code: 
  
  
  
  
  import argparse
  import os
  from functools import reduce
  
  import fairseq
  import torch
  from datasets import load_dataset
  
  from transformers import WavaVecaProcessor, logging
  from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig
  
  # Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
  from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
  from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel
  
  
  logging.set_verbosity_info()
  SCREAMING_SNAKE_CASE__    :    List[Any]								=					logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE__    :    Union[str, Any]								=					{
      "post_extract_proj": "feature_projection.projection",
      "models.0.layer_norm": "feature_projection.layer_norm",
      "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
      "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
      "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
      "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
      "self_attn_layer_norm": "encoder.layers.*.layer_norm",
      "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
      "fc2": "encoder.layers.*.feed_forward.output_dense",
      "final_layer_norm": "encoder.layers.*.final_layer_norm",
      "encoder.layer_norm": "encoder.layer_norm",
      "w2v_model.layer_norm": "feature_projection.layer_norm",
      "w2v_encoder.proj": "lm_head",
      "mask_emb": "masked_spec_embed",
  }
  SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					[
      "lm_head",
  ]
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Tuple:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			for attribute in key.split('.'   ):
  						SCREAMING_SNAKE_CASE_			=	getattr(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  
  			if weight_type is not None:
  						SCREAMING_SNAKE_CASE_			=	getattr(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ).shape
  			else:
  						SCREAMING_SNAKE_CASE_			=	hf_pointer.shape
  
  			if hf_shape != value.shape:
  						raise ValueError(
  						    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
  						    F' {value.shape} for {full_name}'   )
  
  			if weight_type == "weight":
  						SCREAMING_SNAKE_CASE_			=	value
  			elif weight_type == "weight_g":
  						SCREAMING_SNAKE_CASE_			=	value
  			elif weight_type == "weight_v":
  						SCREAMING_SNAKE_CASE_			=	value
  			elif weight_type == "bias":
  						SCREAMING_SNAKE_CASE_			=	value
  			else:
  						SCREAMING_SNAKE_CASE_			=	value
  
  			logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       List[Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	[]
  			SCREAMING_SNAKE_CASE_			=	fairseq_model.state_dict()
  
  			if not is_headless:
  						SCREAMING_SNAKE_CASE_			=	hf_model.dataavec_audio.feature_extractor
  						SCREAMING_SNAKE_CASE_			=	hf_model.dataavec_audio.encoder.pos_conv_embed
  
  			else:
  						SCREAMING_SNAKE_CASE_			=	hf_model.feature_extractor
  						SCREAMING_SNAKE_CASE_			=	hf_model.encoder.pos_conv_embed
  
  			for name, value in fairseq_dict.items():
  						SCREAMING_SNAKE_CASE_			=	False
  						if "conv_layers" in name:
  									load_conv_layer(
  									    SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						)
  									SCREAMING_SNAKE_CASE_			=	True
  						elif "pos_conv" in name:
  									load_pos_conv_layer(
  									    SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						)
  									SCREAMING_SNAKE_CASE_			=	True
  						else:
  									for key, mapped_key in MAPPING.items():
  												if not is_headless:
  															SCREAMING_SNAKE_CASE_			=	'data2vec_audio.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
  												if key in name or key.split('w2v_model.'   )[-1] == name.split('.'   )[0]:
  															SCREAMING_SNAKE_CASE_			=	True
  															if "*" in mapped_key:
  																		SCREAMING_SNAKE_CASE_			=	name.split(SCREAMING_SNAKE_CASE   )[0].split('.'   )[-2]
  																		SCREAMING_SNAKE_CASE_			=	mapped_key.replace('*'					,						SCREAMING_SNAKE_CASE   )
  															if "weight_g" in name:
  																		SCREAMING_SNAKE_CASE_			=	'weight_g'
  															elif "weight_v" in name:
  																		SCREAMING_SNAKE_CASE_			=	'weight_v'
  															elif "bias" in name:
  																		SCREAMING_SNAKE_CASE_			=	'bias'
  															elif "weight" in name:
  																		# TODO: don't match quantizer.weight_proj
  																		SCREAMING_SNAKE_CASE_			=	'weight'
  															else:
  																		SCREAMING_SNAKE_CASE_			=	None
  															set_recursively(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  												continue
  						if not is_used:
  									unused_weights.append(SCREAMING_SNAKE_CASE   )
  
  			logger.warning(F'Unused weights: {unused_weights}'   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Any:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	path.split('.'   )
  			return reduce(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Optional[int]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	access_by_string(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	hf_weight.data
  
  			if fsq_value.shape != hf_value.shape:
  						raise ValueError(F'{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'   )
  			SCREAMING_SNAKE_CASE_			=	fsq_value
  			logger.info(F'{full_name} was correctly initialized from {hf_weight_path}.'   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Tuple:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	full_name.split('conv_layers.'   )[-1]
  			SCREAMING_SNAKE_CASE_			=	name.split('.'   )
  			SCREAMING_SNAKE_CASE_			=	int(items[0]   )
  			SCREAMING_SNAKE_CASE_			=	int(items[1]   )
  
  			SCREAMING_SNAKE_CASE_			=	name.split('.'   )[-1]
  			if type_id == 0:
  						SCREAMING_SNAKE_CASE_			=	'conv'
  			elif type_id == 2:
  						SCREAMING_SNAKE_CASE_			=	'layer_norm'
  			else:
  						unused_weights.append(SCREAMING_SNAKE_CASE   )
  						return
  
  			set_weights(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						F'conv_layers.{layer_id}.{layer_type}.{weight_type}'   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       List[str]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	full_name.split('pos_conv.'   )[-1]
  			SCREAMING_SNAKE_CASE_			=	name.split('.'   )
  			SCREAMING_SNAKE_CASE_			=	int(items[0]   )
  			SCREAMING_SNAKE_CASE_			=	int(items[1]   )
  
  			SCREAMING_SNAKE_CASE_			=	name.split('.'   )[-1]
  			if type_id != 0:
  						unused_weights.append(SCREAMING_SNAKE_CASE   )
  						return
  			else:
  						SCREAMING_SNAKE_CASE_			=	'conv'
  
  			set_weights(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						F'layers.{layer_id}.{layer_type}.{weight_type}'   )
  
  @torch.no_grad()
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE=None					,						SCREAMING_SNAKE_CASE=None					,						SCREAMING_SNAKE_CASE=True   ) ->       Any:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			if config_path is not None:
  						SCREAMING_SNAKE_CASE_			=	DataaVecAudioConfig.from_pretrained(SCREAMING_SNAKE_CASE   )
  			else:
  						SCREAMING_SNAKE_CASE_			=	DataaVecAudioConfig()
  
  			if not is_finetuned:
  						# Modify final_proj layer name
  						SCREAMING_SNAKE_CASE_			=	DataaVecAudioModel(SCREAMING_SNAKE_CASE   )
  						SCREAMING_SNAKE_CASE_			=	os.path.dirname(SCREAMING_SNAKE_CASE   )
  
  						SCREAMING_SNAKE_CASE_			=	torch.load(SCREAMING_SNAKE_CASE   )
  						SCREAMING_SNAKE_CASE_			=	state_dict['model'].pop('final_proj.0.weight'   )
  						SCREAMING_SNAKE_CASE_			=	state_dict['model'].pop('final_proj.0.bias'   )
  						SCREAMING_SNAKE_CASE_			=	os.path.join(SCREAMING_SNAKE_CASE					,						'converted.pt'   )
  						torch.save(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  			else:
  						SCREAMING_SNAKE_CASE_			=	DataaVecAudioForCTC(SCREAMING_SNAKE_CASE   )
  						SCREAMING_SNAKE_CASE_			=	checkpoint_path
  
  			def load_dataavec(SCREAMING_SNAKE_CASE   ):
  						SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	fairseq.checkpoint_utils.load_model_ensemble_and_task([path]   )
  						return model[0].eval()
  
  			SCREAMING_SNAKE_CASE_			=	load_dataavec(SCREAMING_SNAKE_CASE   )
  
  			recursively_load_weights(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						not is_finetuned   )
  
  			SCREAMING_SNAKE_CASE_			=	WavaVecaProcessor.from_pretrained('facebook/wav2vec2-large-lv60'   )
  
  			SCREAMING_SNAKE_CASE_			=	load_dataset('patrickvonplaten/librispeech_asr_dummy'					,						'clean'					,						split='validation'   )
  			SCREAMING_SNAKE_CASE_			=	[x['array'] for x in ds[:4]['audio']]
  
  			SCREAMING_SNAKE_CASE_			=	processor(SCREAMING_SNAKE_CASE					,						return_tensors='pt'					,						padding=SCREAMING_SNAKE_CASE   )
  
  			SCREAMING_SNAKE_CASE_			=	inputs.input_values
  			SCREAMING_SNAKE_CASE_			=	inputs.attention_mask
  			#    input_values = inputs.input_values[:, :-1]
  			#    attention_mask = inputs.attention_mask[:, :-1]
  
  			hf_wavavec.eval()
  			model.eval()
  			if is_finetuned:
  						SCREAMING_SNAKE_CASE_			=	model(source=SCREAMING_SNAKE_CASE					,						padding_mask=(1 - attention_mask)					,						mask=SCREAMING_SNAKE_CASE					,						features_only=SCREAMING_SNAKE_CASE   )[
  						    'encoder_out'
  						].transpose(0					,						1   )
  						SCREAMING_SNAKE_CASE_			=	hf_wavavec(SCREAMING_SNAKE_CASE					,						attention_mask=SCREAMING_SNAKE_CASE   )['logits']
  
  						SCREAMING_SNAKE_CASE_			=	torch.argmax(SCREAMING_SNAKE_CASE					,						dim=-1   )
  						SCREAMING_SNAKE_CASE_			=	processor.batch_decode(SCREAMING_SNAKE_CASE   )
  
  						print(F'Expected Output: {ds[:4]["text"]}, Pred: {output_string}'   )
  			else:
  						SCREAMING_SNAKE_CASE_			=	model(source=SCREAMING_SNAKE_CASE					,						padding_mask=(1 - attention_mask)					,						mask=SCREAMING_SNAKE_CASE					,						features_only=SCREAMING_SNAKE_CASE   )[
  						    'layer_results'
  						][-1][0].transpose(0					,						1   )
  						SCREAMING_SNAKE_CASE_			=	hf_wavavec(SCREAMING_SNAKE_CASE					,						attention_mask=SCREAMING_SNAKE_CASE   )['last_hidden_state']
  
  			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(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						atol=1E-3   )
  			print('Do both models output the same tensors?'					,						'🔥' if success else '💩'   )
  			if not success:
  						raise Exception('Something went wRoNg'   )
  
  			hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE   )
  
  			if is_finetuned:
  						processor.save_pretrained(SCREAMING_SNAKE_CASE   )
  			else:
  						processor.feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE   )
  
  
  if __name__ == "__main__":
  				SCREAMING_SNAKE_CASE__    :    Any								=					argparse.ArgumentParser()
  				parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  				parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
  				parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
  				parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  				parser.add_argument(
  				    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
  				)
  				SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					parser.parse_args()
  				convert_wavaveca_checkpoint(
  				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  				)
  
style_context: 
  
  
  
  
  import os
  import pickle
  import unittest
  from collections import UserDict, namedtuple
  
  import torch
  
  from accelerate.test_utils.testing import require_cuda, require_torch_min_version
  from accelerate.test_utils.training import RegressionModel
  from accelerate.utils import (
      convert_outputs_to_fpaa,
      extract_model_from_parallel,
      find_device,
      listify,
      patch_environment,
      recursively_apply,
      send_to_device,
  )
  
  
  SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					namedtuple("ExampleNamedTuple", "a b c")
  
  
  
  class 			a_					(    unittest.TestCase    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	torch.randn(5			,    2			)
  				SCREAMING_SNAKE_CASE_			=	torch.device('cuda'			) if torch.cuda.is_available() else torch.device('cpu'			)
  
  				SCREAMING_SNAKE_CASE_			=	send_to_device(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta.cpu()			,    SCREAMING_SNAKE_CASE			)			)
  
  				SCREAMING_SNAKE_CASE_			=	send_to_device((tensor, [tensor, tensor], 1)			,    SCREAMING_SNAKE_CASE			)
  				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta[0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertIsInstance(resulta[1]			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta[1][0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertTrue(torch.equal(resulta[1][1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertEqual(resulta[2]			,    1			)
  
  				SCREAMING_SNAKE_CASE_			=	send_to_device({'a': tensor, 'b': [tensor, tensor], 'c': 1}			,    SCREAMING_SNAKE_CASE			)
  				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta['a'].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertIsInstance(resulta['b']			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta['b'][0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertTrue(torch.equal(resulta['b'][1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertEqual(resulta['c']			,    1			)
  
  				SCREAMING_SNAKE_CASE_			=	send_to_device(ExampleNamedTuple(a=SCREAMING_SNAKE_CASE			,    b=[tensor, tensor]			,    c=1			)			,    SCREAMING_SNAKE_CASE			)
  				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta.a.cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertIsInstance(resulta.b			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta.b[0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertTrue(torch.equal(resulta.b[1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertEqual(resulta.c			,    1			)
  
  				SCREAMING_SNAKE_CASE_			=	send_to_device(UserDict({'a': tensor, 'b': [tensor, tensor], 'c': 1}			)			,    SCREAMING_SNAKE_CASE			)
  				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta['a'].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertIsInstance(resulta['b']			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta['b'][0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertTrue(torch.equal(resulta['b'][1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertEqual(resulta['c']			,    1			)
  
  
  
  
  
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				with self.assertRaises(SCREAMING_SNAKE_CASE			) as cm:
  							SCREAMING_SNAKE_CASE_			=	recursively_apply(torch.tensor			,    (torch.tensor(1			), 1)			,    error_on_other_type=SCREAMING_SNAKE_CASE			)
  				self.assertEqual(
  				    str(cm.exception			)			,    'Unsupported types (<class \'int\'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.'			,    )
  
  
  
  
  
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	torch.tensor([1, 2, 3, 4, 5]			)
  				self.assertEqual(listify(SCREAMING_SNAKE_CASE			)			,    [1, 2, 3, 4, 5]			)
  
  				SCREAMING_SNAKE_CASE_			=	torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]			)
  				self.assertEqual(listify(SCREAMING_SNAKE_CASE			)			,    [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]			)
  
  				SCREAMING_SNAKE_CASE_			=	torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]			)
  				self.assertEqual(
  				    listify(SCREAMING_SNAKE_CASE			)			,    [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				with patch_environment(aa=1			,    BB=2			):
  							self.assertEqual(os.environ.get('AA'			)			,    '1'			)
  							self.assertEqual(os.environ.get('BB'			)			,    '2'			)
  
  				self.assertNotIn('AA'			,    os.environ			)
  				self.assertNotIn('BB'			,    os.environ			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	RegressionModel()
  				SCREAMING_SNAKE_CASE_			=	model.forward
  				SCREAMING_SNAKE_CASE_			=	convert_outputs_to_fpaa(model.forward			)
  				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			,    keep_fpaa_wrapper=SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	pickle.dumps(SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	@require_cuda
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	RegressionModel()
  				SCREAMING_SNAKE_CASE_			=	model.forward
  				SCREAMING_SNAKE_CASE_			=	torch.cuda.amp.autocast(dtype=torch.floataa			)(model.forward			)
  				SCREAMING_SNAKE_CASE_			=	convert_outputs_to_fpaa(model.forward			)
  				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			,    keep_fpaa_wrapper=SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	pickle.dumps(SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	@require_cuda
  	@require_torch_min_version(version='2.0'			)
  	def 	A_(       self			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	RegressionModel()
  				SCREAMING_SNAKE_CASE_			=	model.forward
  				SCREAMING_SNAKE_CASE_			=	torch.cuda.amp.autocast(dtype=torch.floataa			)(model.forward			)
  				SCREAMING_SNAKE_CASE_			=	convert_outputs_to_fpaa(model.forward			)
  				SCREAMING_SNAKE_CASE_			=	torch.compile(model.forward			,    backend='inductor'			)
  				SCREAMING_SNAKE_CASE_			=	torch.randn(4			,    10			).cuda()
  				SCREAMING_SNAKE_CASE_			=	model(SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			)     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	RegressionModel()
  				# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
  				SCREAMING_SNAKE_CASE_			=	torch.nn.parallel.DataParallel(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			)
  
  				self.assertEqual(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	@require_torch_min_version(version='2.0'			)
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	RegressionModel()
  				SCREAMING_SNAKE_CASE_			=	torch.compile(SCREAMING_SNAKE_CASE			)
  
  				# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
  				SCREAMING_SNAKE_CASE_			=	torch.nn.parallel.DataParallel(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	torch.compile(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			)
  
  				self.assertEqual(compiled_model._orig_mod			,    compiled_model_unwrapped._orig_mod			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				self.assertEqual(find_device([1, 'a', torch.tensor([1, 2, 3]			)]			)			,    torch.device('cpu'			)			)
  				self.assertEqual(find_device({'a': 1, 'b': torch.tensor([1, 2, 3]			)}			)			,    torch.device('cpu'			)			)
  				self.assertIsNone(find_device([1, 'a']			)			)
  