
import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Union[str, Any]       ) ->       Dict:

				"""simple docstring"""







				A__  :	Optional[int]         =							[]
				embed.append(
				    (
				        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
				        F"stage{idx}.patch_embed.proj.weight",
				    )       )
				embed.append(
				    (
				        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
				        F"stage{idx}.patch_embed.proj.bias",
				    )       )
				embed.append(
				    (
				        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
				        F"stage{idx}.patch_embed.norm.weight",
				    )       )
				embed.append(
				    (
				        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
				        F"stage{idx}.patch_embed.norm.bias",
				    )       )
				return embed

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[int]							,						__UpperCamelCase :						Any       ) ->       Any:

				"""simple docstring"""







				A__  :	Tuple         =							[]
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight",
				        F"stage{idx}.blocks.{cnt}.attn.proj_q.weight",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias",
				        F"stage{idx}.blocks.{cnt}.attn.proj_q.bias",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight",
				        F"stage{idx}.blocks.{cnt}.attn.proj_k.weight",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias",
				        F"stage{idx}.blocks.{cnt}.attn.proj_k.bias",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight",
				        F"stage{idx}.blocks.{cnt}.attn.proj_v.weight",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias",
				        F"stage{idx}.blocks.{cnt}.attn.proj_v.bias",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight",
				        F"stage{idx}.blocks.{cnt}.attn.proj.weight",
				    )       )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias",
				        F"stage{idx}.blocks.{cnt}.attn.proj.bias",
				    )       )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc1.weight")       )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc1.bias")       )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc2.weight")       )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc2.bias")       )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight", F"stage{idx}.blocks.{cnt}.norm1.weight")       )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias", F"stage{idx}.blocks.{cnt}.norm1.bias")       )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight", F"stage{idx}.blocks.{cnt}.norm2.weight")       )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias", F"stage{idx}.blocks.{cnt}.norm2.bias")       )
				return attention_weights

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						str       ) ->       Optional[Any]:

				"""simple docstring"""







				A__  :	Optional[Any]         =							[]
				token.append((F"cvt.encoder.stages.{idx}.cls_token", '''stage2.cls_token''')       )
				return token

def 				SCREAMING_SNAKE_CASE     (      ) ->       Tuple:

				"""simple docstring"""







				A__  :	Optional[int]         =							[]
				head.append(('''layernorm.weight''', '''norm.weight''')       )
				head.append(('''layernorm.bias''', '''norm.bias''')       )
				head.append(('''classifier.weight''', '''head.weight''')       )
				head.append(('''classifier.bias''', '''head.bias''')       )
				return head

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Dict							,						__UpperCamelCase :						Dict							,						__UpperCamelCase :						str							,						__UpperCamelCase :						List[Any]       ) ->       Union[str, Any]:

				"""simple docstring"""







				A__  :	List[Any]         =							'''imagenet-1k-id2label.json'''
				A__  :	str         =							10_00

				A__  :	str         =							'''huggingface/label-files'''
				A__  :	Union[str, Any]         =							num_labels
				A__  :	Tuple         =							json.load(open(cached_download(hf_hub_url(__UpperCamelCase							,						__UpperCamelCase							,						repo_type='''dataset'''       )       )							,						'''r'''       )       )
				A__  :	List[str]         =							{int(__UpperCamelCase       ): v for k, v in idalabel.items()}

				A__  :	int         =							idalabel
				A__  :	Optional[int]         =							{v: k for k, v in idalabel.items()}

				A__  :	int         =							CvtConfig(num_labels=__UpperCamelCase							,						idalabel=__UpperCamelCase							,						labelaid=__UpperCamelCase       )

				# For depth size 13 (13 = 1+2+10)
				if cvt_model.rsplit('''/'''							,						1       )[-1][4:6] == "13":
								A__  :	Any         =							[1, 2, 10]

				# For depth size 21 (21 = 1+4+16)
				elif cvt_model.rsplit('''/'''							,						1       )[-1][4:6] == "21":
								A__  :	Tuple         =							[1, 4, 16]

				# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
				else:
								A__  :	List[str]         =							[2, 2, 20]
								A__  :	Optional[int]         =							[3, 12, 16]
								A__  :	List[str]         =							[1_92, 7_68, 10_24]

				A__  :	Dict         =							CvtForImageClassification(__UpperCamelCase       )
				A__  :	List[Any]         =							AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''       )
				A__  :	Union[str, Any]         =							image_size
				A__  :	List[Any]         =							torch.load(__UpperCamelCase							,						map_location=torch.device('''cpu'''       )       )

				A__  :	Any         =							OrderedDict()
				A__  :	Dict         =							[]

				for idx in range(len(config.depth       )       ):
								if config.cls_token[idx]:
												A__  :	int         =							list_of_state_dict + cls_token(__UpperCamelCase       )
								A__  :	List[str]         =							list_of_state_dict + embeddings(__UpperCamelCase       )
								for cnt in range(config.depth[idx]       ):
												A__  :	int         =							list_of_state_dict + attention(__UpperCamelCase							,						__UpperCamelCase       )

				A__  :	Dict         =							list_of_state_dict + final()
				for gg in list_of_state_dict:
								print(__UpperCamelCase       )
				for i in range(len(__UpperCamelCase       )       ):
								A__  :	Any         =							original_weights[list_of_state_dict[i][1]]

				model.load_state_dict(__UpperCamelCase       )
				model.save_pretrained(__UpperCamelCase       )
				image_processor.save_pretrained(__UpperCamelCase       )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
					_SCREAMING_SNAKE_CASE    :      Tuple          =			argparse.ArgumentParser()
					parser.add_argument(
					    '--cvt_model',
					    default='cvt-w24',
					    type=str,
					    help='Name of the cvt model you\'d like to convert.',
					)
					parser.add_argument(
					    '--image_size',
					    default=3_8_4,
					    type=int,
					    help='Input Image Size',
					)
					parser.add_argument(
					    '--cvt_file_name',
					    default=r'cvtmodels\CvT-w24-384x384-IN-22k.pth',
					    type=str,
					    help='Input Image Size',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
					)

					_SCREAMING_SNAKE_CASE    :      Optional[int]          =			parser.parse_args()
					convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)

import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format







class  UpperCamelCase__		:







	'''simple docstring'''
	_lowerCAmelCase							       =				None





	def   __snake_case				(			self		):
					A__  :	Dict         =							self.feature_extraction_class(**self.feat_extract_dict		)
					A__  :	Tuple         =							json.loads(feat_extract.to_json_string()		)
					for key, value in self.feat_extract_dict.items():
									self.assertEqual(obj[key]				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Any         =							self.feature_extraction_class(**self.feat_extract_dict		)

					with tempfile.TemporaryDirectory() as tmpdirname:
									A__  :	Any         =							os.path.join(UpperCamelCase__				,       '''feat_extract.json'''		)
									feat_extract_first.to_json_file(UpperCamelCase__		)
									A__  :	Dict         =							self.feature_extraction_class.from_json_file(UpperCamelCase__		)

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





	def   __snake_case				(			self		):
					A__  :	Any         =							self.feature_extraction_class(**self.feat_extract_dict		)

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

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





	def   __snake_case				(			self		):
					A__  :	str         =							self.feature_extraction_class()
					self.assertIsNotNone(UpperCamelCase__		)