
"""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 						_snake_case				(		snake_case__   :     List[Any]     ):
	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(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 						_snake_case				(		snake_case__   :     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 						_snake_case				(		snake_case__   :     str	,      snake_case__   :     Any	,      snake_case__   :     Dict=False     ):
	# 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': 256}	,      resample=PILImageResampling.BILINEAR	,      do_center_crop=snake_case__	,      crop_size=224	,      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(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(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__":
		_lowercase							   =				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.''',
		)

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

"""simple docstring"""


from math import pi, sqrt






def       _snake_case				(		snake_case__   :     float     ):
	if num <= 0:
		raise ValueError('math domain error'     )
	if num > 171.5:
		raise OverflowError('math range error'     )
	elif num - int(snake_case__     ) not in (0, 0.5):
		raise NotImplementedError('num must be an integer or a half-integer'     )
	elif num == 0.5:
		return sqrt(snake_case__     )
	else:
		return 1.0 if num == 1 else (num - 1) * gamma(num - 1     )






def       _snake_case				(		):
	assert gamma(0.5     ) == sqrt(snake_case__     )
	assert gamma(1     ) == 1.0
	assert gamma(2     ) == 1.0


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

		testmod()
		_lowercase							   =				1.0
		while num:
				_lowercase							   =				float(input('''Gamma of: '''))
				print(F"""gamma({num}) = {gamma(num)}""")
				print('''\nEnter 0 to exit...''')