
'''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 					__SCREAMING_SNAKE_CASE      (  _SCREAMING_SNAKE_CASE      ):
							_snake_case   =  [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
							_snake_case   =  True if '''large''' in model_name or '''huge''' in model_name else False
							_snake_case   =  True if '''large''' in model_name or '''huge''' in model_name else False
							_snake_case   =  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:
																					_snake_case   =  [3, 3, 3, 3]
																					_snake_case   =  [5, 5, 5, 5]
														elif "fl4" in model_name:
																					_snake_case   =  [4, 4, 4, 4]
																					_snake_case   =  [3, 3, 3, 3]

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

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

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

							_snake_case   =  json.load(open(hf_hub_download(lowercase__							,					lowercase__							,					repo_type="""dataset"""      )							,					"""r"""      )      )
							_snake_case   =  {int(lowercase__      ): v for k, v in idalabel.items()}
							_snake_case   =  {v: k for k, v in idalabel.items()}

							_snake_case   =  FocalNetConfig(
							    embed_dim=lowercase__							,					depths=lowercase__							,					focal_levels=lowercase__							,					focal_windows=lowercase__							,					use_conv_embed=lowercase__							,					idalabel=lowercase__							,					labelaid=lowercase__							,					use_post_layernorm=lowercase__							,					use_layerscale=lowercase__							,					)

							return config






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

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

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

							return name






def 					__SCREAMING_SNAKE_CASE      (  _SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE=False      ):
							# fmt: off
							_snake_case   =  {
							    '''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

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

							# rename keys
							for key in state_dict.copy().keys():
														_snake_case   =  state_dict.pop(lowercase__      )
														_snake_case   =  val

							_snake_case   =  get_focalnet_config(lowercase__      )
							_snake_case   =  FocalNetForImageClassification(lowercase__      )
							model.eval()

							# load state dict
							model.load_state_dict(lowercase__      )

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

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

							_snake_case   =  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]      ),
							    ]      )

							_snake_case   =  image_transforms(lowercase__      ).unsqueeze(0      )

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

							_snake_case   =  model(**lowercase__      )

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

							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     							=       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     							=       parser.parse_args()
				convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



from __future__ import annotations

import bisect


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__ = 0    ,       lowercase__ = -1      ):
						if hi < 0:
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		len(lowercase__      )

						while lo < hi:
												__SCREAMING_SNAKE_CASE						:     Any					=		lo + (hi - lo) // 2
												if sorted_collection[mid] < item:
																		__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		mid + 1
												else:
																		__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		mid

						return lo


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__ = 0    ,       lowercase__ = -1      ):
						if hi < 0:
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		len(lowercase__      )

						while lo < hi:
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		lo + (hi - lo) // 2
												if sorted_collection[mid] <= item:
																		__SCREAMING_SNAKE_CASE						:     Any					=		mid + 1
												else:
																		__SCREAMING_SNAKE_CASE						:     Optional[int]					=		mid

						return lo


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__ = 0    ,       lowercase__ = -1      ):
						sorted_collection.insert(bisect_left(lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      )    ,       lowercase__      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__ = 0    ,       lowercase__ = -1      ):
						sorted_collection.insert(bisect_right(lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      )    ,       lowercase__      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Any					=		0
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		len(lowercase__      ) - 1

						while left <= right:
												__SCREAMING_SNAKE_CASE						:     str					=		left + (right - left) // 2
												__SCREAMING_SNAKE_CASE						:     List[str]					=		sorted_collection[midpoint]
												if current_item == item:
																		return midpoint
												elif item < current_item:
																		__SCREAMING_SNAKE_CASE						:     int					=		midpoint - 1
												else:
																		__SCREAMING_SNAKE_CASE						:     Dict					=		midpoint + 1
						return None


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		bisect.bisect_left(lowercase__    ,       lowercase__      )
						if index != len(lowercase__      ) and sorted_collection[index] == item:
												return index
						return None


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      ):
						if right < left:
												return None

						__SCREAMING_SNAKE_CASE						:     int					=		left + (right - left) // 2

						if sorted_collection[midpoint] == item:
												return midpoint
						elif sorted_collection[midpoint] > item:
												return binary_search_by_recursion(lowercase__    ,       lowercase__    ,       lowercase__    ,       midpoint - 1      )
						else:
												return binary_search_by_recursion(lowercase__    ,       lowercase__    ,       midpoint + 1    ,       lowercase__      )


if __name__ == "__main__":
						__lowerCAmelCase	: Dict  				=input('Enter numbers separated by comma:\n').strip()
						__lowerCAmelCase	: str  				=sorted(int(item) for item in user_input.split(','))
						__lowerCAmelCase	: Tuple  				=int(input('Enter a single number to be found in the list:\n'))
						__lowerCAmelCase	: Tuple  				=binary_search(collection, target)
						if result is None:
												print(f"""{target} was not found in {collection}.""")
						else:
												print(f"""{target} was found at position {result} in {collection}.""")
