



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        lowerCAmelCase_(   lowercase_     :							Dict							)	->       Optional[Any]:
				_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        lowerCAmelCase_(   lowercase_     :							List[Any]						,   lowercase_     :							int							)	->       str:
				_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        lowerCAmelCase_(   lowercase_     :							Union[str, Any]							)	->       List[Any]:
				_lowerCamelCase        =				[]
				token.append((F"""cvt.encoder.stages.{idx}.cls_token""", '''stage2.cls_token''')							)
				return token



def        lowerCAmelCase_(   )	->       Any:
				_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        lowerCAmelCase_(   lowercase_     :							str						,   lowercase_     :							Dict						,   lowercase_     :							str						,   lowercase_     :							Dict							)	->       Any:
				_lowerCamelCase        =				'''imagenet-1k-id2label.json'''
				_lowerCamelCase        =				10_00

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

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

				_lowerCamelCase        =				_lowerCamelCase        =				CvtConfig(num_labels=lowerCAmelCase__						,   idalabel=lowerCAmelCase__						,   labelaid=lowerCAmelCase__							)

				# 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        =				[1_92, 7_68, 10_24]

				_lowerCamelCase        =				CvtForImageClassification(lowerCAmelCase__							)
				_lowerCamelCase        =				AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''							)
				_lowerCamelCase        =				image_size
				_lowerCamelCase        =				torch.load(lowerCAmelCase__						,   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(lowerCAmelCase__							)
								_lowerCamelCase        =				list_of_state_dict + embeddings(lowerCAmelCase__							)
								for cnt in range(config.depth[idx]							):
												_lowerCamelCase        =				list_of_state_dict + attention(lowerCAmelCase__						,   lowerCAmelCase__							)

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

				model.load_state_dict(lowerCAmelCase__							)
				model.save_pretrained(lowerCAmelCase__							)
				image_processor.save_pretrained(lowerCAmelCase__							)


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

if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      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  :						List[str]    							=      parser.parse_args()
			convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)





"""simple docstring"""
from __future__ import annotations

from typing import Any



def        lowerCAmelCase_(   lowercase_     :							list[Any]							)	->       None:
				create_state_space_tree(lowercase_						,   []						,   0							)



def        lowerCAmelCase_(   lowercase_     :							list[Any]						,   lowercase_     :							list[Any]						,   lowercase_     :							int							)	->       None:

				if index == len(lowercase_							):
								print(lowercase_							)
								return

				create_state_space_tree(lowercase_						,   lowercase_						,   index + 1							)
				current_subsequence.append(sequence[index]							)
				create_state_space_tree(lowercase_						,   lowercase_						,   index + 1							)
				current_subsequence.pop()


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						list[Any]    							=      [3, 1, 2, 4]
			generate_all_subsequences(seq)

			seq.clear()
			seq.extend(['''A''', '''B''', '''C'''])
			generate_all_subsequences(seq)
