






from ...processing_utils import ProcessorMixin



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"SpeechT5FeatureExtractor"
					a_			=	"SpeechT5Tokenizer"



					def __init__(      self  :						Optional[int]							,	__A  :						Any							,	__A  :						Any ):
						super().__init__(__A							,	__A )



					def __call__(      self  :						Any							,	*__A  :						Tuple							,	**__A  :						List[Any] ):
						snake_case__				:   int     =						kwargs.pop("audio"							,	__A )
						snake_case__				:   Optional[int]     =						kwargs.pop("text"							,	__A )
						snake_case__				:   str     =						kwargs.pop("text_target"							,	__A )
						snake_case__				:   List[Any]     =						kwargs.pop("audio_target"							,	__A )
						snake_case__				:   Any     =						kwargs.pop("sampling_rate"							,	__A )

						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:
							snake_case__				:   Union[str, Any]     =						self.feature_extractor(__A							,	*__A							,	sampling_rate=__A							,	**__A )
						elif text is not None:
							snake_case__				:   Dict     =						self.tokenizer(__A							,	**__A )
						else:
							snake_case__				:   Optional[int]     =						None

						if audio_target is not None:
							snake_case__				:   List[str]     =						self.feature_extractor(audio_target=__A							,	*__A							,	sampling_rate=__A							,	**__A )
							snake_case__				:   str     =						targets["input_values"]
						elif text_target is not None:
							snake_case__				:   List[str]     =						self.tokenizer(__A							,	**__A )
							snake_case__				:   List[Any]     =						targets["input_ids"]
						else:
							snake_case__				:   int     =						None

						if inputs is None:
							return targets

						if targets is not None:
							snake_case__				:   Optional[int]     =						labels

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

						return inputs



					def  _lowercase  (      self  :						Optional[int]							,	*__A  :						Any							,	**__A  :						List[Any] ):
						snake_case__				:   Optional[Any]     =						kwargs.pop("input_values"							,	__A )
						snake_case__				:   List[Any]     =						kwargs.pop("input_ids"							,	__A )
						snake_case__				:   Optional[Any]     =						kwargs.pop("labels"							,	__A )

						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:
							snake_case__				:   Tuple     =						self.feature_extractor.pad(__A							,	*__A							,	**__A )
						elif input_ids is not None:
							snake_case__				:   List[Any]     =						self.tokenizer.pad(__A							,	**__A )
						else:
							snake_case__				:   Dict     =						None

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

						if inputs is None:
							return targets

						if targets is not None:
							snake_case__				:   int     =						labels

							snake_case__				:   str     =						targets.get("attention_mask" )
							if decoder_attention_mask is not None:
								snake_case__				:   Optional[int]     =						decoder_attention_mask

						return inputs



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






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











# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__lowerCamelCase      :    Dict   		=   abspath(join(dirname(dirname(__file__)), """src"""))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							str     ):
	from diffusers.utils.testing_utils import pytest_addoption_shared

	pytest_addoption_shared(snake_case_     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Any     ):
	from diffusers.utils.testing_utils import pytest_terminal_summary_main

	snake_case__				:   Optional[int]     =						terminalreporter.config.getoption("--make-reports"     )
	if make_reports:
		pytest_terminal_summary_main(snake_case_  ,						id=snake_case_     )



