
'''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 A (__lowerCamelCase :Dict						):
						_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 A (__lowerCamelCase :Optional[int]					,      __lowerCamelCase :List[Any]						):
						_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 A (__lowerCamelCase :Optional[int]						):
						_lowerCAmelCase    		=					[]
						token.append((f'cvt.encoder.stages.{idx}.cls_token', """stage2.cls_token""")						)
						return token


def A ():
						_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 A (__lowerCamelCase :Optional[int]					,      __lowerCamelCase :Dict					,      __lowerCamelCase :List[str]					,      __lowerCamelCase :Dict						):
						_lowerCAmelCase    		=					"""imagenet-1k-id2label.json"""
						_lowerCAmelCase    		=					1000

						_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    		=					[192, 768, 1024]

						_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__":
						_lowercase          =						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."""
						)

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


'''simple docstring'''



import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
						print("""Googling.....""")
						_lowercase          =						"""https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
						_lowercase          =						requests.get(url, headers={"""UserAgent""": UserAgent().random})
						# res.raise_for_status()
						with open("""project1a.html""", """wb""") as out_file:  # only for knowing the class
												for data in res.iter_content(10000):
																		out_file.write(data)
						_lowercase          =						BeautifulSoup(res.text, """html.parser""")
						_lowercase          =						list(soup.select(""".eZt8xd"""))[:5]

						print(len(links))
						for link in links:
												if link.text == "Maps":
																		webbrowser.open(link.get("""href"""))
												else:
																		webbrowser.open(F"""https://google.com{link.get('href')}""")
