'''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 __UpperCamelCase(   _A					:				Union[str, Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
   UpperCAmelCase__							:				Optional[Any]   				=  True if '''large''' in model_name or '''huge''' in model_name else False
   UpperCAmelCase__							:				Tuple   				=  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__							:				Optional[Any]   				=  [3, 3, 3, 3]
         UpperCAmelCase__							:				Dict   				=  [5, 5, 5, 5]
      elif "fl4" in model_name:
         UpperCAmelCase__							:				Any   				=  [4, 4, 4, 4]
         UpperCAmelCase__							:				Any   				=  [3, 3, 3, 3]

   if "tiny" in model_name or "small" in model_name or "base" in model_name:
      UpperCAmelCase__							:				Union[str, Any]   				=  [3, 3, 3, 3]
      if "lrf" in model_name:
         UpperCAmelCase__							:				List[str]   				=  [3, 3, 3, 3]
      else:
         UpperCAmelCase__							:				Dict   				=  [2, 2, 2, 2]

   if "tiny" in model_name:
      UpperCAmelCase__							:				Tuple   				=  96
   elif "small" in model_name:
      UpperCAmelCase__							:				str   				=  96
   elif "base" in model_name:
      UpperCAmelCase__							:				List[Any]   				=  1_28
   elif "large" in model_name:
      UpperCAmelCase__							:				str   				=  1_92
   elif "xlarge" in model_name:
      UpperCAmelCase__							:				Optional[Any]   				=  2_56
   elif "huge" in model_name:
      UpperCAmelCase__							:				List[Any]   				=  3_52

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

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

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

   return config







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

   if name == "norm.weight":
      UpperCAmelCase__							:				Dict   				=  '''layernorm.weight'''
   if name == "norm.bias":
      UpperCAmelCase__							:				Dict   				=  '''layernorm.bias'''

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

   return name







def __UpperCamelCase(   _A					:				Optional[Any]  ,			_A					:				int  ,			_A					:				Dict=False    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  {
       '''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__							:				Tuple   				=  model_name_to_url[model_name]
   print('''Checkpoint URL: '''  ,			_A    )
   UpperCAmelCase__							:				List[Any]   				=  torch.hub.load_state_dict_from_url(_A  ,			map_location='''cpu'''    )['''model''']

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

   UpperCAmelCase__							:				Any   				=  get_focalnet_config(_A    )
   UpperCAmelCase__							:				Union[str, Any]   				=  FocalNetForImageClassification(_A    )
   model.eval()

   # load state dict
   model.load_state_dict(_A    )

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

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

   UpperCAmelCase__							:				List[Any]   				=  transforms.Compose(
       [
           transforms.Resize(2_56    ),
           transforms.CenterCrop(2_24    ),
           transforms.ToTensor(),
           transforms.Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]  ,			std=[0.2_2_9, 0.2_2_4, 0.2_2_5]    ),
       ]    )

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

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

   UpperCAmelCase__							:				Union[str, Any]   				=  model(**_A    )

   UpperCAmelCase__							:				str   				=  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__							:				Union[str, Any]   				=  torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1]    )
   elif model_name == "focalnet-tiny-lrf":
      UpperCAmelCase__							:				str   				=  torch.tensor([1.1_6_6_9, 0.0_1_2_5, -0.1_6_9_5]    )
   elif model_name == "focalnet-small":
      UpperCAmelCase__							:				List[str]   				=  torch.tensor([0.4_9_1_7, -0.0_4_3_0, 0.1_3_4_1]    )
   elif model_name == "focalnet-small-lrf":
      UpperCAmelCase__							:				List[str]   				=  torch.tensor([-0.2_5_8_8, -0.5_3_4_2, -0.2_3_3_1]    )
   elif model_name == "focalnet-base":
      UpperCAmelCase__							:				Any   				=  torch.tensor([-0.1_6_5_5, -0.4_0_9_0, -0.1_7_3_0]    )
   elif model_name == "focalnet-base-lrf":
      UpperCAmelCase__							:				Tuple   				=  torch.tensor([0.5_3_0_6, -0.0_4_8_3, -0.3_9_2_8]    )
   assert torch.allclose(outputs.logits[0, :3]  ,			_A  ,			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(_A    )
      processor.save_pretrained(_A    )

   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__":
  UpperCamelCase__     :	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.',
  )

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





'''simple docstring'''

import math







def __UpperCamelCase(   _A					:				float  ,			_A					:				float    ):
   '''simple docstring'''
   return math.pow(_A  ,			2    ) - a







def __UpperCamelCase(   _A					:				float    ):
   '''simple docstring'''
   return 2 * x







def __UpperCamelCase(   _A					:				float    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  2.0

   while start <= a:
      UpperCAmelCase__							:				int   				=  math.pow(_A  ,			2    )

   return start







def __UpperCamelCase(   _A					:				float  ,			_A					:				int = 99_99  ,			_A					:				float = 0.0_0_0_0_0_0_0_0_0_0_0_0_0_1    ):
   '''simple docstring'''

   if a < 0:
      raise ValueError('''math domain error'''    )

   UpperCAmelCase__							:				Any   				=  get_initial_point(_A    )

   for _ in range(_A    ):
      UpperCAmelCase__							:				Optional[Any]   				=  value
      UpperCAmelCase__							:				Tuple   				=  value - fx(_A  ,			_A    ) / fx_derivative(_A    )
      if abs(prev_value - value    ) < tolerance:
         return value

   return value


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

  testmod()




