

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   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:int      	=					["""image_processor""", """tokenizer"""]
 __SCREAMING_SNAKE_CASE					:List[str]      	=					"""BridgeTowerImageProcessor"""
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					("""RobertaTokenizer""", """RobertaTokenizerFast""")



 def __init__(   self		:   Dict			,				a__		:   Optional[int]			,				a__		:   Tuple      ):
      super().__init__(a__			,				a__      )



 def __call__(   self		:   Tuple			,				a__		:   Dict			,				a__		:   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,				a__		:   bool = True			,				a__		:   Union[bool, str, PaddingStrategy] = False			,				a__		:   Union[bool, str, TruncationStrategy] = None			,				a__		:   Optional[int] = None			,				a__		:   int = 0			,				a__		:   Optional[int] = None			,				a__		:   Optional[bool] = None			,				a__		:   Optional[bool] = None			,				a__		:   bool = False			,				a__		:   bool = False			,				a__		:   bool = False			,				a__		:   bool = False			,				a__		:   bool = True			,				a__		:   Optional[Union[str, TensorType]] = None			,				**a__		:   Tuple			,				):
      __magic_name__						       =    self.tokenizer(
          text=a__			,				add_special_tokens=a__			,				padding=a__			,				truncation=a__			,				max_length=a__			,				stride=a__			,				pad_to_multiple_of=a__			,				return_token_type_ids=a__			,				return_attention_mask=a__			,				return_overflowing_tokens=a__			,				return_special_tokens_mask=a__			,				return_offsets_mapping=a__			,				return_length=a__			,				verbose=a__			,				return_tensors=a__			,				**a__			,				)
      # add pixel_values + pixel_mask
      __magic_name__						       =    self.image_processor(
          a__			,				return_tensors=a__			,				do_normalize=a__			,				do_center_crop=a__			,				**a__      )
      encoding.update(a__      )

      return encoding



 def 			snake_case__  (   self		:   Dict			,				*a__		:   Optional[int]			,				**a__		:   Union[str, Any]      ):
      return self.tokenizer.batch_decode(*a__			,				**a__      )



 def 			snake_case__  (   self		:   Dict			,				*a__		:   Tuple			,				**a__		:   int      ):
      return self.tokenizer.decode(*a__			,				**a__      )



 @property
 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    self.tokenizer.model_input_names
      __magic_name__						       =    self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )









'''simple docstring'''




import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
_lowerCAmelCase         =						object()

# For specifying empty leaf dict `{}`
_lowerCAmelCase         =						object()




def 						UpperCamelCase	(			a		,						a			)				->     Union[str, Any]:






     '''simple docstring'''
     __magic_name__						       =    tuple((re.compile(x + '''$'''			) for x in qs)			)
     for i in range(len(a			) - len(a			) + 1			):
          __magic_name__						       =    [x.match(a			) for x, y in zip(a		,						ks[i:]			)]
          if matches and all(a			):
               return True
     return False




def 						UpperCamelCase	(			a			)				->     Tuple:






     '''simple docstring'''
     def replace(a		,						a			):
          for rule, replacement in rules:
               if _match(a		,						a			):
                    return replacement
          return val

     return replace




def 						UpperCamelCase	(			)				->     Union[str, Any]:






     '''simple docstring'''
     return [
         # embeddings
         (("transformer", "wpe", "embedding"), P('''mp'''		,						a			)),
         (("transformer", "wte", "embedding"), P('''mp'''		,						a			)),
         # atention
         (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(a		,						'''mp'''			)),
         (("attention", "out_proj", "kernel"), P('''mp'''		,						a			)),
         (("attention", "out_proj", "bias"), None),
         # mlp
         (("mlp", "c_fc", "kernel"), P(a		,						'''mp'''			)),
         (("mlp", "c_fc", "bias"), P('''mp'''			)),
         (("mlp", "c_proj", "kernel"), P('''mp'''		,						a			)),
         (("mlp", "c_proj", "bias"), None),
         # layer norms
         ((r"ln_\d+", "bias"), None),
         ((r"\d+", r"ln_\d+", "scale"), None),
         (("ln_f", "bias"), None),
         (("ln_f", "scale"), None),
     ]




def 						UpperCamelCase	(			a			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    _get_partition_rules()
     __magic_name__						       =    _replacement_rules(a			)
     __magic_name__						       =    {k: _unmatched for k in flatten_dict(a			)}
     __magic_name__						       =    {k: replace(a		,						a			) for k, v in initd.items()}
     assert _unmatched not in result.values(), "Incomplete partition spec."
     return freeze(unflatten_dict(a			)			)






