






from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)








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)				)
