






from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
							from .features import FeatureType




@dataclass
class 		UpperCAmelCase_						:


						'''simple docstring'''




						A     :   List[str]
						A     :   Optional[str]						=					None
						# Automatically constructed
						A     :   ClassVar[str]						=					"dict"
						A     :   ClassVar[Any]						=					None
						A     :   str						=					field(default='Translation'					,  init=__snake_case					,  repr=__snake_case					)






						def __call__(	self				) ->				str:
								return pa.struct({lang: pa.string() for lang in sorted(self.languages				)}				)






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								from .features import Value

								return {k: Value("string"				) for k in sorted(self.languages				)}




@dataclass
class 		UpperCAmelCase_						:


						'''simple docstring'''




						A     :   Optional[List]						=					None
						A     :   Optional[int]						=					None
						A     :   Optional[str]						=					None
						# Automatically constructed
						A     :   ClassVar[str]						=					"dict"
						A     :   ClassVar[Any]						=					None
						A     :   str						=					field(default='TranslationVariableLanguages'					,  init=__snake_case					,  repr=__snake_case					)






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								snake_case_				:							List[Any]						 =       sorted(set(self.languages				)				) if self.languages else None
								snake_case_				:							str						 =       len(self.languages				) if self.languages else None






						def __call__(	self				) ->				List[Any]:
								return pa.struct({"language": pa.list_(pa.string()				), "translation": pa.list_(pa.string()				)}				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Optional[int]:
								snake_case_				:							Dict						 =       set(self.languages				)
								if self.languages and set(A_				) - lang_set:
										raise ValueError(
										    f'''Some languages in example ({', '.join(sorted(set(A_				) - lang_set				)				)}) are not in valid set ({', '.join(A_				)}).'''				)

								# Convert dictionary into tuples, splitting out cases where there are
								# multiple translations for a single language.
								snake_case_				:							List[str]						 =       []
								for lang, text in translation_dict.items():
										if isinstance(A_		,				A_				):
												translation_tuples.append((lang, text)				)
										else:
												translation_tuples.extend([(lang, el) for el in text]				)

										# Ensure translations are in ascending order by language code.
								snake_case_				:							Dict						 =       zip(*sorted(A_				)				)

								return {"language": languages, "translation": translations}






						def       _lowerCAmelCase       (	self				) ->				Any:
								from .features import Sequence, Value

								return {
								    "language": Sequence(Value("string"				)				),
								    "translation": Sequence(Value("string"				)				),
								}









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


lowercase  :							Any			  =    logging.get_logger(__name__)

lowercase  :							str			  =    {
    '''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_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Tuple						=					'donut-swin'

						A     :   Union[str, Any]						=					{
						    'num_attention_heads': 'num_heads',
						    'num_hidden_layers': 'num_layers',
						}






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=224		,				_SCREAMING_SNAKE_CASE=4		,				_SCREAMING_SNAKE_CASE=3		,				_SCREAMING_SNAKE_CASE=96		,				_SCREAMING_SNAKE_CASE=[2, 2, 6, 2]		,				_SCREAMING_SNAKE_CASE=[3, 6, 12, 24]		,				_SCREAMING_SNAKE_CASE=7		,				_SCREAMING_SNAKE_CASE=4.0		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE="gelu"		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=0.02		,				_SCREAMING_SNAKE_CASE=1e-5		,				**_SCREAMING_SNAKE_CASE		,				) ->				List[str]:
								super().__init__(**_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[int]						 =       image_size
								snake_case_				:							Any						 =       patch_size
								snake_case_				:							str						 =       num_channels
								snake_case_				:							Dict						 =       embed_dim
								snake_case_				:							Tuple						 =       depths
								snake_case_				:							List[Any]						 =       len(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Optional[int]						 =       num_heads
								snake_case_				:							Optional[int]						 =       window_size
								snake_case_				:							Any						 =       mlp_ratio
								snake_case_				:							str						 =       qkv_bias
								snake_case_				:							Optional[Any]						 =       hidden_dropout_prob
								snake_case_				:							Union[str, Any]						 =       attention_probs_dropout_prob
								snake_case_				:							str						 =       drop_path_rate
								snake_case_				:							List[str]						 =       hidden_act
								snake_case_				:							Optional[int]						 =       use_absolute_embeddings
								snake_case_				:							Tuple						 =       layer_norm_eps
								snake_case_				:							List[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
								snake_case_				:							Any						 =       int(embed_dim * 2 ** (len(_SCREAMING_SNAKE_CASE				) - 1)				)
