


'''simple docstring'''







from ...processing_utils import ProcessorMixin


class  lowerCAmelCase_						(       snake_case__      ):





		"""simple docstring"""



		a_       :List[Any]												="""SpeechT5FeatureExtractor"""
		a_       :Dict												="""SpeechT5Tokenizer"""

		def __init__(					self     :							int				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]						):





							'''simple docstring'''

							super().__init__(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)

		def __call__(					self     :							Union[str, Any]				,							*SCREAMING_SNAKE_CASE__     :							Optional[Any]				,							**SCREAMING_SNAKE_CASE__     :							Tuple						):





							'''simple docstring'''

							__a       =   kwargs.pop("""audio"""				,							SCREAMING_SNAKE_CASE__						)
							__a       =   kwargs.pop("""text"""				,							SCREAMING_SNAKE_CASE__						)
							__a       =   kwargs.pop("""text_target"""				,							SCREAMING_SNAKE_CASE__						)
							__a       =   kwargs.pop("""audio_target"""				,							SCREAMING_SNAKE_CASE__						)
							__a       =   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:
												__a       =   self.feature_extractor(SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__				,							sampling_rate=SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)
							elif text is not None:
												__a       =   self.tokenizer(SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)
							else:
												__a       =   None

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

							if inputs is None:
												return targets

							if targets is not None:
												__a       =   labels

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

							return inputs

		def __a						(					self     :							int				,							*SCREAMING_SNAKE_CASE__     :							List[str]				,							**SCREAMING_SNAKE_CASE__     :							List[Any]						):





							'''simple docstring'''

							__a       =   kwargs.pop("""input_values"""				,							SCREAMING_SNAKE_CASE__						)
							__a       =   kwargs.pop("""input_ids"""				,							SCREAMING_SNAKE_CASE__						)
							__a       =   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:
												__a       =   self.feature_extractor.pad(SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)
							elif input_ids is not None:
												__a       =   self.tokenizer.pad(SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)
							else:
												__a       =   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]):
																	__a       =   self.tokenizer.pad(SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)
																	__a       =   targets["""input_ids"""]
												else:
																	__a       =   self.feature_extractor.feature_size
																	__a       =   self.feature_extractor.num_mel_bins
																	__a       =   self.feature_extractor.pad(SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)
																	__a       =   feature_size_hack
																	__a       =   targets["""input_values"""]
							else:
												__a       =   None

							if inputs is None:
												return targets

							if targets is not None:
												__a       =   labels

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

							return inputs

		def __a						(					self     :							Union[str, Any]				,							*SCREAMING_SNAKE_CASE__     :							Any				,							**SCREAMING_SNAKE_CASE__     :							Optional[Any]						):





							'''simple docstring'''

							return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)


		def __a						(					self     :							int				,							*SCREAMING_SNAKE_CASE__     :							str				,							**SCREAMING_SNAKE_CASE__     :							Tuple						):





							'''simple docstring'''

							return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)










'''simple docstring'''







from __future__ import annotations

import os
from typing import Any

import requests

SCREAMING_SNAKE_CASE_      =			'https://api.github.com'

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
SCREAMING_SNAKE_CASE_      =			BASE_URL + '/user'

# https://github.com/settings/tokens
SCREAMING_SNAKE_CASE_      =			os.environ.get('USER_TOKEN', '')




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					dict[Any, Any]:
     """simple docstring"""




     __a       =   {
         """Authorization""": F'''token {auth_token}''',
         """Accept""": """application/vnd.github.v3+json""",
     }
     return requests.get(__SCREAMING_SNAKE_CASE				,  headers=__SCREAMING_SNAKE_CASE		).json()


if __name__ == "__main__":  # pragma: no cover
 if USER_TOKEN:
  for key, value in fetch_github_info(USER_TOKEN).items():
   print(f"""{key}: {value}""")
 else:
  raise ValueError('\'USER_TOKEN\' field cannot be empty.')






