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			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					List[Any]           =							[2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
						SCREAMING_SNAKE_CASE							:					Any           =							True if '''large''' in model_name or '''huge''' in model_name else False
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							True if '''large''' in model_name or '''huge''' in model_name else False
						SCREAMING_SNAKE_CASE							:					Optional[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:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							[3, 3, 3, 3]
																		SCREAMING_SNAKE_CASE							:					List[str]           =							[5, 5, 5, 5]
												elif "fl4" in model_name:
																		SCREAMING_SNAKE_CASE							:					Any           =							[4, 4, 4, 4]
																		SCREAMING_SNAKE_CASE							:					str           =							[3, 3, 3, 3]

						if "tiny" in model_name or "small" in model_name or "base" in model_name:
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[3, 3, 3, 3]
												if "lrf" in model_name:
																		SCREAMING_SNAKE_CASE							:					Tuple           =							[3, 3, 3, 3]
												else:
																		SCREAMING_SNAKE_CASE							:					Any           =							[2, 2, 2, 2]

						if "tiny" in model_name:
												SCREAMING_SNAKE_CASE							:					Tuple           =							96
						elif "small" in model_name:
												SCREAMING_SNAKE_CASE							:					List[Any]           =							96
						elif "base" in model_name:
												SCREAMING_SNAKE_CASE							:					Tuple           =							128
						elif "large" in model_name:
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							192
						elif "xlarge" in model_name:
												SCREAMING_SNAKE_CASE							:					Tuple           =							256
						elif "huge" in model_name:
												SCREAMING_SNAKE_CASE							:					List[str]           =							352

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

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

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

						if name == "norm.weight":
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							'''layernorm.weight'''
						if name == "norm.bias":
												SCREAMING_SNAKE_CASE							:					Tuple           =							'''layernorm.bias'''

						if "head" in name:
												SCREAMING_SNAKE_CASE							:					str           =							name.replace('''head'''	,						'''classifier'''		)
						else:
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							'''focalnet.''' + name

						return name
def    A			(					_lowercase	,						_lowercase	,						_lowercase=False		):
						# fmt: off
						SCREAMING_SNAKE_CASE							:					Tuple           =							{
						    '''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

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

						# rename keys
						for key in state_dict.copy().keys():
												SCREAMING_SNAKE_CASE							:					List[str]           =							state_dict.pop(_lowercase		)
												SCREAMING_SNAKE_CASE							:					Any           =							val

						SCREAMING_SNAKE_CASE							:					Any           =							get_focalnet_config(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Dict           =							FocalNetForImageClassification(_lowercase		)
						model.eval()

						# load state dict
						model.load_state_dict(_lowercase		)

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

						SCREAMING_SNAKE_CASE							:					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	,						)
						SCREAMING_SNAKE_CASE							:					str           =							Image.open(requests.get(_lowercase	,						stream=_lowercase		).raw		)
						SCREAMING_SNAKE_CASE							:					str           =							processor(images=_lowercase	,						return_tensors='''pt'''		)

						SCREAMING_SNAKE_CASE							:					List[Any]           =							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]		),
						    ]		)

						SCREAMING_SNAKE_CASE							:					Optional[int]           =							image_transforms(_lowercase		).unsqueeze(0		)

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

						SCREAMING_SNAKE_CASE							:					Dict           =							model(**_lowercase		)

						SCREAMING_SNAKE_CASE							:					Tuple           =							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":
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.tensor([0.2166, -0.4368, 0.2191]		)
						elif model_name == "focalnet-tiny-lrf":
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch.tensor([1.1669, 0.0125, -0.1695]		)
						elif model_name == "focalnet-small":
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch.tensor([0.4917, -0.0430, 0.1341]		)
						elif model_name == "focalnet-small-lrf":
												SCREAMING_SNAKE_CASE							:					List[str]           =							torch.tensor([-0.2588, -0.5342, -0.2331]		)
						elif model_name == "focalnet-base":
												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.tensor([-0.1655, -0.4090, -0.1730]		)
						elif model_name == "focalnet-base-lrf":
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							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__":
					__UpperCamelCase	: List[Any]    		=  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.',
					)

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






import os

try:
					from .build_directory_md import good_file_paths
except ImportError:
					from build_directory_md import good_file_paths  # type: ignore

__UpperCamelCase	: Dict    		=  list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__UpperCamelCase	: Tuple    		=  [file for file in filepaths if file != file.lower()]
if upper_files:
					print(f"""{len(upper_files)} files contain uppercase characters:""")
					print('\n'.join(upper_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if ' ' in file]
if space_files:
					print(f"""{len(space_files)} files contain space characters:""")
					print('\n'.join(space_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if '-' in file]
if hyphen_files:
					print(f"""{len(hyphen_files)} files contain hyphen characters:""")
					print('\n'.join(hyphen_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if os.sep not in file]
if nodir_files:
					print(f"""{len(nodir_files)} files are not in a directory:""")
					print('\n'.join(nodir_files) + '\n')

__UpperCamelCase	: Optional[Any]    		=  len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
					import sys

					sys.exit(bad_files)





