
import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment





@require_huggingface_suite
class 			_lowerCAmelCase      (			unittest.TestCase ):




				def   __a		(      self  )       ->   List[str]:
							lowerCAmelCase_      			=			inspect.getfile(accelerate.test_utils  )
							lowerCAmelCase_      			=			os.path.sep.join(
							    mod_file.split(os.path.sep  )[:-1] + ["scripts", "external_deps", "test_metrics.py"]  )

							from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

							lowerCAmelCase_      			=			test_metrics




				@require_cpu
				def   __a		(      self  )       ->   Dict:
							debug_launcher(self.test_metrics.main       ,       num_processes=1  )




				@require_cpu
				def   __a		(      self  )       ->   List[Any]:
							debug_launcher(self.test_metrics.main  )




				@require_single_gpu
				def   __a		(      self  )       ->   Tuple:
							self.test_metrics.main()




				@require_multi_gpu
				def   __a		(      self  )       ->   str:
							print(f"""Found {torch.cuda.device_count()} devices."""  )
							lowerCAmelCase_      			=			["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
							with patch_environment(omp_num_threads=1  ):
										execute_subprocess_async(_UpperCamelCase       ,       env=os.environ.copy()  )





from ...processing_utils import ProcessorMixin





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	='''SpeechT5FeatureExtractor'''
				_lowercase    	='''SpeechT5Tokenizer'''




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							super().__init__(_UpperCamelCase       ,       _UpperCamelCase  )




				def __call__(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Optional[int]:
							lowerCAmelCase_      			=			kwargs.pop("audio"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("text"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("text_target"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("audio_target"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("sampling_rate"       ,       _UpperCamelCase  )

							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_      			=			self.feature_extractor(_UpperCamelCase       ,       *_UpperCamelCase       ,       sampling_rate=_UpperCamelCase       ,       **_UpperCamelCase  )
							elif text is not None:
										lowerCAmelCase_      			=			self.tokenizer(_UpperCamelCase       ,       **_UpperCamelCase  )
							else:
										lowerCAmelCase_      			=			None

							if audio_target is not None:
										lowerCAmelCase_      			=			self.feature_extractor(audio_target=_UpperCamelCase       ,       *_UpperCamelCase       ,       sampling_rate=_UpperCamelCase       ,       **_UpperCamelCase  )
										lowerCAmelCase_      			=			targets["input_values"]
							elif text_target is not None:
										lowerCAmelCase_      			=			self.tokenizer(_UpperCamelCase       ,       **_UpperCamelCase  )
										lowerCAmelCase_      			=			targets["input_ids"]
							else:
										lowerCAmelCase_      			=			None

							if inputs is None:
										return targets

							if targets is not None:
										lowerCAmelCase_      			=			labels

										lowerCAmelCase_      			=			targets.get("attention_mask"  )
										if decoder_attention_mask is not None:
													lowerCAmelCase_      			=			decoder_attention_mask

							return inputs




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   str:
							lowerCAmelCase_      			=			kwargs.pop("input_values"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("input_ids"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("labels"       ,       _UpperCamelCase  )

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

							if labels is not None:
										if "input_ids" in labels or (isinstance(_UpperCamelCase       ,       _UpperCamelCase  ) and "input_ids" in labels[0]):
													lowerCAmelCase_      			=			self.tokenizer.pad(_UpperCamelCase       ,       **_UpperCamelCase  )
													lowerCAmelCase_      			=			targets["input_ids"]
										else:
													lowerCAmelCase_      			=			self.feature_extractor.feature_size
													lowerCAmelCase_      			=			self.feature_extractor.num_mel_bins
													lowerCAmelCase_      			=			self.feature_extractor.pad(_UpperCamelCase       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )
													lowerCAmelCase_      			=			feature_size_hack
													lowerCAmelCase_      			=			targets["input_values"]
							else:
										lowerCAmelCase_      			=			None

							if inputs is None:
										return targets

							if targets is not None:
										lowerCAmelCase_      			=			labels

										lowerCAmelCase_      			=			targets.get("attention_mask"  )
										if decoder_attention_mask is not None:
													lowerCAmelCase_      			=			decoder_attention_mask

							return inputs




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   List[Any]:
							return self.tokenizer.batch_decode(*_UpperCamelCase       ,       **_UpperCamelCase  )




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Optional[Any]:
							return self.tokenizer.decode(*_UpperCamelCase       ,       **_UpperCamelCase  )



