



"""simple docstring"""







from ...processing_utils import ProcessorMixin







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     """SpeechT5FeatureExtractor"""
      _SCREAMING_SNAKE_CASE  				=     """SpeechT5Tokenizer"""



      def __init__(      self				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      str       ):
        super().__init__(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )



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

        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_   :				Any								= self.feature_extractor(SCREAMING_SNAKE_CASE_    ,			*SCREAMING_SNAKE_CASE_    ,			sampling_rate=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )
        elif text is not None:
          lowerCAmelCase_   :				List[Any]								= self.tokenizer(SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )
        else:
          lowerCAmelCase_   :				List[Any]								= None

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

        if inputs is None:
          return targets

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

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

        return inputs



      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple    ,			*SCREAMING_SNAKE_CASE_				:      Any    ,			**SCREAMING_SNAKE_CASE_				:      Optional[int]       ):
        lowerCAmelCase_   :				Union[str, Any]								= kwargs.pop('input_values'    ,			SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				List[str]								= kwargs.pop('input_ids'    ,			SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Any								= kwargs.pop('labels'    ,			SCREAMING_SNAKE_CASE_       )

        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_   :				Any								= self.feature_extractor.pad(SCREAMING_SNAKE_CASE_    ,			*SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )
        elif input_ids is not None:
          lowerCAmelCase_   :				Tuple								= self.tokenizer.pad(SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )
        else:
          lowerCAmelCase_   :				Any								= None

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

        if inputs is None:
          return targets

        if targets is not None:
          lowerCAmelCase_   :				List[Any]								= labels

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

        return inputs



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]    ,			*SCREAMING_SNAKE_CASE_				:      str    ,			**SCREAMING_SNAKE_CASE_				:      Optional[Any]       ):
        return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]    ,			*SCREAMING_SNAKE_CASE_				:      Any    ,			**SCREAMING_SNAKE_CASE_				:      List[str]       ):
        return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )










"""simple docstring"""







import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment







class       UpperCamelCase__   (	unittest.TestCase  ):




      """simple docstring"""




      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				Union[str, Any]								= inspect.getfile(accelerate.test_utils       )
        lowerCAmelCase_   :				Dict								= os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_script.py']       )
        lowerCAmelCase_   :				int								= os.path.sep.join(
            mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_distributed_data_loop.py']       )
        lowerCAmelCase_   :				Union[str, Any]								= os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_ops.py']       )



      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        print(F"Found {torch.cuda.device_count()} devices."       )
        lowerCAmelCase_   :				Optional[int]								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
        with patch_environment(omp_num_threads=1       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )



      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        print(F"Found {torch.cuda.device_count()} devices."       )
        lowerCAmelCase_   :				int								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", self.operation_file_path]
        print(F"Command: {cmd}"       )
        with patch_environment(omp_num_threads=1       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )



      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				List[Any]								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__       )]
        with patch_environment(omp_num_threads=1       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )





      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        print(F"Found {torch.cuda.device_count()} devices, using 2 devices only"       )
        lowerCAmelCase_   :				Any								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", self.data_loop_file_path]
        with patch_environment(omp_num_threads=1    ,			cuda_visible_devices='0,1'       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )


if __name__ == "__main__":
     lowercase__		:				Dict          =	Accelerator()
     lowercase__		:				List[Any]          =	(accelerator.state.process_index + 2, 1_0)
     lowercase__		:				Any          =	torch.randint(0, 1_0, shape).to(accelerator.device)

     lowercase__		:				List[Any]          =	""""""

     lowercase__		:				Union[str, Any]          =	accelerator.pad_across_processes(tensor)
     if tensora.shape[0] != accelerator.state.num_processes + 1:
          error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
     if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
          error_msg += "Tensors have different values."
     if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
          error_msg += "Padding was not done with the right value (0)."

     lowercase__		:				Optional[Any]          =	accelerator.pad_across_processes(tensor, pad_first=True)
     if tensora.shape[0] != accelerator.state.num_processes + 1:
          error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
     lowercase__		:				Any          =	accelerator.state.num_processes - accelerator.state.process_index - 1
     if not torch.equal(tensora[index:], tensor):
          error_msg += "Tensors have different values."
     if not torch.all(tensora[:index] == 0):
          error_msg += "Padding was not done with the right value (0)."

     # 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)





