


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							(       lowercase_      )		->     Tuple:




						'''simple docstring'''







						__UpperCAmelCase			:							Any	    =	[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			:							List[str]	    =	True if '''large''' in model_name or '''huge''' in model_name else False
						__UpperCAmelCase			:							Union[str, Any]	    =	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			:							Any	    =	[3, 3, 3, 3]
																		__UpperCAmelCase			:							Dict	    =	[5, 5, 5, 5]
												elif "fl4" in model_name:
																		__UpperCAmelCase			:							Optional[int]	    =	[4, 4, 4, 4]
																		__UpperCAmelCase			:							List[Any]	    =	[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			:							Optional[Any]	    =	[2, 2, 2, 2]

						if "tiny" in model_name:
												__UpperCAmelCase			:							List[str]	    =	96
						elif "small" in model_name:
												__UpperCAmelCase			:							Dict	    =	96
						elif "base" in model_name:
												__UpperCAmelCase			:							str	    =	128
						elif "large" in model_name:
												__UpperCAmelCase			:							int	    =	192
						elif "xlarge" in model_name:
												__UpperCAmelCase			:							List[Any]	    =	256
						elif "huge" in model_name:
												__UpperCAmelCase			:							Union[str, Any]	    =	352

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

						__UpperCAmelCase			:							Any	    =	json.load(open(hf_hub_download(lowercase_      ,       lowercase_      ,       repo_type='''dataset'''      )      ,       '''r'''      )      )
						__UpperCAmelCase			:							Optional[Any]	    =	{int(lowercase_      ): v for k, v in idalabel.items()}
						__UpperCAmelCase			:							Tuple	    =	{v: k for k, v in idalabel.items()}

						__UpperCAmelCase			:							int	    =	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							(       lowercase_      )		->     Tuple:




						'''simple docstring'''







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

						if name == "norm.weight":
												__UpperCAmelCase			:							List[Any]	    =	'''layernorm.weight'''
						if name == "norm.bias":
												__UpperCAmelCase			:							Tuple	    =	'''layernorm.bias'''

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

						return name




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_=False      )		->     List[Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							Optional[Any]	    =	{
						    '''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			:							Optional[Any]	    =	model_name_to_url[model_name]
						print('''Checkpoint URL: '''      ,       lowercase_      )
						__UpperCAmelCase			:							Dict	    =	torch.hub.load_state_dict_from_url(lowercase_      ,       map_location='''cpu'''      )['''model''']

						# rename keys
						for key in state_dict.copy().keys():
												__UpperCAmelCase			:							int	    =	state_dict.pop(lowercase_      )
												__UpperCAmelCase			:							Optional[int]	    =	val

						__UpperCAmelCase			:							List[Any]	    =	get_focalnet_config(lowercase_      )
						__UpperCAmelCase			:							Tuple	    =	FocalNetForImageClassification(lowercase_      )
						model.eval()

						# load state dict
						model.load_state_dict(lowercase_      )

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

						__UpperCAmelCase			:							str	    =	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_      ,       )
						__UpperCAmelCase			:							Union[str, Any]	    =	Image.open(requests.get(lowercase_      ,       stream=lowercase_      ).raw      )
						__UpperCAmelCase			:							int	    =	processor(images=lowercase_      ,       return_tensors='''pt'''      )

						__UpperCAmelCase			:							Dict	    =	transforms.Compose(
						    [
						        transforms.Resize(256      ),
						        transforms.CenterCrop(224      ),
						        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			:							List[str]	    =	image_transforms(lowercase_      ).unsqueeze(0      )

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

						__UpperCAmelCase			:							Optional[int]	    =	model(**lowercase_      )

						__UpperCAmelCase			:							List[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			:							str	    =	torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1]      )
						elif model_name == "focalnet-tiny-lrf":
												__UpperCAmelCase			:							Any	    =	torch.tensor([1.1_6_6_9, 0.0_1_2_5, -0.1_6_9_5]      )
						elif model_name == "focalnet-small":
												__UpperCAmelCase			:							Tuple	    =	torch.tensor([0.4_9_1_7, -0.0_4_3_0, 0.1_3_4_1]      )
						elif model_name == "focalnet-small-lrf":
												__UpperCAmelCase			:							Tuple	    =	torch.tensor([-0.2_5_8_8, -0.5_3_4_2, -0.2_3_3_1]      )
						elif model_name == "focalnet-base":
												__UpperCAmelCase			:							List[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			:							Optional[Any]	    =	torch.tensor([0.5_3_0_6, -0.0_4_8_3, -0.3_9_2_8]      )
						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)





import inspect
import os
import sys
import unittest

import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu




class 							lowerCamelCase   (  unittest.TestCase ):


	def  A(    self):
							__UpperCAmelCase			:							List[Any]	    =	inspect.getfile(accelerate.test_utils)
							__UpperCAmelCase			:							Dict	    =	os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ['''scripts''', '''test_script.py'''])
							__UpperCAmelCase			:							Union[str, Any]	    =	os.path.sep.join(inspect.getfile(self.__class__).split(os.path.sep)[:-1])


	@require_tpu
	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	F"\n            {self.test_dir}/xla_spawn.py\n            --num_cores 8\n            {self.test_file_path}\n        ".split()
							__UpperCAmelCase			:							Union[str, Any]	    =	[sys.executable] + distributed_args
							execute_subprocess_async(lowercase__				,     env=os.environ.copy())

