




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):
					_lowerCamelCase						    =					"""AutoFeatureExtractor"""
					_lowerCamelCase						    =					"""Speech2Text2Tokenizer"""

					def __init__(							self					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						super().__init__(__lowercase					,			__lowercase   )
						lowerCamelCase_				:						Optional[int]				  =				self.feature_extractor
						lowerCamelCase_				:						List[Any]				  =				False

					def __call__(							self					,			*__lowercase					,			**__lowercase   ):







						'''simple docstring'''




						if self._in_target_context_manager:
							return self.current_processor(*__lowercase					,			**__lowercase   )

						if "raw_speech" in kwargs:
							warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'   )
							lowerCamelCase_				:						int				  =				kwargs.pop('raw_speech'   )
						else:
							lowerCamelCase_				:						Optional[Any]				  =				kwargs.pop('audio'					,			__lowercase   )
						lowerCamelCase_				:						Optional[int]				  =				kwargs.pop('sampling_rate'					,			__lowercase   )
						lowerCamelCase_				:						int				  =				kwargs.pop('text'					,			__lowercase   )
						if len(__lowercase   ) > 0:
							lowerCamelCase_				:						Optional[int]				  =				args[0]
							lowerCamelCase_				:						Any				  =				args[1:]

						if audio is None and text is None:
							raise ValueError('You need to specify either an `audio` or `text` input to process.'   )

						if audio is not None:
							lowerCamelCase_				:						Any				  =				self.feature_extractor(__lowercase					,			*__lowercase					,			sampling_rate=__lowercase					,			**__lowercase   )
						if text is not None:
							lowerCamelCase_				:						List[str]				  =				self.tokenizer(__lowercase					,			**__lowercase   )

						if text is None:
							return inputs
						elif audio is None:
							return encodings
						else:
							lowerCamelCase_				:						Optional[int]				  =				encodings['input_ids']
							return inputs

					def       lowercase_				(							self					,			*__lowercase					,			**__lowercase   ):







						'''simple docstring'''




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

					def       lowercase_				(							self					,			*__lowercase					,			**__lowercase   ):







						'''simple docstring'''




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





					@contextmanager
					def       lowercase_				(							self   ):







						'''simple docstring'''




						warnings.warn(
						    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
						    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
						    'your audio inputs, or in a separate call.'   )
						lowerCamelCase_				:						Tuple				  =				True
						lowerCamelCase_				:						Optional[Any]				  =				self.tokenizer
						yield
						lowerCamelCase_				:						str				  =				self.feature_extractor
						lowerCamelCase_				:						Optional[int]				  =				False



'''simple docstring'''

from typing import List, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_     =      logging.get_logger(__name__)

lowerCAmelCase_     =      {
    "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/config.json",
    # See all LED models at https://huggingface.co/models?filter=led
}







class lowerCamelCase   (    __lowerCAmelCase				):
				snake_case_							     = '''led'''
				snake_case_							     = {
				    '''num_attention_heads''': '''encoder_attention_heads''',
				    '''hidden_size''': '''d_model''',
				    '''attention_probs_dropout_prob''': '''attention_dropout''',
				    '''initializer_range''': '''init_std''',
				}

				def __init__(       self,						lowercase_=50265,						lowercase_=16384,						lowercase_=1024,						lowercase_=12,						lowercase_=4096,						lowercase_=16,						lowercase_=12,						lowercase_=4096,						lowercase_=16,						lowercase_=0.0,						lowercase_=0.0,						lowercase_=True,						lowercase_=True,						lowercase_="gelu",						lowercase_=1024,						lowercase_=0.1,						lowercase_=0.0,						lowercase_=0.0,						lowercase_=0.02,						lowercase_=2,						lowercase_=0.0,						lowercase_=1,						lowercase_=0,						lowercase_=2,						lowercase_ = 512,						**lowercase_,						)    ->							Union[str, Any]:
					snake_case							=					vocab_size
					snake_case							=					max_encoder_position_embeddings
					snake_case							=					max_decoder_position_embeddings
					snake_case							=					d_model
					snake_case							=					encoder_ffn_dim
					snake_case							=					encoder_layers
					snake_case							=					encoder_attention_heads
					snake_case							=					decoder_ffn_dim
					snake_case							=					decoder_layers
					snake_case							=					decoder_attention_heads
					snake_case							=					dropout
					snake_case							=					attention_dropout
					snake_case							=					activation_dropout
					snake_case							=					activation_function
					snake_case							=					init_std
					snake_case							=					encoder_layerdrop
					snake_case							=					decoder_layerdrop
					snake_case							=					classifier_dropout
					snake_case							=					use_cache
					snake_case							=					encoder_layers
					snake_case							=					attention_window

					super().__init__(
					    pad_token_id=lowercase_,						bos_token_id=lowercase_,						eos_token_id=lowercase_,						is_encoder_decoder=lowercase_,						decoder_start_token_id=lowercase_,						**lowercase_,						)





