
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 						UpperCAmelCase      (     a_						)		->     str:







				"""simple docstring"""



				__A             =			[]
				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 						UpperCAmelCase      (     a_		,       a_						)		->     int:







				"""simple docstring"""



				__A             =			[]
				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 						UpperCAmelCase      (     a_						)		->     Dict:







				"""simple docstring"""



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


def 						UpperCAmelCase      (     )		->     Union[str, Any]:







				"""simple docstring"""



				__A             =			[]
				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 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_						)		->     int:







				"""simple docstring"""



				__A             =			"imagenet-1k-id2label.json"
				__A             =			1_0_0_0

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

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

				__A             =			__A             =			CvtConfig(num_labels=a_		,       idalabel=a_		,       labelaid=a_						)

				# For depth size 13 (13 = 1+2+10)
				if cvt_model.rsplit("/"		,       1						)[-1][4:6] == "13":
								__A             =			[1, 2, 1_0]

				# For depth size 21 (21 = 1+4+16)
				elif cvt_model.rsplit("/"		,       1						)[-1][4:6] == "21":
								__A             =			[1, 4, 1_6]

				# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
				else:
								__A             =			[2, 2, 2_0]
								__A             =			[3, 1_2, 1_6]
								__A             =			[1_9_2, 7_6_8, 1_0_2_4]

				__A             =			CvtForImageClassification(a_						)
				__A             =			AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"						)
				__A             =			image_size
				__A             =			torch.load(a_		,       map_location=torch.device("cpu"						)						)

				__A             =			OrderedDict()
				__A             =			[]

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

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

				model.load_state_dict(a_						)
				model.save_pretrained(a_						)
				image_processor.save_pretrained(a_						)


# 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=384,
					    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[Any]										=    parser.parse_args()
					convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)


import argparse
from collections import defaultdict

import yaml


SCREAMING_SNAKE_CASE							:str										=    'docs/source/en/_toctree.yml'


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				__A             =			defaultdict(a_						)
				for doc in model_doc:
								counts[doc["local"]] += 1
				__A             =			[key for key, value in counts.items() if value > 1]

				__A             =			[]
				for duplicate_key in duplicates:
								__A             =			list({doc["title"] for doc in model_doc if doc["local"] == duplicate_key}						)
								if len(a_						) > 1:
												raise ValueError(
												    F'''{duplicate_key} is present several times in the documentation table of content at '''
												    "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
												    "others."						)
								# Only add this once
								new_doc.append({"local": duplicate_key, "title": titles[0]}						)

				# Add none duplicate-keys
				new_doc.extend([doc for doc in model_doc if counts[doc["local"]] == 1]						)

				# Sort
				return sorted(a_		,       key=lambda a_						: s["title"].lower()						)


def 						UpperCAmelCase      (     a_=False						)		->     List[Any]:







				"""simple docstring"""



				with open(a_		,       encoding="utf-8"						) as f:
								__A             =			yaml.safe_load(f.read()						)

				# Get to the API doc
				__A             =			0
				while content[api_idx]["title"] != "API":
								api_idx += 1
				__A             =			content[api_idx]["sections"]

				# Then to the model doc
				__A             =			0
				while api_doc[model_idx]["title"] != "Models":
								model_idx += 1

				__A             =			api_doc[model_idx]["sections"]

				__A             =			[(idx, section) for idx, section in enumerate(a_						) if "sections" in section]
				__A             =			False
				for idx, modality_doc in modalities_docs:
								__A             =			modality_doc["sections"]
								__A             =			clean_model_doc_toc(a_						)

								if old_modality_doc != new_modality_doc:
												__A             =			True
												if overwrite:
																__A             =			new_modality_doc

				if diff:
								if overwrite:
												__A             =			model_doc
												__A             =			api_doc
												with open(a_		,       "w"		,       encoding="utf-8"						) as f:
																f.write(yaml.dump(a_		,       allow_unicode=a_						)						)
								else:
												raise ValueError(
												    "The model doc part of the table of content is not properly sorted, run `make style` to fix this."						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple										=    argparse.ArgumentParser()
					parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
					SCREAMING_SNAKE_CASE							:List[str]										=    parser.parse_args()

					check_model_doc(args.fix_and_overwrite)
