
'''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       __lowercase  (_SCREAMING_SNAKE_CASE      :Dict       ):
				SCREAMING_SNAKE_CASE  :						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       __lowercase  (_SCREAMING_SNAKE_CASE      :Dict    ,	_SCREAMING_SNAKE_CASE      :Any       ):
				SCREAMING_SNAKE_CASE  :						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       __lowercase  (_SCREAMING_SNAKE_CASE      :Union[str, Any]       ):
				SCREAMING_SNAKE_CASE  :						List[Any]      					= []
				token.append((F'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')       )
				return token






def       __lowercase  ():
				SCREAMING_SNAKE_CASE  :						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       __lowercase  (_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :int       ):
				SCREAMING_SNAKE_CASE  :						Dict      					= '''imagenet-1k-id2label.json'''
				SCREAMING_SNAKE_CASE  :						Any      					= 10_00

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

				SCREAMING_SNAKE_CASE  :						Dict      					= idalabel
				SCREAMING_SNAKE_CASE  :						str      					= {v: k for k, v in idalabel.items()}

				SCREAMING_SNAKE_CASE  :						Optional[int]      					= 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":
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [1, 2, 10]

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

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

				SCREAMING_SNAKE_CASE  :						Any      					= CvtForImageClassification(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Dict      					= AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''       )
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= image_size
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= torch.load(_SCREAMING_SNAKE_CASE    ,	map_location=torch.device('''cpu'''       )       )

				SCREAMING_SNAKE_CASE  :						Any      					= OrderedDict()
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= []

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

				SCREAMING_SNAKE_CASE  :						Any      					= list_of_state_dict + final()
				for gg in list_of_state_dict:
								print(_SCREAMING_SNAKE_CASE       )
				for i in range(len(_SCREAMING_SNAKE_CASE       )       ):
								SCREAMING_SNAKE_CASE  :						Tuple      					= 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=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."""
	)

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









'''simple docstring'''

from collections import deque






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]       ):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= len(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						List[str]      					= deque()
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= [False for _ in range(_SCREAMING_SNAKE_CASE       )]
				SCREAMING_SNAKE_CASE  :						Any      					= [-1 for _ in range(_SCREAMING_SNAKE_CASE       )]
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= index_of[:]

				def strong_connect(_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :List[str]       ):
								SCREAMING_SNAKE_CASE  :						Any      					= index  # the number when this node is seen
								SCREAMING_SNAKE_CASE  :						str      					= index  # lowest rank node reachable from here
								index += 1
								stack.append(_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= True

								for w in g[v]:
												if index_of[w] == -1:
																SCREAMING_SNAKE_CASE  :						List[str]      					= strong_connect(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
																SCREAMING_SNAKE_CASE  :						Optional[int]      					= (
																    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																)
												elif on_stack[w]:
																SCREAMING_SNAKE_CASE  :						List[str]      					= (
																    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																)

								if lowlink_of[v] == index_of[v]:
												SCREAMING_SNAKE_CASE  :						str      					= []
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= stack.pop()
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= False
												component.append(_SCREAMING_SNAKE_CASE       )
												while w != v:
																SCREAMING_SNAKE_CASE  :						int      					= stack.pop()
																SCREAMING_SNAKE_CASE  :						Tuple      					= False
																component.append(_SCREAMING_SNAKE_CASE       )
												components.append(_SCREAMING_SNAKE_CASE       )
								return index

				SCREAMING_SNAKE_CASE  :						int      					= []
				for v in range(_SCREAMING_SNAKE_CASE       ):
								if index_of[v] == -1:
												strong_connect(_SCREAMING_SNAKE_CASE    ,	0    ,	_SCREAMING_SNAKE_CASE       )

				return components






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				SCREAMING_SNAKE_CASE  :						Tuple      					= [[] for _ in range(_SCREAMING_SNAKE_CASE       )]
				for u, v in edges:
								g[u].append(_SCREAMING_SNAKE_CASE       )
				return g


if __name__ == "__main__":
	# Test
	snake_case_			=     7
	snake_case_			=     [0, 0, 1, 2, 3, 3, 4, 4, 6]
	snake_case_			=     [1, 3, 2, 0, 1, 4, 5, 6, 5]
	snake_case_			=     [(u, v) for u, v in zip(source, target)]
	snake_case_			=     create_graph(n_vertices, edges)

	assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)




