






'''simple docstring'''



import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase   :							Optional[Any]         =   logging.get_logger(__name__)

__UpperCamelCase   :							Union[str, Any]         =   {
    """google/pix2struct-textcaps-base""": (
        """https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
    ),
}
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="pix2struct_text_model"
      __a	       =["past_key_values"]
      __a	       ={
          "hidden_size": "hidden_size",
          "num_attention_heads": "num_heads",
          "num_hidden_layers": "num_layers",
      }




      def __init__(	self			, lowerCamelCase=5_0244			, lowerCamelCase=768			, lowerCamelCase=64			, lowerCamelCase=2048			, lowerCamelCase=12			, lowerCamelCase=12			, lowerCamelCase=32			, lowerCamelCase=128			, lowerCamelCase=0.1			, lowerCamelCase=1e-6			, lowerCamelCase=1.0			, lowerCamelCase="gelu_new"			, lowerCamelCase=0			, lowerCamelCase=False			, lowerCamelCase=0			, lowerCamelCase=1			, lowerCamelCase=False			, lowerCamelCase=True			, **lowerCamelCase			, )				->int:



         '''simple docstring'''
         __a					  =     vocab_size
         __a					  =     hidden_size
         __a					  =     d_kv
         __a					  =     d_ff
         __a					  =     num_layers
         __a					  =     num_heads
         __a					  =     relative_attention_num_buckets
         __a					  =     relative_attention_max_distance
         __a					  =     dropout_rate
         __a					  =     layer_norm_epsilon
         __a					  =     initializer_factor
         __a					  =     use_cache

         __a					  =     eos_token_id
         __a					  =     decoder_start_token_id

         # for backwards compatibility
         __a					  =     dense_act_fn

         super().__init__(
             pad_token_id=lowerCamelCase			, eos_token_id=lowerCamelCase			, decoder_start_token_id=lowerCamelCase			, tie_word_embeddings=lowerCamelCase			, is_decoder=lowerCamelCase			, **lowerCamelCase			, )






      @classmethod
      def 			__UpperCamelCase				(	cls			, lowerCamelCase			, **lowerCamelCase   )				->"PretrainedConfig":



         '''simple docstring'''
         cls._set_token_in_kwargs(lowerCamelCase   )

         __a  ,      __a					  =     cls.get_config_dict(lowerCamelCase			, **lowerCamelCase   )

         # get the text config dict if we are loading from Pix2StructConfig
         if config_dict.get('model_type'   ) == "pix2struct":
            __a					  =     config_dict['text_config']

         if "model_type" in config_dict and hasattr(cls			, 'model_type'   ) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

         return cls.from_dict(lowerCamelCase			, **lowerCamelCase   )







class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="pix2struct_vision_model"




      def __init__(	self			, lowerCamelCase=768			, lowerCamelCase=768			, lowerCamelCase=2048			, lowerCamelCase=64			, lowerCamelCase=12			, lowerCamelCase=12			, lowerCamelCase="gelu_new"			, lowerCamelCase=1e-6			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, lowerCamelCase=1e-10			, lowerCamelCase=1.0			, lowerCamelCase=4096			, lowerCamelCase=32			, lowerCamelCase=128			, **lowerCamelCase			, )				->Any:



         '''simple docstring'''
         super().__init__(**lowerCamelCase   )

         __a					  =     hidden_size
         __a					  =     patch_embed_hidden_size
         __a					  =     d_ff
         __a					  =     dropout_rate
         __a					  =     num_hidden_layers
         __a					  =     num_attention_heads
         __a					  =     initializer_range
         __a					  =     initializer_factor
         __a					  =     attention_dropout
         __a					  =     layer_norm_eps
         __a					  =     dense_act_fn
         __a					  =     seq_len
         __a					  =     relative_attention_num_buckets
         __a					  =     relative_attention_max_distance
         __a					  =     d_kv






      @classmethod
      def 			__UpperCamelCase				(	cls			, lowerCamelCase			, **lowerCamelCase   )				->"PretrainedConfig":



         '''simple docstring'''
         cls._set_token_in_kwargs(lowerCamelCase   )

         __a  ,      __a					  =     cls.get_config_dict(lowerCamelCase			, **lowerCamelCase   )

         # get the vision config dict if we are loading from Pix2StructConfig
         if config_dict.get('model_type'   ) == "pix2struct":
            __a					  =     config_dict['vision_config']

         if "model_type" in config_dict and hasattr(cls			, 'model_type'   ) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

         return cls.from_dict(lowerCamelCase			, **lowerCamelCase   )







class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="pix2struct"
      __a	       =True




      def __init__(	self			, lowerCamelCase=None			, lowerCamelCase=None			, lowerCamelCase=1.0			, lowerCamelCase=0.02			, lowerCamelCase=False			, lowerCamelCase=False			, lowerCamelCase=True			, **lowerCamelCase			, )				->Dict:



         '''simple docstring'''
         super().__init__(tie_word_embeddings=lowerCamelCase			, is_encoder_decoder=lowerCamelCase			, **lowerCamelCase   )

         if text_config is None:
            __a					  =     {}
            logger.info('text_config is None. Initializing the Pix2StructTextConfig with default values.'   )

         if vision_config is None:
            __a					  =     {}
            logger.info('vision_config is None. Initializing the Pix2StructVisionConfig with default values.'   )

         __a					  =     PixaStructTextConfig(**lowerCamelCase   )
         __a					  =     PixaStructVisionConfig(**lowerCamelCase   )

         __a					  =     self.text_config.decoder_start_token_id
         __a					  =     self.text_config.pad_token_id
         __a					  =     self.text_config.eos_token_id

         __a					  =     initializer_factor
         __a					  =     initializer_range

         __a					  =     self.initializer_range
         __a					  =     self.initializer_range

         __a					  =     is_vqa




      @classmethod
      def 			__UpperCamelCase				(	cls			, lowerCamelCase			, lowerCamelCase			, **lowerCamelCase   )				->int:



         '''simple docstring'''
         return cls(text_config=text_config.to_dict()			, vision_config=vision_config.to_dict()			, **lowerCamelCase   )






      def 			__UpperCamelCase				(	self   )				->List[Any]:



         '''simple docstring'''
         __a					  =     copy.deepcopy(self.__dict__   )
         __a					  =     self.text_config.to_dict()
         __a					  =     self.vision_config.to_dict()
         __a					  =     self.__class__.model_type
         return output







'''simple docstring'''



import copy
import random

from transformers import CLIPTokenizer
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):




      def __init__(	self			, *lowerCamelCase			, **lowerCamelCase   )				->Union[str, Any]:



         '''simple docstring'''
         super().__init__(*lowerCamelCase			, **lowerCamelCase   )
         __a					  =     {}




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, *lowerCamelCase			, **lowerCamelCase   )				->Dict:



         '''simple docstring'''
         __a					  =     super().add_tokens(lowerCamelCase			, *lowerCamelCase			, **lowerCamelCase   )
         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 			__UpperCamelCase				(	self			, lowerCamelCase			, *lowerCamelCase			, lowerCamelCase=1			, **lowerCamelCase   )				->List[str]:



         '''simple docstring'''
         __a					  =     []
         if num_vec_per_token == 1:
            self.try_adding_tokens(lowerCamelCase			, *lowerCamelCase			, **lowerCamelCase   )
            output.append(lowerCamelCase   )
         else:
            __a					  =     []
            for i in range(lowerCamelCase   ):
               __a					  =     placeholder_token + F"""_{i}"""
               self.try_adding_tokens(lowerCamelCase			, *lowerCamelCase			, **lowerCamelCase   )
               output.append(lowerCamelCase   )
        # 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					  =     output




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase=False			, lowerCamelCase=1.0   )				->int:



         '''simple docstring'''
         if isinstance(lowerCamelCase			, lowerCamelCase   ):
            __a					  =     []
            for i in range(len(lowerCamelCase   )   ):
               output.append(self.replace_placeholder_tokens_in_text(text[i]			, vector_shuffle=lowerCamelCase   )   )
            return output
         for placeholder_token in self.token_map:
            if placeholder_token in text:
               __a					  =     self.token_map[placeholder_token]
               __a					  =     tokens[: 1 + int(len(lowerCamelCase   ) * prop_tokens_to_load   )]
               if vector_shuffle:
                  __a					  =     copy.copy(lowerCamelCase   )
                  random.shuffle(lowerCamelCase   )
               __a					  =     text.replace(lowerCamelCase			, ' '.join(lowerCamelCase   )   )
         return text




      def __call__(	self			, lowerCamelCase			, *lowerCamelCase			, lowerCamelCase=False			, lowerCamelCase=1.0			, **lowerCamelCase   )				->Optional[int]:



         '''simple docstring'''
         return super().__call__(
             self.replace_placeholder_tokens_in_text(
                 lowerCamelCase			, vector_shuffle=lowerCamelCase			, prop_tokens_to_load=lowerCamelCase   )			, *lowerCamelCase			, **lowerCamelCase			, )






      def 			__UpperCamelCase				(	self			, lowerCamelCase			, *lowerCamelCase			, lowerCamelCase=False			, lowerCamelCase=1.0			, **lowerCamelCase   )				->List[Any]:



         '''simple docstring'''
         return super().encode(
             self.replace_placeholder_tokens_in_text(
                 lowerCamelCase			, vector_shuffle=lowerCamelCase			, prop_tokens_to_load=lowerCamelCase   )			, *lowerCamelCase			, **lowerCamelCase			, )