"""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    lowerCAmelCase__    (			_UpperCamelCase       :				List[Any]					)					->				Tuple:





						"""simple docstring"""






						snake_case								=			[]
						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__    (			_UpperCamelCase       :				int ,			_UpperCamelCase       :				Union[str, Any]					)					->				Optional[Any]:





						"""simple docstring"""






						snake_case								=			[]
						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__    (			_UpperCamelCase       :				Any					)					->				Any:





						"""simple docstring"""






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





def    lowerCAmelCase__    (			)					->				Any:





						"""simple docstring"""






						snake_case								=			[]
						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__    (			_UpperCamelCase       :				str ,			_UpperCamelCase       :				int ,			_UpperCamelCase       :				Union[str, Any] ,			_UpperCamelCase       :				Dict					)					->				Optional[int]:





						"""simple docstring"""






						snake_case								=			'imagenet-1k-id2label.json'
						snake_case								=			1_0_0_0

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

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

						snake_case								=			snake_case								=			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":
												snake_case								=			[1, 2, 1_0]

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

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

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

						snake_case								=			OrderedDict()
						snake_case								=			[]

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

						snake_case								=			list_of_state_dict + final()
						for gg in list_of_state_dict:
												print(_UpperCamelCase					)
						for i in range(len(_UpperCamelCase					)					):
												snake_case								=			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__      						=  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__      						=  parser.parse_args()
					convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)



"""simple docstring"""
def    lowerCAmelCase__    (			_UpperCamelCase       :				int ,			_UpperCamelCase       :				int					)					->				int:





						"""simple docstring"""






						return int((input_a, input_a).count(0					) == 0					)





def    lowerCAmelCase__    (			)					->				None:





						"""simple docstring"""






						assert and_gate(0 ,			0					) == 0
						assert and_gate(0 ,			1					) == 0
						assert and_gate(1 ,			0					) == 0
						assert and_gate(1 ,			1					) == 1


if __name__ == "__main__":
					test_and_gate()
					print(and_gate(1, 0))
					print(and_gate(0, 0))
					print(and_gate(0, 1))
					print(and_gate(1, 1))


