




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


__lowerCAmelCase	:       Optional[Any]     =	logging.get_logger(__name__)

__lowerCAmelCase	:       List[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 					snake_case__							(_UpperCAmelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							Optional[Any]											=      """donut-swin"""

				SCREAMING_SNAKE_CASE_ :							List[Any]											=      {
				    """num_attention_heads""": """num_heads""",
				    """num_hidden_layers""": """num_layers""",
				}





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

							a					=    image_size
							a					=    patch_size
							a					=    num_channels
							a					=    embed_dim
							a					=    depths
							a					=    len(lowerCamelCase_							)
							a					=    num_heads
							a					=    window_size
							a					=    mlp_ratio
							a					=    qkv_bias
							a					=    hidden_dropout_prob
							a					=    attention_probs_dropout_prob
							a					=    drop_path_rate
							a					=    hidden_act
							a					=    use_absolute_embeddings
							a					=    layer_norm_eps
							a					=    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
							a					=    int(embed_dim * 2 ** (len(lowerCamelCase_							) - 1)							)












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCAmelCase	:       Dict     =	{
    'configuration_blip': [
        'BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlipConfig',
        'BlipTextConfig',
        'BlipVisionConfig',
    ],
    'processing_blip': ['BlipProcessor'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       Optional[Any]     =	['BlipImageProcessor']


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       List[Any]     =	[
		    'BLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BlipModel',
		    'BlipPreTrainedModel',
		    'BlipForConditionalGeneration',
		    'BlipForQuestionAnswering',
		    'BlipVisionModel',
		    'BlipTextModel',
		    'BlipForImageTextRetrieval',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       Any     =	[
		    'TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFBlipModel',
		    'TFBlipPreTrainedModel',
		    'TFBlipForConditionalGeneration',
		    'TFBlipForQuestionAnswering',
		    'TFBlipVisionModel',
		    'TFBlipTextModel',
		    'TFBlipForImageTextRetrieval',
		]

if TYPE_CHECKING:
		from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
		from .processing_blip import BlipProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_blip import BlipImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip import (
				    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipForConditionalGeneration,
				    BlipForImageTextRetrieval,
				    BlipForQuestionAnswering,
				    BlipModel,
				    BlipPreTrainedModel,
				    BlipTextModel,
				    BlipVisionModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blip import (
				    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFBlipForConditionalGeneration,
				    TFBlipForImageTextRetrieval,
				    TFBlipForQuestionAnswering,
				    TFBlipModel,
				    TFBlipPreTrainedModel,
				    TFBlipTextModel,
				    TFBlipVisionModel,
				)

else:
		import sys

		__lowerCAmelCase	:       List[str]     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






