


import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler







@dataclass
class        snake_case_   (_UpperCAmelCase      ):
					UpperCAmelCase__					:    int          =			0
					UpperCAmelCase__					:    bool          =			False
					UpperCAmelCase__					:    float          =			3.0







class        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :int      )					->    Optional[int]:
												# If no defaults are changed, `to_kwargs` returns an empty dict.
												self.assertDictEqual(MockClass().to_kwargs()			,{}      )
												self.assertDictEqual(MockClass(a=2      ).to_kwargs()			,{'a': 2}      )
												self.assertDictEqual(MockClass(a=2			,b=UpperCAmelCase_      ).to_kwargs()			,{'a': 2, 'b': True}      )
												self.assertDictEqual(MockClass(a=2			,c=2.25      ).to_kwargs()			,{'a': 2, 'c': 2.25}      )
					@require_cuda
					def  lowerCamelCase__(      self   :Optional[int]      )					->    Union[str, Any]:
												# If no defaults are changed, `to_kwargs` returns an empty dict.
												a__             =     GradScalerKwargs(init_scale=10_24			,growth_factor=2      )
												AcceleratorState._reset_state()
												a__             =     Accelerator(mixed_precision='fp16'			,kwargs_handlers=[scaler_handler]      )
												print(accelerator.use_fpaa      )
												a__             =     accelerator.scaler

												# Check the kwargs have been applied
												self.assertEqual(scaler._init_scale			,10_24.0      )
												self.assertEqual(scaler._growth_factor			,2.0      )

												# Check the other values are at the default
												self.assertEqual(scaler._backoff_factor			,0.5      )
												self.assertEqual(scaler._growth_interval			,20_00      )
												self.assertEqual(scaler._enabled			,UpperCAmelCase_      )



					@require_multi_gpu
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    Union[str, Any]:
												a__             =     ['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__      )]
												execute_subprocess_async(UpperCAmelCase_			,env=os.environ.copy()      )


if __name__ == "__main__":
						snake_case							:       Optional[Any]     			=      DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
						snake_case							:       Union[str, Any]     			=      Accelerator(kwargs_handlers=[ddp_scaler])
						snake_case							:       Optional[Any]     			=      torch.nn.Linear(1_00, 2_00)
						snake_case							:       int     			=      accelerator.prepare(model)

						# Check the values changed in kwargs
						snake_case							:       Any     			=      ''''''
						snake_case							:       Optional[int]     			=      model.bucket_bytes_cap // (10_24 * 10_24)
						if observed_bucket_cap_map != 15:
												error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
						if model.find_unused_parameters is not True:
												error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

						# Check the values of the defaults
						if model.dim != 0:
												error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
						if model.broadcast_buffers is not True:
												error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
						if model.gradient_as_bucket_view is not False:
												error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

						# Raise error at the end to make sure we don't stop at the first failure.
						if len(error_msg) > 0:
												raise ValueError(error_msg)






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   Union[str, Any]				       ="""Wav2Vec2FeatureExtractor"""
	A__    :   Any				       ="""AutoTokenizer"""
	def __init__(			self							:   List[Any]       ,   UpperCAmelCase_							:   List[str]       ,   UpperCAmelCase_							:   str							):
						super().__init__(UpperCAmelCase_       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= self.feature_extractor
						SCREAMING_SNAKE_CASE__													= False
	@classmethod
	def      A_				(			cls							:   Union[str, Any]       ,   UpperCAmelCase_							:   Any       ,   **UpperCAmelCase_							:   Optional[Any]							):
						try:
											return super().from_pretrained(UpperCAmelCase_       ,   **UpperCAmelCase_							)
						except OSError:
											warnings.warn(
											    F'Loading a tokenizer inside {cls.__name__} from a config that does not'
											    ' include a `tokenizer_class` attribute is deprecated and will be '
											    'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
											    ' attribute to either your `config.json` or `tokenizer_config.json` '
											    'file to suppress this warning: '       ,   UpperCAmelCase_       ,   )

											SCREAMING_SNAKE_CASE__													= WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase_       ,   **UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= WavaVecaCTCTokenizer.from_pretrained(UpperCAmelCase_       ,   **UpperCAmelCase_							)

											return cls(feature_extractor=UpperCAmelCase_       ,   tokenizer=UpperCAmelCase_							)
	def __call__(			self							:   Any       ,   *UpperCAmelCase_							:   Dict       ,   **UpperCAmelCase_							:   Any							):
						# For backward compatibility
						if self._in_target_context_manager:
											return self.current_processor(*UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if "raw_speech" in kwargs:
											warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'							)
											SCREAMING_SNAKE_CASE__													= kwargs.pop('raw_speech'							)
						else:
											SCREAMING_SNAKE_CASE__													= kwargs.pop('audio'       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= kwargs.pop('sampling_rate'       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= kwargs.pop('text'       ,   UpperCAmelCase_							)
						if len(UpperCAmelCase_							) > 0:
											SCREAMING_SNAKE_CASE__													= args[0]
											SCREAMING_SNAKE_CASE__													= args[1:]

						if audio is None and text is None:
											raise ValueError('You need to specify either an `audio` or `text` input to process.'							)

						if audio is not None:
											SCREAMING_SNAKE_CASE__													= self.feature_extractor(UpperCAmelCase_       ,   *UpperCAmelCase_       ,   sampling_rate=UpperCAmelCase_       ,   **UpperCAmelCase_							)
						if text is not None:
											SCREAMING_SNAKE_CASE__													= self.tokenizer(UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if text is None:
											return inputs
						elif audio is None:
											return encodings
						else:
											SCREAMING_SNAKE_CASE__													= encodings['input_ids']
											return inputs
	def      A_				(			self							:   List[str]       ,   *UpperCAmelCase_							:   List[Any]       ,   **UpperCAmelCase_							:   Optional[int]							):
						# For backward compatibility
						if self._in_target_context_manager:
											return self.current_processor.pad(*UpperCAmelCase_       ,   **UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= kwargs.pop('input_features'       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= kwargs.pop('labels'       ,   UpperCAmelCase_							)
						if len(UpperCAmelCase_							) > 0:
											SCREAMING_SNAKE_CASE__													= args[0]
											SCREAMING_SNAKE_CASE__													= args[1:]

						if input_features is not None:
											SCREAMING_SNAKE_CASE__													= self.feature_extractor.pad(UpperCAmelCase_       ,   *UpperCAmelCase_       ,   **UpperCAmelCase_							)
						if labels is not None:
											SCREAMING_SNAKE_CASE__													= self.tokenizer.pad(UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if labels is None:
											return input_features
						elif input_features is None:
											return labels
						else:
											SCREAMING_SNAKE_CASE__													= labels['input_ids']
											return input_features
	def      A_				(			self							:   List[Any]       ,   *UpperCAmelCase_							:   List[str]       ,   **UpperCAmelCase_							:   List[str]							):
						return self.tokenizer.batch_decode(*UpperCAmelCase_       ,   **UpperCAmelCase_							)
	def      A_				(			self							:   Tuple       ,   *UpperCAmelCase_							:   int       ,   **UpperCAmelCase_							:   List[str]							):
						return self.tokenizer.decode(*UpperCAmelCase_       ,   **UpperCAmelCase_							)





	@contextmanager
	def      A_				(			self							:   str							):
						warnings.warn(
						    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
						    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
						    'your audio inputs, or in a separate call.'							)
						SCREAMING_SNAKE_CASE__													= True
						SCREAMING_SNAKE_CASE__													= self.tokenizer
						yield
						SCREAMING_SNAKE_CASE__													= self.feature_extractor
						SCREAMING_SNAKE_CASE__													= False





