


from ...processing_utils import ProcessorMixin

class 							lowercase  (						_UpperCAmelCase			):
     lowerCamelCase			:			List[str]      = '''SpeechT5FeatureExtractor'''
     lowerCamelCase			:			Optional[Any]      = '''SpeechT5Tokenizer'''







     def __init__(						self :						int   ,   _lowercase :						List[str]   ,   _lowercase :						str      ):
           super().__init__(_lowercase   ,   _lowercase      )







     def __call__(						self :						Dict   ,   *_lowercase :						Any   ,   **_lowercase :						Tuple      ):
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		kwargs.pop('''audio'''   ,   _lowercase      )
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		kwargs.pop('''text'''   ,   _lowercase      )
           SCREAMING_SNAKE_CASE__       :  Dict						     =		kwargs.pop('''text_target'''   ,   _lowercase      )
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		kwargs.pop('''audio_target'''   ,   _lowercase      )
           SCREAMING_SNAKE_CASE__       :  int						     =		kwargs.pop('''sampling_rate'''   ,   _lowercase      )

           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:
                 SCREAMING_SNAKE_CASE__       :  Dict						     =		self.feature_extractor(_lowercase   ,   *_lowercase   ,   sampling_rate=_lowercase   ,   **_lowercase      )
           elif text is not None:
                 SCREAMING_SNAKE_CASE__       :  int						     =		self.tokenizer(_lowercase   ,   **_lowercase      )
           else:
                 SCREAMING_SNAKE_CASE__       :  Tuple						     =		None

           if audio_target is not None:
                 SCREAMING_SNAKE_CASE__       :  str						     =		self.feature_extractor(audio_target=_lowercase   ,   *_lowercase   ,   sampling_rate=_lowercase   ,   **_lowercase      )
                 SCREAMING_SNAKE_CASE__       :  Tuple						     =		targets['''input_values''']
           elif text_target is not None:
                 SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		self.tokenizer(_lowercase   ,   **_lowercase      )
                 SCREAMING_SNAKE_CASE__       :  str						     =		targets['''input_ids''']
           else:
                 SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		None

           if inputs is None:
                 return targets

           if targets is not None:
                 SCREAMING_SNAKE_CASE__       :  List[str]						     =		labels

                 SCREAMING_SNAKE_CASE__       :  List[Any]						     =		targets.get('''attention_mask'''      )
                 if decoder_attention_mask is not None:
                       SCREAMING_SNAKE_CASE__       :  List[str]						     =		decoder_attention_mask

           return inputs







     def 							lowercase__       (						self :						List[str]   ,   *_lowercase :						Optional[Any]   ,   **_lowercase :						Dict      ):
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		kwargs.pop('''input_values'''   ,   _lowercase      )
           SCREAMING_SNAKE_CASE__       :  Any						     =		kwargs.pop('''input_ids'''   ,   _lowercase      )
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		kwargs.pop('''labels'''   ,   _lowercase      )

           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:
                 SCREAMING_SNAKE_CASE__       :  str						     =		self.feature_extractor.pad(_lowercase   ,   *_lowercase   ,   **_lowercase      )
           elif input_ids is not None:
                 SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		self.tokenizer.pad(_lowercase   ,   **_lowercase      )
           else:
                 SCREAMING_SNAKE_CASE__       :  str						     =		None

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

           if inputs is None:
                 return targets

           if targets is not None:
                 SCREAMING_SNAKE_CASE__       :  int						     =		labels

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

           return inputs







     def 							lowercase__       (						self :						Optional[int]   ,   *_lowercase :						Any   ,   **_lowercase :						Optional[Any]      ):
           return self.tokenizer.batch_decode(*_lowercase   ,   **_lowercase      )







     def 							lowercase__       (						self :						List[str]   ,   *_lowercase :						Any   ,   **_lowercase :						List[str]      ):
           return self.tokenizer.decode(*_lowercase   ,   **_lowercase      )














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 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					0
	lowerCAmelCase						=					False
	lowerCAmelCase						=					3.0






class 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								# 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=a   ).to_kwargs()			,     {'a': 2, 'b': True}   )
								self.assertDictEqual(MockClass(a=2			,     c=2.25   ).to_kwargs()			,     {'a': 2, 'c': 2.25}   )




	@require_cuda
	def 				_UpperCamelCase     (			self   )			->       str:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								snake_case_           =				GradScalerKwargs(init_scale=10_24			,     growth_factor=2   )
								AcceleratorState._reset_state()
								snake_case_           =				Accelerator(mixed_precision='fp16'			,     kwargs_handlers=[scaler_handler]   )
								print(accelerator.use_fpaa   )
								snake_case_           =				accelerator.scaler

								# Check the kwargs have been applied
								self.assertEqual(scaler._init_scale			,     1_024.0   )
								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			,     a   )






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


if __name__ == "__main__":
	lowercase    				=   DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
	lowercase    				=   Accelerator(kwargs_handlers=[ddp_scaler])
	lowercase    				=   torch.nn.Linear(100, 200)
	lowercase    				=   accelerator.prepare(model)

	# Check the values changed in kwargs
	lowercase    				=   ""
	lowercase    				=   model.bucket_bytes_cap // (1024 * 1024)
	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)





