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


SCREAMING_SNAKE_CASE			:						Any				  =		logging.get_logger(__name__)



def 	__A		(      _A       ):



 """simple docstring"""


 __a							=	r"\w+[.]\d+"
 __a							=	re.findall(_A				,					_A       )
 for pat in pats:
  __a							=	key.replace(_A				,					"_".join(pat.split("."       )       )       )
 return key



def 	__A		(      _A				,					_A				,					_A       ):



 """simple docstring"""


 __a							=	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)
 ):
  __a							=	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:
  __a							=	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:
  __a							=	pt_tuple_key[:-1] + ("embedding",)
  return renamed_pt_tuple_key, pt_tensor

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

 # linear layer
 __a							=	pt_tuple_key[:-1] + ("kernel",)
 if pt_tuple_key[-1] == "weight":
  __a							=	pt_tensor.T
  return renamed_pt_tuple_key, pt_tensor

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

 # old PyTorch layer norm bias
 __a							=	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 	__A		(      _A				,					_A				,					_A=42       ):



 """simple docstring"""


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

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

 __a							=	flatten_dict(_A       )
 __a							=	{}

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

  # Correctly rename weight parameters
  __a							,  __a							=	rename_key_and_reshape_tensor(_A				,					_A				,					_A       )

  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
  __a							=	jnp.asarray(_A       )

 return unflatten_dict(_A       )



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      ["""image_processor""", """tokenizer"""]
  _SCREAMING_SNAKE_CASE     					=      """ChineseCLIPImageProcessor"""
  _SCREAMING_SNAKE_CASE     					=      ("""BertTokenizer""", """BertTokenizerFast""")

  def __init__(		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Any=None  ,				__SCREAMING_SNAKE_CASE			:   Tuple=None  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]							):
   __a							=	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  ,				)
    __a							=	kwargs.pop("feature_extractor"							)

   __a							=	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							)
   __a							=	self.image_processor

  def __call__(		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Dict=None  ,				__SCREAMING_SNAKE_CASE			:   Dict=None  ,				__SCREAMING_SNAKE_CASE			:   Tuple=None  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]							):

   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(__SCREAMING_SNAKE_CASE  ,				return_tensors=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   if images is not None:
    __a							=	self.image_processor(__SCREAMING_SNAKE_CASE  ,				return_tensors=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_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(**__SCREAMING_SNAKE_CASE							)  ,				tensor_type=__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   int  ,				*__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				**__SCREAMING_SNAKE_CASE			:   str							):
   return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				*__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]							):
   return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  @property
  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	self.tokenizer.model_input_names
   __a							=	self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)

  @property
  def 		_UpperCAmelCase (		self			:   Any							):
   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


