




"""simple docstring"""



from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				['''image_processor''', '''tokenizer''']
    lowercase__						 =				'''AutoImageProcessor'''
    lowercase__						 =				'''AutoTokenizer'''




    def __init__(   self						:	str     ,			snake_case_						:	Dict     ,			snake_case_						:	List[str]				)				->							str:




     '''simple docstring'''

     super().__init__(snake_case_     ,			snake_case_				)
     A__	=    self.image_processor




    def __call__(   self						:	int     ,			snake_case_						:	Any=None     ,			snake_case_						:	Any=None     ,			snake_case_						:	Union[str, Any]=None     ,			**snake_case_						:	Optional[int]				)				->							Optional[int]:




     '''simple docstring'''

     if text is None and images is None:
      raise ValueError("You have to specify either text or images. Both cannot be none."				)

     if text is not None:
      A__	=    self.tokenizer(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if images is not None:
      A__	=    self.image_processor(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if text is not None and images is not None:
      A__	=    image_features.pixel_values
      return encoding
     elif text is not None:
      return encoding
     else:
      return BatchEncoding(data=dict(**snake_case_				)     ,			tensor_type=snake_case_				)




    def 							__magic_name__							(   self						:	Optional[int]     ,			*snake_case_						:	Union[str, Any]     ,			**snake_case_						:	List[Any]				)				->							int:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	List[str]     ,			*snake_case_						:	List[str]     ,			**snake_case_						:	Optional[int]				)				->							Tuple:




     '''simple docstring'''

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




    @property
    def 							__magic_name__							(   self						:	List[Any]				)				->							List[Any]:




     '''simple docstring'''

     return ["input_ids", "attention_mask", "pixel_values"]











"""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
SCREAMING_SNAKE_CASE    =	object()

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







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		List[Any]:
 A__	=    tuple((re.compile(x + "$"						) for x in qs)						)
 for i in range(len(lowercase_						) - len(lowercase_						) + 1						):
  A__	=    [x.match(lowercase_						) for x, y in zip(lowercase_      ,			ks[i:]						)]
  if matches and all(lowercase_						):
   return True
 return False







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Optional[int]:
 def replace(lowercase_      ,			lowercase_						):
  for rule, replacement in rules:
   if _match(lowercase_      ,			lowercase_						):
    return replacement
  return val

 return replace







def   _SCREAMING_SNAKE_CASE       (							)						->		List[str]:
 return [
     # embeddings
     (("transformer", "wpe", "embedding"), P("mp"      ,			lowercase_						)),
     (("transformer", "wte", "embedding"), P("mp"      ,			lowercase_						)),
     # atention
     (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(lowercase_      ,			"mp"						)),
     (("attention", "out_proj", "kernel"), P("mp"      ,			lowercase_						)),
     (("attention", "out_proj", "bias"), None),
     # mlp
     (("mlp", "c_fc", "kernel"), P(lowercase_      ,			"mp"						)),
     (("mlp", "c_fc", "bias"), P("mp"						)),
     (("mlp", "c_proj", "kernel"), P("mp"      ,			lowercase_						)),
     (("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   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		int:
 A__	=    _get_partition_rules()
 A__	=    _replacement_rules(lowercase_						)
 A__	=    {k: _unmatched for k in flatten_dict(lowercase_						)}
 A__	=    {k: replace(lowercase_      ,			lowercase_						) for k, v in initd.items()}
 assert _unmatched not in result.values(), "Incomplete partition spec."
 return freeze(unflatten_dict(lowercase_						)						)





