



"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}




class 			lowercase__			(      A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					'''roberta'''






							def __init__(  self	,       snake_case=50265	,       snake_case=768	,       snake_case=12	,       snake_case=12	,       snake_case=3072	,       snake_case="gelu"	,       snake_case=0.1	,       snake_case=0.1	,       snake_case=512	,       snake_case=2	,       snake_case=0.02	,       snake_case=1E-12	,       snake_case=1	,       snake_case=0	,       snake_case=2	,       snake_case="absolute"	,       snake_case=True	,       snake_case=None	,       **snake_case	,       )			->							Tuple:
									super().__init__(pad_token_id=snake_case	,       bos_token_id=snake_case	,       eos_token_id=snake_case	,       **snake_case						)

									_UpperCAmelCase							 =		vocab_size
									_UpperCAmelCase							 =		hidden_size
									_UpperCAmelCase							 =		num_hidden_layers
									_UpperCAmelCase							 =		num_attention_heads
									_UpperCAmelCase							 =		hidden_act
									_UpperCAmelCase							 =		intermediate_size
									_UpperCAmelCase							 =		hidden_dropout_prob
									_UpperCAmelCase							 =		attention_probs_dropout_prob
									_UpperCAmelCase							 =		max_position_embeddings
									_UpperCAmelCase							 =		type_vocab_size
									_UpperCAmelCase							 =		initializer_range
									_UpperCAmelCase							 =		layer_norm_eps
									_UpperCAmelCase							 =		position_embedding_type
									_UpperCAmelCase							 =		use_cache
									_UpperCAmelCase							 =		classifier_dropout







class 			lowercase__			(      A    ):
							'''simple docstring'''







							@property
							def        lowerCamelCase_   (  self						)			->							Mapping[str, Mapping[int, str]]:
									if self.task == "multiple-choice":
											_UpperCAmelCase							 =		{0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
											_UpperCAmelCase							 =		{0: 'batch', 1: 'sequence'}
									return OrderedDict(
									    [
									        ('input_ids', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									    ]						)







"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{
    '''microsoft/swin-tiny-patch4-window7-224''': (
        '''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}




class 			lowercase__			(      A,					A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					'''swin'''

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






							def __init__(  self	,       snake_case=224	,       snake_case=4	,       snake_case=3	,       snake_case=96	,       snake_case=[2, 2, 6, 2]	,       snake_case=[3, 6, 12, 24]	,       snake_case=7	,       snake_case=4.0	,       snake_case=True	,       snake_case=0.0	,       snake_case=0.0	,       snake_case=0.1	,       snake_case="gelu"	,       snake_case=False	,       snake_case=0.02	,       snake_case=1E-5	,       snake_case=32	,       snake_case=None	,       snake_case=None	,       **snake_case	,       )			->							List[Any]:
									super().__init__(**snake_case						)

									_UpperCAmelCase							 =		image_size
									_UpperCAmelCase							 =		patch_size
									_UpperCAmelCase							 =		num_channels
									_UpperCAmelCase							 =		embed_dim
									_UpperCAmelCase							 =		depths
									_UpperCAmelCase							 =		len(snake_case						)
									_UpperCAmelCase							 =		num_heads
									_UpperCAmelCase							 =		window_size
									_UpperCAmelCase							 =		mlp_ratio
									_UpperCAmelCase							 =		qkv_bias
									_UpperCAmelCase							 =		hidden_dropout_prob
									_UpperCAmelCase							 =		attention_probs_dropout_prob
									_UpperCAmelCase							 =		drop_path_rate
									_UpperCAmelCase							 =		hidden_act
									_UpperCAmelCase							 =		use_absolute_embeddings
									_UpperCAmelCase							 =		layer_norm_eps
									_UpperCAmelCase							 =		initializer_range
									_UpperCAmelCase							 =		encoder_stride
									# 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
									_UpperCAmelCase							 =		int(embed_dim * 2 ** (len(snake_case						) - 1)						)
									_UpperCAmelCase							 =		['stem'] + [f'stage{idx}' for idx in range(1	,       len(snake_case						) + 1						)]
									_UpperCAmelCase ,					_UpperCAmelCase							 =		get_aligned_output_features_output_indices(
									    out_features=snake_case	,       out_indices=snake_case	,       stage_names=self.stage_names						)







class 			lowercase__			(      A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					version.parse('''1.11'''    )






							@property
							def        lowerCamelCase_   (  self						)			->							Mapping[str, Mapping[int, str]]:
									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									    ]						)






							@property
							def        lowerCamelCase_   (  self						)			->							float:
									return 1E-4


