


'''simple docstring'''






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


__A	:	Dict     =     logging.get_logger(__name__)

__A	:	List[str]     =     {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__A	:	List[Any]     =     {
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

__A	:	List[Any]     =     {
    'junnyu/roformer_chinese_small': 1_536,
    'junnyu/roformer_chinese_base': 1_536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


__A	:	str     =     {
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}
class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Optional[int]   		=  VOCAB_FILES_NAMES
							lowercase			:		Optional[Any]   		=  PRETRAINED_VOCAB_FILES_MAP
							lowercase			:		List[Any]   		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase			:		Union[str, Any]   		=  PRETRAINED_INIT_CONFIGURATION
							lowercase			:		str   		=  RoFormerTokenizer





							def __init__(  self		:Optional[Any]  ,_UpperCamelCase		:List[Any]=None  ,_UpperCamelCase		:Any=None  ,_UpperCamelCase		:Optional[Any]=True  ,_UpperCamelCase		:Tuple="[UNK]"  ,_UpperCamelCase		:List[Any]="[SEP]"  ,_UpperCamelCase		:str="[PAD]"  ,_UpperCamelCase		:List[Any]="[CLS]"  ,_UpperCamelCase		:Optional[Any]="[MASK]"  ,_UpperCamelCase		:Optional[Any]=True  ,_UpperCamelCase		:Optional[Any]=None  ,**_UpperCamelCase		:int  ,):
									super().__init__(
									    _UpperCamelCase  ,tokenizer_file=_UpperCamelCase  ,do_lower_case=_UpperCamelCase  ,unk_token=_UpperCamelCase  ,sep_token=_UpperCamelCase  ,pad_token=_UpperCamelCase  ,cls_token=_UpperCamelCase  ,mask_token=_UpperCamelCase  ,tokenize_chinese_chars=_UpperCamelCase  ,strip_accents=_UpperCamelCase  ,**_UpperCamelCase  ,)

									snake_case_		:							Tuple			       =						json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
									if (
									    pre_tok_state.get("""lowercase"""  ,_UpperCamelCase			) != do_lower_case
									    or pre_tok_state.get("""strip_accents"""  ,_UpperCamelCase			) != strip_accents
									):
											snake_case_		:							Any			       =						getattr(_UpperCamelCase  ,pre_tok_state.pop("""type"""			)			)
											snake_case_		:							List[Any]			       =						do_lower_case
											snake_case_		:							Any			       =						strip_accents
											snake_case_		:							List[str]			       =						pre_tok_class(**_UpperCamelCase			)

									snake_case_		:							Optional[Any]			       =						do_lower_case





							def __getstate__(  self		:str			):
									snake_case_		:							Any			       =						self.__dict__.copy()
									snake_case_		:							int			       =						BertPreTokenizer()
									return state





							def __setstate__(  self		:List[Any]  ,_UpperCamelCase		:Any			):
									snake_case_		:							Optional[int]			       =						d
									snake_case_		:							int			       =						self.__dict__["""_tokenizer"""].get_vocab()
									snake_case_		:							Union[str, Any]			       =						PreTokenizer.custom(JiebaPreTokenizer(_UpperCamelCase			)			)





							def 		a__   (  self		:Union[str, Any]  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:List[str]=None			):
									snake_case_		:							List[Any]			       =						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

									if token_ids_a:
											output += token_ids_a + [self.sep_token_id]

									return output





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:List[int]  ,_UpperCamelCase		:Optional[List[int]] = None			):
									snake_case_		:							Union[str, Any]			       =						[self.sep_token_id]
									snake_case_		:							Tuple			       =						[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		:Any  ,_UpperCamelCase		:str  ,_UpperCamelCase		:Optional[str] = None			):
									snake_case_		:							int			       =						self._tokenizer.model.save(_UpperCamelCase  ,name=_UpperCamelCase			)
									return tuple(_UpperCamelCase			)





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:int  ,_UpperCamelCase		:Dict=None  ,_UpperCamelCase		:List[Any]=None  ,_UpperCamelCase		:Dict=False  ,**_UpperCamelCase		:Tuple  ,):
									snake_case_		:							int			       =						BertPreTokenizer()
									return super().save_pretrained(_UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase  ,**_UpperCamelCase			)



'''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