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       ):



 """simple docstring"""


 __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							=	128
 elif "large" in model_name:
  __a							=	192
 elif "xlarge" in model_name:
  __a							=	256
 elif "huge" in model_name:
  __a							=	352

 # 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(_A				,					_A				,					repo_type="dataset"       )				,					"r"       )       )
 __a							=	{int(_A       ): v for k, v in idalabel.items()}
 __a							=	{v: k for k, v in idalabel.items()}

 __a							=	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 	__A		(      _A       ):



 """simple docstring"""


 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				,					_A				,					_A=False       ):



 """simple docstring"""


 __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: "				,					_A       )
 __a							=	torch.hub.load_state_dict_from_url(_A				,					map_location="cpu"       )["model"]

 # rename keys
 for key in state_dict.copy().keys():
  __a							=	state_dict.pop(_A       )
  __a							=	val

 __a							=	get_focalnet_config(_A       )
 __a							=	FocalNetForImageClassification(_A       )
 model.eval()

 # load state dict
 model.load_state_dict(_A       )

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

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

 __a							=	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]       ),
     ]       )

 __a							=	image_transforms(_A       ).unsqueeze(0       )

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

 __a							=	model(**_A       )

 __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]				,					_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__":
  SCREAMING_SNAKE_CASE			:						Optional[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.""",
  )

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



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


SCREAMING_SNAKE_CASE			:						List[Any]				  =		logging.get_logger(__name__)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      ["""pixel_values"""]

  def __init__(		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Dict[str, int] = None  ,				__SCREAMING_SNAKE_CASE			:   PILImageResampling = PILImageResampling.BICUBIC  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Union[int, float] = 1 / 2_55  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, List[float]]] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, List[float]]] = None  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)
   __a							=	size if size is not None else {"height": 3_84, "width": 3_84}
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE  ,				default_to_square=__SCREAMING_SNAKE_CASE							)

   __a							=	do_resize
   __a							=	size
   __a							=	resample
   __a							=	do_rescale
   __a							=	rescale_factor
   __a							=	do_normalize
   __a							=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
   __a							=	image_std if image_std is not None else OPENAI_CLIP_STD
   __a							=	do_convert_rgb

  def 		_UpperCAmelCase (		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   np.ndarray  ,				__SCREAMING_SNAKE_CASE			:   Dict[str, int]  ,				__SCREAMING_SNAKE_CASE			:   PILImageResampling = PILImageResampling.BICUBIC  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, ChannelDimension]] = None  ,				**__SCREAMING_SNAKE_CASE			:   Any  ,				):
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE  ,				default_to_square=__SCREAMING_SNAKE_CASE							)
   if "height" not in size or "width" not in size:
    raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""							)
   __a							=	(size["height"], size["width"])
   return resize(__SCREAMING_SNAKE_CASE  ,				size=__SCREAMING_SNAKE_CASE  ,				resample=__SCREAMING_SNAKE_CASE  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   np.ndarray  ,				__SCREAMING_SNAKE_CASE			:   Union[int, float]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, ChannelDimension]] = None  ,				**__SCREAMING_SNAKE_CASE			:   str  ,				):
   return rescale(__SCREAMING_SNAKE_CASE  ,				scale=__SCREAMING_SNAKE_CASE  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   np.ndarray  ,				__SCREAMING_SNAKE_CASE			:   Union[float, List[float]]  ,				__SCREAMING_SNAKE_CASE			:   Union[float, List[float]]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, ChannelDimension]] = None  ,				**__SCREAMING_SNAKE_CASE			:   int  ,				):
   return normalize(__SCREAMING_SNAKE_CASE  ,				mean=__SCREAMING_SNAKE_CASE  ,				std=__SCREAMING_SNAKE_CASE  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   ImageInput  ,				__SCREAMING_SNAKE_CASE			:   Optional[bool] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Dict[str, int]] = None  ,				__SCREAMING_SNAKE_CASE			:   PILImageResampling = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[bool] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[float] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[bool] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, List[float]]] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, List[float]]] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, TensorType]] = None  ,				__SCREAMING_SNAKE_CASE			:   bool = None  ,				__SCREAMING_SNAKE_CASE			:   ChannelDimension = ChannelDimension.FIRST  ,				**__SCREAMING_SNAKE_CASE			:   Any  ,				):
   __a							=	do_resize if do_resize is not None else self.do_resize
   __a							=	resample if resample is not None else self.resample
   __a							=	do_rescale if do_rescale is not None else self.do_rescale
   __a							=	rescale_factor if rescale_factor is not None else self.rescale_factor
   __a							=	do_normalize if do_normalize is not None else self.do_normalize
   __a							=	image_mean if image_mean is not None else self.image_mean
   __a							=	image_std if image_std is not None else self.image_std
   __a							=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   __a							=	size if size is not None else self.size
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE  ,				default_to_square=__SCREAMING_SNAKE_CASE							)

   __a							=	make_list_of_images(__SCREAMING_SNAKE_CASE							)

   if not valid_images(__SCREAMING_SNAKE_CASE							):
    raise ValueError(
        "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
        "torch.Tensor, tf.Tensor or jax.ndarray."							)

   if do_resize and size is None or resample is None:
    raise ValueError("Size and resample must be specified if do_resize is True."							)

   if do_rescale and rescale_factor is None:
    raise ValueError("Rescale factor must be specified if do_rescale is True."							)

   if do_normalize and (image_mean is None or image_std is None):
    raise ValueError("Image mean and std must be specified if do_normalize is True."							)

   # PIL RGBA images are converted to RGB
   if do_convert_rgb:
    __a							=	[convert_to_rgb(__SCREAMING_SNAKE_CASE							) for image in images]

   # All transformations expect numpy arrays.
   __a							=	[to_numpy_array(__SCREAMING_SNAKE_CASE							) for image in images]

   if do_resize:
    __a							=	[self.resize(image=__SCREAMING_SNAKE_CASE  ,				size=__SCREAMING_SNAKE_CASE  ,				resample=__SCREAMING_SNAKE_CASE							) for image in images]

   if do_rescale:
    __a							=	[self.rescale(image=__SCREAMING_SNAKE_CASE  ,				scale=__SCREAMING_SNAKE_CASE							) for image in images]

   if do_normalize:
    __a							=	[self.normalize(image=__SCREAMING_SNAKE_CASE  ,				mean=__SCREAMING_SNAKE_CASE  ,				std=__SCREAMING_SNAKE_CASE							) for image in images]

   __a							=	[to_channel_dimension_format(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							) for image in images]

   __a							=	BatchFeature(data={"pixel_values": images}  ,				tensor_type=__SCREAMING_SNAKE_CASE							)

   return encoded_outputs


