



"""simple docstring"""






import json
import os
import tempfile
from unittest.mock import patch

import torch
from torch.utils.data import DataLoader, TensorDataset

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment






def 					snake_case__       (		):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]		     =torch.nn.Linear(2							,		4     )
			lowerCamelCase__		:		List[str]		     =torch.optim.AdamW(model.parameters()							,		lr=1.0     )
			lowerCamelCase__		:		int		     =torch.optim.lr_scheduler.OneCycleLR(__lowerCamelCase							,		max_lr=0.01							,		steps_per_epoch=2							,		epochs=1     )
			lowerCamelCase__		:		Dict		     =DataLoader(TensorDataset(torch.tensor([1, 2, 3]     )     )     )
			lowerCamelCase__		:		Optional[int]		     =DataLoader(TensorDataset(torch.tensor([4, 5, 6]     )     )     )

			return model, optimizer, scheduler, train_dl, valid_dl






def 					snake_case__       (		__lowerCamelCase    :   List[str]     ):
			"""simple docstring"""
			return (model.weight.abs().sum() + model.bias.abs().sum()).item()






def 					snake_case__       (		__lowerCamelCase    :   Union[str, Any]     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]		     =torch.nn.Linear(*tuple(model.weight.T.shape     )     ).state_dict()
			model.load_state_dict(__lowerCamelCase     )

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					@require_cuda
					def 		snake_case	(	self							:  Any					)-> Optional[int]:
								lowerCamelCase__		:		str		     =Accelerator()
								assert PartialState._shared_state["_cpu"] is False
								assert PartialState._shared_state["device"].type == "cuda"
								with self.assertRaises(lowerCamelCase					):
											lowerCamelCase__		:		Union[str, Any]		     =Accelerator(cpu=lowerCamelCase					)



					def 		snake_case	(	self							:  Dict					)-> Tuple:
								lowerCamelCase__		:		List[Any]		     =Accelerator()
								lowerCamelCase__		:		Union[str, Any]		     =GradientState()
								assert state.num_steps == 1
								lowerCamelCase__		:		Tuple		     =4
								assert state.num_steps == 4

								assert state.sync_gradients is True
								lowerCamelCase__		:		Any		     =False
								assert state.sync_gradients is False
								GradientState._reset_state()



					def 		snake_case	(	self							:  Any					)-> Optional[int]:
								lowerCamelCase__		:		Dict		     =Accelerator()
								lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__		:		int		     =create_components()

								(
								    (
								    lowerCamelCase__
								)     ,       (
								    lowerCamelCase__
								)     ,       (
								    lowerCamelCase__
								)     ,       (
								    lowerCamelCase__
								)     ,       (
								    lowerCamelCase__
								)     ,       
								)		:		Optional[int]		     =accelerator.prepare(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase					)

								self.assertTrue(prepared_model in accelerator._models					)
								self.assertTrue(prepared_optimizer in accelerator._optimizers					)
								self.assertTrue(prepared_scheduler in accelerator._schedulers					)
								self.assertTrue(prepared_train_dl in accelerator._dataloaders					)
								self.assertTrue(prepared_valid_dl in accelerator._dataloaders					)



					def 		snake_case	(	self							:  str					)-> Optional[Any]:
								lowerCamelCase__		:		Dict		     =Accelerator()
								lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__		:		Dict		     =create_components()
								accelerator.prepare(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase					)
								accelerator.free_memory()

								self.assertTrue(len(accelerator._models					) == 0					)
								self.assertTrue(len(accelerator._optimizers					) == 0					)
								self.assertTrue(len(accelerator._schedulers					) == 0					)
								self.assertTrue(len(accelerator._dataloaders					) == 0					)



					def 		snake_case	(	self							:  int					)-> Dict:
								PartialState._reset_state()

								# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
								def noop(*lowerCamelCase							:  List[Any],  **lowerCamelCase							:  Tuple					):
											pass

								with patch('''torch.cuda.set_device''',  lowerCamelCase					), patch_environment(ACCELERATE_TORCH_DEVICE='''cuda:64'''					):
											lowerCamelCase__		:		Optional[int]		     =Accelerator()
											self.assertEqual(str(accelerator.state.device					),  '''cuda:64'''					)



					def 		snake_case	(	self							:  str					)-> Any:
								lowerCamelCase__		:		Union[str, Any]		     =Accelerator()
								lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__		:		int		     =create_components()
								accelerator.prepare(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase					)

								lowerCamelCase__		:		Optional[Any]		     =get_signature(lowerCamelCase					)

								with tempfile.TemporaryDirectory() as tmpdirname:
											accelerator.save_state(lowerCamelCase					)

											# make sure random weights don't match
											load_random_weights(lowerCamelCase					)
											self.assertTrue(abs(model_signature - get_signature(lowerCamelCase					)					) > 1E-3					)

											# make sure loaded weights match
											accelerator.load_state(lowerCamelCase					)
											self.assertTrue(abs(model_signature - get_signature(lowerCamelCase					)					) < 1E-3					)



					def 		snake_case	(	self							:  List[Any]					)-> str:
								lowerCamelCase__		:		List[str]		     =Accelerator()
								lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__		:		Tuple		     =create_components()
								accelerator.prepare(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =get_signature(lowerCamelCase					)

								# saving hook
								def save_config(lowerCamelCase							:  List[Any],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Any					):
											lowerCamelCase__		:		Any		     ={'''class_name''': models[0].__class__.__name__}

											with open(os.path.join(lowerCamelCase,  '''data.json'''					),  '''w'''					) as f:
														json.dump(lowerCamelCase,  lowerCamelCase					)

        # loading hook
								def load_config(lowerCamelCase							:  str,  lowerCamelCase							:  Any					):
											with open(os.path.join(lowerCamelCase,  '''data.json'''					),  '''r'''					) as f:
														lowerCamelCase__		:		Tuple		     =json.load(lowerCamelCase					)

											lowerCamelCase__		:		List[Any]		     =config['''class_name''']

								lowerCamelCase__		:		Tuple		     =accelerator.register_save_state_pre_hook(lowerCamelCase					)
								lowerCamelCase__		:		Optional[Any]		     =accelerator.register_load_state_pre_hook(lowerCamelCase					)

								with tempfile.TemporaryDirectory() as tmpdirname:
											accelerator.save_state(lowerCamelCase					)

											# make sure random weights don't match with hooks
											load_random_weights(lowerCamelCase					)
											self.assertTrue(abs(model_signature - get_signature(lowerCamelCase					)					) > 1E-3					)

											# random class name to verify correct one is loaded
											lowerCamelCase__		:		Union[str, Any]		     ='''random'''

											# make sure loaded weights match with hooks
											accelerator.load_state(lowerCamelCase					)
											self.assertTrue(abs(model_signature - get_signature(lowerCamelCase					)					) < 1E-3					)

											# mode.class_name is loaded from config
											self.assertTrue(model.class_name == model.__class__.__name__					)

								# remove hooks
								save_hook.remove()
								load_hook.remove()

								with tempfile.TemporaryDirectory() as tmpdirname:
											accelerator.save_state(lowerCamelCase					)

											# make sure random weights don't match with hooks removed
											load_random_weights(lowerCamelCase					)
											self.assertTrue(abs(model_signature - get_signature(lowerCamelCase					)					) > 1E-3					)

											# random class name to verify correct one is loaded
											lowerCamelCase__		:		List[Any]		     ='''random'''

											# make sure loaded weights match with hooks removed
											accelerator.load_state(lowerCamelCase					)
											self.assertTrue(abs(model_signature - get_signature(lowerCamelCase					)					) < 1E-3					)

											# mode.class_name is NOT loaded from config
											self.assertTrue(model.class_name != model.__class__.__name__					)



					def 		snake_case	(	self							:  Optional[int]					)-> Tuple:
								lowerCamelCase__		:		str		     =Accelerator()
								lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__		:		Any		     =create_components()
								lowerCamelCase__		:		Tuple		     =None

								# This should work
								lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__		:		Optional[Any]		     =accelerator.prepare(
								    lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase					)
								self.assertTrue(dummy_obj is None					)



					def 		snake_case	(	self							:  Dict					)-> Tuple:
								lowerCamelCase__		:		List[str]		     =Accelerator()
								lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__		:		Tuple		     =create_components()
								lowerCamelCase__		:		List[str]		     =[1, 2, 3]

								# This should work
								lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__		:		List[str]		     =accelerator.prepare(
								    lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase					)
								self.assertEqual(
								    getattr(lowerCamelCase,  '''_is_accelerate_prepared''',  lowerCamelCase					),  lowerCamelCase,  '''Dummy object should have `_is_accelerate_prepared` set to `True`''',  )
								self.assertEqual(
								    getattr(lowerCamelCase,  '''_is_accelerate_prepared''',  lowerCamelCase					),  lowerCamelCase,  '''Model is missing `_is_accelerator_prepared` or is set to `False`''',  )
								self.assertEqual(
								    getattr(lowerCamelCase,  '''_is_accelerate_prepared''',  lowerCamelCase					),  lowerCamelCase,  '''Optimizer is missing `_is_accelerator_prepared` or is set to `False`''',  )
								self.assertEqual(
								    getattr(lowerCamelCase,  '''_is_accelerate_prepared''',  lowerCamelCase					),  lowerCamelCase,  '''Scheduler is missing `_is_accelerator_prepared` or is set to `False`''',  )
								self.assertEqual(
								    getattr(lowerCamelCase,  '''_is_accelerate_prepared''',  lowerCamelCase					),  lowerCamelCase,  '''Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`''',  )
								self.assertEqual(
								    getattr(lowerCamelCase,  '''_is_accelerate_prepared''',  lowerCamelCase					),  lowerCamelCase,  '''Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`''',  )



					@slow
					@require_bnb
					def 		snake_case	(	self							:  Tuple					)-> List[Any]:
								from transformers import AutoModelForCausalLM

								lowerCamelCase__		:		Dict		     =AutoModelForCausalLM.from_pretrained(
								    '''EleutherAI/gpt-neo-125m''',  load_in_abit=lowerCamelCase,  device_map={'''''': 0},  )
								lowerCamelCase__		:		Optional[Any]		     =Accelerator()

								# This should work
								lowerCamelCase__		:		Tuple		     =accelerator.prepare(lowerCamelCase					)



					@slow
					@require_bnb
					def 		snake_case	(	self							:  Dict					)-> List[Any]:
								from transformers import AutoModelForCausalLM

								lowerCamelCase__		:		int		     =Accelerator()

								with init_empty_weights():
											lowerCamelCase__		:		Optional[int]		     =AutoModelForCausalLM.from_pretrained(
											    '''EleutherAI/gpt-neo-125m''',  )
											model.tie_weights()
											lowerCamelCase__		:		List[Any]		     =infer_auto_device_map(lowerCamelCase					)
											lowerCamelCase__		:		Any		     ='''cpu'''

								lowerCamelCase__		:		Any		     =AutoModelForCausalLM.from_pretrained(
								    '''EleutherAI/gpt-neo-125m''',  device_map=lowerCamelCase,  load_in_abit=lowerCamelCase,  llm_inta_enable_fpaa_cpu_offload=lowerCamelCase					)

								# This should not work and get value error
								with self.assertRaises(lowerCamelCase					):
											lowerCamelCase__		:		Optional[Any]		     =accelerator.prepare(lowerCamelCase					)



					@slow
					@require_bnb
					@require_multi_gpu
					def 		snake_case	(	self							:  Any					)-> Dict:
								from transformers import AutoModelForCausalLM

								lowerCamelCase__		:		Dict		     ={'''distributed_type''': DistributedType.MULTI_GPU}

								with init_empty_weights():
											lowerCamelCase__		:		Tuple		     =AutoModelForCausalLM.from_pretrained(
											    '''EleutherAI/gpt-neo-125m''',  )
											model.tie_weights()
											lowerCamelCase__		:		Union[str, Any]		     =infer_auto_device_map(lowerCamelCase					)
											lowerCamelCase__		:		int		     =1

								lowerCamelCase__		:		int		     =AutoModelForCausalLM.from_pretrained(
								    '''EleutherAI/gpt-neo-125m''',  load_in_abit=lowerCamelCase,  device_map=lowerCamelCase,  )
								lowerCamelCase__		:		List[Any]		     =Accelerator()

								# This should not work and get value error
								with self.assertRaises(lowerCamelCase					):
											lowerCamelCase__		:		int		     =accelerator.prepare(lowerCamelCase					)

								PartialState._reset_state()



					@slow
					@require_bnb
					@require_multi_gpu
					def 		snake_case	(	self							:  Any					)-> List[str]:
								from transformers import AutoModelForCausalLM

								with init_empty_weights():
											lowerCamelCase__		:		List[str]		     =AutoModelForCausalLM.from_pretrained(
											    '''EleutherAI/gpt-neo-125m''',  )
											lowerCamelCase__		:		Optional[Any]		     =infer_auto_device_map(lowerCamelCase					)
											lowerCamelCase__		:		Union[str, Any]		     =1

								lowerCamelCase__		:		Any		     =AutoModelForCausalLM.from_pretrained(
								    '''EleutherAI/gpt-neo-125m''',  load_in_abit=lowerCamelCase,  device_map=lowerCamelCase,  )
								lowerCamelCase__		:		Any		     =Accelerator()

								# This should work
								lowerCamelCase__		:		Optional[Any]		     =accelerator.prepare(lowerCamelCase					)




					@require_cuda
					def 		snake_case	(	self							:  Any					)-> List[Any]:
								lowerCamelCase__		:		Dict		     =torch.nn.Linear(10,  10					)
								lowerCamelCase__		:		str		     =torch.optim.SGD(model.parameters(),  lr=0.01					)
								lowerCamelCase__		:		Dict		     =Accelerator(cpu=lowerCamelCase					)
								lowerCamelCase__		:		Optional[Any]		     =accelerator.prepare(lowerCamelCase					)









"""simple docstring"""






from ...processing_utils import ProcessorMixin

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							'SpeechT5FeatureExtractor'
					_a             =							'SpeechT5Tokenizer'



					def __init__(	self							:  Dict,  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  str					)-> Any:
								super().__init__(lowerCamelCase,  lowerCamelCase					)



					def __call__(	self							:  Tuple,  *lowerCamelCase							:  List[str],  **lowerCamelCase							:  Optional[int]					)-> List[str]:
								lowerCamelCase__		:		List[Any]		     =kwargs.pop('''audio''',  lowerCamelCase					)
								lowerCamelCase__		:		List[str]		     =kwargs.pop('''text''',  lowerCamelCase					)
								lowerCamelCase__		:		int		     =kwargs.pop('''text_target''',  lowerCamelCase					)
								lowerCamelCase__		:		Dict		     =kwargs.pop('''audio_target''',  lowerCamelCase					)
								lowerCamelCase__		:		Any		     =kwargs.pop('''sampling_rate''',  lowerCamelCase					)

								if audio is not None and text is not None:
											raise ValueError(
											    '''Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'''					)
								if audio_target is not None and text_target is not None:
											raise ValueError(
											    '''Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'''					)
								if audio is None and audio_target is None and text is None and text_target is None:
											raise ValueError(
											    '''You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'''					)

								if audio is not None:
											lowerCamelCase__		:		Union[str, Any]		     =self.feature_extractor(lowerCamelCase,  *lowerCamelCase,  sampling_rate=lowerCamelCase,  **lowerCamelCase					)
								elif text is not None:
											lowerCamelCase__		:		List[Any]		     =self.tokenizer(lowerCamelCase,  **lowerCamelCase					)
								else:
											lowerCamelCase__		:		Any		     =None

								if audio_target is not None:
											lowerCamelCase__		:		List[str]		     =self.feature_extractor(audio_target=lowerCamelCase,  *lowerCamelCase,  sampling_rate=lowerCamelCase,  **lowerCamelCase					)
											lowerCamelCase__		:		Tuple		     =targets['''input_values''']
								elif text_target is not None:
											lowerCamelCase__		:		Dict		     =self.tokenizer(lowerCamelCase,  **lowerCamelCase					)
											lowerCamelCase__		:		int		     =targets['''input_ids''']
								else:
											lowerCamelCase__		:		List[str]		     =None

								if inputs is None:
											return targets

								if targets is not None:
											lowerCamelCase__		:		Dict		     =labels

											lowerCamelCase__		:		Any		     =targets.get('''attention_mask'''					)
											if decoder_attention_mask is not None:
														lowerCamelCase__		:		Dict		     =decoder_attention_mask

								return inputs



					def 		snake_case	(	self							:  int,  *lowerCamelCase							:  Optional[Any],  **lowerCamelCase							:  Optional[int]					)-> Optional[Any]:
								lowerCamelCase__		:		List[Any]		     =kwargs.pop('''input_values''',  lowerCamelCase					)
								lowerCamelCase__		:		Union[str, Any]		     =kwargs.pop('''input_ids''',  lowerCamelCase					)
								lowerCamelCase__		:		Optional[Any]		     =kwargs.pop('''labels''',  lowerCamelCase					)

								if input_values is not None and input_ids is not None:
											raise ValueError('''Cannot process both `input_values` and `input_ids` inputs.'''					)
								if input_values is None and input_ids is None and labels is None:
											raise ValueError(
											    '''You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'''					)

								if input_values is not None:
											lowerCamelCase__		:		List[str]		     =self.feature_extractor.pad(lowerCamelCase,  *lowerCamelCase,  **lowerCamelCase					)
								elif input_ids is not None:
											lowerCamelCase__		:		Tuple		     =self.tokenizer.pad(lowerCamelCase,  **lowerCamelCase					)
								else:
											lowerCamelCase__		:		Any		     =None

								if labels is not None:
											if "input_ids" in labels or (isinstance(lowerCamelCase,  lowerCamelCase					) and "input_ids" in labels[0]):
														lowerCamelCase__		:		str		     =self.tokenizer.pad(lowerCamelCase,  **lowerCamelCase					)
														lowerCamelCase__		:		List[Any]		     =targets['''input_ids''']
											else:
														lowerCamelCase__		:		Any		     =self.feature_extractor.feature_size
														lowerCamelCase__		:		Optional[Any]		     =self.feature_extractor.num_mel_bins
														lowerCamelCase__		:		Optional[int]		     =self.feature_extractor.pad(lowerCamelCase,  *lowerCamelCase,  **lowerCamelCase					)
														lowerCamelCase__		:		List[Any]		     =feature_size_hack
														lowerCamelCase__		:		Tuple		     =targets['''input_values''']
								else:
											lowerCamelCase__		:		Optional[Any]		     =None

								if inputs is None:
											return targets

								if targets is not None:
											lowerCamelCase__		:		Tuple		     =labels

											lowerCamelCase__		:		Optional[int]		     =targets.get('''attention_mask'''					)
											if decoder_attention_mask is not None:
														lowerCamelCase__		:		Optional[Any]		     =decoder_attention_mask

								return inputs



					def 		snake_case	(	self							:  List[str],  *lowerCamelCase							:  Union[str, Any],  **lowerCamelCase							:  List[Any]					)-> List[Any]:
								return self.tokenizer.batch_decode(*lowerCamelCase,  **lowerCamelCase					)




					def 		snake_case	(	self							:  List[str],  *lowerCamelCase							:  List[Any],  **lowerCamelCase							:  Tuple					)-> int:
								return self.tokenizer.decode(*lowerCamelCase,  **lowerCamelCase					)




