

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      A__					(			__A  :						List[Any]			)					->Tuple:
      __A    =[2, 2, 6, 2] if """tiny""" in model_name else [2, 2, 18, 2]
      __A    =True if """large""" in model_name or """huge""" in model_name else False
      __A    =True if """large""" in model_name or """huge""" in model_name else False
      __A    =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:
                  __A    =[3, 3, 3, 3]
                  __A    =[5, 5, 5, 5]
            elif "fl4" in model_name:
                  __A    =[4, 4, 4, 4]
                  __A    =[3, 3, 3, 3]

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

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

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

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

      __A    =FocalNetConfig(
          embed_dim=snake_case__     ,				depths=snake_case__     ,				focal_levels=snake_case__     ,				focal_windows=snake_case__     ,				use_conv_embed=snake_case__     ,				idalabel=snake_case__     ,				labelaid=snake_case__     ,				use_post_layernorm=snake_case__     ,				use_layerscale=snake_case__     ,				)

      return config





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

      if name == "norm.weight":
            __A    ="""layernorm.weight"""
      if name == "norm.bias":
            __A    ="""layernorm.bias"""

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

      return name





def      A__					(			__A  :						Dict     ,				__A  :						Optional[int]     ,				__A  :						Optional[Any]=False			)					->Optional[int]:
      # fmt: off
      __A    ={
          """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

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

      # rename keys
      for key in state_dict.copy().keys():
            __A    =state_dict.pop(snake_case__			)
            __A    =val

      __A    =get_focalnet_config(snake_case__			)
      __A    =FocalNetForImageClassification(snake_case__			)
      model.eval()

      # load state dict
      model.load_state_dict(snake_case__			)

      # verify conversion
      __A    ="""http://images.cocodataset.org/val2017/000000039769.jpg"""

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

      __A    =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]			),
          ]			)

      __A    =image_transforms(snake_case__			).unsqueeze(0			)

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

      __A    =model(**snake_case__			)

      __A    =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":
            __A    =torch.tensor([0.2166, -0.4368, 0.2191]			)
      elif model_name == "focalnet-tiny-lrf":
            __A    =torch.tensor([1.1669, 0.0125, -0.1695]			)
      elif model_name == "focalnet-small":
            __A    =torch.tensor([0.4917, -0.0430, 0.1341]			)
      elif model_name == "focalnet-small-lrf":
            __A    =torch.tensor([-0.2588, -0.5342, -0.2331]			)
      elif model_name == "focalnet-base":
            __A    =torch.tensor([-0.1655, -0.4090, -0.1730]			)
      elif model_name == "focalnet-base-lrf":
            __A    =torch.tensor([0.5306, -0.0483, -0.3928]			)
      assert torch.allclose(outputs.logits[0, :3]     ,				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(snake_case__			)
            processor.save_pretrained(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__":
   _lowerCamelCase      :					Tuple					    =      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      :					List[Any]					    =      parser.parse_args()
   convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










"""simple docstring"""
from typing import Dict

from .base import GenericTensor, Pipeline



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	def  lowerCamelCase(self  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  **lowerCAmelCase_				):
		if tokenize_kwargs is None:
			A_		:    Dict						     = {}

		if truncation is not None:
			if "truncation" in tokenize_kwargs:
				raise ValueError(
				    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""				)
			A_		:    Optional[Any]						     = truncation

		A_		:    Optional[int]						     = tokenize_kwargs

		A_		:    Optional[Any]						     = {}
		if return_tensors is not None:
			A_		:    Optional[Any]						     = return_tensors

		return preprocess_params, {}, postprocess_params





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				):
		A_		:    List[Any]						     = self.framework
		A_		:    Optional[int]						     = self.tokenizer(lowerCAmelCase_  ,  return_tensors=lowerCAmelCase_  ,  **lowerCAmelCase_				)
		return model_inputs





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    List[Any]						     = self.model(**lowerCAmelCase_				)
		return model_outputs





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=False				):
		# [0] is the first available tensor, logits or last_hidden_state.
		if return_tensors:
			return model_outputs[0]
		if self.framework == "pt":
			return model_outputs[0].tolist()
		elif self.framework == "tf":
			return model_outputs[0].numpy().tolist()





	def __call__(self  ,  *lowerCAmelCase_  ,  **lowerCAmelCase_				):
		return super().__call__(*lowerCAmelCase_  ,  **lowerCAmelCase_				)

