
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 		UpperCAmelCase     (      )						->      Optional[int]:







				"""simple docstring"""



				__A       =					torch.nn.Linear(2						,  4		)
				__A       =					torch.optim.AdamW(model.parameters()						,  lr=1.0		)
				__A       =					torch.optim.lr_scheduler.OneCycleLR(a_						,  max_lr=0.01						,  steps_per_epoch=2						,  epochs=1		)
				__A       =					DataLoader(TensorDataset(torch.tensor([1, 2, 3]		)		)		)
				__A       =					DataLoader(TensorDataset(torch.tensor([4, 5, 6]		)		)		)

				return model, optimizer, scheduler, train_dl, valid_dl


def 		UpperCAmelCase     (      a_		)						->      Dict:







				"""simple docstring"""



				return (model.weight.abs().sum() + model.bias.abs().sum()).item()


def 		UpperCAmelCase     (      a_		)						->      Dict:







				"""simple docstring"""



				__A       =					torch.nn.Linear(*tuple(model.weight.T.shape		)		).state_dict()
				model.load_state_dict(a_		)





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	@require_cuda
	def 							UpperCamelCase_    (			self   :					Dict					):
					__A       =					Accelerator()
					assert PartialState._shared_state["_cpu"] is False
					assert PartialState._shared_state["device"].type == "cuda"
					with self.assertRaises(A					):
									__A       =					Accelerator(cpu=A					)






	def 							UpperCamelCase_    (			self   :					Dict					):
					__A       =					Accelerator()
					__A       =					GradientState()
					assert state.num_steps == 1
					__A       =					4
					assert state.num_steps == 4

					assert state.sync_gradients is True
					__A       =					False
					assert state.sync_gradients is False
					GradientState._reset_state()






	def 							UpperCamelCase_    (			self   :					str					):
					__A       =					Accelerator()
					__A     ,				__A     ,				__A     ,				__A     ,				__A       =					create_components()

					(
					    (
					    __A
					)     ,				(
					    __A
					)     ,				(
					    __A
					)     ,				(
					    __A
					)     ,				(
					    __A
					)     ,				
					)       =					accelerator.prepare(A				,A				,A				,A				,A					)

					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 							UpperCamelCase_    (			self   :					Optional[int]					):
					__A       =					Accelerator()
					__A     ,				__A     ,				__A     ,				__A     ,				__A       =					create_components()
					accelerator.prepare(A				,A				,A				,A				,A					)
					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 							UpperCamelCase_    (			self   :					str					):
					PartialState._reset_state()

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

					with patch("torch.cuda.set_device"				,A					), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64"					):
									__A       =					Accelerator()
									self.assertEqual(str(accelerator.state.device					)				,"cuda:64"					)






	def 							UpperCamelCase_    (			self   :					Tuple					):
					__A       =					Accelerator()
					__A     ,				__A     ,				__A     ,				__A     ,				__A       =					create_components()
					accelerator.prepare(A				,A				,A				,A				,A					)

					__A       =					get_signature(A					)

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

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

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






	def 							UpperCamelCase_    (			self   :					Dict					):
					__A       =					Accelerator()
					__A     ,				__A     ,				__A     ,				__A     ,				__A       =					create_components()
					accelerator.prepare(A				,A				,A				,A				,A					)

					__A       =					get_signature(A					)

					# saving hook
					def save_config(A   :					Optional[int]				,A   :					List[Any]				,A   :					int					):
									__A       =					{"class_name": models[0].__class__.__name__}

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

        # loading hook
					def load_config(A   :					List[Any]				,A   :					Any					):
									with open(os.path.join(A				,"data.json"					)				,"r"					) as f:
													__A       =					json.load(A					)

									__A       =					config["class_name"]

					__A       =					accelerator.register_save_state_pre_hook(A					)
					__A       =					accelerator.register_load_state_pre_hook(A					)

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

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

									# random class name to verify correct one is loaded
									__A       =					"random"

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

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

									# random class name to verify correct one is loaded
									__A       =					"random"

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

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






	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					__A       =					Accelerator()
					__A     ,				__A     ,				__A     ,				__A     ,				__A       =					create_components()
					__A       =					None

					# This should work
					__A     ,				__A     ,				__A     ,				__A     ,				__A     ,				__A       =					accelerator.prepare(
					    A				,A				,A				,A				,A				,A					)
					self.assertTrue(dummy_obj is None					)






	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					__A       =					Accelerator()
					__A     ,				__A     ,				__A     ,				__A     ,				__A       =					create_components()
					__A       =					[1, 2, 3]

					# This should work
					__A     ,				__A     ,				__A     ,				__A     ,				__A     ,				__A       =					accelerator.prepare(
					    A				,A				,A				,A				,A				,A					)
					self.assertEqual(
					    getattr(A				,"_is_accelerate_prepared"				,A					)				,A				,"Dummy object should have `_is_accelerate_prepared` set to `True`"				,)
					self.assertEqual(
					    getattr(A				,"_is_accelerate_prepared"				,A					)				,A				,"Model is missing `_is_accelerator_prepared` or is set to `False`"				,)
					self.assertEqual(
					    getattr(A				,"_is_accelerate_prepared"				,A					)				,A				,"Optimizer is missing `_is_accelerator_prepared` or is set to `False`"				,)
					self.assertEqual(
					    getattr(A				,"_is_accelerate_prepared"				,A					)				,A				,"Scheduler is missing `_is_accelerator_prepared` or is set to `False`"				,)
					self.assertEqual(
					    getattr(A				,"_is_accelerate_prepared"				,A					)				,A				,"Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`"				,)
					self.assertEqual(
					    getattr(A				,"_is_accelerate_prepared"				,A					)				,A				,"Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`"				,)






	@slow
	@require_bnb
	def 							UpperCamelCase_    (			self   :					Tuple					):
					from transformers import AutoModelForCausalLM

					__A       =					AutoModelForCausalLM.from_pretrained(
					    "EleutherAI/gpt-neo-125m"				,load_in_abit=A				,device_map={"": 0}				,)
					__A       =					Accelerator()

					# This should work
					__A       =					accelerator.prepare(A					)






	@slow
	@require_bnb
	def 							UpperCamelCase_    (			self   :					Any					):
					from transformers import AutoModelForCausalLM

					__A       =					Accelerator()

					with init_empty_weights():
									__A       =					AutoModelForCausalLM.from_pretrained(
									    "EleutherAI/gpt-neo-125m"				,)
									model.tie_weights()
									__A       =					infer_auto_device_map(A					)
									__A       =					"cpu"

					__A       =					AutoModelForCausalLM.from_pretrained(
					    "EleutherAI/gpt-neo-125m"				,device_map=A				,load_in_abit=A				,llm_inta_enable_fpaa_cpu_offload=A					)

					# This should not work and get value error
					with self.assertRaises(A					):
									__A       =					accelerator.prepare(A					)






	@slow
	@require_bnb
	@require_multi_gpu
	def 							UpperCamelCase_    (			self   :					int					):
					from transformers import AutoModelForCausalLM

					__A       =					{"distributed_type": DistributedType.MULTI_GPU}

					with init_empty_weights():
									__A       =					AutoModelForCausalLM.from_pretrained(
									    "EleutherAI/gpt-neo-125m"				,)
									model.tie_weights()
									__A       =					infer_auto_device_map(A					)
									__A       =					1

					__A       =					AutoModelForCausalLM.from_pretrained(
					    "EleutherAI/gpt-neo-125m"				,load_in_abit=A				,device_map=A				,)
					__A       =					Accelerator()

					# This should not work and get value error
					with self.assertRaises(A					):
									__A       =					accelerator.prepare(A					)

					PartialState._reset_state()






	@slow
	@require_bnb
	@require_multi_gpu
	def 							UpperCamelCase_    (			self   :					Dict					):
					from transformers import AutoModelForCausalLM

					with init_empty_weights():
									__A       =					AutoModelForCausalLM.from_pretrained(
									    "EleutherAI/gpt-neo-125m"				,)
									__A       =					infer_auto_device_map(A					)
									__A       =					1

					__A       =					AutoModelForCausalLM.from_pretrained(
					    "EleutherAI/gpt-neo-125m"				,load_in_abit=A				,device_map=A				,)
					__A       =					Accelerator()

					# This should work
					__A       =					accelerator.prepare(A					)







	@require_cuda
	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					__A       =					torch.nn.Linear(10				,10					)
					__A       =					torch.optim.SGD(model.parameters()				,lr=0.01					)
					__A       =					Accelerator(cpu=A					)
					__A       =					accelerator.prepare(A					)






'''simple docstring'''



from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


UpperCamelCase_    	=				logging.get_logger(__name__)






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				str        =						['''audio_values''', '''audio_mask''']







       def __init__(      self,							A=2_048,							A=1,							A=[16, 16],							A=128,							A=44_100,							A=86,							A=2_048,							A=0.0,							**A,							):







             '''simple docstring'''







             super().__init__(
                 feature_size=A,							sampling_rate=A,							padding_value=A,							**A,							)

             SCREAMING_SNAKE_CASE   :      str    					=						spectrogram_length
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						num_channels
             SCREAMING_SNAKE_CASE   :      List[str]    					=						patch_size
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						feature_size // self.patch_size[1]
             SCREAMING_SNAKE_CASE   :      Dict    					=						n_fft
             SCREAMING_SNAKE_CASE   :      Tuple    					=						sampling_rate // hop_length_to_sampling_rate
             SCREAMING_SNAKE_CASE   :      str    					=						sampling_rate
             SCREAMING_SNAKE_CASE   :      int    					=						padding_value
             SCREAMING_SNAKE_CASE   :      Any    					=						mel_filter_bank(
                 num_frequency_bins=1 + n_fft // 2,							num_mel_filters=A,							min_frequency=0.0,							max_frequency=2_20_50.0,							sampling_rate=A,							norm='slaney',							mel_scale='slaney',							).T







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						spectrogram(
                 A,							window_function(self.n_fft,							'hann'  ),							frame_length=self.n_fft,							hop_length=self.hop_length,							power=2.0,							mel_filters=self.mel_filters.T,							log_mel='dB',							db_range=80.0,							)
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						log_spec[:, :-1]
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						log_spec - 20.0
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						np.clip(log_spec / 40.0,							-2.0,							0.0  ) + 1.0
             return log_spec







       def __call__(      self,							A,							A = None,							A = True,							A = None,							A = False,							A = False,							**A,							):







             '''simple docstring'''







             if sampling_rate is not None:
                   if sampling_rate != self.sampling_rate:
                         raise ValueError(
                             'This feature extractor is set to support sampling rate'
                             F" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"
                             F" with {self.sampling_rate} and not {sampling_rate}."  )
             else:
                   logger.warning(
                       'It is strongly recommended to pass the `sampling_rate` argument to this function. '
                       'Failing to do so can result in silent errors that might be hard to debug.'  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						isinstance(A,							np.ndarray  ) and len(raw_speech.shape  ) > 1
             if is_batched_numpy and len(raw_speech.shape  ) > 2:
                   raise ValueError(F"Only mono-channel audio is supported for input to {self}"  )
             SCREAMING_SNAKE_CASE   :      int    					=						is_batched_numpy or (
                 isinstance(A,							(list, tuple)  ) and (isinstance(raw_speech[0],							(np.ndarray, tuple, list)  ))
             )
             if is_batched:
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[np.asarray([speech],							dtype=np.floataa  ).T for speech in raw_speech]
             elif not is_batched and not isinstance(A,							np.ndarray  ):
                   SCREAMING_SNAKE_CASE   :      Any    					=						np.asarray(A,							dtype=np.floataa  )
             elif isinstance(A,							np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						raw_speech.astype(np.floataa  )
             # always return batch
             if not is_batched:
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[np.asarray([raw_speech]  ).T]

             # Convert audio signals to log mel spectrograms, truncate by time axis
             SCREAMING_SNAKE_CASE   :      int    					=						[
                 self._np_extract_fbank_features(waveform.squeeze()  ).T[: self.spectrogram_length] for waveform in raw_speech
             ]
             if isinstance(audio_features[0],							A  ):
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[np.asarray(A,							dtype=np.floataa  ) for feature in audio_features]

             # Create audio attention mask
             SCREAMING_SNAKE_CASE   :      Tuple    					=						max(
                 [ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len for feature in audio_features]  )  # The maximum number of audio patches in a batch
             if return_attention_mask:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						[
                       (ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len) * [1]
                       + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len) * [0]
                       for feature in audio_features
                   ]
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						np.array(A  ).astype(np.floataa  )

             # convert into correct format for padding
             SCREAMING_SNAKE_CASE   :      Tuple    					=						max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						np.ones([len(A  ), 1, max_time_len, self.feature_size]  ).astype(np.floataa  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						padded_audio_features * self.padding_value
             for i in range(len(A  )  ):
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						audio_features[i]
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						feature

             # return as BatchFeature
             if return_attention_mask:
                   SCREAMING_SNAKE_CASE   :      Any    					=						{'audio_values': padded_audio_features, 'audio_mask': audio_mask}
             else:
                   SCREAMING_SNAKE_CASE   :      Dict    					=						{'audio_values': padded_audio_features}

             SCREAMING_SNAKE_CASE   :      str    					=						BatchFeature(data=A,							tensor_type=A  )
             return encoded_inputs


