

import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def 							__SCREAMING_SNAKE_CASE     (   )					->				str:
	raise RuntimeError("""CUDA out of memory."""       )






class      lowerCamelCase_      (	nn.Module	):




	def __init__(     self						:     str							):
		super().__init__()
		__A :			Optional[int]		    =			nn.Linear(3					,      4							)
		__A :			List[str]		    =			nn.BatchNormad(4							)
		__A :			int		    =			nn.Linear(4					,      5							)




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     Union[str, Any]							):
		return self.lineara(self.batchnorm(self.lineara(__A							)							)							)






class      lowerCamelCase_      (	unittest.TestCase	):




	def        lowerCAmelCase_       (     self						:     int							):
		__A :			int		    =			[]

		@find_executable_batch_size(starting_batch_size=128							)
		def mock_training_loop_function(__A						:     Optional[int]							):
			nonlocal batch_sizes
			batch_sizes.append(__A							)
			if batch_size != 8:
				raise_fake_out_of_memory()

		mock_training_loop_function()
		self.assertListEqual(__A					,      [128, 64, 32, 16, 8]							)




	def        lowerCAmelCase_       (     self						:     Any							):
		__A :			Dict		    =			[]

		@find_executable_batch_size(starting_batch_size=128							)
		def mock_training_loop_function(__A						:     str					,      __A						:     Any							):
			nonlocal batch_sizes
			batch_sizes.append(__A							)
			if batch_size != 8:
				raise_fake_out_of_memory()
			return batch_size, arga

		__A    ,				__A :			Union[str, Any]		    =			mock_training_loop_function("""hello"""							)
		self.assertListEqual(__A					,      [128, 64, 32, 16, 8]							)
		self.assertListEqual([bs, arga]					,      [8, """hello"""]							)




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		@find_executable_batch_size(starting_batch_size=0							)
		def mock_training_loop_function(__A						:     List[Any]							):
			pass

		with self.assertRaises(__A							) as cm:
			mock_training_loop_function()
			self.assertIn("""No executable batch size found, reached zero."""					,      cm.exception.args[0]							)




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		@find_executable_batch_size(starting_batch_size=16							)
		def mock_training_loop_function(__A						:     Union[str, Any]							):
			if batch_size > 0:
				raise_fake_out_of_memory()
			pass

		with self.assertRaises(__A							) as cm:
			mock_training_loop_function()
			self.assertIn("""No executable batch size found, reached zero."""					,      cm.exception.args[0]							)




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		@find_executable_batch_size(starting_batch_size=128							)
		def mock_training_loop_function(__A						:     str					,      __A						:     List[str]					,      __A						:     Union[str, Any]							):
			if batch_size != 8:
				raise raise_fake_out_of_memory()

		with self.assertRaises(__A							) as cm:
			mock_training_loop_function(128					,      """hello"""					,      """world"""							)
			self.assertIn("""Batch size was passed into `f`"""					,      cm.exception.args[0]							)
			self.assertIn("""`f(arg1='hello', arg2='world')"""					,      cm.exception.args[0]							)




	def        lowerCAmelCase_       (     self						:     Tuple							):
		@find_executable_batch_size(starting_batch_size=16							)
		def mock_training_loop_function(__A						:     Any							):
			raise ValueError("""Oops, we had an error!"""							)

		with self.assertRaises(__A							) as cm:
			mock_training_loop_function()
			self.assertIn("""Oops, we had an error!"""					,      cm.exception.args[0]							)




	@require_cuda
	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			Optional[int]		    =			torch.cuda.memory_allocated()
		__A :			Optional[Any]		    =			ModelForTest()
		model.cuda()
		self.assertGreater(torch.cuda.memory_allocated()					,      __A							)
		__A :			Optional[int]		    =			release_memory(__A							)
		self.assertEqual(torch.cuda.memory_allocated()					,      __A							)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase_							:					Optional[Any]		 =   {
    '''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
    '''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
    '''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
    '''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					Optional[Any]		 =   [
			    '''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''Wav2Vec2ForAudioFrameClassification''',
			    '''Wav2Vec2ForCTC''',
			    '''Wav2Vec2ForMaskedLM''',
			    '''Wav2Vec2ForPreTraining''',
			    '''Wav2Vec2ForSequenceClassification''',
			    '''Wav2Vec2ForXVector''',
			    '''Wav2Vec2Model''',
			    '''Wav2Vec2PreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					List[Any]		 =   [
			    '''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFWav2Vec2ForCTC''',
			    '''TFWav2Vec2Model''',
			    '''TFWav2Vec2PreTrainedModel''',
			    '''TFWav2Vec2ForSequenceClassification''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					Dict		 =   [
			    '''FlaxWav2Vec2ForCTC''',
			    '''FlaxWav2Vec2ForPreTraining''',
			    '''FlaxWav2Vec2Model''',
			    '''FlaxWav2Vec2PreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
			from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
			from .processing_wavaveca import WavaVecaProcessor
			from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavaveca import (
						    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavaVecaForAudioFrameClassification,
						    WavaVecaForCTC,
						    WavaVecaForMaskedLM,
						    WavaVecaForPreTraining,
						    WavaVecaForSequenceClassification,
						    WavaVecaForXVector,
						    WavaVecaModel,
						    WavaVecaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_wavaveca import (
						    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFWavaVecaForCTC,
						    TFWavaVecaForSequenceClassification,
						    TFWavaVecaModel,
						    TFWavaVecaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_wavaveca import (
						    FlaxWavaVecaForCTC,
						    FlaxWavaVecaForPreTraining,
						    FlaxWavaVecaModel,
						    FlaxWavaVecaPreTrainedModel,
						)


else:
			import sys

			UpperCAmelCase_							:					Optional[Any]		 =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





