

'''simple docstring'''




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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[Any]    ):
  UpperCAmelCase    				=						[2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
  UpperCAmelCase    				=						True if 'large' in model_name or 'huge' in model_name else False
  UpperCAmelCase    				=						True if 'large' in model_name or 'huge' in model_name else False
  UpperCAmelCase    				=						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:
      UpperCAmelCase    				=						[3, 3, 3, 3]
      UpperCAmelCase    				=						[5, 5, 5, 5]
    elif "fl4" in model_name:
      UpperCAmelCase    				=						[4, 4, 4, 4]
      UpperCAmelCase    				=						[3, 3, 3, 3]

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

  if "tiny" in model_name:
    UpperCAmelCase    				=						96
  elif "small" in model_name:
    UpperCAmelCase    				=						96
  elif "base" in model_name:
    UpperCAmelCase    				=						128
  elif "large" in model_name:
    UpperCAmelCase    				=						192
  elif "xlarge" in model_name:
    UpperCAmelCase    				=						256
  elif "huge" in model_name:
    UpperCAmelCase    				=						352

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

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

  UpperCAmelCase    				=						FocalNetConfig(
      embed_dim=SCREAMING_SNAKE_CASE							, depths=SCREAMING_SNAKE_CASE							, focal_levels=SCREAMING_SNAKE_CASE							, focal_windows=SCREAMING_SNAKE_CASE							, use_conv_embed=SCREAMING_SNAKE_CASE							, idalabel=SCREAMING_SNAKE_CASE							, labelaid=SCREAMING_SNAKE_CASE							, use_post_layernorm=SCREAMING_SNAKE_CASE							, use_layerscale=SCREAMING_SNAKE_CASE							, )

  return config


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

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

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

  return name


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[Any]							, SCREAMING_SNAKE_CASE   : Optional[int]							, SCREAMING_SNAKE_CASE   : List[str]=False    ):
  # fmt: off
  UpperCAmelCase    				=						{
      '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

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

  # rename keys
  for key in state_dict.copy().keys():
    UpperCAmelCase    				=						state_dict.pop(SCREAMING_SNAKE_CASE    )
    UpperCAmelCase    				=						val

  UpperCAmelCase    				=						get_focalnet_config(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						FocalNetForImageClassification(SCREAMING_SNAKE_CASE    )
  model.eval()

  # load state dict
  model.load_state_dict(SCREAMING_SNAKE_CASE    )

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

  UpperCAmelCase    				=						BitImageProcessor(
      do_resize=SCREAMING_SNAKE_CASE							, size={'shortest_edge': 256}							, resample=PILImageResampling.BILINEAR							, do_center_crop=SCREAMING_SNAKE_CASE							, crop_size=224							, do_normalize=SCREAMING_SNAKE_CASE							, image_mean=SCREAMING_SNAKE_CASE							, image_std=SCREAMING_SNAKE_CASE							, )
  UpperCAmelCase    				=						Image.open(requests.get(SCREAMING_SNAKE_CASE							, stream=SCREAMING_SNAKE_CASE    ).raw    )
  UpperCAmelCase    				=						processor(images=SCREAMING_SNAKE_CASE							, return_tensors='pt'    )

  UpperCAmelCase    				=						transforms.Compose(
      [
          transforms.Resize(256    ),
          transforms.CenterCrop(224    ),
          transforms.ToTensor(),
          transforms.Normalize(mean=[0.485, 0.456, 0.406]							, std=[0.229, 0.224, 0.225]    ),
      ]    )

  UpperCAmelCase    				=						image_transforms(SCREAMING_SNAKE_CASE    ).unsqueeze(0    )

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

  UpperCAmelCase    				=						model(**SCREAMING_SNAKE_CASE    )

  UpperCAmelCase    				=						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":
    UpperCAmelCase    				=						torch.tensor([0.2166, -0.4368, 0.2191]    )
  elif model_name == "focalnet-tiny-lrf":
    UpperCAmelCase    				=						torch.tensor([1.1669, 0.0125, -0.1695]    )
  elif model_name == "focalnet-small":
    UpperCAmelCase    				=						torch.tensor([0.4917, -0.0430, 0.1341]    )
  elif model_name == "focalnet-small-lrf":
    UpperCAmelCase    				=						torch.tensor([-0.2588, -0.5342, -0.2331]    )
  elif model_name == "focalnet-base":
    UpperCAmelCase    				=						torch.tensor([-0.1655, -0.4090, -0.1730]    )
  elif model_name == "focalnet-base-lrf":
    UpperCAmelCase    				=						torch.tensor([0.5306, -0.0483, -0.3928]    )
  assert torch.allclose(outputs.logits[0, :3]							, SCREAMING_SNAKE_CASE							, 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(SCREAMING_SNAKE_CASE    )
    processor.save_pretrained(SCREAMING_SNAKE_CASE    )

  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__":
  _a					:			List[str]							=    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.',
  )

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





'''simple docstring'''




def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : list[int]							, SCREAMING_SNAKE_CASE   : list[int]							, SCREAMING_SNAKE_CASE   : int    ):
  return not any(
      neighbour == 1 and colored_vertices[i] == color
      for i, neighbour in enumerate(SCREAMING_SNAKE_CASE    )    )


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : list[list[int]]							, SCREAMING_SNAKE_CASE   : int							, SCREAMING_SNAKE_CASE   : list[int]							, SCREAMING_SNAKE_CASE   : int    ):

  # Base Case
  if index == len(SCREAMING_SNAKE_CASE    ):
    return True

  # Recursive Step
  for i in range(SCREAMING_SNAKE_CASE    ):
    if valid_coloring(graph[index]							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    ):
      # Color current vertex
      UpperCAmelCase    				=						i
      # Validate coloring
      if util_color(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, index + 1    ):
        return True
      # Backtrack
      UpperCAmelCase    				=						-1
  return False



def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : list[list[int]]							, SCREAMING_SNAKE_CASE   : int    ):
  UpperCAmelCase    				=						[-1] * len(SCREAMING_SNAKE_CASE    )

  if util_color(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, 0    ):
    return colored_vertices

  return []


