


'''simple docstring'''






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_rembert import RemBertTokenizer
else:
							__A	:	Tuple     =     None

__A	:	Union[str, Any]     =     logging.get_logger(__name__)
__A	:	Optional[Any]     =     {'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

__A	:	Union[str, Any]     =     {
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
    'tokenizer_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/tokenizer.json',
    },
}

__A	:	Optional[Any]     =     {
    'google/rembert': 256,
}

__A	:	Optional[int]     =     '▁'
class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		List[Any]   		=  VOCAB_FILES_NAMES
							lowercase			:		Any   		=  PRETRAINED_VOCAB_FILES_MAP
							lowercase			:		Optional[int]   		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase			:		Optional[Any]   		=  RemBertTokenizer





							def __init__(  self		:str  ,_UpperCamelCase		:List[Any]=None  ,_UpperCamelCase		:Any=None  ,_UpperCamelCase		:Dict=True  ,_UpperCamelCase		:Optional[int]=True  ,_UpperCamelCase		:Tuple=False  ,_UpperCamelCase		:Union[str, Any]="[CLS]"  ,_UpperCamelCase		:str="[SEP]"  ,_UpperCamelCase		:Optional[Any]="<unk>"  ,_UpperCamelCase		:Optional[int]="[SEP]"  ,_UpperCamelCase		:Dict="<pad>"  ,_UpperCamelCase		:Optional[int]="[CLS]"  ,_UpperCamelCase		:Any="[MASK]"  ,**_UpperCamelCase		:Dict  ,):
									# Mask token behave like a normal word, i.e. include the space before it
									snake_case_		:							Tuple			       =						AddedToken(_UpperCamelCase  ,lstrip=_UpperCamelCase  ,rstrip=_UpperCamelCase			) if isinstance(_UpperCamelCase  ,_UpperCamelCase			) else mask_token

									super().__init__(
									    _UpperCamelCase  ,tokenizer_file=_UpperCamelCase  ,do_lower_case=_UpperCamelCase  ,remove_space=_UpperCamelCase  ,keep_accents=_UpperCamelCase  ,bos_token=_UpperCamelCase  ,eos_token=_UpperCamelCase  ,unk_token=_UpperCamelCase  ,sep_token=_UpperCamelCase  ,pad_token=_UpperCamelCase  ,cls_token=_UpperCamelCase  ,mask_token=_UpperCamelCase  ,**_UpperCamelCase  ,)

									snake_case_		:							Dict			       =						do_lower_case
									snake_case_		:							int			       =						remove_space
									snake_case_		:							Optional[int]			       =						keep_accents
									snake_case_		:							Union[str, Any]			       =						vocab_file
									snake_case_		:							Union[str, Any]			       =						False if not self.vocab_file else True





							def 		a__   (  self		:List[str]  ,_UpperCamelCase		:List[int]  ,_UpperCamelCase		:Optional[List[int]] = None			):
									snake_case_		:							Optional[int]			       =						[self.sep_token_id]
									snake_case_		:							Tuple			       =						[self.cls_token_id]
									if token_ids_a is None:
											return cls + token_ids_a + sep
									return cls + token_ids_a + sep + token_ids_a + sep





							def 		a__   (  self		:Union[str, Any]  ,_UpperCamelCase		:List[int]  ,_UpperCamelCase		:Optional[List[int]] = None  ,_UpperCamelCase		:bool = False			):

									if already_has_special_tokens:
											if token_ids_a is not None:
													raise ValueError(
													    """You should not supply a second sequence if the provided sequence of """
													    """ids is already formatted with special tokens for the model."""			)
											return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

									if token_ids_a is not None:
											return [1] + ([0] * len(_UpperCamelCase			)) + [1] + ([0] * len(_UpperCamelCase			)) + [1]
									return [1] + ([0] * len(_UpperCamelCase			)) + [1]





							def 		a__   (  self		:str  ,_UpperCamelCase		:List[int]  ,_UpperCamelCase		:Optional[List[int]] = None			):
									snake_case_		:							int			       =						[self.sep_token_id]
									snake_case_		:							Optional[Any]			       =						[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			) * [0] + len(token_ids_a + sep			) * [1]





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:str  ,_UpperCamelCase		:Optional[str] = None			):
									if not os.path.isdir(_UpperCamelCase			):
											logger.error("""Vocabulary path ({}) should be a directory""".format(_UpperCamelCase			)			)
											return
									snake_case_		:							Optional[int]			       =						os.path.join(
									    _UpperCamelCase  ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)

									if os.path.abspath(self.vocab_file			) != os.path.abspath(_UpperCamelCase			):
											copyfile(self.vocab_file  ,_UpperCamelCase			)

									return (out_vocab_file,)



'''simple docstring'''






import copy
import os
from typing import Union

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


__A	:	str     =     logging.get_logger(__name__)

__A	:	Dict     =     {
    'google/pix2struct-textcaps-base': (
        'https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'
    ),
}
class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Tuple   		=  'pix2struct_text_model'
							lowercase			:		int   		=  ['past_key_values']
							lowercase			:		int   		=  {
							    'hidden_size': 'hidden_size',
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}





							def __init__(  self		:int  ,_UpperCamelCase		:List[str]=5_0_2_4_4  ,_UpperCamelCase		:Optional[Any]=7_6_8  ,_UpperCamelCase		:Dict=6_4  ,_UpperCamelCase		:Dict=2_0_4_8  ,_UpperCamelCase		:Optional[int]=1_2  ,_UpperCamelCase		:Union[str, Any]=1_2  ,_UpperCamelCase		:List[str]=3_2  ,_UpperCamelCase		:Union[str, Any]=1_2_8  ,_UpperCamelCase		:Tuple=0.1  ,_UpperCamelCase		:List[str]=1E-6  ,_UpperCamelCase		:List[Any]=1.0  ,_UpperCamelCase		:Optional[int]="gelu_new"  ,_UpperCamelCase		:Dict=0  ,_UpperCamelCase		:List[str]=False  ,_UpperCamelCase		:Optional[int]=0  ,_UpperCamelCase		:Dict=1  ,_UpperCamelCase		:List[Any]=False  ,_UpperCamelCase		:Tuple=True  ,**_UpperCamelCase		:List[Any]  ,):
									snake_case_		:							List[str]			       =						vocab_size
									snake_case_		:							Any			       =						hidden_size
									snake_case_		:							Any			       =						d_kv
									snake_case_		:							List[Any]			       =						d_ff
									snake_case_		:							Union[str, Any]			       =						num_layers
									snake_case_		:							Union[str, Any]			       =						num_heads
									snake_case_		:							str			       =						relative_attention_num_buckets
									snake_case_		:							Optional[int]			       =						relative_attention_max_distance
									snake_case_		:							Tuple			       =						dropout_rate
									snake_case_		:							Tuple			       =						layer_norm_epsilon
									snake_case_		:							Any			       =						initializer_factor
									snake_case_		:							List[Any]			       =						use_cache

									snake_case_		:							Optional[int]			       =						eos_token_id
									snake_case_		:							List[Any]			       =						decoder_start_token_id

									# for backwards compatibility
									snake_case_		:							List[str]			       =						dense_act_fn

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





							@classmethod
							def 		a__   (  cls		:Optional[int]  ,_UpperCamelCase		:Union[str, os.PathLike]  ,**_UpperCamelCase		:Optional[int]			):
									cls._set_token_in_kwargs(_UpperCamelCase			)

									snake_case_       ,      snake_case_		:							Optional[Any]			       =						cls.get_config_dict(_UpperCamelCase  ,**_UpperCamelCase			)

									# get the text config dict if we are loading from Pix2StructConfig
									if config_dict.get("""model_type"""			) == "pix2struct":
											snake_case_		:							Optional[int]			       =						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(_UpperCamelCase  ,**_UpperCamelCase			)





class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Dict   		=  'pix2struct_vision_model'





							def __init__(  self		:List[str]  ,_UpperCamelCase		:Any=7_6_8  ,_UpperCamelCase		:List[str]=7_6_8  ,_UpperCamelCase		:List[Any]=2_0_4_8  ,_UpperCamelCase		:Union[str, Any]=6_4  ,_UpperCamelCase		:int=1_2  ,_UpperCamelCase		:int=1_2  ,_UpperCamelCase		:Any="gelu_new"  ,_UpperCamelCase		:Optional[int]=1E-6  ,_UpperCamelCase		:List[Any]=0.0  ,_UpperCamelCase		:Union[str, Any]=0.0  ,_UpperCamelCase		:int=1E-1_0  ,_UpperCamelCase		:str=1.0  ,_UpperCamelCase		:Optional[int]=4_0_9_6  ,_UpperCamelCase		:str=3_2  ,_UpperCamelCase		:Union[str, Any]=1_2_8  ,**_UpperCamelCase		:Union[str, Any]  ,):
									super().__init__(**_UpperCamelCase			)

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





							@classmethod
							def 		a__   (  cls		:Optional[Any]  ,_UpperCamelCase		:Union[str, os.PathLike]  ,**_UpperCamelCase		:List[str]			):
									cls._set_token_in_kwargs(_UpperCamelCase			)

									snake_case_       ,      snake_case_		:							Tuple			       =						cls.get_config_dict(_UpperCamelCase  ,**_UpperCamelCase			)

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





class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Union[str, Any]   		=  'pix2struct'
							lowercase			:		int   		=  True





							def __init__(  self		:int  ,_UpperCamelCase		:Tuple=None  ,_UpperCamelCase		:Optional[int]=None  ,_UpperCamelCase		:Dict=1.0  ,_UpperCamelCase		:Optional[int]=0.02  ,_UpperCamelCase		:Tuple=False  ,_UpperCamelCase		:List[str]=False  ,_UpperCamelCase		:Dict=True  ,**_UpperCamelCase		:Union[str, Any]  ,):
									super().__init__(tie_word_embeddings=_UpperCamelCase  ,is_encoder_decoder=_UpperCamelCase  ,**_UpperCamelCase			)

									if text_config is None:
											snake_case_		:							Optional[int]			       =						{}
											logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values."""			)

									if vision_config is None:
											snake_case_		:							List[str]			       =						{}
											logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values."""			)

									snake_case_		:							Optional[int]			       =						PixaStructTextConfig(**_UpperCamelCase			)
									snake_case_		:							List[str]			       =						PixaStructVisionConfig(**_UpperCamelCase			)

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

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

									snake_case_		:							Union[str, Any]			       =						self.initializer_range
									snake_case_		:							str			       =						self.initializer_range

									snake_case_		:							Dict			       =						is_vqa





							@classmethod
							def 		a__   (  cls		:Any  ,_UpperCamelCase		:PixaStructTextConfig  ,_UpperCamelCase		:PixaStructVisionConfig  ,**_UpperCamelCase		:Any			):
									return cls(text_config=text_config.to_dict()  ,vision_config=vision_config.to_dict()  ,**_UpperCamelCase			)





							def 		a__   (  self		:Optional[int]			):
									snake_case_		:							int			       =						copy.deepcopy(self.__dict__			)
									snake_case_		:							str			       =						self.text_config.to_dict()
									snake_case_		:							Tuple			       =						self.vision_config.to_dict()
									snake_case_		:							Any			       =						self.__class__.model_type
									return output