>> predictions = [0, 2, 1, 0, 0, 1]\n >>> references = [0, 1, 2, 0, 1, 2]\n >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")\n >>> print(round(results[\'f1\'], 2))\n 0.27\n >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")\n >>> print(round(results[\'f1\'], 2))\n 0.33\n >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")\n >>> print(round(results[\'f1\'], 2))\n 0.27\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n >>> print(results)\n {\'f1\': array([0.8, 0. , 0. ])}\n'
SCREAMING_SNAKE_CASE :List[str] = '\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase ( datasets.Metric ):
'''simple docstring'''
def UpperCamelCase_ ( self : str ):
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("int32" ) ),
"references": datasets.Sequence(datasets.Value("int32" ) ),
}
if self.config_name == "multilabel"
else {
"predictions": datasets.Value("int32" ),
"references": datasets.Value("int32" ),
} ) ,reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"] ,)
def UpperCamelCase_ ( self : Dict ,A : Dict ,A : str ,A : str=None ,A : Tuple=1 ,A : Any="binary" ,A : List[Any]=None ):
__A = fa_score(
A ,A ,labels=A ,pos_label=A ,average=A ,sample_weight=A )
return {"f1": float(A ) if score.size == 1 else score}
| 55
|
from __future__ import annotations
SCREAMING_SNAKE_CASE_:Tuple = """#"""
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self ):
A : dict = {}
def _lowerCAmelCase ( self, lowerCamelCase__ ):
A : List[Any] = self._trie
for char in text:
if char not in trie:
A : str = {}
A : str = trie[char]
A : Optional[int] = True
def _lowerCAmelCase ( self, lowerCamelCase__ ):
A : Dict = self._trie
for char in prefix:
if char in trie:
A : Optional[Any] = trie[char]
else:
return []
return self._elements(lowerCamelCase__ )
def _lowerCAmelCase ( self, lowerCamelCase__ ):
A : int = []
for c, v in d.items():
A : List[Any] = [""" """] if c == END else [(c + s) for s in self._elements(lowerCamelCase__ )]
result.extend(lowerCamelCase__ )
return tuple(lowerCamelCase__ )
SCREAMING_SNAKE_CASE_:Any = Trie()
SCREAMING_SNAKE_CASE_:Tuple = ("""depart""", """detergent""", """daring""", """dog""", """deer""", """deal""")
for word in words:
trie.insert_word(word)
def __UpperCamelCase ( _lowerCAmelCase ) -> tuple:
"""simple docstring"""
A : List[str] = trie.find_word(_lowerCAmelCase )
return tuple(string + word for word in suffixes )
def __UpperCamelCase ( ) -> None:
"""simple docstring"""
print(autocomplete_using_trie("""de""" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 662
| 0
|
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
lowerCamelCase__ = {
"vocab_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-german-cased": (
"https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"
),
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"
),
},
}
lowerCamelCase__ = {
"distilbert-base-uncased": 512,
"distilbert-base-uncased-distilled-squad": 512,
"distilbert-base-cased": 512,
"distilbert-base-cased-distilled-squad": 512,
"distilbert-base-german-cased": 512,
"distilbert-base-multilingual-cased": 512,
}
lowerCamelCase__ = {
"distilbert-base-uncased": {"do_lower_case": True},
"distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
"distilbert-base-cased": {"do_lower_case": False},
"distilbert-base-cased-distilled-squad": {"do_lower_case": False},
"distilbert-base-german-cased": {"do_lower_case": False},
"distilbert-base-multilingual-cased": {"do_lower_case": False},
}
class __SCREAMING_SNAKE_CASE ( _snake_case ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :int = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ :Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ :Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ :List[Any] = PRETRAINED_INIT_CONFIGURATION
SCREAMING_SNAKE_CASE__ :str = ['input_ids', 'attention_mask']
SCREAMING_SNAKE_CASE__ :str = DistilBertTokenizer
def __init__( self : Union[str, Any] , __a : Optional[int]=None , __a : List[str]=None , __a : int=True , __a : Any="[UNK]" , __a : Tuple="[SEP]" , __a : Dict="[PAD]" , __a : Optional[Any]="[CLS]" , __a : int="[MASK]" , __a : Union[str, Any]=True , __a : Tuple=None , **__a : Any , ) -> Tuple:
super().__init__(
__a , tokenizer_file=__a , do_lower_case=__a , unk_token=__a , sep_token=__a , pad_token=__a , cls_token=__a , mask_token=__a , tokenize_chinese_chars=__a , strip_accents=__a , **__a , )
_UpperCamelCase : Union[str, Any] = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , __a ) != do_lower_case
or normalizer_state.get("strip_accents" , __a ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , __a ) != tokenize_chinese_chars
):
_UpperCamelCase : List[str] = getattr(__a , normalizer_state.pop("type" ) )
_UpperCamelCase : Dict = do_lower_case
_UpperCamelCase : Dict = strip_accents
_UpperCamelCase : Optional[Any] = tokenize_chinese_chars
_UpperCamelCase : Optional[int] = normalizer_class(**__a )
_UpperCamelCase : Union[str, Any] = do_lower_case
def __SCREAMING_SNAKE_CASE ( self : Union[str, Any] , __a : List[str] , __a : Dict=None ) -> Any:
_UpperCamelCase : Optional[Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __SCREAMING_SNAKE_CASE ( self : Dict , __a : List[int] , __a : Optional[List[int]] = None ) -> str:
_UpperCamelCase : List[Any] = [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 ) * [0] + len(token_ids_a + sep ) * [1]
def __SCREAMING_SNAKE_CASE ( self : Optional[Any] , __a : str , __a : Optional[str] = None ) -> Any:
_UpperCamelCase : Dict = self._tokenizer.model.save(__a , name=__a )
return tuple(__a )
| 713
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase__ = {
"configuration_owlvit": [
"OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"OwlViTConfig",
"OwlViTOnnxConfig",
"OwlViTTextConfig",
"OwlViTVisionConfig",
],
"processing_owlvit": ["OwlViTProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = ["OwlViTFeatureExtractor"]
lowerCamelCase__ = ["OwlViTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
"OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"OwlViTModel",
"OwlViTPreTrainedModel",
"OwlViTTextModel",
"OwlViTVisionModel",
"OwlViTForObjectDetection",
]
if TYPE_CHECKING:
from .configuration_owlvit import (
OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OwlViTConfig,
OwlViTOnnxConfig,
OwlViTTextConfig,
OwlViTVisionConfig,
)
from .processing_owlvit import OwlViTProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_owlvit import OwlViTFeatureExtractor
from .image_processing_owlvit import OwlViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_owlvit import (
OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
OwlViTForObjectDetection,
OwlViTModel,
OwlViTPreTrainedModel,
OwlViTTextModel,
OwlViTVisionModel,
)
else:
import sys
lowerCamelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 51
| 0
|
'''simple docstring'''
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _a ( SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
A : List[str] = GPTSanJapaneseTokenizer
A : Optional[Any] = False
A : List[Any] = {'''do_clean_text''': False, '''add_prefix_space''': False}
def UpperCamelCase_ ( self ):
'''simple docstring'''
super().setUp()
# fmt: off
SCREAMING_SNAKE_CASE : Union[str, Any] = ['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
# fmt: on
SCREAMING_SNAKE_CASE : Optional[Any] = {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}} # 😀
SCREAMING_SNAKE_CASE : Tuple = {'unk_token': '<unk>'}
SCREAMING_SNAKE_CASE : Any = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['vocab_file'] )
SCREAMING_SNAKE_CASE : List[str] = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['emoji_file'] )
with open(self.vocab_file, 'w', encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
with open(self.emoji_file, 'w' ) as emoji_writer:
emoji_writer.write(json.dumps(A ) )
def UpperCamelCase_ ( self, **A ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname, **A )
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = 'こんにちは、世界。 \nこんばんは、㔺界。😀'
SCREAMING_SNAKE_CASE : Union[str, Any] = 'こんにちは、世界。 \nこんばんは、世界。😀'
return input_text, output_text
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = self.get_input_output_texts(A )
SCREAMING_SNAKE_CASE : str = tokenizer.encode(A, add_special_tokens=A )
SCREAMING_SNAKE_CASE : Tuple = tokenizer.decode(A, clean_up_tokenization_spaces=A )
return text, ids
def UpperCamelCase_ ( self ):
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase_ ( self ):
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase_ ( self ):
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_tokenizer()
# Testing tokenization
SCREAMING_SNAKE_CASE : int = 'こんにちは、世界。 こんばんは、㔺界。'
SCREAMING_SNAKE_CASE : str = ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer.tokenize(A )
self.assertListEqual(A, A )
# Testing conversion to ids without special tokens
SCREAMING_SNAKE_CASE : Union[str, Any] = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.convert_tokens_to_ids(A )
self.assertListEqual(A, A )
# Testing conversion to ids with special tokens
SCREAMING_SNAKE_CASE : Union[str, Any] = tokens + [tokenizer.unk_token]
SCREAMING_SNAKE_CASE : Optional[int] = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.convert_tokens_to_ids(A )
self.assertListEqual(A, A )
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = self.get_tokenizer()
# Testing tokenization
SCREAMING_SNAKE_CASE : int = 'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
SCREAMING_SNAKE_CASE : List[str] = 'こんにちは、、、、世界。こんばんは、、、、世界。'
SCREAMING_SNAKE_CASE : str = tokenizer.encode(A )
SCREAMING_SNAKE_CASE : str = tokenizer.decode(A )
self.assertEqual(A, A )
@slow
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
# Testing tokenization
SCREAMING_SNAKE_CASE : List[Any] = 'こんにちは、世界。'
SCREAMING_SNAKE_CASE : Dict = 'こんばんは、㔺界。😀'
SCREAMING_SNAKE_CASE : List[Any] = 'こんにちは、世界。こんばんは、世界。😀'
SCREAMING_SNAKE_CASE : Tuple = tokenizer.encode(prefix_text + input_text )
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer.encode('', prefix_text=prefix_text + input_text )
SCREAMING_SNAKE_CASE : int = tokenizer.encode(A, prefix_text=A )
SCREAMING_SNAKE_CASE : List[str] = tokenizer.decode(A )
SCREAMING_SNAKE_CASE : Any = tokenizer.decode(A )
SCREAMING_SNAKE_CASE : Any = tokenizer.decode(A )
self.assertEqual(A, A )
self.assertEqual(A, A )
self.assertEqual(A, A )
@slow
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
# Testing tokenization
SCREAMING_SNAKE_CASE : Optional[Any] = 'こんにちは、世界。'
SCREAMING_SNAKE_CASE : Dict = 'こんばんは、㔺界。😀'
SCREAMING_SNAKE_CASE : str = len(tokenizer.encode(A ) ) - 2
SCREAMING_SNAKE_CASE : Optional[Any] = len(tokenizer.encode(A ) ) - 2
SCREAMING_SNAKE_CASE : Union[str, Any] = [1] + [0] * (len_prefix + len_text + 1)
SCREAMING_SNAKE_CASE : Optional[Any] = [1] * (len_prefix + len_text + 1) + [0]
SCREAMING_SNAKE_CASE : List[str] = [1] + [1] * (len_prefix) + [0] * (len_text + 1)
SCREAMING_SNAKE_CASE : List[Any] = tokenizer(prefix_text + input_text ).token_type_ids
SCREAMING_SNAKE_CASE : Tuple = tokenizer('', prefix_text=prefix_text + input_text ).token_type_ids
SCREAMING_SNAKE_CASE : List[str] = tokenizer(A, prefix_text=A ).token_type_ids
self.assertListEqual(A, A )
self.assertListEqual(A, A )
self.assertListEqual(A, A )
@slow
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
SCREAMING_SNAKE_CASE : str = tokenizer.encode('あンいワ' )
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer.encode('', prefix_text='あンいワ' )
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.encode('いワ', prefix_text='あン' )
self.assertEqual(tokenizer.decode(A ), tokenizer.decode(A ) )
self.assertEqual(tokenizer.decode(A ), tokenizer.decode(A ) )
self.assertNotEqual(A, A )
self.assertNotEqual(A, A )
self.assertEqual(x_token_a[1], x_token_a[-1] ) # SEG token
self.assertEqual(x_token_a[1], x_token_a[3] ) # SEG token
@slow
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
SCREAMING_SNAKE_CASE : Any = [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
SCREAMING_SNAKE_CASE : Any = tokenizer(A, padding=A )
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.batch_encode_plus(A, padding=A )
# fmt: off
SCREAMING_SNAKE_CASE : int = [[35_993, 8_640, 25_948, 35_998, 30_647, 35_675, 35_999, 35_999], [35_993, 10_382, 9_868, 35_998, 30_646, 9_459, 30_646, 35_675]]
SCREAMING_SNAKE_CASE : str = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
SCREAMING_SNAKE_CASE : List[Any] = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
# fmt: on
self.assertListEqual(x_token.input_ids, A )
self.assertListEqual(x_token.token_type_ids, A )
self.assertListEqual(x_token.attention_mask, A )
self.assertListEqual(x_token_a.input_ids, A )
self.assertListEqual(x_token_a.token_type_ids, A )
self.assertListEqual(x_token_a.attention_mask, A )
def UpperCamelCase_ ( self ):
'''simple docstring'''
pass
def UpperCamelCase_ ( self ):
'''simple docstring'''
pass
| 28
|
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def _a ( lowerCamelCase_ ):
return 1.0 / (1.0 + np.exp(-_outputs ))
def _a ( lowerCamelCase_ ):
snake_case : Union[str, Any] =np.max(_outputs , axis=-1 , keepdims=lowerCamelCase_ )
snake_case : Optional[int] =np.exp(_outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=lowerCamelCase_ )
class lowerCAmelCase_ ( a_ ):
__UpperCAmelCase = 'sigmoid'
__UpperCAmelCase = 'softmax'
__UpperCAmelCase = 'none'
@add_end_docstrings(
a_ , R'\n return_all_scores (`bool`, *optional*, defaults to `False`):\n Whether to return all prediction scores or just the one of the predicted class.\n function_to_apply (`str`, *optional*, defaults to `"default"`):\n The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n has several labels, will apply the softmax function on the output.\n - `"sigmoid"`: Applies the sigmoid function on the output.\n - `"softmax"`: Applies the softmax function on the output.\n - `"none"`: Does not apply any function on the output.\n ' , )
class lowerCAmelCase_ ( a_ ):
__UpperCAmelCase = False
__UpperCAmelCase = ClassificationFunction.NONE
def __init__( self : List[str], **_snake_case : List[Any] ):
'''simple docstring'''
super().__init__(**_snake_case )
self.check_model_type(
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if self.framework == '''tf'''
else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING )
def __snake_case ( self : List[Any], _snake_case : str=None, _snake_case : Union[str, Any]=None, _snake_case : Optional[int]="", **_snake_case : List[Any] ):
'''simple docstring'''
snake_case : int =tokenizer_kwargs
snake_case : str ={}
if hasattr(self.model.config, '''return_all_scores''' ) and return_all_scores is None:
snake_case : int =self.model.config.return_all_scores
if isinstance(_snake_case, _snake_case ) or top_k is None:
snake_case : int =top_k
snake_case : Optional[Any] =False
elif return_all_scores is not None:
warnings.warn(
'''`return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of'''
''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.''', _snake_case, )
if return_all_scores:
snake_case : List[Any] =None
else:
snake_case : Dict =1
if isinstance(_snake_case, _snake_case ):
snake_case : List[str] =ClassificationFunction[function_to_apply.upper()]
if function_to_apply is not None:
snake_case : Tuple =function_to_apply
return preprocess_params, {}, postprocess_params
def __call__( self : Tuple, *_snake_case : Union[str, Any], **_snake_case : int ):
'''simple docstring'''
snake_case : Dict =super().__call__(*_snake_case, **_snake_case )
# TODO try and retrieve it in a nicer way from _sanitize_parameters.
snake_case : Optional[int] ='''top_k''' not in kwargs
if isinstance(args[0], _snake_case ) and _legacy:
# This pipeline is odd, and return a list when single item is run
return [result]
else:
return result
def __snake_case ( self : List[Any], _snake_case : List[str], **_snake_case : Dict ):
'''simple docstring'''
snake_case : Optional[Any] =self.framework
if isinstance(_snake_case, _snake_case ):
return self.tokenizer(**_snake_case, return_tensors=_snake_case, **_snake_case )
elif isinstance(_snake_case, _snake_case ) and len(_snake_case ) == 1 and isinstance(inputs[0], _snake_case ) and len(inputs[0] ) == 2:
# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
return self.tokenizer(
text=inputs[0][0], text_pair=inputs[0][1], return_tensors=_snake_case, **_snake_case )
elif isinstance(_snake_case, _snake_case ):
# This is likely an invalid usage of the pipeline attempting to pass text pairs.
raise ValueError(
'''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.''' )
return self.tokenizer(_snake_case, return_tensors=_snake_case, **_snake_case )
def __snake_case ( self : Tuple, _snake_case : Union[str, Any] ):
'''simple docstring'''
return self.model(**_snake_case )
def __snake_case ( self : Tuple, _snake_case : Optional[int], _snake_case : str=None, _snake_case : Any=1, _snake_case : Optional[int]=True ):
'''simple docstring'''
if function_to_apply is None:
if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
snake_case : Tuple =ClassificationFunction.SIGMOID
elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
snake_case : str =ClassificationFunction.SOFTMAX
elif hasattr(self.model.config, '''function_to_apply''' ) and function_to_apply is None:
snake_case : Tuple =self.model.config.function_to_apply
else:
snake_case : Optional[Any] =ClassificationFunction.NONE
snake_case : List[str] =model_outputs['''logits'''][0]
snake_case : Union[str, Any] =outputs.numpy()
if function_to_apply == ClassificationFunction.SIGMOID:
snake_case : Optional[int] =sigmoid(_snake_case )
elif function_to_apply == ClassificationFunction.SOFTMAX:
snake_case : Optional[Any] =softmax(_snake_case )
elif function_to_apply == ClassificationFunction.NONE:
snake_case : Union[str, Any] =outputs
else:
raise ValueError(f'''Unrecognized `function_to_apply` argument: {function_to_apply}''' )
if top_k == 1 and _legacy:
return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}
snake_case : int =[
{'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(_snake_case )
]
if not _legacy:
dict_scores.sort(key=lambda _snake_case : x["score"], reverse=_snake_case )
if top_k is not None:
snake_case : List[Any] =dict_scores[:top_k]
return dict_scores
| 349
| 0
|
'''simple docstring'''
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
snake_case_ = {
'facebook/maskformer-swin-base-ade': (
'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
snake_case_ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
_A = 'maskformer'
_A = {'hidden_size': 'mask_feature_size'}
_A = ['resnet', 'swin']
_A = ['detr']
def __init__( self , lowercase__ = 256 , lowercase__ = 256 , lowercase__ = 0.1 , lowercase__ = False , lowercase__ = None , lowercase__ = None , lowercase__ = 0.02 , lowercase__ = 1.0 , lowercase__ = 1.0 , lowercase__ = 1.0 , lowercase__ = 20.0 , lowercase__ = None , **lowercase__ , ):
"""simple docstring"""
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
SCREAMING_SNAKE_CASE_ : Union[str, Any] = SwinConfig(
image_size=384 , in_channels=3 , patch_size=4 , embed_dim=128 , depths=[2, 2, 18, 2] , num_heads=[4, 8, 16, 32] , window_size=12 , drop_path_rate=0.3 , out_features=["stage1", "stage2", "stage3", "stage4"] , )
if isinstance(lowercase__ , lowercase__ ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = backbone_config.pop("model_type" )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE_ : Optional[Any] = config_class.from_dict(lowercase__ )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
F"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. "
F"Supported model types: {','.join(self.backbones_supported )}" )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
SCREAMING_SNAKE_CASE_ : Optional[int] = DetrConfig()
else:
# verify that the decoder is supported
SCREAMING_SNAKE_CASE_ : Dict = (
decoder_config.pop("model_type" ) if isinstance(lowercase__ , lowercase__ ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
F"Transformer Decoder {decoder_type} not supported, please use one of"
F" {','.join(self.decoders_supported )}" )
if isinstance(lowercase__ , lowercase__ ):
SCREAMING_SNAKE_CASE_ : List[str] = CONFIG_MAPPING[decoder_type]
SCREAMING_SNAKE_CASE_ : int = config_class.from_dict(lowercase__ )
SCREAMING_SNAKE_CASE_ : Optional[Any] = backbone_config
SCREAMING_SNAKE_CASE_ : int = decoder_config
# main feature dimension for the model
SCREAMING_SNAKE_CASE_ : Tuple = fpn_feature_size
SCREAMING_SNAKE_CASE_ : Dict = mask_feature_size
# initializer
SCREAMING_SNAKE_CASE_ : Union[str, Any] = init_std
SCREAMING_SNAKE_CASE_ : List[Any] = init_xavier_std
# Hungarian matcher && loss
SCREAMING_SNAKE_CASE_ : int = cross_entropy_weight
SCREAMING_SNAKE_CASE_ : List[str] = dice_weight
SCREAMING_SNAKE_CASE_ : List[Any] = mask_weight
SCREAMING_SNAKE_CASE_ : Dict = use_auxiliary_loss
SCREAMING_SNAKE_CASE_ : Dict = no_object_weight
SCREAMING_SNAKE_CASE_ : List[Any] = output_auxiliary_logits
SCREAMING_SNAKE_CASE_ : List[Any] = self.decoder_config.encoder_attention_heads
SCREAMING_SNAKE_CASE_ : Tuple = self.decoder_config.num_hidden_layers
super().__init__(**lowercase__ )
@classmethod
def __lowerCamelCase ( cls , lowercase__ , lowercase__ , **lowercase__ ):
"""simple docstring"""
return cls(
backbone_config=lowercase__ , decoder_config=lowercase__ , **lowercase__ , )
def __lowerCamelCase ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.backbone_config.to_dict()
SCREAMING_SNAKE_CASE_ : List[str] = self.decoder_config.to_dict()
SCREAMING_SNAKE_CASE_ : Optional[Any] = self.__class__.model_type
return output
| 701
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
_A = "xmod"
def __init__( self , lowercase__=3_0522 , lowercase__=768 , lowercase__=12 , lowercase__=12 , lowercase__=3072 , lowercase__="gelu" , lowercase__=0.1 , lowercase__=0.1 , lowercase__=512 , lowercase__=2 , lowercase__=0.02 , lowercase__=1e-12 , lowercase__=1 , lowercase__=0 , lowercase__=2 , lowercase__="absolute" , lowercase__=True , lowercase__=None , lowercase__=False , lowercase__=2 , lowercase__=False , lowercase__=True , lowercase__=True , lowercase__=("en_XX",) , lowercase__=None , **lowercase__ , ):
"""simple docstring"""
super().__init__(pad_token_id=lowercase__ , bos_token_id=lowercase__ , eos_token_id=lowercase__ , **lowercase__ )
SCREAMING_SNAKE_CASE_ : Tuple = vocab_size
SCREAMING_SNAKE_CASE_ : Optional[Any] = hidden_size
SCREAMING_SNAKE_CASE_ : Tuple = num_hidden_layers
SCREAMING_SNAKE_CASE_ : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE_ : List[str] = hidden_act
SCREAMING_SNAKE_CASE_ : Optional[int] = intermediate_size
SCREAMING_SNAKE_CASE_ : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ : List[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE_ : Dict = type_vocab_size
SCREAMING_SNAKE_CASE_ : str = initializer_range
SCREAMING_SNAKE_CASE_ : List[str] = layer_norm_eps
SCREAMING_SNAKE_CASE_ : Tuple = position_embedding_type
SCREAMING_SNAKE_CASE_ : str = use_cache
SCREAMING_SNAKE_CASE_ : Optional[int] = classifier_dropout
SCREAMING_SNAKE_CASE_ : int = pre_norm
SCREAMING_SNAKE_CASE_ : Optional[int] = adapter_reduction_factor
SCREAMING_SNAKE_CASE_ : List[str] = adapter_layer_norm
SCREAMING_SNAKE_CASE_ : List[str] = adapter_reuse_layer_norm
SCREAMING_SNAKE_CASE_ : int = ln_before_adapter
SCREAMING_SNAKE_CASE_ : List[Any] = list(lowercase__ )
SCREAMING_SNAKE_CASE_ : Any = default_language
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
@property
def __lowerCamelCase ( self ):
"""simple docstring"""
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE_ : Tuple = {0: "batch", 1: "choice", 2: "sequence"}
else:
SCREAMING_SNAKE_CASE_ : Optional[int] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 68
| 0
|
__UpperCAmelCase = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
15: '''f''',
}
def UpperCamelCase ( snake_case__ : float ) -> str:
assert type(snake_case__ ) in (int, float) and decimal == int(snake_case__ )
UpperCamelCase : Any = int(snake_case__ )
UpperCamelCase : Any = ''
UpperCamelCase : List[str] = False
if decimal < 0:
UpperCamelCase : Optional[Any] = True
decimal *= -1
while decimal > 0:
UpperCamelCase , UpperCamelCase : Union[str, Any] = divmod(snake_case__ , 16 )
UpperCamelCase : int = values[remainder] + hexadecimal
UpperCamelCase : int = '0x' + hexadecimal
if negative:
UpperCamelCase : str = '-' + hexadecimal
return hexadecimal
if __name__ == "__main__":
import doctest
doctest.testmod()
| 40
|
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}
# See all LED models at https://huggingface.co/models?filter=LED
lowerCAmelCase = {
"""vocab_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
},
"""merges_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
},
"""tokenizer_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
},
}
lowerCAmelCase = {
"""allenai/led-base-16384""": 16_384,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def __SCREAMING_SNAKE_CASE ( ) -> Dict:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = (
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
__UpperCAmelCase : Tuple = bs[:]
__UpperCAmelCase : Dict = 0
for b in range(2**8 ):
if b not in bs:
bs.append(lowercase_ )
cs.append(2**8 + n )
n += 1
__UpperCAmelCase : Union[str, Any] = [chr(lowercase_ ) for n in cs]
return dict(zip(lowercase_ , lowercase_ ) )
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> Optional[Any]:
'''simple docstring'''
__UpperCAmelCase : Any = set()
__UpperCAmelCase : Tuple = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__UpperCAmelCase : Tuple = char
return pairs
class lowerCamelCase ( _UpperCamelCase ):
_lowerCAmelCase : List[Any] = VOCAB_FILES_NAMES
_lowerCAmelCase : int = PRETRAINED_VOCAB_FILES_MAP
_lowerCAmelCase : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCAmelCase : int = ['''input_ids''', '''attention_mask''']
def __init__( self , lowercase__ , lowercase__ , lowercase__="replace" , lowercase__="<s>" , lowercase__="</s>" , lowercase__="</s>" , lowercase__="<s>" , lowercase__="<unk>" , lowercase__="<pad>" , lowercase__="<mask>" , lowercase__=False , **lowercase__ , ):
__UpperCAmelCase : List[Any] = AddedToken(lowercase__ , lstrip=lowercase__ , rstrip=lowercase__) if isinstance(lowercase__ , lowercase__) else bos_token
__UpperCAmelCase : int = AddedToken(lowercase__ , lstrip=lowercase__ , rstrip=lowercase__) if isinstance(lowercase__ , lowercase__) else eos_token
__UpperCAmelCase : Optional[Any] = AddedToken(lowercase__ , lstrip=lowercase__ , rstrip=lowercase__) if isinstance(lowercase__ , lowercase__) else sep_token
__UpperCAmelCase : Any = AddedToken(lowercase__ , lstrip=lowercase__ , rstrip=lowercase__) if isinstance(lowercase__ , lowercase__) else cls_token
__UpperCAmelCase : Any = 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 : Optional[Any] = 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 : Optional[int] = json.load(lowercase__)
__UpperCAmelCase : List[str] = {v: k for k, v in self.encoder.items()}
__UpperCAmelCase : Optional[Any] = errors # how to handle errors in decoding
__UpperCAmelCase : str = bytes_to_unicode()
__UpperCAmelCase : List[str] = {v: k for k, v in self.byte_encoder.items()}
with open(lowercase__ , encoding='''utf-8''') as merges_handle:
__UpperCAmelCase : Optional[int] = merges_handle.read().split('''\n''')[1:-1]
__UpperCAmelCase : int = [tuple(merge.split()) for merge in bpe_merges]
__UpperCAmelCase : str = dict(zip(lowercase__ , range(len(lowercase__))))
__UpperCAmelCase : Optional[int] = {}
__UpperCAmelCase : Optional[int] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
__UpperCAmelCase : List[Any] = re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''')
@property
# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
def A( self):
return len(self.encoder)
def A( self):
return dict(self.encoder , **self.added_tokens_encoder)
def A( self , lowercase__):
if token in self.cache:
return self.cache[token]
__UpperCAmelCase : int = tuple(lowercase__)
__UpperCAmelCase : int = get_pairs(lowercase__)
if not pairs:
return token
while True:
__UpperCAmelCase : Union[str, Any] = min(lowercase__ , key=lambda lowercase__: self.bpe_ranks.get(lowercase__ , float('''inf''')))
if bigram not in self.bpe_ranks:
break
__UpperCAmelCase , __UpperCAmelCase : Tuple = bigram
__UpperCAmelCase : List[str] = []
__UpperCAmelCase : List[str] = 0
while i < len(lowercase__):
try:
__UpperCAmelCase : List[Any] = word.index(lowercase__ , lowercase__)
except ValueError:
new_word.extend(word[i:])
break
else:
new_word.extend(word[i:j])
__UpperCAmelCase : str = 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 : Union[str, Any] = tuple(lowercase__)
__UpperCAmelCase : Dict = new_word
if len(lowercase__) == 1:
break
else:
__UpperCAmelCase : Optional[int] = get_pairs(lowercase__)
__UpperCAmelCase : List[Any] = ''' '''.join(lowercase__)
__UpperCAmelCase : Tuple = word
return word
def A( self , lowercase__):
__UpperCAmelCase : str = []
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 A( self , lowercase__):
return self.encoder.get(lowercase__ , self.encoder.get(self.unk_token))
def A( self , lowercase__):
return self.decoder.get(lowercase__)
def A( self , lowercase__):
__UpperCAmelCase : str = ''''''.join(lowercase__)
__UpperCAmelCase : Optional[int] = bytearray([self.byte_decoder[c] for c in text]).decode('''utf-8''' , errors=self.errors)
return text
def A( self , lowercase__ , lowercase__ = None):
if not os.path.isdir(lowercase__):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
__UpperCAmelCase : List[Any] = os.path.join(
lowercase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
__UpperCAmelCase : Optional[Any] = 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 : Optional[int] = token_index
writer.write(''' '''.join(lowercase__) + '''\n''')
index += 1
return vocab_file, merge_file
def A( self , lowercase__ , lowercase__ = None):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__UpperCAmelCase : Optional[Any] = [self.cls_token_id]
__UpperCAmelCase : Union[str, Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def A( self , lowercase__ , lowercase__ = None , lowercase__ = False):
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 A( self , lowercase__ , lowercase__ = None):
__UpperCAmelCase : List[Any] = [self.sep_token_id]
__UpperCAmelCase : List[Any] = [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 A( self , lowercase__ , lowercase__=False , **lowercase__):
__UpperCAmelCase : List[Any] = 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)
def A( self , lowercase__ , lowercase__ = None , lowercase__ = PaddingStrategy.DO_NOT_PAD , lowercase__ = None , lowercase__ = None , ):
__UpperCAmelCase : Optional[Any] = super()._pad(
encoded_inputs=lowercase__ , max_length=lowercase__ , padding_strategy=lowercase__ , pad_to_multiple_of=lowercase__ , return_attention_mask=lowercase__ , )
# Load from model defaults
if return_attention_mask is None:
__UpperCAmelCase : Optional[Any] = '''attention_mask''' in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
__UpperCAmelCase : Dict = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
__UpperCAmelCase : int = len(encoded_inputs['''global_attention_mask''']) != len(lowercase__)
if needs_to_be_padded:
__UpperCAmelCase : Dict = len(lowercase__) - len(encoded_inputs['''global_attention_mask'''])
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
__UpperCAmelCase : Optional[Any] = (
encoded_inputs['''global_attention_mask'''] + [-1] * difference
)
elif self.padding_side == "left":
__UpperCAmelCase : int = [-1] * difference + encoded_inputs[
'''global_attention_mask'''
]
else:
raise ValueError('''Invalid padding strategy:''' + str(self.padding_side))
return encoded_inputs
| 462
| 0
|
'''simple docstring'''
import math
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
return math.sqrt(__A ) * math.sqrt(__A ) == num
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
A : str = 0
A : int = n
while left <= right:
A : List[Any] = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
A : Dict = mid - 1
else:
A : Tuple = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 701
|
'''simple docstring'''
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')
class A :
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = True , SCREAMING_SNAKE_CASE = False ) -> int:
"""simple docstring"""
A : Any = scheduler
A : Tuple = optimizers if isinstance(SCREAMING_SNAKE_CASE , (list, tuple) ) else [optimizers]
A : Dict = split_batches
A : Tuple = step_with_optimizer
A : Any = GradientState()
def __lowerCAmelCase ( self , *SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
if not self.step_with_optimizer:
# No link between scheduler and optimizer -> just step
self.scheduler.step(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
return
# Otherwise, first make sure the optimizer was stepped.
if not self.gradient_state.sync_gradients:
if self.gradient_state.adjust_scheduler:
self.scheduler._step_count += 1
return
for opt in self.optimizers:
if opt.step_was_skipped:
return
if self.split_batches:
# Split batches -> the training dataloader batch size is not changed so one step per training step
self.scheduler.step(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
else:
# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
# num_processes steps per training step
A : str = AcceleratorState().num_processes
for _ in range(SCREAMING_SNAKE_CASE ):
# Special case when using OneCycle and `drop_last` was not used
if hasattr(self.scheduler , '''total_steps''' ):
if self.scheduler._step_count <= self.scheduler.total_steps:
self.scheduler.step(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
else:
self.scheduler.step(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
return self.scheduler.get_last_lr()
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
return self.scheduler.state_dict()
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
self.scheduler.load_state_dict(SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self ) -> Any:
"""simple docstring"""
return self.scheduler.get_lr()
def __lowerCAmelCase ( self , *SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
return self.scheduler.print_lr(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
| 343
| 0
|
"""simple docstring"""
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __snake_case (lowerCamelCase ):
__a = ['''image_processor''', '''tokenizer''']
__a = '''Pix2StructImageProcessor'''
__a = ('''T5Tokenizer''', '''T5TokenizerFast''')
def __init__( self: Dict , A_: List[str] , A_: Optional[int] ):
__lowerCamelCase = False
super().__init__(A_ , A_ )
def __call__( self: Tuple , A_: str=None , A_: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , A_: bool = True , A_: Union[bool, str, PaddingStrategy] = False , A_: Union[bool, str, TruncationStrategy] = None , A_: Optional[int] = None , A_: Optional[int] = 20_48 , A_: int = 0 , A_: Optional[int] = None , A_: Optional[bool] = None , A_: bool = False , A_: bool = False , A_: bool = False , A_: bool = False , A_: bool = False , A_: bool = True , A_: Optional[Union[str, TensorType]] = None , **A_: List[str] , ):
if images is None and text is None:
raise ValueError("""You have to specify either images or text.""" )
# Get only text
if images is None and not self.image_processor.is_vqa:
__lowerCamelCase = self.tokenizer
__lowerCamelCase = self.tokenizer(
text=A_ , add_special_tokens=A_ , padding=A_ , truncation=A_ , max_length=A_ , stride=A_ , pad_to_multiple_of=A_ , return_attention_mask=A_ , return_overflowing_tokens=A_ , return_special_tokens_mask=A_ , return_offsets_mapping=A_ , return_token_type_ids=A_ , return_length=A_ , verbose=A_ , return_tensors=A_ , **A_ , )
return text_encoding
if not self.image_processor.is_vqa:
# add pixel_values
__lowerCamelCase = self.image_processor(
A_ , return_tensors=A_ , max_patches=A_ , **A_ )
else:
# add pixel_values and bbox
__lowerCamelCase = self.image_processor(
A_ , return_tensors=A_ , max_patches=A_ , header_text=A_ , **A_ )
if text is not None and not self.image_processor.is_vqa:
__lowerCamelCase = self.tokenizer(
text=A_ , add_special_tokens=A_ , padding=A_ , truncation=A_ , max_length=A_ , stride=A_ , pad_to_multiple_of=A_ , return_attention_mask=A_ , return_overflowing_tokens=A_ , return_special_tokens_mask=A_ , return_offsets_mapping=A_ , return_token_type_ids=A_ , return_length=A_ , verbose=A_ , return_tensors=A_ , **A_ , )
if "attention_mask" in text_encoding:
__lowerCamelCase = text_encoding.pop("""attention_mask""" )
if "input_ids" in text_encoding:
__lowerCamelCase = text_encoding.pop("""input_ids""" )
else:
__lowerCamelCase = None
if text_encoding is not None:
encoding_image_processor.update(A_ )
return encoding_image_processor
def __a ( self: List[str] , *A_: Tuple , **A_: Optional[Any] ):
return self.tokenizer.batch_decode(*A_ , **A_ )
def __a ( self: int , *A_: Optional[int] , **A_: Optional[Any] ):
return self.tokenizer.decode(*A_ , **A_ )
@property
def __a ( self: Dict ):
__lowerCamelCase = self.tokenizer.model_input_names
__lowerCamelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 281
|
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __snake_case (lowerCamelCase ):
__a = ['''image_processor''', '''tokenizer''']
__a = '''LayoutLMv2ImageProcessor'''
__a = ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')
def __init__( self: Any , A_: Optional[Any]=None , A_: Dict=None , **A_: Any ):
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , A_ , )
__lowerCamelCase = kwargs.pop("""feature_extractor""" )
__lowerCamelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(A_ , A_ )
def __call__( self: List[str] , A_: Any , A_: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , A_: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , A_: Union[List[List[int]], List[List[List[int]]]] = None , A_: Optional[Union[List[int], List[List[int]]]] = None , A_: bool = True , A_: Union[bool, str, PaddingStrategy] = False , A_: Union[bool, str, TruncationStrategy] = None , A_: Optional[int] = None , A_: int = 0 , A_: Optional[int] = None , A_: Optional[bool] = None , A_: Optional[bool] = None , A_: bool = False , A_: bool = False , A_: bool = False , A_: bool = False , A_: bool = True , A_: Optional[Union[str, TensorType]] = None , **A_: Any , ):
# verify input
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
"""You cannot provide bounding boxes """
"""if you initialized the image processor with apply_ocr set to True.""" )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
"""You cannot provide word labels if you initialized the image processor with apply_ocr set to True.""" )
if return_overflowing_tokens is True and return_offsets_mapping is False:
raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping.""" )
# first, apply the image processor
__lowerCamelCase = self.image_processor(images=A_ , return_tensors=A_ )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(A_ , A_ ):
__lowerCamelCase = [text] # add batch dimension (as the image processor always adds a batch dimension)
__lowerCamelCase = features["""words"""]
__lowerCamelCase = self.tokenizer(
text=text if text is not None else features["""words"""] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features["""boxes"""] , word_labels=A_ , add_special_tokens=A_ , padding=A_ , truncation=A_ , max_length=A_ , stride=A_ , pad_to_multiple_of=A_ , return_token_type_ids=A_ , return_attention_mask=A_ , return_overflowing_tokens=A_ , return_special_tokens_mask=A_ , return_offsets_mapping=A_ , return_length=A_ , verbose=A_ , return_tensors=A_ , **A_ , )
# add pixel values
__lowerCamelCase = features.pop("""pixel_values""" )
if return_overflowing_tokens is True:
__lowerCamelCase = self.get_overflowing_images(A_ , encoded_inputs["""overflow_to_sample_mapping"""] )
__lowerCamelCase = images
return encoded_inputs
def __a ( self: Optional[Any] , A_: Any , A_: Tuple ):
# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
__lowerCamelCase = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(A_ ) != len(A_ ):
raise ValueError(
"""Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
f' {len(A_ )} and {len(A_ )}' )
return images_with_overflow
def __a ( self: Optional[Any] , *A_: str , **A_: Dict ):
return self.tokenizer.batch_decode(*A_ , **A_ )
def __a ( self: Union[str, Any] , *A_: Optional[Any] , **A_: Tuple ):
return self.tokenizer.decode(*A_ , **A_ )
@property
def __a ( self: Union[str, Any] ):
return ["input_ids", "bbox", "attention_mask", "image"]
@property
def __a ( self: Optional[Any] ):
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , A_ , )
return self.image_processor_class
@property
def __a ( self: List[Any] ):
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , A_ , )
return self.image_processor
| 281
| 1
|
"""simple docstring"""
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def _snake_case ( snake_case__ : Tuple , snake_case__ : Optional[Any] ):
A = []
for part_id in partition_order:
A = df.where(F'SPARK_PARTITION_ID() = {part_id}' ).collect()
for row_idx, row in enumerate(snake_case__ ):
expected_row_ids_and_row_dicts.append((F'{part_id}_{row_idx}', row.asDict()) )
return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def _snake_case ( ):
A = pyspark.sql.SparkSession.builder.master('local[*]' ).appName('pyspark' ).getOrCreate()
A = spark.range(100 ).repartition(1 )
A = Spark(snake_case__ )
# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
# that each partition can hold 2 rows.
spark_builder._repartition_df_if_needed(max_shard_size=16 )
# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
assert spark_builder.df.rdd.getNumPartitions() == 50
@require_not_windows
@require_dill_gt_0_3_2
def _snake_case ( ):
A = pyspark.sql.SparkSession.builder.master('local[*]' ).appName('pyspark' ).getOrCreate()
A = spark.range(10 ).repartition(2 )
A = [1, 0]
A = _generate_iterable_examples(snake_case__ , snake_case__ ) # Reverse the partitions.
A = _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__ , snake_case__ )
for i, (row_id, row_dict) in enumerate(generate_fn() ):
A , A = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def _snake_case ( ):
A = pyspark.sql.SparkSession.builder.master('local[*]' ).appName('pyspark' ).getOrCreate()
A = spark.range(10 ).repartition(1 )
A = SparkExamplesIterable(snake_case__ )
assert it.n_shards == 1
for i, (row_id, row_dict) in enumerate(snake_case__ ):
assert row_id == F'0_{i}'
assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def _snake_case ( ):
A = pyspark.sql.SparkSession.builder.master('local[*]' ).appName('pyspark' ).getOrCreate()
A = spark.range(30 ).repartition(3 )
# Mock the generator so that shuffle reverses the partition indices.
with patch('numpy.random.Generator' ) as generator_mock:
A = lambda snake_case__ : x.reverse()
A = _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__ , [2, 1, 0] )
A = SparkExamplesIterable(snake_case__ ).shuffle_data_sources(snake_case__ )
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(snake_case__ ):
A , A = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def _snake_case ( ):
A = pyspark.sql.SparkSession.builder.master('local[*]' ).appName('pyspark' ).getOrCreate()
A = spark.range(20 ).repartition(4 )
# Partitions 0 and 2
A = SparkExamplesIterable(snake_case__ ).shard_data_sources(worker_id=0 , num_workers=2 )
assert shard_it_a.n_shards == 2
A = _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__ , [0, 2] )
for i, (row_id, row_dict) in enumerate(snake_case__ ):
A , A = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
A = SparkExamplesIterable(snake_case__ ).shard_data_sources(worker_id=1 , num_workers=2 )
assert shard_it_a.n_shards == 2
A = _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__ , [1, 3] )
for i, (row_id, row_dict) in enumerate(snake_case__ ):
A , A = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def _snake_case ( ):
A = pyspark.sql.SparkSession.builder.master('local[*]' ).appName('pyspark' ).getOrCreate()
A = spark.range(100 ).repartition(1 )
A = Spark(snake_case__ )
# Choose a small max_shard_size for maximum partitioning.
spark_builder._repartition_df_if_needed(max_shard_size=1 )
# The new number of partitions should not be greater than the number of rows.
assert spark_builder.df.rdd.getNumPartitions() == 100
| 701
|
"""simple docstring"""
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput
_lowercase = 8
def _snake_case ( snake_case__ : Tuple , snake_case__ : Optional[int]=BITS ):
A = x.device
A = (x * 255).int().clamp(0 , 255 )
A = 2 ** torch.arange(bits - 1 , -1 , -1 , device=snake_case__ )
A = rearrange(snake_case__ , 'd -> d 1 1' )
A = rearrange(snake_case__ , 'b c h w -> b c 1 h w' )
A = ((x & mask) != 0).float()
A = rearrange(snake_case__ , 'b c d h w -> b (c d) h w' )
A = bits * 2 - 1
return bits
def _snake_case ( snake_case__ : Any , snake_case__ : Any=BITS ):
A = x.device
A = (x > 0).int()
A = 2 ** torch.arange(bits - 1 , -1 , -1 , device=snake_case__ , dtype=torch.intaa )
A = rearrange(snake_case__ , 'd -> d 1 1' )
A = rearrange(snake_case__ , 'b (c d) h w -> b c d h w' , d=8 )
A = reduce(x * mask , 'b c d h w -> b c h w' , 'sum' )
return (dec / 255).clamp(0.0 , 1.0 )
def _snake_case ( self : Optional[int] , snake_case__ : torch.FloatTensor , snake_case__ : int , snake_case__ : torch.FloatTensor , snake_case__ : float = 0.0 , snake_case__ : bool = True , snake_case__ : List[str]=None , snake_case__ : bool = True , ):
if self.num_inference_steps is None:
raise ValueError(
'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler' )
# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
# Ideally, read DDIM paper in-detail understanding
# Notation (<variable name> -> <name in paper>
# - pred_noise_t -> e_theta(x_t, t)
# - pred_original_sample -> f_theta(x_t, t) or x_0
# - std_dev_t -> sigma_t
# - eta -> η
# - pred_sample_direction -> "direction pointing to x_t"
# - pred_prev_sample -> "x_t-1"
# 1. get previous step value (=t-1)
A = timestep - self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
A = self.alphas_cumprod[timestep]
A = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
A = 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
A = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
# 4. Clip "predicted x_0"
A = self.bit_scale
if self.config.clip_sample:
A = torch.clamp(snake_case__ , -scale , snake_case__ )
# 5. compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
A = self._get_variance(snake_case__ , snake_case__ )
A = eta * variance ** 0.5
if use_clipped_model_output:
# the model_output is always re-derived from the clipped x_0 in Glide
A = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
A = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
A = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if eta > 0:
# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
A = model_output.device if torch.is_tensor(snake_case__ ) else 'cpu'
A = torch.randn(model_output.shape , dtype=model_output.dtype , generator=snake_case__ ).to(snake_case__ )
A = self._get_variance(snake_case__ , snake_case__ ) ** 0.5 * eta * noise
A = prev_sample + variance
if not return_dict:
return (prev_sample,)
return DDIMSchedulerOutput(prev_sample=snake_case__ , pred_original_sample=snake_case__ )
def _snake_case ( self : Dict , snake_case__ : torch.FloatTensor , snake_case__ : int , snake_case__ : torch.FloatTensor , snake_case__ : Tuple="epsilon" , snake_case__ : List[str]=None , snake_case__ : bool = True , ):
A = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
A , A = torch.split(snake_case__ , sample.shape[1] , dim=1 )
else:
A = None
# 1. compute alphas, betas
A = self.alphas_cumprod[t]
A = self.alphas_cumprod[t - 1] if t > 0 else self.one
A = 1 - alpha_prod_t
A = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if prediction_type == "epsilon":
A = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif prediction_type == "sample":
A = model_output
else:
raise ValueError(F'Unsupported prediction_type {prediction_type}.' )
# 3. Clip "predicted x_0"
A = self.bit_scale
if self.config.clip_sample:
A = torch.clamp(snake_case__ , -scale , snake_case__ )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
A = (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
A = self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
A = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
A = 0
if t > 0:
A = torch.randn(
model_output.size() , dtype=model_output.dtype , layout=model_output.layout , generator=snake_case__ ).to(model_output.device )
A = (self._get_variance(snake_case__ , predicted_variance=snake_case__ ) ** 0.5) * noise
A = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return DDPMSchedulerOutput(prev_sample=snake_case__ , pred_original_sample=snake_case__ )
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,A_ : UNetaDConditionModel ,A_ : Union[DDIMScheduler, DDPMScheduler] ,A_ : Optional[float] = 1.0 ,) -> Optional[int]:
super().__init__()
A = bit_scale
A = (
ddim_bit_scheduler_step if isinstance(A_ ,A_ ) else ddpm_bit_scheduler_step
)
self.register_modules(unet=A_ ,scheduler=A_ )
@torch.no_grad()
def __call__( self : Tuple ,A_ : Optional[int] = 256 ,A_ : Optional[int] = 256 ,A_ : Optional[int] = 50 ,A_ : Optional[torch.Generator] = None ,A_ : Optional[int] = 1 ,A_ : Optional[str] = "pil" ,A_ : bool = True ,**A_ : Optional[Any] ,) -> Union[Tuple, ImagePipelineOutput]:
A = torch.randn(
(batch_size, self.unet.config.in_channels, height, width) ,generator=A_ ,)
A = decimal_to_bits(A_ ) * self.bit_scale
A = latents.to(self.device )
self.scheduler.set_timesteps(A_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# predict the noise residual
A = self.unet(A_ ,A_ ).sample
# compute the previous noisy sample x_t -> x_t-1
A = self.scheduler.step(A_ ,A_ ,A_ ).prev_sample
A = bits_to_decimal(A_ )
if output_type == "pil":
A = self.numpy_to_pil(A_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=A_ )
| 22
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
a = {
'''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
'''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = ['''Speech2TextTokenizer''']
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = ['''Speech2TextFeatureExtractor''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = [
'''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFSpeech2TextForConditionalGeneration''',
'''TFSpeech2TextModel''',
'''TFSpeech2TextPreTrainedModel''',
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = [
'''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Speech2TextForConditionalGeneration''',
'''Speech2TextModel''',
'''Speech2TextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
a = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 7
|
"""simple docstring"""
import os
import sys
UpperCamelCase__ :Union[str, Any] = os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
UpperCamelCase__ :List[Any] = [
"""torch""",
"""numpy""",
"""tokenizers""",
"""filelock""",
"""requests""",
"""tqdm""",
"""regex""",
"""sentencepiece""",
"""sacremoses""",
"""importlib_metadata""",
"""huggingface_hub""",
]
@add_start_docstrings(AutoConfig.__doc__ )
def A_ ( *snake_case__ , **snake_case__ ) -> int:
return AutoConfig.from_pretrained(*snake_case__ , **snake_case__ )
@add_start_docstrings(AutoTokenizer.__doc__ )
def A_ ( *snake_case__ , **snake_case__ ) -> int:
return AutoTokenizer.from_pretrained(*snake_case__ , **snake_case__ )
@add_start_docstrings(AutoModel.__doc__ )
def A_ ( *snake_case__ , **snake_case__ ) -> Dict:
return AutoModel.from_pretrained(*snake_case__ , **snake_case__ )
@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def A_ ( *snake_case__ , **snake_case__ ) -> Dict:
return AutoModelForCausalLM.from_pretrained(*snake_case__ , **snake_case__ )
@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def A_ ( *snake_case__ , **snake_case__ ) -> Dict:
return AutoModelForMaskedLM.from_pretrained(*snake_case__ , **snake_case__ )
@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def A_ ( *snake_case__ , **snake_case__ ) -> Dict:
return AutoModelForSequenceClassification.from_pretrained(*snake_case__ , **snake_case__ )
@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def A_ ( *snake_case__ , **snake_case__ ) -> Union[str, Any]:
return AutoModelForQuestionAnswering.from_pretrained(*snake_case__ , **snake_case__ )
| 355
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {
'configuration_blenderbot': [
'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BlenderbotConfig',
'BlenderbotOnnxConfig',
],
'tokenization_blenderbot': ['BlenderbotTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ['BlenderbotTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BlenderbotForCausalLM',
'BlenderbotForConditionalGeneration',
'BlenderbotModel',
'BlenderbotPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
'TFBlenderbotForConditionalGeneration',
'TFBlenderbotModel',
'TFBlenderbotPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
'FlaxBlenderbotForConditionalGeneration',
'FlaxBlenderbotModel',
'FlaxBlenderbotPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 467
|
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Dict , lowerCAmelCase: Union[str, Any] ) -> Union[str, Any]:
_UpperCAmelCase : Dict = b.T
_UpperCAmelCase : Dict = np.sum(np.square(lowerCAmelCase ) , axis=1 )
_UpperCAmelCase : Optional[Any] = np.sum(np.square(lowerCAmelCase ) , axis=0 )
_UpperCAmelCase : str = np.matmul(lowerCAmelCase , lowerCAmelCase )
_UpperCAmelCase : Any = aa[:, None] - 2 * ab + ba[None, :]
return d
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Union[str, Any] , lowerCAmelCase: Dict ) -> int:
_UpperCAmelCase : Any = x.reshape(-1 , 3 )
_UpperCAmelCase : List[str] = squared_euclidean_distance(lowerCAmelCase , lowerCAmelCase )
return np.argmin(lowerCAmelCase , axis=1 )
class a ( UpperCAmelCase ):
_lowercase = ["pixel_values"]
def __init__( self , A_ = None , A_ = True , A_ = None , A_ = PILImageResampling.BILINEAR , A_ = True , A_ = True , **A_ , ):
'''simple docstring'''
super().__init__(**A_ )
_UpperCAmelCase : Optional[Any] = size if size is not None else {"height": 256, "width": 256}
_UpperCAmelCase : Optional[int] = get_size_dict(A_ )
_UpperCAmelCase : Union[str, Any] = np.array(A_ ) if clusters is not None else None
_UpperCAmelCase : int = do_resize
_UpperCAmelCase : Union[str, Any] = size
_UpperCAmelCase : Optional[Any] = resample
_UpperCAmelCase : str = do_normalize
_UpperCAmelCase : List[str] = do_color_quantize
def _UpperCAmelCase ( self , A_ , A_ , A_ = PILImageResampling.BILINEAR , A_ = None , **A_ , ):
'''simple docstring'''
_UpperCAmelCase : int = get_size_dict(A_ )
if "height" not in size or "width" not in size:
raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}' )
return resize(
A_ , size=(size["height"], size["width"]) , resample=A_ , data_format=A_ , **A_ )
def _UpperCAmelCase ( self , A_ , A_ = None , ):
'''simple docstring'''
_UpperCAmelCase : Dict = rescale(image=A_ , scale=1 / 1_27.5 , data_format=A_ )
_UpperCAmelCase : List[Any] = image - 1
return image
def _UpperCAmelCase ( self , A_ , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = ChannelDimension.FIRST , **A_ , ):
'''simple docstring'''
_UpperCAmelCase : Optional[int] = do_resize if do_resize is not None else self.do_resize
_UpperCAmelCase : Any = size if size is not None else self.size
_UpperCAmelCase : Dict = get_size_dict(A_ )
_UpperCAmelCase : List[Any] = resample if resample is not None else self.resample
_UpperCAmelCase : Any = do_normalize if do_normalize is not None else self.do_normalize
_UpperCAmelCase : Optional[Any] = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
_UpperCAmelCase : Any = clusters if clusters is not None else self.clusters
_UpperCAmelCase : Optional[int] = np.array(A_ )
_UpperCAmelCase : List[str] = 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 or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_color_quantize and clusters is None:
raise ValueError("Clusters must be specified if do_color_quantize is True." )
# All transformations expect numpy arrays.
_UpperCAmelCase : List[str] = [to_numpy_array(A_ ) for image in images]
if do_resize:
_UpperCAmelCase : int = [self.resize(image=A_ , size=A_ , resample=A_ ) for image in images]
if do_normalize:
_UpperCAmelCase : List[str] = [self.normalize(image=A_ ) for image in images]
if do_color_quantize:
_UpperCAmelCase : Tuple = [to_channel_dimension_format(A_ , ChannelDimension.LAST ) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
_UpperCAmelCase : List[str] = np.array(A_ )
_UpperCAmelCase : List[Any] = color_quantize(A_ , A_ ).reshape(images.shape[:-1] )
# flatten to (batch_size, height*width)
_UpperCAmelCase : Any = images.shape[0]
_UpperCAmelCase : List[Any] = images.reshape(A_ , -1 )
# We need to convert back to a list of images to keep consistent behaviour across processors.
_UpperCAmelCase : Union[str, Any] = list(A_ )
else:
_UpperCAmelCase : Optional[Any] = [to_channel_dimension_format(A_ , A_ ) for image in images]
_UpperCAmelCase : List[Any] = {"input_ids": images}
return BatchFeature(data=A_ , tensor_type=A_ )
| 467
| 1
|
'''simple docstring'''
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def UpperCAmelCase_ ( A , A , A ):
'''simple docstring'''
_a : List[Any] = AutoConfig.from_pretrained(lowerCamelCase_ )
_a : List[str] = FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCamelCase_ )
_a : Any = checkpoints.load_tax_checkpoint(lowerCamelCase_ )
_a : List[Any] = 'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']
if config.model_type == "t5":
_a : Tuple = 'SelfAttention'
if config.model_type == "longt5" and config.encoder_attention_type == "local":
_a : int = 'LocalSelfAttention'
elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
_a : int = 'TransientGlobalSelfAttention'
else:
raise ValueError(
'Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'
' attribute with a value from [\'local\', \'transient-global].' )
# Encoder
for layer_index in range(config.num_layers ):
_a : Union[str, Any] = f'''layers_{str(lowerCamelCase_ )}'''
# Self-Attention
_a : Any = tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
_a : Any = tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
_a : Dict = tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
_a : Dict = tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
_a : List[Any] = tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']
# Layer Normalization
_a : Tuple = tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']
if split_mlp_wi:
_a : Any = tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
_a : Optional[int] = tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
else:
_a : Any = tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']
_a : Any = tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']
# Layer Normalization
_a : Optional[Any] = tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']
# Assigning
_a : Optional[int] = flax_model.params['encoder']['block'][str(lowerCamelCase_ )]['layer']
_a : Tuple = tax_attention_key
_a : Dict = tax_attention_out
_a : Union[str, Any] = tax_attention_query
_a : Any = tax_attention_value
_a : Tuple = tax_attention_layer_norm
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
_a : str = tax_global_layer_norm
if split_mlp_wi:
_a : Union[str, Any] = tax_mlp_wi_a
_a : Dict = tax_mlp_wi_a
else:
_a : Any = tax_mlp_wi
_a : List[Any] = tax_mlp_wo
_a : Optional[int] = tax_mlp_layer_norm
_a : List[Any] = flax_model_encoder_layer_block
# Only for layer 0:
_a : List[Any] = tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
_a : List[str] = tax_encoder_rel_embedding
# Side/global relative position_bias + layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
_a : List[Any] = tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
_a : List[str] = tax_encoder_global_rel_embedding
# Assigning
_a : Dict = tax_model['target']['encoder']['encoder_norm']['scale']
_a : List[str] = tax_encoder_norm
# Decoder
for layer_index in range(config.num_layers ):
_a : Any = f'''layers_{str(lowerCamelCase_ )}'''
# Self-Attention
_a : int = tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
_a : Optional[int] = tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
_a : Union[str, Any] = tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
_a : Dict = tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']
# Layer Normalization
_a : str = tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
'scale'
]
# Encoder-Decoder-Attention
_a : int = tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
_a : List[Any] = tax_enc_dec_attention_module['key']['kernel']
_a : Any = tax_enc_dec_attention_module['out']['kernel']
_a : List[str] = tax_enc_dec_attention_module['query']['kernel']
_a : Optional[int] = tax_enc_dec_attention_module['value']['kernel']
# Layer Normalization
_a : str = tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']
# MLP
if split_mlp_wi:
_a : str = tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
_a : Optional[Any] = tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
else:
_a : List[str] = tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']
_a : Optional[int] = tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']
# Layer Normalization
_a : Optional[int] = tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']
# Assigning
_a : str = flax_model.params['decoder']['block'][str(lowerCamelCase_ )]['layer']
_a : List[str] = tax_attention_key
_a : Union[str, Any] = tax_attention_out
_a : Optional[Any] = tax_attention_query
_a : str = tax_attention_value
_a : Any = tax_pre_attention_layer_norm
_a : Any = tax_enc_dec_attention_key
_a : Union[str, Any] = tax_enc_dec_attention_out
_a : Dict = tax_enc_dec_attention_query
_a : str = tax_enc_dec_attention_value
_a : Optional[Any] = tax_cross_layer_norm
if split_mlp_wi:
_a : Dict = tax_mlp_wi_a
_a : Union[str, Any] = tax_mlp_wi_a
else:
_a : Union[str, Any] = tax_mlp_wi
_a : Optional[Any] = tax_mlp_wo
_a : Tuple = txa_mlp_layer_norm
_a : Union[str, Any] = flax_model_decoder_layer_block
# Decoder Normalization
_a : Union[str, Any] = tax_model['target']['decoder']['decoder_norm']['scale']
_a : Any = txa_decoder_norm
# Only for layer 0:
_a : Dict = tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
_a : List[Any] = tax_decoder_rel_embedding
# Token Embeddings
_a : List[Any] = tax_model['target']['token_embedder']['embedding']
_a : List[str] = txa_token_embeddings
# LM Head (only in v1.1 and LongT5 checkpoints)
if "logits_dense" in tax_model["target"]["decoder"]:
_a : int = tax_model['target']['decoder']['logits_dense']['kernel']
flax_model.save_pretrained(lowerCamelCase_ )
print('T5X Model was sucessfully converted!' )
if __name__ == "__main__":
UpperCAmelCase_ : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint."
)
parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.")
parser.add_argument(
"--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
)
UpperCAmelCase_ : Optional[int] = parser.parse_args()
convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
| 120
|
'''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'):
__magic_name__ : str ={
'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:
__magic_name__ : Tuple ={
'linear': PIL.Image.LINEAR,
'bilinear': PIL.Image.BILINEAR,
'bicubic': PIL.Image.BICUBIC,
'lanczos': PIL.Image.LANCZOS,
'nearest': PIL.Image.NEAREST,
}
def __snake_case ( lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
__magic_name__ = (images / 2 + 0.5).clamp(0 , 1 )
__magic_name__ = images.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
__magic_name__ = numpy_to_pil(lowerCamelCase_ )
return images
def __snake_case ( lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
if images.ndim == 3:
__magic_name__ = images[None, ...]
__magic_name__ = (images * 255).round().astype("uint8" )
if images.shape[-1] == 1:
# special case for grayscale (single channel) images
__magic_name__ = [Image.fromarray(image.squeeze() , mode="L" ) for image in images]
else:
__magic_name__ = [Image.fromarray(lowerCamelCase_ ) for image in images]
return pil_images
| 664
| 0
|
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def UpperCAmelCase ( A__: str ) -> Tuple:
__lowerCamelCase : List[str] = image.size
__lowerCamelCase : Dict = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
__lowerCamelCase : str = image.resize((w, h) , resample=PIL_INTERPOLATION['lanczos'] )
__lowerCamelCase : int = np.array(A__ ).astype(np.floataa ) / 255.0
__lowerCamelCase : Optional[Any] = image[None].transpose(0 , 3 , 1 , 2 )
__lowerCamelCase : Any = torch.from_numpy(A__ )
return 2.0 * image - 1.0
class __lowercase( lowercase__ ):
'''simple docstring'''
def __init__( self , __a , __a , __a , ):
super().__init__()
self.register_modules(vqvae=__a , unet=__a , scheduler=__a )
@torch.no_grad()
def __call__( self , __a = None , __a = 1 , __a = 100 , __a = 0.0 , __a = None , __a = "pil" , __a = True , ):
if isinstance(__a , PIL.Image.Image ):
__lowerCamelCase : Dict = 1
elif isinstance(__a , torch.Tensor ):
__lowerCamelCase : List[str] = image.shape[0]
else:
raise ValueError(f'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__a )}''' )
if isinstance(__a , PIL.Image.Image ):
__lowerCamelCase : int = preprocess(__a )
__lowerCamelCase : Optional[int] = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
__lowerCamelCase : Union[str, Any] = (batch_size, self.unet.config.in_channels // 2, height, width)
__lowerCamelCase : List[Any] = next(self.unet.parameters() ).dtype
__lowerCamelCase : str = randn_tensor(__a , generator=__a , device=self.device , dtype=__a )
__lowerCamelCase : Optional[int] = image.to(device=self.device , dtype=__a )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(__a , device=self.device )
__lowerCamelCase : Union[str, Any] = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
__lowerCamelCase : Optional[Any] = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
__lowerCamelCase : str = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
__lowerCamelCase : List[str] = {}
if accepts_eta:
__lowerCamelCase : Tuple = eta
for t in self.progress_bar(__a ):
# concat latents and low resolution image in the channel dimension.
__lowerCamelCase : str = torch.cat([latents, image] , dim=1 )
__lowerCamelCase : Optional[int] = self.scheduler.scale_model_input(__a , __a )
# predict the noise residual
__lowerCamelCase : Union[str, Any] = self.unet(__a , __a ).sample
# compute the previous noisy sample x_t -> x_t-1
__lowerCamelCase : Dict = self.scheduler.step(__a , __a , __a , **__a ).prev_sample
# decode the image latents with the VQVAE
__lowerCamelCase : int = self.vqvae.decode(__a ).sample
__lowerCamelCase : int = torch.clamp(__a , -1.0 , 1.0 )
__lowerCamelCase : Tuple = image / 2 + 0.5
__lowerCamelCase : List[str] = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__lowerCamelCase : Union[str, Any] = self.numpy_to_pil(__a )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__a )
| 708
|
"""simple docstring"""
import unittest
from transformers import DebertaVaConfig, is_torch_available
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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaVaForMaskedLM,
DebertaVaForMultipleChoice,
DebertaVaForQuestionAnswering,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaModel,
)
from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class __lowercase( lowercase__ ):
'''simple docstring'''
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=16 , __a=2 , __a=0.02 , __a=False , __a=True , __a="None" , __a=3 , __a=4 , __a=None , ):
__lowerCamelCase : List[str] = parent
__lowerCamelCase : Dict = batch_size
__lowerCamelCase : str = seq_length
__lowerCamelCase : Optional[int] = is_training
__lowerCamelCase : Dict = use_input_mask
__lowerCamelCase : Dict = use_token_type_ids
__lowerCamelCase : Dict = use_labels
__lowerCamelCase : Optional[Any] = vocab_size
__lowerCamelCase : Any = hidden_size
__lowerCamelCase : List[Any] = num_hidden_layers
__lowerCamelCase : Tuple = num_attention_heads
__lowerCamelCase : Any = intermediate_size
__lowerCamelCase : Optional[Any] = hidden_act
__lowerCamelCase : Any = hidden_dropout_prob
__lowerCamelCase : Optional[Any] = attention_probs_dropout_prob
__lowerCamelCase : List[Any] = max_position_embeddings
__lowerCamelCase : Optional[Any] = type_vocab_size
__lowerCamelCase : Dict = type_sequence_label_size
__lowerCamelCase : Any = initializer_range
__lowerCamelCase : Union[str, Any] = num_labels
__lowerCamelCase : Tuple = num_choices
__lowerCamelCase : str = relative_attention
__lowerCamelCase : Optional[int] = position_biased_input
__lowerCamelCase : int = pos_att_type
__lowerCamelCase : str = scope
def snake_case_ ( self ):
__lowerCamelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase : int = None
if self.use_input_mask:
__lowerCamelCase : int = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
__lowerCamelCase : Tuple = None
if self.use_token_type_ids:
__lowerCamelCase : Any = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase : Optional[Any] = None
__lowerCamelCase : Optional[Any] = None
__lowerCamelCase : int = None
if self.use_labels:
__lowerCamelCase : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCamelCase : List[Any] = ids_tensor([self.batch_size] , self.num_choices )
__lowerCamelCase : Optional[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case_ ( self ):
return 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 , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def snake_case_ ( self , __a ):
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : int = DebertaVaModel(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : str = model(__a , attention_mask=__a , token_type_ids=__a )[0]
__lowerCamelCase : str = model(__a , token_type_ids=__a )[0]
__lowerCamelCase : Optional[Any] = model(__a )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : List[str] = DebertaVaForMaskedLM(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : Union[str, Any] = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : Optional[int] = self.num_labels
__lowerCamelCase : List[Any] = DebertaVaForSequenceClassification(__a )
model.to(__a )
model.eval()
__lowerCamelCase : Optional[Any] = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(__a )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : int = self.num_labels
__lowerCamelCase : Dict = DebertaVaForTokenClassification(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : int = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : Optional[Any] = DebertaVaForQuestionAnswering(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : Any = model(
__a , attention_mask=__a , token_type_ids=__a , start_positions=__a , end_positions=__a , )
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 snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : Any = DebertaVaForMultipleChoice(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : Union[str, Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase : Optional[int] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase : int = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase : List[Any] = model(
__a , attention_mask=__a , token_type_ids=__a , labels=__a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def snake_case_ ( self ):
__lowerCamelCase : Any = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) ,
) : List[str] = config_and_inputs
__lowerCamelCase : int = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class __lowercase( lowercase__ , lowercase__ , unittest.TestCase ):
'''simple docstring'''
__a : Dict = (
(
DebertaVaModel,
DebertaVaForMaskedLM,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaForQuestionAnswering,
DebertaVaForMultipleChoice,
)
if is_torch_available()
else ()
)
__a : Tuple = (
{
'feature-extraction': DebertaVaModel,
'fill-mask': DebertaVaForMaskedLM,
'question-answering': DebertaVaForQuestionAnswering,
'text-classification': DebertaVaForSequenceClassification,
'token-classification': DebertaVaForTokenClassification,
'zero-shot': DebertaVaForSequenceClassification,
}
if is_torch_available()
else {}
)
__a : str = True
__a : Dict = False
__a : Tuple = False
__a : Optional[Any] = False
__a : List[Any] = False
def snake_case_ ( self ):
__lowerCamelCase : List[str] = DebertaVaModelTester(self )
__lowerCamelCase : Tuple = ConfigTester(self , config_class=__a , hidden_size=37 )
def snake_case_ ( self ):
self.config_tester.run_common_tests()
def snake_case_ ( self ):
__lowerCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*__a )
def snake_case_ ( self ):
__lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*__a )
def snake_case_ ( self ):
__lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*__a )
def snake_case_ ( self ):
__lowerCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*__a )
def snake_case_ ( self ):
__lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*__a )
def snake_case_ ( self ):
__lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_multiple_choice(*__a )
@slow
def snake_case_ ( self ):
for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase : Tuple = DebertaVaModel.from_pretrained(__a )
self.assertIsNotNone(__a )
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowercase( unittest.TestCase ):
'''simple docstring'''
@unittest.skip(reason='Model not available yet' )
def snake_case_ ( self ):
pass
@slow
def snake_case_ ( self ):
__lowerCamelCase : Any = DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge' )
__lowerCamelCase : Any = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
__lowerCamelCase : Any = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__lowerCamelCase : Union[str, Any] = model(__a , attention_mask=__a )[0]
# compare the actual values for a slice.
__lowerCamelCase : str = torch.tensor(
[[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __a , atol=1E-4 ) , f'''{output[:, 1:4, 1:4]}''' )
| 263
| 0
|
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def a__ ( A__ ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = FileLock(str(tmpdir / 'foo.lock' ) )
SCREAMING_SNAKE_CASE_ : List[str] = FileLock(str(tmpdir / 'foo.lock' ) )
SCREAMING_SNAKE_CASE_ : Tuple = 0.01
with locka.acquire():
with pytest.raises(A__ ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = time.time()
locka.acquire(A__ )
assert time.time() - _start > timeout
def a__ ( A__ ):
SCREAMING_SNAKE_CASE_ : List[Any] = 'a' * 1_0_0_0 + '.lock'
SCREAMING_SNAKE_CASE_ : int = 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 ) ) <= 2_5_5
SCREAMING_SNAKE_CASE_ : Optional[int] = FileLock(tmpdir / filename )
with locka.acquire():
with pytest.raises(A__ ):
locka.acquire(0 )
| 101
|
def a__ ( A__, A__ ):
def get_matched_characters(A__, A__ ) -> str:
SCREAMING_SNAKE_CASE_ : Dict = []
SCREAMING_SNAKE_CASE_ : Any = min(len(_stra ), len(_stra ) ) // 2
for i, l in enumerate(_stra ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = int(max(0, i - limit ) )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = int(min(i + limit + 1, len(_stra ) ) )
if l in _stra[left:right]:
matched.append(A__ )
SCREAMING_SNAKE_CASE_ : List[str] = F'''{_stra[0:_stra.index(A__ )]} {_stra[_stra.index(A__ ) + 1:]}'''
return "".join(A__ )
# matching characters
SCREAMING_SNAKE_CASE_ : Union[str, Any] = get_matched_characters(A__, A__ )
SCREAMING_SNAKE_CASE_ : int = get_matched_characters(A__, A__ )
SCREAMING_SNAKE_CASE_ : Any = len(A__ )
# transposition
SCREAMING_SNAKE_CASE_ : Optional[int] = (
len([(ca, ca) for ca, ca in zip(A__, A__ ) if ca != ca] ) // 2
)
if not match_count:
SCREAMING_SNAKE_CASE_ : Dict = 0.0
else:
SCREAMING_SNAKE_CASE_ : Optional[Any] = (
1
/ 3
* (
match_count / len(A__ )
+ match_count / len(A__ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
SCREAMING_SNAKE_CASE_ : List[Any] = 0
for ca, ca in zip(stra[:4], stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('hello', 'world'))
| 101
| 1
|
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def _a ( UpperCAmelCase ) -> Dict:
"""simple docstring"""
lowerCamelCase__ : int = SwinaSRConfig()
if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
lowerCamelCase__ : Tuple = 4
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
lowerCamelCase__ : Dict = 4
lowerCamelCase__ : Optional[Any] = 48
lowerCamelCase__ : str = '''pixelshuffle_aux'''
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
lowerCamelCase__ : List[str] = [6, 6, 6, 6]
lowerCamelCase__ : Any = 60
lowerCamelCase__ : int = [6, 6, 6, 6]
lowerCamelCase__ : Dict = '''pixelshuffledirect'''
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
lowerCamelCase__ : int = 4
lowerCamelCase__ : List[Any] = '''nearest+conv'''
elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
lowerCamelCase__ : List[Any] = 1
lowerCamelCase__ : Union[str, Any] = 1
lowerCamelCase__ : List[Any] = 126
lowerCamelCase__ : Union[str, Any] = 7
lowerCamelCase__ : Union[str, Any] = 255.0
lowerCamelCase__ : Optional[Any] = ''''''
return config
def _a ( UpperCAmelCase , UpperCAmelCase ) -> Optional[int]:
"""simple docstring"""
if "patch_embed.proj" in name and "layers" not in name:
lowerCamelCase__ : Optional[int] = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
lowerCamelCase__ : List[str] = name.replace('''patch_embed.norm''' , '''embeddings.patch_embeddings.layernorm''' )
if "layers" in name:
lowerCamelCase__ : str = name.replace('''layers''' , '''encoder.stages''' )
if "residual_group.blocks" in name:
lowerCamelCase__ : List[str] = name.replace('''residual_group.blocks''' , '''layers''' )
if "attn.proj" in name:
lowerCamelCase__ : Any = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name:
lowerCamelCase__ : Union[str, Any] = name.replace('''attn''' , '''attention.self''' )
if "norm1" in name:
lowerCamelCase__ : Dict = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
lowerCamelCase__ : Union[str, Any] = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
lowerCamelCase__ : Union[str, Any] = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
lowerCamelCase__ : Union[str, Any] = name.replace('''mlp.fc2''' , '''output.dense''' )
if "q_bias" in name:
lowerCamelCase__ : int = name.replace('''q_bias''' , '''query.bias''' )
if "k_bias" in name:
lowerCamelCase__ : Optional[int] = name.replace('''k_bias''' , '''key.bias''' )
if "v_bias" in name:
lowerCamelCase__ : Optional[Any] = name.replace('''v_bias''' , '''value.bias''' )
if "cpb_mlp" in name:
lowerCamelCase__ : Optional[Any] = name.replace('''cpb_mlp''' , '''continuous_position_bias_mlp''' )
if "patch_embed.proj" in name:
lowerCamelCase__ : Tuple = name.replace('''patch_embed.proj''' , '''patch_embed.projection''' )
if name == "norm.weight":
lowerCamelCase__ : Dict = '''layernorm.weight'''
if name == "norm.bias":
lowerCamelCase__ : str = '''layernorm.bias'''
if "conv_first" in name:
lowerCamelCase__ : List[Any] = name.replace('''conv_first''' , '''first_convolution''' )
if (
"upsample" in name
or "conv_before_upsample" in name
or "conv_bicubic" in name
or "conv_up" in name
or "conv_hr" in name
or "conv_last" in name
or "aux" in name
):
# heads
if "conv_last" in name:
lowerCamelCase__ : Any = name.replace('''conv_last''' , '''final_convolution''' )
if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
if "conv_before_upsample.0" in name:
lowerCamelCase__ : Union[str, Any] = name.replace('''conv_before_upsample.0''' , '''conv_before_upsample''' )
if "upsample.0" in name:
lowerCamelCase__ : Optional[Any] = name.replace('''upsample.0''' , '''upsample.convolution_0''' )
if "upsample.2" in name:
lowerCamelCase__ : List[str] = name.replace('''upsample.2''' , '''upsample.convolution_1''' )
lowerCamelCase__ : List[str] = '''upsample.''' + name
elif config.upsampler == "pixelshuffledirect":
lowerCamelCase__ : int = name.replace('''upsample.0.weight''' , '''upsample.conv.weight''' )
lowerCamelCase__ : int = name.replace('''upsample.0.bias''' , '''upsample.conv.bias''' )
else:
pass
else:
lowerCamelCase__ : Any = '''swin2sr.''' + name
return name
def _a ( UpperCAmelCase , UpperCAmelCase ) -> List[Any]:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
lowerCamelCase__ : str = orig_state_dict.pop(UpperCAmelCase )
if "qkv" in key:
lowerCamelCase__ : Union[str, Any] = key.split('''.''' )
lowerCamelCase__ : Dict = int(key_split[1] )
lowerCamelCase__ : int = int(key_split[4] )
lowerCamelCase__ : Optional[Any] = config.embed_dim
if "weight" in key:
lowerCamelCase__ : str = val[:dim, :]
lowerCamelCase__ : Tuple = val[dim : dim * 2, :]
lowerCamelCase__ : Tuple = val[-dim:, :]
else:
lowerCamelCase__ : List[str] = val[:dim]
lowerCamelCase__ : Any = val[dim : dim * 2]
lowerCamelCase__ : Union[str, Any] = val[-dim:]
pass
else:
lowerCamelCase__ : Optional[int] = val
return orig_state_dict
def _a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase__ : Tuple = get_config(UpperCAmelCase )
lowerCamelCase__ : List[Any] = SwinaSRForImageSuperResolution(UpperCAmelCase )
model.eval()
lowerCamelCase__ : Any = torch.hub.load_state_dict_from_url(UpperCAmelCase , map_location='''cpu''' )
lowerCamelCase__ : List[str] = convert_state_dict(UpperCAmelCase , UpperCAmelCase )
lowerCamelCase__ : Optional[Any] = model.load_state_dict(UpperCAmelCase , strict=UpperCAmelCase )
if len(UpperCAmelCase ) > 0:
raise ValueError('''Missing keys when converting: {}'''.format(UpperCAmelCase ) )
for key in unexpected_keys:
if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
raise ValueError(f"Unexpected key {key} in state_dict" )
# verify values
lowerCamelCase__ : Dict = '''https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'''
lowerCamelCase__ : Union[str, Any] = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw ).convert('''RGB''' )
lowerCamelCase__ : List[Any] = SwinaSRImageProcessor()
# pixel_values = processor(image, return_tensors="pt").pixel_values
lowerCamelCase__ : List[str] = 126 if '''Jpeg''' in checkpoint_url else 256
lowerCamelCase__ : str = Compose(
[
Resize((image_size, image_size) ),
ToTensor(),
Normalize(mean=[0.4_85, 0.4_56, 0.4_06] , std=[0.2_29, 0.2_24, 0.2_25] ),
] )
lowerCamelCase__ : str = transforms(UpperCAmelCase ).unsqueeze(0 )
if config.num_channels == 1:
lowerCamelCase__ : str = pixel_values[:, 0, :, :].unsqueeze(1 )
lowerCamelCase__ : List[Any] = model(UpperCAmelCase )
# assert values
if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
lowerCamelCase__ : int = torch.Size([1, 3, 512, 512] )
lowerCamelCase__ : Optional[int] = torch.tensor(
[[-0.70_87, -0.71_38, -0.67_21], [-0.83_40, -0.80_95, -0.72_98], [-0.91_49, -0.84_14, -0.79_40]] )
elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
lowerCamelCase__ : Optional[int] = torch.Size([1, 3, 1024, 1024] )
lowerCamelCase__ : int = torch.tensor(
[[-0.77_75, -0.81_05, -0.89_33], [-0.77_64, -0.83_56, -0.92_25], [-0.79_76, -0.86_86, -0.95_79]] )
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
# TODO values didn't match exactly here
lowerCamelCase__ : Any = torch.Size([1, 3, 1024, 1024] )
lowerCamelCase__ : int = torch.tensor(
[[-0.80_35, -0.75_04, -0.74_91], [-0.85_38, -0.81_24, -0.77_82], [-0.88_04, -0.86_51, -0.84_93]] )
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
lowerCamelCase__ : Optional[int] = torch.Size([1, 3, 512, 512] )
lowerCamelCase__ : List[Any] = torch.tensor(
[[-0.76_69, -0.86_62, -0.87_67], [-0.88_10, -0.99_62, -0.98_20], [-0.93_40, -1.03_22, -1.11_49]] )
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
lowerCamelCase__ : Optional[Any] = torch.Size([1, 3, 1024, 1024] )
lowerCamelCase__ : int = torch.tensor(
[[-0.52_38, -0.55_57, -0.63_21], [-0.60_16, -0.59_03, -0.63_91], [-0.62_44, -0.63_34, -0.68_89]] )
assert (
outputs.reconstruction.shape == expected_shape
), f"Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"
assert torch.allclose(outputs.reconstruction[0, 0, :3, :3] , UpperCAmelCase , atol=1E-3 )
print('''Looks ok!''' )
lowerCamelCase__ : Optional[int] = {
'''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''': (
'''swin2SR-classical-sr-x2-64'''
),
'''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth''': (
'''swin2SR-classical-sr-x4-64'''
),
'''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth''': (
'''swin2SR-compressed-sr-x4-48'''
),
'''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth''': (
'''swin2SR-lightweight-x2-64'''
),
'''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth''': (
'''swin2SR-realworld-sr-x4-64-bsrgan-psnr'''
),
}
lowerCamelCase__ : Optional[int] = url_to_name[checkpoint_url]
if pytorch_dump_folder_path is not None:
print(f"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(UpperCAmelCase )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
processor.save_pretrained(UpperCAmelCase )
if push_to_hub:
model.push_to_hub(f"caidas/{model_name}" )
processor.push_to_hub(f"caidas/{model_name}" )
if __name__ == "__main__":
_A : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth',
type=str,
help='URL of the original Swin2SR checkpoint 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 converted model to the hub.')
_A : List[Any] = parser.parse_args()
convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 720
|
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCamelCase ( self : Any , A : str ) ->int:
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['''bs'''] , model_result['''ss'''] ):
lowerCamelCase__ : Any = model_result['''result'''][batch_size][sequence_length]
self.assertIsNotNone(A )
def __lowerCamelCase ( self : List[str] ) ->List[str]:
lowerCamelCase__ : Optional[Any] = '''sshleifer/tiny-gpt2'''
lowerCamelCase__ : List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=A , multi_process=A , )
lowerCamelCase__ : Tuple = TensorFlowBenchmark(A )
lowerCamelCase__ : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __lowerCamelCase ( self : Dict ) ->Optional[Any]:
lowerCamelCase__ : Tuple = '''sgugger/tiny-distilbert-classification'''
lowerCamelCase__ : Dict = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , only_pretrain_model=A , )
lowerCamelCase__ : str = TensorFlowBenchmark(A )
lowerCamelCase__ : List[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __lowerCamelCase ( self : Tuple ) ->Dict:
lowerCamelCase__ : int = '''sshleifer/tiny-gpt2'''
lowerCamelCase__ : List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
lowerCamelCase__ : str = TensorFlowBenchmark(A )
lowerCamelCase__ : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __lowerCamelCase ( self : Union[str, Any] ) ->Tuple:
lowerCamelCase__ : Optional[int] = '''sshleifer/tiny-gpt2'''
lowerCamelCase__ : List[Any] = AutoConfig.from_pretrained(A )
lowerCamelCase__ : Tuple = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=A , multi_process=A , )
lowerCamelCase__ : List[Any] = TensorFlowBenchmark(A , [config] )
lowerCamelCase__ : List[str] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __lowerCamelCase ( self : List[Any] ) ->Any:
lowerCamelCase__ : Optional[int] = '''sshleifer/tiny-gpt2'''
lowerCamelCase__ : List[str] = AutoConfig.from_pretrained(A )
lowerCamelCase__ : List[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
lowerCamelCase__ : Optional[Any] = TensorFlowBenchmark(A , [config] )
lowerCamelCase__ : List[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __lowerCamelCase ( self : Any ) ->Optional[Any]:
lowerCamelCase__ : List[str] = '''sshleifer/tiny-gpt2'''
lowerCamelCase__ : str = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
lowerCamelCase__ : Union[str, Any] = TensorFlowBenchmark(A )
lowerCamelCase__ : Any = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def __lowerCamelCase ( self : Union[str, Any] ) ->Any:
lowerCamelCase__ : Tuple = '''sshleifer/tiny-gpt2'''
lowerCamelCase__ : Optional[int] = AutoConfig.from_pretrained(A )
lowerCamelCase__ : Dict = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
lowerCamelCase__ : str = TensorFlowBenchmark(A , [config] )
lowerCamelCase__ : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def __lowerCamelCase ( self : Any ) ->Any:
lowerCamelCase__ : Dict = '''patrickvonplaten/t5-tiny-random'''
lowerCamelCase__ : int = AutoConfig.from_pretrained(A )
lowerCamelCase__ : Optional[int] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
lowerCamelCase__ : Dict = TensorFlowBenchmark(A , configs=[config] )
lowerCamelCase__ : int = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('''GPU''' ) ) == 0 , '''Cannot do xla on CPU.''' )
def __lowerCamelCase ( self : Dict ) ->Dict:
lowerCamelCase__ : Dict = '''sshleifer/tiny-gpt2'''
lowerCamelCase__ : Union[str, Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , use_xla=A , multi_process=A , )
lowerCamelCase__ : List[Any] = TensorFlowBenchmark(A )
lowerCamelCase__ : Optional[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __lowerCamelCase ( self : Any ) ->Optional[Any]:
lowerCamelCase__ : List[str] = '''sshleifer/tiny-gpt2'''
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCamelCase__ : Optional[int] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=A , save_to_csv=A , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(A , '''inf_time.csv''' ) , inference_memory_csv_file=os.path.join(A , '''inf_mem.csv''' ) , env_info_csv_file=os.path.join(A , '''env.csv''' ) , multi_process=A , )
lowerCamelCase__ : Tuple = TensorFlowBenchmark(A )
benchmark.run()
self.assertTrue(Path(os.path.join(A , '''inf_time.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(A , '''inf_mem.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(A , '''env.csv''' ) ).exists() )
def __lowerCamelCase ( self : Tuple ) ->Optional[int]:
lowerCamelCase__ : List[Any] = '''sshleifer/tiny-gpt2'''
def _check_summary_is_not_empty(A : int ):
self.assertTrue(hasattr(A , '''sequential''' ) )
self.assertTrue(hasattr(A , '''cumulative''' ) )
self.assertTrue(hasattr(A , '''current''' ) )
self.assertTrue(hasattr(A , '''total''' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCamelCase__ : List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=A , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(A , '''log.txt''' ) , log_print=A , trace_memory_line_by_line=A , eager_mode=A , multi_process=A , )
lowerCamelCase__ : Any = TensorFlowBenchmark(A )
lowerCamelCase__ : Optional[Any] = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
self.assertTrue(Path(os.path.join(A , '''log.txt''' ) ).exists() )
| 130
| 0
|
"""simple docstring"""
from math import factorial
def lowercase__ ( lowerCAmelCase__ : Any , lowerCAmelCase__ : Tuple ) -> Optional[int]:
'''simple docstring'''
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n >= k" )
return factorial(_UpperCamelCase ) // (factorial(_UpperCamelCase ) * factorial(n - k ))
if __name__ == "__main__":
print(
'''The number of five-card hands possible from a standard''',
f"fifty-two card deck is: {combinations(52, 5)}\n",
)
print(
'''If a class of 40 students must be arranged into groups of''',
f"4 for group projects, there are {combinations(40, 4)} ways",
'''to arrange them.\n''',
)
print(
'''If 10 teams are competing in a Formula One race, there''',
f"are {combinations(10, 3)} ways that first, second and",
'''third place can be awarded.''',
)
| 642
|
'''simple docstring'''
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
"""simple docstring"""
lowercase_ : Optional[int] = 2
lowercase_ : Tuple = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_UpperCamelCase )
if n > 1:
factors.append(_UpperCamelCase )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 620
| 0
|
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCAmelCase ( __snake_case ):
lowercase = ["""image_processor""", """tokenizer"""]
lowercase = """LayoutLMv2ImageProcessor"""
lowercase = ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")
def __init__( self : str , __magic_name__ : Tuple=None , __magic_name__ : Union[str, Any]=None , **__magic_name__ : Optional[Any] ):
"""simple docstring"""
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , __magic_name__ , )
UpperCamelCase = kwargs.pop("""feature_extractor""" )
UpperCamelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(__magic_name__ , __magic_name__ )
def __call__( self : Tuple , __magic_name__ : str , __magic_name__ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , __magic_name__ : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , __magic_name__ : Union[List[List[int]], List[List[List[int]]]] = None , __magic_name__ : Optional[Union[List[int], List[List[int]]]] = None , __magic_name__ : bool = True , __magic_name__ : Union[bool, str, PaddingStrategy] = False , __magic_name__ : Union[bool, str, TruncationStrategy] = None , __magic_name__ : Optional[int] = None , __magic_name__ : int = 0 , __magic_name__ : Optional[int] = None , __magic_name__ : Optional[bool] = None , __magic_name__ : Optional[bool] = None , __magic_name__ : bool = False , __magic_name__ : bool = False , __magic_name__ : bool = False , __magic_name__ : bool = False , __magic_name__ : bool = True , __magic_name__ : Optional[Union[str, TensorType]] = None , **__magic_name__ : int , ):
"""simple docstring"""
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
"""You cannot provide bounding boxes """
"""if you initialized the image processor with apply_ocr set to True.""" )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
"""You cannot provide word labels if you initialized the image processor with apply_ocr set to True.""" )
if return_overflowing_tokens is True and return_offsets_mapping is False:
raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping.""" )
# first, apply the image processor
UpperCamelCase = self.image_processor(images=__magic_name__ , return_tensors=__magic_name__ )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(__magic_name__ , __magic_name__ ):
UpperCamelCase = [text] # add batch dimension (as the image processor always adds a batch dimension)
UpperCamelCase = features["""words"""]
UpperCamelCase = self.tokenizer(
text=text if text is not None else features["""words"""] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features["""boxes"""] , word_labels=__magic_name__ , add_special_tokens=__magic_name__ , padding=__magic_name__ , truncation=__magic_name__ , max_length=__magic_name__ , stride=__magic_name__ , pad_to_multiple_of=__magic_name__ , return_token_type_ids=__magic_name__ , return_attention_mask=__magic_name__ , return_overflowing_tokens=__magic_name__ , return_special_tokens_mask=__magic_name__ , return_offsets_mapping=__magic_name__ , return_length=__magic_name__ , verbose=__magic_name__ , return_tensors=__magic_name__ , **__magic_name__ , )
# add pixel values
UpperCamelCase = features.pop("""pixel_values""" )
if return_overflowing_tokens is True:
UpperCamelCase = self.get_overflowing_images(__magic_name__ , encoded_inputs["""overflow_to_sample_mapping"""] )
UpperCamelCase = images
return encoded_inputs
def lowerCamelCase_ ( self : Optional[int] , __magic_name__ : Dict , __magic_name__ : List[Any] ):
"""simple docstring"""
UpperCamelCase = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(__magic_name__ ) != len(__magic_name__ ):
raise ValueError(
"""Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
F' {len(__magic_name__ )} and {len(__magic_name__ )}' )
return images_with_overflow
def lowerCamelCase_ ( self : List[str] , *__magic_name__ : Optional[Any] , **__magic_name__ : Tuple ):
"""simple docstring"""
return self.tokenizer.batch_decode(*__magic_name__ , **__magic_name__ )
def lowerCamelCase_ ( self : str , *__magic_name__ : Tuple , **__magic_name__ : Dict ):
"""simple docstring"""
return self.tokenizer.decode(*__magic_name__ , **__magic_name__ )
@property
def lowerCamelCase_ ( self : Tuple ):
"""simple docstring"""
return ["input_ids", "bbox", "attention_mask", "image"]
@property
def lowerCamelCase_ ( self : str ):
"""simple docstring"""
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , __magic_name__ , )
return self.image_processor_class
@property
def lowerCamelCase_ ( self : str ):
"""simple docstring"""
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , __magic_name__ , )
return self.image_processor
| 181
|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_mbart import MBartTokenizer
else:
__snake_case = None
__snake_case = logging.get_logger(__name__)
__snake_case = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
__snake_case = {
"vocab_file": {
"facebook/mbart-large-en-ro": (
"https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
),
"facebook/mbart-large-cc25": (
"https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
"facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
},
}
__snake_case = {
"facebook/mbart-large-en-ro": 1_024,
"facebook/mbart-large-cc25": 1_024,
}
# fmt: off
__snake_case = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]
class UpperCAmelCase ( __snake_case ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = ["""input_ids""", """attention_mask"""]
lowercase = MBartTokenizer
lowercase = []
lowercase = []
def __init__( self : Optional[int] , __magic_name__ : str=None , __magic_name__ : Optional[int]=None , __magic_name__ : Dict="<s>" , __magic_name__ : Union[str, Any]="</s>" , __magic_name__ : List[str]="</s>" , __magic_name__ : Optional[int]="<s>" , __magic_name__ : int="<unk>" , __magic_name__ : str="<pad>" , __magic_name__ : List[str]="<mask>" , __magic_name__ : Tuple=None , __magic_name__ : Dict=None , __magic_name__ : Dict=None , **__magic_name__ : Tuple , ):
"""simple docstring"""
UpperCamelCase = AddedToken(__magic_name__ , lstrip=__magic_name__ , rstrip=__magic_name__ ) if isinstance(__magic_name__ , __magic_name__ ) else mask_token
super().__init__(
vocab_file=__magic_name__ , tokenizer_file=__magic_name__ , bos_token=__magic_name__ , eos_token=__magic_name__ , sep_token=__magic_name__ , cls_token=__magic_name__ , unk_token=__magic_name__ , pad_token=__magic_name__ , mask_token=__magic_name__ , src_lang=__magic_name__ , tgt_lang=__magic_name__ , additional_special_tokens=__magic_name__ , **__magic_name__ , )
UpperCamelCase = vocab_file
UpperCamelCase = False if not self.vocab_file else True
UpperCamelCase = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} )
UpperCamelCase = {
lang_code: self.convert_tokens_to_ids(__magic_name__ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
UpperCamelCase = src_lang if src_lang is not None else """en_XX"""
UpperCamelCase = self.convert_tokens_to_ids(self._src_lang )
UpperCamelCase = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def lowerCamelCase_ ( self : int ):
"""simple docstring"""
return self._src_lang
@src_lang.setter
def lowerCamelCase_ ( self : List[str] , __magic_name__ : str ):
"""simple docstring"""
UpperCamelCase = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def lowerCamelCase_ ( self : Optional[Any] , __magic_name__ : List[int] , __magic_name__ : Optional[List[int]] = None ):
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def lowerCamelCase_ ( self : int , __magic_name__ : List[int] , __magic_name__ : Optional[List[int]] = None ):
"""simple docstring"""
UpperCamelCase = [self.sep_token_id]
UpperCamelCase = [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 lowerCamelCase_ ( self : int , __magic_name__ : List[Any] , __magic_name__ : str , __magic_name__ : Optional[str] , __magic_name__ : Optional[str] , **__magic_name__ : Tuple ):
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
UpperCamelCase = src_lang
UpperCamelCase = self(__magic_name__ , add_special_tokens=__magic_name__ , return_tensors=__magic_name__ , **__magic_name__ )
UpperCamelCase = self.convert_tokens_to_ids(__magic_name__ )
UpperCamelCase = tgt_lang_id
return inputs
def lowerCamelCase_ ( self : Dict , __magic_name__ : List[str] , __magic_name__ : str = "en_XX" , __magic_name__ : Optional[List[str]] = None , __magic_name__ : str = "ro_RO" , **__magic_name__ : int , ):
"""simple docstring"""
UpperCamelCase = src_lang
UpperCamelCase = tgt_lang
return super().prepare_seqaseq_batch(__magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
return self.set_src_lang_special_tokens(self.src_lang )
def lowerCamelCase_ ( self : Any ):
"""simple docstring"""
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def lowerCamelCase_ ( self : int , __magic_name__ : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = self.convert_tokens_to_ids(__magic_name__ )
UpperCamelCase = []
UpperCamelCase = [self.eos_token_id, self.cur_lang_code]
UpperCamelCase = self.convert_ids_to_tokens(self.prefix_tokens )
UpperCamelCase = self.convert_ids_to_tokens(self.suffix_tokens )
UpperCamelCase = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def lowerCamelCase_ ( self : Tuple , __magic_name__ : str ):
"""simple docstring"""
UpperCamelCase = self.convert_tokens_to_ids(__magic_name__ )
UpperCamelCase = []
UpperCamelCase = [self.eos_token_id, self.cur_lang_code]
UpperCamelCase = self.convert_ids_to_tokens(self.prefix_tokens )
UpperCamelCase = self.convert_ids_to_tokens(self.suffix_tokens )
UpperCamelCase = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def lowerCamelCase_ ( self : Any , __magic_name__ : str , __magic_name__ : Optional[str] = None ):
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(__magic_name__ ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory.' )
return
UpperCamelCase = os.path.join(
__magic_name__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__magic_name__ ):
copyfile(self.vocab_file , __magic_name__ )
return (out_vocab_file,)
| 181
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase_ = {
"configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"ResNetForImageClassification",
"ResNetModel",
"ResNetPreTrainedModel",
"ResNetBackbone",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFResNetForImageClassification",
"TFResNetModel",
"TFResNetPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"FlaxResNetForImageClassification",
"FlaxResNetModel",
"FlaxResNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_resnet import (
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
ResNetBackbone,
ResNetForImageClassification,
ResNetModel,
ResNetPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_resnet import (
TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFResNetForImageClassification,
TFResNetModel,
TFResNetPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 256
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
UpperCamelCase_ = {
"configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST",
"ErnieForCausalLM",
"ErnieForMaskedLM",
"ErnieForMultipleChoice",
"ErnieForNextSentencePrediction",
"ErnieForPreTraining",
"ErnieForQuestionAnswering",
"ErnieForSequenceClassification",
"ErnieForTokenClassification",
"ErnieModel",
"ErniePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 256
| 1
|
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowercase = get_tests_dir("fixtures/spiece.model")
@require_sentencepiece
@require_tokenizers
class _UpperCAmelCase ( A__ , unittest.TestCase ):
UpperCamelCase__ = DebertaVaTokenizer
UpperCamelCase__ = DebertaVaTokenizerFast
UpperCamelCase__ = True
UpperCamelCase__ = True
def snake_case_ ( self):
super().setUp()
# We have a SentencePiece fixture for testing
A__ = DebertaVaTokenizer(a__ , unk_token='''<unk>''')
tokenizer.save_pretrained(self.tmpdirname)
def snake_case_ ( self , a__):
A__ = '''this is a test'''
A__ = '''this is a test'''
return input_text, output_text
def snake_case_ ( self):
A__ = '''<pad>'''
A__ = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(a__) , a__)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(a__) , a__)
def snake_case_ ( self):
A__ = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , '''<pad>''')
self.assertEqual(vocab_keys[1] , '''<unk>''')
self.assertEqual(vocab_keys[-1] , '''[PAD]''')
self.assertEqual(len(a__) , 3_0_0_0_1)
def snake_case_ ( self):
self.assertEqual(self.get_tokenizer().vocab_size , 3_0_0_0_0)
def snake_case_ ( self):
# fmt: off
A__ = ''' \tHeLLo!how \n Are yoU? '''
A__ = ['''▁hello''', '''!''', '''how''', '''▁are''', '''▁you''', '''?''']
# fmt: on
A__ = DebertaVaTokenizer(a__ , do_lower_case=a__)
A__ = tokenizer.convert_ids_to_tokens(tokenizer.encode(a__ , add_special_tokens=a__))
self.assertListEqual(a__ , a__)
A__ = DebertaVaTokenizerFast(a__ , do_lower_case=a__)
A__ = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(a__ , add_special_tokens=a__))
self.assertListEqual(a__ , a__)
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''')
def snake_case_ ( self):
pass
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''')
def snake_case_ ( self):
pass
def snake_case_ ( self):
# fmt: off
A__ = '''I was born in 92000, and this is falsé.'''
A__ = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
A__ = DebertaVaTokenizer(a__ , split_by_punct=a__)
A__ = tokenizer.convert_ids_to_tokens(tokenizer.encode(a__ , add_special_tokens=a__))
self.assertListEqual(a__ , a__)
A__ = DebertaVaTokenizerFast(a__ , split_by_punct=a__)
A__ = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(a__ , add_special_tokens=a__))
self.assertListEqual(a__ , a__)
def snake_case_ ( self):
# fmt: off
A__ = '''I was born in 92000, and this is falsé.'''
A__ = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
A__ = DebertaVaTokenizer(a__ , do_lower_case=a__ , split_by_punct=a__)
A__ = tokenizer.convert_ids_to_tokens(tokenizer.encode(a__ , add_special_tokens=a__))
self.assertListEqual(a__ , a__)
A__ = DebertaVaTokenizerFast(a__ , do_lower_case=a__ , split_by_punct=a__)
A__ = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(a__ , add_special_tokens=a__))
self.assertListEqual(a__ , a__)
def snake_case_ ( self):
# fmt: off
A__ = '''I was born in 92000, and this is falsé.'''
A__ = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
A__ = DebertaVaTokenizer(a__ , do_lower_case=a__ , split_by_punct=a__)
A__ = tokenizer.convert_ids_to_tokens(tokenizer.encode(a__ , add_special_tokens=a__))
self.assertListEqual(a__ , a__)
A__ = DebertaVaTokenizerFast(a__ , do_lower_case=a__ , split_by_punct=a__)
A__ = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(a__ , add_special_tokens=a__))
self.assertListEqual(a__ , a__)
def snake_case_ ( self):
# fmt: off
A__ = '''I was born in 92000, and this is falsé.'''
A__ = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
A__ = DebertaVaTokenizer(a__ , do_lower_case=a__ , split_by_punct=a__)
A__ = tokenizer.convert_ids_to_tokens(tokenizer.encode(a__ , add_special_tokens=a__))
self.assertListEqual(a__ , a__)
A__ = DebertaVaTokenizerFast(a__ , do_lower_case=a__ , split_by_punct=a__)
A__ = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(a__ , add_special_tokens=a__))
self.assertListEqual(a__ , a__)
def snake_case_ ( self):
# fmt: off
A__ = ''' \tHeLLo!how \n Are yoU? '''
A__ = ['''▁''', '''<unk>''', '''e''', '''<unk>''', '''o''', '''!''', '''how''', '''▁''', '''<unk>''', '''re''', '''▁yo''', '''<unk>''', '''?''']
# fmt: on
A__ = DebertaVaTokenizer(a__ , do_lower_case=a__ , split_by_punct=a__)
A__ = tokenizer.convert_ids_to_tokens(tokenizer.encode(a__ , add_special_tokens=a__))
self.assertListEqual(a__ , a__)
A__ = DebertaVaTokenizerFast(a__ , do_lower_case=a__ , split_by_punct=a__)
A__ = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(a__ , add_special_tokens=a__))
self.assertListEqual(a__ , a__)
def snake_case_ ( self):
A__ = self.get_tokenizer()
A__ = self.get_rust_tokenizer()
A__ = '''I was born in 92000, and this is falsé.'''
A__ = tokenizer.convert_ids_to_tokens(tokenizer.encode(a__ , add_special_tokens=a__))
A__ = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(a__ , add_special_tokens=a__))
self.assertListEqual(a__ , a__)
A__ = tokenizer.encode(a__ , add_special_tokens=a__)
A__ = rust_tokenizer.encode(a__ , add_special_tokens=a__)
self.assertListEqual(a__ , a__)
A__ = self.get_rust_tokenizer()
A__ = tokenizer.encode(a__)
A__ = rust_tokenizer.encode(a__)
self.assertListEqual(a__ , a__)
def snake_case_ ( self):
A__ = '''This is a test'''
A__ = [1_3, 1, 4_3_9_8, 2_5, 2_1, 1_2_8_9]
A__ = ['''▁''', '''T''', '''his''', '''▁is''', '''▁a''', '''▁test''']
A__ = ['''▁''', '''<unk>''', '''his''', '''▁is''', '''▁a''', '''▁test''']
A__ = DebertaVaTokenizer(a__ , keep_accents=a__)
A__ = DebertaVaTokenizerFast(a__ , keep_accents=a__)
A__ = tokenizer.encode(a__ , add_special_tokens=a__)
self.assertListEqual(a__ , a__)
A__ = tokenizer.tokenize(a__)
self.assertListEqual(a__ , a__)
A__ = tokenizer.convert_ids_to_tokens(a__)
self.assertListEqual(a__ , a__)
A__ = rust_tokenizer.encode(a__ , add_special_tokens=a__)
self.assertListEqual(a__ , a__)
A__ = rust_tokenizer.tokenize(a__)
self.assertListEqual(a__ , a__)
A__ = rust_tokenizer.convert_ids_to_tokens(a__)
self.assertListEqual(a__ , a__)
# fmt: off
A__ = '''I was born in 92000, and this is falsé.'''
A__ = [1_3, 1, 2_3, 3_8_6, 1_9, 5_6_1, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]
A__ = ['''▁''', '''I''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''', ]
A__ = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
A__ = tokenizer.encode(a__ , add_special_tokens=a__)
self.assertListEqual(a__ , a__)
A__ = tokenizer.tokenize(a__)
self.assertListEqual(a__ , a__)
A__ = tokenizer.convert_ids_to_tokens(a__)
self.assertListEqual(a__ , a__)
A__ = rust_tokenizer.encode(a__ , add_special_tokens=a__)
self.assertListEqual(a__ , a__)
A__ = rust_tokenizer.tokenize(a__)
self.assertListEqual(a__ , a__)
A__ = rust_tokenizer.convert_ids_to_tokens(a__)
self.assertListEqual(a__ , a__)
def snake_case_ ( self):
A__ = DebertaVaTokenizer(a__)
A__ = tokenizer.encode('''sequence builders''')
A__ = tokenizer.encode('''multi-sequence build''')
A__ = tokenizer.build_inputs_with_special_tokens(a__)
A__ = tokenizer.build_inputs_with_special_tokens(a__ , a__)
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , a__)
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , a__ , )
@slow
def snake_case_ ( self):
# fmt: off
A__ = {'''input_ids''': [[1, 3_9_8_6_7, 3_6, 1_9_3_9_0, 4_8_6, 2_7, 3_5_0_5_2, 8_1_4_3_6, 1_8, 6_0_6_8_5, 1_2_2_5, 7, 3_5_0_5_2, 8_1_4_3_6, 1_8, 9_3_6_7, 1_6_8_9_9, 1_8, 1_5_9_3_7, 5_3, 5_9_4, 7_7_3, 1_8, 1_6_2_8_7, 3_0_4_6_5, 3_6, 1_5_9_3_7, 6, 4_1_1_3_9, 3_8, 3_6_9_7_9, 6_0_7_6_3, 1_9_1, 6, 3_4_1_3_2, 9_9, 6, 5_0_5_3_8, 3_9_0, 4_3_2_3_0, 6, 3_4_1_3_2, 2_7_7_9, 2_0_8_5_0, 1_4, 6_9_9, 1_0_7_2, 1_1_9_4, 3_6, 3_8_2, 1_0_9_0_1, 5_3, 7, 6_9_9, 1_0_7_2, 2_0_8_4, 3_6, 2_0_4_2_2, 6_3_0, 5_3, 1_9, 1_0_5, 3_0_4_9, 1_8_9_6, 1_0_5_3, 1_6_8_9_9, 1_5_0_6, 1_1, 3_7_9_7_8, 4_2_4_3, 7, 1_2_3_7, 3_1_8_6_9, 2_0_0, 1_6_5_6_6, 6_5_4, 6, 3_5_0_5_2, 8_1_4_3_6, 7, 5_5_6_3_0, 1_3_5_9_3, 4, 2], [1, 2_6, 1_5_0_1_1, 1_3, 6_6_7, 8, 1_0_5_3, 1_8, 2_3_6_1_1, 1_2_3_7, 7_2_3_5_6, 1_2_8_2_0, 3_4, 1_0_4_1_3_4, 1_2_0_9, 3_5, 1_3_3_1_3, 6_6_2_7, 2_1, 2_0_2, 3_4_7, 7, 1_6_4, 2_3_9_9, 1_1, 4_6, 4_4_8_5, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 5, 1_2_3_2, 2_8_6_4, 1_5_7_8_5, 1_4_9_5_1, 1_0_5, 5, 8_5_8_1, 1_2_5_0, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]], '''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=a__ , model_name='''microsoft/deberta-v2-xlarge''' , revision='''ad6e42c1532ddf3a15c39246b63f5559d558b670''' , )
| 526
|
def lowerCAmelCase__ ( UpperCamelCase_ : str , UpperCamelCase_ : str )-> float:
def get_matched_characters(UpperCamelCase_ : str , UpperCamelCase_ : str ) -> str:
A__ = []
A__ = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
A__ = int(max(0 , i - limit ) )
A__ = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(UpperCamelCase_ )
A__ = f"{_stra[0:_stra.index(UpperCamelCase_ )]} {_stra[_stra.index(UpperCamelCase_ ) + 1:]}"
return "".join(UpperCamelCase_ )
# matching characters
A__ = get_matched_characters(UpperCamelCase_ , UpperCamelCase_ )
A__ = get_matched_characters(UpperCamelCase_ , UpperCamelCase_ )
A__ = len(UpperCamelCase_ )
# transposition
A__ = (
len([(ca, ca) for ca, ca in zip(UpperCamelCase_ , UpperCamelCase_ ) if ca != ca] ) // 2
)
if not match_count:
A__ = 0.0
else:
A__ = (
1
/ 3
* (
match_count / len(UpperCamelCase_ )
+ match_count / len(UpperCamelCase_ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
A__ = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler("hello", "world"))
| 526
| 1
|
"""simple docstring"""
from math import pow
def _snake_case ( snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : int , ):
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution.
solutions_count += 1
return current_sum, solutions_count
A = int(pow(snake_case__ , snake_case__ ) )
if current_sum + i_to_n <= needed_sum:
# If the sum of the powers is less than needed_sum, then continue adding powers.
current_sum += i_to_n
A , A = backtrack(
snake_case__ , snake_case__ , current_number + 1 , snake_case__ , snake_case__ )
current_sum -= i_to_n
if i_to_n < needed_sum:
# If the power of i is less than needed_sum, then try with the next power.
A , A = backtrack(
snake_case__ , snake_case__ , current_number + 1 , snake_case__ , snake_case__ )
return current_sum, solutions_count
def _snake_case ( snake_case__ : int , snake_case__ : int ):
if not (1 <= needed_sum <= 1000 and 2 <= power <= 10):
raise ValueError(
'Invalid input\n'
'needed_sum must be between 1 and 1000, power between 2 and 10.' )
return backtrack(snake_case__ , snake_case__ , 1 , 0 , 0 )[1] # Return the solutions_count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 91
|
"""simple docstring"""
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
_lowercase = get_logger(__name__)
def _snake_case ( snake_case__ : Optional[Any] , snake_case__ : List[Any] , snake_case__ : int , snake_case__ : int , snake_case__ : str=0 ):
os.makedirs(snake_case__ , exist_ok=snake_case__ )
with FSDP.state_dict_type(
snake_case__ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
A = model.state_dict()
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
A = F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
A = os.path.join(snake_case__ , snake_case__ )
if accelerator.process_index == 0:
logger.info(F'Saving model to {output_model_file}' )
torch.save(snake_case__ , snake_case__ )
logger.info(F'Model saved to {output_model_file}' )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
A = (
F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
if model_index == 0
else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
)
A = os.path.join(snake_case__ , snake_case__ )
logger.info(F'Saving model to {output_model_file}' )
torch.save(snake_case__ , snake_case__ )
logger.info(F'Model saved to {output_model_file}' )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
A = os.path.join(snake_case__ , F'{MODEL_NAME}_{model_index}' )
os.makedirs(snake_case__ , exist_ok=snake_case__ )
logger.info(F'Saving model to {ckpt_dir}' )
A = {'model': state_dict}
dist_cp.save_state_dict(
state_dict=snake_case__ , storage_writer=dist_cp.FileSystemWriter(snake_case__ ) , planner=DefaultSavePlanner() , )
logger.info(F'Model saved to {ckpt_dir}' )
def _snake_case ( snake_case__ : int , snake_case__ : List[str] , snake_case__ : str , snake_case__ : str , snake_case__ : Any=0 ):
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
snake_case__ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if type(snake_case__ ) != FSDP and accelerator.process_index != 0:
if not fsdp_plugin.sync_module_states:
raise ValueError(
'Set the `sync_module_states` flag to `True` so that model states are synced across processes when '
'initializing FSDP object' )
return
A = F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
A = os.path.join(snake_case__ , snake_case__ )
logger.info(F'Loading model from {input_model_file}' )
A = torch.load(snake_case__ )
logger.info(F'Model loaded from {input_model_file}' )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
A = (
F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
if model_index == 0
else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
)
A = os.path.join(snake_case__ , snake_case__ )
logger.info(F'Loading model from {input_model_file}' )
A = torch.load(snake_case__ )
logger.info(F'Model loaded from {input_model_file}' )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
A = (
os.path.join(snake_case__ , F'{MODEL_NAME}_{model_index}' )
if F'{MODEL_NAME}' not in input_dir
else input_dir
)
logger.info(F'Loading model from {ckpt_dir}' )
A = {'model': model.state_dict()}
dist_cp.load_state_dict(
state_dict=snake_case__ , storage_reader=dist_cp.FileSystemReader(snake_case__ ) , planner=DefaultLoadPlanner() , )
A = state_dict['model']
logger.info(F'Model loaded from {ckpt_dir}' )
model.load_state_dict(snake_case__ )
def _snake_case ( snake_case__ : Tuple , snake_case__ : Optional[int] , snake_case__ : Optional[int] , snake_case__ : Dict , snake_case__ : List[str] , snake_case__ : Any=0 ):
os.makedirs(snake_case__ , exist_ok=snake_case__ )
with FSDP.state_dict_type(
snake_case__ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
A = FSDP.optim_state_dict(snake_case__ , snake_case__ )
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if accelerator.process_index == 0:
A = (
F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
)
A = os.path.join(snake_case__ , snake_case__ )
logger.info(F'Saving Optimizer state to {output_optimizer_file}' )
torch.save(snake_case__ , snake_case__ )
logger.info(F'Optimizer state saved in {output_optimizer_file}' )
else:
A = os.path.join(snake_case__ , F'{OPTIMIZER_NAME}_{optimizer_index}' )
os.makedirs(snake_case__ , exist_ok=snake_case__ )
logger.info(F'Saving Optimizer state to {ckpt_dir}' )
dist_cp.save_state_dict(
state_dict={'optimizer': optim_state} , storage_writer=dist_cp.FileSystemWriter(snake_case__ ) , planner=DefaultSavePlanner() , )
logger.info(F'Optimizer state saved in {ckpt_dir}' )
def _snake_case ( snake_case__ : str , snake_case__ : Optional[int] , snake_case__ : List[str] , snake_case__ : List[str] , snake_case__ : int , snake_case__ : Optional[int]=0 ):
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
snake_case__ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
A = None
# below check should work but currently it isn't working (mostly opytorch issue),
# in the meantime disabling it at the cost of excess memory usage
# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
A = (
F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
)
A = os.path.join(snake_case__ , snake_case__ )
logger.info(F'Loading Optimizer state from {input_optimizer_file}' )
A = torch.load(snake_case__ )
logger.info(F'Optimizer state loaded from {input_optimizer_file}' )
else:
A = (
os.path.join(snake_case__ , F'{OPTIMIZER_NAME}_{optimizer_index}' )
if F'{OPTIMIZER_NAME}' not in input_dir
else input_dir
)
logger.info(F'Loading Optimizer from {ckpt_dir}' )
A = load_sharded_optimizer_state_dict(
model_state_dict=model.state_dict() , optimizer_key='optimizer' , storage_reader=dist_cp.FileSystemReader(snake_case__ ) , )
A = optim_state['optimizer']
logger.info(F'Optimizer loaded from {ckpt_dir}' )
A = FSDP.optim_state_dict_to_load(snake_case__ , snake_case__ , snake_case__ )
optimizer.load_state_dict(snake_case__ )
| 91
| 1
|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, 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 (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class __snake_case :
def __init__( self, A, A=13, A=7, A=True, A=True, A=True, A=True, A=99, A=32, A=2, A=4, A=37, A="gelu", A=0.1, A=0.1, A=512, A=16, A=2, A=0.02, A=3, A=4, A=None, ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] = parent
lowerCamelCase : Union[str, Any] = 13
lowerCamelCase : Tuple = 7
lowerCamelCase : Tuple = True
lowerCamelCase : str = True
lowerCamelCase : int = True
lowerCamelCase : List[Any] = True
lowerCamelCase : Dict = 99
lowerCamelCase : Union[str, Any] = 32
lowerCamelCase : Optional[Any] = 2
lowerCamelCase : Dict = 4
lowerCamelCase : Any = 37
lowerCamelCase : Dict = 'gelu'
lowerCamelCase : Dict = 0.1
lowerCamelCase : Dict = 0.1
lowerCamelCase : Any = 512
lowerCamelCase : Any = 16
lowerCamelCase : List[Any] = 2
lowerCamelCase : List[Any] = 0.02
lowerCamelCase : Union[str, Any] = 3
lowerCamelCase : List[str] = 4
lowerCamelCase : Optional[Any] = None
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : List[str] = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
lowerCamelCase : Any = None
if self.use_input_mask:
lowerCamelCase : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase : Any = None
if self.use_token_type_ids:
lowerCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
lowerCamelCase : Any = None
lowerCamelCase : Optional[Any] = None
lowerCamelCase : Optional[int] = None
if self.use_labels:
lowerCamelCase : Tuple = ids_tensor([self.batch_size], self.type_sequence_label_size )
lowerCamelCase : str = ids_tensor([self.batch_size, self.seq_length], self.num_labels )
lowerCamelCase : Optional[int] = ids_tensor([self.batch_size], self.num_choices )
lowerCamelCase : int = 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, initializer_range=self.initializer_range, return_dict=A, )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase_ ( self, A, A, A, A, A, A, A ):
"""simple docstring"""
lowerCamelCase : Optional[int] = TFRoFormerModel(config=A )
lowerCamelCase : Dict = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
lowerCamelCase : Any = [input_ids, input_mask]
lowerCamelCase : Any = model(A )
lowerCamelCase : List[Any] = model(A )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ ( self, A, A, A, A, A, A, A ):
"""simple docstring"""
lowerCamelCase : Optional[Any] = True
lowerCamelCase : Tuple = TFRoFormerForCausalLM(config=A )
lowerCamelCase : str = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
lowerCamelCase : Any = model(A )['logits']
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ), [self.batch_size, self.seq_length, self.vocab_size] )
def UpperCAmelCase_ ( self, A, A, A, A, A, A, A ):
"""simple docstring"""
lowerCamelCase : List[str] = TFRoFormerForMaskedLM(config=A )
lowerCamelCase : Tuple = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
lowerCamelCase : Tuple = model(A )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase_ ( self, A, A, A, A, A, A, A ):
"""simple docstring"""
lowerCamelCase : Optional[Any] = self.num_labels
lowerCamelCase : List[Any] = TFRoFormerForSequenceClassification(config=A )
lowerCamelCase : str = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
lowerCamelCase : Dict = model(A )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def UpperCAmelCase_ ( self, A, A, A, A, A, A, A ):
"""simple docstring"""
lowerCamelCase : List[str] = self.num_choices
lowerCamelCase : List[str] = TFRoFormerForMultipleChoice(config=A )
lowerCamelCase : Union[str, Any] = tf.tile(tf.expand_dims(A, 1 ), (1, self.num_choices, 1) )
lowerCamelCase : List[str] = tf.tile(tf.expand_dims(A, 1 ), (1, self.num_choices, 1) )
lowerCamelCase : Any = tf.tile(tf.expand_dims(A, 1 ), (1, self.num_choices, 1) )
lowerCamelCase : int = {
'input_ids': multiple_choice_inputs_ids,
'attention_mask': multiple_choice_input_mask,
'token_type_ids': multiple_choice_token_type_ids,
}
lowerCamelCase : Union[str, Any] = model(A )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices) )
def UpperCAmelCase_ ( self, A, A, A, A, A, A, A ):
"""simple docstring"""
lowerCamelCase : Optional[Any] = self.num_labels
lowerCamelCase : str = TFRoFormerForTokenClassification(config=A )
lowerCamelCase : Tuple = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
lowerCamelCase : Optional[int] = model(A )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase_ ( self, A, A, A, A, A, A, A ):
"""simple docstring"""
lowerCamelCase : List[Any] = TFRoFormerForQuestionAnswering(config=A )
lowerCamelCase : Optional[Any] = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
lowerCamelCase : List[Any] = model(A )
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 UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Tuple = self.prepare_config_and_inputs()
(
lowerCamelCase
) : Dict = config_and_inputs
lowerCamelCase : Optional[Any] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_tf
class __snake_case ( a__ , a__ , unittest.TestCase):
_lowerCAmelCase = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
_lowerCAmelCase = (
{
'''feature-extraction''': TFRoFormerModel,
'''fill-mask''': TFRoFormerForMaskedLM,
'''question-answering''': TFRoFormerForQuestionAnswering,
'''text-classification''': TFRoFormerForSequenceClassification,
'''text-generation''': TFRoFormerForCausalLM,
'''token-classification''': TFRoFormerForTokenClassification,
'''zero-shot''': TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
_lowerCAmelCase = False
_lowerCAmelCase = False
def UpperCAmelCase_ ( self, A, A, A, A, A ):
"""simple docstring"""
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : str = TFRoFormerModelTester(self )
lowerCamelCase : Tuple = ConfigTester(self, config_class=A, hidden_size=37 )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*A )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*A )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A )
@slow
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Dict = TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base' )
self.assertIsNotNone(A )
@require_tf
class __snake_case ( unittest.TestCase):
@slow
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Tuple = TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base' )
lowerCamelCase : Optional[int] = tf.constant([[0, 1, 2, 3, 4, 5]] )
lowerCamelCase : List[Any] = model(A )[0]
# TODO Replace vocab size
lowerCamelCase : List[str] = 5_0000
lowerCamelCase : Dict = [1, 6, vocab_size]
self.assertEqual(output.shape, A )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
lowerCamelCase : Optional[Any] = tf.constant(
[
[
[-0.1205_3341, -1.026_4901, 0.2922_1946],
[-1.513_3783, 0.19_7433, 0.1519_0607],
[-5.013_5403, -3.90_0256, -0.8403_8764],
]
] )
tf.debugging.assert_near(output[:, :3, :3], A, atol=1e-4 )
@require_tf
class __snake_case ( unittest.TestCase):
_lowerCAmelCase = 1E-4
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Dict = tf.constant([[4, 10]] )
lowerCamelCase : Tuple = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6, embedding_dim=6 )
lowerCamelCase : List[str] = emba(input_ids.shape )
lowerCamelCase : Tuple = tf.constant(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]] )
tf.debugging.assert_near(A, A, atol=self.tolerance )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : int = tf.constant(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
] )
lowerCamelCase : int = TFRoFormerSinusoidalPositionalEmbedding(num_positions=512, embedding_dim=512 )
emba([2, 16, 512] )
lowerCamelCase : Optional[Any] = emba.weight[:3, :5]
tf.debugging.assert_near(A, A, atol=self.tolerance )
@require_tf
class __snake_case ( unittest.TestCase):
_lowerCAmelCase = 1E-4
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Dict = tf.reshape(tf.range(2 * 12 * 16 * 64, dtype=tf.floataa ), shape=(2, 12, 16, 64) ) / 100
lowerCamelCase : Any = -tf.reshape(tf.range(2 * 12 * 16 * 64, dtype=tf.floataa ), shape=(2, 12, 16, 64) ) / 100
lowerCamelCase : str = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32, embedding_dim=64 )
lowerCamelCase : Tuple = embed_positions([2, 16, 768] )[None, None, :, :]
lowerCamelCase : List[str] = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
A, A, A )
lowerCamelCase : List[str] = tf.constant(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
] )
lowerCamelCase : str = tf.constant(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8], A, atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8], A, atol=self.tolerance )
| 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
|
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _A ( _lowerCamelCase ):
def __a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
return [
{"col_1": 3, "col_2": "a"},
{"col_1": 2, "col_2": "b"},
{"col_1": 1, "col_2": "c"},
{"col_1": 0, "col_2": "d"},
]
def __a ( self : List[Any] ) -> List[str]:
"""simple docstring"""
lowercase : Union[str, Any] = {'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
return Dataset.from_dict(_A )
def __a ( self : List[str] ) -> Dict:
"""simple docstring"""
lowercase : Union[str, Any] = self._create_example_records()
lowercase : List[str] = Dataset.from_list(_A )
self.assertListEqual(dset.column_names , ['''col_1''', '''col_2'''] )
for i, r in enumerate(_A ):
self.assertDictEqual(_A , example_records[i] )
def __a ( self : Any ) -> List[Any]:
"""simple docstring"""
lowercase : Dict = self._create_example_records()
lowercase : List[Any] = Dataset.from_list(_A )
lowercase : Union[str, Any] = Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]} )
self.assertEqual(dset.info , dset_from_dict.info )
def __a ( self : Dict ) -> Union[str, Any]: # checks what happens with missing columns
"""simple docstring"""
lowercase : Optional[Any] = [{'''col_1''': 1}, {'''col_2''': '''x'''}]
lowercase : Optional[int] = Dataset.from_list(_A )
self.assertDictEqual(dset[0] , {'''col_1''': 1} )
self.assertDictEqual(dset[1] , {'''col_1''': None} ) # NB: first record is used for columns
def __a ( self : Tuple ) -> Optional[Any]: # checks if the type can be inferred from the second record
"""simple docstring"""
lowercase : Any = [{'''col_1''': []}, {'''col_1''': [1, 2]}]
lowercase : Optional[int] = Dataset.from_list(_A )
self.assertEqual(dset.info.features['''col_1'''] , Sequence(Value('''int64''' ) ) )
def __a ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
lowercase : Any = Dataset.from_list([] )
self.assertEqual(len(_A ) , 0 )
self.assertListEqual(dset.column_names , [] )
| 217
|
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _A ( _lowerCamelCase , unittest.TestCase ):
_UpperCamelCase : Optional[int] = KandinskyVaaImgaImgPipeline
_UpperCamelCase : Dict = ['''image_embeds''', '''negative_image_embeds''', '''image''']
_UpperCamelCase : Tuple = [
'''image_embeds''',
'''negative_image_embeds''',
'''image''',
]
_UpperCamelCase : Tuple = [
'''generator''',
'''height''',
'''width''',
'''strength''',
'''guidance_scale''',
'''num_inference_steps''',
'''return_dict''',
'''guidance_scale''',
'''num_images_per_prompt''',
'''output_type''',
'''return_dict''',
]
_UpperCamelCase : int = False
@property
def __a ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
return 32
@property
def __a ( self : str ) -> List[str]:
"""simple docstring"""
return 32
@property
def __a ( self : str ) -> int:
"""simple docstring"""
return self.time_input_dim
@property
def __a ( self : Dict ) -> Any:
"""simple docstring"""
return self.time_input_dim * 4
@property
def __a ( self : Any ) -> Optional[int]:
"""simple docstring"""
return 100
@property
def __a ( self : Tuple ) -> str:
"""simple docstring"""
torch.manual_seed(0 )
lowercase : Union[str, Any] = {
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
lowercase : Any = UNetaDConditionModel(**_A )
return model
@property
def __a ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase : Dict = VQModel(**self.dummy_movq_kwargs )
return model
def __a ( self : List[Any] ) -> int:
"""simple docstring"""
lowercase : int = self.dummy_unet
lowercase : str = self.dummy_movq
lowercase : Union[str, Any] = {
'''num_train_timesteps''': 1_000,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.00_085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
lowercase : Optional[Any] = DDIMScheduler(**_A )
lowercase : str = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __a ( self : Tuple , _A : int , _A : Any=0 ) -> List[str]:
"""simple docstring"""
lowercase : Dict = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(_A ) ).to(_A )
lowercase : str = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
_A )
# create init_image
lowercase : List[Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(_A ) ).to(_A )
lowercase : Optional[Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase : str = Image.fromarray(np.uinta(_A ) ).convert('''RGB''' ).resize((256, 256) )
if str(_A ).startswith('''mps''' ):
lowercase : int = torch.manual_seed(_A )
else:
lowercase : Tuple = torch.Generator(device=_A ).manual_seed(_A )
lowercase : List[str] = {
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def __a ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
lowercase : Optional[int] = '''cpu'''
lowercase : Any = self.get_dummy_components()
lowercase : Any = self.pipeline_class(**_A )
lowercase : int = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
lowercase : Optional[int] = pipe(**self.get_dummy_inputs(_A ) )
lowercase : str = output.images
lowercase : int = pipe(
**self.get_dummy_inputs(_A ) , return_dict=_A , )[0]
lowercase : int = image[0, -3:, -3:, -1]
lowercase : str = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase : List[Any] = np.array(
[0.6_199_778, 0.63_984_406, 0.46_145_785, 0.62_944_984, 0.5_622_215, 0.47_306_132, 0.47_441_456, 0.4_607_606, 0.48_719_263] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
@slow
@require_torch_gpu
class _A ( unittest.TestCase ):
def __a ( self : Optional[int] ) -> str:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self : List[str] ) -> Any:
"""simple docstring"""
lowercase : Optional[int] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_img2img_frog.npy''' )
lowercase : Tuple = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
lowercase : Optional[Any] = '''A red cartoon frog, 4k'''
lowercase : List[Any] = KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(_A )
lowercase : Any = KandinskyVaaImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder''' , torch_dtype=torch.floataa )
lowercase : Optional[Any] = pipeline.to(_A )
pipeline.set_progress_bar_config(disable=_A )
lowercase : Any = torch.Generator(device='''cpu''' ).manual_seed(0 )
lowercase , lowercase : Dict = pipe_prior(
_A , generator=_A , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
lowercase : Tuple = pipeline(
image=_A , image_embeds=_A , negative_image_embeds=_A , generator=_A , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type='''np''' , )
lowercase : Optional[int] = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_A , _A )
| 217
| 1
|
import os
def lowerCAmelCase_ ( A_ = "matrix.txt"):
with open(os.path.join(os.path.dirname(A_) ,A_)) as in_file:
UpperCamelCase__: Optional[int] = in_file.read()
UpperCamelCase__: int = [[int(A_) for cell in row.split(",")] for row in data.strip().splitlines()]
UpperCamelCase__: List[Any] = [[0 for cell in row] for row in grid]
UpperCamelCase__: int = len(grid[0])
UpperCamelCase__: int = [[0 for i in range(A_)] for j in range(A_)]
UpperCamelCase__: Any = grid[0][0]
for i in range(1 ,A_):
UpperCamelCase__: Optional[int] = grid[0][i] + dp[0][i - 1]
for i in range(1 ,A_):
UpperCamelCase__: Optional[int] = grid[i][0] + dp[i - 1][0]
for i in range(1 ,A_):
for j in range(1 ,A_):
UpperCamelCase__: List[Any] = grid[i][j] + min(dp[i - 1][j] ,dp[i][j - 1])
return dp[-1][-1]
if __name__ == "__main__":
print(f"{solution() = }")
| 221
|
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
A__: Any = logging.get_logger(__name__)
@add_end_docstrings(UpperCamelCase__)
class _a ( UpperCamelCase__):
"""simple docstring"""
def __init__( self: List[str] , **__lowerCamelCase: Union[str, Any] ):
'''simple docstring'''
super().__init__(**__lowerCamelCase )
requires_backends(self , "vision" )
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == "tf"
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
def __call__( self: Optional[Any] , __lowerCamelCase: Union[str, List[str], "Image", List["Image"]] , **__lowerCamelCase: Optional[int] ):
'''simple docstring'''
return super().__call__(__lowerCamelCase , **__lowerCamelCase )
def UpperCAmelCase_ ( self: str , **__lowerCamelCase: Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__: Optional[int] = {}
if "candidate_labels" in kwargs:
UpperCamelCase__: Any = kwargs["candidate_labels"]
if "hypothesis_template" in kwargs:
UpperCamelCase__: Dict = kwargs["hypothesis_template"]
return preprocess_params, {}, {}
def UpperCAmelCase_ ( self: Dict , __lowerCamelCase: Dict , __lowerCamelCase: str=None , __lowerCamelCase: Dict="This is a photo of {}." ):
'''simple docstring'''
UpperCamelCase__: Union[str, Any] = load_image(__lowerCamelCase )
UpperCamelCase__: Dict = self.image_processor(images=[image] , return_tensors=self.framework )
UpperCamelCase__: Union[str, Any] = candidate_labels
UpperCamelCase__: Optional[Any] = [hypothesis_template.format(__lowerCamelCase ) for x in candidate_labels]
UpperCamelCase__: Optional[int] = self.tokenizer(__lowerCamelCase , return_tensors=self.framework , padding=__lowerCamelCase )
UpperCamelCase__: Dict = [text_inputs]
return inputs
def UpperCAmelCase_ ( self: Optional[Any] , __lowerCamelCase: Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__: Optional[Any] = model_inputs.pop("candidate_labels" )
UpperCamelCase__: Optional[Any] = model_inputs.pop("text_inputs" )
if isinstance(text_inputs[0] , __lowerCamelCase ):
UpperCamelCase__: Optional[int] = text_inputs[0]
else:
# Batching case.
UpperCamelCase__: str = text_inputs[0][0]
UpperCamelCase__: str = self.model(**__lowerCamelCase , **__lowerCamelCase )
UpperCamelCase__: Dict = {
"candidate_labels": candidate_labels,
"logits": outputs.logits_per_image,
}
return model_outputs
def UpperCAmelCase_ ( self: Any , __lowerCamelCase: Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__: Tuple = model_outputs.pop("candidate_labels" )
UpperCamelCase__: Optional[Any] = model_outputs["logits"][0]
if self.framework == "pt":
UpperCamelCase__: List[str] = logits.softmax(dim=-1 ).squeeze(-1 )
UpperCamelCase__: str = probs.tolist()
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
UpperCamelCase__: Any = [scores]
elif self.framework == "tf":
UpperCamelCase__: int = stable_softmax(__lowerCamelCase , axis=-1 )
UpperCamelCase__: int = probs.numpy().tolist()
else:
raise ValueError(F"Unsupported framework: {self.framework}" )
UpperCamelCase__: List[Any] = [
{"score": score, "label": candidate_label}
for score, candidate_label in sorted(zip(__lowerCamelCase , __lowerCamelCase ) , key=lambda __lowerCamelCase : -x[0] )
]
return result
| 221
| 1
|
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
A_ : Tuple = logging.get_logger(__name__)
def snake_case () -> Optional[Any]:
# Get the sagemaker specific mp parameters from smp_options variable.
UpperCamelCase_: Optional[Any] = os.getenv('SM_HP_MP_PARAMETERS' , '{}' )
try:
# Parse it and check the field "partitions" is included, it is required for model parallel.
UpperCamelCase_: List[str] = json.loads(UpperCAmelCase__ )
if "partitions" not in smp_options:
return False
except json.JSONDecodeError:
return False
# Get the sagemaker specific framework parameters from mpi_options variable.
UpperCamelCase_: Any = os.getenv('SM_FRAMEWORK_PARAMS' , '{}' )
try:
# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
UpperCamelCase_: Tuple = json.loads(UpperCAmelCase__ )
if not mpi_options.get('sagemaker_mpi_enabled' , UpperCAmelCase__ ):
return False
except json.JSONDecodeError:
return False
# Lastly, check if the `smdistributed` module is present.
return importlib.util.find_spec('smdistributed' ) is not None
if is_sagemaker_model_parallel_available():
import smdistributed.modelparallel.torch as smp
smp.init()
@dataclass
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
a : str =field(
default='''''' , metadata={'''help''': '''Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'''} , )
def _a ( self ):
super().__post_init__()
warnings.warn(
'`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '
'`TrainingArguments` instead.' , _lowerCamelCase , )
@cached_property
def _a ( self ):
logger.info('PyTorch: setting up devices' )
if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
logger.warning(
'torch.distributed process group is initialized, but local_rank == -1. '
'In order to use Torch DDP, launch your script with `python -m torch.distributed.launch' )
if self.no_cuda:
UpperCamelCase_: str = torch.device('cpu' )
UpperCamelCase_: Optional[Any] = 0
elif is_sagemaker_model_parallel_available():
UpperCamelCase_: Optional[int] = smp.local_rank()
UpperCamelCase_: Any = torch.device('cuda' , _lowerCamelCase )
UpperCamelCase_: int = 1
elif is_sagemaker_dp_enabled():
import smdistributed.dataparallel.torch.torch_smddp # noqa: F401
torch.distributed.init_process_group(backend='smddp' , timeout=self.ddp_timeout_delta )
UpperCamelCase_: Optional[int] = int(os.getenv('SMDATAPARALLEL_LOCAL_RANK' ) )
UpperCamelCase_: Dict = torch.device('cuda' , self.local_rank )
UpperCamelCase_: Union[str, Any] = 1
elif self.local_rank == -1:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# trigger an error that a device index is missing. Index 0 takes into account the
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
# will use the first GPU in that env, i.e. GPU#1
UpperCamelCase_: Union[str, Any] = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu' )
# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
# the default value.
UpperCamelCase_: Any = torch.cuda.device_count()
else:
# Here, we'll use torch.distributed.
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend='nccl' , timeout=self.ddp_timeout_delta )
UpperCamelCase_: Optional[Any] = torch.device('cuda' , self.local_rank )
UpperCamelCase_: Optional[int] = 1
if device.type == "cuda":
torch.cuda.set_device(_lowerCamelCase )
return device
@property
def _a ( self ):
if is_sagemaker_model_parallel_available():
return smp.dp_size()
return super().world_size
@property
def _a ( self ):
return not is_sagemaker_model_parallel_available()
@property
def _a ( self ):
return False
| 57
|
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
__lowerCamelCase : str = get_logger(__name__)
class a__ ( enum.Enum ):
A = 'all_checks'
A = 'basic_checks'
A = 'no_checks'
class a__ ( A__ ):
pass
class a__ ( A__ ):
pass
class a__ ( A__ ):
pass
class a__ ( A__ ):
pass
def _snake_case ( lowerCAmelCase : Optional[dict] , lowerCAmelCase : dict , lowerCAmelCase : List[Any]=None ):
"""simple docstring"""
if expected_checksums is None:
logger.info("Unable to verify checksums." )
return
if len(set(lowerCAmelCase ) - set(lowerCAmelCase ) ) > 0:
raise ExpectedMoreDownloadedFiles(str(set(lowerCAmelCase ) - set(lowerCAmelCase ) ) )
if len(set(lowerCAmelCase ) - set(lowerCAmelCase ) ) > 0:
raise UnexpectedDownloadedFile(str(set(lowerCAmelCase ) - set(lowerCAmelCase ) ) )
SCREAMING_SNAKE_CASE_ : int = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
SCREAMING_SNAKE_CASE_ : List[str] = " for " + verification_name if verification_name is not None else ""
if len(lowerCAmelCase ) > 0:
raise NonMatchingChecksumError(
f'Checksums didn\'t match{for_verification_name}:\n'
f'{bad_urls}\n'
"Set `verification_mode='no_checks'` to skip checksums verification and ignore this error" )
logger.info("All the checksums matched successfully" + for_verification_name )
class a__ ( A__ ):
pass
class a__ ( A__ ):
pass
class a__ ( A__ ):
pass
class a__ ( A__ ):
pass
def _snake_case ( lowerCAmelCase : Optional[dict] , lowerCAmelCase : dict ):
"""simple docstring"""
if expected_splits is None:
logger.info("Unable to verify splits sizes." )
return
if len(set(lowerCAmelCase ) - set(lowerCAmelCase ) ) > 0:
raise ExpectedMoreSplits(str(set(lowerCAmelCase ) - set(lowerCAmelCase ) ) )
if len(set(lowerCAmelCase ) - set(lowerCAmelCase ) ) > 0:
raise UnexpectedSplits(str(set(lowerCAmelCase ) - set(lowerCAmelCase ) ) )
SCREAMING_SNAKE_CASE_ : Tuple = [
{"expected": expected_splits[name], "recorded": recorded_splits[name]}
for name in expected_splits
if expected_splits[name].num_examples != recorded_splits[name].num_examples
]
if len(lowerCAmelCase ) > 0:
raise NonMatchingSplitsSizesError(str(lowerCAmelCase ) )
logger.info("All the splits matched successfully." )
def _snake_case ( lowerCAmelCase : str , lowerCAmelCase : bool = True ):
"""simple docstring"""
if record_checksum:
SCREAMING_SNAKE_CASE_ : int = shaaaa()
with open(lowerCAmelCase , "rb" ) as f:
for chunk in iter(lambda: f.read(1 << 2_0 ) , B"" ):
m.update(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : List[str] = m.hexdigest()
else:
SCREAMING_SNAKE_CASE_ : Optional[Any] = None
return {"num_bytes": os.path.getsize(lowerCAmelCase ), "checksum": checksum}
def _snake_case ( lowerCAmelCase : str ):
"""simple docstring"""
if dataset_size and config.IN_MEMORY_MAX_SIZE:
return dataset_size < config.IN_MEMORY_MAX_SIZE
else:
return False
| 216
| 0
|
'''simple docstring'''
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def UpperCAmelCase_ ( lowerCamelCase_ ):
"""simple docstring"""
lowerCAmelCase__ : Dict = [
"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(lowerCamelCase_ , lowerCamelCase_ )
def UpperCAmelCase_ ( lowerCamelCase_ ):
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ : Optional[int] = emb.weight.shape
lowerCAmelCase__ : Optional[Any] = nn.Linear(lowerCamelCase_ , lowerCamelCase_ , bias=lowerCamelCase_ )
lowerCAmelCase__ : Union[str, Any] = emb.weight.data
return lin_layer
def UpperCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_=None ):
"""simple docstring"""
lowerCAmelCase__ : List[str] = {}
for old_key in state_dict.keys():
lowerCAmelCase__ : str = old_key
if "moe_layer.experts." in key:
if expert_idx is not None:
lowerCAmelCase__ : Tuple = key.replace("moe_layer.experts.0" , f'''ffn.experts.expert_{expert_idx}''' )
else:
lowerCAmelCase__ : List[str] = key.replace("moe_layer.experts." , "ffn.experts.expert_" )
if "gate" in key:
lowerCAmelCase__ : Union[str, Any] = key.replace(".moe_layer.gate.wg" , ".ffn.router.classifier" )
if "fc2" and "experts" not in key:
lowerCAmelCase__ : List[Any] = key.replace(".fc2." , ".ffn.fc2." )
if "fc1" and "experts" not in key:
lowerCAmelCase__ : List[str] = key.replace(".fc1." , ".ffn.fc1." )
if ".encoder_attn." in key:
lowerCAmelCase__ : Tuple = key.replace(".encoder_attn." , ".cross_attention." )
if "encoder_attn_layer_norm" in key:
lowerCAmelCase__ : Any = key.replace("encoder_attn_layer_norm" , "cross_attention_layer_norm" )
if "final_layer_norm" in key:
lowerCAmelCase__ : Dict = key.replace("final_layer_norm" , "ff_layer_norm" )
lowerCAmelCase__ : Tuple = state_dict[old_key]
return new_dict
def UpperCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = WEIGHTS_NAME ):
"""simple docstring"""
lowerCAmelCase__ : List[str] = []
lowerCAmelCase__ : Any = 0
os.makedirs(lowerCamelCase_ , exist_ok=lowerCamelCase_ )
for expert in range(lowerCamelCase_ ):
lowerCAmelCase__ : Any = switch_checkpoint_path + f'''-rank-{expert}.pt'''
if os.path.isfile(lowerCamelCase_ ):
lowerCAmelCase__ : str = torch.load(lowerCamelCase_ )["model"]
remove_ignore_keys_(lowerCamelCase_ )
lowerCAmelCase__ : Optional[int] = rename_fairseq_keys(lowerCamelCase_ , lowerCamelCase_ )
lowerCAmelCase__ : str = os.path.join(
lowerCamelCase_ , weights_name.replace(".bin" , f'''-{len(lowerCamelCase_ )+1:05d}-of-???.bin''' ) )
torch.save(lowerCamelCase_ , lowerCamelCase_ )
sharded_state_dicts.append(expert_state.keys() )
total_size += sum([value.numel() for key, value in expert_state.items()] ) * dtype_byte_size(
expert_state[list(lowerCamelCase_ )[0]].dtype )
# Add the last block
lowerCAmelCase__ : int = os.path.join(lowerCamelCase_ , weights_name.replace(".bin" , f'''-{len(lowerCamelCase_ )+1:05d}-of-???.bin''' ) )
lowerCAmelCase__ : Tuple = torch.load(switch_checkpoint_path + "-shared.pt" )["model"]
remove_ignore_keys_(lowerCamelCase_ )
lowerCAmelCase__ : Dict = rename_fairseq_keys(lowerCamelCase_ , lowerCamelCase_ )
lowerCAmelCase__ : str = shared_weights["decoder.embed_tokens.weight"]
sharded_state_dicts.append(shared_weights.keys() )
# If we only have the shared weights (dummy model/experts saved on the same file)
if len(lowerCamelCase_ ) == 1:
lowerCAmelCase__ : str = os.path.join(lowerCamelCase_ , lowerCamelCase_ )
torch.save(lowerCamelCase_ , lowerCamelCase_ )
return {weights_name: sharded_state_dicts[0]}, None
else:
torch.save(lowerCamelCase_ , lowerCamelCase_ )
# Otherwise, let's build the index
lowerCAmelCase__ : Dict = {}
for idx, shard in enumerate(lowerCamelCase_ ):
lowerCAmelCase__ : int = weights_name.replace(".bin" , f'''-{idx+1:05d}-of-{len(lowerCamelCase_ ):05d}.bin''' )
lowerCAmelCase__ : Tuple = os.path.join(lowerCamelCase_ , weights_name.replace(".bin" , f'''-{idx+1:05d}-of-???.bin''' ) )
os.rename(lowerCamelCase_ , os.path.join(lowerCamelCase_ , lowerCamelCase_ ) )
for key in shard:
lowerCAmelCase__ : Any = shard_file
# Add the metadata
lowerCAmelCase__ : List[str] = {"total_size": total_size}
lowerCAmelCase__ : List[str] = {"metadata": metadata, "weight_map": weight_map}
with open(os.path.join(lowerCamelCase_ , lowerCamelCase_ ) , "w" , encoding="utf-8" ) as f:
lowerCAmelCase__ : List[str] = json.dumps(lowerCamelCase_ , indent=2 , sort_keys=lowerCamelCase_ ) + "\n"
f.write(lowerCamelCase_ )
return metadata, index
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--nllb_moe_checkpoint_path""",
default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000""",
type=str,
required=False,
help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
)
parser.add_argument("""--dtype""", default="""float32""", type=str, required=False, help="""dtype of the saved model""")
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b""",
type=str,
required=False,
help="""Path to the output pytorch model.""",
)
snake_case = parser.parse_args()
snake_case = shard_on_the_fly(
args.nllb_moe_checkpoint_path,
args.pytorch_dump_folder_path,
1_28,
args.dtype,
)
snake_case = NllbMoeConfig.from_pretrained(
"""facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_28
)
config.save_pretrained(args.pytorch_dump_folder_path)
snake_case = NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
print("""Done""")
model.save_pretrained(args.pytorch_dump_folder_path)
| 720
|
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def UpperCAmelCase_ ( lowerCamelCase_ ):
"""simple docstring"""
if "img_encoder.pos_embed" in name:
lowerCAmelCase__ : int = name.replace("img_encoder.pos_embed" , "vision_model.embeddings.position_embeddings" )
if "img_encoder.patch_embed.proj" in name:
lowerCAmelCase__ : Optional[int] = name.replace("img_encoder.patch_embed.proj" , "vision_model.embeddings.patch_embeddings.projection" )
if "img_encoder.patch_embed.norm" in name:
lowerCAmelCase__ : Optional[Any] = name.replace("img_encoder.patch_embed.norm" , "vision_model.embeddings.layernorm" )
if "img_encoder.layers" in name:
lowerCAmelCase__ : Optional[Any] = name.replace("img_encoder.layers" , "vision_model.encoder.stages" )
if "blocks" in name and "res" not in name:
lowerCAmelCase__ : int = name.replace("blocks" , "layers" )
if "attn" in name and "pre_assign" not in name:
lowerCAmelCase__ : Optional[Any] = name.replace("attn" , "self_attn" )
if "proj" in name and "self_attn" in name and "text" not in name:
lowerCAmelCase__ : Optional[int] = name.replace("proj" , "out_proj" )
if "pre_assign_attn.attn.proj" in name:
lowerCAmelCase__ : int = name.replace("pre_assign_attn.attn.proj" , "pre_assign_attn.attn.out_proj" )
if "norm1" in name:
lowerCAmelCase__ : List[str] = name.replace("norm1" , "layer_norm1" )
if "norm2" in name and "pre_assign" not in name:
lowerCAmelCase__ : Tuple = name.replace("norm2" , "layer_norm2" )
if "img_encoder.norm" in name:
lowerCAmelCase__ : int = name.replace("img_encoder.norm" , "vision_model.layernorm" )
# text encoder
if "text_encoder.token_embedding" in name:
lowerCAmelCase__ : Any = name.replace("text_encoder.token_embedding" , "text_model.embeddings.token_embedding" )
if "text_encoder.positional_embedding" in name:
lowerCAmelCase__ : int = name.replace("text_encoder.positional_embedding" , "text_model.embeddings.position_embedding.weight" )
if "text_encoder.transformer.resblocks." in name:
lowerCAmelCase__ : Optional[Any] = name.replace("text_encoder.transformer.resblocks." , "text_model.encoder.layers." )
if "ln_1" in name:
lowerCAmelCase__ : Optional[Any] = name.replace("ln_1" , "layer_norm1" )
if "ln_2" in name:
lowerCAmelCase__ : Optional[Any] = name.replace("ln_2" , "layer_norm2" )
if "c_fc" in name:
lowerCAmelCase__ : Optional[Any] = name.replace("c_fc" , "fc1" )
if "c_proj" in name:
lowerCAmelCase__ : List[str] = name.replace("c_proj" , "fc2" )
if "text_encoder" in name:
lowerCAmelCase__ : Tuple = name.replace("text_encoder" , "text_model" )
if "ln_final" in name:
lowerCAmelCase__ : str = name.replace("ln_final" , "final_layer_norm" )
# projection layers
if "img_projector.linear_hidden." in name:
lowerCAmelCase__ : Optional[int] = name.replace("img_projector.linear_hidden." , "visual_projection." )
if "img_projector.linear_out." in name:
lowerCAmelCase__ : Tuple = name.replace("img_projector.linear_out." , "visual_projection.3." )
if "text_projector.linear_hidden" in name:
lowerCAmelCase__ : List[str] = name.replace("text_projector.linear_hidden" , "text_projection" )
if "text_projector.linear_out" in name:
lowerCAmelCase__ : Optional[Any] = name.replace("text_projector.linear_out" , "text_projection.3" )
return name
def UpperCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
lowerCAmelCase__ : int = orig_state_dict.pop(lowerCamelCase_ )
if "qkv" in key:
# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
lowerCAmelCase__ : Any = key.split("." )
lowerCAmelCase__ , lowerCAmelCase__ : str = int(key_split[2] ), int(key_split[4] )
lowerCAmelCase__ : List[str] = config.vision_config.hidden_size
if "weight" in key:
lowerCAmelCase__ : Dict = val[:dim, :]
lowerCAmelCase__ : Any = val[dim : dim * 2, :]
lowerCAmelCase__ : Optional[int] = val[-dim:, :]
else:
lowerCAmelCase__ : List[str] = val[:dim]
lowerCAmelCase__ : List[Any] = val[dim : dim * 2]
lowerCAmelCase__ : str = val[-dim:]
elif "in_proj" in key:
# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
lowerCAmelCase__ : List[Any] = key.split("." )
lowerCAmelCase__ : Dict = int(key_split[3] )
lowerCAmelCase__ : str = config.text_config.hidden_size
if "weight" in key:
lowerCAmelCase__ : List[Any] = val[:dim, :]
lowerCAmelCase__ : Any = val[
dim : dim * 2, :
]
lowerCAmelCase__ : Dict = val[-dim:, :]
else:
lowerCAmelCase__ : str = val[:dim]
lowerCAmelCase__ : str = val[dim : dim * 2]
lowerCAmelCase__ : Optional[int] = val[-dim:]
else:
lowerCAmelCase__ : List[str] = rename_key(lowerCamelCase_ )
# squeeze if necessary
if (
"text_projection.0" in new_name
or "text_projection.3" in new_name
or "visual_projection.0" in new_name
or "visual_projection.3" in new_name
):
lowerCAmelCase__ : Optional[Any] = val.squeeze_()
else:
lowerCAmelCase__ : List[str] = val
return orig_state_dict
def UpperCAmelCase_ ( ):
"""simple docstring"""
lowerCAmelCase__ : Any = "http://images.cocodataset.org/val2017/000000039769.jpg"
lowerCAmelCase__ : List[Any] = Image.open(requests.get(lowerCamelCase_ , stream=lowerCamelCase_ ).raw )
return im
@torch.no_grad()
def UpperCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_="groupvit-gcc-yfcc" , lowerCamelCase_=False ):
"""simple docstring"""
lowerCAmelCase__ : List[str] = GroupViTConfig()
lowerCAmelCase__ : Any = GroupViTModel(lowerCamelCase_ ).eval()
lowerCAmelCase__ : Tuple = torch.load(lowerCamelCase_ , map_location="cpu" )["model"]
lowerCAmelCase__ : Optional[int] = convert_state_dict(lowerCamelCase_ , lowerCamelCase_ )
lowerCAmelCase__ , lowerCAmelCase__ : Dict = model.load_state_dict(lowerCamelCase_ , strict=lowerCamelCase_ )
assert missing_keys == ["text_model.embeddings.position_ids"]
assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowerCamelCase_ ) == 0)
# verify result
lowerCAmelCase__ : Union[str, Any] = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32" )
lowerCAmelCase__ : str = prepare_img()
lowerCAmelCase__ : int = processor(text=["a photo of a cat", "a photo of a dog"] , images=lowerCamelCase_ , padding=lowerCamelCase_ , return_tensors="pt" )
with torch.no_grad():
lowerCAmelCase__ : Union[str, Any] = model(**lowerCamelCase_ )
if model_name == "groupvit-gcc-yfcc":
lowerCAmelCase__ : Union[str, Any] = torch.tensor([[13.3523, 6.3629]] )
elif model_name == "groupvit-gcc-redcaps":
lowerCAmelCase__ : int = torch.tensor([[16.1873, 8.6230]] )
else:
raise ValueError(f'''Model name {model_name} not supported.''' )
assert torch.allclose(outputs.logits_per_image , lowerCamelCase_ , atol=1e-3 )
processor.save_pretrained(lowerCamelCase_ )
model.save_pretrained(lowerCamelCase_ )
print("Successfully saved processor and model to" , lowerCamelCase_ )
if push_to_hub:
print("Pushing to the hub..." )
processor.push_to_hub(lowerCamelCase_ , organization="nielsr" )
model.push_to_hub(lowerCamelCase_ , organization="nielsr" )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to dump the processor and PyTorch model."""
)
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to GroupViT checkpoint""")
parser.add_argument(
"""--model_name""",
default="""groupvit-gccy-fcc""",
type=str,
help="""Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'""",
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.""",
)
snake_case = parser.parse_args()
convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 568
| 0
|
"""simple docstring"""
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available
if is_torch_available():
from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification
if is_tf_available():
from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification
if is_flax_available():
from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification
_UpperCAmelCase = DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co
_UpperCAmelCase = """main"""
# Default branch name
_UpperCAmelCase = """f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"""
# One particular commit (not the top of `main`)
_UpperCAmelCase = """aaaaaaa"""
# This commit does not exist, so we should 404.
_UpperCAmelCase = """d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"""
# Sha-1 of config.json on the top of `main`, for checking purposes
_UpperCAmelCase = """4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"""
@contextlib.contextmanager
def __magic_name__ ( ):
print("""Welcome!""" )
yield
print("""Bye!""" )
@contextlib.contextmanager
def __magic_name__ ( ):
print("""Bonjour!""" )
yield
print("""Au revoir!""" )
class a ( unittest.TestCase ):
def lowerCamelCase__ ( self : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
assert transformers.__spec__ is not None
assert importlib.util.find_spec("""transformers""" ) is not None
class a ( unittest.TestCase ):
@unittest.mock.patch("""sys.stdout""" , new_callable=io.StringIO )
def lowerCamelCase__ ( self : Union[str, Any] , lowerCAmelCase : Tuple ) -> Tuple:
'''simple docstring'''
with ContextManagers([] ):
print("""Transformers are awesome!""" )
# The print statement adds a new line at the end of the output
self.assertEqual(mock_stdout.getvalue() , """Transformers are awesome!\n""" )
@unittest.mock.patch("""sys.stdout""" , new_callable=io.StringIO )
def lowerCamelCase__ ( self : List[Any] , lowerCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
with ContextManagers([context_en()] ):
print("""Transformers are awesome!""" )
# The output should be wrapped with an English welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , """Welcome!\nTransformers are awesome!\nBye!\n""" )
@unittest.mock.patch("""sys.stdout""" , new_callable=io.StringIO )
def lowerCamelCase__ ( self : List[Any] , lowerCAmelCase : List[Any] ) -> str:
'''simple docstring'''
with ContextManagers([context_fr(), context_en()] ):
print("""Transformers are awesome!""" )
# The output should be wrapped with an English and French welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , """Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n""" )
@require_torch
def lowerCamelCase__ ( self : Union[str, Any] ) -> List[str]:
'''simple docstring'''
self.assertEqual(find_labels(lowerCAmelCase ) , ["""labels"""] )
self.assertEqual(find_labels(lowerCAmelCase ) , ["""labels""", """next_sentence_label"""] )
self.assertEqual(find_labels(lowerCAmelCase ) , ["""start_positions""", """end_positions"""] )
class a ( UpperCAmelCase__ ):
pass
self.assertEqual(find_labels(lowerCAmelCase ) , ["""labels"""] )
@require_tf
def lowerCamelCase__ ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
self.assertEqual(find_labels(lowerCAmelCase ) , ["""labels"""] )
self.assertEqual(find_labels(lowerCAmelCase ) , ["""labels""", """next_sentence_label"""] )
self.assertEqual(find_labels(lowerCAmelCase ) , ["""start_positions""", """end_positions"""] )
class a ( UpperCAmelCase__ ):
pass
self.assertEqual(find_labels(lowerCAmelCase ) , ["""labels"""] )
@require_flax
def lowerCamelCase__ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
self.assertEqual(find_labels(lowerCAmelCase ) , [] )
self.assertEqual(find_labels(lowerCAmelCase ) , [] )
self.assertEqual(find_labels(lowerCAmelCase ) , [] )
class a ( UpperCAmelCase__ ):
pass
self.assertEqual(find_labels(lowerCAmelCase ) , [] )
| 409
|
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
_UpperCamelCase = logging.get_logger(__name__)
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A : int = b.T
__A : Optional[Any] = np.sum(np.square(SCREAMING_SNAKE_CASE ) , axis=1 )
__A : List[Any] = np.sum(np.square(SCREAMING_SNAKE_CASE ) , axis=0 )
__A : List[Any] = np.matmul(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__A : str = aa[:, None] - 2 * ab + ba[None, :]
return d
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A : Union[str, Any] = x.reshape(-1 , 3 )
__A : Any = squared_euclidean_distance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return np.argmin(SCREAMING_SNAKE_CASE , axis=1 )
class __magic_name__ ( lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase__ = ['pixel_values']
def __init__( self , lowerCamelCase = None , lowerCamelCase = True , lowerCamelCase = None , lowerCamelCase = PILImageResampling.BILINEAR , lowerCamelCase = True , lowerCamelCase = True , **lowerCamelCase , ):
'''simple docstring'''
super().__init__(**lowerCamelCase )
__A : Optional[int] = size if size is not None else {"height": 256, "width": 256}
__A : int = get_size_dict(lowerCamelCase )
__A : str = np.array(lowerCamelCase ) if clusters is not None else None
__A : List[Any] = do_resize
__A : Dict = size
__A : str = resample
__A : Optional[Any] = do_normalize
__A : Dict = do_color_quantize
def lowerCAmelCase__ ( self , lowerCamelCase , lowerCamelCase , lowerCamelCase = PILImageResampling.BILINEAR , lowerCamelCase = None , **lowerCamelCase , ):
'''simple docstring'''
__A : Optional[Any] = get_size_dict(lowerCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f"Size dictionary must contain both height and width keys. Got {size.keys()}" )
return resize(
lowerCamelCase , size=(size["height"], size["width"]) , resample=lowerCamelCase , data_format=lowerCamelCase , **lowerCamelCase )
def lowerCAmelCase__ ( self , lowerCamelCase , lowerCamelCase = None , ):
'''simple docstring'''
__A : List[str] = rescale(image=lowerCamelCase , scale=1 / 127.5 , data_format=lowerCamelCase )
__A : List[Any] = image - 1
return image
def lowerCAmelCase__ ( self , lowerCamelCase , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = ChannelDimension.FIRST , **lowerCamelCase , ):
'''simple docstring'''
__A : List[Any] = do_resize if do_resize is not None else self.do_resize
__A : int = size if size is not None else self.size
__A : Tuple = get_size_dict(lowerCamelCase )
__A : Tuple = resample if resample is not None else self.resample
__A : Union[str, Any] = do_normalize if do_normalize is not None else self.do_normalize
__A : Tuple = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
__A : Any = clusters if clusters is not None else self.clusters
__A : Dict = np.array(lowerCamelCase )
__A : Optional[int] = make_list_of_images(lowerCamelCase )
if not valid_images(lowerCamelCase ):
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 or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_color_quantize and clusters is None:
raise ValueError("Clusters must be specified if do_color_quantize is True." )
# All transformations expect numpy arrays.
__A : Optional[Any] = [to_numpy_array(lowerCamelCase ) for image in images]
if do_resize:
__A : Dict = [self.resize(image=lowerCamelCase , size=lowerCamelCase , resample=lowerCamelCase ) for image in images]
if do_normalize:
__A : List[Any] = [self.normalize(image=lowerCamelCase ) for image in images]
if do_color_quantize:
__A : Union[str, Any] = [to_channel_dimension_format(lowerCamelCase , ChannelDimension.LAST ) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
__A : int = np.array(lowerCamelCase )
__A : Any = color_quantize(lowerCamelCase , lowerCamelCase ).reshape(images.shape[:-1] )
# flatten to (batch_size, height*width)
__A : Tuple = images.shape[0]
__A : List[Any] = images.reshape(lowerCamelCase , -1 )
# We need to convert back to a list of images to keep consistent behaviour across processors.
__A : Optional[int] = list(lowerCamelCase )
else:
__A : Tuple = [to_channel_dimension_format(lowerCamelCase , lowerCamelCase ) for image in images]
__A : Optional[int] = {"input_ids": images}
return BatchFeature(data=lowerCamelCase , tensor_type=lowerCamelCase )
| 111
| 0
|
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
BarkConfig,
BarkFineConfig,
BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
BarkCoarseGenerationConfig,
BarkFineGenerationConfig,
BarkGenerationConfig,
BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase_ = logging.get_logger(__name__)
set_seed(770)
lowerCamelCase_ = {
'''c_attn''': '''att_proj''',
'''c_proj''': '''out_proj''',
'''c_fc''': '''in_proj''',
'''transformer.''': '''''',
'''h.''': '''layers.''',
'''ln_1''': '''layernorm_1''',
'''ln_2''': '''layernorm_2''',
'''ln_f''': '''layernorm_final''',
'''wpe''': '''position_embeds_layer''',
'''wte''': '''input_embeds_layer''',
}
lowerCamelCase_ = {
'''text_small''': {
'''repo_id''': '''suno/bark''',
'''file_name''': '''text.pt''',
},
'''coarse_small''': {
'''repo_id''': '''suno/bark''',
'''file_name''': '''coarse.pt''',
},
'''fine_small''': {
'''repo_id''': '''suno/bark''',
'''file_name''': '''fine.pt''',
},
'''text''': {
'''repo_id''': '''suno/bark''',
'''file_name''': '''text_2.pt''',
},
'''coarse''': {
'''repo_id''': '''suno/bark''',
'''file_name''': '''coarse_2.pt''',
},
'''fine''': {
'''repo_id''': '''suno/bark''',
'''file_name''': '''fine_2.pt''',
},
}
lowerCamelCase_ = os.path.dirname(os.path.abspath(__file__))
lowerCamelCase_ = os.path.join(os.path.expanduser('''~'''), '''.cache''')
lowerCamelCase_ = os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')
def UpperCamelCase( lowercase_ , lowercase_=False ) -> Tuple:
'''simple docstring'''
snake_case_ = model_type
if use_small:
key += "_small"
return os.path.join(lowercase_ , REMOTE_MODEL_PATHS[key]["""file_name"""] )
def UpperCamelCase( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
os.makedirs(lowercase_ , exist_ok=lowercase_ )
hf_hub_download(repo_id=lowercase_ , filename=lowercase_ , local_dir=lowercase_ )
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_=False , lowercase_="text" ) -> List[str]:
'''simple docstring'''
if model_type == "text":
snake_case_ = BarkSemanticModel
snake_case_ = BarkSemanticConfig
snake_case_ = BarkSemanticGenerationConfig
elif model_type == "coarse":
snake_case_ = BarkCoarseModel
snake_case_ = BarkCoarseConfig
snake_case_ = BarkCoarseGenerationConfig
elif model_type == "fine":
snake_case_ = BarkFineModel
snake_case_ = BarkFineConfig
snake_case_ = BarkFineGenerationConfig
else:
raise NotImplementedError()
snake_case_ = f'''{model_type}_small''' if use_small else model_type
snake_case_ = REMOTE_MODEL_PATHS[model_key]
if not os.path.exists(lowercase_ ):
logger.info(f'''{model_type} model not found, downloading into `{CACHE_DIR}`.''' )
_download(model_info["""repo_id"""] , model_info["""file_name"""] )
snake_case_ = torch.load(lowercase_ , map_location=lowercase_ )
# this is a hack
snake_case_ = checkpoint["""model_args"""]
if "input_vocab_size" not in model_args:
snake_case_ = model_args["""vocab_size"""]
snake_case_ = model_args["""vocab_size"""]
del model_args["vocab_size"]
# convert Bark model arguments to HF Bark model arguments
snake_case_ = model_args.pop("""n_head""" )
snake_case_ = model_args.pop("""n_embd""" )
snake_case_ = model_args.pop("""n_layer""" )
snake_case_ = ConfigClass(**checkpoint["""model_args"""] )
snake_case_ = ModelClass(config=lowercase_ )
snake_case_ = GenerationConfigClass()
snake_case_ = model_generation_config
snake_case_ = checkpoint["""model"""]
# fixup checkpoint
snake_case_ = """_orig_mod."""
for k, v in list(state_dict.items() ):
if k.startswith(lowercase_ ):
# replace part of the key with corresponding layer name in HF implementation
snake_case_ = k[len(lowercase_ ) :]
for old_layer_name in new_layer_name_dict:
snake_case_ = new_k.replace(lowercase_ , new_layer_name_dict[old_layer_name] )
snake_case_ = state_dict.pop(lowercase_ )
snake_case_ = set(state_dict.keys() ) - set(model.state_dict().keys() )
snake_case_ = {k for k in extra_keys if not k.endswith(""".attn.bias""" )}
snake_case_ = set(model.state_dict().keys() ) - set(state_dict.keys() )
snake_case_ = {k for k in missing_keys if not k.endswith(""".attn.bias""" )}
if len(lowercase_ ) != 0:
raise ValueError(f'''extra keys found: {extra_keys}''' )
if len(lowercase_ ) != 0:
raise ValueError(f'''missing keys: {missing_keys}''' )
model.load_state_dict(lowercase_ , strict=lowercase_ )
snake_case_ = model.num_parameters(exclude_embeddings=lowercase_ )
snake_case_ = checkpoint["""best_val_loss"""].item()
logger.info(f'''model loaded: {round(n_params/1e6 , 1 )}M params, {round(lowercase_ , 3 )} loss''' )
model.eval()
model.to(lowercase_ )
del checkpoint, state_dict
return model
def UpperCamelCase( lowercase_ , lowercase_=False , lowercase_="text" ) -> Union[str, Any]:
'''simple docstring'''
if model_type not in ("text", "coarse", "fine"):
raise NotImplementedError()
snake_case_ = """cpu""" # do conversion on cpu
snake_case_ = _get_ckpt_path(lowercase_ , use_small=lowercase_ )
snake_case_ = _load_model(lowercase_ , lowercase_ , model_type=lowercase_ , use_small=lowercase_ )
# load bark initial model
snake_case_ = _bark_load_model(lowercase_ , """cpu""" , model_type=lowercase_ , use_small=lowercase_ )
if model_type == "text":
snake_case_ = bark_model["""model"""]
if model.num_parameters(exclude_embeddings=lowercase_ ) != bark_model.get_num_params():
raise ValueError("""initial and new models don't have the same number of parameters""" )
# check if same output as the bark model
snake_case_ = 5
snake_case_ = 10
if model_type in ["text", "coarse"]:
snake_case_ = torch.randint(256 , (batch_size, sequence_length) , dtype=torch.int )
snake_case_ = bark_model(lowercase_ )[0]
snake_case_ = model(lowercase_ )
# take last logits
snake_case_ = output_new_model_total.logits[:, [-1], :]
else:
snake_case_ = 3
snake_case_ = 8
snake_case_ = torch.randint(256 , (batch_size, sequence_length, n_codes_total) , dtype=torch.int )
snake_case_ = model(lowercase_ , lowercase_ )
snake_case_ = bark_model(lowercase_ , lowercase_ )
snake_case_ = output_new_model_total.logits
# output difference should come from the difference of self-attention implementation design
if output_new_model.shape != output_old_model.shape:
raise ValueError("""initial and new outputs don't have the same shape""" )
if (output_new_model - output_old_model).abs().max().item() > 1e-3:
raise ValueError("""initial and new outputs are not equal""" )
Path(lowercase_ ).mkdir(exist_ok=lowercase_ )
model.save_pretrained(lowercase_ )
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , ) -> Dict:
'''simple docstring'''
snake_case_ = os.path.join(lowercase_ , lowercase_ )
snake_case_ = BarkSemanticConfig.from_pretrained(os.path.join(lowercase_ , """config.json""" ) )
snake_case_ = BarkCoarseConfig.from_pretrained(os.path.join(lowercase_ , """config.json""" ) )
snake_case_ = BarkFineConfig.from_pretrained(os.path.join(lowercase_ , """config.json""" ) )
snake_case_ = EncodecConfig.from_pretrained("""facebook/encodec_24khz""" )
snake_case_ = BarkSemanticModel.from_pretrained(lowercase_ )
snake_case_ = BarkCoarseModel.from_pretrained(lowercase_ )
snake_case_ = BarkFineModel.from_pretrained(lowercase_ )
snake_case_ = EncodecModel.from_pretrained("""facebook/encodec_24khz""" )
snake_case_ = BarkConfig.from_sub_model_configs(
lowercase_ , lowercase_ , lowercase_ , lowercase_ )
snake_case_ = BarkGenerationConfig.from_sub_model_configs(
semantic.generation_config , coarseAcoustic.generation_config , fineAcoustic.generation_config )
snake_case_ = BarkModel(lowercase_ )
snake_case_ = semantic
snake_case_ = coarseAcoustic
snake_case_ = fineAcoustic
snake_case_ = codec
snake_case_ = bark_generation_config
Path(lowercase_ ).mkdir(exist_ok=lowercase_ )
bark.save_pretrained(lowercase_ , repo_id=lowercase_ , push_to_hub=lowercase_ )
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''model_type''', type=str, help='''text, coarse or fine.''')
parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--is_small''', action='''store_true''', help='''convert the small version instead of the large.''')
lowerCamelCase_ = parser.parse_args()
load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
| 714
|
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowerCamelCase_ = pytest.mark.integration
@pytest.mark.parametrize("""path""" , ["""paws""", """csv"""] )
def UpperCamelCase( lowercase_ , lowercase_ ) -> List[Any]:
'''simple docstring'''
inspect_dataset(lowercase_ , lowercase_ )
snake_case_ = path + """.py"""
assert script_name in os.listdir(lowercase_ )
assert "__pycache__" not in os.listdir(lowercase_ )
@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning""" )
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""" )
@pytest.mark.parametrize("""path""" , ["""accuracy"""] )
def UpperCamelCase( lowercase_ , lowercase_ ) -> int:
'''simple docstring'''
inspect_metric(lowercase_ , lowercase_ )
snake_case_ = path + """.py"""
assert script_name in os.listdir(lowercase_ )
assert "__pycache__" not in os.listdir(lowercase_ )
@pytest.mark.parametrize(
"""path, config_name, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> List[str]:
'''simple docstring'''
snake_case_ = get_dataset_config_info(lowercase_ , config_name=lowercase_ )
assert info.config_name == config_name
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> Dict:
'''simple docstring'''
with pytest.raises(lowercase_ ):
get_dataset_config_info(lowercase_ , config_name=lowercase_ )
@pytest.mark.parametrize(
"""path, expected""" , [
("""squad""", """plain_text"""),
("""acronym_identification""", """default"""),
("""lhoestq/squad""", """plain_text"""),
("""lhoestq/test""", """default"""),
("""lhoestq/demo1""", """lhoestq--demo1"""),
("""dalle-mini/wit""", """dalle-mini--wit"""),
] , )
def UpperCamelCase( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
snake_case_ = get_dataset_config_names(lowercase_ )
assert expected in config_names
@pytest.mark.parametrize(
"""path, expected_configs, expected_splits_in_first_config""" , [
("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
] , )
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> List[str]:
'''simple docstring'''
snake_case_ = get_dataset_infos(lowercase_ )
assert list(infos.keys() ) == expected_configs
snake_case_ = expected_configs[0]
assert expected_config in infos
snake_case_ = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits_in_first_config
@pytest.mark.parametrize(
"""path, expected_config, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> List[str]:
'''simple docstring'''
snake_case_ = get_dataset_infos(lowercase_ )
assert expected_config in infos
snake_case_ = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
with pytest.raises(lowercase_ ):
get_dataset_split_names(lowercase_ , config_name=lowercase_ )
| 161
| 0
|
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowerCAmelCase ( __snake_case ):
'''simple docstring'''
def __init__(self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = None , ) -> List[str]:
super().__init__()
self.register_modules(transformer=UpperCAmelCase , vae=UpperCAmelCase , scheduler=UpperCAmelCase )
# create a imagenet -> id dictionary for easier use
_snake_case = {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split(""",""" ):
_snake_case = int(UpperCAmelCase )
_snake_case = dict(sorted(self.labels.items() ) )
def lowercase (self , UpperCAmelCase ) -> List[int]:
if not isinstance(UpperCAmelCase , UpperCAmelCase ):
_snake_case = list(UpperCAmelCase )
for l in label:
if l not in self.labels:
raise ValueError(
f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.""" )
return [self.labels[l] for l in label]
@torch.no_grad()
def __call__(self , UpperCAmelCase , UpperCAmelCase = 4.0 , UpperCAmelCase = None , UpperCAmelCase = 50 , UpperCAmelCase = "pil" , UpperCAmelCase = True , ) -> Union[ImagePipelineOutput, Tuple]:
_snake_case = len(UpperCAmelCase )
_snake_case = self.transformer.config.sample_size
_snake_case = self.transformer.config.in_channels
_snake_case = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=UpperCAmelCase , device=self.device , dtype=self.transformer.dtype , )
_snake_case = torch.cat([latents] * 2 ) if guidance_scale > 1 else latents
_snake_case = torch.tensor(UpperCAmelCase , device=self.device ).reshape(-1 )
_snake_case = torch.tensor([1000] * batch_size , device=self.device )
_snake_case = torch.cat([class_labels, class_null] , 0 ) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(UpperCAmelCase )
for t in self.progress_bar(self.scheduler.timesteps ):
if guidance_scale > 1:
_snake_case = latent_model_input[: len(UpperCAmelCase ) // 2]
_snake_case = torch.cat([half, half] , dim=0 )
_snake_case = self.scheduler.scale_model_input(UpperCAmelCase , UpperCAmelCase )
_snake_case = t
if not torch.is_tensor(UpperCAmelCase ):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
_snake_case = latent_model_input.device.type == """mps"""
if isinstance(UpperCAmelCase , UpperCAmelCase ):
_snake_case = torch.floataa if is_mps else torch.floataa
else:
_snake_case = torch.intaa if is_mps else torch.intaa
_snake_case = torch.tensor([timesteps] , dtype=UpperCAmelCase , device=latent_model_input.device )
elif len(timesteps.shape ) == 0:
_snake_case = timesteps[None].to(latent_model_input.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
_snake_case = timesteps.expand(latent_model_input.shape[0] )
# predict noise model_output
_snake_case = self.transformer(
UpperCAmelCase , timestep=UpperCAmelCase , class_labels=UpperCAmelCase ).sample
# perform guidance
if guidance_scale > 1:
_snake_case, _snake_case = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
_snake_case, _snake_case = torch.split(UpperCAmelCase , len(UpperCAmelCase ) // 2 , dim=0 )
_snake_case = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
_snake_case = torch.cat([half_eps, half_eps] , dim=0 )
_snake_case = torch.cat([eps, rest] , dim=1 )
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
_snake_case, _snake_case = torch.split(UpperCAmelCase , UpperCAmelCase , dim=1 )
else:
_snake_case = noise_pred
# compute previous image: x_t -> x_t-1
_snake_case = self.scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ).prev_sample
if guidance_scale > 1:
_snake_case, _snake_case = latent_model_input.chunk(2 , dim=0 )
else:
_snake_case = latent_model_input
_snake_case = 1 / self.vae.config.scaling_factor * latents
_snake_case = self.vae.decode(UpperCAmelCase ).sample
_snake_case = (samples / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
_snake_case = samples.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
_snake_case = self.numpy_to_pil(UpperCAmelCase )
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=UpperCAmelCase )
| 585
|
'''simple docstring'''
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
if len(_SCREAMING_SNAKE_CASE ) == 0:
return []
_snake_case, _snake_case = min(_SCREAMING_SNAKE_CASE ), max(_SCREAMING_SNAKE_CASE )
_snake_case = int(max_value - min_value ) + 1
_snake_case = [[] for _ in range(_SCREAMING_SNAKE_CASE )]
for i in my_list:
buckets[int(i - min_value )].append(_SCREAMING_SNAKE_CASE )
return [v for bucket in buckets for v in sorted(_SCREAMING_SNAKE_CASE )]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]
| 585
| 1
|
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
),
}
class a__ ( UpperCAmelCase__ ):
"""simple docstring"""
A__ : Union[str, Any] = '''pix2struct_text_model'''
A__ : Union[str, Any] = ['''past_key_values''']
A__ : Optional[Any] = {
'''hidden_size''': '''hidden_size''',
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self :Optional[int] , lowercase__ :int=5_0244 , lowercase__ :str=768 , lowercase__ :List[Any]=64 , lowercase__ :Union[str, Any]=2048 , lowercase__ :Any=12 , lowercase__ :Any=12 , lowercase__ :Optional[int]=32 , lowercase__ :str=128 , lowercase__ :int=0.1 , lowercase__ :int=1E-6 , lowercase__ :str=1.0 , lowercase__ :Optional[int]="gelu_new" , lowercase__ :List[str]=0 , lowercase__ :Union[str, Any]=False , lowercase__ :Optional[int]=0 , lowercase__ :List[Any]=1 , lowercase__ :Dict=False , lowercase__ :int=True , **lowercase__ :List[str] , ):
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=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , decoder_start_token_id=__lowerCAmelCase , tie_word_embeddings=__lowerCAmelCase , is_decoder=__lowerCAmelCase , **__lowerCAmelCase , )
@classmethod
def __UpperCAmelCase ( cls :Dict , lowercase__ :Union[str, os.PathLike] , **lowercase__ :Dict ):
cls._set_token_in_kwargs(__lowerCAmelCase )
lowercase , lowercase = cls.get_config_dict(__lowerCAmelCase , **__lowerCAmelCase )
# 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(__lowerCAmelCase , **__lowerCAmelCase )
class a__ ( UpperCAmelCase__ ):
"""simple docstring"""
A__ : List[Any] = '''pix2struct_vision_model'''
def __init__( self :List[Any] , lowercase__ :List[Any]=768 , lowercase__ :str=768 , lowercase__ :Optional[int]=2048 , lowercase__ :int=64 , lowercase__ :Any=12 , lowercase__ :str=12 , lowercase__ :Optional[Any]="gelu_new" , lowercase__ :Any=1E-6 , lowercase__ :Optional[Any]=0.0 , lowercase__ :Tuple=0.0 , lowercase__ :int=1E-10 , lowercase__ :int=1.0 , lowercase__ :Optional[Any]=4096 , lowercase__ :str=32 , lowercase__ :Any=128 , **lowercase__ :Optional[Any] , ):
super().__init__(**__lowerCAmelCase )
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 __UpperCAmelCase ( cls :List[str] , lowercase__ :Union[str, os.PathLike] , **lowercase__ :str ):
cls._set_token_in_kwargs(__lowerCAmelCase )
lowercase , lowercase = cls.get_config_dict(__lowerCAmelCase , **__lowerCAmelCase )
# 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(__lowerCAmelCase , **__lowerCAmelCase )
class a__ ( UpperCAmelCase__ ):
"""simple docstring"""
A__ : List[Any] = '''pix2struct'''
A__ : int = True
def __init__( self :Optional[Any] , lowercase__ :Union[str, Any]=None , lowercase__ :Union[str, Any]=None , lowercase__ :Optional[Any]=1.0 , lowercase__ :str=0.02 , lowercase__ :Optional[Any]=False , lowercase__ :Dict=False , lowercase__ :Tuple=True , **lowercase__ :Any , ):
super().__init__(tie_word_embeddings=__lowerCAmelCase , is_encoder_decoder=__lowerCAmelCase , **__lowerCAmelCase )
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(**__lowerCAmelCase )
lowercase = PixaStructVisionConfig(**__lowerCAmelCase )
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 __UpperCAmelCase ( cls :Optional[Any] , lowercase__ :PixaStructTextConfig , lowercase__ :PixaStructVisionConfig , **lowercase__ :List[Any] ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__lowerCAmelCase )
def __UpperCAmelCase ( self :Optional[int] ):
lowercase = copy.deepcopy(self.__dict__ )
lowercase = self.text_config.to_dict()
lowercase = self.vision_config.to_dict()
lowercase = self.__class__.model_type
return output
| 710
|
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'''
''' Distillation'''
)
)
parser.add_argument('''--model_type''', default='''roberta''', choices=['''roberta''', '''gpt2'''])
parser.add_argument('''--model_name''', default='''roberta-large''', type=str)
parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_roberta_048131723.pth''', type=str)
parser.add_argument('''--vocab_transform''', action='''store_true''')
__magic_name__ = parser.parse_args()
if args.model_type == "roberta":
__magic_name__ = RobertaForMaskedLM.from_pretrained(args.model_name)
__magic_name__ = '''roberta'''
elif args.model_type == "gpt2":
__magic_name__ = GPTaLMHeadModel.from_pretrained(args.model_name)
__magic_name__ = '''transformer'''
__magic_name__ = model.state_dict()
__magic_name__ = {}
# Embeddings #
if args.model_type == "gpt2":
for param_name in ["wte.weight", "wpe.weight"]:
__magic_name__ = state_dict[F"""{prefix}.{param_name}"""]
else:
for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
__magic_name__ = F"""{prefix}.embeddings.{w}.weight"""
__magic_name__ = state_dict[param_name]
for w in ["weight", "bias"]:
__magic_name__ = F"""{prefix}.embeddings.LayerNorm.{w}"""
__magic_name__ = state_dict[param_name]
# Transformer Blocks #
__magic_name__ = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
if args.model_type == "gpt2":
for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
for w in ["weight", "bias"]:
__magic_name__ = state_dict[
F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
]
__magic_name__ = state_dict[F"""{prefix}.h.{teacher_idx}.attn.bias"""]
else:
for layer in [
"attention.self.query",
"attention.self.key",
"attention.self.value",
"attention.output.dense",
"attention.output.LayerNorm",
"intermediate.dense",
"output.dense",
"output.LayerNorm",
]:
for w in ["weight", "bias"]:
__magic_name__ = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
]
std_idx += 1
# Language Modeling Head ###s
if args.model_type == "roberta":
for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
__magic_name__ = state_dict[F"""{layer}"""]
if args.vocab_transform:
for w in ["weight", "bias"]:
__magic_name__ = state_dict[F"""lm_head.dense.{w}"""]
__magic_name__ = state_dict[F"""lm_head.layer_norm.{w}"""]
elif args.model_type == "gpt2":
for w in ["weight", "bias"]:
__magic_name__ = state_dict[F"""{prefix}.ln_f.{w}"""]
__magic_name__ = state_dict['''lm_head.weight''']
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)
| 314
| 0
|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez import BarthezTokenizer
else:
__magic_name__ = None
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
__magic_name__ = {
'''vocab_file''': {
'''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
'''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
'''moussaKam/barthez-orangesum-title''': (
'''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
),
},
'''tokenizer_file''': {
'''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
'''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
'''moussaKam/barthez-orangesum-title''': (
'''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
),
},
}
__magic_name__ = {
'''moussaKam/mbarthez''': 1_024,
'''moussaKam/barthez''': 1_024,
'''moussaKam/barthez-orangesum-title''': 1_024,
}
__magic_name__ = '''▁'''
class __SCREAMING_SNAKE_CASE ( UpperCamelCase):
"""simple docstring"""
__UpperCAmelCase = VOCAB_FILES_NAMES
__UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase = ["input_ids", "attention_mask"]
__UpperCAmelCase = BarthezTokenizer
def __init__( self , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase="<s>" , _UpperCAmelCase="</s>" , _UpperCAmelCase="</s>" , _UpperCAmelCase="<s>" , _UpperCAmelCase="<unk>" , _UpperCAmelCase="<pad>" , _UpperCAmelCase="<mask>" , **_UpperCAmelCase , ):
# Mask token behave like a normal word, i.e. include the space before it
__snake_case : Any = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else mask_token
super().__init__(
_UpperCAmelCase , tokenizer_file=_UpperCAmelCase , bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , cls_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , **_UpperCAmelCase , )
__snake_case : Tuple = vocab_file
__snake_case : Optional[int] = False if not self.vocab_file else True
def lowercase_ ( self , _UpperCAmelCase , _UpperCAmelCase = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__snake_case : List[Any] = [self.cls_token_id]
__snake_case : str = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowercase_ ( self , _UpperCAmelCase , _UpperCAmelCase = None ):
__snake_case : Dict = [self.sep_token_id]
__snake_case : int = [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 lowercase_ ( self , _UpperCAmelCase , _UpperCAmelCase = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(_UpperCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
__snake_case : Optional[Any] = os.path.join(
_UpperCAmelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ):
copyfile(self.vocab_file , _UpperCAmelCase )
return (out_vocab_file,)
| 576
|
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def UpperCAmelCase__( __UpperCAmelCase : int ):
__snake_case : Tuple = filter(lambda __UpperCAmelCase : p.requires_grad , model.parameters() )
__snake_case : List[str] = sum([np.prod(p.size() ) for p in model_parameters] )
return params
__magic_name__ = logging.getLogger(__name__)
def UpperCAmelCase__( __UpperCAmelCase : Any , __UpperCAmelCase : Optional[Any] ):
if metric == "rouge2":
__snake_case : Dict = '{val_avg_rouge2:.4f}-{step_count}'
elif metric == "bleu":
__snake_case : Any = '{val_avg_bleu:.4f}-{step_count}'
elif metric == "em":
__snake_case : Dict = '{val_avg_em:.4f}-{step_count}'
else:
raise NotImplementedError(
F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
' function.' )
__snake_case : List[Any] = ModelCheckpoint(
dirpath=__UpperCAmelCase , filename=__UpperCAmelCase , monitor=F"""val_{metric}""" , mode='max' , save_top_k=3 , every_n_epochs=1 , )
return checkpoint_callback
def UpperCAmelCase__( __UpperCAmelCase : List[str] , __UpperCAmelCase : Dict ):
return EarlyStopping(
monitor=F"""val_{metric}""" , mode='min' if 'loss' in metric else 'max' , patience=__UpperCAmelCase , verbose=__UpperCAmelCase , )
class __SCREAMING_SNAKE_CASE ( pl.Callback):
"""simple docstring"""
def lowercase_ ( self , _UpperCAmelCase , _UpperCAmelCase ):
__snake_case : List[str] = {F"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(_UpperCAmelCase )
@rank_zero_only
def lowercase_ ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=True ):
logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****""" )
__snake_case : List[str] = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']} )
# Log results
__snake_case : Dict = Path(pl_module.hparams.output_dir )
if type_path == "test":
__snake_case : Union[str, Any] = od / 'test_results.txt'
__snake_case : Union[str, Any] = od / 'test_generations.txt'
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
__snake_case : Tuple = od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
__snake_case : List[str] = od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
results_file.parent.mkdir(exist_ok=_UpperCAmelCase )
generations_file.parent.mkdir(exist_ok=_UpperCAmelCase )
with open(_UpperCAmelCase , 'a+' ) as writer:
for key in sorted(_UpperCAmelCase ):
if key in ["log", "progress_bar", "preds"]:
continue
__snake_case : Tuple = metrics[key]
if isinstance(_UpperCAmelCase , torch.Tensor ):
__snake_case : List[Any] = val.item()
__snake_case : Dict = F"""{key}: {val:.6f}\n"""
writer.write(_UpperCAmelCase )
if not save_generations:
return
if "preds" in metrics:
__snake_case : Optional[int] = '\n'.join(metrics['preds'] )
generations_file.open('w+' ).write(_UpperCAmelCase )
@rank_zero_only
def lowercase_ ( self , _UpperCAmelCase , _UpperCAmelCase ):
try:
__snake_case : Any = pl_module.model.model.num_parameters()
except AttributeError:
__snake_case : List[Any] = pl_module.model.num_parameters()
__snake_case : List[str] = count_trainable_parameters(_UpperCAmelCase )
# mp stands for million parameters
trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6} )
@rank_zero_only
def lowercase_ ( self , _UpperCAmelCase , _UpperCAmelCase ):
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(_UpperCAmelCase , _UpperCAmelCase , 'test' )
@rank_zero_only
def lowercase_ ( self , _UpperCAmelCase , _UpperCAmelCase ):
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 576
| 1
|
"""simple docstring"""
import sys
a : Union[str, Any] = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def _UpperCamelCase ( _A ) -> int:
"""simple docstring"""
_UpperCAmelCase = 1
for digit in s:
product *= int(__A )
return product
def _UpperCamelCase ( _A = N ) -> int:
"""simple docstring"""
_UpperCAmelCase = -sys.maxsize - 1
_UpperCAmelCase = n[:1_3]
_UpperCAmelCase = 1_3
while cur_index < len(__A ) - 1_3:
if int(n[cur_index] ) >= int(substr[0] ):
_UpperCAmelCase = substr[1:] + n[cur_index]
cur_index += 1
else:
_UpperCAmelCase = max(__A , str_eval(__A ) )
_UpperCAmelCase = n[cur_index : cur_index + 1_3]
cur_index += 1_3
return largest_product
if __name__ == "__main__":
print(F"{solution() = }")
| 708
|
"""simple docstring"""
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DPTImageProcessor
class a_ :
def __init__( self : Union[str, Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : Optional[int]=2 , __UpperCamelCase : int=32 , __UpperCamelCase : Tuple=16 , __UpperCamelCase : Dict=3 , __UpperCamelCase : Dict=True , __UpperCamelCase : Optional[Any]=True , __UpperCamelCase : Optional[Any]=32 , __UpperCamelCase : Any=4 , __UpperCamelCase : Optional[int]=[0, 1, 2, 3] , __UpperCamelCase : str=4 , __UpperCamelCase : Optional[Any]=37 , __UpperCamelCase : str="gelu" , __UpperCamelCase : Optional[int]=0.1 , __UpperCamelCase : Any=0.1 , __UpperCamelCase : Any=0.0_2 , __UpperCamelCase : Optional[int]=3 , __UpperCamelCase : int=[1, 3_84, 24, 24] , __UpperCamelCase : Union[str, Any]=True , __UpperCamelCase : Any=None , ) ->Optional[Any]:
'''simple docstring'''
_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 = backbone_out_indices
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_labels
_UpperCAmelCase = backbone_featmap_shape
_UpperCAmelCase = scope
_UpperCAmelCase = is_hybrid
# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
_UpperCAmelCase = (image_size // patch_size) ** 2
_UpperCAmelCase = num_patches + 1
def _snake_case ( self : str ) ->int:
'''simple docstring'''
_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.image_size, self.image_size] , self.num_labels )
_UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def _snake_case ( self : List[str] ) ->List[Any]:
'''simple docstring'''
_UpperCAmelCase = {
"""global_padding""": """same""",
"""layer_type""": """bottleneck""",
"""depths""": [3, 4, 9],
"""out_features""": ["""stage1""", """stage2""", """stage3"""],
"""embedding_dynamic_padding""": True,
"""hidden_sizes""": [96, 1_92, 3_84, 7_68],
"""num_groups""": 2,
}
return DPTConfig(
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 , backbone_out_indices=self.backbone_out_indices , 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=__UpperCamelCase , initializer_range=self.initializer_range , is_hybrid=self.is_hybrid , backbone_config=__UpperCamelCase , backbone_featmap_shape=self.backbone_featmap_shape , )
def _snake_case ( self : Any , __UpperCamelCase : Dict , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Optional[Any] ) ->Dict:
'''simple docstring'''
_UpperCAmelCase = DPTModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = model(__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _snake_case ( self : List[Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : str , __UpperCamelCase : List[Any] ) ->int:
'''simple docstring'''
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = DPTForDepthEstimation(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = model(__UpperCamelCase )
self.parent.assertEqual(result.predicted_depth.shape , (self.batch_size, self.image_size, self.image_size) )
def _snake_case ( self : Any , __UpperCamelCase : List[Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Union[str, Any] ) ->List[Any]:
'''simple docstring'''
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = DPTForSemanticSegmentation(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = model(__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size) )
def _snake_case ( self : Tuple ) ->Any:
'''simple docstring'''
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class a_ ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
a : Dict = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
a : int = (
{
'depth-estimation': DPTForDepthEstimation,
'feature-extraction': DPTModel,
'image-segmentation': DPTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
a : str = False
a : List[str] = False
a : Dict = False
def _snake_case ( self : Any ) ->Any:
'''simple docstring'''
_UpperCAmelCase = DPTModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=__UpperCamelCase , has_text_modality=__UpperCamelCase , hidden_size=37 )
def _snake_case ( self : Optional[int] ) ->Any:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="""DPT does not use inputs_embeds""" )
def _snake_case ( self : Tuple ) ->Tuple:
'''simple docstring'''
pass
def _snake_case ( self : int ) ->Any:
'''simple docstring'''
_UpperCAmelCase ,_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(__UpperCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_UpperCAmelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCamelCase , nn.Linear ) )
def _snake_case ( self : Optional[int] ) ->Optional[Any]:
'''simple docstring'''
_UpperCAmelCase ,_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(__UpperCamelCase )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __UpperCamelCase )
def _snake_case ( self : Optional[Any] ) ->Dict:
'''simple docstring'''
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def _snake_case ( self : str ) ->int:
'''simple docstring'''
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_depth_estimation(*__UpperCamelCase )
def _snake_case ( self : Any ) ->Tuple:
'''simple docstring'''
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase )
def _snake_case ( self : str ) ->Any:
'''simple docstring'''
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
_UpperCAmelCase ,_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase = True
if model_class in get_values(__UpperCamelCase ):
continue
_UpperCAmelCase = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.train()
_UpperCAmelCase = self._prepare_for_class(__UpperCamelCase , __UpperCamelCase , return_labels=__UpperCamelCase )
_UpperCAmelCase = model(**__UpperCamelCase ).loss
loss.backward()
def _snake_case ( self : List[str] ) ->Dict:
'''simple docstring'''
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
_UpperCAmelCase ,_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase = False
_UpperCAmelCase = True
if model_class in get_values(__UpperCamelCase ) or not model_class.supports_gradient_checkpointing:
continue
_UpperCAmelCase = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.gradient_checkpointing_enable()
model.train()
_UpperCAmelCase = self._prepare_for_class(__UpperCamelCase , __UpperCamelCase , return_labels=__UpperCamelCase )
_UpperCAmelCase = model(**__UpperCamelCase ).loss
loss.backward()
def _snake_case ( self : Tuple ) ->List[str]:
'''simple docstring'''
_UpperCAmelCase ,_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase = _config_zero_init(__UpperCamelCase )
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(config=__UpperCamelCase )
# Skip the check for the backbone
_UpperCAmelCase = []
for name, module in model.named_modules():
if module.__class__.__name__ == "DPTViTHybridEmbeddings":
_UpperCAmelCase = [f"""{name}.{key}""" for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _snake_case ( self : Dict ) ->Tuple:
'''simple docstring'''
pass
@slow
def _snake_case ( self : Optional[int] ) ->List[Any]:
'''simple docstring'''
for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
_UpperCAmelCase = DPTModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def _snake_case ( self : List[Any] ) ->Optional[int]:
'''simple docstring'''
_UpperCAmelCase ,_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase = """add"""
with self.assertRaises(__UpperCamelCase ):
_UpperCAmelCase = DPTForDepthEstimation(__UpperCamelCase )
def _UpperCamelCase ( ) -> int:
"""simple docstring"""
_UpperCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
@slow
class a_ ( unittest.TestCase ):
def _snake_case ( self : Any ) ->Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas""" )
_UpperCAmelCase = DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas""" ).to(__UpperCamelCase )
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=__UpperCamelCase , return_tensors="""pt""" ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(**__UpperCamelCase )
_UpperCAmelCase = outputs.predicted_depth
# verify the predicted depth
_UpperCAmelCase = torch.Size((1, 3_84, 3_84) )
self.assertEqual(predicted_depth.shape , __UpperCamelCase )
_UpperCAmelCase = torch.tensor(
[[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]] ).to(__UpperCamelCase )
self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00 , __UpperCamelCase , atol=1e-4 ) )
| 19
| 0
|
import torch
from torch import nn
class __a ( nn.Module ):
def __init__( self : int , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : Dict=1 , SCREAMING_SNAKE_CASE : List[str]=False ):
'''simple docstring'''
super().__init__()
UpperCamelCase__ : Dict = n_token
UpperCamelCase__ : str = d_embed
UpperCamelCase__ : List[Any] = d_proj
UpperCamelCase__ : List[Any] = cutoffs + [n_token]
UpperCamelCase__ : Optional[int] = [0] + self.cutoffs
UpperCamelCase__ : List[str] = div_val
UpperCamelCase__ : str = self.cutoffs[0]
UpperCamelCase__ : Any = len(self.cutoffs ) - 1
UpperCamelCase__ : List[str] = self.shortlist_size + self.n_clusters
if self.n_clusters > 0:
UpperCamelCase__ : int = nn.Parameter(torch.zeros(self.n_clusters , self.d_embed ) )
UpperCamelCase__ : Dict = nn.Parameter(torch.zeros(self.n_clusters ) )
UpperCamelCase__ : Any = nn.ModuleList()
UpperCamelCase__ : Union[str, Any] = nn.ParameterList()
if div_val == 1:
for i in range(len(self.cutoffs ) ):
if d_proj != d_embed:
self.out_projs.append(nn.Parameter(torch.FloatTensor(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) ) )
else:
self.out_projs.append(SCREAMING_SNAKE_CASE )
self.out_layers.append(nn.Linear(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
else:
for i in range(len(self.cutoffs ) ):
UpperCamelCase__ , UpperCamelCase__ : Tuple = self.cutoff_ends[i], self.cutoff_ends[i + 1]
UpperCamelCase__ : int = d_embed // (div_val**i)
self.out_projs.append(nn.Parameter(torch.FloatTensor(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) ) )
self.out_layers.append(nn.Linear(SCREAMING_SNAKE_CASE , r_idx - l_idx ) )
UpperCamelCase__ : Optional[int] = keep_order
def __lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
if proj is None:
UpperCamelCase__ : Tuple = nn.functional.linear(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , bias=SCREAMING_SNAKE_CASE )
else:
# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
UpperCamelCase__ : int = nn.functional.linear(SCREAMING_SNAKE_CASE , proj.t().contiguous() )
UpperCamelCase__ : int = nn.functional.linear(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , bias=SCREAMING_SNAKE_CASE )
# else:
# logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
# if bias is not None:
# logit = logit + bias
return logit
def __lowercase ( self : Tuple , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Dict=None , SCREAMING_SNAKE_CASE : Optional[int]=False ):
'''simple docstring'''
if labels is not None:
# Shift so that tokens < n predict n
UpperCamelCase__ : Dict = hidden[..., :-1, :].contiguous()
UpperCamelCase__ : Optional[Any] = labels[..., 1:].contiguous()
UpperCamelCase__ : str = hidden.view(-1 , hidden.size(-1 ) )
UpperCamelCase__ : Optional[int] = labels.view(-1 )
if hidden.size(0 ) != labels.size(0 ):
raise RuntimeError("Input and labels should have the same size in the batch dimension." )
else:
UpperCamelCase__ : str = hidden.view(-1 , hidden.size(-1 ) )
if self.n_clusters == 0:
UpperCamelCase__ : List[Any] = self._compute_logit(SCREAMING_SNAKE_CASE , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
if labels is not None:
UpperCamelCase__ : str = labels != -1_00
UpperCamelCase__ : str = torch.zeros_like(SCREAMING_SNAKE_CASE , dtype=hidden.dtype , device=hidden.device )
UpperCamelCase__ : Optional[int] = (
-nn.functional.log_softmax(SCREAMING_SNAKE_CASE , dim=-1 )[mask].gather(1 , labels[mask].unsqueeze(1 ) ).squeeze(1 )
)
else:
UpperCamelCase__ : List[str] = nn.functional.log_softmax(SCREAMING_SNAKE_CASE , dim=-1 )
else:
# construct weights and biases
UpperCamelCase__ , UpperCamelCase__ : int = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
UpperCamelCase__ , UpperCamelCase__ : Tuple = self.cutoff_ends[i], self.cutoff_ends[i + 1]
UpperCamelCase__ : List[Any] = self.out_layers[0].weight[l_idx:r_idx]
UpperCamelCase__ : Dict = self.out_layers[0].bias[l_idx:r_idx]
else:
UpperCamelCase__ : str = self.out_layers[i].weight
UpperCamelCase__ : str = self.out_layers[i].bias
if i == 0:
UpperCamelCase__ : List[Any] = torch.cat([weight_i, self.cluster_weight] , dim=0 )
UpperCamelCase__ : List[str] = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(SCREAMING_SNAKE_CASE )
biases.append(SCREAMING_SNAKE_CASE )
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : Optional[Any] = weights[0], biases[0], self.out_projs[0]
UpperCamelCase__ : Tuple = self._compute_logit(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Any = nn.functional.log_softmax(SCREAMING_SNAKE_CASE , dim=1 )
if labels is None:
UpperCamelCase__ : List[str] = hidden.new_empty((head_logit.size(0 ), self.n_token) )
else:
UpperCamelCase__ : Optional[Any] = torch.zeros_like(SCREAMING_SNAKE_CASE , dtype=hidden.dtype , device=hidden.device )
UpperCamelCase__ : str = 0
UpperCamelCase__ : Union[str, Any] = [0] + self.cutoffs
for i in range(len(SCREAMING_SNAKE_CASE ) - 1 ):
UpperCamelCase__ , UpperCamelCase__ : str = cutoff_values[i], cutoff_values[i + 1]
if labels is not None:
UpperCamelCase__ : Optional[Any] = (labels >= l_idx) & (labels < r_idx)
UpperCamelCase__ : List[Any] = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
UpperCamelCase__ : Optional[Any] = labels.index_select(0 , SCREAMING_SNAKE_CASE ) - l_idx
UpperCamelCase__ : Optional[int] = head_logprob.index_select(0 , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : str = hidden.index_select(0 , SCREAMING_SNAKE_CASE )
else:
UpperCamelCase__ : Any = hidden
if i == 0:
if labels is not None:
UpperCamelCase__ : str = head_logprob_i.gather(1 , target_i[:, None] ).squeeze(1 )
else:
UpperCamelCase__ : Tuple = head_logprob[:, : self.cutoffs[0]]
else:
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : Dict = weights[i], biases[i], self.out_projs[i]
UpperCamelCase__ : Dict = self._compute_logit(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Any = nn.functional.log_softmax(SCREAMING_SNAKE_CASE , dim=1 )
UpperCamelCase__ : Union[str, Any] = self.cutoffs[0] + i - 1 # No probability for the head cluster
if labels is not None:
UpperCamelCase__ : List[Any] = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
1 , target_i[:, None] ).squeeze(1 )
else:
UpperCamelCase__ : Dict = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
UpperCamelCase__ : List[str] = logprob_i
if labels is not None:
if (hasattr(self , "keep_order" ) and self.keep_order) or keep_order:
out.index_copy_(0 , SCREAMING_SNAKE_CASE , -logprob_i )
else:
out[offset : offset + logprob_i.size(0 )].copy_(-logprob_i )
offset += logprob_i.size(0 )
return out
def __lowercase ( self : int , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if self.n_clusters == 0:
UpperCamelCase__ : List[Any] = self._compute_logit(SCREAMING_SNAKE_CASE , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
return nn.functional.log_softmax(SCREAMING_SNAKE_CASE , dim=-1 )
else:
# construct weights and biases
UpperCamelCase__ , UpperCamelCase__ : List[str] = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
UpperCamelCase__ , UpperCamelCase__ : Dict = self.cutoff_ends[i], self.cutoff_ends[i + 1]
UpperCamelCase__ : Union[str, Any] = self.out_layers[0].weight[l_idx:r_idx]
UpperCamelCase__ : Any = self.out_layers[0].bias[l_idx:r_idx]
else:
UpperCamelCase__ : Tuple = self.out_layers[i].weight
UpperCamelCase__ : List[str] = self.out_layers[i].bias
if i == 0:
UpperCamelCase__ : Optional[int] = torch.cat([weight_i, self.cluster_weight] , dim=0 )
UpperCamelCase__ : Tuple = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(SCREAMING_SNAKE_CASE )
biases.append(SCREAMING_SNAKE_CASE )
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : List[Any] = weights[0], biases[0], self.out_projs[0]
UpperCamelCase__ : int = self._compute_logit(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[Any] = hidden.new_empty((head_logit.size(0 ), self.n_token) )
UpperCamelCase__ : Optional[Any] = nn.functional.log_softmax(SCREAMING_SNAKE_CASE , dim=1 )
UpperCamelCase__ : Dict = [0] + self.cutoffs
for i in range(len(SCREAMING_SNAKE_CASE ) - 1 ):
UpperCamelCase__ , UpperCamelCase__ : Optional[Any] = cutoff_values[i], cutoff_values[i + 1]
if i == 0:
UpperCamelCase__ : str = head_logprob[:, : self.cutoffs[0]]
else:
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : int = weights[i], biases[i], self.out_projs[i]
UpperCamelCase__ : Tuple = self._compute_logit(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[str] = nn.functional.log_softmax(SCREAMING_SNAKE_CASE , dim=1 )
UpperCamelCase__ : Any = head_logprob[:, -i] + tail_logprob_i
UpperCamelCase__ : int = logprob_i
return out
| 228
|
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __a :
def __lowercase ( self : Optional[Any] , SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
raise NotImplementedError()
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
raise NotImplementedError()
class __a ( A__ ):
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE : "AutoTokenizer" , SCREAMING_SNAKE_CASE : bool = False , **SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
UpperCamelCase__ : Any = tokenizer
UpperCamelCase__ : Tuple = skip_prompt
UpperCamelCase__ : List[str] = decode_kwargs
# variables used in the streaming process
UpperCamelCase__ : Union[str, Any] = []
UpperCamelCase__ : Union[str, Any] = 0
UpperCamelCase__ : int = True
def __lowercase ( self : Dict , SCREAMING_SNAKE_CASE : Dict ):
'''simple docstring'''
if len(value.shape ) > 1 and value.shape[0] > 1:
raise ValueError("TextStreamer only supports batch size 1" )
elif len(value.shape ) > 1:
UpperCamelCase__ : Dict = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
UpperCamelCase__ : Union[str, Any] = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist() )
UpperCamelCase__ : int = self.tokenizer.decode(self.token_cache , **self.decode_kwargs )
# After the symbol for a new line, we flush the cache.
if text.endswith("\n" ):
UpperCamelCase__ : Any = text[self.print_len :]
UpperCamelCase__ : Dict = []
UpperCamelCase__ : int = 0
# If the last token is a CJK character, we print the characters.
elif len(SCREAMING_SNAKE_CASE ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
UpperCamelCase__ : List[str] = text[self.print_len :]
self.print_len += len(SCREAMING_SNAKE_CASE )
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
UpperCamelCase__ : Dict = text[self.print_len : text.rfind(" " ) + 1]
self.print_len += len(SCREAMING_SNAKE_CASE )
self.on_finalized_text(SCREAMING_SNAKE_CASE )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
if len(self.token_cache ) > 0:
UpperCamelCase__ : int = self.tokenizer.decode(self.token_cache , **self.decode_kwargs )
UpperCamelCase__ : Tuple = text[self.print_len :]
UpperCamelCase__ : List[Any] = []
UpperCamelCase__ : int = 0
else:
UpperCamelCase__ : List[str] = ""
UpperCamelCase__ : Dict = True
self.on_finalized_text(SCREAMING_SNAKE_CASE , stream_end=SCREAMING_SNAKE_CASE )
def __lowercase ( self : Any , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : bool = False ):
'''simple docstring'''
print(SCREAMING_SNAKE_CASE , flush=SCREAMING_SNAKE_CASE , end="" if not stream_end else None )
def __lowercase ( self : int , SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
if (
(cp >= 0X4e_00 and cp <= 0X9f_ff)
or (cp >= 0X34_00 and cp <= 0X4d_bf) #
or (cp >= 0X2_00_00 and cp <= 0X2_a6_df) #
or (cp >= 0X2_a7_00 and cp <= 0X2_b7_3f) #
or (cp >= 0X2_b7_40 and cp <= 0X2_b8_1f) #
or (cp >= 0X2_b8_20 and cp <= 0X2_ce_af) #
or (cp >= 0Xf9_00 and cp <= 0Xfa_ff)
or (cp >= 0X2_f8_00 and cp <= 0X2_fa_1f) #
): #
return True
return False
class __a ( A__ ):
def __init__( self : Dict , SCREAMING_SNAKE_CASE : "AutoTokenizer" , SCREAMING_SNAKE_CASE : bool = False , SCREAMING_SNAKE_CASE : Optional[float] = None , **SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
super().__init__(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Optional[Any] = Queue()
UpperCamelCase__ : str = None
UpperCamelCase__ : str = timeout
def __lowercase ( self : Any , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : bool = False ):
'''simple docstring'''
self.text_queue.put(SCREAMING_SNAKE_CASE , timeout=self.timeout )
if stream_end:
self.text_queue.put(self.stop_signal , timeout=self.timeout )
def __iter__( self : Optional[Any] ):
'''simple docstring'''
return self
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCamelCase__ : Any = self.text_queue.get(timeout=self.timeout )
if value == self.stop_signal:
raise StopIteration()
else:
return value
| 228
| 1
|
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
lowercase__ : List[str] = logging.get_logger(__name__)
lowercase__ : List[str] = {
"EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
_snake_case = 'gptj'
_snake_case = {
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self , SCREAMING_SNAKE_CASE_=50400 , SCREAMING_SNAKE_CASE_=2048 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=28 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=64 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_="gelu_new" , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=1E-5 , SCREAMING_SNAKE_CASE_=0.0_2 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=50256 , SCREAMING_SNAKE_CASE_=50256 , SCREAMING_SNAKE_CASE_=False , **SCREAMING_SNAKE_CASE_ , )-> Optional[int]:
'''simple docstring'''
__UpperCamelCase = vocab_size
__UpperCamelCase = n_positions
__UpperCamelCase = n_embd
__UpperCamelCase = n_layer
__UpperCamelCase = n_head
__UpperCamelCase = n_inner
__UpperCamelCase = rotary_dim
__UpperCamelCase = activation_function
__UpperCamelCase = resid_pdrop
__UpperCamelCase = embd_pdrop
__UpperCamelCase = attn_pdrop
__UpperCamelCase = layer_norm_epsilon
__UpperCamelCase = initializer_range
__UpperCamelCase = use_cache
__UpperCamelCase = bos_token_id
__UpperCamelCase = eos_token_id
super().__init__(
bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , tie_word_embeddings=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = "default" , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False , )-> int:
'''simple docstring'''
super().__init__(SCREAMING_SNAKE_CASE_ , task=SCREAMING_SNAKE_CASE_ , patching_specs=SCREAMING_SNAKE_CASE_ , use_past=SCREAMING_SNAKE_CASE_ )
if not getattr(self._config , '''pad_token_id''' , SCREAMING_SNAKE_CASE_ ):
# TODO: how to do that better?
__UpperCamelCase = 0
@property
def A__ ( self )-> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
__UpperCamelCase = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_ , direction='''inputs''' )
__UpperCamelCase = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
__UpperCamelCase = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def A__ ( self )-> int:
'''simple docstring'''
return self._config.n_layer
@property
def A__ ( self )-> int:
'''simple docstring'''
return self._config.n_head
def A__ ( 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'''
__UpperCamelCase = super(SCREAMING_SNAKE_CASE_ , self ).generate_dummy_inputs(
SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ , seq_length=SCREAMING_SNAKE_CASE_ , is_pair=SCREAMING_SNAKE_CASE_ , framework=SCREAMING_SNAKE_CASE_ )
# We need to order the input in the way they appears in the forward()
__UpperCamelCase = OrderedDict({'''input_ids''': common_inputs['''input_ids''']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
__UpperCamelCase , __UpperCamelCase = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
__UpperCamelCase = seqlen + 2
__UpperCamelCase = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
__UpperCamelCase = [
(torch.zeros(SCREAMING_SNAKE_CASE_ ), torch.zeros(SCREAMING_SNAKE_CASE_ )) for _ in range(self.num_layers )
]
__UpperCamelCase = common_inputs['''attention_mask''']
if self.use_past:
__UpperCamelCase = ordered_inputs['''attention_mask'''].dtype
__UpperCamelCase = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , dtype=SCREAMING_SNAKE_CASE_ )] , dim=1 )
return ordered_inputs
@property
def A__ ( self )-> int:
'''simple docstring'''
return 13
| 709
|
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=32 * 8 , SCREAMING_SNAKE_CASE_=32 * 8 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=64 , )-> Dict:
'''simple docstring'''
__UpperCamelCase = parent
__UpperCamelCase = batch_size
__UpperCamelCase = is_training
__UpperCamelCase = use_auxiliary_loss
__UpperCamelCase = num_queries
__UpperCamelCase = num_channels
__UpperCamelCase = min_size
__UpperCamelCase = max_size
__UpperCamelCase = num_labels
__UpperCamelCase = hidden_dim
__UpperCamelCase = hidden_dim
def A__ ( self )-> Optional[int]:
'''simple docstring'''
__UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = torch.ones([self.batch_size, self.min_size, self.max_size] , device=SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=SCREAMING_SNAKE_CASE_ ) > 0.5
).float()
__UpperCamelCase = (torch.rand((self.batch_size, self.num_labels) , device=SCREAMING_SNAKE_CASE_ ) > 0.5).long()
__UpperCamelCase = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def A__ ( self )-> Optional[Any]:
'''simple docstring'''
__UpperCamelCase = MaskaFormerConfig(
hidden_size=self.hidden_dim , )
__UpperCamelCase = self.num_queries
__UpperCamelCase = self.num_labels
__UpperCamelCase = [1, 1, 1, 1]
__UpperCamelCase = self.num_channels
__UpperCamelCase = 64
__UpperCamelCase = 128
__UpperCamelCase = self.hidden_dim
__UpperCamelCase = self.hidden_dim
__UpperCamelCase = self.hidden_dim
return config
def A__ ( self )-> Any:
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = self.prepare_config_and_inputs()
__UpperCamelCase = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def A__ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> Any:
'''simple docstring'''
__UpperCamelCase = output.encoder_hidden_states
__UpperCamelCase = output.pixel_decoder_hidden_states
__UpperCamelCase = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_ ) , config.decoder_layers )
def A__ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False )-> Tuple:
'''simple docstring'''
with torch.no_grad():
__UpperCamelCase = MaskaFormerModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
__UpperCamelCase = model(pixel_values=SCREAMING_SNAKE_CASE_ , pixel_mask=SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = model(SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def A__ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> List[str]:
'''simple docstring'''
__UpperCamelCase = MaskaFormerForUniversalSegmentation(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
def comm_check_on_output(SCREAMING_SNAKE_CASE_ ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__UpperCamelCase = model(pixel_values=SCREAMING_SNAKE_CASE_ , pixel_mask=SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = model(SCREAMING_SNAKE_CASE_ )
comm_check_on_output(SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = model(
pixel_values=SCREAMING_SNAKE_CASE_ , pixel_mask=SCREAMING_SNAKE_CASE_ , mask_labels=SCREAMING_SNAKE_CASE_ , class_labels=SCREAMING_SNAKE_CASE_ )
comm_check_on_output(SCREAMING_SNAKE_CASE_ )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
"""simple docstring"""
_snake_case = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
_snake_case = {'feature-extraction': MaskaFormerModel} if is_torch_available() else {}
_snake_case = False
_snake_case = False
_snake_case = False
_snake_case = False
def A__ ( self )-> Optional[Any]:
'''simple docstring'''
__UpperCamelCase = MaskaFormerModelTester(self )
__UpperCamelCase = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , has_text_modality=SCREAMING_SNAKE_CASE_ )
def A__ ( self )-> List[str]:
'''simple docstring'''
self.config_tester.run_common_tests()
def A__ ( self )-> int:
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ )
def A__ ( self )-> Tuple:
'''simple docstring'''
__UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*SCREAMING_SNAKE_CASE_ )
@unittest.skip(reason='''Mask2Former does not use inputs_embeds''' )
def A__ ( self )-> Any:
'''simple docstring'''
pass
@unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method''' )
def A__ ( self )-> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''Mask2Former is not a generative model''' )
def A__ ( self )-> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''Mask2Former does not use token embeddings''' )
def A__ ( self )-> List[str]:
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def A__ ( self )-> Dict:
'''simple docstring'''
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def A__ ( self )-> str:
'''simple docstring'''
pass
def A__ ( self )-> str:
'''simple docstring'''
__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.forward )
# 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_ )
@slow
def A__ ( self )-> Any:
'''simple docstring'''
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
__UpperCamelCase = MaskaFormerModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def A__ ( self )-> Optional[int]:
'''simple docstring'''
__UpperCamelCase = (self.model_tester.min_size,) * 2
__UpperCamelCase = {
'''pixel_values''': torch.randn((2, 3, *size) , device=SCREAMING_SNAKE_CASE_ ),
'''mask_labels''': torch.randn((2, 10, *size) , device=SCREAMING_SNAKE_CASE_ ),
'''class_labels''': torch.zeros(2 , 10 , device=SCREAMING_SNAKE_CASE_ ).long(),
}
__UpperCamelCase = self.model_tester.get_config()
__UpperCamelCase = MaskaFormerForUniversalSegmentation(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = model(**SCREAMING_SNAKE_CASE_ )
self.assertTrue(outputs.loss is not None )
def A__ ( self )-> Tuple:
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ )
def A__ ( self )-> Any:
'''simple docstring'''
__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_ ).to(SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = model(**SCREAMING_SNAKE_CASE_ , output_attentions=SCREAMING_SNAKE_CASE_ )
self.assertTrue(outputs.attentions is not None )
def A__ ( self )-> Any:
'''simple docstring'''
if not self.model_tester.is_training:
return
__UpperCamelCase = self.all_model_classes[1]
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = self.model_tester.prepare_config_and_inputs()
__UpperCamelCase = model_class(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.train()
__UpperCamelCase = model(SCREAMING_SNAKE_CASE_ , mask_labels=SCREAMING_SNAKE_CASE_ , class_labels=SCREAMING_SNAKE_CASE_ ).loss
loss.backward()
def A__ ( self )-> Tuple:
'''simple docstring'''
__UpperCamelCase = self.all_model_classes[1]
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = self.model_tester.prepare_config_and_inputs()
__UpperCamelCase = True
__UpperCamelCase = True
__UpperCamelCase = model_class(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
model.train()
__UpperCamelCase = model(SCREAMING_SNAKE_CASE_ , mask_labels=SCREAMING_SNAKE_CASE_ , class_labels=SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__UpperCamelCase = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
__UpperCamelCase = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__UpperCamelCase = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
lowercase__ : Any = 1e-4
def A_ ( ) -> List[Any]:
'''simple docstring'''
__UpperCamelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def A__ ( self )-> List[Any]:
'''simple docstring'''
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def A__ ( self )-> Any:
'''simple docstring'''
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def A__ ( self )-> List[str]:
'''simple docstring'''
__UpperCamelCase = MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = self.default_image_processor
__UpperCamelCase = prepare_img()
__UpperCamelCase = image_processor(SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' ).to(SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(SCREAMING_SNAKE_CASE_ , (1, 3, 384, 384) )
with torch.no_grad():
__UpperCamelCase = model(**SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = torch.tensor(
[[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]] ).to(SCREAMING_SNAKE_CASE_ )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=SCREAMING_SNAKE_CASE_ ) )
__UpperCamelCase = torch.tensor(
[[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]] ).to(SCREAMING_SNAKE_CASE_ )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=SCREAMING_SNAKE_CASE_ ) )
__UpperCamelCase = torch.tensor(
[[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]] ).to(SCREAMING_SNAKE_CASE_ )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=SCREAMING_SNAKE_CASE_ ) )
def A__ ( self )-> List[Any]:
'''simple docstring'''
__UpperCamelCase = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(SCREAMING_SNAKE_CASE_ ).eval()
__UpperCamelCase = self.default_image_processor
__UpperCamelCase = prepare_img()
__UpperCamelCase = image_processor(SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' ).to(SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(SCREAMING_SNAKE_CASE_ , (1, 3, 384, 384) )
with torch.no_grad():
__UpperCamelCase = model(**SCREAMING_SNAKE_CASE_ )
# masks_queries_logits
__UpperCamelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
__UpperCamelCase = [
[-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
[-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
[-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
]
__UpperCamelCase = torch.tensor(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=SCREAMING_SNAKE_CASE_ ) )
# class_queries_logits
__UpperCamelCase = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
__UpperCamelCase = torch.tensor(
[
[1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
[0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
[0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
] ).to(SCREAMING_SNAKE_CASE_ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=SCREAMING_SNAKE_CASE_ ) )
def A__ ( self )-> str:
'''simple docstring'''
__UpperCamelCase = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(SCREAMING_SNAKE_CASE_ ).eval()
__UpperCamelCase = self.default_image_processor
__UpperCamelCase = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors='''pt''' , )
__UpperCamelCase = inputs['''pixel_values'''].to(SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = [el.to(SCREAMING_SNAKE_CASE_ ) for el in inputs['''mask_labels''']]
__UpperCamelCase = [el.to(SCREAMING_SNAKE_CASE_ ) for el in inputs['''class_labels''']]
with torch.no_grad():
__UpperCamelCase = model(**SCREAMING_SNAKE_CASE_ )
self.assertTrue(outputs.loss is not None )
| 451
| 0
|
'''simple docstring'''
from collections.abc import Iterable
from typing import Generic, TypeVar
UpperCamelCase_ = TypeVar("""_T""")
class __SCREAMING_SNAKE_CASE ( Generic[_T] ):
def __init__( self : int , UpperCAmelCase__ : Iterable[_T] | None = None ):
'''simple docstring'''
lowercase : Dict =list(iterable or [] )
lowercase : Union[str, Any] =[]
def __len__( self : Tuple ):
'''simple docstring'''
return len(self._stacka ) + len(self._stacka )
def __repr__( self : Any ):
'''simple docstring'''
return F'''Queue({tuple(self._stacka[::-1] + self._stacka )})'''
def lowerCamelCase_ ( self : int , UpperCAmelCase__ : _T ):
'''simple docstring'''
self._stacka.append(a_ )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
lowercase : Optional[Any] =self._stacka.pop
lowercase : Dict =self._stacka.append
if not self._stacka:
while self._stacka:
stacka_append(stacka_pop() )
if not self._stacka:
raise IndexError('''Queue is empty''' )
return self._stacka.pop()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 92
|
from sklearn.metrics import recall_score
import datasets
a__ : str = """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
"""
a__ : Dict = """
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.
- `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.
- `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.
- `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
- `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.
- `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
- `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.
- `0`: If there is a zero division, the return value is `0`.
- `1`: If there is a zero division, the return value is `1`.
Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.
Examples:
Example 1-A simple example with some errors
>>> recall_metric = datasets.load_metric('recall')
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
>>> print(results)
{'recall': 0.6666666666666666}
Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
>>> recall_metric = datasets.load_metric('recall')
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
>>> print(results)
{'recall': 0.5}
Example 3-The same example as Example 1, but with `sample_weight` included.
>>> recall_metric = datasets.load_metric('recall')
>>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
>>> print(results)
{'recall': 0.55}
Example 4-A multiclass example, using different averages.
>>> recall_metric = datasets.load_metric('recall')
>>> predictions = [0, 2, 1, 0, 0, 1]
>>> references = [0, 1, 2, 0, 1, 2]
>>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')
>>> print(results)
{'recall': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')
>>> print(results)
{'recall': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')
>>> print(results)
{'recall': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
>>> print(results)
{'recall': array([1., 0., 0.])}
"""
a__ : List[Any] = """
@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase ( datasets.Metric ):
"""simple docstring"""
def _UpperCamelCase ( self : List[Any] ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""int32""" ) ),
"""references""": datasets.Sequence(datasets.Value("""int32""" ) ),
}
if self.config_name == """multilabel"""
else {
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"""] , )
def _UpperCamelCase ( self : Dict , a_ : Tuple , a_ : Optional[Any] , a_ : Union[str, Any]=None , a_ : List[Any]=1 , a_ : List[str]="binary" , a_ : List[str]=None , a_ : int="warn" , ):
"""simple docstring"""
lowerCamelCase__ = recall_score(
a_ , a_ , labels=a_ , pos_label=a_ , average=a_ , sample_weight=a_ , zero_division=a_ , )
return {"recall": float(a_ ) if score.size == 1 else score}
| 165
| 0
|
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [range(10 )]),
({"num_shards": 10, "max_num_jobs": 10}, [range(a_ , i + 1 ) for i in range(10 )]),
({"num_shards": 1, "max_num_jobs": 10}, [range(1 )]),
({"num_shards": 10, "max_num_jobs": 3}, [range(0 , 4 ), range(4 , 7 ), range(7 , 10 )]),
({"num_shards": 3, "max_num_jobs": 10}, [range(0 , 1 ), range(1 , 2 ), range(2 , 3 )]),
] , )
def UpperCamelCase_ ( a_ , a_ ) ->Dict:
A =_distribute_shards(**a_ )
assert out == expected
@pytest.mark.parametrize(
"gen_kwargs, max_num_jobs, expected" , [
({"foo": 0}, 10, [{"foo": 0}]),
({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
] , )
def UpperCamelCase_ ( a_ , a_ , a_ ) ->Any:
A =_split_gen_kwargs(a_ , a_ )
assert out == expected
@pytest.mark.parametrize(
"gen_kwargs, expected" , [
({"foo": 0}, 1),
({"shards": [0]}, 1),
({"shards": [0, 1, 2, 3]}, 4),
({"shards": [0, 1, 2, 3], "foo": 0}, 4),
({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
] , )
def UpperCamelCase_ ( a_ , a_ ) ->List[str]:
if expected is RuntimeError:
with pytest.raises(a_ ):
_number_of_shards_in_gen_kwargs(a_ )
else:
A =_number_of_shards_in_gen_kwargs(a_ )
assert out == expected
| 689
|
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()
__a = logging.get_logger(__name__)
__a = ["""model.decoder.embed_positions.weights"""]
def UpperCamelCase_ ( a_ ) ->List[str]:
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 UpperCamelCase_ ( a_ , a_ ) ->Tuple[Dict, Dict]:
A =list(state_dict.keys() )
A ={}
for key in keys:
A =state_dict.pop(a_ )
A =rename_keys(a_ )
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 UpperCamelCase_ ( a_ ) ->MusicgenDecoderConfig:
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=a_ , ffn_dim=hidden_size * 4 , num_hidden_layers=a_ , num_attention_heads=a_ , )
return config
@torch.no_grad()
def UpperCamelCase_ ( a_ , a_=None , a_=None , a_="cpu" ) ->Union[str, Any]:
A =MusicGen.get_pretrained(a_ , device=a_ )
A =decoder_config_from_checkpoint(a_ )
A =fairseq_model.lm.state_dict()
A , A =rename_state_dict(
a_ , hidden_size=decoder_config.hidden_size )
A =TaEncoderModel.from_pretrained("t5-base" )
A =EncodecModel.from_pretrained("facebook/encodec_32khz" )
A =MusicgenForCausalLM(a_ ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
A , A =decoder.load_state_dict(a_ , strict=a_ )
for key in missing_keys.copy():
if key.startswith(("text_encoder", "audio_encoder") ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(a_ )
if len(a_ ) > 0:
raise ValueError(f'''Missing key(s) in state_dict: {missing_keys}''' )
if len(a_ ) > 0:
raise ValueError(f'''Unexpected key(s) in state_dict: {unexpected_keys}''' )
# init the composite model
A =MusicgenForConditionalGeneration(text_encoder=a_ , audio_encoder=a_ , decoder=a_ )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(a_ )
# 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=a_ , decoder_input_ids=a_ ).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=a_ , tokenizer=a_ )
# 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(a_ ).mkdir(exist_ok=a_ )
logger.info(f'''Saving model {checkpoint} to {pytorch_dump_folder}''' )
model.save_pretrained(a_ )
processor.save_pretrained(a_ )
if repo_id:
logger.info(f'''Pushing model {checkpoint} to {repo_id}''' )
model.push_to_hub(a_ )
processor.push_to_hub(a_ )
if __name__ == "__main__":
__a = 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."""
)
__a = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 689
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__lowerCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase = ['GPTSw3Tokenizer']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_swa import GPTSwaTokenizer
else:
import sys
__lowerCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 585
|
"""simple docstring"""
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
a = logging.get_logger(__name__)
a = {
'''tensor(bool)''': np.bool_,
'''tensor(int8)''': np.inta,
'''tensor(uint8)''': np.uinta,
'''tensor(int16)''': np.intaa,
'''tensor(uint16)''': np.uintaa,
'''tensor(int32)''': np.intaa,
'''tensor(uint32)''': np.uintaa,
'''tensor(int64)''': np.intaa,
'''tensor(uint64)''': np.uintaa,
'''tensor(float16)''': np.floataa,
'''tensor(float)''': np.floataa,
'''tensor(double)''': np.floataa,
}
class lowercase_ :
'''simple docstring'''
def __init__( self : Optional[Any] , _UpperCAmelCase : Dict=None , **_UpperCAmelCase : Optional[Any] ):
logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.' )
_A = model
_A = kwargs.get('model_save_dir' , _UpperCAmelCase )
_A = kwargs.get('latest_model_name' , _UpperCAmelCase )
def __call__( self : Dict , **_UpperCAmelCase : List[Any] ):
_A = {k: np.array(_UpperCAmelCase ) for k, v in kwargs.items()}
return self.model.run(_UpperCAmelCase , _UpperCAmelCase )
@staticmethod
def lowerCAmelCase_ ( _UpperCAmelCase : Union[str, Path] , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : List[Any]=None ):
if provider is None:
logger.info('No onnxruntime provider specified, using CPUExecutionProvider' )
_A = 'CPUExecutionProvider'
return ort.InferenceSession(_UpperCAmelCase , providers=[provider] , sess_options=_UpperCAmelCase )
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : Union[str, Path] , _UpperCAmelCase : Optional[str] = None , **_UpperCAmelCase : List[Any] ):
_A = file_name if file_name is not None else ONNX_WEIGHTS_NAME
_A = self.model_save_dir.joinpath(self.latest_model_name )
_A = Path(_UpperCAmelCase ).joinpath(_UpperCAmelCase )
try:
shutil.copyfile(_UpperCAmelCase , _UpperCAmelCase )
except shutil.SameFileError:
pass
# copy external weights (for models >2GB)
_A = self.model_save_dir.joinpath(_UpperCAmelCase )
if src_path.exists():
_A = Path(_UpperCAmelCase ).joinpath(_UpperCAmelCase )
try:
shutil.copyfile(_UpperCAmelCase , _UpperCAmelCase )
except shutil.SameFileError:
pass
def lowerCAmelCase_ ( self : List[Any] , _UpperCAmelCase : Union[str, os.PathLike] , **_UpperCAmelCase : List[str] , ):
if os.path.isfile(_UpperCAmelCase ):
logger.error(F'''Provided path ({save_directory}) should be a directory, not a file''' )
return
os.makedirs(_UpperCAmelCase , exist_ok=_UpperCAmelCase )
# saving model weights/files
self._save_pretrained(_UpperCAmelCase , **_UpperCAmelCase )
@classmethod
def lowerCAmelCase_ ( cls : Tuple , _UpperCAmelCase : Union[str, Path] , _UpperCAmelCase : Optional[Union[bool, str, None]] = None , _UpperCAmelCase : Optional[Union[str, None]] = None , _UpperCAmelCase : bool = False , _UpperCAmelCase : Optional[str] = None , _UpperCAmelCase : Optional[str] = None , _UpperCAmelCase : Optional[str] = None , _UpperCAmelCase : Optional["ort.SessionOptions"] = None , **_UpperCAmelCase : Union[str, Any] , ):
_A = file_name if file_name is not None else ONNX_WEIGHTS_NAME
# load model from local directory
if os.path.isdir(_UpperCAmelCase ):
_A = OnnxRuntimeModel.load_model(
os.path.join(_UpperCAmelCase , _UpperCAmelCase ) , provider=_UpperCAmelCase , sess_options=_UpperCAmelCase )
_A = Path(_UpperCAmelCase )
# load model from hub
else:
# download model
_A = hf_hub_download(
repo_id=_UpperCAmelCase , filename=_UpperCAmelCase , use_auth_token=_UpperCAmelCase , revision=_UpperCAmelCase , cache_dir=_UpperCAmelCase , force_download=_UpperCAmelCase , )
_A = Path(_UpperCAmelCase ).parent
_A = Path(_UpperCAmelCase ).name
_A = OnnxRuntimeModel.load_model(_UpperCAmelCase , provider=_UpperCAmelCase , sess_options=_UpperCAmelCase )
return cls(model=_UpperCAmelCase , **_UpperCAmelCase )
@classmethod
def lowerCAmelCase_ ( cls : List[Any] , _UpperCAmelCase : Union[str, Path] , _UpperCAmelCase : bool = True , _UpperCAmelCase : Optional[str] = None , _UpperCAmelCase : Optional[str] = None , **_UpperCAmelCase : Tuple , ):
_A = None
if len(str(_UpperCAmelCase ).split('@' ) ) == 2:
_A , _A = model_id.split('@' )
return cls._from_pretrained(
model_id=_UpperCAmelCase , revision=_UpperCAmelCase , cache_dir=_UpperCAmelCase , force_download=_UpperCAmelCase , use_auth_token=_UpperCAmelCase , **_UpperCAmelCase , )
| 7
| 0
|
"""simple docstring"""
def _A ( _a : int , _a : Optional[Any] ):
"""simple docstring"""
A = 0
A = len(_a ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
A = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(_a ):
return None
A = sorted_collection[point]
if current_item == item:
return point
else:
if point < left:
A = left
A = point
elif point > right:
A = right
A = point
else:
if item < current_item:
A = point - 1
else:
A = point + 1
return None
def _A ( _a : Any , _a : Optional[Any] , _a : Tuple , _a : Tuple ):
"""simple docstring"""
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
A = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(_a ):
return None
if sorted_collection[point] == item:
return point
elif point < left:
return interpolation_search_by_recursion(_a , _a , _a , _a )
elif point > right:
return interpolation_search_by_recursion(_a , _a , _a , _a )
else:
if sorted_collection[point] > item:
return interpolation_search_by_recursion(
_a , _a , _a , point - 1 )
else:
return interpolation_search_by_recursion(
_a , _a , point + 1 , _a )
def _A ( _a : Optional[Any] ):
"""simple docstring"""
if collection != sorted(_a ):
raise ValueError("""Collection must be ascending sorted""" )
return True
if __name__ == "__main__":
import sys
UpperCAmelCase =0
if debug == 1:
UpperCAmelCase =[10, 30, 40, 45, 50, 66, 77, 93]
try:
__assert_sorted(collection)
except ValueError:
sys.exit("Sequence must be ascending sorted to apply interpolation search")
UpperCAmelCase =67
UpperCAmelCase =interpolation_search(collection, target)
if result is not None:
print(f"""{target} found at positions: {result}""")
else:
print("Not found")
| 255
|
"""simple docstring"""
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowerCamelCase = (DDPMParallelScheduler,)
def UpperCamelCase__ ( self ,**lowerCamelCase_ ) -> List[Any]:
A = {
"""num_train_timesteps""": 1_0_0_0,
"""beta_start""": 0.00_01,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**lowerCamelCase_ )
return config
def UpperCamelCase__ ( self ) -> Tuple:
for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=lowerCamelCase_ )
def UpperCamelCase__ ( self ) -> Dict:
for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1] ,[0.0_02, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=lowerCamelCase_ ,beta_end=lowerCamelCase_ )
def UpperCamelCase__ ( self ) -> List[Any]:
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=lowerCamelCase_ )
def UpperCamelCase__ ( self ) -> str:
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=lowerCamelCase_ )
def UpperCamelCase__ ( self ) -> Any:
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=lowerCamelCase_ )
def UpperCamelCase__ ( self ) -> int:
self.check_over_configs(thresholding=lowerCamelCase_ )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=lowerCamelCase_ ,prediction_type=lowerCamelCase_ ,sample_max_value=lowerCamelCase_ ,)
def UpperCamelCase__ ( self ) -> Union[str, Any]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCamelCase_ )
def UpperCamelCase__ ( self ) -> Union[str, Any]:
for t in [0, 5_0_0, 9_9_9]:
self.check_over_forward(time_step=lowerCamelCase_ )
def UpperCamelCase__ ( self ) -> Optional[Any]:
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**lowerCamelCase_ )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(4_8_7 ) - 0.0_09_79 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(9_9_9 ) - 0.02 ) ) < 1E-5
def UpperCamelCase__ ( self ) -> Optional[Any]:
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**lowerCamelCase_ )
A = len(lowerCamelCase_ )
A = self.dummy_model()
A = self.dummy_sample_deter
A = self.dummy_sample_deter + 0.1
A = self.dummy_sample_deter - 0.1
A = samplea.shape[0]
A = torch.stack([samplea, samplea, samplea] ,dim=0 )
A = torch.arange(lowerCamelCase_ )[0:3, None].repeat(1 ,lowerCamelCase_ )
A = model(samples.flatten(0 ,1 ) ,timesteps.flatten(0 ,1 ) )
A = scheduler.batch_step_no_noise(lowerCamelCase_ ,timesteps.flatten(0 ,1 ) ,samples.flatten(0 ,1 ) )
A = torch.sum(torch.abs(lowerCamelCase_ ) )
A = torch.mean(torch.abs(lowerCamelCase_ ) )
assert abs(result_sum.item() - 11_53.18_33 ) < 1E-2
assert abs(result_mean.item() - 0.50_05 ) < 1E-3
def UpperCamelCase__ ( self ) -> Optional[int]:
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**lowerCamelCase_ )
A = len(lowerCamelCase_ )
A = self.dummy_model()
A = self.dummy_sample_deter
A = torch.manual_seed(0 )
for t in reversed(range(lowerCamelCase_ ) ):
# 1. predict noise residual
A = model(lowerCamelCase_ ,lowerCamelCase_ )
# 2. predict previous mean of sample x_t-1
A = scheduler.step(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,generator=lowerCamelCase_ ).prev_sample
A = pred_prev_sample
A = torch.sum(torch.abs(lowerCamelCase_ ) )
A = torch.mean(torch.abs(lowerCamelCase_ ) )
assert abs(result_sum.item() - 2_58.96_06 ) < 1E-2
assert abs(result_mean.item() - 0.33_72 ) < 1E-3
def UpperCamelCase__ ( self ) -> int:
A = self.scheduler_classes[0]
A = self.get_scheduler_config(prediction_type="""v_prediction""" )
A = scheduler_class(**lowerCamelCase_ )
A = len(lowerCamelCase_ )
A = self.dummy_model()
A = self.dummy_sample_deter
A = torch.manual_seed(0 )
for t in reversed(range(lowerCamelCase_ ) ):
# 1. predict noise residual
A = model(lowerCamelCase_ ,lowerCamelCase_ )
# 2. predict previous mean of sample x_t-1
A = scheduler.step(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,generator=lowerCamelCase_ ).prev_sample
A = pred_prev_sample
A = torch.sum(torch.abs(lowerCamelCase_ ) )
A = torch.mean(torch.abs(lowerCamelCase_ ) )
assert abs(result_sum.item() - 2_02.02_96 ) < 1E-2
assert abs(result_mean.item() - 0.26_31 ) < 1E-3
def UpperCamelCase__ ( self ) -> Optional[Any]:
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**lowerCamelCase_ )
A = [1_0_0, 8_7, 5_0, 1, 0]
scheduler.set_timesteps(timesteps=lowerCamelCase_ )
A = scheduler.timesteps
for i, timestep in enumerate(lowerCamelCase_ ):
if i == len(lowerCamelCase_ ) - 1:
A = -1
else:
A = timesteps[i + 1]
A = scheduler.previous_timestep(lowerCamelCase_ )
A = prev_t.item()
self.assertEqual(lowerCamelCase_ ,lowerCamelCase_ )
def UpperCamelCase__ ( self ) -> str:
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**lowerCamelCase_ )
A = [1_0_0, 8_7, 5_0, 5_1, 0]
with self.assertRaises(lowerCamelCase_ ,msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=lowerCamelCase_ )
def UpperCamelCase__ ( self ) -> str:
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**lowerCamelCase_ )
A = [1_0_0, 8_7, 5_0, 1, 0]
A = len(lowerCamelCase_ )
with self.assertRaises(lowerCamelCase_ ,msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=lowerCamelCase_ ,timesteps=lowerCamelCase_ )
def UpperCamelCase__ ( self ) -> List[str]:
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**lowerCamelCase_ )
A = [scheduler.config.num_train_timesteps]
with self.assertRaises(
lowerCamelCase_ ,msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" ,):
scheduler.set_timesteps(timesteps=lowerCamelCase_ )
| 255
| 1
|
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'nvidia/segformer-b0-finetuned-ade-512-512': (
'https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'
),
# See all SegFormer models at https://huggingface.co/models?filter=segformer
}
class lowerCAmelCase__ ( lowerCamelCase_ ):
'''simple docstring'''
lowerCAmelCase_ = """segformer"""
def __init__( self : Optional[int] , snake_case__ : Optional[Any]=3 , snake_case__ : int=4 , snake_case__ : Optional[Any]=[2, 2, 2, 2] , snake_case__ : Optional[int]=[8, 4, 2, 1] , snake_case__ : Tuple=[3_2, 6_4, 1_6_0, 2_5_6] , snake_case__ : Any=[7, 3, 3, 3] , snake_case__ : Union[str, Any]=[4, 2, 2, 2] , snake_case__ : Optional[Any]=[1, 2, 5, 8] , snake_case__ : Optional[Any]=[4, 4, 4, 4] , snake_case__ : str="gelu" , snake_case__ : Optional[int]=0.0 , snake_case__ : Tuple=0.0 , snake_case__ : Tuple=0.1 , snake_case__ : Optional[Any]=0.02 , snake_case__ : Union[str, Any]=0.1 , snake_case__ : Union[str, Any]=1e-6 , snake_case__ : Dict=2_5_6 , snake_case__ : List[Any]=2_5_5 , **snake_case__ : Optional[int] , ) -> Tuple:
super().__init__(**_UpperCAmelCase )
if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
warnings.warn(
'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
' removed, as the behaviour will default to that of reshape_last_stage = True.' , _UpperCAmelCase , )
_lowerCamelCase = num_channels
_lowerCamelCase = num_encoder_blocks
_lowerCamelCase = depths
_lowerCamelCase = sr_ratios
_lowerCamelCase = hidden_sizes
_lowerCamelCase = patch_sizes
_lowerCamelCase = strides
_lowerCamelCase = mlp_ratios
_lowerCamelCase = num_attention_heads
_lowerCamelCase = hidden_act
_lowerCamelCase = hidden_dropout_prob
_lowerCamelCase = attention_probs_dropout_prob
_lowerCamelCase = classifier_dropout_prob
_lowerCamelCase = initializer_range
_lowerCamelCase = drop_path_rate
_lowerCamelCase = layer_norm_eps
_lowerCamelCase = decoder_hidden_size
_lowerCamelCase = kwargs.get('reshape_last_stage' , _UpperCAmelCase )
_lowerCamelCase = semantic_loss_ignore_index
class lowerCAmelCase__ ( lowerCamelCase_ ):
'''simple docstring'''
lowerCAmelCase_ = version.parse('1.11' )
@property
def _snake_case ( self : int ) -> Tuple:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def _snake_case ( self : Tuple ) -> Optional[int]:
return 1e-4
@property
def _snake_case ( self : Tuple ) -> str:
return 1_2
| 544
|
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AutoformerConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'AutoformerForPrediction',
'AutoformerModel',
'AutoformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_autoformer import (
AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_autoformer import (
AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
AutoformerForPrediction,
AutoformerModel,
AutoformerPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 603
| 0
|
'''simple docstring'''
from math import sqrt
def lowerCAmelCase__ ( a_ : int = 1_0_0_0_0_0_0 ) -> int:
UpperCAmelCase__ : int = 0
UpperCAmelCase__ : int = 0
UpperCAmelCase__ : int
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 * max_cuboid_size + 1 ):
if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():
num_cuboids += (
min(a_ , sum_shortest_sides // 2 )
- max(1 , sum_shortest_sides - max_cuboid_size )
+ 1
)
return max_cuboid_size
if __name__ == "__main__":
print(f'{solution() = }')
| 599
|
'''simple docstring'''
def lowerCAmelCase__ ( a_ : list , a_ : list ) -> float:
_validate_point(a_ )
_validate_point(a_ )
if len(a_ ) != len(a_ ):
raise ValueError('''Both points must be in the same n-dimensional space''' )
return float(sum(abs(a - b ) for a, b in zip(a_ , a_ ) ) )
def lowerCAmelCase__ ( a_ : list[float] ) -> None:
if point:
if isinstance(a_ , a_ ):
for item in point:
if not isinstance(a_ , (int, float) ):
UpperCAmelCase__ : Optional[Any] = (
'''Expected a list of numbers as input, found '''
f"""{type(a_ ).__name__}"""
)
raise TypeError(a_ )
else:
UpperCAmelCase__ : Tuple = f"""Expected a list of numbers as input, found {type(a_ ).__name__}"""
raise TypeError(a_ )
else:
raise ValueError('''Missing an input''' )
def lowerCAmelCase__ ( a_ : list , a_ : list ) -> float:
_validate_point(a_ )
_validate_point(a_ )
if len(a_ ) != len(a_ ):
raise ValueError('''Both points must be in the same n-dimensional space''' )
return float(sum(abs(x - y ) for x, y in zip(a_ , a_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 599
| 1
|
'''simple docstring'''
import unittest
import numpy as np
from transformers import DistilBertConfig, 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.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : List[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Union[str, Any]=1_3 , _lowerCAmelCase : Optional[int]=7 , _lowerCAmelCase : Any=True , _lowerCAmelCase : Tuple=True , _lowerCAmelCase : Optional[Any]=True , _lowerCAmelCase : Optional[Any]=True , _lowerCAmelCase : Optional[Any]=9_9 , _lowerCAmelCase : List[Any]=3_2 , _lowerCAmelCase : Union[str, Any]=5 , _lowerCAmelCase : Optional[int]=4 , _lowerCAmelCase : Any=3_7 , _lowerCAmelCase : int="gelu" , _lowerCAmelCase : List[Any]=0.1 , _lowerCAmelCase : Union[str, Any]=0.1 , _lowerCAmelCase : Union[str, Any]=5_1_2 , _lowerCAmelCase : List[Any]=1_6 , _lowerCAmelCase : Optional[Any]=2 , _lowerCAmelCase : Optional[Any]=0.02 , _lowerCAmelCase : List[str]=4 , ):
'''simple docstring'''
__lowercase =parent
__lowercase =batch_size
__lowercase =seq_length
__lowercase =is_training
__lowercase =use_attention_mask
__lowercase =use_token_type_ids
__lowercase =use_labels
__lowercase =vocab_size
__lowercase =hidden_size
__lowercase =num_hidden_layers
__lowercase =num_attention_heads
__lowercase =intermediate_size
__lowercase =hidden_act
__lowercase =hidden_dropout_prob
__lowercase =attention_probs_dropout_prob
__lowercase =max_position_embeddings
__lowercase =type_vocab_size
__lowercase =type_sequence_label_size
__lowercase =initializer_range
__lowercase =num_choices
def __lowerCamelCase ( self : str):
'''simple docstring'''
__lowercase =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
__lowercase =None
if self.use_attention_mask:
__lowercase =random_attention_mask([self.batch_size, self.seq_length])
__lowercase =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 , tie_weights_=__A , )
return config, input_ids, attention_mask
def __lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
__lowercase =self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase =config_and_inputs
__lowercase ={'input_ids': input_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class _UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __lowerCamelCase ( self : int):
'''simple docstring'''
__lowercase =FlaxDistilBertModelTester(self)
@slow
def __lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
for model_class_name in self.all_model_classes:
__lowercase =model_class_name.from_pretrained('distilbert-base-uncased')
__lowercase =model(np.ones((1, 1)))
self.assertIsNotNone(__A)
@require_flax
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def __lowerCamelCase ( self : List[str]):
'''simple docstring'''
__lowercase =FlaxDistilBertModel.from_pretrained('distilbert-base-uncased')
__lowercase =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]])
__lowercase =np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
__lowercase =model(__A , attention_mask=__A)[0]
__lowercase =(1, 1_1, 7_6_8)
self.assertEqual(output.shape , __A)
__lowercase =np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]])
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , __A , atol=1e-4))
| 474
|
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
RandomResizedCrop,
Resize,
ToTensor,
)
import transformers
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForImageClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__magic_name__ : str = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-classification/requirements.txt''')
__magic_name__ : int = list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__magic_name__ : int = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def A__ ( A_ ) -> Any:
with open(A_ , "rb" ) as f:
_lowercase = Image.open(A_ )
return im.convert("RGB" )
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
UpperCAmelCase__ = field(
default=lowerCamelCase__ , metadata={
'help': 'Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'
} , )
UpperCAmelCase__ = field(
default=lowerCamelCase__ , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
UpperCAmelCase__ = field(default=lowerCamelCase__ , metadata={'help': 'A folder containing the training data.'} )
UpperCAmelCase__ = field(default=lowerCamelCase__ , metadata={'help': 'A folder containing the validation data.'} )
UpperCAmelCase__ = field(
default=0.15 , metadata={'help': 'Percent to split off of train for validation.'} )
UpperCAmelCase__ = field(
default=lowerCamelCase__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
UpperCAmelCase__ = field(
default=lowerCamelCase__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
def snake_case ( self : int ):
"""simple docstring"""
if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
raise ValueError(
"You must specify either a dataset name from the hub or a train and/or validation directory." )
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
UpperCAmelCase__ = field(
default='google/vit-base-patch16-224-in21k' , metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} , )
UpperCAmelCase__ = field(
default=lowerCamelCase__ , metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(lowerCamelCase__ )} , )
UpperCAmelCase__ = field(
default=lowerCamelCase__ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
UpperCAmelCase__ = field(
default=lowerCamelCase__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'} )
UpperCAmelCase__ = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
UpperCAmelCase__ = field(default=lowerCamelCase__ , metadata={'help': 'Name or path of preprocessor config.'} )
UpperCAmelCase__ = field(
default=lowerCamelCase__ , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
UpperCAmelCase__ = field(
default=lowerCamelCase__ , metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'} , )
def A__ ( A_ ) -> Optional[Any]:
_lowercase = torch.stack([example["pixel_values"] for example in examples] )
_lowercase = torch.tensor([example["labels"] for example in examples] )
return {"pixel_values": pixel_values, "labels": labels}
def A__ ( ) -> Optional[Any]:
# 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.
_lowercase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_lowercase , _lowercase , _lowercase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_lowercase , _lowercase , _lowercase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_image_classification" , A_ , A_ )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_lowercase = training_args.get_process_log_level()
logger.setLevel(A_ )
transformers.utils.logging.set_verbosity(A_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(F"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
_lowercase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_lowercase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
"Use --overwrite_output_dir to overcome." )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )
# Set seed before initializing model.
set_seed(training_args.seed )
# Initialize our dataset and prepare it for the 'image-classification' task.
if data_args.dataset_name is not None:
_lowercase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir , task="image-classification" , use_auth_token=True if model_args.use_auth_token else None , )
else:
_lowercase = {}
if data_args.train_dir is not None:
_lowercase = os.path.join(data_args.train_dir , "**" )
if data_args.validation_dir is not None:
_lowercase = os.path.join(data_args.validation_dir , "**" )
_lowercase = load_dataset(
"imagefolder" , data_files=A_ , cache_dir=model_args.cache_dir , task="image-classification" , )
# If we don't have a validation split, split off a percentage of train as validation.
_lowercase = None if "validation" in dataset.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , A_ ) and data_args.train_val_split > 0.0:
_lowercase = dataset["train"].train_test_split(data_args.train_val_split )
_lowercase = split["train"]
_lowercase = split["test"]
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
_lowercase = dataset["train"].features["labels"].names
_lowercase , _lowercase = {}, {}
for i, label in enumerate(A_ ):
_lowercase = str(A_ )
_lowercase = label
# Load the accuracy metric from the datasets package
_lowercase = evaluate.load("accuracy" )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(A_ ):
return metric.compute(predictions=np.argmax(p.predictions , axis=1 ) , references=p.label_ids )
_lowercase = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(A_ ) , labelaid=A_ , idalabel=A_ , finetuning_task="image-classification" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
_lowercase = AutoModelForImageClassification.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 , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
_lowercase = AutoImageProcessor.from_pretrained(
model_args.image_processor_name or model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Define torchvision transforms to be applied to each image.
if "shortest_edge" in image_processor.size:
_lowercase = image_processor.size["shortest_edge"]
else:
_lowercase = (image_processor.size["height"], image_processor.size["width"])
_lowercase = Normalize(mean=image_processor.image_mean , std=image_processor.image_std )
_lowercase = Compose(
[
RandomResizedCrop(A_ ),
RandomHorizontalFlip(),
ToTensor(),
normalize,
] )
_lowercase = Compose(
[
Resize(A_ ),
CenterCrop(A_ ),
ToTensor(),
normalize,
] )
def train_transforms(A_ ):
_lowercase = [
_train_transforms(pil_img.convert("RGB" ) ) for pil_img in example_batch["image"]
]
return example_batch
def val_transforms(A_ ):
_lowercase = [_val_transforms(pil_img.convert("RGB" ) ) for pil_img in example_batch["image"]]
return example_batch
if training_args.do_train:
if "train" not in dataset:
raise ValueError("--do_train requires a train dataset" )
if data_args.max_train_samples is not None:
_lowercase = (
dataset["train"].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
dataset["train"].set_transform(A_ )
if training_args.do_eval:
if "validation" not in dataset:
raise ValueError("--do_eval requires a validation dataset" )
if data_args.max_eval_samples is not None:
_lowercase = (
dataset["validation"].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
dataset["validation"].set_transform(A_ )
# Initalize our trainer
_lowercase = Trainer(
model=A_ , args=A_ , train_dataset=dataset["train"] if training_args.do_train else None , eval_dataset=dataset["validation"] if training_args.do_eval else None , compute_metrics=A_ , tokenizer=A_ , data_collator=A_ , )
# Training
if training_args.do_train:
_lowercase = None
if training_args.resume_from_checkpoint is not None:
_lowercase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_lowercase = last_checkpoint
_lowercase = trainer.train(resume_from_checkpoint=A_ )
trainer.save_model()
trainer.log_metrics("train" , train_result.metrics )
trainer.save_metrics("train" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_lowercase = trainer.evaluate()
trainer.log_metrics("eval" , A_ )
trainer.save_metrics("eval" , A_ )
# Write model card and (optionally) push to hub
_lowercase = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "image-classification",
"dataset": data_args.dataset_name,
"tags": ["image-classification", "vision"],
}
if training_args.push_to_hub:
trainer.push_to_hub(**A_ )
else:
trainer.create_model_card(**A_ )
if __name__ == "__main__":
main()
| 497
| 0
|
"""simple docstring"""
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed
SCREAMING_SNAKE_CASE_ = logging.getLogger(__name__)
def lowercase (_lowerCAmelCase=2 , _lowerCAmelCase=3 , _lowerCAmelCase=16 , _lowerCAmelCase = 10 , _lowerCAmelCase = 2 ):
def get_dataset(_lowerCAmelCase ):
__lowerCAmelCase = torch.randn(batch_size * n_batches , 1 )
return TensorDataset(_lowerCAmelCase , a * x + b + 0.1 * torch.randn(batch_size * n_batches , 1 ) )
__lowerCAmelCase = get_dataset(_lowerCAmelCase )
__lowerCAmelCase = get_dataset(_lowerCAmelCase )
__lowerCAmelCase = DataLoader(_lowerCAmelCase , shuffle=_lowerCAmelCase , batch_size=_lowerCAmelCase , num_workers=4 )
__lowerCAmelCase = DataLoader(_lowerCAmelCase , shuffle=_lowerCAmelCase , batch_size=_lowerCAmelCase , num_workers=4 )
return (train_dataloader, valid_dataloader)
def lowercase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=None ):
__lowerCAmelCase = []
for epoch in range(_lowerCAmelCase ):
# Train quickly
model.train()
for batch in dataloader:
__lowerCAmelCase = batch
__lowerCAmelCase = model(_lowerCAmelCase )
__lowerCAmelCase = torch.nn.functional.mse_loss(_lowerCAmelCase , _lowerCAmelCase )
accelerator.backward(_lowerCAmelCase )
optimizer.step()
optimizer.zero_grad()
rands.append(random.random() ) # Introduce some randomness
if scheduler is not None:
scheduler.step()
return rands
class lowerCAmelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self ) -> Optional[int]:
super().__init__()
__lowerCAmelCase = nn.Parameter(torch.randn(1 ) )
__lowerCAmelCase = nn.Parameter(torch.randn(1 ) )
def A__ ( self , snake_case_ ) -> List[str]:
return x * self.a + self.b
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def A__ ( self ) -> Union[str, Any]:
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
__lowerCAmelCase = DummyModel()
__lowerCAmelCase = torch.optim.Adam(params=model.parameters() , lr=1e-3 )
__lowerCAmelCase = dummy_dataloaders()
__lowerCAmelCase = ProjectConfiguration(total_limit=1 , project_dir=_a , automatic_checkpoint_naming=_a )
# Train baseline
__lowerCAmelCase = Accelerator(project_config=_a )
__lowerCAmelCase = accelerator.prepare(
_a , _a , _a , _a )
# Save initial
accelerator.save_state()
# Save second state
accelerator.save_state()
self.assertEqual(len(os.listdir(accelerator.project_dir ) ) , 1 )
def A__ ( self ) -> List[str]:
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
__lowerCAmelCase = DummyModel()
__lowerCAmelCase = torch.optim.Adam(params=model.parameters() , lr=1e-3 )
__lowerCAmelCase = dummy_dataloaders()
# Train baseline
__lowerCAmelCase = Accelerator()
__lowerCAmelCase = accelerator.prepare(
_a , _a , _a , _a )
# Save initial
__lowerCAmelCase = os.path.join(_a , """initial""" )
accelerator.save_state(_a )
(__lowerCAmelCase) = model.a.item(), model.b.item()
__lowerCAmelCase = optimizer.state_dict()
__lowerCAmelCase = train(3 , _a , _a , _a , _a )
(__lowerCAmelCase) = model.a.item(), model.b.item()
__lowerCAmelCase = optimizer.state_dict()
# Train partially
set_seed(42 )
__lowerCAmelCase = DummyModel()
__lowerCAmelCase = torch.optim.Adam(params=model.parameters() , lr=1e-3 )
__lowerCAmelCase = dummy_dataloaders()
__lowerCAmelCase = Accelerator()
__lowerCAmelCase = accelerator.prepare(
_a , _a , _a , _a )
accelerator.load_state(_a )
(__lowerCAmelCase) = model.a.item(), model.b.item()
__lowerCAmelCase = optimizer.state_dict()
self.assertEqual(_a , _a )
self.assertEqual(_a , _a )
self.assertEqual(_a , _a )
__lowerCAmelCase = train(2 , _a , _a , _a , _a )
# Save everything
__lowerCAmelCase = os.path.join(_a , """checkpoint""" )
accelerator.save_state(_a )
# Load everything back in and make sure all states work
accelerator.load_state(_a )
test_rands += train(1 , _a , _a , _a , _a )
(__lowerCAmelCase) = model.a.item(), model.b.item()
__lowerCAmelCase = optimizer.state_dict()
self.assertEqual(_a , _a )
self.assertEqual(_a , _a )
self.assertEqual(_a , _a )
self.assertEqual(_a , _a )
def A__ ( self ) -> str:
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
__lowerCAmelCase = DummyModel()
__lowerCAmelCase = torch.optim.Adam(params=model.parameters() , lr=1e-3 )
__lowerCAmelCase = dummy_dataloaders()
__lowerCAmelCase = ProjectConfiguration(automatic_checkpoint_naming=_a )
# Train baseline
__lowerCAmelCase = Accelerator(project_dir=_a , project_config=_a )
__lowerCAmelCase = accelerator.prepare(
_a , _a , _a , _a )
# Save initial
accelerator.save_state()
(__lowerCAmelCase) = model.a.item(), model.b.item()
__lowerCAmelCase = optimizer.state_dict()
__lowerCAmelCase = train(3 , _a , _a , _a , _a )
(__lowerCAmelCase) = model.a.item(), model.b.item()
__lowerCAmelCase = optimizer.state_dict()
# Train partially
set_seed(42 )
__lowerCAmelCase = DummyModel()
__lowerCAmelCase = torch.optim.Adam(params=model.parameters() , lr=1e-3 )
__lowerCAmelCase = dummy_dataloaders()
__lowerCAmelCase = ProjectConfiguration(iteration=1 , automatic_checkpoint_naming=_a )
__lowerCAmelCase = Accelerator(project_dir=_a , project_config=_a )
__lowerCAmelCase = accelerator.prepare(
_a , _a , _a , _a )
accelerator.load_state(os.path.join(_a , """checkpoints""" , """checkpoint_0""" ) )
(__lowerCAmelCase) = model.a.item(), model.b.item()
__lowerCAmelCase = optimizer.state_dict()
self.assertEqual(_a , _a )
self.assertEqual(_a , _a )
self.assertEqual(_a , _a )
__lowerCAmelCase = train(2 , _a , _a , _a , _a )
# Save everything
accelerator.save_state()
# Load everything back in and make sure all states work
accelerator.load_state(os.path.join(_a , """checkpoints""" , """checkpoint_1""" ) )
test_rands += train(1 , _a , _a , _a , _a )
(__lowerCAmelCase) = model.a.item(), model.b.item()
__lowerCAmelCase = optimizer.state_dict()
self.assertEqual(_a , _a )
self.assertEqual(_a , _a )
self.assertEqual(_a , _a )
self.assertEqual(_a , _a )
def A__ ( self ) -> Optional[Any]:
__lowerCAmelCase = torch.tensor([1, 2, 3] )
__lowerCAmelCase = torch.tensor([2, 3, 4] )
__lowerCAmelCase = DummyModel()
__lowerCAmelCase = torch.optim.Adam(net.parameters() )
__lowerCAmelCase = Accelerator()
with self.assertRaises(_a ) as ve:
accelerator.register_for_checkpointing(_a , _a , _a , _a )
__lowerCAmelCase = str(ve.exception )
self.assertTrue("""Item at index 0""" in message )
self.assertTrue("""Item at index 1""" in message )
self.assertFalse("""Item at index 2""" in message )
self.assertFalse("""Item at index 3""" in message )
def A__ ( self ) -> Dict:
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
__lowerCAmelCase = DummyModel()
__lowerCAmelCase = torch.optim.Adam(params=model.parameters() , lr=1e-3 )
__lowerCAmelCase = torch.optim.lr_scheduler.StepLR(_a , step_size=1 , gamma=0.99 )
__lowerCAmelCase = dummy_dataloaders()
__lowerCAmelCase = ProjectConfiguration(automatic_checkpoint_naming=_a )
# Train baseline
__lowerCAmelCase = Accelerator(project_dir=_a , project_config=_a )
__lowerCAmelCase = accelerator.prepare(
_a , _a , _a , _a , _a )
# Save initial
accelerator.save_state()
__lowerCAmelCase = scheduler.state_dict()
train(3 , _a , _a , _a , _a , _a )
self.assertNotEqual(_a , scheduler.state_dict() )
# Load everything back in and make sure all states work
accelerator.load_state(os.path.join(_a , """checkpoints""" , """checkpoint_0""" ) )
self.assertEqual(_a , scheduler.state_dict() )
def A__ ( self ) -> str:
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
__lowerCAmelCase = DummyModel()
__lowerCAmelCase = ProjectConfiguration(automatic_checkpoint_naming=_a , total_limit=2 )
# Train baseline
__lowerCAmelCase = Accelerator(project_dir=_a , project_config=_a )
__lowerCAmelCase = accelerator.prepare(_a )
# Save 3 states:
for _ in range(11 ):
accelerator.save_state()
self.assertTrue(not os.path.exists(os.path.join(_a , """checkpoints""" , """checkpoint_0""" ) ) )
self.assertTrue(os.path.exists(os.path.join(_a , """checkpoints""" , """checkpoint_9""" ) ) )
self.assertTrue(os.path.exists(os.path.join(_a , """checkpoints""" , """checkpoint_10""" ) ) )
@require_cuda
def A__ ( self ) -> str:
__lowerCAmelCase = ["""torchrun""", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
execute_subprocess_async(_a , env=os.environ.copy() )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = '''/tmp/accelerate/state_checkpointing'''
SCREAMING_SNAKE_CASE_ = DummyModel()
SCREAMING_SNAKE_CASE_ = torch.optim.Adam(params=model.parameters(), lr=1E-3)
SCREAMING_SNAKE_CASE_ = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = dummy_dataloaders()
SCREAMING_SNAKE_CASE_ = ProjectConfiguration(automatic_checkpoint_naming=True)
# Train baseline
SCREAMING_SNAKE_CASE_ = Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='''no''')
if accelerator.process_index == 0:
if os.path.exists(savedir):
shutil.rmtree(savedir)
os.makedirs(savedir)
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = accelerator.prepare(
model, optimizer, train_dataloader, valid_dataloader, scheduler
)
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = accelerator.prepare(model, optimizer)
train(3, model, train_dataloader, optimizer, accelerator, scheduler)
# Check that the intial optimizer is loaded on the GPU
for group in optimizer.param_groups:
SCREAMING_SNAKE_CASE_ = group['''params'''][0].device
break
assert param_device.type == accelerator.device.type
SCREAMING_SNAKE_CASE_ = model.cpu()
accelerator.wait_for_everyone()
accelerator.save_state()
accelerator.wait_for_everyone()
# Check CPU state
accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''cpu''')
for group in optimizer.param_groups:
SCREAMING_SNAKE_CASE_ = group['''params'''][0].device
break
assert (
param_device.type == torch.device('''cpu''').type
), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"
# Check device state
model.to(accelerator.device)
accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''on_device''')
for group in optimizer.param_groups:
SCREAMING_SNAKE_CASE_ = group['''params'''][0].device
break
assert (
param_device.type == accelerator.device.type
), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"
# Check error
with pytest.raises(TypeError, match='''Unsupported optimizer map location passed'''):
accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''invalid''')
accelerator.wait_for_everyone()
if accelerator.process_index == 0:
shutil.rmtree(savedir)
accelerator.wait_for_everyone()
| 705
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
class lowerCAmelCase_ ( A__ ):
'''simple docstring'''
def __init__( self , *snake_case_ , **snake_case_ ) -> None:
warnings.warn(
"""The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use GLPNImageProcessor instead.""" , snake_case_ , )
super().__init__(*snake_case_ , **snake_case_ )
| 573
| 0
|
'''simple docstring'''
from __future__ import annotations
a : Optional[Any] = '''Muhammad Umer Farooq'''
a : int = '''MIT'''
a : Dict = '''1.0.0'''
a : Optional[int] = '''Muhammad Umer Farooq'''
a : Optional[Any] = '''contact@muhammadumerfarooq.me'''
a : Union[str, Any] = '''Alpha'''
import re
from html.parser import HTMLParser
from urllib import parse
import requests
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
def __init__( self : Optional[Any] , a_ : str ):
"""simple docstring"""
super().__init__()
__snake_case = []
__snake_case = domain
def A ( self : str , a_ : str , a_ : list[tuple[str, str | None]] ):
"""simple docstring"""
if tag == "a":
# Check the list of defined attributes.
for name, value in attrs:
# If href is defined, and not empty nor # print it.
if name == "href" and value != "#" and value != "":
# If not already in urls.
if value not in self.urls:
__snake_case = parse.urljoin(self.domain , a_ )
self.urls.append(a_ )
def __UpperCAmelCase ( _UpperCAmelCase : str ) -> str:
return ".".join(get_sub_domain_name(_UpperCAmelCase ).split("." )[-2:] )
def __UpperCAmelCase ( _UpperCAmelCase : str ) -> str:
return parse.urlparse(_UpperCAmelCase ).netloc
def __UpperCAmelCase ( _UpperCAmelCase : str = "https://github.com" ) -> list[str]:
__snake_case = get_domain_name(_UpperCAmelCase )
# Initialize the parser
__snake_case = Parser(_UpperCAmelCase )
try:
# Open URL
__snake_case = requests.get(_UpperCAmelCase )
# pass the raw HTML to the parser to get links
parser.feed(r.text )
# Get links and loop through
__snake_case = set()
for link in parser.urls:
# open URL.
# read = requests.get(link)
try:
__snake_case = requests.get(_UpperCAmelCase )
# Get the valid email.
__snake_case = re.findall("[a-zA-Z0-9]+@" + domain , read.text )
# If not in list then append it.
for email in emails:
valid_emails.add(_UpperCAmelCase )
except ValueError:
pass
except ValueError:
raise SystemExit(1 )
# Finally return a sorted list of email addresses with no duplicates.
return sorted(_UpperCAmelCase )
if __name__ == "__main__":
a : Any = emails_from_url('''https://github.com''')
print(F'''{len(emails)} emails found:''')
print('''\n'''.join(sorted(emails)))
| 69
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
'configuration_trajectory_transformer': [
'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TrajectoryTransformerConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TrajectoryTransformerModel',
'TrajectoryTransformerPreTrainedModel',
'load_tf_weights_in_trajectory_transformer',
]
if TYPE_CHECKING:
from .configuration_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TrajectoryTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TrajectoryTransformerModel,
TrajectoryTransformerPreTrainedModel,
load_tf_weights_in_trajectory_transformer,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 596
| 0
|
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
A__ : Optional[Any] = logging.get_logger(__name__)
def _snake_case ( lowerCamelCase__ : List[str] , lowerCamelCase__ : Dict=False ) -> int:
lowerCamelCase_ : Union[str, Any] =[]
# fmt: off
# stem:
rename_keys.append(("cls_token", "vit.embeddings.cls_token") )
rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings") )
rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias") )
# backbone
rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight") )
rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight") )
rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias") )
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""") )
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""") )
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""") )
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""") )
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""") )
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""") )
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""") )
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""") )
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""") )
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""") )
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""") )
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""") )
# transformer encoder
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""") )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("norm.weight", "layernorm.weight"),
("norm.bias", "layernorm.bias"),
("pre_logits.fc.weight", "pooler.dense.weight"),
("pre_logits.fc.bias", "pooler.dense.bias"),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
lowerCamelCase_ : Dict =[(pair[0], pair[1][4:]) if pair[1].startswith("vit" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("norm.weight", "vit.layernorm.weight"),
("norm.bias", "vit.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
] )
# fmt: on
return rename_keys
def _snake_case ( lowerCamelCase__ : List[Any] , lowerCamelCase__ : List[str] , lowerCamelCase__ : Any=False ) -> Optional[Any]:
for i in range(config.num_hidden_layers ):
if base_model:
lowerCamelCase_ : Union[str, Any] =""
else:
lowerCamelCase_ : Any ="vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowerCamelCase_ : Optional[int] =state_dict.pop(F"""blocks.{i}.attn.qkv.weight""" )
lowerCamelCase_ : Dict =state_dict.pop(F"""blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
lowerCamelCase_ : Dict =in_proj_weight[
: config.hidden_size, :
]
lowerCamelCase_ : Any =in_proj_bias[: config.hidden_size]
lowerCamelCase_ : Union[str, Any] =in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowerCamelCase_ : Tuple =in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowerCamelCase_ : Dict =in_proj_weight[
-config.hidden_size :, :
]
lowerCamelCase_ : List[str] =in_proj_bias[-config.hidden_size :]
def _snake_case ( lowerCamelCase__ : Any ) -> str:
lowerCamelCase_ : Optional[Any] =["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(lowerCamelCase__ , lowerCamelCase__ )
def _snake_case ( lowerCamelCase__ : Any , lowerCamelCase__ : str , lowerCamelCase__ : int ) -> int:
lowerCamelCase_ : Union[str, Any] =dct.pop(lowerCamelCase__ )
lowerCamelCase_ : int =val
def _snake_case ( ) -> List[str]:
lowerCamelCase_ : Optional[int] ="http://images.cocodataset.org/val2017/000000039769.jpg"
lowerCamelCase_ : List[Any] =Image.open(requests.get(lowerCamelCase__ , stream=lowerCamelCase__ ).raw )
return im
@torch.no_grad()
def _snake_case ( lowerCamelCase__ : Dict , lowerCamelCase__ : Dict , lowerCamelCase__ : Any=False ) -> int:
lowerCamelCase_ : Optional[Any] =BitConfig(
global_padding="same" , layer_type="bottleneck" , depths=(3, 4, 9) , out_features=["stage3"] , embedding_dynamic_padding=lowerCamelCase__ , )
lowerCamelCase_ : int =ViTHybridConfig(backbone_config=lowerCamelCase__ , image_size=384 , num_labels=1_000 )
lowerCamelCase_ : Optional[Any] =False
# load original model from timm
lowerCamelCase_ : Optional[Any] =timm.create_model(lowerCamelCase__ , pretrained=lowerCamelCase__ )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
lowerCamelCase_ : int =timm_model.state_dict()
if base_model:
remove_classification_head_(lowerCamelCase__ )
lowerCamelCase_ : Any =create_rename_keys(lowerCamelCase__ , lowerCamelCase__ )
for src, dest in rename_keys:
rename_key(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
read_in_q_k_v(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowerCamelCase_ : Union[str, Any] ="huggingface/label-files"
lowerCamelCase_ : Any ="imagenet-1k-id2label.json"
lowerCamelCase_ : List[Any] =json.load(open(hf_hub_download(lowerCamelCase__ , lowerCamelCase__ , repo_type="dataset" ) , "r" ) )
lowerCamelCase_ : str ={int(lowerCamelCase__ ): v for k, v in idalabel.items()}
lowerCamelCase_ : List[str] =idalabel
lowerCamelCase_ : Union[str, Any] ={v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
lowerCamelCase_ : Dict =ViTHybridModel(lowerCamelCase__ ).eval()
else:
lowerCamelCase_ : List[str] =ViTHybridForImageClassification(lowerCamelCase__ ).eval()
model.load_state_dict(lowerCamelCase__ )
# create image processor
lowerCamelCase_ : Optional[Any] =create_transform(**resolve_data_config({} , model=lowerCamelCase__ ) )
lowerCamelCase_ : int =transform.transforms
lowerCamelCase_ : Any ={
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
lowerCamelCase_ : List[str] =ViTHybridImageProcessor(
do_resize=lowerCamelCase__ , size={"shortest_edge": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=lowerCamelCase__ , crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]} , do_normalize=lowerCamelCase__ , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
lowerCamelCase_ : int =prepare_img()
lowerCamelCase_ : List[Any] =transform(lowerCamelCase__ ).unsqueeze(0 )
lowerCamelCase_ : str =processor(lowerCamelCase__ , return_tensors="pt" ).pixel_values
# verify pixel values
assert torch.allclose(lowerCamelCase__ , lowerCamelCase__ )
# verify logits
with torch.no_grad():
lowerCamelCase_ : List[Any] =model(lowerCamelCase__ )
lowerCamelCase_ : Dict =outputs.logits
print("Predicted class:" , logits.argmax(-1 ).item() )
if base_model:
lowerCamelCase_ : List[Any] =timm_model.forward_features(lowerCamelCase__ )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(lowerCamelCase__ , outputs.pooler_output , atol=1e-3 )
else:
lowerCamelCase_ : Optional[int] =timm_model(lowerCamelCase__ )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(lowerCamelCase__ , outputs.logits , atol=1e-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(lowerCamelCase__ ).mkdir(exist_ok=lowerCamelCase__ )
print(F"""Saving model {vit_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(lowerCamelCase__ )
print(F"""Saving processor to {pytorch_dump_folder_path}""" )
processor.save_pretrained(lowerCamelCase__ )
if push_to_hub:
print(F"""Pushing model and processor to the hub {vit_name}""" )
model.push_to_hub(F"""ybelkada/{vit_name}""" )
processor.push_to_hub(F"""ybelkada/{vit_name}""" )
if __name__ == "__main__":
A__ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--vit_name',
default='vit_base_r50_s16_384',
type=str,
help='Name of the hybrid ViT 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 upload the model to the HuggingFace hub.'
)
A__ : Optional[int] = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 244
|
"""simple docstring"""
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class lowercase__ :
def __init__( self : Union[str, Any] , snake_case__ : Optional[int] , ):
lowerCamelCase_ : Optional[int] =parent
lowerCamelCase_ : str =13
lowerCamelCase_ : str =7
lowerCamelCase_ : str =30
lowerCamelCase_ : List[str] =self.seq_length + self.mem_len
lowerCamelCase_ : Optional[int] =15
lowerCamelCase_ : Union[str, Any] =True
lowerCamelCase_ : int =True
lowerCamelCase_ : Union[str, Any] =99
lowerCamelCase_ : Optional[int] =[10, 50, 80]
lowerCamelCase_ : Tuple =32
lowerCamelCase_ : Optional[int] =32
lowerCamelCase_ : Optional[int] =4
lowerCamelCase_ : List[Any] =8
lowerCamelCase_ : Optional[Any] =128
lowerCamelCase_ : Optional[int] =2
lowerCamelCase_ : Dict =2
lowerCamelCase_ : Union[str, Any] =None
lowerCamelCase_ : Optional[int] =1
lowerCamelCase_ : Any =0
lowerCamelCase_ : Optional[int] =3
lowerCamelCase_ : List[str] =self.vocab_size - 1
lowerCamelCase_ : Optional[Any] =0.01
def UpperCAmelCase__ ( self : Optional[int] ):
lowerCamelCase_ : str =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase_ : str =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase_ : List[Any] =None
if self.use_labels:
lowerCamelCase_ : List[str] =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase_ : Tuple =TransfoXLConfig(
vocab_size=self.vocab_size , mem_len=self.mem_len , clamp_len=self.clamp_len , cutoffs=self.cutoffs , d_model=self.hidden_size , d_embed=self.d_embed , n_head=self.num_attention_heads , d_head=self.d_head , d_inner=self.d_inner , div_val=self.div_val , n_layer=self.num_hidden_layers , eos_token_id=self.eos_token_id , pad_token_id=self.vocab_size - 1 , init_range=self.init_range , num_labels=self.num_labels , )
return (config, input_ids_a, input_ids_a, lm_labels)
def UpperCAmelCase__ ( self : Optional[Any] ):
random.seed(self.seed )
tf.random.set_seed(self.seed )
def UpperCAmelCase__ ( self : str , snake_case__ : int , snake_case__ : List[str] , snake_case__ : List[str] , snake_case__ : Union[str, Any] ):
lowerCamelCase_ : Union[str, Any] =TFTransfoXLModel(snake_case__ )
lowerCamelCase_ , lowerCamelCase_ : str =model(snake_case__ ).to_tuple()
lowerCamelCase_ : int ={"input_ids": input_ids_a, "mems": mems_a}
lowerCamelCase_ , lowerCamelCase_ : Tuple =model(snake_case__ ).to_tuple()
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def UpperCAmelCase__ ( self : int , snake_case__ : List[Any] , snake_case__ : Optional[int] , snake_case__ : Optional[Any] , snake_case__ : Optional[int] ):
lowerCamelCase_ : Optional[int] =TFTransfoXLLMHeadModel(snake_case__ )
lowerCamelCase_ , lowerCamelCase_ : List[Any] =model(snake_case__ ).to_tuple()
lowerCamelCase_ : int ={"input_ids": input_ids_a, "labels": lm_labels}
lowerCamelCase_ , lowerCamelCase_ : str =model(snake_case__ ).to_tuple()
lowerCamelCase_ , lowerCamelCase_ : List[Any] =model([input_ids_a, mems_a] ).to_tuple()
lowerCamelCase_ : Union[str, Any] ={"input_ids": input_ids_a, "mems": mems_a, "labels": lm_labels}
lowerCamelCase_ , lowerCamelCase_ : Optional[int] =model(snake_case__ ).to_tuple()
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def UpperCAmelCase__ ( self : Any , snake_case__ : Optional[int] , snake_case__ : List[Any] , snake_case__ : Union[str, Any] , snake_case__ : Dict ):
lowerCamelCase_ : Tuple =TFTransfoXLForSequenceClassification(snake_case__ )
lowerCamelCase_ : List[str] =model(snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase__ ( self : str ):
lowerCamelCase_ : Optional[Any] =self.prepare_config_and_inputs()
((lowerCamelCase_) , (lowerCamelCase_) , (lowerCamelCase_) , (lowerCamelCase_)) : List[Any] =config_and_inputs
lowerCamelCase_ : int ={"input_ids": input_ids_a}
return config, inputs_dict
@require_tf
class lowercase__ ( snake_case__, snake_case__, unittest.TestCase ):
_UpperCAmelCase :Optional[Any] = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
_UpperCAmelCase :Union[str, Any] = () if is_tf_available() else ()
_UpperCAmelCase :List[str] = (
{
"feature-extraction": TFTransfoXLModel,
"text-classification": TFTransfoXLForSequenceClassification,
"text-generation": TFTransfoXLLMHeadModel,
"zero-shot": TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
_UpperCAmelCase :Union[str, Any] = False
_UpperCAmelCase :Optional[int] = False
_UpperCAmelCase :int = False
_UpperCAmelCase :Any = False
def UpperCAmelCase__ ( self : Union[str, Any] , snake_case__ : Any , snake_case__ : List[Any] , snake_case__ : str , snake_case__ : int , snake_case__ : List[Any] ):
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def UpperCAmelCase__ ( self : Tuple ):
lowerCamelCase_ : List[str] =TFTransfoXLModelTester(self )
lowerCamelCase_ : List[str] =ConfigTester(self , config_class=snake_case__ , d_embed=37 )
def UpperCAmelCase__ ( self : str ):
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self : Dict ):
self.model_tester.set_seed()
lowerCamelCase_ : List[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*snake_case__ )
def UpperCAmelCase__ ( self : Optional[int] ):
self.model_tester.set_seed()
lowerCamelCase_ : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*snake_case__ )
def UpperCAmelCase__ ( self : Optional[Any] ):
lowerCamelCase_ : int =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*snake_case__ )
def UpperCAmelCase__ ( self : Optional[Any] ):
lowerCamelCase_ , lowerCamelCase_ : int =self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase_ : Dict =[TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
lowerCamelCase_ : List[Any] =model_class(snake_case__ )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class in list_other_models_with_output_ebd:
lowerCamelCase_ : Optional[Any] =model.get_output_embeddings()
assert isinstance(snake_case__ , tf.keras.layers.Layer )
lowerCamelCase_ : Any =model.get_bias()
assert name is None
else:
lowerCamelCase_ : List[Any] =model.get_output_embeddings()
assert x is None
lowerCamelCase_ : int =model.get_bias()
assert name is None
def UpperCAmelCase__ ( self : str ):
# TODO JP: Make TransfoXL XLA compliant
pass
@slow
def UpperCAmelCase__ ( self : Optional[Any] ):
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ : Dict =TFTransfoXLModel.from_pretrained(snake_case__ )
self.assertIsNotNone(snake_case__ )
@unittest.skip(reason="This model doesn't play well with fit() due to not returning a single loss." )
def UpperCAmelCase__ ( self : Optional[int] ):
pass
@require_tf
class lowercase__ ( unittest.TestCase ):
@unittest.skip("Skip test until #12651 is resolved." )
@slow
def UpperCAmelCase__ ( self : str ):
lowerCamelCase_ : Optional[Any] =TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103" )
# fmt: off
lowerCamelCase_ : str =tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]] , dtype=tf.intaa ) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
lowerCamelCase_ : int =[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
lowerCamelCase_ : Optional[int] =model.generate(snake_case__ , max_length=200 , do_sample=snake_case__ )
self.assertListEqual(output_ids[0].numpy().tolist() , snake_case__ )
| 244
| 1
|
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
get_torch_dist_unique_port,
require_apex,
require_bitsandbytes,
require_fairscale,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
require_torch_non_multi_gpu,
slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed
A_ = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f'''{bindir}/../../examples/pytorch/translation'''):
from run_translation import main # noqa
set_seed(42)
A_ = "sshleifer/student_marian_en_ro_6_1"
A_ = "sshleifer/tiny-mbart"
@require_torch
class _snake_case ( _a ):
def __UpperCamelCase ( self : Optional[int] ,SCREAMING_SNAKE_CASE__ : Optional[int]=False ,SCREAMING_SNAKE_CASE__ : int=None ,SCREAMING_SNAKE_CASE__ : int=True ,SCREAMING_SNAKE_CASE__ : Optional[int]=True ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=True ,SCREAMING_SNAKE_CASE__ : Dict=True ,):
SCREAMING_SNAKE_CASE:Optional[int] = self.run_trainer(
eval_steps=1 ,max_len=12 ,model_name=SCREAMING_SNAKE_CASE__ ,num_train_epochs=1 ,distributed=SCREAMING_SNAKE_CASE__ ,extra_args_str=SCREAMING_SNAKE_CASE__ ,predict_with_generate=SCREAMING_SNAKE_CASE__ ,do_train=SCREAMING_SNAKE_CASE__ ,do_eval=SCREAMING_SNAKE_CASE__ ,do_predict=SCREAMING_SNAKE_CASE__ ,)
SCREAMING_SNAKE_CASE:List[Any] = TrainerState.load_from_json(os.path.join(SCREAMING_SNAKE_CASE__ ,"trainer_state.json" ) ).log_history
if not do_eval:
return
SCREAMING_SNAKE_CASE:int = [log for log in logs if "eval_loss" in log.keys()]
SCREAMING_SNAKE_CASE:List[str] = eval_metrics[0]
if predict_with_generate:
assert "eval_bleu" in first_step_stats
SCREAMING_SNAKE_CASE:Optional[int] = eval_metrics[-1]
assert isinstance(last_step_stats["eval_bleu"] ,SCREAMING_SNAKE_CASE__ )
assert not math.isnan(float(last_step_stats["eval_loss"] ) ), "eval_loss must not be `nan`"
@require_torch_non_multi_gpu
def __UpperCamelCase ( self : str ):
self.run_seqaseq_quick()
@require_torch_multi_gpu
def __UpperCamelCase ( self : Optional[int] ):
self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__ )
@require_torch_multi_gpu
def __UpperCamelCase ( self : List[Any] ):
self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__ )
@unittest.skip("Requires an update of the env running those tests" )
@require_torch_multi_gpu
@require_fairscale
def __UpperCamelCase ( self : Dict ):
self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__ ,extra_args_str="--sharded_ddp simple" )
@unittest.skip("Requires an update of the env running those tests" )
@require_torch_multi_gpu
@require_fairscale
def __UpperCamelCase ( self : Optional[Any] ):
self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__ ,extra_args_str="--sharded_ddp simple --fp16" )
@unittest.skip("Requires an update of the env running those tests" )
@require_torch_multi_gpu
@require_fairscale
def __UpperCamelCase ( self : Any ):
self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__ ,extra_args_str="--sharded_ddp zero_dp_2" ,predict_with_generate=SCREAMING_SNAKE_CASE__ )
@unittest.skip("Requires an update of the env running those tests" )
@require_torch_multi_gpu
@require_fairscale
def __UpperCamelCase ( self : Tuple ):
self.run_seqaseq_quick(
distributed=SCREAMING_SNAKE_CASE__ ,extra_args_str="--sharded_ddp zero_dp_2 --fp16" ,predict_with_generate=SCREAMING_SNAKE_CASE__ )
@require_apex
@require_torch_gpu
def __UpperCamelCase ( self : List[Any] ):
# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
# program and it breaks other tests that run from the same pytest worker, therefore until this is
# sorted out it must be run only in an external program, that is distributed=True in this
# test and only under one or more gpus - if we want cpu will need to make a special test
#
# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
# 2nd main() call it botches the future eval.
#
self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__ ,extra_args_str="--fp16 --fp16_backend=apex" )
# test 2nd time - was getting eval_loss': nan'
# to reproduce the problem set distributed=False
self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__ ,extra_args_str="--fp16 --fp16_backend=apex" )
@parameterized.expand(["base", "low", "high", "mixed"] )
@require_torch_multi_gpu
def __UpperCamelCase ( self : List[Any] ,SCREAMING_SNAKE_CASE__ : Any ):
# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
SCREAMING_SNAKE_CASE:List[Any] = {
# test with the default log_level - should be info and thus log info once
"base": {"extra_args_str": "", "n_matches": 1},
# test with low log_level and log_level_replica - should be noisy on all processes
# now the info string should appear twice on 2 processes
"low": {"extra_args_str": "--log_level debug --log_level_replica debug", "n_matches": 2},
# test with high log_level and low log_level_replica
# now the info string should appear once only on the replica
"high": {"extra_args_str": "--log_level error --log_level_replica debug", "n_matches": 1},
# test with high log_level and log_level_replica - should be quiet on all processes
"mixed": {"extra_args_str": "--log_level error --log_level_replica error", "n_matches": 0},
}
SCREAMING_SNAKE_CASE:str = experiments[experiment_id]
SCREAMING_SNAKE_CASE:List[str] = {"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
SCREAMING_SNAKE_CASE:Optional[int] = "Running training"
with CaptureStderr() as cl:
self.run_seqaseq_quick(**SCREAMING_SNAKE_CASE__ ,extra_args_str=data["extra_args_str"] )
SCREAMING_SNAKE_CASE:Union[str, Any] = len(re.findall(SCREAMING_SNAKE_CASE__ ,cl.err ) )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,data["n_matches"] )
@slow
def __UpperCamelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE:List[Any] = self.run_trainer(
eval_steps=2 ,max_len=128 ,model_name=SCREAMING_SNAKE_CASE__ ,learning_rate=3e-4 ,num_train_epochs=10 ,distributed=SCREAMING_SNAKE_CASE__ ,)
# Check metrics
SCREAMING_SNAKE_CASE:str = TrainerState.load_from_json(os.path.join(SCREAMING_SNAKE_CASE__ ,"trainer_state.json" ) ).log_history
SCREAMING_SNAKE_CASE:Dict = [log for log in logs if "eval_loss" in log.keys()]
SCREAMING_SNAKE_CASE:Optional[Any] = eval_metrics[0]
SCREAMING_SNAKE_CASE:List[str] = eval_metrics[-1]
assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
assert isinstance(last_step_stats["eval_bleu"] ,SCREAMING_SNAKE_CASE__ )
# test if do_predict saves generations and metrics
SCREAMING_SNAKE_CASE:int = os.listdir(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Union[str, Any] = {os.path.basename(SCREAMING_SNAKE_CASE__ ) for p in contents}
assert "generated_predictions.txt" in contents
assert "predict_results.json" in contents
@slow
@require_bitsandbytes
def __UpperCamelCase ( self : List[Any] ):
from transformers.training_args import OptimizerNames
def train_and_return_metrics(SCREAMING_SNAKE_CASE__ : str ) -> Tuple[int, float]:
SCREAMING_SNAKE_CASE:Union[str, Any] = "--skip_memory_metrics 0"
SCREAMING_SNAKE_CASE:Optional[Any] = self.run_trainer(
max_len=128 ,model_name=SCREAMING_SNAKE_CASE__ ,learning_rate=3e-4 ,num_train_epochs=1 ,optim=SCREAMING_SNAKE_CASE__ ,distributed=SCREAMING_SNAKE_CASE__ ,extra_args_str=SCREAMING_SNAKE_CASE__ ,do_eval=SCREAMING_SNAKE_CASE__ ,do_predict=SCREAMING_SNAKE_CASE__ ,n_gpus_to_use=1 ,)
# Check metrics
SCREAMING_SNAKE_CASE:int = TrainerState.load_from_json(Path(SCREAMING_SNAKE_CASE__ ,"trainer_state.json" ) ).log_history
SCREAMING_SNAKE_CASE:int = int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20 )
SCREAMING_SNAKE_CASE:Tuple = int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20 )
SCREAMING_SNAKE_CASE:str = logs[0]["train_loss"]
return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE:Tuple = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE:Union[str, Any] = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )
SCREAMING_SNAKE_CASE:Optional[Any] = gpu_alloc_mem_orig - gpu_alloc_mem_bnb
SCREAMING_SNAKE_CASE:Optional[Any] = gpu_peak_mem_orig + gpu_alloc_mem_orig
SCREAMING_SNAKE_CASE:Dict = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
SCREAMING_SNAKE_CASE:Optional[Any] = gpu_total_mem_orig - gpu_total_mem_bnb
# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
# in 2 bytes and the diff in optim memory usage is derived as so:
#
# - normal 25*8=~200MB (8 bytes per param)
# - bnb 25*2= ~50MB (2 bytes per param)
#
# Thus we should expect ~150MB total memory saved.
#
# Peak memory should be the same - the total should be different by about that same margin
#
# After leaving a small margin to accommodate for differences between gpus let's check
# that we have at least 120MB in savings
SCREAMING_SNAKE_CASE:Tuple = 120
# uncomment the following if this test starts failing - requires py38 for a new print feature
# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
# print(f" {gpu_alloc_mem_bnb=}MB {gpu_peak_mem_bnb=}MB {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
# print(f"{gpu_alloc_mem_diff=}MB")
# print(f"{gpu_peak_mem_diff=}MB")
# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")
self.assertGreater(
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,"should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"
F''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
F''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB''' ,)
self.assertGreater(
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,"should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"
F''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
F''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB''' ,)
self.assertEqual(
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}''' )
def __UpperCamelCase ( self : str ,SCREAMING_SNAKE_CASE__ : int ,SCREAMING_SNAKE_CASE__ : str ,SCREAMING_SNAKE_CASE__ : int ,SCREAMING_SNAKE_CASE__ : float = 3e-3 ,SCREAMING_SNAKE_CASE__ : str = "adafactor" ,SCREAMING_SNAKE_CASE__ : bool = False ,SCREAMING_SNAKE_CASE__ : str = None ,SCREAMING_SNAKE_CASE__ : int = 0 ,SCREAMING_SNAKE_CASE__ : bool = True ,SCREAMING_SNAKE_CASE__ : bool = True ,SCREAMING_SNAKE_CASE__ : bool = True ,SCREAMING_SNAKE_CASE__ : bool = True ,SCREAMING_SNAKE_CASE__ : int = None ,):
SCREAMING_SNAKE_CASE:List[Any] = self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
SCREAMING_SNAKE_CASE:Optional[int] = self.get_auto_remove_tmp_dir()
SCREAMING_SNAKE_CASE:Tuple = F'''
--model_name_or_path {model_name}
--train_file {data_dir}/train.json
--validation_file {data_dir}/val.json
--test_file {data_dir}/test.json
--output_dir {output_dir}
--overwrite_output_dir
--max_train_samples 8
--max_source_length {max_len}
--max_target_length {max_len}
--do_train
--num_train_epochs {str(SCREAMING_SNAKE_CASE__ )}
--per_device_train_batch_size 4
--learning_rate {learning_rate}
--warmup_steps 8
--logging_steps 0
--logging_strategy no
--save_steps {str(SCREAMING_SNAKE_CASE__ )}
--group_by_length
--label_smoothing_factor 0.1
--target_lang ro_RO
--source_lang en_XX
'''.split()
SCREAMING_SNAKE_CASE:List[Any] = F'''
--do_eval
--per_device_eval_batch_size 4
--max_eval_samples 8
--val_max_target_length {max_len}
--evaluation_strategy steps
--eval_steps {str(SCREAMING_SNAKE_CASE__ )}
'''.split()
SCREAMING_SNAKE_CASE:Optional[Any] = "\n --do_predict\n ".split()
SCREAMING_SNAKE_CASE:Optional[Any] = []
if do_train:
args += args_train
if do_eval:
args += args_eval
if do_predict:
args += args_predict
if predict_with_generate:
args += "--predict_with_generate".split()
if do_train:
if optim == "adafactor":
args += "--adafactor".split()
else:
args += F'''--optim {optim}'''.split()
if extra_args_str is not None:
args += extra_args_str.split()
if distributed:
if n_gpus_to_use is None:
SCREAMING_SNAKE_CASE:Union[str, Any] = get_gpu_count()
SCREAMING_SNAKE_CASE:Any = get_torch_dist_unique_port()
SCREAMING_SNAKE_CASE:Optional[Any] = F'''
-m torch.distributed.run
--nproc_per_node={n_gpus_to_use}
--master_port={master_port}
{self.examples_dir_str}/pytorch/translation/run_translation.py
'''.split()
SCREAMING_SNAKE_CASE:Any = [sys.executable] + distributed_args + args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(SCREAMING_SNAKE_CASE__ ,env=self.get_env() )
else:
SCREAMING_SNAKE_CASE:Optional[int] = ["run_translation.py"] + args
with patch.object(SCREAMING_SNAKE_CASE__ ,"argv" ,SCREAMING_SNAKE_CASE__ ):
main()
return output_dir
| 143
|
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def A_ ( snake_case , snake_case , snake_case , snake_case , snake_case=True , snake_case="pt" ):
SCREAMING_SNAKE_CASE:Optional[int] = {"add_prefix_space": True} if isinstance(snake_case , snake_case ) and not line.startswith(" " ) else {}
SCREAMING_SNAKE_CASE:Any = padding_side
return tokenizer(
[line] , max_length=snake_case , padding="max_length" if pad_to_max_length else None , truncation=snake_case , return_tensors=snake_case , add_special_tokens=snake_case , **snake_case , )
def A_ ( snake_case , snake_case , snake_case=None , ):
SCREAMING_SNAKE_CASE:List[str] = input_ids.ne(snake_case ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class _snake_case ( _a ):
def __init__( self : Optional[int] ,SCREAMING_SNAKE_CASE__ : int ,SCREAMING_SNAKE_CASE__ : Optional[int] ,SCREAMING_SNAKE_CASE__ : Optional[Any] ,SCREAMING_SNAKE_CASE__ : int ,SCREAMING_SNAKE_CASE__ : Tuple="train" ,SCREAMING_SNAKE_CASE__ : List[Any]=None ,SCREAMING_SNAKE_CASE__ : Optional[int]=None ,SCREAMING_SNAKE_CASE__ : List[str]=None ,SCREAMING_SNAKE_CASE__ : Any="" ,):
super().__init__()
SCREAMING_SNAKE_CASE:int = Path(SCREAMING_SNAKE_CASE__ ).joinpath(type_path + ".source" )
SCREAMING_SNAKE_CASE:Optional[int] = Path(SCREAMING_SNAKE_CASE__ ).joinpath(type_path + ".target" )
SCREAMING_SNAKE_CASE:List[str] = self.get_char_lens(self.src_file )
SCREAMING_SNAKE_CASE:Tuple = max_source_length
SCREAMING_SNAKE_CASE:Any = max_target_length
assert min(self.src_lens ) > 0, F'''found empty line in {self.src_file}'''
SCREAMING_SNAKE_CASE:List[Any] = tokenizer
SCREAMING_SNAKE_CASE:str = prefix
if n_obs is not None:
SCREAMING_SNAKE_CASE:Union[str, Any] = self.src_lens[:n_obs]
SCREAMING_SNAKE_CASE:Dict = src_lang
SCREAMING_SNAKE_CASE:Optional[int] = tgt_lang
def __len__( self : Union[str, Any] ):
return len(self.src_lens )
def __getitem__( self : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : str ):
SCREAMING_SNAKE_CASE:List[str] = index + 1 # linecache starts at 1
SCREAMING_SNAKE_CASE:Union[str, Any] = self.prefix + linecache.getline(str(self.src_file ) ,SCREAMING_SNAKE_CASE__ ).rstrip("\n" )
SCREAMING_SNAKE_CASE:Union[str, Any] = linecache.getline(str(self.tgt_file ) ,SCREAMING_SNAKE_CASE__ ).rstrip("\n" )
assert source_line, F'''empty source line for index {index}'''
assert tgt_line, F'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer ,SCREAMING_SNAKE_CASE__ ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
SCREAMING_SNAKE_CASE:str = (
self.tokenizer.question_encoder if isinstance(self.tokenizer ,SCREAMING_SNAKE_CASE__ ) else self.tokenizer
)
SCREAMING_SNAKE_CASE:Optional[Any] = self.tokenizer.generator if isinstance(self.tokenizer ,SCREAMING_SNAKE_CASE__ ) else self.tokenizer
SCREAMING_SNAKE_CASE:int = encode_line(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,self.max_source_length ,"right" )
SCREAMING_SNAKE_CASE:List[Any] = encode_line(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,self.max_target_length ,"right" )
SCREAMING_SNAKE_CASE:Dict = source_inputs["input_ids"].squeeze()
SCREAMING_SNAKE_CASE:List[str] = target_inputs["input_ids"].squeeze()
SCREAMING_SNAKE_CASE:List[str] = source_inputs["attention_mask"].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def __UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Dict ):
return [len(SCREAMING_SNAKE_CASE__ ) for x in Path(SCREAMING_SNAKE_CASE__ ).open().readlines()]
def __UpperCamelCase ( self : Optional[Any] ,SCREAMING_SNAKE_CASE__ : Optional[Any] ):
SCREAMING_SNAKE_CASE:Dict = torch.stack([x["input_ids"] for x in batch] )
SCREAMING_SNAKE_CASE:Union[str, Any] = torch.stack([x["attention_mask"] for x in batch] )
SCREAMING_SNAKE_CASE:int = torch.stack([x["decoder_input_ids"] for x in batch] )
SCREAMING_SNAKE_CASE:Union[str, Any] = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer ,SCREAMING_SNAKE_CASE__ )
else self.tokenizer.pad_token_id
)
SCREAMING_SNAKE_CASE:Dict = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer ,SCREAMING_SNAKE_CASE__ )
else self.tokenizer.pad_token_id
)
SCREAMING_SNAKE_CASE:Dict = trim_batch(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE:List[Any] = trim_batch(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,attention_mask=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Tuple = {
"input_ids": source_ids,
"attention_mask": source_mask,
"decoder_input_ids": y,
}
return batch
A_ = getLogger(__name__)
def A_ ( snake_case ):
return list(itertools.chain.from_iterable(snake_case ) )
def A_ ( snake_case ):
SCREAMING_SNAKE_CASE:Tuple = get_git_info()
save_json(snake_case , os.path.join(snake_case , "git_log.json" ) )
def A_ ( snake_case , snake_case , snake_case=4 , **snake_case ):
with open(snake_case , "w" ) as f:
json.dump(snake_case , snake_case , indent=snake_case , **snake_case )
def A_ ( snake_case ):
with open(snake_case ) as f:
return json.load(snake_case )
def A_ ( ):
SCREAMING_SNAKE_CASE:int = git.Repo(search_parent_directories=snake_case )
SCREAMING_SNAKE_CASE:Any = {
"repo_id": str(snake_case ),
"repo_sha": str(repo.head.object.hexsha ),
"repo_branch": str(repo.active_branch ),
"hostname": str(socket.gethostname() ),
}
return repo_infos
def A_ ( snake_case , snake_case ):
return list(map(snake_case , snake_case ) )
def A_ ( snake_case , snake_case ):
with open(snake_case , "wb" ) as f:
return pickle.dump(snake_case , snake_case )
def A_ ( snake_case ):
def remove_articles(snake_case ):
return re.sub(r"\b(a|an|the)\b" , " " , snake_case )
def white_space_fix(snake_case ):
return " ".join(text.split() )
def remove_punc(snake_case ):
SCREAMING_SNAKE_CASE:Optional[int] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(snake_case ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(snake_case ) ) ) )
def A_ ( snake_case , snake_case ):
SCREAMING_SNAKE_CASE:Optional[Any] = normalize_answer(snake_case ).split()
SCREAMING_SNAKE_CASE:Optional[int] = normalize_answer(snake_case ).split()
SCREAMING_SNAKE_CASE:Optional[int] = Counter(snake_case ) & Counter(snake_case )
SCREAMING_SNAKE_CASE:List[str] = sum(common.values() )
if num_same == 0:
return 0
SCREAMING_SNAKE_CASE:Union[str, Any] = 1.0 * num_same / len(snake_case )
SCREAMING_SNAKE_CASE:List[Any] = 1.0 * num_same / len(snake_case )
SCREAMING_SNAKE_CASE:str = (2 * precision * recall) / (precision + recall)
return fa
def A_ ( snake_case , snake_case ):
return normalize_answer(snake_case ) == normalize_answer(snake_case )
def A_ ( snake_case , snake_case ):
assert len(snake_case ) == len(snake_case )
SCREAMING_SNAKE_CASE:Optional[Any] = 0
for hypo, pred in zip(snake_case , snake_case ):
em += exact_match_score(snake_case , snake_case )
if len(snake_case ) > 0:
em /= len(snake_case )
return {"em": em}
def A_ ( snake_case ):
return model_prefix.startswith("rag" )
def A_ ( snake_case , snake_case , snake_case ):
SCREAMING_SNAKE_CASE:List[str] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
SCREAMING_SNAKE_CASE:Dict = "dropout_rate"
for p in extra_params:
if getattr(snake_case , snake_case , snake_case ):
if not hasattr(snake_case , snake_case ) and not hasattr(snake_case , equivalent_param[p] ):
logger.info("config doesn't have a `{}` attribute".format(snake_case ) )
delattr(snake_case , snake_case )
continue
SCREAMING_SNAKE_CASE:Optional[int] = p if hasattr(snake_case , snake_case ) else equivalent_param[p]
setattr(snake_case , snake_case , getattr(snake_case , snake_case ) )
delattr(snake_case , snake_case )
return hparams, config
| 143
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A = {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = ["""XLNetTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = ["""XLNetTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
"""XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""XLNetForMultipleChoice""",
"""XLNetForQuestionAnswering""",
"""XLNetForQuestionAnsweringSimple""",
"""XLNetForSequenceClassification""",
"""XLNetForTokenClassification""",
"""XLNetLMHeadModel""",
"""XLNetModel""",
"""XLNetPreTrainedModel""",
"""load_tf_weights_in_xlnet""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
"""TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFXLNetForMultipleChoice""",
"""TFXLNetForQuestionAnsweringSimple""",
"""TFXLNetForSequenceClassification""",
"""TFXLNetForTokenClassification""",
"""TFXLNetLMHeadModel""",
"""TFXLNetMainLayer""",
"""TFXLNetModel""",
"""TFXLNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet import XLNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet_fast import XLNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlnet import (
XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
XLNetForMultipleChoice,
XLNetForQuestionAnswering,
XLNetForQuestionAnsweringSimple,
XLNetForSequenceClassification,
XLNetForTokenClassification,
XLNetLMHeadModel,
XLNetModel,
XLNetPreTrainedModel,
load_tf_weights_in_xlnet,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlnet import (
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLNetForMultipleChoice,
TFXLNetForQuestionAnsweringSimple,
TFXLNetForSequenceClassification,
TFXLNetForTokenClassification,
TFXLNetLMHeadModel,
TFXLNetMainLayer,
TFXLNetModel,
TFXLNetPreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 701
|
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
__UpperCAmelCase : int = grid[0]
for row_n in range(1 , len(UpperCamelCase ) ):
__UpperCAmelCase : int = grid[row_n]
__UpperCAmelCase : str = fill_row(UpperCamelCase , UpperCamelCase )
__UpperCAmelCase : List[str] = grid[row_n]
return grid[-1][-1]
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> list:
"""simple docstring"""
current_row[0] += row_above[0]
for cell_n in range(1 , len(UpperCamelCase ) ):
current_row[cell_n] += min(current_row[cell_n - 1] , row_above[cell_n] )
return current_row
if __name__ == "__main__":
import doctest
doctest.testmod()
| 487
| 0
|
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
)
class A :
def __init__( self, UpperCamelCase__, UpperCamelCase__=14, UpperCamelCase__=7, UpperCamelCase__=True, UpperCamelCase__=True, UpperCamelCase__=True, UpperCamelCase__=True, UpperCamelCase__=True, UpperCamelCase__=99, UpperCamelCase__=32, UpperCamelCase__=5, UpperCamelCase__=4, UpperCamelCase__=37, UpperCamelCase__="gelu", UpperCamelCase__=0.1, UpperCamelCase__=0.1, UpperCamelCase__=512, UpperCamelCase__=16, UpperCamelCase__=2, UpperCamelCase__=0.02, UpperCamelCase__=3, UpperCamelCase__=4, UpperCamelCase__=None, ):
"""simple docstring"""
lowerCAmelCase_ = parent
lowerCAmelCase_ = batch_size
lowerCAmelCase_ = seq_length
lowerCAmelCase_ = is_training
lowerCAmelCase_ = use_token_type_ids
lowerCAmelCase_ = use_input_mask
lowerCAmelCase_ = use_labels
lowerCAmelCase_ = use_mc_token_ids
lowerCAmelCase_ = vocab_size
lowerCAmelCase_ = hidden_size
lowerCAmelCase_ = num_hidden_layers
lowerCAmelCase_ = num_attention_heads
lowerCAmelCase_ = intermediate_size
lowerCAmelCase_ = hidden_act
lowerCAmelCase_ = hidden_dropout_prob
lowerCAmelCase_ = attention_probs_dropout_prob
lowerCAmelCase_ = max_position_embeddings
lowerCAmelCase_ = type_vocab_size
lowerCAmelCase_ = type_sequence_label_size
lowerCAmelCase_ = initializer_range
lowerCAmelCase_ = num_labels
lowerCAmelCase_ = num_choices
lowerCAmelCase_ = scope
lowerCAmelCase_ = self.vocab_size - 1
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
lowerCAmelCase_ = None
if self.use_input_mask:
lowerCAmelCase_ = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase_ = None
if self.use_token_type_ids:
lowerCAmelCase_ = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
lowerCAmelCase_ = None
if self.use_mc_token_ids:
lowerCAmelCase_ = ids_tensor([self.batch_size, self.num_choices], self.seq_length )
lowerCAmelCase_ = None
lowerCAmelCase_ = None
lowerCAmelCase_ = None
if self.use_labels:
lowerCAmelCase_ = ids_tensor([self.batch_size], self.type_sequence_label_size )
lowerCAmelCase_ = ids_tensor([self.batch_size, self.seq_length], self.num_labels )
lowerCAmelCase_ = ids_tensor([self.batch_size], self.num_choices )
lowerCAmelCase_ = self.get_config()
lowerCAmelCase_ = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2 )
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
return CTRLConfig(
vocab_size=self.vocab_size, n_embd=self.hidden_size, n_layer=self.num_hidden_layers, n_head=self.num_attention_heads, n_positions=self.max_position_embeddings, pad_token_id=self.pad_token_id, )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, *UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = CTRLModel(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
model(UpperCamelCase__, token_type_ids=UpperCamelCase__, head_mask=UpperCamelCase__ )
model(UpperCamelCase__, token_type_ids=UpperCamelCase__ )
lowerCAmelCase_ = model(UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(len(result.past_key_values ), config.n_layer )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, *UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = CTRLLMHeadModel(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
lowerCAmelCase_ = model(UpperCamelCase__, token_type_ids=UpperCamelCase__, labels=UpperCamelCase__ )
self.parent.assertEqual(result.loss.shape, () )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.prepare_config_and_inputs()
(
(
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) ,
) = config_and_inputs
lowerCAmelCase_ = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, *UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = self.num_labels
lowerCAmelCase_ = CTRLForSequenceClassification(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
lowerCAmelCase_ = ids_tensor([self.batch_size], self.type_sequence_label_size )
lowerCAmelCase_ = model(UpperCamelCase__, token_type_ids=UpperCamelCase__, labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
@require_torch
class A ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
__snake_case = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
__snake_case = (CTRLLMHeadModel,) if is_torch_available() else ()
__snake_case = (
{
'feature-extraction': CTRLModel,
'text-classification': CTRLForSequenceClassification,
'text-generation': CTRLLMHeadModel,
'zero-shot': CTRLForSequenceClassification,
}
if is_torch_available()
else {}
)
__snake_case = True
__snake_case = False
__snake_case = False
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
# config could not be created.
return True
return False
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = CTRLModelTester(self )
lowerCAmelCase_ = ConfigTester(self, config_class=UpperCamelCase__, n_embd=37 )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*UpperCamelCase__ )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
pass
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ = CTRLModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
@unittest.skip('''The model doesn\'t support left padding''' ) # and it's not used enough to be worth fixing :)
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
pass
@require_torch
class A ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = CTRLLMHeadModel.from_pretrained('''ctrl''' )
model.to(UpperCamelCase__ )
lowerCAmelCase_ = torch.tensor(
[[1_1859, 0, 1611, 8]], dtype=torch.long, device=UpperCamelCase__ ) # Legal the president is
lowerCAmelCase_ = [
1_1859,
0,
1611,
8,
5,
150,
2_6449,
2,
19,
348,
469,
3,
2595,
48,
2_0740,
24_6533,
24_6533,
19,
30,
5,
] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
lowerCAmelCase_ = model.generate(UpperCamelCase__, do_sample=UpperCamelCase__ )
self.assertListEqual(output_ids[0].tolist(), UpperCamelCase__ )
| 431
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_A = {
'''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
'''FALCON_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FalconForCausalLM''',
'''FalconModel''',
'''FalconPreTrainedModel''',
'''FalconForSequenceClassification''',
'''FalconForTokenClassification''',
'''FalconForQuestionAnswering''',
]
if TYPE_CHECKING:
from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_falcon import (
FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
FalconPreTrainedModel,
)
else:
import sys
_A = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 431
| 1
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
UpperCAmelCase_ : Any = logging.get_logger(__name__)
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self : Optional[int] , *lowercase_ : Dict , **lowercase_ : Dict):
'''simple docstring'''
warnings.warn(
'''The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use DeformableDetrImageProcessor instead.''' , lowercase_ , )
super().__init__(*lowercase_ , **lowercase_)
| 710
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
UpperCAmelCase_ : Dict = {
"""facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = "data2vec-text"
def __init__( self : Any , lowercase_ : Any=30522 , lowercase_ : Any=768 , lowercase_ : Union[str, Any]=12 , lowercase_ : Dict=12 , lowercase_ : List[Any]=3072 , lowercase_ : str="gelu" , lowercase_ : int=0.1 , lowercase_ : Dict=0.1 , lowercase_ : str=512 , lowercase_ : Optional[int]=2 , lowercase_ : int=0.02 , lowercase_ : int=1e-12 , lowercase_ : Any=1 , lowercase_ : Any=0 , lowercase_ : List[Any]=2 , lowercase_ : Tuple="absolute" , lowercase_ : Optional[int]=True , lowercase_ : int=None , **lowercase_ : Tuple , ):
'''simple docstring'''
super().__init__(pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_)
SCREAMING_SNAKE_CASE_ : List[Any] = vocab_size
SCREAMING_SNAKE_CASE_ : Tuple = hidden_size
SCREAMING_SNAKE_CASE_ : Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE_ : List[Any] = num_attention_heads
SCREAMING_SNAKE_CASE_ : Optional[Any] = hidden_act
SCREAMING_SNAKE_CASE_ : Any = intermediate_size
SCREAMING_SNAKE_CASE_ : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ : Tuple = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ : Optional[int] = max_position_embeddings
SCREAMING_SNAKE_CASE_ : Union[str, Any] = type_vocab_size
SCREAMING_SNAKE_CASE_ : Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE_ : List[Any] = layer_norm_eps
SCREAMING_SNAKE_CASE_ : str = position_embedding_type
SCREAMING_SNAKE_CASE_ : Optional[int] = use_cache
SCREAMING_SNAKE_CASE_ : str = classifier_dropout
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
@property
def _SCREAMING_SNAKE_CASE ( self : int):
'''simple docstring'''
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE_ : List[Any] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
SCREAMING_SNAKE_CASE_ : Any = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
])
| 176
| 0
|
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A_ ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Tuple = OpenAIGPTTokenizer
_UpperCamelCase : List[Any] = OpenAIGPTTokenizerFast
_UpperCamelCase : int = True
_UpperCamelCase : List[Any] = False
def SCREAMING_SNAKE_CASE__ ( self ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowercase = [
'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>',
]
lowercase = dict(zip(snake_case , range(len(snake_case ) ) ) )
lowercase = ['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']
lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' ) as fp:
fp.write(json.dumps(snake_case ) )
with open(self.merges_file , 'w' ) as fp:
fp.write('\n'.join(snake_case ) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
return "lower newer", "lower newer"
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = OpenAIGPTTokenizer(self.vocab_file , self.merges_file )
lowercase = 'lower'
lowercase = ['low', 'er</w>']
lowercase = tokenizer.tokenize(snake_case )
self.assertListEqual(snake_case , snake_case )
lowercase = tokens + ['<unk>']
lowercase = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case ) , snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case=15 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase = self.rust_tokenizer_class.from_pretrained(snake_case , **snake_case )
# Simple input
lowercase = 'This is a simple input'
lowercase = ['This is a simple input 1', 'This is a simple input 2']
lowercase = ('This is a simple input', 'This is a pair')
lowercase = [
('This is a simple input 1', 'This is a simple input 2'),
('This is a simple pair 1', 'This is a simple pair 2'),
]
# Simple input tests
self.assertRaises(snake_case , tokenizer_r.encode , snake_case , max_length=snake_case , padding='max_length' )
# Simple input
self.assertRaises(snake_case , tokenizer_r.encode_plus , snake_case , max_length=snake_case , padding='max_length' )
# Simple input
self.assertRaises(
snake_case , tokenizer_r.batch_encode_plus , snake_case , max_length=snake_case , padding='max_length' , )
# Pair input
self.assertRaises(snake_case , tokenizer_r.encode , snake_case , max_length=snake_case , padding='max_length' )
# Pair input
self.assertRaises(snake_case , tokenizer_r.encode_plus , snake_case , max_length=snake_case , padding='max_length' )
# Pair input
self.assertRaises(
snake_case , tokenizer_r.batch_encode_plus , snake_case , max_length=snake_case , padding='max_length' , )
def SCREAMING_SNAKE_CASE__ ( self ):
pass
@require_ftfy
@require_spacy
@require_tokenizers
class A_ ( __lowerCamelCase ):
'''simple docstring'''
pass
| 84
|
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _UpperCAmelCase ( A ):
'''simple docstring'''
UpperCAmelCase__ =int(A )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ =t // 3600, (t // 60) % 60, t % 60
return F"""{h}:{m:02d}:{s:02d}""" if h != 0 else F"""{m:02d}:{s:02d}"""
def _UpperCAmelCase ( A , A , A , A , A=300 ):
'''simple docstring'''
return F"""
<div>
{prefix}
<progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>
{label}
</div>
"""
def _UpperCAmelCase ( A ):
'''simple docstring'''
UpperCAmelCase__ ="<table border=\"1\" class=\"dataframe\">\n"
html_code += """ <thead>\n <tr style="text-align: left;">\n"""
for i in items[0]:
html_code += F""" <th>{i}</th>\n"""
html_code += " </tr>\n </thead>\n <tbody>\n"
for line in items[1:]:
html_code += " <tr>\n"
for elt in line:
UpperCAmelCase__ =F"""{elt:.6f}""" if isinstance(A , A ) else str(A )
html_code += F""" <td>{elt}</td>\n"""
html_code += " </tr>\n"
html_code += " </tbody>\n</table><p>"
return html_code
class snake_case_ :
'''simple docstring'''
__UpperCamelCase = 5
__UpperCamelCase = 0.2
def __init__( self, A_, A_ = None, A_ = True, A_ = None, A_ = 300, ) -> Optional[Any]:
UpperCAmelCase__ =total
UpperCAmelCase__ ="" if prefix is None else prefix
UpperCAmelCase__ =leave
UpperCAmelCase__ =parent
UpperCAmelCase__ =width
UpperCAmelCase__ =None
UpperCAmelCase__ =None
UpperCAmelCase__ =None
def __UpperCAmelCase ( self, A_, A_ = False, A_ = None ) -> Any:
UpperCAmelCase__ =value
if comment is not None:
UpperCAmelCase__ =comment
if self.last_value is None:
UpperCAmelCase__ =UpperCAmelCase__ =time.time()
UpperCAmelCase__ =UpperCAmelCase__ =value
UpperCAmelCase__ =UpperCAmelCase__ =None
UpperCAmelCase__ =self.warmup
UpperCAmelCase__ =1
self.update_bar(A_ )
elif value <= self.last_value and not force_update:
return
elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for, self.total ):
if self.first_calls > 0:
self.first_calls -= 1
UpperCAmelCase__ =time.time()
UpperCAmelCase__ =current_time - self.start_time
# We could have value = self.start_value if the update is called twixe with the same start value.
if value > self.start_value:
UpperCAmelCase__ =self.elapsed_time / (value - self.start_value)
else:
UpperCAmelCase__ =None
if value >= self.total:
UpperCAmelCase__ =self.total
UpperCAmelCase__ =None
if not self.leave:
self.close()
elif self.average_time_per_item is not None:
UpperCAmelCase__ =self.average_time_per_item * (self.total - value)
self.update_bar(A_ )
UpperCAmelCase__ =value
UpperCAmelCase__ =current_time
if self.average_time_per_item is None:
UpperCAmelCase__ =1
else:
UpperCAmelCase__ =max(int(self.update_every / self.average_time_per_item ), 1 )
def __UpperCAmelCase ( self, A_, A_=None ) -> Dict:
UpperCAmelCase__ =" " * (len(str(self.total ) ) - len(str(A_ ) )) + str(A_ )
if self.elapsed_time is None:
UpperCAmelCase__ =f"""[{spaced_value}/{self.total} : < :"""
elif self.predicted_remaining is None:
UpperCAmelCase__ =f"""[{spaced_value}/{self.total} {format_time(self.elapsed_time )}"""
else:
UpperCAmelCase__ =(
f"""[{spaced_value}/{self.total} {format_time(self.elapsed_time )} <"""
f""" {format_time(self.predicted_remaining )}"""
)
self.label += f""", {1/self.average_time_per_item:.2f} it/s"""
self.label += "]" if self.comment is None or len(self.comment ) == 0 else f""", {self.comment}]"""
self.display()
def __UpperCAmelCase ( self ) -> Tuple:
UpperCAmelCase__ =html_progress_bar(self.value, self.total, self.prefix, self.label, self.width )
if self.parent is not None:
# If this is a child bar, the parent will take care of the display.
self.parent.display()
return
if self.output is None:
UpperCAmelCase__ =disp.display(disp.HTML(self.html_code ), display_id=A_ )
else:
self.output.update(disp.HTML(self.html_code ) )
def __UpperCAmelCase ( self ) -> str:
if self.parent is None and self.output is not None:
self.output.update(disp.HTML("" ) )
class snake_case_ ( a ):
'''simple docstring'''
def __init__( self, A_, A_=None ) -> Dict:
super().__init__(A_ )
UpperCAmelCase__ =None if column_names is None else [column_names]
UpperCAmelCase__ =None
def __UpperCAmelCase ( self ) -> int:
UpperCAmelCase__ =html_progress_bar(self.value, self.total, self.prefix, self.label, self.width )
if self.inner_table is not None:
self.html_code += text_to_html_table(self.inner_table )
if self.child_bar is not None:
self.html_code += self.child_bar.html_code
if self.output is None:
UpperCAmelCase__ =disp.display(disp.HTML(self.html_code ), display_id=A_ )
else:
self.output.update(disp.HTML(self.html_code ) )
def __UpperCAmelCase ( self, A_ ) -> Tuple:
if self.inner_table is None:
UpperCAmelCase__ =[list(values.keys() ), list(values.values() )]
else:
UpperCAmelCase__ =self.inner_table[0]
if len(self.inner_table ) == 1:
# We give a chance to update the column names at the first iteration
for key in values.keys():
if key not in columns:
columns.append(A_ )
UpperCAmelCase__ =columns
self.inner_table.append([values[c] for c in columns] )
def __UpperCAmelCase ( self, A_, A_=None, A_=300 ) -> Union[str, Any]:
UpperCAmelCase__ =NotebookProgressBar(A_, prefix=A_, parent=self, width=A_ )
return self.child_bar
def __UpperCAmelCase ( self ) -> Tuple:
UpperCAmelCase__ =None
self.display()
class snake_case_ ( a ):
'''simple docstring'''
def __init__( self ) -> Optional[int]:
UpperCAmelCase__ =None
UpperCAmelCase__ =None
UpperCAmelCase__ =False
def __UpperCAmelCase ( self, A_, A_, A_, **A_ ) -> str:
UpperCAmelCase__ ="Epoch" if args.evaluation_strategy == IntervalStrategy.EPOCH else "Step"
UpperCAmelCase__ =0
UpperCAmelCase__ =0
UpperCAmelCase__ =[self.first_column] + ["Training Loss"]
if args.evaluation_strategy != IntervalStrategy.NO:
column_names.append("Validation Loss" )
UpperCAmelCase__ =NotebookTrainingTracker(state.max_steps, A_ )
def __UpperCAmelCase ( self, A_, A_, A_, **A_ ) -> str:
UpperCAmelCase__ =int(state.epoch ) if int(state.epoch ) == state.epoch else f"""{state.epoch:.2f}"""
self.training_tracker.update(
state.global_step + 1, comment=f"""Epoch {epoch}/{state.num_train_epochs}""", force_update=self._force_next_update, )
UpperCAmelCase__ =False
def __UpperCAmelCase ( self, A_, A_, A_, A_=None, **A_ ) -> int:
if not has_length(A_ ):
return
if self.prediction_bar is None:
if self.training_tracker is not None:
UpperCAmelCase__ =self.training_tracker.add_child(len(A_ ) )
else:
UpperCAmelCase__ =NotebookProgressBar(len(A_ ) )
self.prediction_bar.update(1 )
else:
self.prediction_bar.update(self.prediction_bar.value + 1 )
def __UpperCAmelCase ( self, A_, A_, A_, **A_ ) -> Optional[Any]:
if self.prediction_bar is not None:
self.prediction_bar.close()
UpperCAmelCase__ =None
def __UpperCAmelCase ( self, A_, A_, A_, A_=None, **A_ ) -> str:
# Only for when there is no evaluation
if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
UpperCAmelCase__ ={"Training Loss": logs["loss"]}
# First column is necessarily Step sine we're not in epoch eval strategy
UpperCAmelCase__ =state.global_step
self.training_tracker.write_line(A_ )
def __UpperCAmelCase ( self, A_, A_, A_, A_=None, **A_ ) -> str:
if self.training_tracker is not None:
UpperCAmelCase__ ={"Training Loss": "No log", "Validation Loss": "No log"}
for log in reversed(state.log_history ):
if "loss" in log:
UpperCAmelCase__ =log["loss"]
break
if self.first_column == "Epoch":
UpperCAmelCase__ =int(state.epoch )
else:
UpperCAmelCase__ =state.global_step
UpperCAmelCase__ ="eval"
for k in metrics:
if k.endswith("_loss" ):
UpperCAmelCase__ =re.sub(R"\_loss$", "", A_ )
UpperCAmelCase__ =metrics.pop("total_flos", A_ )
UpperCAmelCase__ =metrics.pop("epoch", A_ )
UpperCAmelCase__ =metrics.pop(f"""{metric_key_prefix}_runtime""", A_ )
UpperCAmelCase__ =metrics.pop(f"""{metric_key_prefix}_samples_per_second""", A_ )
UpperCAmelCase__ =metrics.pop(f"""{metric_key_prefix}_steps_per_second""", A_ )
UpperCAmelCase__ =metrics.pop(f"""{metric_key_prefix}_jit_compilation_time""", A_ )
for k, v in metrics.items():
if k == f"""{metric_key_prefix}_loss""":
UpperCAmelCase__ =v
else:
UpperCAmelCase__ =k.split("_" )
UpperCAmelCase__ =" ".join([part.capitalize() for part in splits[1:]] )
UpperCAmelCase__ =v
self.training_tracker.write_line(A_ )
self.training_tracker.remove_child()
UpperCAmelCase__ =None
# Evaluation takes a long time so we should force the next update.
UpperCAmelCase__ =True
def __UpperCAmelCase ( self, A_, A_, A_, **A_ ) -> List[str]:
self.training_tracker.update(
state.global_step, comment=f"""Epoch {int(state.epoch )}/{state.num_train_epochs}""", force_update=A_ )
UpperCAmelCase__ =None
| 625
| 0
|
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def UpperCamelCase (lowercase_: List[str] , lowercase_: Optional[int] ) -> Optional[int]:
A__ : Optional[int] = old_name
if "patch_embed" in old_name:
A__ , A__ , A__ : Optional[int] = old_name.split(""".""" )
if layer == "0":
A__ : Dict = old_name.replace("""0""" , """convolution1""" )
elif layer == "1":
A__ : str = old_name.replace("""1""" , """batchnorm_before""" )
elif layer == "3":
A__ : Union[str, Any] = old_name.replace("""3""" , """convolution2""" )
else:
A__ : Any = old_name.replace("""4""" , """batchnorm_after""" )
if "network" in old_name and re.search(r"""\d\.\d""" , UpperCAmelCase__ ):
A__ : Dict = r"""\b\d{2}\b"""
if bool(re.search(UpperCAmelCase__ , UpperCAmelCase__ ) ):
A__ : Dict = re.search(r"""\d\.\d\d.""" , UpperCAmelCase__ ).group()
else:
A__ : Optional[Any] = re.search(r"""\d\.\d.""" , UpperCAmelCase__ ).group()
if int(match[0] ) < 6:
A__ : Tuple = old_name.replace(UpperCAmelCase__ , """""" )
A__ : Optional[int] = trimmed_name.replace("""network""" , match[0] + """.meta4D_layers.blocks.""" + match[2:-1] )
A__ : List[str] = """intermediate_stages.""" + trimmed_name
else:
A__ : Union[str, Any] = old_name.replace(UpperCAmelCase__ , """""" )
if int(match[2] ) < num_meta4D_last_stage:
A__ : Tuple = trimmed_name.replace("""network""" , """meta4D_layers.blocks.""" + match[2] )
else:
A__ : Dict = str(int(match[2] ) - num_meta4D_last_stage )
A__ : Any = trimmed_name.replace("""network""" , """meta3D_layers.blocks.""" + layer_index )
if "norm1" in old_name:
A__ : Tuple = trimmed_name.replace("""norm1""" , """layernorm1""" )
elif "norm2" in old_name:
A__ : Union[str, Any] = trimmed_name.replace("""norm2""" , """layernorm2""" )
elif "fc1" in old_name:
A__ : str = trimmed_name.replace("""fc1""" , """linear_in""" )
elif "fc2" in old_name:
A__ : Optional[int] = trimmed_name.replace("""fc2""" , """linear_out""" )
A__ : int = """last_stage.""" + trimmed_name
elif "network" in old_name and re.search(r""".\d.""" , UpperCAmelCase__ ):
A__ : Dict = old_name.replace("""network""" , """intermediate_stages""" )
if "fc" in new_name:
A__ : List[str] = new_name.replace("""fc""" , """convolution""" )
elif ("norm1" in new_name) and ("layernorm1" not in new_name):
A__ : int = new_name.replace("""norm1""" , """batchnorm_before""" )
elif ("norm2" in new_name) and ("layernorm2" not in new_name):
A__ : List[str] = new_name.replace("""norm2""" , """batchnorm_after""" )
if "proj" in new_name:
A__ : Any = new_name.replace("""proj""" , """projection""" )
if "dist_head" in new_name:
A__ : Tuple = new_name.replace("""dist_head""" , """distillation_classifier""" )
elif "head" in new_name:
A__ : Dict = new_name.replace("""head""" , """classifier""" )
elif "patch_embed" in new_name:
A__ : Dict = """efficientformer.""" + new_name
elif new_name == "norm.weight" or new_name == "norm.bias":
A__ : List[str] = new_name.replace("""norm""" , """layernorm""" )
A__ : Union[str, Any] = """efficientformer.""" + new_name
else:
A__ : Optional[Any] = """efficientformer.encoder.""" + new_name
return new_name
def UpperCamelCase (lowercase_: str , lowercase_: List[Any] ) -> Tuple:
for key in checkpoint.copy().keys():
A__ : Optional[int] = checkpoint.pop(UpperCAmelCase__ )
A__ : Any = val
return checkpoint
def UpperCamelCase () -> List[str]:
A__ : Tuple = """http://images.cocodataset.org/val2017/000000039769.jpg"""
A__ : Optional[int] = Image.open(requests.get(UpperCAmelCase__ , stream=UpperCAmelCase__ ).raw )
return image
def UpperCamelCase (lowercase_: List[str] , lowercase_: List[Any] , lowercase_: Dict , lowercase_: Tuple ) -> Tuple:
A__ : int = torch.load(UpperCAmelCase__ , map_location="""cpu""" )["""model"""]
A__ : Tuple = EfficientFormerConfig.from_json_file(UpperCAmelCase__ )
A__ : List[str] = EfficientFormerForImageClassificationWithTeacher(UpperCAmelCase__ )
A__ : Dict = """_""".join(checkpoint_path.split("""/""" )[-1].split(""".""" )[0].split("""_""" )[:-1] )
A__ : Optional[Any] = config.depths[-1] - config.num_metaad_blocks + 1
A__ : Any = convert_torch_checkpoint(UpperCAmelCase__ , UpperCAmelCase__ )
model.load_state_dict(UpperCAmelCase__ )
model.eval()
A__ : List[str] = {
"""bilinear""": PILImageResampling.BILINEAR,
"""bicubic""": PILImageResampling.BICUBIC,
"""nearest""": PILImageResampling.NEAREST,
}
# prepare image
A__ : str = prepare_img()
A__ : Any = 256
A__ : Optional[int] = 224
A__ : List[Any] = EfficientFormerImageProcessor(
size={"""shortest_edge""": image_size} , crop_size={"""height""": crop_size, """width""": crop_size} , resample=pillow_resamplings["""bicubic"""] , )
A__ : Optional[int] = processor(images=UpperCAmelCase__ , return_tensors="""pt""" ).pixel_values
# original processing pipeline
A__ : Union[str, Any] = Compose(
[
Resize(UpperCAmelCase__ , interpolation=pillow_resamplings["""bicubic"""] ),
CenterCrop(UpperCAmelCase__ ),
ToTensor(),
Normalize(UpperCAmelCase__ , UpperCAmelCase__ ),
] )
A__ : Tuple = image_transforms(UpperCAmelCase__ ).unsqueeze(0 )
assert torch.allclose(UpperCAmelCase__ , UpperCAmelCase__ )
A__ : Union[str, Any] = model(UpperCAmelCase__ )
A__ : Optional[Any] = outputs.logits
A__ : Tuple = (1, 1000)
if "l1" in model_name:
A__ : Union[str, Any] = torch.Tensor(
[-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328] )
assert torch.allclose(logits[0, :10] , UpperCAmelCase__ , atol=1E-3 )
assert logits.shape == expected_shape
elif "l3" in model_name:
A__ : List[str] = torch.Tensor(
[-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127] )
assert torch.allclose(logits[0, :10] , UpperCAmelCase__ , atol=1E-3 )
assert logits.shape == expected_shape
elif "l7" in model_name:
A__ : Optional[int] = torch.Tensor(
[-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878] )
assert logits.shape == expected_shape
else:
raise ValueError(
f"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7""" )
# Save Checkpoints
Path(UpperCAmelCase__ ).mkdir(exist_ok=UpperCAmelCase__ )
model.save_pretrained(UpperCAmelCase__ )
print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )
processor.save_pretrained(UpperCAmelCase__ )
print(f"""Processor successfuly saved at {pytorch_dump_path}""" )
if push_to_hub:
print("""Pushing model to the hub...""" )
model.push_to_hub(
repo_id=f"""Bearnardd/{pytorch_dump_path}""" , commit_message="""Add model""" , use_temp_dir=UpperCAmelCase__ , )
processor.push_to_hub(
repo_id=f"""Bearnardd/{pytorch_dump_path}""" , commit_message="""Add image processor""" , use_temp_dir=UpperCAmelCase__ , )
if __name__ == "__main__":
A_ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--pytorch_model_path',
default=None,
type=str,
required=True,
help='Path to EfficientFormer pytorch checkpoint.',
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help='The json file for EfficientFormer model config.',
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
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',
)
parser.set_defaults(push_to_hub=True)
A_ : Optional[Any] = parser.parse_args()
convert_efficientformer_checkpoint(
checkpoint_path=args.pytorch_model_path,
efficientformer_config_file=args.config_file,
pytorch_dump_path=args.pytorch_dump_path,
push_to_hub=args.push_to_hub,
)
| 713
|
from __future__ import annotations
from collections.abc import Callable
A_ : List[Any] = list[list[float | int]]
def UpperCamelCase (lowercase_: Matrix , lowercase_: Matrix ) -> Matrix:
A__ : int = len(lowercase_ )
A__ : Matrix = [[0 for _ in range(size + 1 )] for _ in range(lowercase_ )]
A__ : int
A__ : int
A__ : int
A__ : int
A__ : int
A__ : float
for row in range(lowercase_ ):
for col in range(lowercase_ ):
A__ : List[str] = matrix[row][col]
A__ : int = vector[row][0]
A__ : Optional[int] = 0
A__ : str = 0
while row < size and col < size:
# pivoting
A__ : int = max((abs(augmented[rowa][col] ), rowa) for rowa in range(lowercase_ , lowercase_ ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
A__ , A__ : Union[str, Any] = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , lowercase_ ):
A__ : List[Any] = augmented[rowa][col] / augmented[row][col]
A__ : Dict = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , lowercase_ ):
for row in range(lowercase_ ):
A__ : List[str] = augmented[row][col] / augmented[col][col]
for cola in range(lowercase_ , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(lowercase_ )
]
def UpperCamelCase (lowercase_: list[int] ) -> Callable[[int], int]:
A__ : int = len(lowercase_ )
A__ : Matrix = [[0 for _ in range(lowercase_ )] for _ in range(lowercase_ )]
A__ : Matrix = [[0] for _ in range(lowercase_ )]
A__ : Matrix
A__ : int
A__ : int
A__ : int
for x_val, y_val in enumerate(lowercase_ ):
for col in range(lowercase_ ):
A__ : Dict = (x_val + 1) ** (size - col - 1)
A__ : Any = y_val
A__ : Union[str, Any] = solve(lowercase_ , lowercase_ )
def interpolated_func(lowercase_: int ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(lowercase_ ) )
return interpolated_func
def UpperCamelCase (lowercase_: int ) -> int:
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def UpperCamelCase (lowercase_: Callable[[int], int] = question_function , lowercase_: int = 10 ) -> int:
A__ : list[int] = [func(lowercase_ ) for x_val in range(1 , order + 1 )]
A__ : list[Callable[[int], int]] = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
A__ : int = 0
A__ : Callable[[int], int]
A__ : int
for poly in polynomials:
A__ : List[str] = 1
while func(lowercase_ ) == poly(lowercase_ ):
x_val += 1
ret += poly(lowercase_ )
return ret
if __name__ == "__main__":
print(f'''{solution() = }''')
| 64
| 0
|
'''simple docstring'''
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from transformers import OneFormerImageProcessor
from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput
if is_vision_available():
from PIL import Image
def a__ ( a__ , a__="shi-labs/oneformer_demo" ):
"""simple docstring"""
with open(hf_hub_download(a__ , a__ , repo_type="""dataset""" ) , """r""" ) as f:
__SCREAMING_SNAKE_CASE = json.load(a__ )
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
for key, info in class_info.items():
__SCREAMING_SNAKE_CASE = info["""name"""]
class_names.append(info["""name"""] )
if info["isthing"]:
thing_ids.append(int(a__ ) )
__SCREAMING_SNAKE_CASE = thing_ids
__SCREAMING_SNAKE_CASE = class_names
return metadata
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Any , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any]=7 , __SCREAMING_SNAKE_CASE : Union[str, Any]=3 , __SCREAMING_SNAKE_CASE : int=30 , __SCREAMING_SNAKE_CASE : str=400 , __SCREAMING_SNAKE_CASE : str=None , __SCREAMING_SNAKE_CASE : Union[str, Any]=True , __SCREAMING_SNAKE_CASE : Optional[Any]=True , __SCREAMING_SNAKE_CASE : Tuple=[0.5, 0.5, 0.5] , __SCREAMING_SNAKE_CASE : Optional[int]=[0.5, 0.5, 0.5] , __SCREAMING_SNAKE_CASE : Optional[Any]=10 , __SCREAMING_SNAKE_CASE : List[str]=False , __SCREAMING_SNAKE_CASE : List[str]=255 , __SCREAMING_SNAKE_CASE : Union[str, Any]="shi-labs/oneformer_demo" , __SCREAMING_SNAKE_CASE : Optional[int]="ade20k_panoptic.json" , __SCREAMING_SNAKE_CASE : Union[str, Any]=10 , ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = min_resolution
__SCREAMING_SNAKE_CASE = max_resolution
__SCREAMING_SNAKE_CASE = do_resize
__SCREAMING_SNAKE_CASE = {"""shortest_edge""": 32, """longest_edge""": 1_333} if size is None else size
__SCREAMING_SNAKE_CASE = do_normalize
__SCREAMING_SNAKE_CASE = image_mean
__SCREAMING_SNAKE_CASE = image_std
__SCREAMING_SNAKE_CASE = class_info_file
__SCREAMING_SNAKE_CASE = prepare_metadata(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = num_text
__SCREAMING_SNAKE_CASE = repo_path
# for the post_process_functions
__SCREAMING_SNAKE_CASE = 2
__SCREAMING_SNAKE_CASE = 10
__SCREAMING_SNAKE_CASE = 10
__SCREAMING_SNAKE_CASE = 3
__SCREAMING_SNAKE_CASE = 4
__SCREAMING_SNAKE_CASE = num_labels
__SCREAMING_SNAKE_CASE = do_reduce_labels
__SCREAMING_SNAKE_CASE = ignore_index
def UpperCAmelCase__ ( self : str ) -> int:
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"num_labels": self.num_labels,
"do_reduce_labels": self.do_reduce_labels,
"ignore_index": self.ignore_index,
"class_info_file": self.class_info_file,
"metadata": self.metadata,
"num_text": self.num_text,
}
def UpperCAmelCase__ ( self : Any , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Optional[int]=False ) -> List[Any]:
"""simple docstring"""
if not batched:
__SCREAMING_SNAKE_CASE = image_inputs[0]
if isinstance(__SCREAMING_SNAKE_CASE , Image.Image ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = image.size
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = image.shape[1], image.shape[2]
if w < h:
__SCREAMING_SNAKE_CASE = int(self.size["""shortest_edge"""] * h / w )
__SCREAMING_SNAKE_CASE = self.size["""shortest_edge"""]
elif w > h:
__SCREAMING_SNAKE_CASE = self.size["""shortest_edge"""]
__SCREAMING_SNAKE_CASE = int(self.size["""shortest_edge"""] * w / h )
else:
__SCREAMING_SNAKE_CASE = self.size["""shortest_edge"""]
__SCREAMING_SNAKE_CASE = self.size["""shortest_edge"""]
else:
__SCREAMING_SNAKE_CASE = []
for image in image_inputs:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__SCREAMING_SNAKE_CASE = max(__SCREAMING_SNAKE_CASE , key=lambda __SCREAMING_SNAKE_CASE : item[0] )[0]
__SCREAMING_SNAKE_CASE = max(__SCREAMING_SNAKE_CASE , key=lambda __SCREAMING_SNAKE_CASE : item[1] )[1]
return expected_height, expected_width
def UpperCAmelCase__ ( self : Optional[Any] ) -> int:
"""simple docstring"""
return OneFormerForUniversalSegmentationOutput(
# +1 for null class
class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) ) , masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) ) , )
@require_torch
@require_vision
class lowerCAmelCase__ ( a , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase__ = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
# only for test_image_processing_common.test_image_proc_to_json_string
lowerCAmelCase__ = image_processing_class
def UpperCAmelCase__ ( self : Dict ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = OneFormerImageProcessorTester(self )
@property
def UpperCAmelCase__ ( self : Union[str, Any] ) -> int:
"""simple docstring"""
return self.image_processing_tester.prepare_image_processor_dict()
def UpperCAmelCase__ ( self : str ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , """image_mean""" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , """image_std""" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , """do_normalize""" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , """do_resize""" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , """size""" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , """ignore_index""" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , """class_info_file""" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , """num_text""" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , """repo_path""" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , """metadata""" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , """do_reduce_labels""" ) )
def UpperCAmelCase__ ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
pass
def UpperCAmelCase__ ( self : Dict ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processing_tester , equal_resolution=__SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(__SCREAMING_SNAKE_CASE , Image.Image )
# Test not batched input
__SCREAMING_SNAKE_CASE = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE , batched=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = image_processor(
__SCREAMING_SNAKE_CASE , ["""semantic"""] * len(__SCREAMING_SNAKE_CASE ) , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCAmelCase__ ( self : str ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processing_tester , equal_resolution=__SCREAMING_SNAKE_CASE , numpify=__SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(__SCREAMING_SNAKE_CASE , np.ndarray )
# Test not batched input
__SCREAMING_SNAKE_CASE = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE , batched=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = image_processor(
__SCREAMING_SNAKE_CASE , ["""semantic"""] * len(__SCREAMING_SNAKE_CASE ) , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processing_tester , equal_resolution=__SCREAMING_SNAKE_CASE , torchify=__SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(__SCREAMING_SNAKE_CASE , torch.Tensor )
# Test not batched input
__SCREAMING_SNAKE_CASE = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE , batched=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = image_processor(
__SCREAMING_SNAKE_CASE , ["""semantic"""] * len(__SCREAMING_SNAKE_CASE ) , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCAmelCase__ ( self : Tuple , __SCREAMING_SNAKE_CASE : Optional[int]=False , __SCREAMING_SNAKE_CASE : int=False , __SCREAMING_SNAKE_CASE : str="np" ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# prepare image and target
__SCREAMING_SNAKE_CASE = self.image_processing_tester.num_labels
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processing_tester , equal_resolution=__SCREAMING_SNAKE_CASE )
if with_segmentation_maps:
__SCREAMING_SNAKE_CASE = num_labels
if is_instance_map:
__SCREAMING_SNAKE_CASE = list(range(__SCREAMING_SNAKE_CASE ) ) * 2
__SCREAMING_SNAKE_CASE = dict(enumerate(__SCREAMING_SNAKE_CASE ) )
__SCREAMING_SNAKE_CASE = [
np.random.randint(0 , high * 2 , (img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
]
if segmentation_type == "pil":
__SCREAMING_SNAKE_CASE = [Image.fromarray(__SCREAMING_SNAKE_CASE ) for annotation in annotations]
__SCREAMING_SNAKE_CASE = image_processor(
__SCREAMING_SNAKE_CASE , ["""semantic"""] * len(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE , return_tensors="""pt""" , instance_id_to_semantic_id=__SCREAMING_SNAKE_CASE , pad_and_return_pixel_mask=__SCREAMING_SNAKE_CASE , )
return inputs
def UpperCAmelCase__ ( self : Optional[Any] ) -> str:
"""simple docstring"""
pass
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
def common(__SCREAMING_SNAKE_CASE : List[Any]=False , __SCREAMING_SNAKE_CASE : str=None ):
__SCREAMING_SNAKE_CASE = self.comm_get_image_processor_inputs(
with_segmentation_maps=__SCREAMING_SNAKE_CASE , is_instance_map=__SCREAMING_SNAKE_CASE , segmentation_type=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = inputs["""mask_labels"""]
__SCREAMING_SNAKE_CASE = inputs["""class_labels"""]
__SCREAMING_SNAKE_CASE = inputs["""pixel_values"""]
__SCREAMING_SNAKE_CASE = inputs["""text_inputs"""]
# check the batch_size
for mask_label, class_label, text_input in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
self.assertEqual(mask_label.shape[0] , class_label.shape[0] )
# this ensure padding has happened
self.assertEqual(mask_label.shape[1:] , pixel_values.shape[2:] )
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , self.image_processing_tester.num_text )
common()
common(is_instance_map=__SCREAMING_SNAKE_CASE )
common(is_instance_map=__SCREAMING_SNAKE_CASE , segmentation_type="""pil""" )
common(is_instance_map=__SCREAMING_SNAKE_CASE , segmentation_type="""pil""" )
def UpperCAmelCase__ ( self : Dict ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = np.zeros((20, 50) )
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = binary_mask_to_rle(__SCREAMING_SNAKE_CASE )
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , 4 )
self.assertEqual(rle[0] , 21 )
self.assertEqual(rle[1] , 45 )
def UpperCAmelCase__ ( self : Any ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , )
__SCREAMING_SNAKE_CASE = self.image_processing_tester.get_fake_oneformer_outputs()
__SCREAMING_SNAKE_CASE = fature_extractor.post_process_semantic_segmentation(__SCREAMING_SNAKE_CASE )
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , self.image_processing_tester.batch_size )
self.assertEqual(
segmentation[0].shape , (
self.image_processing_tester.height,
self.image_processing_tester.width,
) , )
__SCREAMING_SNAKE_CASE = [(1, 4) for i in range(self.image_processing_tester.batch_size )]
__SCREAMING_SNAKE_CASE = fature_extractor.post_process_semantic_segmentation(__SCREAMING_SNAKE_CASE , target_sizes=__SCREAMING_SNAKE_CASE )
self.assertEqual(segmentation[0].shape , target_sizes[0] )
def UpperCAmelCase__ ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , )
__SCREAMING_SNAKE_CASE = self.image_processing_tester.get_fake_oneformer_outputs()
__SCREAMING_SNAKE_CASE = image_processor.post_process_instance_segmentation(__SCREAMING_SNAKE_CASE , threshold=0 )
self.assertTrue(len(__SCREAMING_SNAKE_CASE ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("""segmentation""" in el )
self.assertTrue("""segments_info""" in el )
self.assertEqual(type(el["""segments_info"""] ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(
el["""segmentation"""].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
def UpperCAmelCase__ ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , )
__SCREAMING_SNAKE_CASE = self.image_processing_tester.get_fake_oneformer_outputs()
__SCREAMING_SNAKE_CASE = image_processor.post_process_panoptic_segmentation(__SCREAMING_SNAKE_CASE , threshold=0 )
self.assertTrue(len(__SCREAMING_SNAKE_CASE ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("""segmentation""" in el )
self.assertTrue("""segments_info""" in el )
self.assertEqual(type(el["""segments_info"""] ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(
el["""segmentation"""].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
| 627
|
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse('0.12.2'):
raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
raise Exception('requires fairseq < v2')
logging.set_verbosity_info()
UpperCAmelCase : List[str] = logging.get_logger(__name__)
UpperCAmelCase : Tuple = 'Hello, World!'
UpperCAmelCase : Any = 'en_XX'
def a__ ( a__ , a__ , a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = Path("""data_bin""" )
__SCREAMING_SNAKE_CASE = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(a__ ).parent ) , checkpoint_file=Path(a__ ).name , _name="""xmod_base""" , arch="""xmod_base""" , task="""multilingual_masked_lm""" , data_name_or_path=str(a__ ) , bpe="""sentencepiece""" , sentencepiece_model=str(Path(a__ ).parent / """sentencepiece.bpe.model""" ) , src_dict=str(data_dir / """dict.txt""" ) , )
xmod.eval() # disable dropout
print(a__ )
__SCREAMING_SNAKE_CASE = xmod.model.encoder.sentence_encoder
__SCREAMING_SNAKE_CASE = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=5_14 , type_vocab_size=1 , layer_norm_eps=1E-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , """bottleneck""" , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
__SCREAMING_SNAKE_CASE = xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
print("""Our X-MOD config:""" , a__ )
__SCREAMING_SNAKE_CASE = XmodForSequenceClassification(a__ ) if classification_head else XmodForMaskedLM(a__ )
model.eval()
# Now let's copy all the weights.
# Embeddings
__SCREAMING_SNAKE_CASE = xmod_sent_encoder.embed_tokens.weight
__SCREAMING_SNAKE_CASE = xmod_sent_encoder.embed_positions.weight
__SCREAMING_SNAKE_CASE = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
__SCREAMING_SNAKE_CASE = xmod_sent_encoder.layernorm_embedding.weight
__SCREAMING_SNAKE_CASE = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
__SCREAMING_SNAKE_CASE = model.roberta.encoder.layer[i]
__SCREAMING_SNAKE_CASE = xmod_sent_encoder.layers[i]
# self attention
__SCREAMING_SNAKE_CASE = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError("""Dimensions of self-attention weights do not match.""" )
__SCREAMING_SNAKE_CASE = xmod_layer.self_attn.q_proj.weight
__SCREAMING_SNAKE_CASE = xmod_layer.self_attn.q_proj.bias
__SCREAMING_SNAKE_CASE = xmod_layer.self_attn.k_proj.weight
__SCREAMING_SNAKE_CASE = xmod_layer.self_attn.k_proj.bias
__SCREAMING_SNAKE_CASE = xmod_layer.self_attn.v_proj.weight
__SCREAMING_SNAKE_CASE = xmod_layer.self_attn.v_proj.bias
# self-attention output
__SCREAMING_SNAKE_CASE = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError("""Dimensions of self-attention output weights do not match.""" )
__SCREAMING_SNAKE_CASE = xmod_layer.self_attn.out_proj.weight
__SCREAMING_SNAKE_CASE = xmod_layer.self_attn.out_proj.bias
__SCREAMING_SNAKE_CASE = xmod_layer.self_attn_layer_norm.weight
__SCREAMING_SNAKE_CASE = xmod_layer.self_attn_layer_norm.bias
# intermediate
__SCREAMING_SNAKE_CASE = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of intermediate weights do not match.""" )
__SCREAMING_SNAKE_CASE = xmod_layer.fca.weight
__SCREAMING_SNAKE_CASE = xmod_layer.fca.bias
# output
__SCREAMING_SNAKE_CASE = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of feed-forward weights do not match.""" )
__SCREAMING_SNAKE_CASE = xmod_layer.fca.weight
__SCREAMING_SNAKE_CASE = xmod_layer.fca.bias
__SCREAMING_SNAKE_CASE = xmod_layer.final_layer_norm.weight
__SCREAMING_SNAKE_CASE = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
__SCREAMING_SNAKE_CASE = xmod_layer.adapter_layer_norm.weight
__SCREAMING_SNAKE_CASE = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError("""Lists of language adapters do not match.""" )
for lang_code, adapter in xmod_layer.adapter_modules.items():
__SCREAMING_SNAKE_CASE = bert_output.adapter_modules[lang_code]
__SCREAMING_SNAKE_CASE = xmod_layer.adapter_modules[lang_code]
__SCREAMING_SNAKE_CASE = from_adapter.fca.weight
__SCREAMING_SNAKE_CASE = from_adapter.fca.bias
__SCREAMING_SNAKE_CASE = from_adapter.fca.weight
__SCREAMING_SNAKE_CASE = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
__SCREAMING_SNAKE_CASE = xmod_sent_encoder.layer_norm.weight
__SCREAMING_SNAKE_CASE = xmod_sent_encoder.layer_norm.bias
if classification_head:
__SCREAMING_SNAKE_CASE = xmod.model.classification_heads["""mnli"""].dense.weight
__SCREAMING_SNAKE_CASE = xmod.model.classification_heads["""mnli"""].dense.bias
__SCREAMING_SNAKE_CASE = xmod.model.classification_heads["""mnli"""].out_proj.weight
__SCREAMING_SNAKE_CASE = xmod.model.classification_heads["""mnli"""].out_proj.bias
else:
# LM Head
__SCREAMING_SNAKE_CASE = xmod.model.encoder.lm_head.dense.weight
__SCREAMING_SNAKE_CASE = xmod.model.encoder.lm_head.dense.bias
__SCREAMING_SNAKE_CASE = xmod.model.encoder.lm_head.layer_norm.weight
__SCREAMING_SNAKE_CASE = xmod.model.encoder.lm_head.layer_norm.bias
__SCREAMING_SNAKE_CASE = xmod.model.encoder.lm_head.weight
__SCREAMING_SNAKE_CASE = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
__SCREAMING_SNAKE_CASE = xmod.encode(a__ ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(a__ )
__SCREAMING_SNAKE_CASE = model(a__ )[0]
if classification_head:
__SCREAMING_SNAKE_CASE = xmod.model.classification_heads["""mnli"""](xmod.extract_features(a__ ) )
else:
__SCREAMING_SNAKE_CASE = xmod.model(a__ , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
__SCREAMING_SNAKE_CASE = torch.max(torch.abs(our_output - their_output ) ).item()
print(F'max_absolute_diff = {max_absolute_diff}' ) # ~ 1e-7
__SCREAMING_SNAKE_CASE = torch.allclose(a__ , a__ , atol=1E-3 )
print("""Do both models output the same tensors?""" , """🔥""" if success else """💩""" )
if not success:
raise Exception("""Something went wRoNg""" )
Path(a__ ).mkdir(parents=a__ , exist_ok=a__ )
print(F'Saving model to {pytorch_dump_folder_path}' )
model.save_pretrained(a__ )
if __name__ == "__main__":
UpperCAmelCase : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--xmod_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.'
)
parser.add_argument(
'--classification_head', action='store_true', help='Whether to convert a final classification head.'
)
UpperCAmelCase : Optional[int] = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 627
| 1
|
import math
import qiskit
def _lowercase ( SCREAMING_SNAKE_CASE_ : int = 1 , SCREAMING_SNAKE_CASE_ : int = 1 , SCREAMING_SNAKE_CASE_ : int = 1 ):
"""simple docstring"""
if (
isinstance(snake_case__ , snake_case__ )
or isinstance(snake_case__ , snake_case__ )
or isinstance(snake_case__ , snake_case__ )
):
raise TypeError("""inputs must be integers.""" )
if (input_a < 0) or (input_a < 0) or (carry_in < 0):
raise ValueError("""inputs must be positive.""" )
if (
(math.floor(snake_case__ ) != input_a)
or (math.floor(snake_case__ ) != input_a)
or (math.floor(snake_case__ ) != carry_in)
):
raise ValueError("""inputs must be exact integers.""" )
if (input_a > 2) or (input_a > 2) or (carry_in > 2):
raise ValueError("""inputs must be less or equal to 2.""" )
# build registers
UpperCamelCase = qiskit.QuantumRegister(4 , """qr""" )
UpperCamelCase = qiskit.ClassicalRegister(2 , """cr""" )
# list the entries
UpperCamelCase = [input_a, input_a, carry_in]
UpperCamelCase = qiskit.QuantumCircuit(snake_case__ , snake_case__ )
for i in range(0 , 3 ):
if entry[i] == 2:
quantum_circuit.h(snake_case__ ) # for hadamard entries
elif entry[i] == 1:
quantum_circuit.x(snake_case__ ) # for 1 entries
elif entry[i] == 0:
quantum_circuit.i(snake_case__ ) # for 0 entries
# build the circuit
quantum_circuit.ccx(0 , 1 , 3 ) # ccx = toffoli gate
quantum_circuit.cx(0 , 1 )
quantum_circuit.ccx(1 , 2 , 3 )
quantum_circuit.cx(1 , 2 )
quantum_circuit.cx(0 , 1 )
quantum_circuit.measure([2, 3] , snake_case__ ) # measure the last two qbits
UpperCamelCase = qiskit.Aer.get_backend("""aer_simulator""" )
UpperCamelCase = qiskit.execute(snake_case__ , snake_case__ , shots=1_000 )
return job.result().get_counts(snake_case__ )
if __name__ == "__main__":
print(F'''Total sum count for state is: {quantum_full_adder(1, 1, 1)}''')
| 700
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"SCUT-DLVCLab/lilt-roberta-en-base": (
"https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
),
}
class UpperCAmelCase ( __snake_case ):
lowercase = """lilt"""
def __init__( self : Tuple , __magic_name__ : Tuple=3_0_5_2_2 , __magic_name__ : List[str]=7_6_8 , __magic_name__ : Optional[Any]=1_2 , __magic_name__ : str=1_2 , __magic_name__ : Tuple=3_0_7_2 , __magic_name__ : List[Any]="gelu" , __magic_name__ : Dict=0.1 , __magic_name__ : str=0.1 , __magic_name__ : Dict=5_1_2 , __magic_name__ : Any=2 , __magic_name__ : Optional[int]=0.02 , __magic_name__ : Optional[int]=1e-12 , __magic_name__ : Optional[int]=0 , __magic_name__ : List[str]="absolute" , __magic_name__ : List[Any]=None , __magic_name__ : str=4 , __magic_name__ : Any=1_0_2_4 , **__magic_name__ : List[str] , ):
"""simple docstring"""
super().__init__(pad_token_id=__magic_name__ , **__magic_name__ )
UpperCamelCase = vocab_size
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = hidden_act
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = type_vocab_size
UpperCamelCase = initializer_range
UpperCamelCase = layer_norm_eps
UpperCamelCase = position_embedding_type
UpperCamelCase = classifier_dropout
UpperCamelCase = channel_shrink_ratio
UpperCamelCase = max_ad_position_embeddings
| 181
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
'https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'
),
}
class _lowercase ( snake_case_ ):
lowercase = 'gptsan-japanese'
lowercase = [
'past_key_values',
]
lowercase = {
'hidden_size': 'd_model',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : List[str] , snake_case : Any=3_6_0_0_0 , snake_case : Tuple=1_2_8_0 , snake_case : Any=1_0_2_4 , snake_case : Optional[Any]=8_1_9_2 , snake_case : Tuple=4_0_9_6 , snake_case : Dict=1_2_8 , snake_case : Optional[Any]=1_0 , snake_case : int=0 , snake_case : List[Any]=1_6 , snake_case : List[str]=1_6 , snake_case : int=1_2_8 , snake_case : List[Any]=0.0 , snake_case : Any=1e-5 , snake_case : Tuple=False , snake_case : int=0.0 , snake_case : Dict="float32" , snake_case : int=False , snake_case : int=False , snake_case : Union[str, Any]=False , snake_case : List[Any]=0.002 , snake_case : Any=False , snake_case : Any=True , snake_case : int=3_5_9_9_8 , snake_case : Optional[Any]=3_5_9_9_5 , snake_case : int=3_5_9_9_9 , **snake_case : Dict , ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ : Any = vocab_size
UpperCamelCase_ : str = max_position_embeddings
UpperCamelCase_ : str = d_model
UpperCamelCase_ : Optional[int] = d_ff
UpperCamelCase_ : Any = d_ext
UpperCamelCase_ : int = d_spout
UpperCamelCase_ : List[str] = num_switch_layers
UpperCamelCase_ : int = num_ext_layers
UpperCamelCase_ : List[str] = num_switch_layers + num_ext_layers
UpperCamelCase_ : Union[str, Any] = num_heads
UpperCamelCase_ : str = num_experts
UpperCamelCase_ : Optional[Any] = expert_capacity
UpperCamelCase_ : Optional[Any] = dropout_rate
UpperCamelCase_ : Tuple = layer_norm_epsilon
UpperCamelCase_ : List[str] = router_bias
UpperCamelCase_ : str = router_jitter_noise
UpperCamelCase_ : Dict = router_dtype
UpperCamelCase_ : str = router_ignore_padding_tokens
UpperCamelCase_ : Tuple = output_hidden_states
UpperCamelCase_ : int = output_attentions
UpperCamelCase_ : Optional[int] = initializer_factor
UpperCamelCase_ : Optional[int] = output_router_logits
UpperCamelCase_ : List[str] = use_cache
super().__init__(
separator_token_id=snake_case , pad_token_id=snake_case , eos_token_id=snake_case , **snake_case , )
| 417
|
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = '▁'
a_ = {'vocab_file': 'spiece.model'}
a_ = {
'vocab_file': {
'google/reformer-crime-and-punishment': (
'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
)
}
}
a_ = {
'google/reformer-crime-and-punishment': 524_288,
}
class _lowercase ( snake_case_ ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = ['input_ids', 'attention_mask']
def __init__( self : Any , snake_case : List[Any] , snake_case : Any="</s>" , snake_case : Optional[Any]="<unk>" , snake_case : str=[] , snake_case : Optional[Dict[str, Any]] = None , **snake_case : str , ) -> None:
"""simple docstring"""
UpperCamelCase_ : Dict = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=snake_case , unk_token=snake_case , additional_special_tokens=snake_case , sp_model_kwargs=self.sp_model_kwargs , **snake_case , )
UpperCamelCase_ : Dict = vocab_file
UpperCamelCase_ : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(snake_case )
@property
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
return self.sp_model.get_piece_size()
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Dict[str, int]:
"""simple docstring"""
UpperCamelCase_ : List[str] = {self.convert_ids_to_tokens(snake_case ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Tuple ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ : Dict = self.__dict__.copy()
UpperCamelCase_ : Any = None
return state
def __setstate__( self : Optional[Any] , snake_case : Any ) -> Dict:
"""simple docstring"""
UpperCamelCase_ : Dict = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
UpperCamelCase_ : Optional[int] = {}
UpperCamelCase_ : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE__ ( self : Tuple , snake_case : str ) -> List[str]:
"""simple docstring"""
return self.sp_model.encode(snake_case , out_type=snake_case )
def SCREAMING_SNAKE_CASE__ ( self : str , snake_case : Optional[int] ) -> int:
"""simple docstring"""
return self.sp_model.piece_to_id(snake_case )
def SCREAMING_SNAKE_CASE__ ( self : Any , snake_case : Union[str, Any] ) -> str:
"""simple docstring"""
if index < self.sp_model.get_piece_size():
UpperCamelCase_ : Tuple = self.sp_model.IdToPiece(snake_case )
return token
def SCREAMING_SNAKE_CASE__ ( self : Tuple , snake_case : List[Any] ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ : Any = []
UpperCamelCase_ : Tuple = ''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(snake_case ) + token
UpperCamelCase_ : int = []
else:
current_sub_tokens.append(snake_case )
out_string += self.sp_model.decode(snake_case )
return out_string.strip()
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , snake_case : str , snake_case : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(snake_case ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
UpperCamelCase_ : Union[str, Any] = os.path.join(
snake_case , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , snake_case )
elif not os.path.isfile(self.vocab_file ):
with open(snake_case , 'wb' ) as fi:
UpperCamelCase_ : str = self.sp_model.serialized_model_proto()
fi.write(snake_case )
return (out_vocab_file,)
| 417
| 1
|
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
lowerCamelCase_ : Any = ["small", "medium", "large"]
lowerCamelCase_ : Dict = "lm_head.decoder.weight"
lowerCamelCase_ : Tuple = "lm_head.weight"
def __lowercase( __snake_case : str ,__snake_case : str ) -> str:
__snake_case = torch.load(__snake_case )
__snake_case = d.pop(__snake_case )
os.makedirs(__snake_case ,exist_ok=__snake_case )
torch.save(__snake_case ,os.path.join(__snake_case ,__snake_case ) )
if __name__ == "__main__":
lowerCamelCase_ : List[Any] = argparse.ArgumentParser()
parser.add_argument("--dialogpt_path", default=".", type=str)
lowerCamelCase_ : Union[str, Any] = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
lowerCamelCase_ : Dict = os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
lowerCamelCase_ : Any = f"""./DialoGPT-{MODEL}"""
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 345
|
from ..utils import DummyObject, requires_backends
class _lowerCamelCase (metaclass=lowerCamelCase ):
lowercase__ = ["""flax"""]
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(self , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
class _lowerCamelCase (metaclass=lowerCamelCase ):
lowercase__ = ["""flax"""]
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(self , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
class _lowerCamelCase (metaclass=lowerCamelCase ):
lowercase__ = ["""flax"""]
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(self , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
class _lowerCamelCase (metaclass=lowerCamelCase ):
lowercase__ = ["""flax"""]
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(self , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
class _lowerCamelCase (metaclass=lowerCamelCase ):
lowercase__ = ["""flax"""]
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(self , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
class _lowerCamelCase (metaclass=lowerCamelCase ):
lowercase__ = ["""flax"""]
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(self , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
class _lowerCamelCase (metaclass=lowerCamelCase ):
lowercase__ = ["""flax"""]
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(self , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
class _lowerCamelCase (metaclass=lowerCamelCase ):
lowercase__ = ["""flax"""]
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(self , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
class _lowerCamelCase (metaclass=lowerCamelCase ):
lowercase__ = ["""flax"""]
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(self , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
class _lowerCamelCase (metaclass=lowerCamelCase ):
lowercase__ = ["""flax"""]
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(self , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
class _lowerCamelCase (metaclass=lowerCamelCase ):
lowercase__ = ["""flax"""]
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(self , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
class _lowerCamelCase (metaclass=lowerCamelCase ):
lowercase__ = ["""flax"""]
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(self , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
class _lowerCamelCase (metaclass=lowerCamelCase ):
lowercase__ = ["""flax"""]
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(self , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
@classmethod
def __lowerCamelCase ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ['flax'] )
| 345
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ["FNetTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ["FNetTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"FNetForMaskedLM",
"FNetForMultipleChoice",
"FNetForNextSentencePrediction",
"FNetForPreTraining",
"FNetForQuestionAnswering",
"FNetForSequenceClassification",
"FNetForTokenClassification",
"FNetLayer",
"FNetModel",
"FNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 68
|
# 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
__UpperCamelCase : Any = 'tiny-wmt19-en-ru'
# Build
# borrowed from a test
__UpperCamelCase : 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>',
]
__UpperCamelCase : Any = dict(zip(vocab, range(len(vocab))))
__UpperCamelCase : Optional[int] = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']
with tempfile.TemporaryDirectory() as tmpdirname:
__UpperCamelCase : Optional[Any] = Path(tmpdirname)
__UpperCamelCase : Tuple = build_dir / VOCAB_FILES_NAMES['src_vocab_file']
__UpperCamelCase : Optional[int] = build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
__UpperCamelCase : Union[str, Any] = 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))
__UpperCamelCase : Any = 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,
)
__UpperCamelCase : Tuple = FSMTConfig(
langs=['ru', 'en'],
src_vocab_size=1000,
tgt_vocab_size=1000,
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,
)
__UpperCamelCase : Optional[int] = FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")
# Test
__UpperCamelCase : str = tokenizer(['Making tiny model'], return_tensors='pt')
__UpperCamelCase : Any = 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
| 248
| 0
|
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
AutoTokenizer,
DataCollatorForWholeWordMask,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
lowerCamelCase = logging.getLogger(__name__)
lowerCamelCase = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
lowerCamelCase = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class snake_case_ :
"""simple docstring"""
__UpperCAmelCase =field(
default=_a , metadata={
"""help""": (
"""The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
)
} , )
__UpperCAmelCase =field(
default=_a , metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(_a )} , )
__UpperCAmelCase =field(
default=_a , metadata={
"""help""": (
"""Override some existing default config settings when a model is trained from scratch. Example: """
"""n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
)
} , )
__UpperCAmelCase =field(
default=_a , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
__UpperCAmelCase =field(
default=_a , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
__UpperCAmelCase =field(
default=_a , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
__UpperCAmelCase =field(
default=_a , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
__UpperCAmelCase =field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
__UpperCAmelCase =field(
default=_a , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
def A__ ( self ):
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
'--config_overrides can\'t be used in combination with --config_name or --model_name_or_path' )
@dataclass
class snake_case_ :
"""simple docstring"""
__UpperCAmelCase =field(
default=_a , metadata={"""help""": """The name of the dataset to use (via the datasets library)."""} )
__UpperCAmelCase =field(
default=_a , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
__UpperCAmelCase =field(default=_a , metadata={"""help""": """The input training data file (a text file)."""} )
__UpperCAmelCase =field(
default=_a , metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""} , )
__UpperCAmelCase =field(
default=_a , metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""} , )
__UpperCAmelCase =field(
default=_a , metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""} , )
__UpperCAmelCase =field(
default=_a , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
__UpperCAmelCase =field(
default=5 , metadata={
"""help""": """The percentage of the train set used as validation set in case there's no validation split"""
} , )
__UpperCAmelCase =field(
default=_a , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated. Default to the max input length of the model."""
)
} , )
__UpperCAmelCase =field(
default=_a , metadata={"""help""": """The number of processes to use for the preprocessing."""} , )
__UpperCAmelCase =field(
default=0.15 , metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""} )
__UpperCAmelCase =field(
default=_a , metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
} , )
def A__ ( self ):
if self.train_file is not None:
__lowerCAmelCase = self.train_file.split('.' )[-1]
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
__lowerCAmelCase = self.validation_file.split('.' )[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
def __lowercase ( UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
with open(UpperCAmelCase__ , 'r' , encoding='utf-8' ) as f:
__lowerCAmelCase = [json.loads(UpperCAmelCase__ ) for line in f.read().splitlines() if (len(UpperCAmelCase__ ) > 0 and not line.isspace())]
assert len(UpperCAmelCase__ ) == len(UpperCAmelCase__ )
__lowerCAmelCase = {c: dataset[c] for c in dataset.column_names}
__lowerCAmelCase = refs
return Dataset.from_dict(UpperCAmelCase__ )
def __lowercase ( ):
"""simple docstring"""
__lowerCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
__lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
__lowerCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__lowerCAmelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None:
logger.info(
F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# Log on each process the small summary:
logger.warning(
F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
# 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' , UpperCAmelCase__ )
# Set seed before initializing model.
set_seed(training_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).
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
__lowerCAmelCase = load_dataset(data_args.dataset_name , data_args.dataset_config_name )
if "validation" not in datasets.keys():
__lowerCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F"""train[:{data_args.validation_split_percentage}%]""" , )
__lowerCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F"""train[{data_args.validation_split_percentage}%:]""" , )
else:
__lowerCAmelCase = {}
if data_args.train_file is not None:
__lowerCAmelCase = data_args.train_file
if data_args.validation_file is not None:
__lowerCAmelCase = data_args.validation_file
__lowerCAmelCase = data_args.train_file.split('.' )[-1]
if extension == "txt":
__lowerCAmelCase = 'text'
__lowerCAmelCase = load_dataset(UpperCAmelCase__ , data_files=UpperCAmelCase__ )
# 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.
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__lowerCAmelCase = {
'cache_dir': model_args.cache_dir,
'revision': model_args.model_revision,
'use_auth_token': True if model_args.use_auth_token else None,
}
if model_args.config_name:
__lowerCAmelCase = AutoConfig.from_pretrained(model_args.config_name , **UpperCAmelCase__ )
elif model_args.model_name_or_path:
__lowerCAmelCase = AutoConfig.from_pretrained(model_args.model_name_or_path , **UpperCAmelCase__ )
else:
__lowerCAmelCase = CONFIG_MAPPING[model_args.model_type]()
logger.warning('You are instantiating a new config instance from scratch.' )
if model_args.config_overrides is not None:
logger.info(F"""Overriding config: {model_args.config_overrides}""" )
config.update_from_string(model_args.config_overrides )
logger.info(F"""New config: {config}""" )
__lowerCAmelCase = {
'cache_dir': model_args.cache_dir,
'use_fast': model_args.use_fast_tokenizer,
'revision': model_args.model_revision,
'use_auth_token': True if model_args.use_auth_token else None,
}
if model_args.tokenizer_name:
__lowerCAmelCase = AutoTokenizer.from_pretrained(model_args.tokenizer_name , **UpperCAmelCase__ )
elif model_args.model_name_or_path:
__lowerCAmelCase = AutoTokenizer.from_pretrained(model_args.model_name_or_path , **UpperCAmelCase__ )
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.' )
if model_args.model_name_or_path:
__lowerCAmelCase = AutoModelForMaskedLM.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=UpperCAmelCase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('Training new model from scratch' )
__lowerCAmelCase = AutoModelForMaskedLM.from_config(UpperCAmelCase__ )
model.resize_token_embeddings(len(UpperCAmelCase__ ) )
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
__lowerCAmelCase = datasets['train'].column_names
else:
__lowerCAmelCase = datasets['validation'].column_names
__lowerCAmelCase = 'text' if 'text' in column_names else column_names[0]
__lowerCAmelCase = 'max_length' if data_args.pad_to_max_length else False
def tokenize_function(UpperCAmelCase__ ):
# Remove empty lines
__lowerCAmelCase = [line for line in examples['text'] if len(UpperCAmelCase__ ) > 0 and not line.isspace()]
return tokenizer(examples['text'] , padding=UpperCAmelCase__ , truncation=UpperCAmelCase__ , max_length=data_args.max_seq_length )
__lowerCAmelCase = datasets.map(
UpperCAmelCase__ , batched=UpperCAmelCase__ , num_proc=data_args.preprocessing_num_workers , remove_columns=[text_column_name] , load_from_cache_file=not data_args.overwrite_cache , )
# Add the chinese references if provided
if data_args.train_ref_file is not None:
__lowerCAmelCase = add_chinese_references(tokenized_datasets['train'] , data_args.train_ref_file )
if data_args.validation_ref_file is not None:
__lowerCAmelCase = add_chinese_references(
tokenized_datasets['validation'] , data_args.validation_ref_file )
# If we have ref files, need to avoid it removed by trainer
__lowerCAmelCase = data_args.train_ref_file or data_args.validation_ref_file
if has_ref:
__lowerCAmelCase = False
# Data collator
# This one will take care of randomly masking the tokens.
__lowerCAmelCase = DataCollatorForWholeWordMask(tokenizer=UpperCAmelCase__ , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
__lowerCAmelCase = Trainer(
model=UpperCAmelCase__ , args=UpperCAmelCase__ , train_dataset=tokenized_datasets['train'] if training_args.do_train else None , eval_dataset=tokenized_datasets['validation'] if training_args.do_eval else None , tokenizer=UpperCAmelCase__ , data_collator=UpperCAmelCase__ , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
__lowerCAmelCase = last_checkpoint
elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path ):
__lowerCAmelCase = model_args.model_name_or_path
else:
__lowerCAmelCase = None
__lowerCAmelCase = trainer.train(resume_from_checkpoint=UpperCAmelCase__ )
trainer.save_model() # Saves the tokenizer too for easy upload
__lowerCAmelCase = os.path.join(training_args.output_dir , 'train_results.txt' )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , 'w' ) as writer:
logger.info('***** Train results *****' )
for key, value in sorted(train_result.metrics.items() ):
logger.info(F""" {key} = {value}""" )
writer.write(F"""{key} = {value}\n""" )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , 'trainer_state.json' ) )
# Evaluation
__lowerCAmelCase = {}
if training_args.do_eval:
logger.info('*** Evaluate ***' )
__lowerCAmelCase = trainer.evaluate()
__lowerCAmelCase = math.exp(eval_output['eval_loss'] )
__lowerCAmelCase = perplexity
__lowerCAmelCase = os.path.join(training_args.output_dir , 'eval_results_mlm_wwm.txt' )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , 'w' ) as writer:
logger.info('***** Eval results *****' )
for key, value in sorted(results.items() ):
logger.info(F""" {key} = {value}""" )
writer.write(F"""{key} = {value}\n""" )
return results
def __lowercase ( UpperCAmelCase__ ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 102
|
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowerCamelCase = data_utils.TransfoXLTokenizer
lowerCamelCase = data_utils.TransfoXLCorpus
lowerCamelCase = data_utils
lowerCamelCase = data_utils
def __lowercase ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(UpperCAmelCase__ , 'rb' ) as fp:
__lowerCAmelCase = pickle.load(UpperCAmelCase__ , encoding='latin1' )
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
__lowerCAmelCase = pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
print(F"""Save vocabulary to {pytorch_vocab_dump_path}""" )
__lowerCAmelCase = corpus.vocab.__dict__
torch.save(UpperCAmelCase__ , UpperCAmelCase__ )
__lowerCAmelCase = corpus.__dict__
corpus_dict_no_vocab.pop('vocab' , UpperCAmelCase__ )
__lowerCAmelCase = pytorch_dump_folder_path + '/' + CORPUS_NAME
print(F"""Save dataset to {pytorch_dataset_dump_path}""" )
torch.save(UpperCAmelCase__ , UpperCAmelCase__ )
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
__lowerCAmelCase = os.path.abspath(UpperCAmelCase__ )
__lowerCAmelCase = os.path.abspath(UpperCAmelCase__ )
print(F"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.""" )
# Initialise PyTorch model
if transfo_xl_config_file == "":
__lowerCAmelCase = TransfoXLConfig()
else:
__lowerCAmelCase = TransfoXLConfig.from_json_file(UpperCAmelCase__ )
print(F"""Building PyTorch model from configuration: {config}""" )
__lowerCAmelCase = TransfoXLLMHeadModel(UpperCAmelCase__ )
__lowerCAmelCase = load_tf_weights_in_transfo_xl(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
__lowerCAmelCase = os.path.join(UpperCAmelCase__ , UpperCAmelCase__ )
__lowerCAmelCase = os.path.join(UpperCAmelCase__ , UpperCAmelCase__ )
print(F"""Save PyTorch model to {os.path.abspath(UpperCAmelCase__ )}""" )
torch.save(model.state_dict() , UpperCAmelCase__ )
print(F"""Save configuration file to {os.path.abspath(UpperCAmelCase__ )}""" )
with open(UpperCAmelCase__ , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
lowerCamelCase = argparse.ArgumentParser()
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
)
parser.add_argument(
'''--tf_checkpoint_path''',
default='''''',
type=str,
help='''An optional path to a TensorFlow checkpoint path to be converted.''',
)
parser.add_argument(
'''--transfo_xl_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--transfo_xl_dataset_file''',
default='''''',
type=str,
help='''An optional dataset file to be converted in a vocabulary.''',
)
lowerCamelCase = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 102
| 1
|
"""simple docstring"""
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def A__ ( ) -> int:
'''simple docstring'''
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
_UpperCAmelCase = "__test_patch_submodule_mock__"
with patch_submodule(_test_patching , "os.path.join" , A__ ):
# Every way to access os.path.join must be patched, and the rest must stay untouched
# check os.path.join
assert isinstance(_test_patching.os , _PatchedModuleObj )
assert isinstance(_test_patching.os.path , _PatchedModuleObj )
assert _test_patching.os.path.join is mock
# check path.join
assert isinstance(_test_patching.path , _PatchedModuleObj )
assert _test_patching.path.join is mock
# check join
assert _test_patching.join is mock
# check that the other attributes are untouched
assert _test_patching.os.rename is original_rename
assert _test_patching.path.dirname is original_dirname
assert _test_patching.os.path.dirname is original_dirname
# Even renamed modules or objects must be patched
# check renamed_os.path.join
assert isinstance(_test_patching.renamed_os , _PatchedModuleObj )
assert isinstance(_test_patching.renamed_os.path , _PatchedModuleObj )
assert _test_patching.renamed_os.path.join is mock
# check renamed_path.join
assert isinstance(_test_patching.renamed_path , _PatchedModuleObj )
assert _test_patching.renamed_path.join is mock
# check renamed_join
assert _test_patching.renamed_join is mock
# check that the other attributes are untouched
assert _test_patching.renamed_os.rename is original_rename
assert _test_patching.renamed_path.dirname is original_dirname
assert _test_patching.renamed_os.path.dirname is original_dirname
# check that everthing is back to normal when the patch is over
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
def A__ ( ) -> Union[str, Any]:
'''simple docstring'''
assert _test_patching.open is open
_UpperCAmelCase = "__test_patch_submodule_builtin_mock__"
# _test_patching has "open" in its globals
assert _test_patching.open is open
with patch_submodule(_test_patching , "open" , A__ ):
assert _test_patching.open is mock
# check that everthing is back to normal when the patch is over
assert _test_patching.open is open
def A__ ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = "__test_patch_submodule_missing_mock__"
with patch_submodule(_test_patching , "pandas.read_csv" , A__ ):
pass
def A__ ( ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = "__test_patch_submodule_missing_builtin_mock__"
# _test_patching doesn't have "len" in its globals
assert getattr(_test_patching , "len" , A__ ) is None
with patch_submodule(_test_patching , "len" , A__ ):
assert _test_patching.len is mock
assert _test_patching.len is len
def A__ ( ) -> int:
'''simple docstring'''
_UpperCAmelCase = "__test_patch_submodule_start_and_stop_mock__"
_UpperCAmelCase = patch_submodule(_test_patching , "open" , A__ )
assert _test_patching.open is open
patch.start()
assert _test_patching.open is mock
patch.stop()
assert _test_patching.open is open
def A__ ( ) -> List[str]:
'''simple docstring'''
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
_UpperCAmelCase = "__test_patch_submodule_successive_join__"
_UpperCAmelCase = "__test_patch_submodule_successive_dirname__"
_UpperCAmelCase = "__test_patch_submodule_successive_rename__"
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
with patch_submodule(_test_patching , "os.path.join" , A__ ):
with patch_submodule(_test_patching , "os.rename" , A__ ):
with patch_submodule(_test_patching , "os.path.dirname" , A__ ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
# try another order
with patch_submodule(_test_patching , "os.rename" , A__ ):
with patch_submodule(_test_patching , "os.path.join" , A__ ):
with patch_submodule(_test_patching , "os.path.dirname" , A__ ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
def A__ ( ) -> int:
'''simple docstring'''
_UpperCAmelCase = "__test_patch_submodule_doesnt_exist_mock__"
with patch_submodule(_test_patching , "__module_that_doesn_exist__.__attribute_that_doesn_exist__" , A__ ):
pass
with patch_submodule(_test_patching , "os.__attribute_that_doesn_exist__" , A__ ):
pass
| 426
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
# See all XGLM models at https://huggingface.co/models?filter=xglm
}
class a ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
A__ : int = "xglm"
A__ : List[Any] = ["past_key_values"]
A__ : str = {
"num_attention_heads": "attention_heads",
"hidden_size": "d_model",
"num_hidden_layers": "num_layers",
}
def __init__( self , snake_case_=256008 , snake_case_=2048 , snake_case_=1024 , snake_case_=4096 , snake_case_=24 , snake_case_=16 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=0.02 , snake_case_=True , snake_case_=True , snake_case_=2 , snake_case_=1 , snake_case_=0 , snake_case_=2 , **snake_case_ , ) -> List[str]:
_UpperCAmelCase = vocab_size
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = d_model
_UpperCAmelCase = ffn_dim
_UpperCAmelCase = num_layers
_UpperCAmelCase = attention_heads
_UpperCAmelCase = activation_function
_UpperCAmelCase = dropout
_UpperCAmelCase = attention_dropout
_UpperCAmelCase = activation_dropout
_UpperCAmelCase = layerdrop
_UpperCAmelCase = init_std
_UpperCAmelCase = scale_embedding # scale factor will be sqrt(d_model) if True
_UpperCAmelCase = use_cache
super().__init__(
pad_token_id=snake_case_ , bos_token_id=snake_case_ , eos_token_id=snake_case_ , decoder_start_token_id=snake_case_ , **snake_case_ , )
| 426
| 1
|
from ..utils import DummyObject, requires_backends
class lowercase ( metaclass=A__ ):
__SCREAMING_SNAKE_CASE = ["""flax""", """transformers"""]
def __init__( self , *_snake_case , **_snake_case ) -> Dict:
"""simple docstring"""
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def snake_case_ ( cls , *_snake_case , **_snake_case ) -> int:
"""simple docstring"""
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def snake_case_ ( cls , *_snake_case , **_snake_case ) -> int:
"""simple docstring"""
requires_backends(cls , ['''flax''', '''transformers'''] )
class lowercase ( metaclass=A__ ):
__SCREAMING_SNAKE_CASE = ["""flax""", """transformers"""]
def __init__( self , *_snake_case , **_snake_case ) -> str:
"""simple docstring"""
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def snake_case_ ( cls , *_snake_case , **_snake_case ) -> Tuple:
"""simple docstring"""
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def snake_case_ ( cls , *_snake_case , **_snake_case ) -> str:
"""simple docstring"""
requires_backends(cls , ['''flax''', '''transformers'''] )
class lowercase ( metaclass=A__ ):
__SCREAMING_SNAKE_CASE = ["""flax""", """transformers"""]
def __init__( self , *_snake_case , **_snake_case ) -> Tuple:
"""simple docstring"""
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def snake_case_ ( cls , *_snake_case , **_snake_case ) -> Dict:
"""simple docstring"""
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def snake_case_ ( cls , *_snake_case , **_snake_case ) -> List[str]:
"""simple docstring"""
requires_backends(cls , ['''flax''', '''transformers'''] )
class lowercase ( metaclass=A__ ):
__SCREAMING_SNAKE_CASE = ["""flax""", """transformers"""]
def __init__( self , *_snake_case , **_snake_case ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def snake_case_ ( cls , *_snake_case , **_snake_case ) -> Optional[int]:
"""simple docstring"""
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def snake_case_ ( cls , *_snake_case , **_snake_case ) -> str:
"""simple docstring"""
requires_backends(cls , ['''flax''', '''transformers'''] )
| 704
|
import re
import string
import numpy as np
import datasets
__magic_name__ = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
__magic_name__ = "\nArgs:\n predictions: List of predicted texts.\n references: List of reference texts.\n regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n ignore when calculating the exact matches. Note: these regexes are removed\n from the input data before the changes based on the options below (e.g. ignore_case,\n ignore_punctuation, ignore_numbers) are applied.\n ignore_case: Boolean, defaults to False. If true, turns everything\n to lowercase so that capitalization differences are ignored.\n ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\n ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\nReturns:\n exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results[\"exact_match\"], 1))\n 25.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results[\"exact_match\"], 1))\n 50.0\n\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results[\"exact_match\"], 1))\n 75.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n >>> print(round(results[\"exact_match\"], 1))\n 100.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results[\"exact_match\"], 1))\n 33.3\n\n"
__magic_name__ = "\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase ( datasets.Metric ):
'''simple docstring'''
def snake_case_ ( self ) -> Union[str, Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , reference_urls=[] , )
def snake_case_ ( self , _snake_case , _snake_case , _snake_case=None , _snake_case=False , _snake_case=False , _snake_case=False , ) -> Optional[Any]:
"""simple docstring"""
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
UpperCAmelCase = np.array([re.sub(_snake_case , '''''' , _snake_case ) for x in predictions] )
UpperCAmelCase = np.array([re.sub(_snake_case , '''''' , _snake_case ) for x in references] )
else:
UpperCAmelCase = np.asarray(_snake_case )
UpperCAmelCase = np.asarray(_snake_case )
if ignore_case:
UpperCAmelCase = np.char.lower(_snake_case )
UpperCAmelCase = np.char.lower(_snake_case )
if ignore_punctuation:
UpperCAmelCase = string.punctuation.maketrans('''''' , '''''' , string.punctuation )
UpperCAmelCase = np.char.translate(_snake_case , table=_snake_case )
UpperCAmelCase = np.char.translate(_snake_case , table=_snake_case )
if ignore_numbers:
UpperCAmelCase = string.digits.maketrans('''''' , '''''' , string.digits )
UpperCAmelCase = np.char.translate(_snake_case , table=_snake_case )
UpperCAmelCase = np.char.translate(_snake_case , table=_snake_case )
UpperCAmelCase = predictions == references
return {"exact_match": np.mean(_snake_case ) * 100}
| 391
| 0
|
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
_lowerCamelCase : List[Any] = 256
class lowercase ( SCREAMING_SNAKE_CASE_):
'''simple docstring'''
UpperCAmelCase : Optional[Any] = ['melgan']
def __init__( self : Optional[Any] , snake_case : SpectrogramNotesEncoder , snake_case : SpectrogramContEncoder , snake_case : TaFilmDecoder , snake_case : DDPMScheduler , snake_case : OnnxRuntimeModel if is_onnx_available() else Any , ):
'''simple docstring'''
super().__init__()
# From MELGAN
SCREAMING_SNAKE_CASE : List[Any] = math.log(1E-5 ) # Matches MelGAN training.
SCREAMING_SNAKE_CASE : Optional[Any] = 4.0 # Largest value for most examples
SCREAMING_SNAKE_CASE : List[Any] = 128
self.register_modules(
notes_encoder=snake_case , continuous_encoder=snake_case , decoder=snake_case , scheduler=snake_case , melgan=snake_case , )
def lowerCamelCase_ ( self : int , snake_case : int , snake_case : Union[str, Any]=(-1.0, 1.0) , snake_case : Optional[Any]=False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = output_range
if clip:
SCREAMING_SNAKE_CASE : List[Any] = torch.clip(snake_case , self.min_value , self.max_value )
# Scale to [0, 1].
SCREAMING_SNAKE_CASE : Optional[int] = (features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def lowerCamelCase_ ( self : int , snake_case : str , snake_case : Optional[Any]=(-1.0, 1.0) , snake_case : Tuple=False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = input_range
SCREAMING_SNAKE_CASE : Any = torch.clip(snake_case , snake_case , snake_case ) if clip else outputs
# Scale to [0, 1].
SCREAMING_SNAKE_CASE : Any = (outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def lowerCamelCase_ ( self : List[Any] , snake_case : Dict , snake_case : Optional[int] , snake_case : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = input_tokens > 0
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] = self.notes_encoder(
encoder_input_tokens=snake_case , encoder_inputs_mask=snake_case )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = self.continuous_encoder(
encoder_inputs=snake_case , encoder_inputs_mask=snake_case )
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def lowerCamelCase_ ( self : Dict , snake_case : Dict , snake_case : Tuple , snake_case : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = noise_time
if not torch.is_tensor(snake_case ):
SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device )
elif torch.is_tensor(snake_case ) and len(timesteps.shape ) == 0:
SCREAMING_SNAKE_CASE : Tuple = timesteps[None].to(input_tokens.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
SCREAMING_SNAKE_CASE : Any = timesteps * torch.ones(input_tokens.shape[0] , dtype=timesteps.dtype , device=timesteps.device )
SCREAMING_SNAKE_CASE : Optional[Any] = self.decoder(
encodings_and_masks=snake_case , decoder_input_tokens=snake_case , decoder_noise_time=snake_case )
return logits
@torch.no_grad()
def __call__( self : Dict , snake_case : List[List[int]] , snake_case : Optional[torch.Generator] = None , snake_case : int = 100 , snake_case : bool = True , snake_case : str = "numpy" , snake_case : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , snake_case : int = 1 , ):
'''simple docstring'''
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(snake_case , snake_case ) or callback_steps <= 0)
):
raise ValueError(
f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
f''' {type(snake_case )}.''' )
SCREAMING_SNAKE_CASE : Dict = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa )
SCREAMING_SNAKE_CASE : str = np.zeros([1, 0, self.n_dims] , np.floataa )
SCREAMING_SNAKE_CASE : Optional[int] = torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=snake_case , device=self.device )
for i, encoder_input_tokens in enumerate(snake_case ):
if i == 0:
SCREAMING_SNAKE_CASE : Tuple = torch.from_numpy(pred_mel[:1].copy() ).to(
device=self.device , dtype=self.decoder.dtype )
# The first chunk has no previous context.
SCREAMING_SNAKE_CASE : Tuple = torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=snake_case , device=self.device )
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
SCREAMING_SNAKE_CASE : Dict = ones
SCREAMING_SNAKE_CASE : Union[str, Any] = self.scale_features(
snake_case , output_range=[-1.0, 1.0] , clip=snake_case )
SCREAMING_SNAKE_CASE : Optional[int] = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device ) , continuous_inputs=snake_case , continuous_mask=snake_case , )
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
SCREAMING_SNAKE_CASE : Any = randn_tensor(
shape=encoder_continuous_inputs.shape , generator=snake_case , device=self.device , dtype=self.decoder.dtype , )
# set step values
self.scheduler.set_timesteps(snake_case )
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
SCREAMING_SNAKE_CASE : Optional[int] = self.decode(
encodings_and_masks=snake_case , input_tokens=snake_case , noise_time=t / self.scheduler.config.num_train_timesteps , )
# Compute previous output: x_t -> x_t-1
SCREAMING_SNAKE_CASE : Any = self.scheduler.step(snake_case , snake_case , snake_case , generator=snake_case ).prev_sample
SCREAMING_SNAKE_CASE : Optional[int] = self.scale_to_features(snake_case , input_range=[-1.0, 1.0] )
SCREAMING_SNAKE_CASE : Optional[int] = mel[:1]
SCREAMING_SNAKE_CASE : str = mel.cpu().float().numpy()
SCREAMING_SNAKE_CASE : Optional[int] = np.concatenate([full_pred_mel, pred_mel[:1]] , axis=1 )
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(snake_case , snake_case )
logger.info('Generated segment' , snake_case )
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
'Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.' )
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
'Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.' )
if output_type == "numpy":
SCREAMING_SNAKE_CASE : Union[str, Any] = self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
else:
SCREAMING_SNAKE_CASE : Optional[int] = full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=snake_case )
| 352
|
from __future__ import annotations
def __a ( __lowerCAmelCase , __lowerCAmelCase = None ) -> list[list[str]]:
SCREAMING_SNAKE_CASE : Dict = word_bank or []
# create a table
SCREAMING_SNAKE_CASE : int = len(__lowerCAmelCase ) + 1
SCREAMING_SNAKE_CASE : list[list[list[str]]] = []
for _ in range(__lowerCAmelCase ):
table.append([] )
# seed value
SCREAMING_SNAKE_CASE : Tuple = [[]] # because empty string has empty combination
# iterate through the indices
for i in range(__lowerCAmelCase ):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(__lowerCAmelCase )] == word:
SCREAMING_SNAKE_CASE : list[list[str]] = [
[word, *way] for way in table[i]
]
# adds the word to every combination the current position holds
# now,push that combination to the table[i+len(word)]
table[i + len(__lowerCAmelCase )] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(__lowerCAmelCase )]:
combination.reverse()
return table[len(__lowerCAmelCase )]
if __name__ == "__main__":
print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
print(
all_construct(
"""hexagonosaurus""",
["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
)
)
| 352
| 1
|
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def _UpperCamelCase ( UpperCamelCase_ : List[Any] ) -> Any:
"""simple docstring"""
lowerCAmelCase__ = model.config
lowerCAmelCase__ = DonutSwinConfig(
image_size=original_config.input_size , patch_size=4 , depths=original_config.encoder_layer , num_heads=[4, 8, 16, 32] , window_size=original_config.window_size , embed_dim=128 , )
lowerCAmelCase__ = MBartConfig(
is_decoder=UpperCamelCase_ , is_encoder_decoder=UpperCamelCase_ , add_cross_attention=UpperCamelCase_ , decoder_layers=original_config.decoder_layer , max_position_embeddings=original_config.max_position_embeddings , vocab_size=len(
model.decoder.tokenizer ) , scale_embedding=UpperCamelCase_ , add_final_layer_norm=UpperCamelCase_ , )
return encoder_config, decoder_config
def _UpperCamelCase ( UpperCamelCase_ : Any ) -> List[Any]:
"""simple docstring"""
if "encoder.model" in name:
lowerCAmelCase__ = name.replace('encoder.model' , 'encoder' )
if "decoder.model" in name:
lowerCAmelCase__ = name.replace('decoder.model' , 'decoder' )
if "patch_embed.proj" in name:
lowerCAmelCase__ = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
lowerCAmelCase__ = name.replace('patch_embed.norm' , 'embeddings.norm' )
if name.startswith('encoder' ):
if "layers" in name:
lowerCAmelCase__ = 'encoder.' + name
if "attn.proj" in name:
lowerCAmelCase__ = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name and "mask" not in name:
lowerCAmelCase__ = name.replace('attn' , 'attention.self' )
if "norm1" in name:
lowerCAmelCase__ = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
lowerCAmelCase__ = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
lowerCAmelCase__ = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
lowerCAmelCase__ = name.replace('mlp.fc2' , 'output.dense' )
if name == "encoder.norm.weight":
lowerCAmelCase__ = 'encoder.layernorm.weight'
if name == "encoder.norm.bias":
lowerCAmelCase__ = 'encoder.layernorm.bias'
return name
def _UpperCamelCase ( UpperCamelCase_ : Tuple , UpperCamelCase_ : Any ) -> int:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
lowerCAmelCase__ = orig_state_dict.pop(UpperCamelCase_ )
if "qkv" in key:
lowerCAmelCase__ = key.split('.' )
lowerCAmelCase__ = int(key_split[3] )
lowerCAmelCase__ = int(key_split[5] )
lowerCAmelCase__ = model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
lowerCAmelCase__ = val[:dim, :]
lowerCAmelCase__ = val[dim : dim * 2, :]
lowerCAmelCase__ = val[-dim:, :]
else:
lowerCAmelCase__ = val[:dim]
lowerCAmelCase__ = val[dim : dim * 2]
lowerCAmelCase__ = val[-dim:]
elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
# HuggingFace implementation doesn't use attn_mask buffer
# and model doesn't use final LayerNorms for the encoder
pass
else:
lowerCAmelCase__ = val
return orig_state_dict
def _UpperCamelCase ( UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Tuple=None , UpperCamelCase_ : Tuple=False ) -> int:
"""simple docstring"""
lowerCAmelCase__ = DonutModel.from_pretrained(UpperCamelCase_ ).eval()
# load HuggingFace model
lowerCAmelCase__ , lowerCAmelCase__ = get_configs(UpperCamelCase_ )
lowerCAmelCase__ = DonutSwinModel(UpperCamelCase_ )
lowerCAmelCase__ = MBartForCausalLM(UpperCamelCase_ )
lowerCAmelCase__ = VisionEncoderDecoderModel(encoder=UpperCamelCase_ , decoder=UpperCamelCase_ )
model.eval()
lowerCAmelCase__ = original_model.state_dict()
lowerCAmelCase__ = convert_state_dict(UpperCamelCase_ , UpperCamelCase_ )
model.load_state_dict(UpperCamelCase_ )
# verify results on scanned document
lowerCAmelCase__ = load_dataset('hf-internal-testing/example-documents' )
lowerCAmelCase__ = dataset['test'][0]['image'].convert('RGB' )
lowerCAmelCase__ = XLMRobertaTokenizerFast.from_pretrained(UpperCamelCase_ , from_slow=UpperCamelCase_ )
lowerCAmelCase__ = DonutImageProcessor(
do_align_long_axis=original_model.config.align_long_axis , size=original_model.config.input_size[::-1] )
lowerCAmelCase__ = DonutProcessor(UpperCamelCase_ , UpperCamelCase_ )
lowerCAmelCase__ = processor(UpperCamelCase_ , return_tensors='pt' ).pixel_values
if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
lowerCAmelCase__ = '<s_docvqa><s_question>{user_input}</s_question><s_answer>'
lowerCAmelCase__ = 'When is the coffee break?'
lowerCAmelCase__ = task_prompt.replace('{user_input}' , UpperCamelCase_ )
elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
lowerCAmelCase__ = '<s_rvlcdip>'
elif model_name in [
"naver-clova-ix/donut-base-finetuned-cord-v1",
"naver-clova-ix/donut-base-finetuned-cord-v1-2560",
]:
lowerCAmelCase__ = '<s_cord>'
elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
lowerCAmelCase__ = 's_cord-v2>'
elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
lowerCAmelCase__ = '<s_zhtrainticket>'
elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
# use a random prompt
lowerCAmelCase__ = 'hello world'
else:
raise ValueError('Model name not supported' )
lowerCAmelCase__ = original_model.decoder.tokenizer(UpperCamelCase_ , add_special_tokens=UpperCamelCase_ , return_tensors='pt' )[
'input_ids'
]
lowerCAmelCase__ = original_model.encoder.model.patch_embed(UpperCamelCase_ )
lowerCAmelCase__ , lowerCAmelCase__ = model.encoder.embeddings(UpperCamelCase_ )
assert torch.allclose(UpperCamelCase_ , UpperCamelCase_ , atol=1e-3 )
# verify encoder hidden states
lowerCAmelCase__ = original_model.encoder(UpperCamelCase_ )
lowerCAmelCase__ = model.encoder(UpperCamelCase_ ).last_hidden_state
assert torch.allclose(UpperCamelCase_ , UpperCamelCase_ , atol=1e-2 )
# verify decoder hidden states
lowerCAmelCase__ = original_model(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ).logits
lowerCAmelCase__ = model(UpperCamelCase_ , decoder_input_ids=UpperCamelCase_ ).logits
assert torch.allclose(UpperCamelCase_ , UpperCamelCase_ , atol=1e-3 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(F"Saving model and processor to {pytorch_dump_folder_path}" )
model.save_pretrained(UpperCamelCase_ )
processor.save_pretrained(UpperCamelCase_ )
if push_to_hub:
model.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
processor.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
if __name__ == "__main__":
__snake_case : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""naver-clova-ix/donut-base-finetuned-docvqa""",
required=False,
type=str,
help="""Name of the original model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
required=False,
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 and processor to the 🤗 hub.""",
)
__snake_case : str = parser.parse_args()
convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 365
|
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 __SCREAMING_SNAKE_CASE :
@staticmethod
def UpperCamelCase__ ( *_UpperCamelCase , **_UpperCamelCase ):
"""simple docstring"""
pass
def _UpperCamelCase ( UpperCamelCase_ : Tuple ) -> Any:
"""simple docstring"""
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.
__snake_case : List[str] = (
"""https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"""
)
@is_pipeline_test
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
_SCREAMING_SNAKE_CASE : Dict = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def UpperCamelCase__ ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ = pipeline(
'document-question-answering' , model=_UpperCamelCase , tokenizer=_UpperCamelCase , image_processor=_UpperCamelCase )
lowerCAmelCase__ = INVOICE_URL
lowerCAmelCase__ = list(zip(*apply_tesseract(load_image(_UpperCamelCase ) , _UpperCamelCase , '' ) ) )
lowerCAmelCase__ = 'What is the placebo?'
lowerCAmelCase__ = [
{
'image': load_image(_UpperCamelCase ),
'question': question,
},
{
'image': image,
'question': question,
},
{
'image': image,
'question': question,
'word_boxes': word_boxes,
},
]
return dqa_pipeline, examples
def UpperCamelCase__ ( self , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ = dqa_pipeline(_UpperCamelCase , top_k=2 )
self.assertEqual(
_UpperCamelCase , [
[
{'score': ANY(_UpperCamelCase ), 'answer': ANY(_UpperCamelCase ), 'start': ANY(_UpperCamelCase ), 'end': ANY(_UpperCamelCase )},
{'score': ANY(_UpperCamelCase ), 'answer': ANY(_UpperCamelCase ), 'start': ANY(_UpperCamelCase ), 'end': ANY(_UpperCamelCase )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = pipeline('document-question-answering' , model='hf-internal-testing/tiny-random-layoutlmv2' )
lowerCAmelCase__ = INVOICE_URL
lowerCAmelCase__ = 'How many cats are there?'
lowerCAmelCase__ = [
{'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},
]
lowerCAmelCase__ = dqa_pipeline(image=_UpperCamelCase , question=_UpperCamelCase , top_k=2 )
self.assertEqual(nested_simplify(_UpperCamelCase , decimals=4 ) , _UpperCamelCase )
lowerCAmelCase__ = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(nested_simplify(_UpperCamelCase , decimals=4 ) , _UpperCamelCase )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
lowerCAmelCase__ = './tests/fixtures/tests_samples/COCO/000000039769.png'
lowerCAmelCase__ = dqa_pipeline(image=_UpperCamelCase , question=_UpperCamelCase , top_k=2 )
self.assertEqual(_UpperCamelCase , [] )
# We can optionnally pass directly the words and bounding boxes
lowerCAmelCase__ = './tests/fixtures/tests_samples/COCO/000000039769.png'
lowerCAmelCase__ = []
lowerCAmelCase__ = []
lowerCAmelCase__ = dqa_pipeline(image=_UpperCamelCase , question=_UpperCamelCase , words=_UpperCamelCase , boxes=_UpperCamelCase , top_k=2 )
self.assertEqual(_UpperCamelCase , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = pipeline(
'document-question-answering' , model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' , revision='9977165' , )
lowerCAmelCase__ = INVOICE_URL
lowerCAmelCase__ = 'What is the invoice number?'
lowerCAmelCase__ = dqa_pipeline(image=_UpperCamelCase , question=_UpperCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(_UpperCamelCase , decimals=4 ) , [
{'score': 0.99_44, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.00_09, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
lowerCAmelCase__ = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(_UpperCamelCase , decimals=4 ) , [
{'score': 0.99_44, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.00_09, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
lowerCAmelCase__ = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(_UpperCamelCase , 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 UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = pipeline(
'document-question-answering' , model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' , revision='9977165' , max_seq_len=50 , )
lowerCAmelCase__ = INVOICE_URL
lowerCAmelCase__ = 'What is the invoice number?'
lowerCAmelCase__ = dqa_pipeline(image=_UpperCamelCase , question=_UpperCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(_UpperCamelCase , decimals=4 ) , [
{'score': 0.99_74, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.99_48, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
lowerCAmelCase__ = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(_UpperCamelCase , decimals=4 ) , [
{'score': 0.99_74, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.99_48, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
lowerCAmelCase__ = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(_UpperCamelCase , 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 UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = AutoTokenizer.from_pretrained(
'impira/layoutlm-document-qa' , revision='3dc6de3' , add_prefix_space=_UpperCamelCase )
lowerCAmelCase__ = pipeline(
'document-question-answering' , model='impira/layoutlm-document-qa' , tokenizer=_UpperCamelCase , revision='3dc6de3' , )
lowerCAmelCase__ = INVOICE_URL
lowerCAmelCase__ = 'What is the invoice number?'
lowerCAmelCase__ = dqa_pipeline(image=_UpperCamelCase , question=_UpperCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(_UpperCamelCase , decimals=4 ) , [
{'score': 0.42_51, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.08_19, 'answer': '1110212019', 'start': 23, 'end': 23},
] , )
lowerCAmelCase__ = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(_UpperCamelCase , decimals=4 ) , [
{'score': 0.42_51, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.08_19, 'answer': '1110212019', 'start': 23, 'end': 23},
] , )
lowerCAmelCase__ = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(_UpperCamelCase , decimals=4 ) , [
[
{'score': 0.42_51, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.08_19, 'answer': '1110212019', 'start': 23, 'end': 23},
]
]
* 2 , )
lowerCAmelCase__ = list(zip(*apply_tesseract(load_image(_UpperCamelCase ) , _UpperCamelCase , '' ) ) )
# This model should also work if `image` is set to None
lowerCAmelCase__ = dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(_UpperCamelCase , 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 UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = AutoTokenizer.from_pretrained(
'impira/layoutlm-document-qa' , revision='3dc6de3' , add_prefix_space=_UpperCamelCase )
lowerCAmelCase__ = pipeline(
'document-question-answering' , model='impira/layoutlm-document-qa' , tokenizer=_UpperCamelCase , revision='3dc6de3' , max_seq_len=50 , )
lowerCAmelCase__ = INVOICE_URL
lowerCAmelCase__ = 'What is the invoice number?'
lowerCAmelCase__ = dqa_pipeline(image=_UpperCamelCase , question=_UpperCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(_UpperCamelCase , decimals=4 ) , [
{'score': 0.99_99, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.99_98, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
lowerCAmelCase__ = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(_UpperCamelCase , 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 , )
lowerCAmelCase__ = list(zip(*apply_tesseract(load_image(_UpperCamelCase ) , _UpperCamelCase , '' ) ) )
# This model should also work if `image` is set to None
lowerCAmelCase__ = dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(_UpperCamelCase , 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 UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = 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' , )
lowerCAmelCase__ = INVOICE_URL
lowerCAmelCase__ = 'What is the invoice number?'
lowerCAmelCase__ = dqa_pipeline(image=_UpperCamelCase , question=_UpperCamelCase , top_k=2 )
self.assertEqual(nested_simplify(_UpperCamelCase , decimals=4 ) , [{'answer': 'us-001'}] )
@require_tf
@unittest.skip('Document question answering not implemented in TF' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
pass
| 365
| 1
|
__lowerCamelCase : Any = {str(digit): digit**5 for digit in range(10)}
def SCREAMING_SNAKE_CASE ( snake_case_ : int ):
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(snake_case_ ) )
def SCREAMING_SNAKE_CASE ( ):
return sum(
number
for number in range(1000 , 1000000 )
if number == digits_fifth_powers_sum(snake_case_ ) )
if __name__ == "__main__":
print(solution())
| 297
|
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ , unittest.TestCase ):
"""simple docstring"""
a_ = KandinskyInpaintPipeline
a_ = ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
a_ = [
"prompt",
"negative_prompt",
"image_embeds",
"negative_image_embeds",
"image",
"mask_image",
]
a_ = [
"generator",
"height",
"width",
"latents",
"guidance_scale",
"negative_prompt",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
a_ = False
@property
def _lowercase ( self : int ):
return 3_2
@property
def _lowercase ( self : Any ):
return 3_2
@property
def _lowercase ( self : Dict ):
return self.time_input_dim
@property
def _lowercase ( self : Union[str, Any] ):
return self.time_input_dim * 4
@property
def _lowercase ( self : List[str] ):
return 1_0_0
@property
def _lowercase ( self : List[str] ):
snake_case__ : Tuple = XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base" )
return tokenizer
@property
def _lowercase ( self : Union[str, Any] ):
torch.manual_seed(0 )
snake_case__ : int = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=3_7 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1_0_0_5 , )
snake_case__ : List[str] = MultilingualCLIP(__A )
snake_case__ : List[str] = text_encoder.eval()
return text_encoder
@property
def _lowercase ( self : str ):
torch.manual_seed(0 )
snake_case__ : Optional[int] = {
"in_channels": 9,
# Out channels is double in channels because predicts mean and variance
"out_channels": 8,
"addition_embed_type": "text_image",
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"layers_per_block": 1,
"encoder_hid_dim": self.text_embedder_hidden_size,
"encoder_hid_dim_type": "text_image_proj",
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": None,
}
snake_case__ : List[Any] = UNetaDConditionModel(**__A )
return model
@property
def _lowercase ( self : Dict ):
return {
"block_out_channels": [3_2, 6_4],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 1_2,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def _lowercase ( self : Union[str, Any] ):
torch.manual_seed(0 )
snake_case__ : Any = VQModel(**self.dummy_movq_kwargs )
return model
def _lowercase ( self : Optional[int] ):
snake_case__ : List[Any] = self.dummy_text_encoder
snake_case__ : List[Any] = self.dummy_tokenizer
snake_case__ : Any = self.dummy_unet
snake_case__ : List[Any] = self.dummy_movq
snake_case__ : Optional[int] = DDIMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule="linear" , beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , clip_sample=__A , set_alpha_to_one=__A , steps_offset=1 , prediction_type="epsilon" , thresholding=__A , )
snake_case__ : Union[str, Any] = {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"movq": movq,
}
return components
def _lowercase ( self : Any , __A : Union[str, Any] , __A : int=0 ):
snake_case__ : Dict = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(__A ) ).to(__A )
snake_case__ : Union[str, Any] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(__A )
# create init_image
snake_case__ : List[Any] = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(__A ) ).to(__A )
snake_case__ : int = image.cpu().permute(0 , 2 , 3 , 1 )[0]
snake_case__ : Optional[int] = Image.fromarray(np.uinta(__A ) ).convert("RGB" ).resize((2_5_6, 2_5_6) )
# create mask
snake_case__ : str = np.ones((6_4, 6_4) , dtype=np.floataa )
snake_case__ : str = 0
if str(__A ).startswith("mps" ):
snake_case__ : Optional[Any] = torch.manual_seed(__A )
else:
snake_case__ : List[Any] = torch.Generator(device=__A ).manual_seed(__A )
snake_case__ : Optional[int] = {
"prompt": "horse",
"image": init_image,
"mask_image": mask,
"image_embeds": image_embeds,
"negative_image_embeds": negative_image_embeds,
"generator": generator,
"height": 6_4,
"width": 6_4,
"num_inference_steps": 2,
"guidance_scale": 4.0,
"output_type": "np",
}
return inputs
def _lowercase ( self : Optional[int] ):
snake_case__ : Optional[int] = "cpu"
snake_case__ : Union[str, Any] = self.get_dummy_components()
snake_case__ : Tuple = self.pipeline_class(**__A )
snake_case__ : Any = pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
snake_case__ : str = pipe(**self.get_dummy_inputs(__A ) )
snake_case__ : List[Any] = output.images
snake_case__ : List[str] = pipe(
**self.get_dummy_inputs(__A ) , return_dict=__A , )[0]
snake_case__ : Optional[Any] = image[0, -3:, -3:, -1]
snake_case__ : Tuple = image_from_tuple[0, -3:, -3:, -1]
print(f'''image.shape {image.shape}''' )
assert image.shape == (1, 6_4, 6_4, 3)
snake_case__ : int = np.array(
[0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
def _lowercase ( self : str ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : Any ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self : Union[str, Any] ):
snake_case__ : Optional[int] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy" )
snake_case__ : Optional[int] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
snake_case__ : Optional[Any] = np.ones((7_6_8, 7_6_8) , dtype=np.floataa )
snake_case__ : List[Any] = 0
snake_case__ : str = "a hat"
snake_case__ : Tuple = KandinskyPriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-1-prior" , torch_dtype=torch.floataa )
pipe_prior.to(__A )
snake_case__ : Dict = KandinskyInpaintPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-1-inpaint" , torch_dtype=torch.floataa )
snake_case__ : Union[str, Any] = pipeline.to(__A )
pipeline.set_progress_bar_config(disable=__A )
snake_case__ : List[Any] = torch.Generator(device="cpu" ).manual_seed(0 )
snake_case__, snake_case__ : str = pipe_prior(
__A , generator=__A , num_inference_steps=5 , negative_prompt="" , ).to_tuple()
snake_case__ : int = pipeline(
__A , image=__A , mask_image=__A , image_embeds=__A , negative_image_embeds=__A , generator=__A , num_inference_steps=1_0_0 , height=7_6_8 , width=7_6_8 , output_type="np" , )
snake_case__ : Optional[int] = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(__A , __A )
| 297
| 1
|
'''simple docstring'''
def _lowerCAmelCase (_lowercase , _lowercase ):
"""simple docstring"""
return abs(_lowercase ) if a == 0 else greatest_common_divisor(b % a , _lowercase )
def _lowerCAmelCase (_lowercase , _lowercase ):
"""simple docstring"""
while y: # --> when y=0 then loop will terminate and return x as final GCD.
a__ , a__ = y, x % y
return abs(_lowercase )
def _lowerCAmelCase ():
"""simple docstring"""
try:
a__ = input("Enter two integers separated by comma (,): " ).split("," )
a__ = int(nums[0] )
a__ = int(nums[1] )
print(
F'greatest_common_divisor({num_a}, {num_a}) = '
F'{greatest_common_divisor(_lowercase , _lowercase )}' )
print(F'By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(_lowercase , _lowercase )}' )
except (IndexError, UnboundLocalError, ValueError):
print("Wrong input" )
if __name__ == "__main__":
main()
| 709
|
'''simple docstring'''
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
UpperCamelCase_ : str = """scheduler_config.json"""
class lowerCamelCase__ ( __lowerCamelCase ):
"""simple docstring"""
UpperCamelCase__ = 1
UpperCamelCase__ = 2
UpperCamelCase__ = 3
UpperCamelCase__ = 4
UpperCamelCase__ = 5
@dataclass
class lowerCamelCase__ ( __lowerCamelCase ):
"""simple docstring"""
UpperCamelCase__ = 42
class lowerCamelCase__ :
"""simple docstring"""
UpperCamelCase__ = SCHEDULER_CONFIG_NAME
UpperCamelCase__ = ['''dtype''']
UpperCamelCase__ = []
UpperCamelCase__ = True
@classmethod
def lowerCAmelCase_ ( cls : Optional[Any] ,a__ : Dict[str, Any] = None ,a__ : Optional[str] = None ,a__ : Union[str, Any]=False ,**a__ : Tuple ,):
a__ , a__ = cls.load_config(
pretrained_model_name_or_path=a__ ,subfolder=a__ ,return_unused_kwargs=a__ ,**a__ ,)
a__ , a__ = cls.from_config(a__ ,return_unused_kwargs=a__ ,**a__ )
if hasattr(a__ ,"create_state" ) and getattr(a__ ,"has_state" ,a__ ):
a__ = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state
def lowerCAmelCase_ ( self : Any ,a__ : Union[str, os.PathLike] ,a__ : bool = False ,**a__ : Optional[int] ):
self.save_config(save_directory=a__ ,push_to_hub=a__ ,**a__ )
@property
def lowerCAmelCase_ ( self : List[str] ):
return self._get_compatibles()
@classmethod
def lowerCAmelCase_ ( cls : str ):
a__ = list(set([cls.__name__] + cls._compatibles ) )
a__ = importlib.import_module(__name__.split("." )[0] )
a__ = [
getattr(a__ ,a__ ) for c in compatible_classes_str if hasattr(a__ ,a__ )
]
return compatible_classes
def _lowerCAmelCase (_lowercase , _lowercase ):
"""simple docstring"""
assert len(_lowercase ) >= x.ndim
return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_lowercase ) - x.ndim) ) , _lowercase )
def _lowerCAmelCase (_lowercase , _lowercase=0.999 , _lowercase=jnp.floataa ):
"""simple docstring"""
def alpha_bar(_lowercase ):
return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2
a__ = []
for i in range(_lowercase ):
a__ = i / num_diffusion_timesteps
a__ = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(_lowercase ) / alpha_bar(_lowercase ) , _lowercase ) )
return jnp.array(_lowercase , dtype=_lowercase )
@flax.struct.dataclass
class lowerCamelCase__ :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
@classmethod
def lowerCAmelCase_ ( cls : Tuple ,a__ : List[Any] ):
a__ = scheduler.config
if config.trained_betas is not None:
a__ = jnp.asarray(config.trained_betas ,dtype=scheduler.dtype )
elif config.beta_schedule == "linear":
a__ = jnp.linspace(config.beta_start ,config.beta_end ,config.num_train_timesteps ,dtype=scheduler.dtype )
elif config.beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
a__ = (
jnp.linspace(
config.beta_start**0.5 ,config.beta_end**0.5 ,config.num_train_timesteps ,dtype=scheduler.dtype )
** 2
)
elif config.beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
a__ = betas_for_alpha_bar(config.num_train_timesteps ,dtype=scheduler.dtype )
else:
raise NotImplementedError(
f'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}' )
a__ = 1.0 - betas
a__ = jnp.cumprod(a__ ,axis=0 )
return cls(
alphas=a__ ,betas=a__ ,alphas_cumprod=a__ ,)
def _lowerCAmelCase (_lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
a__ = state.alphas_cumprod
a__ = alphas_cumprod[timesteps] ** 0.5
a__ = sqrt_alpha_prod.flatten()
a__ = broadcast_to_shape_from_left(_lowercase , original_samples.shape )
a__ = (1 - alphas_cumprod[timesteps]) ** 0.5
a__ = sqrt_one_minus_alpha_prod.flatten()
a__ = broadcast_to_shape_from_left(_lowercase , original_samples.shape )
return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def _lowerCAmelCase (_lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
a__ , a__ = get_sqrt_alpha_prod(_lowercase , _lowercase , _lowercase , _lowercase )
a__ = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def _lowerCAmelCase (_lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
a__ , a__ = get_sqrt_alpha_prod(_lowercase , _lowercase , _lowercase , _lowercase )
a__ = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
| 394
| 0
|
'''simple docstring'''
import random
from .binary_exp_mod import bin_exp_mod
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[Any]=1000 ):
'''simple docstring'''
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
UpperCAmelCase__ = n - 1
UpperCAmelCase__ = 0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(2**exp)
UpperCAmelCase__ = 0
while count < prec:
UpperCAmelCase__ = random.randint(2 , n - 1 )
UpperCAmelCase__ = bin_exp_mod(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if b != 1:
UpperCAmelCase__ = True
for _ in range(SCREAMING_SNAKE_CASE__ ):
if b == n - 1:
UpperCAmelCase__ = False
break
UpperCAmelCase__ = b * b
b %= n
if flag:
return False
count += 1
return True
if __name__ == "__main__":
UpperCAmelCase_ = abs(int(input('Enter bound : ').strip()))
print('Here\'s the list of primes:')
print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))
| 603
|
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Any=None , SCREAMING_SNAKE_CASE__ : Any=None ):
'''simple docstring'''
if attention_mask is None:
UpperCAmelCase__ = tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE__ , config.pad_token_id ) , tf.inta )
return {"input_ids": input_ids, "attention_mask": attention_mask}
@require_tf
class lowerCAmelCase_ :
'''simple docstring'''
lowerCAmelCase_ : int = OPTConfig
lowerCAmelCase_ : Optional[Any] = {}
lowerCAmelCase_ : List[Any] = """gelu"""
def __init__( self : int , _UpperCAmelCase : Dict , _UpperCAmelCase : Tuple=13 , _UpperCAmelCase : List[str]=7 , _UpperCAmelCase : int=True , _UpperCAmelCase : Any=False , _UpperCAmelCase : Union[str, Any]=99 , _UpperCAmelCase : int=16 , _UpperCAmelCase : Any=2 , _UpperCAmelCase : Union[str, Any]=4 , _UpperCAmelCase : Any=4 , _UpperCAmelCase : Any="gelu" , _UpperCAmelCase : Any=0.1 , _UpperCAmelCase : Dict=0.1 , _UpperCAmelCase : Optional[int]=20 , _UpperCAmelCase : str=2 , _UpperCAmelCase : Optional[Any]=1 , _UpperCAmelCase : List[str]=0 , _UpperCAmelCase : List[Any]=16 , _UpperCAmelCase : Optional[int]=16 , ):
"""simple docstring"""
UpperCAmelCase__ = parent
UpperCAmelCase__ = batch_size
UpperCAmelCase__ = seq_length
UpperCAmelCase__ = is_training
UpperCAmelCase__ = use_labels
UpperCAmelCase__ = vocab_size
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__ = max_position_embeddings
UpperCAmelCase__ = eos_token_id
UpperCAmelCase__ = pad_token_id
UpperCAmelCase__ = bos_token_id
UpperCAmelCase__ = embed_dim
UpperCAmelCase__ = word_embed_proj_dim
UpperCAmelCase__ = False
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
UpperCAmelCase__ = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
UpperCAmelCase__ = tf.concat([input_ids, eos_tensor] , axis=1 )
UpperCAmelCase__ = self.config_cls(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , 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 , embed_dim=self.embed_dim , word_embed_proj_dim=self.word_embed_proj_dim , is_encoder_decoder=_UpperCAmelCase , **self.config_updates , )
UpperCAmelCase__ = prepare_opt_inputs_dict(_UpperCAmelCase , _UpperCAmelCase )
return config, inputs_dict
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : str ):
"""simple docstring"""
UpperCAmelCase__ = TFOPTModel(config=_UpperCAmelCase )
UpperCAmelCase__ = inputs_dict["""input_ids"""]
UpperCAmelCase__ = input_ids[:1, :]
UpperCAmelCase__ = inputs_dict["""attention_mask"""][:1, :]
UpperCAmelCase__ = 1
# first forward pass
UpperCAmelCase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , use_cache=_UpperCAmelCase )
UpperCAmelCase__ , UpperCAmelCase__ = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
UpperCAmelCase__ = ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCAmelCase__ = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
UpperCAmelCase__ = tf.concat([input_ids, next_tokens] , axis=-1 )
UpperCAmelCase__ = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
UpperCAmelCase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase )[0]
UpperCAmelCase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , past_key_values=_UpperCAmelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
UpperCAmelCase__ = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
UpperCAmelCase__ = output_from_no_past[:, -3:, random_slice_idx]
UpperCAmelCase__ = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(_UpperCAmelCase , _UpperCAmelCase , rtol=1E-3 )
@require_tf
class lowerCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ : str = (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
lowerCAmelCase_ : Dict = (TFOPTForCausalLM,) if is_tf_available() else ()
lowerCAmelCase_ : Dict = (
{"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
)
lowerCAmelCase_ : Tuple = False
lowerCAmelCase_ : Optional[int] = False
lowerCAmelCase_ : str = False
lowerCAmelCase_ : List[Any] = 10
def SCREAMING_SNAKE_CASE__ ( self : int ):
"""simple docstring"""
UpperCAmelCase__ = TFOPTModelTester(self )
UpperCAmelCase__ = ConfigTester(self , config_class=_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : int ):
"""simple docstring"""
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
def _get_word_embedding_weight(_UpperCAmelCase : Optional[int] , _UpperCAmelCase : str ):
if hasattr(_UpperCAmelCase , """weight""" ):
return embedding_layer.weight
else:
# Here we build the word embeddings weights if not exists.
# And then we retry to get the attribute once built.
model.build()
if hasattr(_UpperCAmelCase , """weight""" ):
return embedding_layer.weight
else:
return None
for model_class in self.all_model_classes:
for size in [config.vocab_size - 10, config.vocab_size + 10]:
# build the embeddings
UpperCAmelCase__ = model_class(config=_UpperCAmelCase )
UpperCAmelCase__ = _get_word_embedding_weight(_UpperCAmelCase , model.get_input_embeddings() )
UpperCAmelCase__ = _get_word_embedding_weight(_UpperCAmelCase , model.get_output_embeddings() )
# reshape the embeddings
model.resize_token_embeddings(_UpperCAmelCase )
UpperCAmelCase__ = _get_word_embedding_weight(_UpperCAmelCase , model.get_input_embeddings() )
UpperCAmelCase__ = _get_word_embedding_weight(_UpperCAmelCase , model.get_output_embeddings() )
# check that the resized embeddings size matches the desired size.
UpperCAmelCase__ = size if size is not None else config.vocab_size
self.assertEqual(new_input_embeddings.shape[0] , _UpperCAmelCase )
# check that weights remain the same after resizing
UpperCAmelCase__ = True
for pa, pa in zip(old_input_embeddings.value() , new_input_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
UpperCAmelCase__ = False
self.assertTrue(_UpperCAmelCase )
if old_output_embeddings is not None and new_output_embeddings is not None:
self.assertEqual(new_output_embeddings.shape[0] , _UpperCAmelCase )
UpperCAmelCase__ = True
for pa, pa in zip(old_output_embeddings.value() , new_output_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
UpperCAmelCase__ = False
self.assertTrue(_UpperCAmelCase )
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[int] ):
'''simple docstring'''
return tf.constant(SCREAMING_SNAKE_CASE__ , dtype=tf.intaa )
@require_tf
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ : Any = 99
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
UpperCAmelCase__ = tf.ones((4, 1) , dtype=tf.intaa ) * 2
UpperCAmelCase__ = tf.concat([ids_tensor((4, 6) , self.vocab_size - 3 ) + 3, eos_column_vector] , axis=1 )
UpperCAmelCase__ = input_ids.shape[0]
UpperCAmelCase__ = OPTConfig(
vocab_size=self.vocab_size , hidden_size=24 , num_hidden_layers=2 , num_attention_heads=2 , 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
@require_sentencepiece
@require_tf
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
"""simple docstring"""
UpperCAmelCase__ = TFOPTModel.from_pretrained("""facebook/opt-350m""" )
UpperCAmelCase__ = _long_tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] )
UpperCAmelCase__ = tf.not_equal(_UpperCAmelCase , model.config.pad_token_id )
with tf.GradientTape():
UpperCAmelCase__ = model(input_ids=_UpperCAmelCase , attention_mask=_UpperCAmelCase ).last_hidden_state
UpperCAmelCase__ = (1, 11, 5_12)
self.assertEqual(output.shape , _UpperCAmelCase )
UpperCAmelCase__ = tf.constant(
[[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]] )
self.assertTrue(np.allclose(output[:, :3, :3] , _UpperCAmelCase , atol=4E-3 ) )
UpperCAmelCase__ = tf.function(_UpperCAmelCase , jit_compile=_UpperCAmelCase )
UpperCAmelCase__ = xla_generate(_UpperCAmelCase , _UpperCAmelCase )[0]
self.assertTrue(np.allclose(output[:, :3, :3] , _UpperCAmelCase , atol=4E-2 ) )
@require_tf
@slow
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
super().setUp()
UpperCAmelCase__ = """facebook/opt-350m"""
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = TFOPTForCausalLM.from_pretrained(self.path_model )
UpperCAmelCase__ = GPTaTokenizer.from_pretrained(self.path_model )
UpperCAmelCase__ = [
"""Today is a beautiful day and I want to""",
"""In the city of""",
"""Paris is the capital of France and""",
"""Computers and mobile phones have taken""",
]
# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
UpperCAmelCase__ = tokenizer(_UpperCAmelCase , return_tensors="""tf""" , padding=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
UpperCAmelCase__ = tf.math.reduce_mean(model(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
UpperCAmelCase__ = tf.constant(
[
[1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
[-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
[0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
[6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
] )
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-4 ) )
UpperCAmelCase__ = tf.function(_UpperCAmelCase , jit_compile=_UpperCAmelCase )
UpperCAmelCase__ = tf.math.reduce_mean(xla_generate(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-4 ) )
@require_tf
@slow
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
return [
"Today is a beautiful day and I want",
"In the city of",
"Paris is the capital of France and",
"Computers and mobile phones have taken",
]
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
"""simple docstring"""
UpperCAmelCase__ = """facebook/opt-125m"""
UpperCAmelCase__ = [
"""Today is a beautiful day and I want to""",
"""In the city of New York, the city""",
"""Paris is the capital of France and the capital""",
"""Computers and mobile phones have taken over the""",
]
UpperCAmelCase__ = []
UpperCAmelCase__ = GPTaTokenizer.from_pretrained(_UpperCAmelCase )
UpperCAmelCase__ = TFOPTForCausalLM.from_pretrained(_UpperCAmelCase )
for prompt in self.prompts:
UpperCAmelCase__ = tokenizer(_UpperCAmelCase , return_tensors="""tf""" ).input_ids
UpperCAmelCase__ = model.generate(_UpperCAmelCase , max_length=10 )
UpperCAmelCase__ = tokenizer.batch_decode(_UpperCAmelCase , skip_special_tokens=_UpperCAmelCase )
predicted_outputs += generated_string
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
UpperCAmelCase__ = """facebook/opt-350m"""
UpperCAmelCase__ = GPTaTokenizer.from_pretrained(_UpperCAmelCase )
UpperCAmelCase__ = TFOPTForCausalLM.from_pretrained(_UpperCAmelCase )
UpperCAmelCase__ = """left"""
# use different length sentences to test batching
UpperCAmelCase__ = [
"""Hello, my dog is a little""",
"""Today, I""",
]
UpperCAmelCase__ = tokenizer(_UpperCAmelCase , return_tensors="""tf""" , padding=_UpperCAmelCase )
UpperCAmelCase__ = inputs["""input_ids"""]
UpperCAmelCase__ = model.generate(input_ids=_UpperCAmelCase , attention_mask=inputs["""attention_mask"""] )
UpperCAmelCase__ = tokenizer(sentences[0] , return_tensors="""tf""" ).input_ids
UpperCAmelCase__ = model.generate(input_ids=_UpperCAmelCase )
UpperCAmelCase__ = inputs_non_padded.shape[-1] - tf.math.reduce_sum(
tf.cast(inputs["""attention_mask"""][-1] , tf.intaa ) )
UpperCAmelCase__ = tokenizer(sentences[1] , return_tensors="""tf""" ).input_ids
UpperCAmelCase__ = model.generate(input_ids=_UpperCAmelCase , max_length=model.config.max_length - num_paddings )
UpperCAmelCase__ = tokenizer.batch_decode(_UpperCAmelCase , skip_special_tokens=_UpperCAmelCase )
UpperCAmelCase__ = tokenizer.decode(output_non_padded[0] , skip_special_tokens=_UpperCAmelCase )
UpperCAmelCase__ = tokenizer.decode(output_padded[0] , skip_special_tokens=_UpperCAmelCase )
UpperCAmelCase__ = [
"""Hello, my dog is a little bit of a dork.\nI'm a little bit""",
"""Today, I was in the middle of a conversation with a friend about the""",
]
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , [non_padded_sentence, padded_sentence] )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
UpperCAmelCase__ = """facebook/opt-350m"""
UpperCAmelCase__ = [
"""Today is a beautiful day and I want to""",
"""In the city of San Francisco, the city""",
"""Paris is the capital of France and the capital""",
"""Computers and mobile phones have taken over the""",
]
UpperCAmelCase__ = []
UpperCAmelCase__ = GPTaTokenizer.from_pretrained(_UpperCAmelCase )
UpperCAmelCase__ = TFOPTForCausalLM.from_pretrained(_UpperCAmelCase )
for prompt in self.prompts:
UpperCAmelCase__ = tokenizer(_UpperCAmelCase , return_tensors="""tf""" ).input_ids
UpperCAmelCase__ = model.generate(_UpperCAmelCase , max_length=10 )
UpperCAmelCase__ = tokenizer.batch_decode(_UpperCAmelCase , skip_special_tokens=_UpperCAmelCase )
predicted_outputs += generated_string
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
| 603
| 1
|
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
__a = get_tests_dir('fixtures/test_sentencepiece.model')
__a = get_tests_dir('fixtures/test_sentencepiece_bpe.model')
__a = 'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :Any = CamembertTokenizer
a :Optional[Any] = CamembertTokenizerFast
a :Union[str, Any] = True
a :str = True
def _lowercase ( self : Tuple ) -> Tuple:
super().setUp()
# We have a SentencePiece fixture for testing
lowercase_ = CamembertTokenizer(SCREAMING_SNAKE_CASE_ )
tokenizer.save_pretrained(self.tmpdirname )
def _lowercase ( self : Tuple ) -> Optional[Any]:
lowercase_ = '''<pad>'''
lowercase_ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Union[str, Any] ) -> List[Any]:
lowercase_ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>NOTUSED''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , 1_0_0_4 )
def _lowercase ( self : List[str] ) -> Any:
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_5 )
def _lowercase ( self : str ) -> Tuple:
lowercase_ = CamembertTokenizer(SCREAMING_SNAKE_CASE_ )
tokenizer.save_pretrained(self.tmpdirname )
lowercase_ = CamembertTokenizerFast.from_pretrained(self.tmpdirname )
lowercase_ = '''I was born in 92000, and this is falsé.'''
lowercase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ )
lowercase_ = rust_tokenizer.encode(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowercase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
lowercase_ = rust_tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
lowercase_ = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
lowercase_ = rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Optional[Any] ) -> Optional[Any]:
if not self.test_rust_tokenizer:
return
lowercase_ = self.get_tokenizer()
lowercase_ = self.get_rust_tokenizer()
lowercase_ = '''I was born in 92000, and this is falsé.'''
lowercase_ = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
lowercase_ = rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowercase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
lowercase_ = rust_tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowercase_ = self.get_rust_tokenizer()
lowercase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ )
lowercase_ = rust_tokenizer.encode(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def _lowercase ( self : Optional[Any] ) -> Union[str, Any]:
# fmt: off
lowercase_ = {'''input_ids''': [[5, 5_4, 7_1_9_6, 2_9_7, 3_0, 2_3, 7_7_6, 1_8, 1_1, 3_2_1_5, 3_7_0_5, 8_2_5_2, 2_2, 3_1_6_4, 1_1_8_1, 2_1_1_6, 2_9, 1_6, 8_1_3, 2_5, 7_9_1, 3_3_1_4, 2_0, 3_4_4_6, 3_8, 2_7_5_7_5, 1_2_0, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_6_8, 1_7, 1_1, 9_0_8_8, 2_0, 1_5_1_7, 8, 2_2_8_0_4, 1_8_8_1_8, 1_0, 3_8, 6_2_9, 6_0_7, 6_0_7, 1_4_2, 1_9, 7_1_9_6, 8_6_7, 5_6, 1_0_3_2_6, 2_4, 2_2_6_7, 2_0, 4_1_6, 5_0_7_2, 1_5_6_1_2, 2_3_3, 7_3_4, 7, 2_3_9_9, 2_7, 1_6, 3_0_1_5, 1_6_4_9, 7, 2_4, 2_0, 4_3_3_8, 2_3_9_9, 2_7, 1_3, 3_4_0_0, 1_4, 1_3, 6_1_8_9, 8, 9_3_0, 9, 6]], '''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, 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, 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]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
lowercase_ = [
'''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
'''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
'''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
'''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
'''telles que la traduction et la synthèse de texte.''',
]
self.tokenizer_integration_test_util(
expected_encoding=SCREAMING_SNAKE_CASE_ , model_name='''camembert-base''' , revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf''' , sequences=SCREAMING_SNAKE_CASE_ , )
| 409
|
from __future__ import annotations
from collections.abc import Callable
def a ( snake_case__: Callable[[int | float], int | float] , snake_case__: int | float , snake_case__: int | float , snake_case__: int = 100 , ):
'''simple docstring'''
lowercase_ = x_start
lowercase_ = fnc(snake_case__ )
lowercase_ = 0.0
for _ in range(snake_case__ ):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
lowercase_ = (x_end - x_start) / steps + xa
lowercase_ = fnc(snake_case__ )
area += abs(fxa + fxa ) * (xa - xa) / 2
# Increment step
lowercase_ = xa
lowercase_ = fxa
return area
if __name__ == "__main__":
def a ( snake_case__: List[Any] ):
'''simple docstring'''
return x**3 + x**2
print('f(x) = x^3 + x^2')
print('The area between the curve, x = -5, x = 5 and the x axis is:')
__a = 1_0
while i <= 1_0_0_0_0_0:
print(f"with {i} steps: {trapezoidal_area(f, -5, 5, i)}")
i *= 1_0
| 409
| 1
|
'''simple docstring'''
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging
lowerCAmelCase__ : Union[str, Any] = logging.get_logger(__name__)
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
from transformers import Trainer
class a ( a__ ):
"""simple docstring"""
def __init__( self : int , snake_case_ : Tuple = 1_0_1 ):
'''simple docstring'''
snake_case__ : int = length
def __len__( self : Union[str, Any] ):
'''simple docstring'''
return self.length
def __getitem__( self : Optional[Any] , snake_case_ : Optional[int] ):
'''simple docstring'''
return i
class a :
"""simple docstring"""
def __call__( self : Optional[int] , snake_case_ : Optional[Any] ):
'''simple docstring'''
return {"input_ids": torch.tensor(snake_case_ ), "labels": torch.tensor(snake_case_ )}
class a ( nn.Module ):
"""simple docstring"""
def __init__( self : int ):
'''simple docstring'''
super().__init__()
# Add some (unused) params otherwise DDP will complain.
snake_case__ : str = nn.Linear(1_2_0 , 8_0 )
def __magic_name__ ( self : Optional[Any] , snake_case_ : int , snake_case_ : str=None ):
'''simple docstring'''
if labels is not None:
return torch.tensor(0.0 , device=input_ids.device ), input_ids
else:
return input_ids
class a ( a__ ):
"""simple docstring"""
@require_torch_neuroncore
def __magic_name__ ( self : List[Any] ):
'''simple docstring'''
snake_case__ : List[str] = F"""--nproc_per_node=2
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_trainer_distributed.py
""".split()
snake_case__ : Optional[Any] = self.get_auto_remove_tmp_dir()
snake_case__ : int = F"""--output_dir {output_dir}""".split()
snake_case__ : Optional[Any] = ['torchrun'] + distributed_args + args
execute_subprocess_async(snake_case_ , env=self.get_env() )
# successful return here == success - any errors would have caused an error in the sub-call
class a ( a__ ):
"""simple docstring"""
@require_torch_multi_gpu
def __magic_name__ ( self : Optional[Any] ):
'''simple docstring'''
snake_case__ : List[Any] = F"""--nproc_per_node={torch.cuda.device_count()}
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_trainer_distributed.py
""".split()
snake_case__ : int = self.get_auto_remove_tmp_dir()
snake_case__ : Optional[Any] = F"""--output_dir {output_dir}""".split()
snake_case__ : str = ['torchrun'] + distributed_args + args
execute_subprocess_async(snake_case_ , env=self.get_env() )
# successful return here == success - any errors would have caused an error in the sub-call
if __name__ == "__main__":
# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
#
# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py
lowerCAmelCase__ : Dict = HfArgumentParser((TrainingArguments,))
lowerCAmelCase__ : int = parser.parse_args_into_dataclasses()[0]
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, """
f"""distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"""
)
# Essentially, what we want to verify in the distributed case is that we get all samples back,
# in the right order. (this is crucial for prediction for instance)
for dataset_length in [101, 40, 7]:
lowerCAmelCase__ : Union[str, Any] = DummyDataset(dataset_length)
def _a ( __lowerCAmelCase : EvalPrediction ):
"""simple docstring"""
snake_case__ : Union[str, Any] = list(range(len(UpperCAmelCase__ ) ) )
snake_case__ : Any = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
if not success and training_args.local_rank == 0:
logger.warning(
'''Predictions and/or labels do not match expected results:\n - predictions: '''
F"""{p.predictions.tolist()}\n - labels: {p.label_ids.tolist()}\n - expected: {sequential}""" )
return {"success": success}
lowerCAmelCase__ : List[str] = Trainer(
model=DummyModel(),
args=training_args,
data_collator=DummyDataCollator(),
eval_dataset=dataset,
compute_metrics=compute_metrics,
)
lowerCAmelCase__ : int = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
lowerCAmelCase__ : Any = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
lowerCAmelCase__ : Union[str, Any] = 2
lowerCAmelCase__ : Union[str, Any] = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
lowerCAmelCase__ : Any = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
lowerCAmelCase__ : Optional[Any] = None
| 347
|
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
A = logging.get_logger(__name__)
def UpperCAmelCase ( UpperCAmelCase__ : Union[tf.Tensor, np.ndarray]):
if isinstance(UpperCAmelCase__ , np.ndarray):
return list(tensor.shape)
lowerCamelCase : Optional[Any] = tf.shape(UpperCAmelCase__)
if tensor.shape == tf.TensorShape(UpperCAmelCase__):
return dynamic
lowerCamelCase : int = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(UpperCAmelCase__)]
def UpperCAmelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Optional[str] = None):
return tf.nn.softmax(logits=logits + 1E-9 , axis=UpperCAmelCase__ , name=UpperCAmelCase__)
def UpperCAmelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : List[Any]=1E-5 , UpperCAmelCase__ : List[str]=-1):
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__):
raise NotImplementedError('Only 1D weight and bias tensors are supported for now, with only a single axis.')
# Get mean and variance on the axis to be normalized
lowerCamelCase , lowerCamelCase : Dict = tf.nn.moments(UpperCAmelCase__ , axes=[axis] , keepdims=UpperCAmelCase__)
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
lowerCamelCase : Optional[int] = [1] * inputs.shape.rank
lowerCamelCase : Union[str, Any] = shape_list(UpperCAmelCase__)[axis]
lowerCamelCase : int = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__)
lowerCamelCase : Optional[int] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__)
# Compute layer normalization using the batch_normalization
# function.
lowerCamelCase : List[str] = tf.nn.batch_normalization(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , offset=UpperCAmelCase__ , scale=UpperCAmelCase__ , variance_epsilon=UpperCAmelCase__ , )
return outputs
def UpperCAmelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : str=0 , UpperCAmelCase__ : int=-1):
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
lowerCamelCase : int = tf.shape(UpperCAmelCase__)
lowerCamelCase : Optional[Any] = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1])
lowerCamelCase : Tuple = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0)
return tf.reshape(UpperCAmelCase__ , UpperCAmelCase__)
def UpperCAmelCase ( UpperCAmelCase__ : tf.Tensor):
if not isinstance(UpperCAmelCase__ , tf.Tensor):
lowerCamelCase : Optional[Any] = tf.convert_to_tensor(UpperCAmelCase__) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
lowerCamelCase : Dict = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
lowerCamelCase : Optional[Any] = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
lowerCamelCase : List[Any] = (
tf.cast(1 , encoder_attention_mask.dtype) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def UpperCAmelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : int , UpperCAmelCase__ : str = "input_ids"):
tf.debugging.assert_less(
UpperCAmelCase__ , tf.cast(UpperCAmelCase__ , dtype=tensor.dtype) , message=(
F'''The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCAmelCase__)}) must be smaller than the embedding '''
F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def UpperCAmelCase ( UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Any):
lowerCamelCase : str = 6_45_12
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
lowerCamelCase : Tuple = [x for x in data if len(UpperCAmelCase__) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
'The following attributes cannot be saved to HDF5 file because '
F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
F'''bytes: {bad_attributes}''')
lowerCamelCase : Any = np.asarray(UpperCAmelCase__)
lowerCamelCase : int = 1
lowerCamelCase : List[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__)
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data):
num_chunks += 1
lowerCamelCase : List[str] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__)
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(UpperCAmelCase__):
lowerCamelCase : Optional[int] = chunk_data
else:
lowerCamelCase : List[str] = data
def UpperCAmelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[Any]):
if name in group.attrs:
lowerCamelCase : int = [n.decode('utf8') if hasattr(UpperCAmelCase__ , 'decode') else n for n in group.attrs[name]]
else:
lowerCamelCase : Any = []
lowerCamelCase : Union[str, Any] = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode('utf8') if hasattr(UpperCAmelCase__ , 'decode') else n for n in group.attrs['%s%d' % (name, chunk_id)]])
chunk_id += 1
return data
def UpperCAmelCase ( UpperCAmelCase__ : Any):
def _expand_single_ad_tensor(UpperCAmelCase__ : Any):
if isinstance(UpperCAmelCase__ , tf.Tensor) and t.shape.rank == 1:
return tf.expand_dims(UpperCAmelCase__ , axis=-1)
return t
return tf.nest.map_structure(_expand_single_ad_tensor , UpperCAmelCase__)
| 320
| 0
|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers.models.esm.modeling_tf_esm import (
TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
TFEsmModel,
)
class __UpperCAmelCase :
def __init__( self , lowerCAmelCase_ , ):
"""simple docstring"""
_snake_case = parent
_snake_case = 13
_snake_case = 7
_snake_case = True
_snake_case = True
_snake_case = True
_snake_case = 99
_snake_case = 32
_snake_case = 2
_snake_case = 4
_snake_case = 37
_snake_case = 'gelu'
_snake_case = 0.1
_snake_case = 0.1
_snake_case = 5_12
_snake_case = 16
_snake_case = 2
_snake_case = 0.02
_snake_case = 3
_snake_case = 4
_snake_case = None
def lowerCamelCase ( self ):
"""simple docstring"""
_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 = EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , pad_token_id=1 , 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 , )
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase ( self ):
"""simple docstring"""
(
(
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) ,
) = self.prepare_config_and_inputs()
_snake_case = True
_snake_case = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
_snake_case = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = TFEsmModel(config=_lowerCamelCase )
_snake_case = {'input_ids': input_ids, 'attention_mask': input_mask}
_snake_case = model(_lowerCamelCase )
_snake_case = [input_ids, input_mask]
_snake_case = model(_lowerCamelCase )
_snake_case = model(_lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , ):
"""simple docstring"""
_snake_case = True
_snake_case = TFEsmModel(config=_lowerCamelCase )
_snake_case = {
'input_ids': input_ids,
'attention_mask': input_mask,
'encoder_hidden_states': encoder_hidden_states,
'encoder_attention_mask': encoder_attention_mask,
}
_snake_case = model(_lowerCamelCase )
_snake_case = [input_ids, input_mask]
_snake_case = model(_lowerCamelCase , encoder_hidden_states=_lowerCamelCase )
# Also check the case where encoder outputs are not passed
_snake_case = model(_lowerCamelCase , attention_mask=_lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = TFEsmForMaskedLM(config=_lowerCamelCase )
_snake_case = model([input_ids, input_mask] )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = self.num_labels
_snake_case = TFEsmForTokenClassification(config=_lowerCamelCase )
_snake_case = {'input_ids': input_ids, 'attention_mask': input_mask}
_snake_case = model(_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase ( self ):
"""simple docstring"""
_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_tf
class __UpperCAmelCase ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
__lowercase = (
(
TFEsmModel,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
)
if is_tf_available()
else ()
)
__lowercase = (
{
"feature-extraction": TFEsmModel,
"fill-mask": TFEsmForMaskedLM,
"text-classification": TFEsmForSequenceClassification,
"token-classification": TFEsmForTokenClassification,
"zero-shot": TFEsmForSequenceClassification,
}
if is_tf_available()
else {}
)
__lowercase = False
__lowercase = False
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = TFEsmModelTester(self )
_snake_case = ConfigTester(self , config_class=_lowerCamelCase , hidden_size=37 )
def lowerCamelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*_lowerCamelCase )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase )
@slow
def lowerCamelCase ( self ):
"""simple docstring"""
for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_snake_case = TFEsmModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
@unittest.skip('Protein models do not support embedding resizing.' )
def lowerCamelCase ( self ):
"""simple docstring"""
pass
@unittest.skip('Protein models do not support embedding resizing.' )
def lowerCamelCase ( self ):
"""simple docstring"""
pass
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case , _snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_snake_case = model_class(_lowerCamelCase )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class is TFEsmForMaskedLM:
# Output embedding test differs from the main test because they're a matrix, not a layer
_snake_case = model.get_bias()
assert isinstance(_lowerCamelCase , _lowerCamelCase )
for k, v in name.items():
assert isinstance(_lowerCamelCase , tf.Variable )
else:
_snake_case = model.get_output_embeddings()
assert x is None
_snake_case = model.get_bias()
assert name is None
@require_tf
class __UpperCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = TFEsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D' )
_snake_case = tf.constant([[0, 1, 2, 3, 4, 5]] )
_snake_case = model(_lowerCamelCase )[0]
_snake_case = [1, 6, 33]
self.assertEqual(list(output.numpy().shape ) , _lowerCamelCase )
# compare the actual values for a slice.
_snake_case = tf.constant(
[
[
[8.921518, -10.589814, -6.4671307],
[-6.3967156, -13.911377, -1.1211915],
[-7.781247, -13.951557, -3.740592],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-2 ) )
@slow
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = TFEsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D' )
_snake_case = tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
_snake_case = model(_lowerCamelCase )[0]
# compare the actual values for a slice.
_snake_case = tf.constant(
[
[
[0.14443092, 0.54125327, 0.3247739],
[0.30340484, 0.00526676, 0.31077722],
[0.32278043, -0.24987096, 0.3414628],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 701
|
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowercase : str = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
lowercase : int = "\\n@inproceedings{banarjee2005,\n title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n author = {Banerjee, Satanjeev and Lavie, Alon},\n booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n month = jun,\n year = {2005},\n address = {Ann Arbor, Michigan},\n publisher = {Association for Computational Linguistics},\n url = {https://www.aclweb.org/anthology/W05-0909},\n pages = {65--72},\n}\n"
lowercase : Any = "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"
lowercase : Optional[Any] = "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n 'meteor': meteor score.\nExamples:\n\n >>> meteor = datasets.load_metric('meteor')\n >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n >>> results = meteor.compute(predictions=predictions, references=references)\n >>> print(round(results[\"meteor\"], 4))\n 0.6944\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __UpperCAmelCase ( datasets.Metric ):
def lowerCamelCase ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'] , reference_urls=[
'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
'https://en.wikipedia.org/wiki/METEOR',
] , )
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
import nltk
nltk.download('wordnet' )
if NLTK_VERSION >= version.Version('3.6.5' ):
nltk.download('punkt' )
if NLTK_VERSION >= version.Version('3.6.6' ):
nltk.download('omw-1.4' )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=0.9 , lowerCAmelCase_=3 , lowerCAmelCase_=0.5 ):
"""simple docstring"""
if NLTK_VERSION >= version.Version('3.6.5' ):
_snake_case = [
meteor_score.single_meteor_score(
word_tokenize(lowerCAmelCase_ ) , word_tokenize(lowerCAmelCase_ ) , alpha=lowerCAmelCase_ , beta=lowerCAmelCase_ , gamma=lowerCAmelCase_ )
for ref, pred in zip(lowerCAmelCase_ , lowerCAmelCase_ )
]
else:
_snake_case = [
meteor_score.single_meteor_score(lowerCAmelCase_ , lowerCAmelCase_ , alpha=lowerCAmelCase_ , beta=lowerCAmelCase_ , gamma=lowerCAmelCase_ )
for ref, pred in zip(lowerCAmelCase_ , lowerCAmelCase_ )
]
return {"meteor": np.mean(lowerCAmelCase_ )}
| 542
| 0
|
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
__lowerCAmelCase : int =logging.get_logger(__name__)
__lowerCAmelCase : Tuple ={
'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
# See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class _lowercase ( A__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = '''gpt_neo'''
SCREAMING_SNAKE_CASE__ : int = ['''past_key_values''']
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self :str , lowerCAmelCase__ :Optional[Any]=50_257 , lowerCAmelCase__ :List[Any]=2_048 , lowerCAmelCase__ :Dict=2_048 , lowerCAmelCase__ :Union[str, Any]=24 , lowerCAmelCase__ :str=[[["global", "local"], 12]] , lowerCAmelCase__ :Tuple=16 , lowerCAmelCase__ :Union[str, Any]=None , lowerCAmelCase__ :Optional[Any]=256 , lowerCAmelCase__ :Union[str, Any]="gelu_new" , lowerCAmelCase__ :List[str]=0.0 , lowerCAmelCase__ :Any=0.0 , lowerCAmelCase__ :Optional[int]=0.0 , lowerCAmelCase__ :List[str]=0.1 , lowerCAmelCase__ :Dict=1E-5 , lowerCAmelCase__ :Union[str, Any]=0.02 , lowerCAmelCase__ :List[Any]=True , lowerCAmelCase__ :Optional[Any]=50_256 , lowerCAmelCase__ :Tuple=50_256 , **lowerCAmelCase__ :str , ) -> List[Any]:
__SCREAMING_SNAKE_CASE : Optional[int] = vocab_size
__SCREAMING_SNAKE_CASE : Optional[int] = max_position_embeddings
__SCREAMING_SNAKE_CASE : List[str] = hidden_size
__SCREAMING_SNAKE_CASE : int = num_layers
__SCREAMING_SNAKE_CASE : Optional[Any] = num_heads
__SCREAMING_SNAKE_CASE : int = intermediate_size
__SCREAMING_SNAKE_CASE : Optional[Any] = window_size
__SCREAMING_SNAKE_CASE : List[Any] = activation_function
__SCREAMING_SNAKE_CASE : Union[str, Any] = resid_dropout
__SCREAMING_SNAKE_CASE : Optional[Any] = embed_dropout
__SCREAMING_SNAKE_CASE : List[str] = attention_dropout
__SCREAMING_SNAKE_CASE : List[str] = classifier_dropout
__SCREAMING_SNAKE_CASE : List[str] = layer_norm_epsilon
__SCREAMING_SNAKE_CASE : Dict = initializer_range
__SCREAMING_SNAKE_CASE : List[str] = use_cache
__SCREAMING_SNAKE_CASE : List[str] = bos_token_id
__SCREAMING_SNAKE_CASE : Dict = eos_token_id
__SCREAMING_SNAKE_CASE : Optional[Any] = attention_types
__SCREAMING_SNAKE_CASE : Optional[Any] = self.expand_attention_types_params(lowerCAmelCase__ )
if len(self.attention_layers ) != self.num_layers:
raise ValueError(
'''Configuration for convolutional module is incorrect. '''
'''It is required that `len(config.attention_layers)` == `config.num_layers` '''
f'''but is `len(config.attention_layers) = {len(self.attention_layers )}`, '''
f'''`config.num_layers = {self.num_layers}`. '''
'''`config.attention_layers` is prepared using `config.attention_types`. '''
'''Please verify the value of `config.attention_types` argument.''' )
super().__init__(bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
@staticmethod
def __magic_name__( lowerCAmelCase__ :Dict ) -> List[Any]:
__SCREAMING_SNAKE_CASE : Any = []
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
import torch
__SCREAMING_SNAKE_CASE : List[Any] = input.size()
__SCREAMING_SNAKE_CASE : List[str] = len(lowercase__ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = shape[dimension]
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.arange(0 , lowercase__ , lowercase__ )
__SCREAMING_SNAKE_CASE : Any = torch.div(sizedim - size , lowercase__ , rounding_mode='''floor''' ) + 1
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.arange(lowercase__ ) + low_indices[:min_length][:, None]
__SCREAMING_SNAKE_CASE : str = [slice(lowercase__ )] * rank
__SCREAMING_SNAKE_CASE : int = indices
__SCREAMING_SNAKE_CASE : Dict = input[s]
__SCREAMING_SNAKE_CASE : Optional[int] = list(range(0 , rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(lowercase__ )
def _UpperCamelCase ( lowercase__ , lowercase__ ):
import torch
__SCREAMING_SNAKE_CASE : Any = torch.arange(1 , lowercase__ )
__SCREAMING_SNAKE_CASE : Any = torch.remainder(lowercase__ , lowercase__ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = remainders == 0
__SCREAMING_SNAKE_CASE : Optional[Any] = candidates[divisor_indices]
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.max(lowercase__ )
return largest_divisor, torch.div(lowercase__ , lowercase__ , rounding_mode='''floor''' )
class _lowercase ( A__ ):
'''simple docstring'''
@property
def __magic_name__( self :Tuple ) -> Mapping[str, Mapping[int, str]]:
__SCREAMING_SNAKE_CASE : Union[str, Any] = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
self.fill_with_past_key_values_(lowerCAmelCase__ , direction='''inputs''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
__SCREAMING_SNAKE_CASE : int = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def __magic_name__( self :List[str] ) -> int:
return self._config.num_heads
def __magic_name__( self :Optional[int] , lowerCAmelCase__ :PreTrainedTokenizer , lowerCAmelCase__ :int = -1 , lowerCAmelCase__ :int = -1 , lowerCAmelCase__ :bool = False , lowerCAmelCase__ :Optional[TensorType] = None , ) -> Mapping[str, Any]:
__SCREAMING_SNAKE_CASE : str = super(lowerCAmelCase__ , self ).generate_dummy_inputs(
lowerCAmelCase__ , batch_size=lowerCAmelCase__ , seq_length=lowerCAmelCase__ , is_pair=lowerCAmelCase__ , framework=lowerCAmelCase__ )
# We need to order the input in the way they appears in the forward()
__SCREAMING_SNAKE_CASE : Any = OrderedDict({'''input_ids''': common_inputs['''input_ids''']} )
# Need to add the past_keys
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 , __SCREAMING_SNAKE_CASE : Optional[int] = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
__SCREAMING_SNAKE_CASE : Any = seqlen + 2
__SCREAMING_SNAKE_CASE : Optional[int] = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = [
(torch.zeros(lowerCAmelCase__ ), torch.zeros(lowerCAmelCase__ )) for _ in range(self.num_layers )
]
__SCREAMING_SNAKE_CASE : Optional[Any] = common_inputs['''attention_mask''']
if self.use_past:
__SCREAMING_SNAKE_CASE : int = ordered_inputs['''attention_mask'''].dtype
__SCREAMING_SNAKE_CASE : int = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(lowerCAmelCase__ , lowerCAmelCase__ , dtype=lowerCAmelCase__ )] , dim=1 )
return ordered_inputs
@property
def __magic_name__( self :List[Any] ) -> int:
return 13
| 696
|
from datetime import datetime
import requests
def _UpperCamelCase ( lowercase__ ):
__SCREAMING_SNAKE_CASE : Optional[int] = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
__SCREAMING_SNAKE_CASE : Tuple = requests.get(base_url + url ).json()[0]['''urls'''][0]['''src''']
return requests.get(lowercase__ ).content
if __name__ == "__main__":
__lowerCAmelCase : int =input('Enter Video/IGTV url: ').strip()
__lowerCAmelCase : Union[str, Any] =f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"""
with open(file_name, 'wb') as fp:
fp.write(download_video(url))
print(f"""Done. Video saved to disk as {file_name}.""")
| 696
| 1
|
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
a = {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}
@is_pipeline_test
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : Optional[int] = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
UpperCAmelCase : Union[str, Any] = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
UpperCAmelCase : List[Any] = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
UpperCAmelCase : Dict = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : int ):
_A = ZeroShotClassificationPipeline(
model=_UpperCAmelCase , tokenizer=_UpperCAmelCase , candidate_labels=['polics', 'health'] )
return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int] ):
_A = classifier('Who are you voting for in 2020?' , candidate_labels='politics' )
self.assertEqual(_UpperCAmelCase , {'sequence': ANY(_UpperCAmelCase ), 'labels': [ANY(_UpperCAmelCase )], 'scores': [ANY(_UpperCAmelCase )]} )
# No kwarg
_A = classifier('Who are you voting for in 2020?' , ['politics'] )
self.assertEqual(_UpperCAmelCase , {'sequence': ANY(_UpperCAmelCase ), 'labels': [ANY(_UpperCAmelCase )], 'scores': [ANY(_UpperCAmelCase )]} )
_A = classifier('Who are you voting for in 2020?' , candidate_labels=['politics'] )
self.assertEqual(_UpperCAmelCase , {'sequence': ANY(_UpperCAmelCase ), 'labels': [ANY(_UpperCAmelCase )], 'scores': [ANY(_UpperCAmelCase )]} )
_A = classifier('Who are you voting for in 2020?' , candidate_labels='politics, public health' )
self.assertEqual(
_UpperCAmelCase , {'sequence': ANY(_UpperCAmelCase ), 'labels': [ANY(_UpperCAmelCase ), ANY(_UpperCAmelCase )], 'scores': [ANY(_UpperCAmelCase ), ANY(_UpperCAmelCase )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs['scores'] ) ) , 1.0 )
_A = classifier('Who are you voting for in 2020?' , candidate_labels=['politics', 'public health'] )
self.assertEqual(
_UpperCAmelCase , {'sequence': ANY(_UpperCAmelCase ), 'labels': [ANY(_UpperCAmelCase ), ANY(_UpperCAmelCase )], 'scores': [ANY(_UpperCAmelCase ), ANY(_UpperCAmelCase )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs['scores'] ) ) , 1.0 )
_A = classifier(
'Who are you voting for in 2020?' , candidate_labels='politics' , hypothesis_template='This text is about {}' )
self.assertEqual(_UpperCAmelCase , {'sequence': ANY(_UpperCAmelCase ), 'labels': [ANY(_UpperCAmelCase )], 'scores': [ANY(_UpperCAmelCase )]} )
# https://github.com/huggingface/transformers/issues/13846
_A = classifier(['I am happy'] , ['positive', 'negative'] )
self.assertEqual(
_UpperCAmelCase , [
{'sequence': ANY(_UpperCAmelCase ), 'labels': [ANY(_UpperCAmelCase ), ANY(_UpperCAmelCase )], 'scores': [ANY(_UpperCAmelCase ), ANY(_UpperCAmelCase )]}
for i in range(1 )
] , )
_A = classifier(['I am happy', 'I am sad'] , ['positive', 'negative'] )
self.assertEqual(
_UpperCAmelCase , [
{'sequence': ANY(_UpperCAmelCase ), 'labels': [ANY(_UpperCAmelCase ), ANY(_UpperCAmelCase )], 'scores': [ANY(_UpperCAmelCase ), ANY(_UpperCAmelCase )]}
for i in range(2 )
] , )
with self.assertRaises(_UpperCAmelCase ):
classifier('' , candidate_labels='politics' )
with self.assertRaises(_UpperCAmelCase ):
classifier(_UpperCAmelCase , candidate_labels='politics' )
with self.assertRaises(_UpperCAmelCase ):
classifier('Who are you voting for in 2020?' , candidate_labels='' )
with self.assertRaises(_UpperCAmelCase ):
classifier('Who are you voting for in 2020?' , candidate_labels=_UpperCAmelCase )
with self.assertRaises(_UpperCAmelCase ):
classifier(
'Who are you voting for in 2020?' , candidate_labels='politics' , hypothesis_template='Not formatting template' , )
with self.assertRaises(_UpperCAmelCase ):
classifier(
'Who are you voting for in 2020?' , candidate_labels='politics' , hypothesis_template=_UpperCAmelCase , )
self.run_entailment_id(_UpperCAmelCase )
def lowerCAmelCase_ ( self : Any , _UpperCAmelCase : Pipeline ):
_A = zero_shot_classifier.model.config
_A = config.labelaid
_A = zero_shot_classifier.entailment_id
_A = {'LABEL_0': 0, 'LABEL_1': 1, 'LABEL_2': 2}
self.assertEqual(zero_shot_classifier.entailment_id , -1 )
_A = {'entailment': 0, 'neutral': 1, 'contradiction': 2}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
_A = {'ENTAIL': 0, 'NON-ENTAIL': 1}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
_A = {'ENTAIL': 2, 'NEUTRAL': 1, 'CONTR': 0}
self.assertEqual(zero_shot_classifier.entailment_id , 2 )
_A = original_labelaid
self.assertEqual(_UpperCAmelCase , zero_shot_classifier.entailment_id )
@require_torch
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = pipeline(
'zero-shot-classification' , model='sshleifer/tiny-distilbert-base-cased-distilled-squad' , framework='pt' , )
# There was a regression in 4.10 for this
# Adding a test so we don't make the mistake again.
# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
zero_shot_classifier(
'Who are you voting for in 2020?' * 100 , candidate_labels=['politics', 'public health', 'science'] )
@require_torch
def lowerCAmelCase_ ( self : Tuple ):
_A = pipeline(
'zero-shot-classification' , model='sshleifer/tiny-distilbert-base-cased-distilled-squad' , framework='pt' , )
_A = zero_shot_classifier(
'Who are you voting for in 2020?' , candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(_UpperCAmelCase ) , {
'sequence': 'Who are you voting for in 2020?',
'labels': ['science', 'public health', 'politics'],
'scores': [0.333, 0.333, 0.333],
} , )
@require_tf
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = pipeline(
'zero-shot-classification' , model='sshleifer/tiny-distilbert-base-cased-distilled-squad' , framework='tf' , )
_A = zero_shot_classifier(
'Who are you voting for in 2020?' , candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(_UpperCAmelCase ) , {
'sequence': 'Who are you voting for in 2020?',
'labels': ['science', 'public health', 'politics'],
'scores': [0.333, 0.333, 0.333],
} , )
@slow
@require_torch
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = pipeline('zero-shot-classification' , model='roberta-large-mnli' , framework='pt' )
_A = zero_shot_classifier(
'Who are you voting for in 2020?' , candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(_UpperCAmelCase ) , {
'sequence': 'Who are you voting for in 2020?',
'labels': ['politics', 'public health', 'science'],
'scores': [0.976, 0.015, 0.009],
} , )
_A = zero_shot_classifier(
'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
' machine translation tasks show these models to be superior in quality while being more parallelizable'
' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
' English-to-German translation task, improving over the existing best results, including ensembles by'
' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
' fraction of the training costs of the best models from the literature. We show that the Transformer'
' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
' large and limited training data.' , candidate_labels=['machine learning', 'statistics', 'translation', 'vision'] , multi_label=_UpperCAmelCase , )
self.assertEqual(
nested_simplify(_UpperCAmelCase ) , {
'sequence': (
'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
' networks in an encoder-decoder configuration. The best performing models also connect the'
' encoder and decoder through an attention mechanism. We propose a new simple network'
' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
' superior in quality while being more parallelizable and requiring significantly less time to'
' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
' English-to-French translation task, our model establishes a new single-model state-of-the-art'
' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
' costs of the best models from the literature. We show that the Transformer generalizes well to'
' other tasks by applying it successfully to English constituency parsing both with large and'
' limited training data.'
),
'labels': ['translation', 'machine learning', 'vision', 'statistics'],
'scores': [0.817, 0.713, 0.018, 0.018],
} , )
@slow
@require_tf
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = pipeline('zero-shot-classification' , model='roberta-large-mnli' , framework='tf' )
_A = zero_shot_classifier(
'Who are you voting for in 2020?' , candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(_UpperCAmelCase ) , {
'sequence': 'Who are you voting for in 2020?',
'labels': ['politics', 'public health', 'science'],
'scores': [0.976, 0.015, 0.009],
} , )
_A = zero_shot_classifier(
'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
' machine translation tasks show these models to be superior in quality while being more parallelizable'
' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
' English-to-German translation task, improving over the existing best results, including ensembles by'
' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
' fraction of the training costs of the best models from the literature. We show that the Transformer'
' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
' large and limited training data.' , candidate_labels=['machine learning', 'statistics', 'translation', 'vision'] , multi_label=_UpperCAmelCase , )
self.assertEqual(
nested_simplify(_UpperCAmelCase ) , {
'sequence': (
'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
' networks in an encoder-decoder configuration. The best performing models also connect the'
' encoder and decoder through an attention mechanism. We propose a new simple network'
' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
' superior in quality while being more parallelizable and requiring significantly less time to'
' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
' English-to-French translation task, our model establishes a new single-model state-of-the-art'
' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
' costs of the best models from the literature. We show that the Transformer generalizes well to'
' other tasks by applying it successfully to English constituency parsing both with large and'
' limited training data.'
),
'labels': ['translation', 'machine learning', 'vision', 'statistics'],
'scores': [0.817, 0.713, 0.018, 0.018],
} , )
| 505
|
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : list[int] ) -> list[int]:
'''simple docstring'''
if len(_snake_case ) == 0:
return array
_A , _A = min(_snake_case ), max(_snake_case )
# Compute the variables
_A = _max - _min + 1
_A , _A = [0] * holes_range, [0] * holes_range
# Make the sorting.
for i in array:
_A = i - _min
_A = i
holes_repeat[index] += 1
# Makes the array back by replacing the numbers.
_A = 0
for i in range(_snake_case ):
while holes_repeat[i] > 0:
_A = holes[i]
index += 1
holes_repeat[i] -= 1
# Returns the sorted array.
return array
if __name__ == "__main__":
import doctest
doctest.testmod()
a = input('''Enter numbers separated by comma:\n''')
a = [int(x) for x in user_input.split(''',''')]
print(pigeon_sort(unsorted))
| 505
| 1
|
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __init__( self : Any , UpperCamelCase__ : Union[str, "sqlalchemy.sql.Selectable"] , UpperCamelCase__ : Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] , UpperCamelCase__ : Optional[Features] = None , UpperCamelCase__ : str = None , UpperCamelCase__ : bool = False , **UpperCamelCase__ : Any , ):
'''simple docstring'''
super().__init__(features=UpperCamelCase__ , cache_dir=UpperCamelCase__ , keep_in_memory=UpperCamelCase__ , **UpperCamelCase__)
snake_case__ = Sql(
cache_dir=UpperCamelCase__ , features=UpperCamelCase__ , sql=UpperCamelCase__ , con=UpperCamelCase__ , **UpperCamelCase__ , )
def __magic_name__ ( self : int):
'''simple docstring'''
snake_case__ = None
snake_case__ = None
snake_case__ = None
snake_case__ = None
self.builder.download_and_prepare(
download_config=UpperCamelCase__ , download_mode=UpperCamelCase__ , verification_mode=UpperCamelCase__ , base_path=UpperCamelCase__ , )
# Build dataset for splits
snake_case__ = self.builder.as_dataset(
split="""train""" , verification_mode=UpperCamelCase__ , in_memory=self.keep_in_memory)
return dataset
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCamelCase__ : Dataset , UpperCamelCase__ : str , UpperCamelCase__ : Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] , UpperCamelCase__ : Optional[int] = None , UpperCamelCase__ : Optional[int] = None , **UpperCamelCase__ : Union[str, Any] , ):
'''simple docstring'''
if num_proc is not None and num_proc <= 0:
raise ValueError(F'''num_proc {num_proc} must be an integer > 0.''')
snake_case__ = dataset
snake_case__ = name
snake_case__ = con
snake_case__ = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
snake_case__ = num_proc
snake_case__ = to_sql_kwargs
def __magic_name__ ( self : List[str]):
'''simple docstring'''
snake_case__ = self.to_sql_kwargs.pop("""sql""" , UpperCamelCase__)
snake_case__ = self.to_sql_kwargs.pop("""con""" , UpperCamelCase__)
snake_case__ = self.to_sql_kwargs.pop("""index""" , UpperCamelCase__)
snake_case__ = self._write(index=UpperCamelCase__ , **self.to_sql_kwargs)
return written
def __magic_name__ ( self : int , UpperCamelCase__ : List[str]):
'''simple docstring'''
snake_case__ , snake_case__ , snake_case__ = args
snake_case__ = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
snake_case__ = query_table(
table=self.dataset.data , key=slice(UpperCamelCase__ , offset + self.batch_size) , indices=self.dataset._indices , )
snake_case__ = batch.to_pandas()
snake_case__ = df.to_sql(self.name , self.con , index=UpperCamelCase__ , **UpperCamelCase__)
return num_rows or len(UpperCamelCase__)
def __magic_name__ ( self : List[Any] , UpperCamelCase__ : List[Any] , **UpperCamelCase__ : List[str]):
'''simple docstring'''
snake_case__ = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset) , self.batch_size) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs))
else:
snake_case__ , snake_case__ = len(self.dataset), self.batch_size
with multiprocessing.Pool(self.num_proc) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , UpperCamelCase__ , UpperCamelCase__)] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 654
|
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy
a__ = logging.get_logger(__name__)
a__ = {
"""artists_file""": """artists.json""",
"""lyrics_file""": """lyrics.json""",
"""genres_file""": """genres.json""",
}
a__ = {
"""artists_file""": {
"""jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json""",
},
"""genres_file""": {
"""jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json""",
},
"""lyrics_file""": {
"""jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json""",
},
}
a__ = {
"""jukebox""": 5_1_2,
}
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : str = VOCAB_FILES_NAMES
_lowercase : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
_lowercase : str = PRETRAINED_LYRIC_TOKENS_SIZES
_lowercase : Any = ['''input_ids''', '''attention_mask''']
def __init__( self : int , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : int=["v3", "v2", "v2"] , UpperCamelCase__ : List[str]=5_1_2 , UpperCamelCase__ : Union[str, Any]=5 , UpperCamelCase__ : List[Any]="<|endoftext|>" , **UpperCamelCase__ : List[Any] , ):
'''simple docstring'''
snake_case__ = AddedToken(UpperCamelCase__ , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__) if isinstance(UpperCamelCase__ , UpperCamelCase__) else unk_token
super().__init__(
unk_token=UpperCamelCase__ , n_genres=UpperCamelCase__ , version=UpperCamelCase__ , max_n_lyric_tokens=UpperCamelCase__ , **UpperCamelCase__ , )
snake_case__ = version
snake_case__ = max_n_lyric_tokens
snake_case__ = n_genres
with open(UpperCamelCase__ , encoding="""utf-8""") as vocab_handle:
snake_case__ = json.load(UpperCamelCase__)
with open(UpperCamelCase__ , encoding="""utf-8""") as vocab_handle:
snake_case__ = json.load(UpperCamelCase__)
with open(UpperCamelCase__ , encoding="""utf-8""") as vocab_handle:
snake_case__ = json.load(UpperCamelCase__)
snake_case__ = R"""[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"""
# In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
if len(self.lyrics_encoder) == 7_9:
snake_case__ = oov.replace(R"""\-'""" , R"""\-+'""")
snake_case__ = regex.compile(UpperCamelCase__)
snake_case__ = {v: k for k, v in self.artists_encoder.items()}
snake_case__ = {v: k for k, v in self.genres_encoder.items()}
snake_case__ = {v: k for k, v in self.lyrics_encoder.items()}
@property
def __magic_name__ ( self : List[str]):
'''simple docstring'''
return len(self.artists_encoder) + len(self.genres_encoder) + len(self.lyrics_encoder)
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
return dict(self.artists_encoder , self.genres_encoder , self.lyrics_encoder)
def __magic_name__ ( self : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : int):
'''simple docstring'''
snake_case__ = [self.artists_encoder.get(UpperCamelCase__ , 0) for artist in list_artists]
for genres in range(len(UpperCamelCase__)):
snake_case__ = [self.genres_encoder.get(UpperCamelCase__ , 0) for genre in list_genres[genres]]
snake_case__ = list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]))
snake_case__ = [[self.lyrics_encoder.get(UpperCamelCase__ , 0) for character in list_lyrics[0]], [], []]
return artists_id, list_genres, lyric_ids
def __magic_name__ ( self : Optional[int] , UpperCamelCase__ : Optional[int]):
'''simple docstring'''
return list(UpperCamelCase__)
def __magic_name__ ( self : int , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Any , **UpperCamelCase__ : List[str]):
'''simple docstring'''
snake_case__ , snake_case__ , snake_case__ = self.prepare_for_tokenization(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__)
snake_case__ = self._tokenize(UpperCamelCase__)
return artist, genre, lyrics
def __magic_name__ ( self : List[Any] , UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : bool = False):
'''simple docstring'''
for idx in range(len(self.version)):
if self.version[idx] == "v3":
snake_case__ = artists[idx].lower()
snake_case__ = [genres[idx].lower()]
else:
snake_case__ = self._normalize(artists[idx]) + """.v2"""
snake_case__ = [
self._normalize(UpperCamelCase__) + """.v2""" for genre in genres[idx].split("""_""")
] # split is for the full dictionary with combined genres
if self.version[0] == "v2":
snake_case__ = regex.compile(R"""[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+""")
snake_case__ = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+'\"()[] \t\n"""
snake_case__ = {vocab[index]: index + 1 for index in range(len(UpperCamelCase__))}
snake_case__ = 0
snake_case__ = len(UpperCamelCase__) + 1
snake_case__ = self.vocab
snake_case__ = {v: k for k, v in self.vocab.items()}
snake_case__ = """"""
else:
snake_case__ = regex.compile(R"""[^A-Za-z0-9.,:;!?\-+'\"()\[\] \t\n]+""")
snake_case__ = self._run_strip_accents(UpperCamelCase__)
snake_case__ = lyrics.replace("""\\""" , """\n""")
snake_case__ = self.out_of_vocab.sub("""""" , UpperCamelCase__), [], []
return artists, genres, lyrics
def __magic_name__ ( self : Union[str, Any] , UpperCamelCase__ : str):
'''simple docstring'''
snake_case__ = unicodedata.normalize("""NFD""" , UpperCamelCase__)
snake_case__ = []
for char in text:
snake_case__ = unicodedata.category(UpperCamelCase__)
if cat == "Mn":
continue
output.append(UpperCamelCase__)
return "".join(UpperCamelCase__)
def __magic_name__ ( self : List[str] , UpperCamelCase__ : str):
'''simple docstring'''
snake_case__ = (
[chr(UpperCamelCase__) for i in range(ord("""a""") , ord("""z""") + 1)]
+ [chr(UpperCamelCase__) for i in range(ord("""A""") , ord("""Z""") + 1)]
+ [chr(UpperCamelCase__) for i in range(ord("""0""") , ord("""9""") + 1)]
+ ["""."""]
)
snake_case__ = frozenset(UpperCamelCase__)
snake_case__ = re.compile(R"""_+""")
snake_case__ = """""".join([c if c in accepted else """_""" for c in text.lower()])
snake_case__ = pattern.sub("""_""" , UpperCamelCase__).strip("""_""")
return text
def __magic_name__ ( self : List[Any] , UpperCamelCase__ : List[str]):
'''simple docstring'''
return " ".join(UpperCamelCase__)
def __magic_name__ ( self : Any , UpperCamelCase__ : str , UpperCamelCase__ : Optional[Union[str, TensorType]] = None , UpperCamelCase__ : bool = False):
'''simple docstring'''
if not isinstance(UpperCamelCase__ , UpperCamelCase__):
snake_case__ = TensorType(UpperCamelCase__)
# Get a function reference for the correct framework
if tensor_type == TensorType.TENSORFLOW:
if not is_tf_available():
raise ImportError(
"""Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.""")
import tensorflow as tf
snake_case__ = tf.constant
snake_case__ = tf.is_tensor
elif tensor_type == TensorType.PYTORCH:
if not is_torch_available():
raise ImportError("""Unable to convert output to PyTorch tensors format, PyTorch is not installed.""")
import torch
snake_case__ = torch.tensor
snake_case__ = torch.is_tensor
elif tensor_type == TensorType.JAX:
if not is_flax_available():
raise ImportError("""Unable to convert output to JAX tensors format, JAX is not installed.""")
import jax.numpy as jnp # noqa: F811
snake_case__ = jnp.array
snake_case__ = _is_jax
else:
snake_case__ = np.asarray
snake_case__ = _is_numpy
# Do the tensor conversion in batch
try:
if prepend_batch_axis:
snake_case__ = [inputs]
if not is_tensor(UpperCamelCase__):
snake_case__ = as_tensor(UpperCamelCase__)
except: # noqa E722
raise ValueError(
"""Unable to create tensor, you should probably activate truncation and/or padding """
"""with 'padding=True' 'truncation=True' to have batched tensors with the same length.""")
return inputs
def __call__( self : str , UpperCamelCase__ : Any , UpperCamelCase__ : Dict , UpperCamelCase__ : Any="" , UpperCamelCase__ : Dict="pt"):
'''simple docstring'''
snake_case__ = [0, 0, 0]
snake_case__ = [artist] * len(self.version)
snake_case__ = [genres] * len(self.version)
snake_case__ , snake_case__ , snake_case__ = self.tokenize(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__)
snake_case__ , snake_case__ , snake_case__ = self._convert_token_to_id(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__)
snake_case__ = [-INFINITY] * len(full_tokens[-1])
snake_case__ = [
self.convert_to_tensors(
[input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]] , tensor_type=UpperCamelCase__)
for i in range(len(self.version))
]
return BatchEncoding({"""input_ids""": input_ids, """attention_masks""": attention_masks})
def __magic_name__ ( self : Any , UpperCamelCase__ : str , UpperCamelCase__ : Optional[str] = None):
'''simple docstring'''
if not os.path.isdir(UpperCamelCase__):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
return
snake_case__ = os.path.join(
UpperCamelCase__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""artists_file"""])
with open(UpperCamelCase__ , """w""" , encoding="""utf-8""") as f:
f.write(json.dumps(self.artists_encoder , ensure_ascii=UpperCamelCase__))
snake_case__ = os.path.join(
UpperCamelCase__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""genres_file"""])
with open(UpperCamelCase__ , """w""" , encoding="""utf-8""") as f:
f.write(json.dumps(self.genres_encoder , ensure_ascii=UpperCamelCase__))
snake_case__ = os.path.join(
UpperCamelCase__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""lyrics_file"""])
with open(UpperCamelCase__ , """w""" , encoding="""utf-8""") as f:
f.write(json.dumps(self.lyrics_encoder , ensure_ascii=UpperCamelCase__))
return (artists_file, genres_file, lyrics_file)
def __magic_name__ ( self : List[Any] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Tuple , UpperCamelCase__ : List[str]):
'''simple docstring'''
snake_case__ = self.artists_decoder.get(UpperCamelCase__)
snake_case__ = [self.genres_decoder.get(UpperCamelCase__) for genre in genres_index]
snake_case__ = [self.lyrics_decoder.get(UpperCamelCase__) for character in lyric_index]
return artist, genres, lyrics
| 654
| 1
|
import torch
from diffusers import StableDiffusionPipeline
lowercase_ : Tuple = 'path-to-your-trained-model'
lowercase_ : Dict = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')
lowercase_ : Optional[Any] = 'A photo of sks dog in a bucket'
lowercase_ : Optional[Any] = pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]
image.save('dog-bucket.png')
| 107
|
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class _lowerCamelCase ( unittest.TestCase ):
def __init__( self , lowerCAmelCase , lowerCAmelCase=7 , lowerCAmelCase=3 , lowerCAmelCase=18 , lowerCAmelCase=30 , lowerCAmelCase=400 , lowerCAmelCase=True , lowerCAmelCase=None , lowerCAmelCase=True , ) -> Any:
SCREAMING_SNAKE_CASE__: Tuple= size if size is not None else {'''height''': 18, '''width''': 18}
SCREAMING_SNAKE_CASE__: Dict= parent
SCREAMING_SNAKE_CASE__: Tuple= batch_size
SCREAMING_SNAKE_CASE__: int= num_channels
SCREAMING_SNAKE_CASE__: List[Any]= image_size
SCREAMING_SNAKE_CASE__: Dict= min_resolution
SCREAMING_SNAKE_CASE__: Union[str, Any]= max_resolution
SCREAMING_SNAKE_CASE__: Optional[Any]= do_resize
SCREAMING_SNAKE_CASE__: List[Any]= size
SCREAMING_SNAKE_CASE__: Optional[Any]= apply_ocr
def UpperCamelCase_ ( self ) -> Optional[int]:
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class _lowerCamelCase ( UpperCamelCase_ , unittest.TestCase ):
__a = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def UpperCamelCase_ ( self ) -> Tuple:
SCREAMING_SNAKE_CASE__: List[Any]= LayoutLMvaImageProcessingTester(self )
@property
def UpperCamelCase_ ( self ) -> Tuple:
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase_ ( self ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__: Tuple= self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase , '''do_resize''' ) )
self.assertTrue(hasattr(lowerCAmelCase , '''size''' ) )
self.assertTrue(hasattr(lowerCAmelCase , '''apply_ocr''' ) )
def UpperCamelCase_ ( self ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__: Tuple= self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 18, '''width''': 18} )
SCREAMING_SNAKE_CASE__: Optional[Any]= self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
def UpperCamelCase_ ( self ) -> Any:
pass
def UpperCamelCase_ ( self ) -> List[str]:
# Initialize image_processing
SCREAMING_SNAKE_CASE__: int= self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__: Optional[int]= prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__: str= image_processing(image_inputs[0] , return_tensors='''pt''' )
self.assertEqual(
encoding.pixel_values.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
self.assertIsInstance(encoding.words , lowerCAmelCase )
self.assertIsInstance(encoding.boxes , lowerCAmelCase )
# Test batched
SCREAMING_SNAKE_CASE__: Optional[Any]= image_processing(lowerCAmelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def UpperCamelCase_ ( self ) -> Dict:
# Initialize image_processing
SCREAMING_SNAKE_CASE__: Tuple= self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE__: Dict= prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase , numpify=lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase , np.ndarray )
# Test not batched input
SCREAMING_SNAKE_CASE__: Dict= image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
SCREAMING_SNAKE_CASE__: Union[str, Any]= image_processing(lowerCAmelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def UpperCamelCase_ ( self ) -> str:
# Initialize image_processing
SCREAMING_SNAKE_CASE__: Tuple= self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE__: int= prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase , torchify=lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase , torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE__: Optional[int]= image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
SCREAMING_SNAKE_CASE__: Any= image_processing(lowerCAmelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def UpperCamelCase_ ( self ) -> Optional[Any]:
# with apply_OCR = True
SCREAMING_SNAKE_CASE__: int= LayoutLMvaImageProcessor()
from datasets import load_dataset
SCREAMING_SNAKE_CASE__: int= load_dataset('''hf-internal-testing/fixtures_docvqa''' , split='''test''' )
SCREAMING_SNAKE_CASE__: str= Image.open(ds[0]['''file'''] ).convert('''RGB''' )
SCREAMING_SNAKE_CASE__: str= image_processing(lowerCAmelCase , return_tensors='''pt''' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
self.assertEqual(len(encoding.words ) , len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
SCREAMING_SNAKE_CASE__: Dict= [['''11:14''', '''to''', '''11:39''', '''a.m''', '''11:39''', '''to''', '''11:44''', '''a.m.''', '''11:44''', '''a.m.''', '''to''', '''12:25''', '''p.m.''', '''12:25''', '''to''', '''12:58''', '''p.m.''', '''12:58''', '''to''', '''4:00''', '''p.m.''', '''2:00''', '''to''', '''5:00''', '''p.m.''', '''Coffee''', '''Break''', '''Coffee''', '''will''', '''be''', '''served''', '''for''', '''men''', '''and''', '''women''', '''in''', '''the''', '''lobby''', '''adjacent''', '''to''', '''exhibit''', '''area.''', '''Please''', '''move''', '''into''', '''exhibit''', '''area.''', '''(Exhibits''', '''Open)''', '''TRRF''', '''GENERAL''', '''SESSION''', '''(PART''', '''|)''', '''Presiding:''', '''Lee''', '''A.''', '''Waller''', '''TRRF''', '''Vice''', '''President''', '''“Introductory''', '''Remarks”''', '''Lee''', '''A.''', '''Waller,''', '''TRRF''', '''Vice''', '''Presi-''', '''dent''', '''Individual''', '''Interviews''', '''with''', '''TRRF''', '''Public''', '''Board''', '''Members''', '''and''', '''Sci-''', '''entific''', '''Advisory''', '''Council''', '''Mem-''', '''bers''', '''Conducted''', '''by''', '''TRRF''', '''Treasurer''', '''Philip''', '''G.''', '''Kuehn''', '''to''', '''get''', '''answers''', '''which''', '''the''', '''public''', '''refrigerated''', '''warehousing''', '''industry''', '''is''', '''looking''', '''for.''', '''Plus''', '''questions''', '''from''', '''the''', '''floor.''', '''Dr.''', '''Emil''', '''M.''', '''Mrak,''', '''University''', '''of''', '''Cal-''', '''ifornia,''', '''Chairman,''', '''TRRF''', '''Board;''', '''Sam''', '''R.''', '''Cecil,''', '''University''', '''of''', '''Georgia''', '''College''', '''of''', '''Agriculture;''', '''Dr.''', '''Stanley''', '''Charm,''', '''Tufts''', '''University''', '''School''', '''of''', '''Medicine;''', '''Dr.''', '''Robert''', '''H.''', '''Cotton,''', '''ITT''', '''Continental''', '''Baking''', '''Company;''', '''Dr.''', '''Owen''', '''Fennema,''', '''University''', '''of''', '''Wis-''', '''consin;''', '''Dr.''', '''Robert''', '''E.''', '''Hardenburg,''', '''USDA.''', '''Questions''', '''and''', '''Answers''', '''Exhibits''', '''Open''', '''Capt.''', '''Jack''', '''Stoney''', '''Room''', '''TRRF''', '''Scientific''', '''Advisory''', '''Council''', '''Meeting''', '''Ballroom''', '''Foyer''']] # noqa: E231
SCREAMING_SNAKE_CASE__: List[Any]= [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , lowerCAmelCase )
self.assertListEqual(encoding.boxes , lowerCAmelCase )
# with apply_OCR = False
SCREAMING_SNAKE_CASE__: int= LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase )
SCREAMING_SNAKE_CASE__: Optional[Any]= image_processing(lowerCAmelCase , return_tensors='''pt''' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
| 107
| 1
|
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