


import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				List[str]:
  snake_case__						= SwinConfig()
  snake_case__						= swin_name.split('''_'''				)

  snake_case__						= name_split[1]
  snake_case__						= int(name_split[4]				)
  snake_case__						= int(name_split[3][-1]				)

  if model_size == "tiny":
    snake_case__						= 96
    snake_case__						= (2, 2, 6, 2)
    snake_case__						= (3, 6, 12, 24)
  elif model_size == "small":
    snake_case__						= 96
    snake_case__						= (2, 2, 18, 2)
    snake_case__						= (3, 6, 12, 24)
  elif model_size == "base":
    snake_case__						= 128
    snake_case__						= (2, 2, 18, 2)
    snake_case__						= (4, 8, 16, 32)
  else:
    snake_case__						= 192
    snake_case__						= (2, 2, 18, 2)
    snake_case__						= (6, 12, 24, 48)

  if "in22k" in swin_name:
    snake_case__						= 2_1841
  else:
    snake_case__						= 1000
    snake_case__						= '''huggingface/label-files'''
    snake_case__						= '''imagenet-1k-id2label.json'''
    snake_case__						= json.load(open(hf_hub_download(__lowerCAmelCase							,   __lowerCAmelCase							,   repo_type='''dataset'''				)							,   '''r'''				)				)
    snake_case__						= {int(__lowerCAmelCase				): v for k, v in idalabel.items()}
    snake_case__						= idalabel
    snake_case__						= {v: k for k, v in idalabel.items()}

  snake_case__						= img_size
  snake_case__						= num_classes
  snake_case__						= embed_dim
  snake_case__						= depths
  snake_case__						= num_heads
  snake_case__						= window_size

  return config







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Optional[Any]:
  if "patch_embed.proj" in name:
    snake_case__						= name.replace('''patch_embed.proj'''							,   '''embeddings.patch_embeddings.projection'''				)
  if "patch_embed.norm" in name:
    snake_case__						= name.replace('''patch_embed.norm'''							,   '''embeddings.norm'''				)
  if "layers" in name:
    snake_case__						= '''encoder.''' + name
  if "attn.proj" in name:
    snake_case__						= name.replace('''attn.proj'''							,   '''attention.output.dense'''				)
  if "attn" in name:
    snake_case__						= name.replace('''attn'''							,   '''attention.self'''				)
  if "norm1" in name:
    snake_case__						= name.replace('''norm1'''							,   '''layernorm_before'''				)
  if "norm2" in name:
    snake_case__						= name.replace('''norm2'''							,   '''layernorm_after'''				)
  if "mlp.fc1" in name:
    snake_case__						= name.replace('''mlp.fc1'''							,   '''intermediate.dense'''				)
  if "mlp.fc2" in name:
    snake_case__						= name.replace('''mlp.fc2'''							,   '''output.dense'''				)

  if name == "norm.weight":
    snake_case__						= '''layernorm.weight'''
  if name == "norm.bias":
    snake_case__						= '''layernorm.bias'''

  if "head" in name:
    snake_case__						= name.replace('''head'''							,   '''classifier'''				)
  else:
    snake_case__						= '''swin.''' + name

  return name







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase				)    ->				List[str]:
  for key in orig_state_dict.copy().keys():
    snake_case__						= orig_state_dict.pop(__lowerCAmelCase				)

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

      if "weight" in key:
        snake_case__						= val[:dim, :]
        snake_case__						= val[
            dim : dim * 2, :
        ]
        snake_case__						= val[-dim:, :]
      else:
        snake_case__						= val[
            :dim
        ]
        snake_case__						= val[
            dim : dim * 2
        ]
        snake_case__						= val[
            -dim:
        ]
    else:
      snake_case__						= val

  return orig_state_dict







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase				)    ->				int:
  snake_case__						= timm.create_model(__lowerCAmelCase							,   pretrained=__lowerCAmelCase				)
  timm_model.eval()

  snake_case__						= get_swin_config(__lowerCAmelCase				)
  snake_case__						= SwinForImageClassification(__lowerCAmelCase				)
  model.eval()

  snake_case__						= convert_state_dict(timm_model.state_dict()							,   __lowerCAmelCase				)
  model.load_state_dict(__lowerCAmelCase				)

  snake_case__						= '''http://images.cocodataset.org/val2017/000000039769.jpg'''

  snake_case__						= AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''							,   '''-'''				)				)				)
  snake_case__						= Image.open(requests.get(__lowerCAmelCase							,   stream=__lowerCAmelCase				).raw				)
  snake_case__						= image_processor(images=__lowerCAmelCase							,   return_tensors='''pt'''				)

  snake_case__						= timm_model(inputs['''pixel_values''']				)
  snake_case__						= model(**__lowerCAmelCase				).logits

  assert torch.allclose(__lowerCAmelCase							,   __lowerCAmelCase							,   atol=1e-3				)

  print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}"""				)
  model.save_pretrained(__lowerCAmelCase				)

  print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
  image_processor.save_pretrained(__lowerCAmelCase				)


if __name__ == "__main__":
   lowerCamelCase__							:						Union[str, Any]							     =   argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--swin_name""",
       default="""swin_tiny_patch4_window7_224""",
       type=str,
       help="""Name of the Swin 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."""
   )

   lowerCamelCase__							:						Dict							     =   parser.parse_args()
   convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)





lowerCamelCase__							:						List[str]							     =   """Alexander Joslin"""

import operator as op

from .stack import Stack







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				int:
  snake_case__						= {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}

  snake_case__						= Stack()
  snake_case__						= Stack()

  for i in equation:
    if i.isdigit():
      # RULE 1
      operand_stack.push(int(__lowerCAmelCase				)				)
    elif i in operators:
      # RULE 2
      operator_stack.push(__lowerCAmelCase				)
    elif i == ")":
      # RULE 4
      snake_case__						= operator_stack.peek()
      operator_stack.pop()
      snake_case__						= operand_stack.peek()
      operand_stack.pop()
      snake_case__						= operand_stack.peek()
      operand_stack.pop()

      snake_case__						= operators[opr](__lowerCAmelCase							,   __lowerCAmelCase				)
      operand_stack.push(__lowerCAmelCase				)

    # RULE 5
  return operand_stack.peek()


if __name__ == "__main__":
   lowerCamelCase__							:						Optional[Any]							     =   """(5 + ((4 * 2) * (2 + 3)))"""
   # answer = 45
   print(F"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")

