


'''simple docstring'''




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class 						UpperCAmelCase_ (    __snake_case						):
     lowerCamelCase							:      str            =	'''Speech2TextFeatureExtractor'''
     lowerCamelCase							:      Union[str, Any]            =	'''Speech2TextTokenizer'''





     def __init__(					self			: List[Any]       , UpperCAmelCase__			: List[str]       , UpperCAmelCase__			: Any       )							->    int:
        super().__init__(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       )
        lowerCAmelCase	=     self.feature_extractor
        lowerCAmelCase	=     False





     def __call__(					self			: Any       , *UpperCAmelCase__			: List[Any]       , **UpperCAmelCase__			: Optional[Any]       )							->    List[str]:




        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.'       )
           lowerCAmelCase	=     kwargs.pop('raw_speech'       )
        else:
           lowerCAmelCase	=     kwargs.pop('audio'       , _SCREAMING_SNAKE_CASE       )
        lowerCAmelCase	=     kwargs.pop('sampling_rate'       , _SCREAMING_SNAKE_CASE       )
        lowerCAmelCase	=     kwargs.pop('text'       , _SCREAMING_SNAKE_CASE       )
        if len(_SCREAMING_SNAKE_CASE       ) > 0:
           lowerCAmelCase	=     args[0]
           lowerCAmelCase	=     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	=     self.feature_extractor(_SCREAMING_SNAKE_CASE       , *_SCREAMING_SNAKE_CASE       , sampling_rate=_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE       )
        if text is not None:
           lowerCAmelCase	=     self.tokenizer(_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE       )

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





     def 				__UpperCAmelCase (					self			: Union[str, Any]       , *UpperCAmelCase__			: int       , **UpperCAmelCase__			: List[Any]       )							->    int:
        return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE       )





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





     @contextmanager
     def 				__UpperCAmelCase (					self			: Union[str, Any]       )							->    Optional[int]:
        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	=     True
        lowerCAmelCase	=     self.tokenizer
        yield
        lowerCAmelCase	=     self.feature_extractor
        lowerCAmelCase	=     False













import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__magic_name__   :  Optional[int]        =							logging.get_logger(__name__)
def 		lowercase__  (				_UpperCamelCase) ->			Dict:
      """simple docstring"""

      UpperCamelCase					       =  r'\w+[.]\d+'
      UpperCamelCase					       =  re.findall(_UpperCamelCase       ,							_UpperCamelCase)
      for pat in pats:
            UpperCamelCase					       =  key.replace(_UpperCamelCase       ,							'_'.join(pat.split('.')))
      return key
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Tuple:
      """simple docstring"""

      UpperCamelCase					       =  pt_tuple_key[:-1] + ('scale',)
      if (
          any('norm' in str_ for str_ in pt_tuple_key)
          and (pt_tuple_key[-1] == "bias")
          and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
          and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
      ):
            UpperCamelCase					       =  pt_tuple_key[:-1] + ('scale',)
            return renamed_pt_tuple_key, pt_tensor
      elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
            UpperCamelCase					       =  pt_tuple_key[:-1] + ('scale',)
            return renamed_pt_tuple_key, pt_tensor

      # embedding
      if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
            UpperCamelCase					       =  pt_tuple_key[:-1] + ('embedding',)
            return renamed_pt_tuple_key, pt_tensor

      # conv layer
      UpperCamelCase					       =  pt_tuple_key[:-1] + ('kernel',)
      if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
            UpperCamelCase					       =  pt_tensor.transpose(2       ,							3       ,							1       ,							0)
            return renamed_pt_tuple_key, pt_tensor

      # linear layer
      UpperCamelCase					       =  pt_tuple_key[:-1] + ('kernel',)
      if pt_tuple_key[-1] == "weight":
            UpperCamelCase					       =  pt_tensor.T
            return renamed_pt_tuple_key, pt_tensor

      # old PyTorch layer norm weight
      UpperCamelCase					       =  pt_tuple_key[:-1] + ('weight',)
      if pt_tuple_key[-1] == "gamma":
            return renamed_pt_tuple_key, pt_tensor

      # old PyTorch layer norm bias
      UpperCamelCase					       =  pt_tuple_key[:-1] + ('bias',)
      if pt_tuple_key[-1] == "beta":
            return renamed_pt_tuple_key, pt_tensor

      return pt_tuple_key, pt_tensor
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase=42) ->			Optional[Any]:
      """simple docstring"""

      UpperCamelCase					       =  {k: v.numpy() for k, v in pt_state_dict.items()}

      # Step 2: Since the model is stateless, get random Flax params
      UpperCamelCase					       =  flax_model.init_weights(PRNGKey(_UpperCamelCase))

      UpperCamelCase					       =  flatten_dict(_UpperCamelCase)
      UpperCamelCase					       =  {}

      # Need to change some parameters name to match Flax names
      for pt_key, pt_tensor in pt_state_dict.items():
            UpperCamelCase					       =  rename_key(_UpperCamelCase)
            UpperCamelCase					       =  tuple(renamed_pt_key.split('.'))

            # Correctly rename weight parameters
            UpperCamelCase       ,  UpperCamelCase					       =  rename_key_and_reshape_tensor(_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase)

            if flax_key in random_flax_state_dict:
                  if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                        raise ValueError(
                            F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
                            F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.')

        # also add unexpected weight so that warning is thrown
            UpperCamelCase					       =  jnp.asarray(_UpperCamelCase)

      return unflatten_dict(_UpperCamelCase)



