




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCAmelCase_		:					List[Any]          =  "\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

UpperCAmelCase_		:					int          =  "\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"


UpperCAmelCase_		:					Union[str, Any]          =  "\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=[\"About 95 species are currently accepted .\"]\n    >>> predictions=[\"About 95 you now get in .\"]\n    >>> references=[[\"About 95 species are currently known .\"]]\n    >>> wiki_split = datasets.load_metric(\"wiki_split\")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"




def 		lowerCAmelCase_ (						lowerCamelCase ):

				def remove_articles(lowerCamelCase ):
								__magic_name__						:						Optional[Any]										=re.compile(R"""\b(a|an|the)\b"""						,  re.UNICODE )
								return re.sub(lowerCamelCase						,  """ """						,  lowerCamelCase )

				def white_space_fix(lowerCamelCase ):
								return " ".join(text.split() )

				def remove_punc(lowerCamelCase ):
								__magic_name__						:						int										=set(string.punctuation )
								return "".join(ch for ch in text if ch not in exclude )

				def lower(lowerCamelCase ):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase ) ) ) )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				return int(normalize_answer(lowerCamelCase ) == normalize_answer(lowerCamelCase ) )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						int										=[any(compute_exact(lowerCamelCase						,  lowerCamelCase ) for ref in refs ) for pred, refs in zip(lowerCamelCase						,  lowerCamelCase )]
				return (sum(lowerCamelCase ) / len(lowerCamelCase )) * 100




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Any										=[rgram for rgrams in rgramslist for rgram in rgrams]
				__magic_name__						:						Optional[Any]										=Counter(lowerCamelCase )

				__magic_name__						:						Optional[int]										=Counter(lowerCamelCase )
				__magic_name__						:						List[str]										=Counter()
				for sgram, scount in sgramcounter.items():
								__magic_name__						:						Optional[Any]										=scount * numref

				__magic_name__						:						List[str]										=Counter(lowerCamelCase )
				__magic_name__						:						List[Any]										=Counter()
				for cgram, ccount in cgramcounter.items():
								__magic_name__						:						Optional[Any]										=ccount * numref

				# KEEP
				__magic_name__						:						List[Any]										=sgramcounter_rep & cgramcounter_rep
				__magic_name__						:						Tuple										=keepgramcounter_rep & rgramcounter
				__magic_name__						:						List[str]										=sgramcounter_rep & rgramcounter

				__magic_name__						:						Optional[Any]										=0
				__magic_name__						:						Union[str, Any]										=0
				for keepgram in keepgramcountergood_rep:
								keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
								# Fix an alleged bug [2] in the keep score computation.
								# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
								keeptmpscorea += keepgramcountergood_rep[keepgram]
				# Define 0/0=1 instead of 0 to give higher scores for predictions that match
				#      a target exactly.
				__magic_name__						:						str										=1
				__magic_name__						:						Dict										=1
				if len(lowerCamelCase ) > 0:
								__magic_name__						:						List[Any]										=keeptmpscorea / len(lowerCamelCase )
				if len(lowerCamelCase ) > 0:
								# Fix an alleged bug [2] in the keep score computation.
								# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
								__magic_name__						:						List[str]										=keeptmpscorea / sum(keepgramcounterall_rep.values() )
				__magic_name__						:						List[str]										=0
				if keepscore_precision > 0 or keepscore_recall > 0:
								__magic_name__						:						List[Any]										=2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

				# DELETION
				__magic_name__						:						Optional[int]										=sgramcounter_rep - cgramcounter_rep
				__magic_name__						:						str										=delgramcounter_rep - rgramcounter
				__magic_name__						:						str										=sgramcounter_rep - rgramcounter
				__magic_name__						:						List[str]										=0
				__magic_name__						:						str										=0
				for delgram in delgramcountergood_rep:
								deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
								deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
				# Define 0/0=1 instead of 0 to give higher scores for predictions that match
				# a target exactly.
				__magic_name__						:						Dict										=1
				if len(lowerCamelCase ) > 0:
								__magic_name__						:						Union[str, Any]										=deltmpscorea / len(lowerCamelCase )

				# ADDITION
				__magic_name__						:						List[str]										=set(lowerCamelCase ) - set(lowerCamelCase )
				__magic_name__						:						Optional[int]										=set(lowerCamelCase ) & set(lowerCamelCase )
				__magic_name__						:						Dict										=set(lowerCamelCase ) - set(lowerCamelCase )

				__magic_name__						:						Any										=0
				for addgram in addgramcountergood:
								addtmpscore += 1

				# Define 0/0=1 instead of 0 to give higher scores for predictions that match
				# a target exactly.
				__magic_name__						:						str										=1
				__magic_name__						:						str										=1
				if len(lowerCamelCase ) > 0:
								__magic_name__						:						Optional[Any]										=addtmpscore / len(lowerCamelCase )
				if len(lowerCamelCase ) > 0:
								__magic_name__						:						Any										=addtmpscore / len(lowerCamelCase )
				__magic_name__						:						Optional[int]										=0
				if addscore_precision > 0 or addscore_recall > 0:
								__magic_name__						:						int										=2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

				return (keepscore, delscore_precision, addscore)




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						List[str]										=len(lowerCamelCase )

				__magic_name__						:						Optional[Any]										=ssent.split(""" """ )
				__magic_name__						:						int										=csent.split(""" """ )
				__magic_name__						:						Any										=[]
				__magic_name__						:						Any										=[]
				__magic_name__						:						List[str]										=[]
				__magic_name__						:						Any										=[]
				__magic_name__						:						List[str]										=[]
				__magic_name__						:						Optional[int]										=[]

				__magic_name__						:						Optional[Any]										=[]
				__magic_name__						:						Any										=[]
				__magic_name__						:						Optional[int]										=[]
				__magic_name__						:						Any										=[]
				for rsent in rsents:
								__magic_name__						:						Any										=rsent.split(""" """ )
								__magic_name__						:						Tuple										=[]
								__magic_name__						:						Dict										=[]
								__magic_name__						:						List[str]										=[]
								ragramslist.append(lowerCamelCase )
								for i in range(0						,  len(lowerCamelCase ) - 1 ):
												if i < len(lowerCamelCase ) - 1:
																__magic_name__						:						int										=ragrams[i] + """ """ + ragrams[i + 1]
																ragrams.append(lowerCamelCase )
												if i < len(lowerCamelCase ) - 2:
																__magic_name__						:						Dict										=ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
																ragrams.append(lowerCamelCase )
												if i < len(lowerCamelCase ) - 3:
																__magic_name__						:						int										=ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2] + """ """ + ragrams[i + 3]
																ragrams.append(lowerCamelCase )
								ragramslist.append(lowerCamelCase )
								ragramslist.append(lowerCamelCase )
								ragramslist.append(lowerCamelCase )

				for i in range(0						,  len(lowerCamelCase ) - 1 ):
								if i < len(lowerCamelCase ) - 1:
												__magic_name__						:						List[str]										=sagrams[i] + """ """ + sagrams[i + 1]
												sagrams.append(lowerCamelCase )
								if i < len(lowerCamelCase ) - 2:
												__magic_name__						:						Any										=sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
												sagrams.append(lowerCamelCase )
								if i < len(lowerCamelCase ) - 3:
												__magic_name__						:						Union[str, Any]										=sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
												sagrams.append(lowerCamelCase )

				for i in range(0						,  len(lowerCamelCase ) - 1 ):
								if i < len(lowerCamelCase ) - 1:
												__magic_name__						:						List[str]										=cagrams[i] + """ """ + cagrams[i + 1]
												cagrams.append(lowerCamelCase )
								if i < len(lowerCamelCase ) - 2:
												__magic_name__						:						List[str]										=cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
												cagrams.append(lowerCamelCase )
								if i < len(lowerCamelCase ) - 3:
												__magic_name__						:						List[Any]										=cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
												cagrams.append(lowerCamelCase )

				((__magic_name__)					,			(__magic_name__)					,			(__magic_name__))						:						Any										=SARIngram(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
				((__magic_name__)					,			(__magic_name__)					,			(__magic_name__))						:						List[str]										=SARIngram(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
				((__magic_name__)					,			(__magic_name__)					,			(__magic_name__))						:						Tuple										=SARIngram(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
				((__magic_name__)					,			(__magic_name__)					,			(__magic_name__))						:						Union[str, Any]										=SARIngram(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
				__magic_name__						:						Any										=sum([keepascore, keepascore, keepascore, keepascore] ) / 4
				__magic_name__						:						Tuple										=sum([delascore, delascore, delascore, delascore] ) / 4
				__magic_name__						:						Dict										=sum([addascore, addascore, addascore, addascore] ) / 4
				__magic_name__						:						int										=(avgkeepscore + avgdelscore + avgaddscore) / 3
				return finalscore




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase = True						,  lowerCamelCase = "13a"						,  lowerCamelCase = True ):
				# Normalization is requried for the ASSET dataset (one of the primary
				# datasets in sentence simplification) to allow using space
				# to split the sentence. Even though Wiki-Auto and TURK datasets,
				# do not require normalization, we do it for consistency.
				# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
				# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

				if lowercase:
								__magic_name__						:						Optional[Any]										=sentence.lower()

				if tokenizer in ["13a", "intl"]:
								if version.parse(sacrebleu.__version__ ).major >= 2:
												__magic_name__						:						Union[str, Any]										=sacrebleu.metrics.bleu._get_tokenizer(lowerCamelCase )()(lowerCamelCase )
								else:
												__magic_name__						:						List[Any]										=sacrebleu.TOKENIZERS[tokenizer]()(lowerCamelCase )
				elif tokenizer == "moses":
								__magic_name__						:						Dict										=sacremoses.MosesTokenizer().tokenize(lowerCamelCase						,  return_str=lowerCamelCase						,  escape=lowerCamelCase )
				elif tokenizer == "penn":
								__magic_name__						:						Any										=sacremoses.MosesTokenizer().penn_tokenize(lowerCamelCase						,  return_str=lowerCamelCase )
				else:
								__magic_name__						:						Tuple										=sentence

				if not return_str:
								__magic_name__						:						Optional[Any]										=normalized_sent.split()

				return normalized_sent




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				if not (len(lowerCamelCase ) == len(lowerCamelCase ) == len(lowerCamelCase )):
								raise ValueError("""Sources length must match predictions and references lengths.""" )
				__magic_name__						:						List[Any]										=0
				for src, pred, refs in zip(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
								sari_score += SARIsent(normalize(lowerCamelCase )						,  normalize(lowerCamelCase )						,  [normalize(lowerCamelCase ) for sent in refs] )
				__magic_name__						:						Any										=sari_score / len(lowerCamelCase )
				return 100 * sari_score




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase="exp"						,  lowerCamelCase=None						,  lowerCamelCase=False						,  lowerCamelCase=False						,  lowerCamelCase=False						,  ):
				__magic_name__						:						Tuple										=len(references[0] )
				if any(len(lowerCamelCase ) != references_per_prediction for refs in references ):
								raise ValueError("""Sacrebleu requires the same number of references for each prediction""" )
				__magic_name__						:						int										=[[refs[i] for refs in references] for i in range(lowerCamelCase )]
				__magic_name__						:						Any										=sacrebleu.corpus_bleu(
				    lowerCamelCase						,  lowerCamelCase						,  smooth_method=lowerCamelCase						,  smooth_value=lowerCamelCase						,  force=lowerCamelCase						,  lowercase=lowerCamelCase						,  use_effective_order=lowerCamelCase						,  )
				return output.score




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,    _KWARGS_DESCRIPTION      )
class 		__A   (      datasets.Metric      ):

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										return datasets.MetricInfo(
										    description=_DESCRIPTION     ,			citation=_CITATION     ,			inputs_description=_KWARGS_DESCRIPTION     ,			features=datasets.Features(
										        {
										            """predictions""": datasets.Value("""string"""     ,			id="""sequence"""			),
										            """references""": datasets.Sequence(datasets.Value("""string"""     ,			id="""sequence"""			)     ,			id="""references"""			),
										        }			)     ,			codebase_urls=[
										        """https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py""",
										        """https://github.com/cocoxu/simplification/blob/master/SARI.py""",
										        """https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py""",
										        """https://github.com/mjpost/sacreBLEU""",
										    ]     ,			reference_urls=[
										        """https://www.aclweb.org/anthology/Q16-1029.pdf""",
										        """https://github.com/mjpost/sacreBLEU""",
										        """https://en.wikipedia.org/wiki/BLEU""",
										        """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
										    ]     ,			)


						def      A__    (		self :List[str]     ,			__snake_case :str     ,			__snake_case :Dict     ,			__snake_case :Any			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										={}
										result.update({"""sari""": compute_sari(sources=__snake_case     ,			predictions=__snake_case     ,			references=__snake_case			)}			)
										result.update({"""sacrebleu""": compute_sacrebleu(predictions=__snake_case     ,			references=__snake_case			)}			)
										result.update({"""exact""": compute_em(predictions=__snake_case     ,			references=__snake_case			)}			)
										return result










import inspect
import unittest

import torch
import torch.nn as nn

from accelerate.hooks import (
    AlignDevicesHook,
    ModelHook,
    SequentialHook,
    add_hook_to_module,
    attach_align_device_hook,
    remove_hook_from_module,
    remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu




class 		__A   (      nn.Module      ):

						def __init__(		self :List[Any]			):
										'''simple docstring'''

										super().__init__()
										__magic_name__						:						Tuple										=nn.Linear(3     ,			4			)
										__magic_name__						:						Union[str, Any]										=nn.BatchNormad(4			)
										__magic_name__						:						List[str]										=nn.Linear(4     ,			5			)


						def      A__    (		self :Dict     ,			__snake_case :Tuple			):
										'''simple docstring'''

										return self.lineara(self.batchnorm(self.lineara(__snake_case			)			)			)




class 		__A   (      UpperCamelCase__      ):

						def      A__    (		self :Any     ,			__snake_case :Optional[Any]     ,			*__snake_case :List[Any]     ,			**__snake_case :Any			):
										'''simple docstring'''

										return (args[0] + 1,) + args[1:], kwargs




class 		__A   (      UpperCamelCase__      ):

						def      A__    (		self :List[str]     ,			__snake_case :Tuple     ,			__snake_case :Union[str, Any]			):
										'''simple docstring'''

										return output + 1




class 		__A   (      unittest.TestCase      ):

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						int										=ModelForTest()
										__magic_name__						:						Tuple										=ModelHook()

										add_hook_to_module(__snake_case     ,			__snake_case			)
										self.assertEqual(test_model._hf_hook     ,			__snake_case			)
										self.assertTrue(hasattr(__snake_case     ,			"""_old_forward"""			)			)

										# Check adding the hook did not change the name or the signature
										self.assertEqual(test_model.forward.__name__     ,			"""forward"""			)
										self.assertListEqual(list(inspect.signature(test_model.forward			).parameters			)     ,			["""x"""]			)

										remove_hook_from_module(__snake_case			)
										self.assertFalse(hasattr(__snake_case     ,			"""_hf_hook"""			)			)
										self.assertFalse(hasattr(__snake_case     ,			"""_old_forward"""			)			)

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__						:						int										=ModelForTest()
										__magic_name__						:						List[str]										=ModelHook()

										add_hook_to_module(__snake_case     ,			__snake_case			)
										add_hook_to_module(__snake_case     ,			__snake_case     ,			append=__snake_case			)

										self.assertEqual(isinstance(test_model._hf_hook     ,			__snake_case			)     ,			__snake_case			)
										self.assertEqual(len(test_model._hf_hook.hooks			)     ,			2			)
										self.assertTrue(hasattr(__snake_case     ,			"""_old_forward"""			)			)

										# Check adding the hook did not change the name or the signature
										self.assertEqual(test_model.forward.__name__     ,			"""forward"""			)
										self.assertListEqual(list(inspect.signature(test_model.forward			).parameters			)     ,			["""x"""]			)

										remove_hook_from_module(__snake_case			)
										self.assertFalse(hasattr(__snake_case     ,			"""_hf_hook"""			)			)
										self.assertFalse(hasattr(__snake_case     ,			"""_old_forward"""			)			)

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Any										=ModelForTest()
										__magic_name__						:						Any										=torch.randn(2     ,			3			)
										__magic_name__						:						Any										=test_model(x + 1			)
										__magic_name__						:						Optional[Any]										=test_model(x + 2			)

										__magic_name__						:						int										=PreForwardHook()
										add_hook_to_module(__snake_case     ,			__snake_case			)
										__magic_name__						:						int										=test_model(__snake_case			)
										self.assertTrue(torch.allclose(__snake_case     ,			__snake_case     ,			atol=1E-5			)			)

										# Attaching a hook to a model when it already has one replaces, does not chain
										__magic_name__						:						str										=PreForwardHook()
										add_hook_to_module(__snake_case     ,			__snake_case			)
										__magic_name__						:						List[str]										=test_model(__snake_case			)
										self.assertTrue(torch.allclose(__snake_case     ,			__snake_case     ,			atol=1E-5			)			)

										# You need to use the sequential hook to chain two or more hooks
										__magic_name__						:						Optional[Any]										=SequentialHook(PreForwardHook()     ,			PreForwardHook()			)
										add_hook_to_module(__snake_case     ,			__snake_case			)

										__magic_name__						:						Any										=test_model(__snake_case			)
										assert torch.allclose(__snake_case     ,			__snake_case     ,			atol=1E-5			)

						def      A__    (		self :Any			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=ModelForTest()
										__magic_name__						:						Dict										=torch.randn(2     ,			3			)
										__magic_name__						:						Any										=test_model(__snake_case			)

										__magic_name__						:						Dict										=PostForwardHook()
										add_hook_to_module(__snake_case     ,			__snake_case			)
										__magic_name__						:						Any										=test_model(__snake_case			)
										self.assertTrue(torch.allclose(__snake_case     ,			output + 1     ,			atol=1E-5			)			)

										# Attaching a hook to a model when it already has one replaces, does not chain
										__magic_name__						:						Union[str, Any]										=PostForwardHook()
										add_hook_to_module(__snake_case     ,			__snake_case			)
										__magic_name__						:						Optional[int]										=test_model(__snake_case			)
										self.assertTrue(torch.allclose(__snake_case     ,			output + 1     ,			atol=1E-5			)			)

										# You need to use the sequential hook to chain two or more hooks
										__magic_name__						:						Union[str, Any]										=SequentialHook(PostForwardHook()     ,			PostForwardHook()			)
										add_hook_to_module(__snake_case     ,			__snake_case			)

										__magic_name__						:						Union[str, Any]										=test_model(__snake_case			)
										assert torch.allclose(__snake_case     ,			output + 2     ,			atol=1E-5			)

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=ModelForTest()
										__magic_name__						:						int										=torch.randn(2     ,			3			)
										__magic_name__						:						Union[str, Any]										=test_model(__snake_case			)

										__magic_name__						:						Union[str, Any]										=PostForwardHook()
										add_hook_to_module(__snake_case     ,			__snake_case			)
										__magic_name__						:						Dict										=test_model(__snake_case			)
										self.assertTrue(torch.allclose(__snake_case     ,			output + 1			)			)
										self.assertTrue(outputa.requires_grad			)

										__magic_name__						:						Any										=True
										__magic_name__						:						Any										=test_model(__snake_case			)
										self.assertFalse(outputa.requires_grad			)

						@require_multi_gpu
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=ModelForTest()
										# Everything is on CPU
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""cpu"""			)			)
										self.assertEqual(model.batchnorm.weight.device     ,			torch.device("""cpu"""			)			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""cpu"""			)			)

										# This will move each submodule on different devices
										add_hook_to_module(model.lineara     ,			AlignDevicesHook(execution_device=0			)			)
										add_hook_to_module(model.batchnorm     ,			AlignDevicesHook(execution_device=0			)			)
										add_hook_to_module(model.lineara     ,			AlignDevicesHook(execution_device=1			)			)

										self.assertEqual(model.lineara.weight.device     ,			torch.device(0			)			)
										self.assertEqual(model.batchnorm.weight.device     ,			torch.device(0			)			)
										self.assertEqual(model.batchnorm.running_mean.device     ,			torch.device(0			)			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device(1			)			)

										# We can still make a forward pass. The input does not need to be on any particular device
										__magic_name__						:						List[Any]										=torch.randn(2     ,			3			)
										__magic_name__						:						Optional[Any]										=model(__snake_case			)
										self.assertEqual(output.device     ,			torch.device(1			)			)

										# We can add a general hook to put back output on same device as input.
										add_hook_to_module(__snake_case     ,			AlignDevicesHook(io_same_device=__snake_case			)			)
										__magic_name__						:						int										=torch.randn(2     ,			3			).to(0			)
										__magic_name__						:						Optional[int]										=model(__snake_case			)
										self.assertEqual(output.device     ,			torch.device(0			)			)

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						int										=ModelForTest()

										# Everything is on CPU
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""cpu"""			)			)
										self.assertEqual(model.batchnorm.weight.device     ,			torch.device("""cpu"""			)			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""cpu"""			)			)

										# This will move each submodule on different devices
										__magic_name__						:						int										={"""execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True}

										add_hook_to_module(model.lineara     ,			AlignDevicesHook(**__snake_case			)			)
										add_hook_to_module(model.batchnorm     ,			AlignDevicesHook(**__snake_case			)			)
										add_hook_to_module(model.lineara     ,			AlignDevicesHook(**__snake_case			)			)

										# Parameters have been offloaded, so on the meta device
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""meta"""			)			)
										self.assertEqual(model.batchnorm.weight.device     ,			torch.device("""meta"""			)			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""meta"""			)			)
										# Buffers are not included in the offload by default, so are on the execution device
										__magic_name__						:						Optional[int]										=torch.device(hook_kwargs["""execution_device"""]			)
										self.assertEqual(model.batchnorm.running_mean.device     ,			__snake_case			)

										__magic_name__						:						Union[str, Any]										=torch.randn(2     ,			3			)
										__magic_name__						:						Optional[int]										=model(__snake_case			)
										self.assertEqual(output.device     ,			__snake_case			)

										# Removing hooks loads back the weights in the model.
										remove_hook_from_module(model.lineara			)
										remove_hook_from_module(model.batchnorm			)
										remove_hook_from_module(model.lineara			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""cpu"""			)			)
										self.assertEqual(model.batchnorm.weight.device     ,			torch.device("""cpu"""			)			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""cpu"""			)			)

										# Now test with buffers included in the offload
										__magic_name__						:						Tuple										={
										    """execution_device""": 0 if torch.cuda.is_available() else """cpu""",
										    """offload""": True,
										    """offload_buffers""": True,
										}

										add_hook_to_module(model.lineara     ,			AlignDevicesHook(**__snake_case			)			)
										add_hook_to_module(model.batchnorm     ,			AlignDevicesHook(**__snake_case			)			)
										add_hook_to_module(model.lineara     ,			AlignDevicesHook(**__snake_case			)			)

										# Parameters have been offloaded, so on the meta device, buffers included
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""meta"""			)			)
										self.assertEqual(model.batchnorm.weight.device     ,			torch.device("""meta"""			)			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""meta"""			)			)
										self.assertEqual(model.batchnorm.running_mean.device     ,			torch.device("""meta"""			)			)

										__magic_name__						:						Tuple										=torch.randn(2     ,			3			)
										__magic_name__						:						int										=model(__snake_case			)
										self.assertEqual(output.device     ,			__snake_case			)

										# Removing hooks loads back the weights in the model.
										remove_hook_from_module(model.lineara			)
										remove_hook_from_module(model.batchnorm			)
										remove_hook_from_module(model.lineara			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""cpu"""			)			)
										self.assertEqual(model.batchnorm.weight.device     ,			torch.device("""cpu"""			)			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""cpu"""			)			)

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						Any										=ModelForTest()

										# Everything is on CPU
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""cpu"""			)			)
										self.assertEqual(model.batchnorm.weight.device     ,			torch.device("""cpu"""			)			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""cpu"""			)			)

										# This will move each submodule on different devices
										__magic_name__						:						str										=0 if torch.cuda.is_available() else """cpu"""
										attach_align_device_hook(__snake_case     ,			execution_device=__snake_case     ,			offload=__snake_case			)

										# Parameters have been offloaded, so on the meta device
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""meta"""			)			)
										self.assertEqual(model.batchnorm.weight.device     ,			torch.device("""meta"""			)			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""meta"""			)			)
										# Buffers are not included in the offload by default, so are on the execution device
										__magic_name__						:						Optional[Any]										=torch.device(__snake_case			)
										self.assertEqual(model.batchnorm.running_mean.device     ,			__snake_case			)

										__magic_name__						:						List[Any]										=torch.randn(2     ,			3			)
										__magic_name__						:						str										=model(__snake_case			)
										self.assertEqual(output.device     ,			__snake_case			)

										# Removing hooks loads back the weights in the model.
										remove_hook_from_submodules(__snake_case			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""cpu"""			)			)
										self.assertEqual(model.batchnorm.weight.device     ,			torch.device("""cpu"""			)			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""cpu"""			)			)

										# Now test with buffers included in the offload
										attach_align_device_hook(__snake_case     ,			execution_device=__snake_case     ,			offload=__snake_case     ,			offload_buffers=__snake_case			)

										# Parameters have been offloaded, so on the meta device, buffers included
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""meta"""			)			)
										self.assertEqual(model.batchnorm.weight.device     ,			torch.device("""meta"""			)			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""meta"""			)			)
										self.assertEqual(model.batchnorm.running_mean.device     ,			torch.device("""meta"""			)			)

										__magic_name__						:						Optional[int]										=torch.randn(2     ,			3			)
										__magic_name__						:						Union[str, Any]										=model(__snake_case			)
										self.assertEqual(output.device     ,			__snake_case			)

										# Removing hooks loads back the weights in the model.
										remove_hook_from_submodules(__snake_case			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""cpu"""			)			)
										self.assertEqual(model.batchnorm.weight.device     ,			torch.device("""cpu"""			)			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""cpu"""			)			)


						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Dict										=ModelForTest()

										# Everything is on CPU
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""cpu"""			)			)
										self.assertEqual(model.batchnorm.weight.device     ,			torch.device("""cpu"""			)			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""cpu"""			)			)

										# This will move each submodule on different devices
										__magic_name__						:						List[str]										=0 if torch.cuda.is_available() else """cpu"""
										attach_align_device_hook(
										    __snake_case     ,			execution_device=__snake_case     ,			offload=__snake_case     ,			weights_map=model.state_dict()			)

										# Parameters have been offloaded, so on the meta device
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""meta"""			)			)
										self.assertEqual(model.batchnorm.weight.device     ,			torch.device("""meta"""			)			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""meta"""			)			)
										# Buffers are not included in the offload by default, so are on the execution device
										__magic_name__						:						Optional[Any]										=torch.device(__snake_case			)
										self.assertEqual(model.batchnorm.running_mean.device     ,			__snake_case			)

										__magic_name__						:						int										=torch.randn(2     ,			3			)
										__magic_name__						:						Any										=model(__snake_case			)
										self.assertEqual(output.device     ,			__snake_case			)

										# Removing hooks loads back the weights in the model.
										remove_hook_from_submodules(__snake_case			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""cpu"""			)			)
										self.assertEqual(model.batchnorm.weight.device     ,			torch.device("""cpu"""			)			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""cpu"""			)			)

										# Now test with buffers included in the offload
										attach_align_device_hook(
										    __snake_case     ,			execution_device=__snake_case     ,			offload=__snake_case     ,			weights_map=model.state_dict()     ,			offload_buffers=__snake_case     ,			)

										# Parameters have been offloaded, so on the meta device, buffers included
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""meta"""			)			)
										self.assertEqual(model.batchnorm.weight.device     ,			torch.device("""meta"""			)			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""meta"""			)			)
										self.assertEqual(model.batchnorm.running_mean.device     ,			torch.device("""meta"""			)			)

										__magic_name__						:						List[Any]										=torch.randn(2     ,			3			)
										__magic_name__						:						str										=model(__snake_case			)
										self.assertEqual(output.device     ,			__snake_case			)

										# Removing hooks loads back the weights in the model.
										remove_hook_from_submodules(__snake_case			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""cpu"""			)			)
										self.assertEqual(model.batchnorm.weight.device     ,			torch.device("""cpu"""			)			)
										self.assertEqual(model.lineara.weight.device     ,			torch.device("""cpu"""			)			)




