




import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Union[str, Any]  ):


      '''simple docstring'''



      lowerCamelCase_	  =					[2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
      lowerCamelCase_	  =					True if 'large' in model_name or 'huge' in model_name else False
      lowerCamelCase_	  =					True if 'large' in model_name or 'huge' in model_name else False
      lowerCamelCase_	  =					True if 'large' in model_name or 'huge' in model_name else False

      if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
            if "fl3" in model_name:
                  lowerCamelCase_	  =					[3, 3, 3, 3]
                  lowerCamelCase_	  =					[5, 5, 5, 5]
            elif "fl4" in model_name:
                  lowerCamelCase_	  =					[4, 4, 4, 4]
                  lowerCamelCase_	  =					[3, 3, 3, 3]

      if "tiny" in model_name or "small" in model_name or "base" in model_name:
            lowerCamelCase_	  =					[3, 3, 3, 3]
            if "lrf" in model_name:
                  lowerCamelCase_	  =					[3, 3, 3, 3]
            else:
                  lowerCamelCase_	  =					[2, 2, 2, 2]

      if "tiny" in model_name:
            lowerCamelCase_	  =					96
      elif "small" in model_name:
            lowerCamelCase_	  =					96
      elif "base" in model_name:
            lowerCamelCase_	  =					1_28
      elif "large" in model_name:
            lowerCamelCase_	  =					1_92
      elif "xlarge" in model_name:
            lowerCamelCase_	  =					2_56
      elif "huge" in model_name:
            lowerCamelCase_	  =					3_52

      # set label information
      lowerCamelCase_	  =					'huggingface/label-files'
      if "large" in model_name or "huge" in model_name:
            lowerCamelCase_	  =					'imagenet-22k-id2label.json'
      else:
            lowerCamelCase_	  =					'imagenet-1k-id2label.json'

      lowerCamelCase_	  =					json.load(open(hf_hub_download(lowercase			,							lowercase			,							repo_type='dataset'  )			,							'r'  )  )
      lowerCamelCase_	  =					{int(lowercase  ): v for k, v in idalabel.items()}
      lowerCamelCase_	  =					{v: k for k, v in idalabel.items()}

      lowerCamelCase_	  =					FocalNetConfig(
          embed_dim=lowercase			,							depths=lowercase			,							focal_levels=lowercase			,							focal_windows=lowercase			,							use_conv_embed=lowercase			,							idalabel=lowercase			,							labelaid=lowercase			,							use_post_layernorm=lowercase			,							use_layerscale=lowercase			,							)

      return config

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



      if "patch_embed.proj" in name:
            lowerCamelCase_	  =					name.replace('patch_embed.proj'			,							'embeddings.patch_embeddings.projection'  )
      if "patch_embed.norm" in name:
            lowerCamelCase_	  =					name.replace('patch_embed.norm'			,							'embeddings.norm'  )
      if "layers" in name:
            lowerCamelCase_	  =					'encoder.' + name
      if "encoder.layers" in name:
            lowerCamelCase_	  =					name.replace('encoder.layers'			,							'encoder.stages'  )
      if "downsample.proj" in name:
            lowerCamelCase_	  =					name.replace('downsample.proj'			,							'downsample.projection'  )
      if "blocks" in name:
            lowerCamelCase_	  =					name.replace('blocks'			,							'layers'  )
      if "modulation.f.weight" in name or "modulation.f.bias" in name:
            lowerCamelCase_	  =					name.replace('modulation.f'			,							'modulation.projection_in'  )
      if "modulation.h.weight" in name or "modulation.h.bias" in name:
            lowerCamelCase_	  =					name.replace('modulation.h'			,							'modulation.projection_context'  )
      if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
            lowerCamelCase_	  =					name.replace('modulation.proj'			,							'modulation.projection_out'  )

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

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

      return name

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[int]			,							lowercase			: Tuple			,							lowercase			: List[str]=False  ):


      '''simple docstring'''



      lowerCamelCase_	  =					{
          'focalnet-tiny': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth',
          'focalnet-tiny-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth',
          'focalnet-small': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth',
          'focalnet-small-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth',
          'focalnet-base': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth',
          'focalnet-base-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth',
          'focalnet-large-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth',
          'focalnet-large-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth',
          'focalnet-xlarge-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth',
          'focalnet-xlarge-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth',
      }
      # fmt: on

      lowerCamelCase_	  =					model_name_to_url[model_name]
      print('Checkpoint URL: '			,							lowercase  )
      lowerCamelCase_	  =					torch.hub.load_state_dict_from_url(lowercase			,							map_location='cpu'  )['model']

      # rename keys
      for key in state_dict.copy().keys():
            lowerCamelCase_	  =					state_dict.pop(lowercase  )
            lowerCamelCase_	  =					val

      lowerCamelCase_	  =					get_focalnet_config(lowercase  )
      lowerCamelCase_	  =					FocalNetForImageClassification(lowercase  )
      model.eval()

      # load state dict
      model.load_state_dict(lowercase  )

      # verify conversion
      lowerCamelCase_	  =					'http://images.cocodataset.org/val2017/000000039769.jpg'

      lowerCamelCase_	  =					BitImageProcessor(
          do_resize=lowercase			,							size={'shortest_edge': 2_56}			,							resample=PILImageResampling.BILINEAR			,							do_center_crop=lowercase			,							crop_size=2_24			,							do_normalize=lowercase			,							image_mean=lowercase			,							image_std=lowercase			,							)
      lowerCamelCase_	  =					Image.open(requests.get(lowercase			,							stream=lowercase  ).raw  )
      lowerCamelCase_	  =					processor(images=lowercase			,							return_tensors='pt'  )

      lowerCamelCase_	  =					transforms.Compose(
          [
              transforms.Resize(2_56  ),
              transforms.CenterCrop(2_24  ),
              transforms.ToTensor(),
              transforms.Normalize(mean=[0.485, 0.456, 0.406]			,							std=[0.229, 0.224, 0.225]  ),
          ]  )

      lowerCamelCase_	  =					image_transforms(lowercase  ).unsqueeze(0  )

      # verify pixel_values
      assert torch.allclose(inputs.pixel_values			,							lowercase			,							atol=1e-4  )

      lowerCamelCase_	  =					model(**lowercase  )

      lowerCamelCase_	  =					outputs.logits.argmax(-1  ).item()
      print('Predicted class:'			,							model.config.idalabel[predicted_class_idx]  )

      print('First values of logits:'			,							outputs.logits[0, :3]  )

      if model_name == "focalnet-tiny":
            lowerCamelCase_	  =					torch.tensor([0.2166, -0.4368, 0.2191]  )
      elif model_name == "focalnet-tiny-lrf":
            lowerCamelCase_	  =					torch.tensor([1.1669, 0.0125, -0.1695]  )
      elif model_name == "focalnet-small":
            lowerCamelCase_	  =					torch.tensor([0.4917, -0.0430, 0.1341]  )
      elif model_name == "focalnet-small-lrf":
            lowerCamelCase_	  =					torch.tensor([-0.2588, -0.5342, -0.2331]  )
      elif model_name == "focalnet-base":
            lowerCamelCase_	  =					torch.tensor([-0.1655, -0.4090, -0.1730]  )
      elif model_name == "focalnet-base-lrf":
            lowerCamelCase_	  =					torch.tensor([0.5306, -0.0483, -0.3928]  )
      assert torch.allclose(outputs.logits[0, :3]			,							lowercase			,							atol=1e-4  )
      print('Looks ok!'  )

      if pytorch_dump_folder_path is not None:
            print(f"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}"""  )
            model.save_pretrained(lowercase  )
            processor.save_pretrained(lowercase  )

      if push_to_hub:
            print(f"""Pushing model and processor of {model_name} to the hub..."""  )
            model.push_to_hub(f"""{model_name}"""  )
            processor.push_to_hub(f"""{model_name}"""  )


if __name__ == "__main__":
       lowerCamelCase       :	int									=     argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default="focalnet-tiny",
           type=str,
           help="Name of the FocalNet 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."
       )
       parser.add_argument(
           "--push_to_hub",
           action="store_true",
           help="Whether to push the model and processor to the hub.",
       )

       lowerCamelCase       :	Optional[Any]									=     parser.parse_args()
       convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









lowerCamelCase       :	Dict									=     "Alexander Joslin"

import operator as op

from .stack import Stack

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



      lowerCamelCase_	  =					{'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

      lowerCamelCase_	  =					Stack()
      lowerCamelCase_	  =					Stack()

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

                  lowerCamelCase_	  =					operators[opr](lowercase			,							lowercase  )
                  operand_stack.push(lowercase  )

    # RULE 5
      return operand_stack.peek()


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



