
'''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


__lowerCAmelCase					=			logging.get_logger(__name__)

__lowerCAmelCase					=			{
    '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      _lowerCAmelCase       (       __snake_case	,				__snake_case ):







							'''simple docstring'''

							lowerCAmelCase_   		=		"swin"

							lowerCAmelCase_   		=		{
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}

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

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


class      _lowerCAmelCase       (       __snake_case ):







							'''simple docstring'''

							lowerCAmelCase_   		=		version.parse("1.11" )

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





							@property
							def 			lowercase				(self    )	->  int:
														return 1e-4

import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format
class       _lowerCAmelCase  :

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Optional[Any]       						=None

 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.feature_extraction_class(**self.feat_extract_dict   )
      UpperCamelCase						    =					json.loads(feat_extract.to_json_string()   )
      for key, value in self.feat_extract_dict.items():
           self.assertEqual(obj[key]					, SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      UpperCamelCase						    =					self.feature_extraction_class(**self.feat_extract_dict   )

      with tempfile.TemporaryDirectory() as tmpdirname:
           UpperCamelCase						    =					os.path.join(SCREAMING_SNAKE_CASE__					, 'feat_extract.json'   )
           feat_extract_first.to_json_file(SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					self.feature_extraction_class.from_json_file(SCREAMING_SNAKE_CASE__   )

      self.assertEqual(feat_extract_second.to_dict()					, feat_extract_first.to_dict()   )

 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      UpperCamelCase						    =					self.feature_extraction_class(**self.feat_extract_dict   )

      with tempfile.TemporaryDirectory() as tmpdirname:
           UpperCamelCase						    =					feat_extract_first.save_pretrained(SCREAMING_SNAKE_CASE__   )[0]
           check_json_file_has_correct_format(SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					self.feature_extraction_class.from_pretrained(SCREAMING_SNAKE_CASE__   )

      self.assertEqual(feat_extract_second.to_dict()					, feat_extract_first.to_dict()   )






 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.feature_extraction_class()
      self.assertIsNotNone(SCREAMING_SNAKE_CASE__   )


