




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
__A						:							Optional[int]							=  object()

# For specifying empty leaf dict `{}`
__A						:							Optional[Any]							=  object()
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
   '''simple docstring'''
   SCREAMING_SNAKE_CASE           =					tuple((re.compile(x + """$"""   ) for x in qs)   )
   for i in range(len(SCREAMING_SNAKE_CASE   ) - len(SCREAMING_SNAKE_CASE   ) + 1   ):
      SCREAMING_SNAKE_CASE           =					[x.match(SCREAMING_SNAKE_CASE   ) for x, y in zip(SCREAMING_SNAKE_CASE					,	ks[i:]   )]
      if matches and all(SCREAMING_SNAKE_CASE   ):
         return True
   return False
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
   '''simple docstring'''

   def replace(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
      for rule, replacement in rules:
         if _match(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
            return replacement
      return val

   return replace
def 					lowerCamelCase_    (    ):
   '''simple docstring'''
   return [
       # embeddings
       (("transformer", "wpe", "embedding"), P("""mp"""					,	SCREAMING_SNAKE_CASE   )),
       (("transformer", "wte", "embedding"), P("""mp"""					,	SCREAMING_SNAKE_CASE   )),
       # atention
       (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(SCREAMING_SNAKE_CASE					,	"""mp"""   )),
       (("attention", "out_proj", "kernel"), P("""mp"""					,	SCREAMING_SNAKE_CASE   )),
       (("attention", "out_proj", "bias"), None),
       # mlp
       (("mlp", "c_fc", "kernel"), P(SCREAMING_SNAKE_CASE					,	"""mp"""   )),
       (("mlp", "c_fc", "bias"), P("""mp"""   )),
       (("mlp", "c_proj", "kernel"), P("""mp"""					,	SCREAMING_SNAKE_CASE   )),
       (("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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
   '''simple docstring'''
   SCREAMING_SNAKE_CASE           =					_get_partition_rules()
   SCREAMING_SNAKE_CASE           =					_replacement_rules(SCREAMING_SNAKE_CASE   )
   SCREAMING_SNAKE_CASE           =					{k: _unmatched for k in flatten_dict(SCREAMING_SNAKE_CASE   )}
   SCREAMING_SNAKE_CASE           =					{k: replace(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ) for k, v in initd.items()}
   assert _unmatched not in result.values(), "Incomplete partition spec."
   return freeze(unflatten_dict(SCREAMING_SNAKE_CASE   )   )












import warnings

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




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     ['''image_processor''', '''tokenizer''']
							a__						  =     '''ViTImageProcessor'''
							a__						  =     ('''CLIPTokenizer''', '''CLIPTokenizerFast''')





							def __init__(					self       :	List[str]       ,     a       :	Dict=None       ,     a       :	List[str]=None       ,     **a       :	List[str]			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					None
										if "feature_extractor" in kwargs:
													warnings.warn(
													    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
													    """ instead."""       ,     a       ,     )
													SCREAMING_SNAKE_CASE           =					kwargs.pop("""feature_extractor"""			)

										SCREAMING_SNAKE_CASE           =					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__(a       ,     a			)





							def __call__(					self       :	List[Any]       ,     a       :	int=None       ,     a       :	Dict=None       ,     a       :	int=None       ,     a       :	Tuple=None       ,     **a       :	Any			)			->					Any:
										if text is None and visual_prompt is None and images is None:
													raise ValueError("""You have to specify either text, visual prompt or images."""			)

										if text is not None and visual_prompt is not None:
													raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""			)

										if text is not None:
													SCREAMING_SNAKE_CASE           =					self.tokenizer(a       ,     return_tensors=a       ,     **a			)

										if visual_prompt is not None:
													SCREAMING_SNAKE_CASE           =					self.image_processor(a       ,     return_tensors=a       ,     **a			)

										if images is not None:
													SCREAMING_SNAKE_CASE           =					self.image_processor(a       ,     return_tensors=a       ,     **a			)

										if visual_prompt is not None and images is not None:
													SCREAMING_SNAKE_CASE           =					{
													    """pixel_values""": image_features.pixel_values,
													    """conditional_pixel_values""": prompt_features.pixel_values,
													}
													return encoding
										elif text is not None and images is not None:
													SCREAMING_SNAKE_CASE           =					image_features.pixel_values
													return encoding
										elif text is not None:
													return encoding
										elif visual_prompt is not None:
													SCREAMING_SNAKE_CASE           =					{
													    """conditional_pixel_values""": prompt_features.pixel_values,
													}
													return encoding
										else:
													return BatchEncoding(data=dict(**a			)       ,     tensor_type=a			)





							def    _UpperCAmelCase     (					self       :	Dict       ,     *a       :	Dict       ,     **a       :	Union[str, Any]			)			->					Any:
										return self.tokenizer.batch_decode(*a       ,     **a			)





							def    _UpperCAmelCase     (					self       :	Tuple       ,     *a       :	List[Any]       ,     **a       :	Union[str, Any]			)			->					Dict:
										return self.tokenizer.decode(*a       ,     **a			)





							@property
							def    _UpperCAmelCase     (					self       :	Any			)			->					Optional[Any]:
										warnings.warn(
										    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,     a       ,     )
										return self.image_processor_class







							@property
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					str:
										warnings.warn(
										    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,     a       ,     )
										return self.image_processor






