





"""simple docstring"""




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


lowerCamelCase_     :						Tuple						=					logging.get_logger(__name__)





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Optional[Any]										=  R'\w+[.]\d+'
  A_  :       int										=  re.findall(_UpperCAmelCase ,					_UpperCAmelCase					)
  for pat in pats:
    A_  :       Optional[int]										=  key.replace(_UpperCAmelCase ,					'_'.join(pat.split('.'					)					)					)
  return key





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[Any]										=  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_  :       Union[str, Any]										=  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_  :       List[str]										=  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_  :       Optional[Any]										=  pt_tuple_key[:-1] + ('embedding',)
    return renamed_pt_tuple_key, pt_tensor

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

  # linear layer
  A_  :       Optional[Any]										=  pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight":
    A_  :       Optional[Any]										=  pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

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

  # old PyTorch layer norm bias
  A_  :       Optional[int]										=  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 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase=42					):






  """simple docstring"""





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

  # Step 2: Since the model is stateless, get random Flax params
  A_  :       Union[str, Any]										=  flax_model.init_weights(PRNGKey(_UpperCAmelCase					)					)

  A_  :       Optional[Any]										=  flatten_dict(_UpperCAmelCase					)
  A_  :       Tuple										=  {}

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    A_  :       Any										=  rename_key(_UpperCAmelCase					)
    A_  :       List[str]										=  tuple(renamed_pt_key.split('.'					)					)

    # Correctly rename weight parameters
    A_						,       A_  :       Union[str, Any]										=  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
    A_  :       str										=  jnp.asarray(_UpperCAmelCase					)

  return unflatten_dict(_UpperCAmelCase					)






"""simple docstring"""




import copy
import random

from transformers import CLIPTokenizer

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        super().__init__(*snake_case_					,      **snake_case_  )
        A_  :       Tuple										=  {}

      def 					lowerCamelCase_			(     self					,      snake_case_					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        A_  :       str										=  super().add_tokens(snake_case_					,      *snake_case_					,      **snake_case_  )
        if num_added_tokens == 0:
          raise ValueError(
              F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
              ' `placeholder_token` that is not already in the tokenizer.'  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      *snake_case_					,      snake_case_=1					,      **snake_case_  ):

        """simple docstring"""

        A_  :       Tuple										=  []
        if num_vec_per_token == 1:
          self.try_adding_tokens(snake_case_					,      *snake_case_					,      **snake_case_  )
          output.append(snake_case_  )
        else:
          A_  :       Tuple										=  []
          for i in range(snake_case_  ):
            A_  :       List[str]										=  placeholder_token + F"""_{i}"""
            self.try_adding_tokens(snake_case_					,      *snake_case_					,      **snake_case_  )
            output.append(snake_case_  )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
        for token in self.token_map:
          if token in placeholder_token:
            raise ValueError(
                F"""The tokenizer already has placeholder token {token} that can get confused with"""
                F""" {placeholder_token}keep placeholder tokens independent"""  )
        A_  :       Any										=  output

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_=False					,      snake_case_=1.0  ):

        """simple docstring"""

        if isinstance(snake_case_					,      snake_case_  ):
          A_  :       Optional[Any]										=  []
          for i in range(len(snake_case_  )  ):
            output.append(self.replace_placeholder_tokens_in_text(text[i]					,      vector_shuffle=snake_case_  )  )
          return output
        for placeholder_token in self.token_map:
          if placeholder_token in text:
            A_  :       List[Any]										=  self.token_map[placeholder_token]
            A_  :       Optional[int]										=  tokens[: 1 + int(len(snake_case_  ) * prop_tokens_to_load  )]
            if vector_shuffle:
              A_  :       Optional[Any]										=  copy.copy(snake_case_  )
              random.shuffle(snake_case_  )
            A_  :       List[str]										=  text.replace(snake_case_					,      ' '.join(snake_case_  )  )
        return text

      def __call__(     self					,      snake_case_					,      *snake_case_					,      snake_case_=False					,      snake_case_=1.0					,      **snake_case_  ):

        """simple docstring"""

        return super().__call__(
            self.replace_placeholder_tokens_in_text(
                snake_case_					,      vector_shuffle=snake_case_					,      prop_tokens_to_load=snake_case_  )					,      *snake_case_					,      **snake_case_					,      )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      *snake_case_					,      snake_case_=False					,      snake_case_=1.0					,      **snake_case_  ):

        """simple docstring"""

        return super().encode(
            self.replace_placeholder_tokens_in_text(
                snake_case_					,      vector_shuffle=snake_case_					,      prop_tokens_to_load=snake_case_  )					,      *snake_case_					,      **snake_case_					,      )