






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     snake_case   (UpperCAmelCase__    )			->							str:
  UpperCamelCase_:   Optional[Any]        =							[2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 1_8, 2]
  UpperCamelCase_:   Optional[Any]        =							True if 'large' in model_name or 'huge' in model_name else False
  UpperCamelCase_:   str        =							True if 'large' in model_name or 'huge' in model_name else False
  UpperCamelCase_:   Optional[int]        =							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_:   int        =							[3, 3, 3, 3]
      UpperCamelCase_:   Dict        =							[5, 5, 5, 5]
    elif "fl4" in model_name:
      UpperCamelCase_:   Optional[Any]        =							[4, 4, 4, 4]
      UpperCamelCase_:   Dict        =							[3, 3, 3, 3]

  if "tiny" in model_name or "small" in model_name or "base" in model_name:
    UpperCamelCase_:   List[Any]        =							[3, 3, 3, 3]
    if "lrf" in model_name:
      UpperCamelCase_:   Optional[int]        =							[3, 3, 3, 3]
    else:
      UpperCamelCase_:   str        =							[2, 2, 2, 2]

  if "tiny" in model_name:
    UpperCamelCase_:   Optional[int]        =							9_6
  elif "small" in model_name:
    UpperCamelCase_:   Any        =							9_6
  elif "base" in model_name:
    UpperCamelCase_:   List[str]        =							1_2_8
  elif "large" in model_name:
    UpperCamelCase_:   List[str]        =							1_9_2
  elif "xlarge" in model_name:
    UpperCamelCase_:   str        =							2_5_6
  elif "huge" in model_name:
    UpperCamelCase_:   Union[str, Any]        =							3_5_2

  # set label information
  UpperCamelCase_:   Optional[Any]        =							'huggingface/label-files'
  if "large" in model_name or "huge" in model_name:
    UpperCamelCase_:   Tuple        =							'imagenet-22k-id2label.json'
  else:
    UpperCamelCase_:   Any        =							'imagenet-1k-id2label.json'

  UpperCamelCase_:   Optional[int]        =							json.load(open(hf_hub_download(UpperCAmelCase__      ,       UpperCAmelCase__      ,       repo_type='dataset'    )      ,       'r'    )    )
  UpperCamelCase_:   Dict        =							{int(UpperCAmelCase__    ): v for k, v in idalabel.items()}
  UpperCamelCase_:   List[Any]        =							{v: k for k, v in idalabel.items()}

  UpperCamelCase_:   Tuple        =							FocalNetConfig(
      embed_dim=UpperCAmelCase__      ,       depths=UpperCAmelCase__      ,       focal_levels=UpperCAmelCase__      ,       focal_windows=UpperCAmelCase__      ,       use_conv_embed=UpperCAmelCase__      ,       idalabel=UpperCAmelCase__      ,       labelaid=UpperCAmelCase__      ,       use_post_layernorm=UpperCAmelCase__      ,       use_layerscale=UpperCAmelCase__      ,       )

  return config

def     snake_case   (UpperCAmelCase__    )			->							Tuple:


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

  if name == "norm.weight":
    UpperCamelCase_:   Union[str, Any]        =							'layernorm.weight'
  if name == "norm.bias":
    UpperCamelCase_:   List[Any]        =							'layernorm.bias'

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

  return name

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__=False    )			->							Optional[int]:
  UpperCamelCase_:   Dict        =							{
      '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_:   Any        =							model_name_to_url[model_name]
  print('Checkpoint URL: '      ,       UpperCAmelCase__    )
  UpperCamelCase_:   Any        =							torch.hub.load_state_dict_from_url(UpperCAmelCase__      ,       map_location='cpu'    )['model']

  # rename keys
  for key in state_dict.copy().keys():
    UpperCamelCase_:   Optional[Any]        =							state_dict.pop(UpperCAmelCase__    )
    UpperCamelCase_:   Optional[int]        =							val

  UpperCamelCase_:   List[Any]        =							get_focalnet_config(UpperCAmelCase__    )
  UpperCamelCase_:   str        =							FocalNetForImageClassification(UpperCAmelCase__    )
  model.eval()

  # load state dict
  model.load_state_dict(UpperCAmelCase__    )

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

  UpperCamelCase_:   Union[str, Any]        =							BitImageProcessor(
      do_resize=UpperCAmelCase__      ,       size={'shortest_edge': 2_5_6}      ,       resample=PILImageResampling.BILINEAR      ,       do_center_crop=UpperCAmelCase__      ,       crop_size=2_2_4      ,       do_normalize=UpperCAmelCase__      ,       image_mean=UpperCAmelCase__      ,       image_std=UpperCAmelCase__      ,       )
  UpperCamelCase_:   int        =							Image.open(requests.get(UpperCAmelCase__      ,       stream=UpperCAmelCase__    ).raw    )
  UpperCamelCase_:   int        =							processor(images=UpperCAmelCase__      ,       return_tensors='pt'    )

  UpperCamelCase_:   List[str]        =							transforms.Compose(
      [
          transforms.Resize(2_5_6    ),
          transforms.CenterCrop(2_2_4    ),
          transforms.ToTensor(),
          transforms.Normalize(mean=[0.485, 0.456, 0.406]      ,       std=[0.229, 0.224, 0.225]    ),
      ]    )

  UpperCamelCase_:   Dict        =							image_transforms(UpperCAmelCase__    ).unsqueeze(0    )

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

  UpperCamelCase_:   Dict        =							model(**UpperCAmelCase__    )

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

  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_						:						Dict						      =					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_						:						Optional[int]						      =					parser.parse_args()
   convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''


from math import pi, sqrt
def 						snake_case_       (UpperCamelCase			:    float ):





							'''simple docstring'''


							if num <= 0:
														raise ValueError('''math domain error''' )
							if num > 171.5:
														raise OverflowError('''math range error''' )
							elif num - int(UpperCamelCase ) not in (0, 0.5):
														raise NotImplementedError('''num must be an integer or a half-integer''' )
							elif num == 0.5:
														return sqrt(UpperCamelCase )
							else:
														return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def 						snake_case_       ():





							'''simple docstring'''


							assert gamma(0.5 ) == sqrt(UpperCamelCase )
							assert gamma(1 ) == 1.0
							assert gamma(2 ) == 1.0


if __name__ == "__main__":
				from doctest import testmod

				testmod()
				_snake_case : Optional[Any]  				=						1.0
				while num:
								_snake_case : Dict  				=						float(input('Gamma of: '))
								print(F'''gamma({num}) = {gamma(num)}''')
								print('\nEnter 0 to exit...')

