


from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


__snake_case   							=						logging.get_logger(__name__)

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

__snake_case   							=						{
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

__snake_case   							=						{'''allegro/herbert-base-cased''': 5_1_4}
__snake_case   							=						{}



class 	__lowerCamelCase			(_a						):
       _lowercase											=     VOCAB_FILES_NAMES
       _lowercase											=     PRETRAINED_VOCAB_FILES_MAP
       _lowercase											=     PRETRAINED_INIT_CONFIGURATION
       _lowercase											=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _lowercase											=     HerbertTokenizer







       def __init__(			self: str,A_: str=None,A_: Optional[int]=None,A_: Union[str, Any]=None,A_: Union[str, Any]="<s>",A_: str="<unk>",A_: Optional[Any]="<pad>",A_: Optional[int]="<mask>",A_: Optional[int]="</s>",**A_: Dict,):






            '''simple docstring'''




            super().__init__(
                A_,A_,tokenizer_file=A_,cls_token=A_,unk_token=A_,pad_token=A_,mask_token=A_,sep_token=A_,**A_,)







       def 		snake_case_	(			self: Any,A_: List[int],A_: Optional[List[int]] = None					):






            '''simple docstring'''




            __UpperCamelCase =			[self.cls_token_id]
            __UpperCamelCase =			[self.sep_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 		snake_case_	(			self: Tuple,A_: List[int],A_: Optional[List[int]] = None,A_: bool = False					):






            '''simple docstring'''




            if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=A_,token_ids_a=A_,already_has_special_tokens=A_					)

            if token_ids_a is None:
                 return [1] + ([0] * len(A_					)) + [1]
            return [1] + ([0] * len(A_					)) + [1] + ([0] * len(A_					)) + [1]







       def 		snake_case_	(			self: Union[str, Any],A_: List[int],A_: Optional[List[int]] = None					):






            '''simple docstring'''




            __UpperCamelCase =			[self.sep_token_id]
            __UpperCamelCase =			[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 		snake_case_	(			self: Any,A_: str,A_: Optional[str] = None					):






            '''simple docstring'''




            __UpperCamelCase =			self._tokenizer.model.save(A_,name=A_					)
            return tuple(A_					)









'''simple docstring'''




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


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       {
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''donut-swin'''

					SCREAMING_SNAKE_CASE__				=	{
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}


					def __init__(						self				:			str      ,						lowerCamelCase_				:			Union[str, Any]=2_24      ,						lowerCamelCase_				:			Union[str, Any]=4      ,						lowerCamelCase_				:			Optional[Any]=3      ,						lowerCamelCase_				:			Dict=96      ,						lowerCamelCase_				:			int=[2, 2, 6, 2]      ,						lowerCamelCase_				:			Optional[Any]=[3, 6, 12, 24]      ,						lowerCamelCase_				:			List[str]=7      ,						lowerCamelCase_				:			List[str]=4.0      ,						lowerCamelCase_				:			Optional[Any]=True      ,						lowerCamelCase_				:			List[str]=0.0      ,						lowerCamelCase_				:			Dict=0.0      ,						lowerCamelCase_				:			Dict=0.1      ,						lowerCamelCase_				:			Union[str, Any]="gelu"      ,						lowerCamelCase_				:			List[str]=False      ,						lowerCamelCase_				:			Optional[int]=0.02      ,						lowerCamelCase_				:			Any=1e-5      ,						**lowerCamelCase_				:			Union[str, Any]      ,						):

							'''simple docstring'''



							super().__init__(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				int		=			image_size
							SCREAMING_SNAKE_CASE :				Dict		=			patch_size
							SCREAMING_SNAKE_CASE :				str		=			num_channels
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			embed_dim
							SCREAMING_SNAKE_CASE :				Optional[int]		=			depths
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			len(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			num_heads
							SCREAMING_SNAKE_CASE :				Optional[int]		=			window_size
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			mlp_ratio
							SCREAMING_SNAKE_CASE :				Dict		=			qkv_bias
							SCREAMING_SNAKE_CASE :				Optional[int]		=			hidden_dropout_prob
							SCREAMING_SNAKE_CASE :				Tuple		=			attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE :				Dict		=			drop_path_rate
							SCREAMING_SNAKE_CASE :				List[str]		=			hidden_act
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			use_absolute_embeddings
							SCREAMING_SNAKE_CASE :				Tuple		=			layer_norm_eps
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			initializer_range
							# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							SCREAMING_SNAKE_CASE :				str		=			int(embed_dim * 2 ** (len(lowerCamelCase_						) - 1)						)



