


import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Optional[Any]		):




      """simple docstring"""





      a		:Optional[Any]									=			SwinvaConfig()
      a		:List[Any]									=			swinva_name.split('''_'''		)

      a		:Dict									=			name_split[1]
      if "to" in name_split[3]:
            a		:Optional[Any]									=			int(name_split[3][-3:]		)
      else:
            a		:Optional[Any]									=			int(name_split[3]		)
      if "to" in name_split[2]:
            a		:List[Any]									=			int(name_split[2][-2:]		)
      else:
            a		:Optional[int]									=			int(name_split[2][6:]		)

      if model_size == "tiny":
            a		:List[Any]									=			96
            a		:Dict									=			(2, 2, 6, 2)
            a		:List[Any]									=			(3, 6, 12, 24)
      elif model_size == "small":
            a		:Optional[Any]									=			96
            a		:Union[str, Any]									=			(2, 2, 18, 2)
            a		:str									=			(3, 6, 12, 24)
      elif model_size == "base":
            a		:List[Any]									=			128
            a		:int									=			(2, 2, 18, 2)
            a		:Dict									=			(4, 8, 16, 32)
      else:
            a		:Optional[int]									=			192
            a		:Any									=			(2, 2, 18, 2)
            a		:str									=			(6, 12, 24, 48)

      if "to" in swinva_name:
            a		:Any									=			(12, 12, 12, 6)

      if ("22k" in swinva_name) and ("to" not in swinva_name):
            a		:int									=			2_1841
            a		:int									=			'''huggingface/label-files'''
            a		:Tuple									=			'''imagenet-22k-id2label.json'''
            a		:str									=			json.load(open(hf_hub_download(UpperCAmelCase_					,    UpperCAmelCase_					,    repo_type='''dataset'''		)					,    '''r'''		)		)
            a		:List[str]									=			{int(UpperCAmelCase_		): v for k, v in idalabel.items()}
            a		:List[Any]									=			idalabel
            a		:Dict									=			{v: k for k, v in idalabel.items()}

      else:
            a		:int									=			1000
            a		:Any									=			'''huggingface/label-files'''
            a		:Dict									=			'''imagenet-1k-id2label.json'''
            a		:Tuple									=			json.load(open(hf_hub_download(UpperCAmelCase_					,    UpperCAmelCase_					,    repo_type='''dataset'''		)					,    '''r'''		)		)
            a		:Optional[Any]									=			{int(UpperCAmelCase_		): v for k, v in idalabel.items()}
            a		:Any									=			idalabel
            a		:str									=			{v: k for k, v in idalabel.items()}

      a		:Union[str, Any]									=			img_size
      a		:str									=			num_classes
      a		:Optional[Any]									=			embed_dim
      a		:Optional[int]									=			depths
      a		:List[str]									=			num_heads
      a		:str									=			window_size

      return config
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Union[str, Any]		):




      """simple docstring"""





      if "patch_embed.proj" in name:
            a		:List[Any]									=			name.replace('''patch_embed.proj'''					,    '''embeddings.patch_embeddings.projection'''		)
      if "patch_embed.norm" in name:
            a		:int									=			name.replace('''patch_embed.norm'''					,    '''embeddings.norm'''		)
      if "layers" in name:
            a		:List[Any]									=			'''encoder.''' + name
      if "attn.proj" in name:
            a		:Dict									=			name.replace('''attn.proj'''					,    '''attention.output.dense'''		)
      if "attn" in name:
            a		:List[Any]									=			name.replace('''attn'''					,    '''attention.self'''		)
      if "norm1" in name:
            a		:Dict									=			name.replace('''norm1'''					,    '''layernorm_before'''		)
      if "norm2" in name:
            a		:List[str]									=			name.replace('''norm2'''					,    '''layernorm_after'''		)
      if "mlp.fc1" in name:
            a		:Dict									=			name.replace('''mlp.fc1'''					,    '''intermediate.dense'''		)
      if "mlp.fc2" in name:
            a		:Optional[Any]									=			name.replace('''mlp.fc2'''					,    '''output.dense'''		)
      if "q_bias" in name:
            a		:List[Any]									=			name.replace('''q_bias'''					,    '''query.bias'''		)
      if "k_bias" in name:
            a		:Union[str, Any]									=			name.replace('''k_bias'''					,    '''key.bias'''		)
      if "v_bias" in name:
            a		:Tuple									=			name.replace('''v_bias'''					,    '''value.bias'''		)
      if "cpb_mlp" in name:
            a		:Union[str, Any]									=			name.replace('''cpb_mlp'''					,    '''continuous_position_bias_mlp'''		)
      if name == "norm.weight":
            a		:int									=			'''layernorm.weight'''
      if name == "norm.bias":
            a		:Tuple									=			'''layernorm.bias'''

      if "head" in name:
            a		:int									=			name.replace('''head'''					,    '''classifier'''		)
      else:
            a		:Any									=			'''swinv2.''' + name

      return name
def 							__lowerCamelCase		(					UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						Dict		):




      """simple docstring"""





      for key in orig_state_dict.copy().keys():
            a		:Optional[int]									=			orig_state_dict.pop(UpperCAmelCase_		)

            if "mask" in key:
                  continue
            elif "qkv" in key:
                  a		:int									=			key.split('''.'''		)
                  a		:Optional[int]									=			int(key_split[1]		)
                  a		:Optional[Any]									=			int(key_split[3]		)
                  a		:List[Any]									=			model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        a		:Optional[int]									=			val[:dim, :]
                        a		:Optional[Any]									=			val[dim : dim * 2, :]
                        a		:Union[str, Any]									=			val[-dim:, :]
                  else:
                        a		:Optional[Any]									=			val[:dim]
                        a		:int									=			val[
                            dim : dim * 2
                        ]
                        a		:List[Any]									=			val[-dim:]
            else:
                  a		:List[Any]									=			val

      return orig_state_dict
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Union[str, Any]					,    UpperCAmelCase_		:						List[str]		):




      """simple docstring"""





      a		:List[Any]									=			timm.create_model(UpperCAmelCase_					,    pretrained=UpperCAmelCase_		)
      timm_model.eval()

      a		:Any									=			get_swinva_config(UpperCAmelCase_		)
      a		:List[Any]									=			SwinvaForImageClassification(UpperCAmelCase_		)
      model.eval()

      a		:Any									=			convert_state_dict(timm_model.state_dict()					,    UpperCAmelCase_		)
      model.load_state_dict(UpperCAmelCase_		)

      a		:List[Any]									=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''

      a		:Tuple									=			AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''					,    '''-'''		)		)		)
      a		:List[Any]									=			Image.open(requests.get(UpperCAmelCase_					,    stream=UpperCAmelCase_		).raw		)
      a		:Optional[int]									=			image_processor(images=UpperCAmelCase_					,    return_tensors='''pt'''		)

      a		:List[Any]									=			timm_model(inputs['''pixel_values''']		)
      a		:Tuple									=			model(**UpperCAmelCase_		).logits

      assert torch.allclose(UpperCAmelCase_					,    UpperCAmelCase_					,    atol=1E-3		)

      print(F'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''		)
      model.save_pretrained(UpperCAmelCase_		)

      print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
      image_processor.save_pretrained(UpperCAmelCase_		)

      model.push_to_hub(
          repo_path_or_name=Path(UpperCAmelCase_					,    UpperCAmelCase_		)					,    organization='''nandwalritik'''					,    commit_message='''Add model'''					,    )


if __name__ == "__main__":
     snake_case							:       Tuple										=	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--swinv2_name''',
         default='''swinv2_tiny_patch4_window8_256''',
         type=str,
         help='''Name of the Swinv2 timm model you\'d like to convert.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
     )

     snake_case							:       Any										=	parser.parse_args()
     convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)









import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int = 8		):




      """simple docstring"""





      a		:Optional[int]									=			ascii_letters + digits + punctuation
      return "".join(secrets.choice(UpperCAmelCase_		) for _ in range(UpperCAmelCase_		)		)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      i -= len(UpperCAmelCase_		)
      a		:Tuple									=			i // 3
      a		:int									=			i % 3
      # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
      #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
      a		:Union[str, Any]									=			(
          chars_incl
          + random(UpperCAmelCase_					,    quotient + remainder		)
          + random(UpperCAmelCase_					,    UpperCAmelCase_		)
          + random(UpperCAmelCase_					,    UpperCAmelCase_		)
      )
      a		:Dict									=			list(UpperCAmelCase_		)
      shuffle(UpperCAmelCase_		)
      return "".join(UpperCAmelCase_		)

      # random is a generalised function for letters, characters and numbers
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      return "".join(secrets.choice(UpperCAmelCase_		) for _ in range(UpperCAmelCase_		)		)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Union[str, Any]					,    UpperCAmelCase_		:						Tuple		):




      """simple docstring"""





      pass  # Put your code here...
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Union[str, Any]					,    UpperCAmelCase_		:						str		):




      """simple docstring"""





      pass  # Put your code here...
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Optional[int]		):




      """simple docstring"""





      pass  # Put your code here...
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						int = 8		):




      """simple docstring"""





      if len(UpperCAmelCase_		) < min_length:
            # Your Password must be at least 8 characters long
            return False

      a		:Dict									=			any(char in ascii_uppercase for char in password		)
      a		:Optional[int]									=			any(char in ascii_lowercase for char in password		)
      a		:Tuple									=			any(char in digits for char in password		)
      a		:Any									=			any(char in punctuation for char in password		)

      return upper and lower and num and spec_char
      # Passwords should contain UPPERCASE, lowerase
      # numbers, and special characters
def 							__lowerCamelCase		(					):




      """simple docstring"""





      a		:int									=			int(input('''Please indicate the max length of your password: '''		).strip()		)
      a		:Union[str, Any]									=			input(
          '''Please indicate the characters that must be in your password: '''		).strip()
      print('''Password generated:'''					,    password_generator(UpperCAmelCase_		)		)
      print(
          '''Alternative Password generated:'''					,    alternative_password_generator(UpperCAmelCase_					,    UpperCAmelCase_		)					,    )
      print('''[If you are thinking of using this passsword, You better save it.]'''		)


if __name__ == "__main__":
     main()





