
'''simple docstring'''





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class     a (					__magic_name__     ):
				_snake_case		     =			'''AutoFeatureExtractor'''
				_snake_case		     =			'''Speech2Text2Tokenizer'''




				def __init__(				self						:					int,     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					Dict   ):
										super().__init__(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
										snake_case							:  Tuple			=     self.feature_extractor
										snake_case							:  int			=     False




				def __call__(				self						:					Dict,     *SCREAMING_SNAKE_CASE_						:					Any,     **SCREAMING_SNAKE_CASE_						:					Optional[Any]   ):
										# For backward compatibility
										if self._in_target_context_manager:
																return self.current_processor(*SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )

										if "raw_speech" in kwargs:
																warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''   )
																snake_case							:  List[str]			=     kwargs.pop('''raw_speech'''   )
										else:
																snake_case							:  int			=     kwargs.pop('''audio''',     SCREAMING_SNAKE_CASE_   )
										snake_case							:  str			=     kwargs.pop('''sampling_rate''',     SCREAMING_SNAKE_CASE_   )
										snake_case							:  Tuple			=     kwargs.pop('''text''',     SCREAMING_SNAKE_CASE_   )
										if len(SCREAMING_SNAKE_CASE_   ) > 0:
																snake_case							:  str			=     args[0]
																snake_case							:  List[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:
																snake_case							:  Optional[int]			=     self.feature_extractor(SCREAMING_SNAKE_CASE_,     *SCREAMING_SNAKE_CASE_,     sampling_rate=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )
										if text is not None:
																snake_case							:  Any			=     self.tokenizer(SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )

										if text is None:
																return inputs
										elif audio is None:
																return encodings
										else:
																snake_case							:  List[Any]			=     encodings['''input_ids''']
																return inputs




				def 						__snake_case				(				self						:					List[str],     *SCREAMING_SNAKE_CASE_						:					Dict,     **SCREAMING_SNAKE_CASE_						:					Dict   ):
										return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Union[str, Any],     *SCREAMING_SNAKE_CASE_						:					Optional[int],     **SCREAMING_SNAKE_CASE_						:					Optional[int]   ):
										return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )




				@contextmanager
				def 						__snake_case				(				self						:					Tuple   ):
										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.'''   )
										snake_case							:  Union[str, Any]			=     True
										snake_case							:  List[str]			=     self.tokenizer
										yield
										snake_case							:  Optional[int]			=     self.feature_extractor
										snake_case							:  Tuple			=     False



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowerCamelCase_				:							Union[str, Any]       =    logging.get_logger(__name__)

lowerCamelCase_				:							Optional[Any]       =    {
    "google/long-t5-local-base": "https://huggingface.co/google/long-t5-local-base/blob/main/config.json",
    "google/long-t5-local-large": "https://huggingface.co/google/long-t5-local-large/blob/main/config.json",
    "google/long-t5-tglobal-base": "https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json",
    "google/long-t5-tglobal-large": "https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json",
}







class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""longt5"""
     lowercase__				  =			["""past_key_values"""]
     lowercase__				  =			{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

     def __init__(  self							,       SCREAMING_SNAKE_CASE_=32_128							,       SCREAMING_SNAKE_CASE_=512							,       SCREAMING_SNAKE_CASE_=64							,       SCREAMING_SNAKE_CASE_=2_048							,       SCREAMING_SNAKE_CASE_=6							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=8							,       SCREAMING_SNAKE_CASE_=127							,       SCREAMING_SNAKE_CASE_=16							,       SCREAMING_SNAKE_CASE_=32							,       SCREAMING_SNAKE_CASE_=128							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=1E-6							,       SCREAMING_SNAKE_CASE_=1.0							,       SCREAMING_SNAKE_CASE_="relu"							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_="local"							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=1							,       **SCREAMING_SNAKE_CASE_							,       ):
            __snake_case						       =   vocab_size
            __snake_case						       =   d_model
            __snake_case						       =   d_kv
            __snake_case						       =   d_ff
            __snake_case						       =   num_layers
            # default = symmetry
            __snake_case						       =   num_decoder_layers if num_decoder_layers is not None else self.num_layers
            __snake_case						       =   num_heads
            __snake_case						       =   local_radius
            __snake_case						       =   global_block_size
            __snake_case						       =   relative_attention_num_buckets
            __snake_case						       =   relative_attention_max_distance
            __snake_case						       =   dropout_rate
            __snake_case						       =   layer_norm_epsilon
            __snake_case						       =   initializer_factor
            __snake_case						       =   feed_forward_proj
            __snake_case						       =   encoder_attention_type
            __snake_case						       =   use_cache

            __snake_case						       =   self.feed_forward_proj.split('-'    )
            __snake_case						       =   act_info[-1]
            __snake_case						       =   act_info[0] == 'gated'

            if len(SCREAMING_SNAKE_CASE_    ) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE_    ) > 2:
                   raise ValueError(
                       f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                       'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                       '\'gated-gelu\' or \'relu\''    )

            # for backwards compatibility
            if feed_forward_proj == "gated-gelu":
                   __snake_case						       =   'gelu_new'

            super().__init__(
                pad_token_id=SCREAMING_SNAKE_CASE_							,       eos_token_id=SCREAMING_SNAKE_CASE_							,       is_encoder_decoder=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_							,       )







class  _lowerCamelCase			(lowerCamelCase					):

     @property
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   {
                'input_ids': {0: 'batch', 1: 'encoder_sequence'},
                'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
            }
            if self.use_past:
                   __snake_case						       =   'past_encoder_sequence + sequence'
                   __snake_case						       =   {0: 'batch'}
                   __snake_case						       =   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
            else:
                   __snake_case						       =   {0: 'batch', 1: 'decoder_sequence'}
                   __snake_case						       =   {0: 'batch', 1: 'decoder_sequence'}

            if self.use_past:
                   self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_							,       direction='inputs'    )

            return common_inputs

     @property
     def 						__lowerCamelCase				(  self    ):
            return 13
