



"""simple docstring"""






from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
		from .features import FeatureType







@dataclass
class 	__SCREAMING_SNAKE_CASE     :


					'''simple docstring'''





					_a             =							4_2
					_a             =							None
					# Automatically constructed
					_a             =							'dict'
					_a             =							None
					_a             =							field(default='Translation'		,       init=lowerCAmelCase_		,       repr=lowerCAmelCase_       )



					def __call__(	self							:  Optional[int]					)-> Optional[int]:
								return pa.struct({lang: pa.string() for lang in sorted(self.languages					)}					)




					def 		snake_case	(	self							:  Optional[Any]					)-> Union["FeatureType", Dict[str, "FeatureType"]]:
								from .features import Value

								return {k: Value('''string'''					) for k in sorted(self.languages					)}







@dataclass
class 	__SCREAMING_SNAKE_CASE     :


					'''simple docstring'''





					_a             =							None
					_a             =							None
					_a             =							None
					# Automatically constructed
					_a             =							'dict'
					_a             =							None
					_a             =							field(default='TranslationVariableLanguages'		,       init=lowerCAmelCase_		,       repr=lowerCAmelCase_       )



					def 		snake_case	(	self							:  Tuple					)-> Dict:
								lowerCamelCase__		:		List[str]		     =sorted(set(self.languages					)					) if self.languages else None
								lowerCamelCase__		:		Optional[int]		     =len(self.languages					) if self.languages else None



					def __call__(	self							:  Dict					)-> str:
								return pa.struct({'''language''': pa.list_(pa.string()					), '''translation''': pa.list_(pa.string()					)}					)



					def 		snake_case	(	self							:  int,  lowerCamelCase							:  Optional[Any]					)-> Optional[int]:
								lowerCamelCase__		:		Optional[Any]		     =set(self.languages					)
								if self.languages and set(lowerCamelCase					) - lang_set:
											raise ValueError(
											    F'''Some languages in example ({", ".join(sorted(set(lowerCamelCase					) - lang_set					)					)}) are not in valid set ({", ".join(lowerCamelCase					)}).'''					)

								# Convert dictionary into tuples, splitting out cases where there are
								# multiple translations for a single language.
								lowerCamelCase__		:		int		     =[]
								for lang, text in translation_dict.items():
											if isinstance(lowerCamelCase,  lowerCamelCase					):
														translation_tuples.append((lang, text)					)
											else:
														translation_tuples.extend([(lang, el) for el in text]					)

        # Ensure translations are in ascending order by language code.
								lowerCamelCase__		:		Tuple		     =zip(*sorted(lowerCamelCase					)					)

								return {"language": languages, "translation": translations}




					def 		snake_case	(	self							:  List[str]					)-> Union["FeatureType", Dict[str, "FeatureType"]]:
								from .features import Sequence, Value

								return {
								    "language": Sequence(Value('''string'''					)					),
								    "translation": Sequence(Value('''string'''					)					),
								}









"""simple docstring"""






from ...processing_utils import ProcessorMixin

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							'SpeechT5FeatureExtractor'
					_a             =							'SpeechT5Tokenizer'



					def __init__(	self							:  Dict,  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  str					)-> Any:
								super().__init__(lowerCamelCase,  lowerCamelCase					)



					def __call__(	self							:  Tuple,  *lowerCamelCase							:  List[str],  **lowerCamelCase							:  Optional[int]					)-> List[str]:
								lowerCamelCase__		:		List[Any]		     =kwargs.pop('''audio''',  lowerCamelCase					)
								lowerCamelCase__		:		List[str]		     =kwargs.pop('''text''',  lowerCamelCase					)
								lowerCamelCase__		:		int		     =kwargs.pop('''text_target''',  lowerCamelCase					)
								lowerCamelCase__		:		Dict		     =kwargs.pop('''audio_target''',  lowerCamelCase					)
								lowerCamelCase__		:		Any		     =kwargs.pop('''sampling_rate''',  lowerCamelCase					)

								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__		:		Union[str, Any]		     =self.feature_extractor(lowerCamelCase,  *lowerCamelCase,  sampling_rate=lowerCamelCase,  **lowerCamelCase					)
								elif text is not None:
											lowerCamelCase__		:		List[Any]		     =self.tokenizer(lowerCamelCase,  **lowerCamelCase					)
								else:
											lowerCamelCase__		:		Any		     =None

								if audio_target is not None:
											lowerCamelCase__		:		List[str]		     =self.feature_extractor(audio_target=lowerCamelCase,  *lowerCamelCase,  sampling_rate=lowerCamelCase,  **lowerCamelCase					)
											lowerCamelCase__		:		Tuple		     =targets['''input_values''']
								elif text_target is not None:
											lowerCamelCase__		:		Dict		     =self.tokenizer(lowerCamelCase,  **lowerCamelCase					)
											lowerCamelCase__		:		int		     =targets['''input_ids''']
								else:
											lowerCamelCase__		:		List[str]		     =None

								if inputs is None:
											return targets

								if targets is not None:
											lowerCamelCase__		:		Dict		     =labels

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

								return inputs



					def 		snake_case	(	self							:  int,  *lowerCamelCase							:  Optional[Any],  **lowerCamelCase							:  Optional[int]					)-> Optional[Any]:
								lowerCamelCase__		:		List[Any]		     =kwargs.pop('''input_values''',  lowerCamelCase					)
								lowerCamelCase__		:		Union[str, Any]		     =kwargs.pop('''input_ids''',  lowerCamelCase					)
								lowerCamelCase__		:		Optional[Any]		     =kwargs.pop('''labels''',  lowerCamelCase					)

								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__		:		List[str]		     =self.feature_extractor.pad(lowerCamelCase,  *lowerCamelCase,  **lowerCamelCase					)
								elif input_ids is not None:
											lowerCamelCase__		:		Tuple		     =self.tokenizer.pad(lowerCamelCase,  **lowerCamelCase					)
								else:
											lowerCamelCase__		:		Any		     =None

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

								if inputs is None:
											return targets

								if targets is not None:
											lowerCamelCase__		:		Tuple		     =labels

											lowerCamelCase__		:		Optional[int]		     =targets.get('''attention_mask'''					)
											if decoder_attention_mask is not None:
														lowerCamelCase__		:		Optional[Any]		     =decoder_attention_mask

								return inputs



					def 		snake_case	(	self							:  List[str],  *lowerCamelCase							:  Union[str, Any],  **lowerCamelCase							:  List[Any]					)-> List[Any]:
								return self.tokenizer.batch_decode(*lowerCamelCase,  **lowerCamelCase					)




					def 		snake_case	(	self							:  List[str],  *lowerCamelCase							:  List[Any],  **lowerCamelCase							:  Tuple					)-> int:
								return self.tokenizer.decode(*lowerCamelCase,  **lowerCamelCase					)




