






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class 					A__     (     __snake_case ):

     '''simple docstring'''






     snake_case__					=					["""image_processor""", """tokenizer"""]
     snake_case__					=					"""ViltImageProcessor"""
     snake_case__					=					("""BertTokenizer""", """BertTokenizerFast""")







     def __init__( self      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    Dict=None   ,						_SCREAMING_SNAKE_CASE      :    Any=None   ,						**_SCREAMING_SNAKE_CASE      :    Any ):






           """simple docstring"""




           UpperCamelCase					       =  None
           if "feature_extractor" in kwargs:
                 warnings.warn(
                     'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                     ' instead.'   ,						_SCREAMING_SNAKE_CASE   ,						)
                 UpperCamelCase					       =  kwargs.pop('feature_extractor' )

           UpperCamelCase					       =  image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
                 raise ValueError('You need to specify an `image_processor`.' )
           if tokenizer is None:
                 raise ValueError('You need to specify a `tokenizer`.' )

           super().__init__(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  self.image_processor







     def __call__( self      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    Union[bool, str, PaddingStrategy] = False   ,						_SCREAMING_SNAKE_CASE      :    Union[bool, str, TruncationStrategy] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[int] = None   ,						_SCREAMING_SNAKE_CASE      :    int = 0   ,						_SCREAMING_SNAKE_CASE      :    Optional[int] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[bool] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[bool] = None   ,						_SCREAMING_SNAKE_CASE      :    bool = False   ,						_SCREAMING_SNAKE_CASE      :    bool = False   ,						_SCREAMING_SNAKE_CASE      :    bool = False   ,						_SCREAMING_SNAKE_CASE      :    bool = False   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[str, TensorType]] = None   ,						**_SCREAMING_SNAKE_CASE      :    Optional[int]   ,						):






           """simple docstring"""




           UpperCamelCase					       =  self.tokenizer(
               text=_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE   ,						padding=_SCREAMING_SNAKE_CASE   ,						truncation=_SCREAMING_SNAKE_CASE   ,						max_length=_SCREAMING_SNAKE_CASE   ,						stride=_SCREAMING_SNAKE_CASE   ,						pad_to_multiple_of=_SCREAMING_SNAKE_CASE   ,						return_token_type_ids=_SCREAMING_SNAKE_CASE   ,						return_attention_mask=_SCREAMING_SNAKE_CASE   ,						return_overflowing_tokens=_SCREAMING_SNAKE_CASE   ,						return_special_tokens_mask=_SCREAMING_SNAKE_CASE   ,						return_offsets_mapping=_SCREAMING_SNAKE_CASE   ,						return_length=_SCREAMING_SNAKE_CASE   ,						verbose=_SCREAMING_SNAKE_CASE   ,						return_tensors=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE   ,						)
           # add pixel_values + pixel_mask
           UpperCamelCase					       =  self.image_processor(_SCREAMING_SNAKE_CASE   ,						return_tensors=_SCREAMING_SNAKE_CASE )
           encoding.update(_SCREAMING_SNAKE_CASE )

           return encoding







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int]   ,						*_SCREAMING_SNAKE_CASE      :    Dict   ,						**_SCREAMING_SNAKE_CASE      :    Tuple ):






           """simple docstring"""




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







     def   _SCREAMING_SNAKE_CASE     ( self      :    int   ,						*_SCREAMING_SNAKE_CASE      :    str   ,						**_SCREAMING_SNAKE_CASE      :    Any ):






           """simple docstring"""




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







     @property
     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  self.tokenizer.model_input_names
           UpperCamelCase					       =  self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )







     @property
     def   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """simple docstring"""




           warnings.warn(
               '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'   ,						_SCREAMING_SNAKE_CASE   ,						)
           return self.image_processor_class







     @property
     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           warnings.warn(
               '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'   ,						_SCREAMING_SNAKE_CASE   ,						)
           return self.image_processor











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)



