





import copy
import os
from typing import Union

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


lowerCAmelCase_        =							logging.get_logger(__name__)

lowerCAmelCase_        =							{
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}



class    __lowerCAmelCase			(     _a	):
			lowerCamelCase_	:	Optional[int]			     = '''pix2struct_text_model'''
			lowerCamelCase_	:	str			     = ['''past_key_values''']
			lowerCamelCase_	:	Any			     = {
			    '''hidden_size''': '''hidden_size''',
			    '''num_attention_heads''': '''num_heads''',
			    '''num_hidden_layers''': '''num_layers''',
			}






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


							'''simple docstring'''



							snake_case_		:       str           =    vocab_size
							snake_case_		:       Optional[Any]           =    hidden_size
							snake_case_		:       Union[str, Any]           =    d_kv
							snake_case_		:       Dict           =    d_ff
							snake_case_		:       str           =    num_layers
							snake_case_		:       Tuple           =    num_heads
							snake_case_		:       int           =    relative_attention_num_buckets
							snake_case_		:       Optional[int]           =    relative_attention_max_distance
							snake_case_		:       int           =    dropout_rate
							snake_case_		:       Optional[Any]           =    layer_norm_epsilon
							snake_case_		:       Tuple           =    initializer_factor
							snake_case_		:       Union[str, Any]           =    use_cache

							snake_case_		:       str           =    eos_token_id
							snake_case_		:       Dict           =    decoder_start_token_id

							# for backwards compatibility
							snake_case_		:       Tuple           =    dense_act_fn

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






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


							'''simple docstring'''



							cls._set_token_in_kwargs(__magic_name__       )

							snake_case_     ,				snake_case_		:       Optional[int]           =    cls.get_config_dict(__magic_name__   ,				**__magic_name__       )

							# get the text config dict if we are loading from Pix2StructConfig
							if config_dict.get('''model_type'''       ) == "pix2struct":
											snake_case_		:       Dict           =    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(__magic_name__   ,				**__magic_name__       )




class    __lowerCAmelCase			(     _a	):
			lowerCamelCase_	:	Optional[int]			     = '''pix2struct_vision_model'''






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


							'''simple docstring'''



							super().__init__(**__magic_name__       )

							snake_case_		:       Any           =    hidden_size
							snake_case_		:       int           =    patch_embed_hidden_size
							snake_case_		:       List[Any]           =    d_ff
							snake_case_		:       str           =    dropout_rate
							snake_case_		:       str           =    num_hidden_layers
							snake_case_		:       Optional[int]           =    num_attention_heads
							snake_case_		:       Tuple           =    initializer_range
							snake_case_		:       Any           =    initializer_factor
							snake_case_		:       str           =    attention_dropout
							snake_case_		:       Optional[int]           =    layer_norm_eps
							snake_case_		:       Optional[int]           =    dense_act_fn
							snake_case_		:       List[str]           =    seq_len
							snake_case_		:       Optional[int]           =    relative_attention_num_buckets
							snake_case_		:       List[str]           =    relative_attention_max_distance
							snake_case_		:       Tuple           =    d_kv






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


							'''simple docstring'''



							cls._set_token_in_kwargs(__magic_name__       )

							snake_case_     ,				snake_case_		:       Optional[int]           =    cls.get_config_dict(__magic_name__   ,				**__magic_name__       )

							# get the vision config dict if we are loading from Pix2StructConfig
							if config_dict.get('''model_type'''       ) == "pix2struct":
											snake_case_		:       str           =    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(__magic_name__   ,				**__magic_name__       )




class    __lowerCAmelCase			(     _a	):
			lowerCamelCase_	:	str			     = '''pix2struct'''
			lowerCamelCase_	:	List[str]			     = True






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


							'''simple docstring'''



							super().__init__(tie_word_embeddings=__magic_name__   ,				is_encoder_decoder=__magic_name__   ,				**__magic_name__       )

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

							if vision_config is None:
											snake_case_		:       Union[str, Any]           =    {}
											logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.'''       )

							snake_case_		:       Optional[Any]           =    PixaStructTextConfig(**__magic_name__       )
							snake_case_		:       Optional[int]           =    PixaStructVisionConfig(**__magic_name__       )

							snake_case_		:       Optional[int]           =    self.text_config.decoder_start_token_id
							snake_case_		:       Tuple           =    self.text_config.pad_token_id
							snake_case_		:       List[str]           =    self.text_config.eos_token_id

							snake_case_		:       int           =    initializer_factor
							snake_case_		:       List[str]           =    initializer_range

							snake_case_		:       Tuple           =    self.initializer_range
							snake_case_		:       List[Any]           =    self.initializer_range

							snake_case_		:       int           =    is_vqa






			@classmethod
			def      lowerCamelCase    (cls   ,				__magic_name__   ,				__magic_name__   ,				**__magic_name__       )	->  Optional[Any]:


							'''simple docstring'''



							return cls(text_config=text_config.to_dict()   ,				vision_config=vision_config.to_dict()   ,				**__magic_name__       )






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


							'''simple docstring'''



							snake_case_		:       str           =    copy.deepcopy(self.__dict__       )
							snake_case_		:       Dict           =    self.text_config.to_dict()
							snake_case_		:       Dict           =    self.vision_config.to_dict()
							snake_case_		:       int           =    self.__class__.model_type
							return output




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


SCREAMING_SNAKE_CASE						:str			=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:Dict			=  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE						:str			=  {
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
        '''roberta-base-openai-detector''': (
            '''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
        ),
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
        ),
    },
}

SCREAMING_SNAKE_CASE						:Tuple			=  {
    '''roberta-base''': 5_12,
    '''roberta-large''': 5_12,
    '''roberta-large-mnli''': 5_12,
    '''distilroberta-base''': 5_12,
    '''roberta-base-openai-detector''': 5_12,
    '''roberta-large-openai-detector''': 5_12,
}


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							VOCAB_FILES_NAMES
  _SCREAMING_SNAKE_CASE		     =							PRETRAINED_VOCAB_FILES_MAP
  _SCREAMING_SNAKE_CASE		     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _SCREAMING_SNAKE_CASE		     =							['input_ids', 'attention_mask']
  _SCREAMING_SNAKE_CASE		     =							RobertaTokenizer



  def __init__(  self							:							Tuple     ,					_lowerCAmelCase							:							Union[str, Any]=None     ,					_lowerCAmelCase							:							Tuple=None     ,					_lowerCAmelCase							:							Any=None     ,					_lowerCAmelCase							:							Optional[Any]="replace"     ,					_lowerCAmelCase							:							str="<s>"     ,					_lowerCAmelCase							:							Tuple="</s>"     ,					_lowerCAmelCase							:							Any="</s>"     ,					_lowerCAmelCase							:							Tuple="<s>"     ,					_lowerCAmelCase							:							Any="<unk>"     ,					_lowerCAmelCase							:							Tuple="<pad>"     ,					_lowerCAmelCase							:							List[str]="<mask>"     ,					_lowerCAmelCase							:							str=False     ,					_lowerCAmelCase							:							Union[str, Any]=True     ,					**_lowerCAmelCase							:							int     ,					) ->			str:




     """simple docstring"""
     super().__init__(
         _lowerCAmelCase     ,					_lowerCAmelCase     ,					tokenizer_file=_lowerCAmelCase     ,					errors=_lowerCAmelCase     ,					bos_token=_lowerCAmelCase     ,					eos_token=_lowerCAmelCase     ,					sep_token=_lowerCAmelCase     ,					cls_token=_lowerCAmelCase     ,					unk_token=_lowerCAmelCase     ,					pad_token=_lowerCAmelCase     ,					mask_token=_lowerCAmelCase     ,					add_prefix_space=_lowerCAmelCase     ,					trim_offsets=_lowerCAmelCase     ,					**_lowerCAmelCase     ,					)

     snake_case_            =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
     if pre_tok_state.get("add_prefix_space"     ,					_lowerCAmelCase	) != add_prefix_space:
        snake_case_            =       getattr(_lowerCAmelCase     ,					pre_tok_state.pop("type"	)	)
        snake_case_            =       add_prefix_space
        snake_case_            =       pre_tok_class(**_lowerCAmelCase	)

     snake_case_            =       add_prefix_space

     snake_case_            =       "post_processor"
     snake_case_            =       getattr(self.backend_tokenizer     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)
     if tokenizer_component_instance:
        snake_case_            =       json.loads(tokenizer_component_instance.__getstate__()	)

        # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
        if "sep" in state:
           snake_case_            =       tuple(state["sep"]	)
        if "cls" in state:
           snake_case_            =       tuple(state["cls"]	)

        snake_case_            =       False

        if state.get("add_prefix_space"     ,					_lowerCAmelCase	) != add_prefix_space:
           snake_case_            =       add_prefix_space
           snake_case_            =       True

        if state.get("trim_offsets"     ,					_lowerCAmelCase	) != trim_offsets:
           snake_case_            =       trim_offsets
           snake_case_            =       True

        if changes_to_apply:
           snake_case_            =       getattr(_lowerCAmelCase     ,					state.pop("type"	)	)
           snake_case_            =       component_class(**_lowerCAmelCase	)
           setattr(self.backend_tokenizer     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)



  @property
  def        lowerCAmelCase__	(  self							:							Dict	) ->			str:




     """simple docstring"""
     if self._mask_token is None:
        if self.verbose:
           logger.error("Using mask_token, but it is not set yet."	)
        return None
     return str(self._mask_token	)



  @mask_token.setter
  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							Tuple	) ->			Optional[int]:




     """simple docstring"""
     snake_case_            =       AddedToken(_lowerCAmelCase     ,					lstrip=_lowerCAmelCase     ,					rstrip=_lowerCAmelCase	) if isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	) else value
     snake_case_            =       value



  def        lowerCAmelCase__	(  self							:							Dict     ,					*_lowerCAmelCase							:							Optional[int]     ,					**_lowerCAmelCase							:							Optional[int]	) ->			BatchEncoding:




     """simple docstring"""
     snake_case_            =       kwargs.get("is_split_into_words"     ,					_lowerCAmelCase	)
     assert self.add_prefix_space or not is_split_into_words, (
         F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
         "to use it with pretokenized inputs."
     )

     return super()._batch_encode_plus(*_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					*_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							Any	) ->			BatchEncoding:




     """simple docstring"""
     snake_case_            =       kwargs.get("is_split_into_words"     ,					_lowerCAmelCase	)

     assert self.add_prefix_space or not is_split_into_words, (
         F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
         "to use it with pretokenized inputs."
     )

     return super()._encode_plus(*_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Optional[str] = None	) ->			Tuple[str]:




     """simple docstring"""
     snake_case_            =       self._tokenizer.model.save(_lowerCAmelCase     ,					name=_lowerCAmelCase	)
     return tuple(_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Any     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							List[Any]=None	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
        return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[int]     ,					_lowerCAmelCase							:							Optional[List[int]] = None	) ->			List[int]:




     """simple docstring"""
     snake_case_            =       [self.sep_token_id]
     snake_case_            =       [self.cls_token_id]

     if token_ids_a is None:
        return len(cls + token_ids_a + sep	) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]




