






"""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


lowerCamelCase_       			=   logging.get_logger(__name__)

lowerCamelCase_       			=   {
    '''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       '''levit'''





	def __init__(   self      :				List[str]			,			lowerCAmelCase_      :				int=224			,			lowerCAmelCase_      :				Union[str, Any]=3			,			lowerCAmelCase_      :				Tuple=3			,			lowerCAmelCase_      :				int=2			,			lowerCAmelCase_      :				str=1			,			lowerCAmelCase_      :				Optional[Any]=16			,			lowerCAmelCase_      :				Tuple=[128, 256, 384]			,			lowerCAmelCase_      :				Optional[int]=[4, 8, 12]			,			lowerCAmelCase_      :				str=[4, 4, 4]			,			lowerCAmelCase_      :				Dict=[16, 16, 16]			,			lowerCAmelCase_      :				int=0			,			lowerCAmelCase_      :				Optional[int]=[2, 2, 2]			,			lowerCAmelCase_      :				Any=[2, 2, 2]			,			lowerCAmelCase_      :				int=0.0_2			,			**lowerCAmelCase_      :				List[Any]			,			)		->    List[str]:
				super().__init__(**lowerCAmelCase_		)
				UpperCAmelCase_  :				int      =      image_size
				UpperCAmelCase_  :				List[Any]      =      num_channels
				UpperCAmelCase_  :				str      =      kernel_size
				UpperCAmelCase_  :				List[Any]      =      stride
				UpperCAmelCase_  :				List[str]      =      padding
				UpperCAmelCase_  :				Any      =      hidden_sizes
				UpperCAmelCase_  :				List[str]      =      num_attention_heads
				UpperCAmelCase_  :				List[str]      =      depths
				UpperCAmelCase_  :				int      =      key_dim
				UpperCAmelCase_  :				List[str]      =      drop_path_rate
				UpperCAmelCase_  :				str      =      patch_size
				UpperCAmelCase_  :				Tuple      =      attention_ratio
				UpperCAmelCase_  :				Optional[int]      =      mlp_ratio
				UpperCAmelCase_  :				Union[str, Any]      =      initializer_range
				UpperCAmelCase_  :				List[Any]      =      [
				    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
				    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
				]







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       version.parse('''1.11'''  )





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





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    float:
				return 1e-4











"""simple docstring"""
import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format







class     UpperCamelCase_ :
	__magic_name__			   =       None





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    List[Any]:
				UpperCAmelCase_  :				Tuple      =      self.feature_extraction_class(**self.feat_extract_dict		)
				UpperCAmelCase_  :				Union[str, Any]      =      json.loads(feat_extract.to_json_string()		)
				for key, value in self.feat_extract_dict.items():
							self.assertEqual(obj[key]			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    int:
				UpperCAmelCase_  :				Dict      =      self.feature_extraction_class(**self.feat_extract_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase_  :				List[Any]      =      os.path.join(lowerCAmelCase_			,			"feat_extract.json"		)
							feat_extract_first.to_json_file(lowerCAmelCase_		)
							UpperCAmelCase_  :				Any      =      self.feature_extraction_class.from_json_file(lowerCAmelCase_		)

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Dict:
				UpperCAmelCase_  :				Optional[Any]      =      self.feature_extraction_class(**self.feat_extract_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase_  :				Optional[Any]      =      feat_extract_first.save_pretrained(lowerCAmelCase_		)[0]
							check_json_file_has_correct_format(lowerCAmelCase_		)
							UpperCAmelCase_  :				Any      =      self.feature_extraction_class.from_pretrained(lowerCAmelCase_		)

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    Optional[Any]:
				UpperCAmelCase_  :				Optional[int]      =      self.feature_extraction_class()
				self.assertIsNotNone(lowerCAmelCase_		)



