


'''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


__A		=logging.get_logger(__name__)

__A		={
    '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 						_snake_case   (       a__    ,				a__		):
					lowerCAmelCase :Any						=   '''swin'''

					lowerCAmelCase :Optional[int]						=   {
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}




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

								UpperCAmelCase__					:   Tuple       				=       image_size
								UpperCAmelCase__					:   str       				=       patch_size
								UpperCAmelCase__					:   List[str]       				=       num_channels
								UpperCAmelCase__					:   List[str]       				=       embed_dim
								UpperCAmelCase__					:   List[str]       				=       depths
								UpperCAmelCase__					:   List[str]       				=       len(_lowerCamelCase)
								UpperCAmelCase__					:   int       				=       num_heads
								UpperCAmelCase__					:   int       				=       window_size
								UpperCAmelCase__					:   Dict       				=       mlp_ratio
								UpperCAmelCase__					:   List[str]       				=       qkv_bias
								UpperCAmelCase__					:   Tuple       				=       hidden_dropout_prob
								UpperCAmelCase__					:   Union[str, Any]       				=       attention_probs_dropout_prob
								UpperCAmelCase__					:   List[str]       				=       drop_path_rate
								UpperCAmelCase__					:   str       				=       hidden_act
								UpperCAmelCase__					:   Union[str, Any]       				=       use_absolute_embeddings
								UpperCAmelCase__					:   int       				=       layer_norm_eps
								UpperCAmelCase__					:   Dict       				=       initializer_range
								UpperCAmelCase__					:   Any       				=       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__					:   Optional[int]       				=       int(embed_dim * 2 ** (len(_lowerCamelCase) - 1))
								UpperCAmelCase__					:   Any       				=       ["""stem"""] + [f'''stage{idx}''' for idx in range(1							,							len(_lowerCamelCase) + 1)]
								UpperCAmelCase__				,    UpperCAmelCase__					:   Dict       				=       get_aligned_output_features_output_indices(
								    out_features=_lowerCamelCase							,							out_indices=_lowerCamelCase							,							stage_names=self.stage_names)


class 						_snake_case   (       a__		):
					lowerCAmelCase :Any						=   version.parse('''1.11'''		)




					@property
					def 			snake_case__  ( self):
								return OrderedDict(
								    [
								        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
								    ])




					@property
					def 			snake_case__  ( self):
								return 1e-4



'''simple docstring'''



from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 						_snake_case   (       a__		):
					lowerCAmelCase :Optional[int]						=   ['''image_processor''', '''tokenizer''']
					lowerCAmelCase :Optional[int]						=   '''BridgeTowerImageProcessor'''
					lowerCAmelCase :List[str]						=   ('''RobertaTokenizer''', '''RobertaTokenizerFast''')




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase):
								super().__init__(_lowerCamelCase							,							_lowerCamelCase)




					def __call__( self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = True							,							_lowerCamelCase = False							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = 0							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = True							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__					:   List[str]       				=       self.tokenizer(
								    text=_lowerCamelCase							,							add_special_tokens=_lowerCamelCase							,							padding=_lowerCamelCase							,							truncation=_lowerCamelCase							,							max_length=_lowerCamelCase							,							stride=_lowerCamelCase							,							pad_to_multiple_of=_lowerCamelCase							,							return_token_type_ids=_lowerCamelCase							,							return_attention_mask=_lowerCamelCase							,							return_overflowing_tokens=_lowerCamelCase							,							return_special_tokens_mask=_lowerCamelCase							,							return_offsets_mapping=_lowerCamelCase							,							return_length=_lowerCamelCase							,							verbose=_lowerCamelCase							,							return_tensors=_lowerCamelCase							,							**_lowerCamelCase							,							)
								# add pixel_values + pixel_mask
								UpperCAmelCase__					:   Optional[Any]       				=       self.image_processor(
								    _lowerCamelCase							,							return_tensors=_lowerCamelCase							,							do_normalize=_lowerCamelCase							,							do_center_crop=_lowerCamelCase							,							**_lowerCamelCase)
								encoding.update(_lowerCamelCase)

								return encoding




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return self.tokenizer.batch_decode(*_lowerCamelCase							,							**_lowerCamelCase)




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return self.tokenizer.decode(*_lowerCamelCase							,							**_lowerCamelCase)




					@property
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       self.tokenizer.model_input_names
								UpperCAmelCase__					:   str       				=       self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))