
"""simple docstring"""



import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase							)
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							str							=					field(default="image-classification"      ,				metadata={"include_in_asdict_even_if_is_default": True}							)
				SCREAMING_SNAKE_CASE_:							ClassVar[Features]							=					Features({"image": Image()}							)
				SCREAMING_SNAKE_CASE_:							ClassVar[Features]							=					Features({"labels": ClassLabel}							)
				SCREAMING_SNAKE_CASE_:							str							=					"image"
				SCREAMING_SNAKE_CASE_:							str							=					"labels"


				def 				__lowerCamelCase	(					self	:			str				,     UpperCAmelCase_	:			Optional[Any]  )		->  Dict:







											"""simple docstring"""




											if self.label_column not in features:
																		raise ValueError(F"""Column {self.label_column} is not present in features."""  )
											if not isinstance(features[self.label_column]				,     UpperCAmelCase_  ):
																		raise ValueError(F"""Column {self.label_column} is not a ClassLabel."""  )
											_lowerCAmelCase  	=			copy.deepcopy(self  )
											_lowerCAmelCase  	=			self.label_schema.copy()
											_lowerCAmelCase  	=			features[self.label_column]
											_lowerCAmelCase  	=			label_schema
											return task_template







				@property
				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Dict[str, str]:







											"""simple docstring"""




											return {
											    self.image_column: "image",
											    self.label_column: "labels",
											}








"""simple docstring"""



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 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[int]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							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 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					Tuple						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							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 	__snake_case  (    SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							token.append((f"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token')						)
							return token






def 	__snake_case  (    ):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							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 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[Any]     ,  SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			'imagenet-1k-id2label.json'
							_lowerCAmelCase  	=			1000

							_lowerCAmelCase  	=			'huggingface/label-files'
							_lowerCAmelCase  	=			num_labels
							_lowerCAmelCase  	=			json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  repo_type='dataset'						)						)     ,  'r'						)						)
							_lowerCAmelCase  	=			{int(SCREAMING_SNAKE_CASE						): v for k, v in idalabel.items()}

							_lowerCAmelCase  	=			idalabel
							_lowerCAmelCase  	=			{v: k for k, v in idalabel.items()}

							_lowerCAmelCase  	=			_lowerCAmelCase  	=			CvtConfig(num_labels=SCREAMING_SNAKE_CASE     ,  idalabel=SCREAMING_SNAKE_CASE     ,  labelaid=SCREAMING_SNAKE_CASE						)

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

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

							# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
							else:
														_lowerCAmelCase  	=			[2, 2, 20]
														_lowerCAmelCase  	=			[3, 12, 16]
														_lowerCAmelCase  	=			[192, 768, 1024]

							_lowerCAmelCase  	=			CvtForImageClassification(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'						)
							_lowerCAmelCase  	=			image_size
							_lowerCAmelCase  	=			torch.load(SCREAMING_SNAKE_CASE     ,  map_location=torch.device('cpu'						)						)

							_lowerCAmelCase  	=			OrderedDict()
							_lowerCAmelCase  	=			[]

							for idx in range(len(config.depth						)						):
														if config.cls_token[idx]:
																					_lowerCAmelCase  	=			list_of_state_dict + cls_token(SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase  	=			list_of_state_dict + embeddings(SCREAMING_SNAKE_CASE						)
														for cnt in range(config.depth[idx]						):
																					_lowerCAmelCase  	=			list_of_state_dict + attention(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

							_lowerCAmelCase  	=			list_of_state_dict + final()
							for gg in list_of_state_dict:
														print(SCREAMING_SNAKE_CASE						)
							for i in range(len(SCREAMING_SNAKE_CASE						)						):
														_lowerCAmelCase  	=			original_weights[list_of_state_dict[i][1]]

							model.load_state_dict(SCREAMING_SNAKE_CASE						)
							model.save_pretrained(SCREAMING_SNAKE_CASE						)
							image_processor.save_pretrained(SCREAMING_SNAKE_CASE						)


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

if __name__ == "__main__":
	_snake_case          =						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.'''
	)

	_snake_case          =						parser.parse_args()
	convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)






