


import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin


class      __lowerCAmelCase  (			_a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'MCTCTFeatureExtractor'
  _SCREAMING_SNAKE_CASE		     =							'AutoTokenizer'



  def __init__(  self							:							int     ,					_lowerCAmelCase							:							Dict     ,					_lowerCAmelCase							:							Union[str, Any]	) ->			str:




     """simple docstring"""
     super().__init__(lowerCAmelCase_     ,					lowerCAmelCase_	)
     snake_case_            =       self.feature_extractor
     snake_case_            =       False



  def __call__(  self							:							Dict     ,					*_lowerCAmelCase							:							Dict     ,					**_lowerCAmelCase							:							Optional[int]	) ->			Optional[int]:




     """simple docstring"""


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

     if "raw_speech" in kwargs:
        warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."	)
        snake_case_            =       kwargs.pop("raw_speech"	)
     else:
        snake_case_            =       kwargs.pop("audio"     ,					lowerCAmelCase_	)
     snake_case_            =       kwargs.pop("sampling_rate"     ,					lowerCAmelCase_	)
     snake_case_            =       kwargs.pop("text"     ,					lowerCAmelCase_	)
     if len(lowerCAmelCase_	) > 0:
        snake_case_            =       args[0]
        snake_case_            =       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_            =       self.feature_extractor(lowerCAmelCase_     ,					*lowerCAmelCase_     ,					sampling_rate=lowerCAmelCase_     ,					**lowerCAmelCase_	)
     if text is not None:
        snake_case_            =       self.tokenizer(lowerCAmelCase_     ,					**lowerCAmelCase_	)

     if text is None:
        return inputs
     elif audio is None:
        return encodings
     else:
        snake_case_            =       encodings["input_ids"]
        return inputs



  def        lowerCAmelCase__	(  self							:							str     ,					*_lowerCAmelCase							:							Any     ,					**_lowerCAmelCase							:							Dict	) ->			Union[str, Any]:




     """simple docstring"""
     return self.tokenizer.batch_decode(*lowerCAmelCase_     ,					**lowerCAmelCase_	)



  def        lowerCAmelCase__	(  self							:							Tuple     ,					*_lowerCAmelCase							:							Optional[int]     ,					**_lowerCAmelCase							:							str	) ->			Optional[int]:




     """simple docstring"""


     if self._in_target_context_manager:
        return self.current_processor.pad(*lowerCAmelCase_     ,					**lowerCAmelCase_	)

     snake_case_            =       kwargs.pop("input_features"     ,					lowerCAmelCase_	)
     snake_case_            =       kwargs.pop("labels"     ,					lowerCAmelCase_	)
     if len(lowerCAmelCase_	) > 0:
        snake_case_            =       args[0]
        snake_case_            =       args[1:]

     if input_features is not None:
        snake_case_            =       self.feature_extractor.pad(lowerCAmelCase_     ,					*lowerCAmelCase_     ,					**lowerCAmelCase_	)
     if labels is not None:
        snake_case_            =       self.tokenizer.pad(lowerCAmelCase_     ,					**lowerCAmelCase_	)

     if labels is None:
        return input_features
     elif input_features is None:
        return labels
     else:
        snake_case_            =       labels["input_ids"]
        return input_features



  def        lowerCAmelCase__	(  self							:							str     ,					*_lowerCAmelCase							:							Dict     ,					**_lowerCAmelCase							:							List[Any]	) ->			Tuple:




     """simple docstring"""
     return self.tokenizer.decode(*lowerCAmelCase_     ,					**lowerCAmelCase_	)



  @contextmanager
  def        lowerCAmelCase__	(  self							:							Optional[Any]	) ->			int:




     """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."	)
     snake_case_            =       True
     snake_case_            =       self.tokenizer
     yield
     snake_case_            =       self.feature_extractor
     snake_case_            =       False










'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

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


_snake_case : Optional[int]  				=						logging.get_logger(__name__)

_snake_case : Any  				=						{
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class      A       (						_a       ):
							lowercase_							     =							'roformer'







							def __init__(    self			:    str				,     lowerCAmelCase_			:    int=5_00_00				,     lowerCAmelCase_			:    Any=None				,     lowerCAmelCase_			:    int=7_68				,     lowerCAmelCase_			:    Tuple=12				,     lowerCAmelCase_			:    Any=12				,     lowerCAmelCase_			:    List[str]=30_72				,     lowerCAmelCase_			:    Dict="gelu"				,     lowerCAmelCase_			:    Optional[int]=0.1				,     lowerCAmelCase_			:    List[Any]=0.1				,     lowerCAmelCase_			:    int=15_36				,     lowerCAmelCase_			:    Optional[Any]=2				,     lowerCAmelCase_			:    int=0.0_2				,     lowerCAmelCase_			:    Dict=1e-12				,     lowerCAmelCase_			:    Any=0				,     lowerCAmelCase_			:    Optional[Any]=False				,     lowerCAmelCase_			:    Tuple=True				,     **lowerCAmelCase_			:    Optional[int]				,     )     ->				str:


														"""simple docstring"""


														super().__init__(pad_token_id=lowerCAmelCase_				,     **lowerCAmelCase_   )

														_a      	=							vocab_size
														_a      	=							hidden_size if embedding_size is None else embedding_size
														_a      	=							hidden_size
														_a      	=							num_hidden_layers
														_a      	=							num_attention_heads
														_a      	=							hidden_act
														_a      	=							intermediate_size
														_a      	=							hidden_dropout_prob
														_a      	=							attention_probs_dropout_prob
														_a      	=							max_position_embeddings
														_a      	=							type_vocab_size
														_a      	=							initializer_range
														_a      	=							layer_norm_eps
														_a      	=							rotary_value
														_a      	=							use_cache



class      A       (						_a       ):







							@property
							def   __lowerCAmelCase     (    self			:    Any   )     ->				Mapping[str, Mapping[int, str]]:


														"""simple docstring"""


														if self.task == "multiple-choice":
																					_a      	=							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
														else:
																					_a      	=							{0: '''batch''', 1: '''sequence'''}
														_a      	=							{0: '''batch''', 1: '''sequence'''}
														return OrderedDict(
														    [
														        ('''input_ids''', dynamic_axis),
														        ('''attention_mask''', dynamic_axis),
														        ('''token_type_ids''', dynamic_axis),
														    ]   )

