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


snake_case :		str							      =  logging.get_logger(__name__)

snake_case :		int							      =  {
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    'swinv2'

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







			def __init__(				self						,   _a=224						,   _a=4						,   _a=3						,   _a=96						,   _a=[2, 2, 6, 2]						,   _a=[3, 6, 12, 24]						,   _a=7						,   _a=4.0						,   _a=True						,   _a=0.0						,   _a=0.0						,   _a=0.1						,   _a="gelu"						,   _a=False						,   _a=0.02						,   _a=1e-5						,   _a=32						,   **_a						,   ):
				super().__init__(**_a		)

				__magic_name__		:		Tuple	   =     image_size
				__magic_name__		:		str	   =     patch_size
				__magic_name__		:		Optional[int]	   =     num_channels
				__magic_name__		:		Tuple	   =     embed_dim
				__magic_name__		:		Optional[Any]	   =     depths
				__magic_name__		:		int	   =     len(_a		)
				__magic_name__		:		str	   =     num_heads
				__magic_name__		:		str	   =     window_size
				__magic_name__		:		Optional[int]	   =     mlp_ratio
				__magic_name__		:		Optional[Any]	   =     qkv_bias
				__magic_name__		:		int	   =     hidden_dropout_prob
				__magic_name__		:		int	   =     attention_probs_dropout_prob
				__magic_name__		:		Tuple	   =     drop_path_rate
				__magic_name__		:		Union[str, Any]	   =     hidden_act
				__magic_name__		:		Optional[int]	   =     use_absolute_embeddings
				__magic_name__		:		str	   =     layer_norm_eps
				__magic_name__		:		str	   =     initializer_range
				__magic_name__		:		Optional[int]	   =     encoder_stride
				# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
				# this indicates the channel dimension after the last stage of the model
				__magic_name__		:		List[Any]	   =     int(embed_dim * 2 ** (len(_a		) - 1)		)
				__magic_name__		:		Tuple	   =     (0, 0, 0, 0)


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,
)
