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

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

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

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

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

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

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

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

							return name






def _lowerCamelCase  (		snake_case     , snake_case     , snake_case=False  ):
							# fmt: off
							_lowerCAmelCase							=							{
							    '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

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

							# rename keys
							for key in state_dict.copy().keys():
														_lowerCAmelCase							=							state_dict.pop(snake_case  )
														_lowerCAmelCase							=							val

							_lowerCAmelCase							=							get_focalnet_config(snake_case  )
							_lowerCAmelCase							=							FocalNetForImageClassification(snake_case  )
							model.eval()

							# load state dict
							model.load_state_dict(snake_case  )

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

							_lowerCAmelCase							=							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     , )
							_lowerCAmelCase							=							Image.open(requests.get(snake_case     , stream=snake_case  ).raw  )
							_lowerCAmelCase							=							processor(images=snake_case     , return_tensors='pt'  )

							_lowerCAmelCase							=							transforms.Compose(
							    [
							        transforms.Resize(256  ),
							        transforms.CenterCrop(224  ),
							        transforms.ToTensor(),
							        transforms.Normalize(mean=[0.4_85, 0.4_56, 0.4_06]     , std=[0.2_29, 0.2_24, 0.2_25]  ),
							    ]  )

							_lowerCAmelCase							=							image_transforms(snake_case  ).unsqueeze(0  )

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

							_lowerCAmelCase							=							model(**snake_case  )

							_lowerCAmelCase							=							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":
														_lowerCAmelCase							=							torch.tensor([0.21_66, -0.43_68, 0.21_91]  )
							elif model_name == "focalnet-tiny-lrf":
														_lowerCAmelCase							=							torch.tensor([1.16_69, 0.01_25, -0.16_95]  )
							elif model_name == "focalnet-small":
														_lowerCAmelCase							=							torch.tensor([0.49_17, -0.04_30, 0.13_41]  )
							elif model_name == "focalnet-small-lrf":
														_lowerCAmelCase							=							torch.tensor([-0.25_88, -0.53_42, -0.23_31]  )
							elif model_name == "focalnet-base":
														_lowerCAmelCase							=							torch.tensor([-0.16_55, -0.40_90, -0.17_30]  )
							elif model_name == "focalnet-base-lrf":
														_lowerCAmelCase							=							torch.tensor([0.53_06, -0.04_83, -0.39_28]  )
							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:	Optional[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.''',
				)

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







import argparse
import os
import re


_lowercase:	Optional[Any]       			=		'''src/transformers/models/auto'''


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_lowercase:	Union[str, Any]       			=		re.compile(R'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict''')
# re pattern that matches identifiers in mappings
_lowercase:	Optional[Any]       			=		re.compile(R'''\s*\(\s*"(\S[^"]+)"''')






def _lowerCamelCase  (		snake_case     , snake_case = False  ):
							with open(snake_case     , 'r'     , encoding='utf-8'  ) as f:
														_lowerCAmelCase							=							f.read()

							_lowerCAmelCase							=							content.split('\n'  )
							_lowerCAmelCase							=							[]
							_lowerCAmelCase							=							0
							while line_idx < len(snake_case  ):
														if _re_intro_mapping.search(lines[line_idx]  ) is not None:
																					_lowerCAmelCase							=							len(re.search(R'^(\s*)\S'     , lines[line_idx]  ).groups()[0]  ) + 8
																					# Start of a new mapping!
																					while not lines[line_idx].startswith(' ' * indent + '('  ):
																												new_lines.append(lines[line_idx]  )
																												line_idx += 1

																					_lowerCAmelCase							=							[]
																					while lines[line_idx].strip() != "]":
																												# Blocks either fit in one line or not
																												if lines[line_idx].strip() == "(":
																																			_lowerCAmelCase							=							line_idx
																																			while not lines[line_idx].startswith(' ' * indent + ')'  ):
																																										line_idx += 1
																																			blocks.append('\n'.join(lines[start_idx : line_idx + 1]  )  )
																												else:
																																			blocks.append(lines[line_idx]  )
																												line_idx += 1

																					# Sort blocks by their identifiers
																					_lowerCAmelCase							=							sorted(snake_case     , key=lambda snake_case  : _re_identifier.search(snake_case  ).groups()[0]  )
																					new_lines += blocks
														else:
																					new_lines.append(lines[line_idx]  )
																					line_idx += 1

							if overwrite:
														with open(snake_case     , 'w'     , encoding='utf-8'  ) as f:
																					f.write('\n'.join(snake_case  )  )
							elif "\n".join(snake_case  ) != content:
														return True






def _lowerCamelCase  (		snake_case = False  ):
							_lowerCAmelCase							=							[os.path.join(snake_case     , snake_case  ) for f in os.listdir(snake_case  ) if f.endswith('.py'  )]
							_lowerCAmelCase							=							[sort_auto_mapping(snake_case     , overwrite=snake_case  ) for fname in fnames]

							if not overwrite and any(snake_case  ):
														_lowerCAmelCase							=							[f for f, d in zip(snake_case     , snake_case  ) if d]
														raise ValueError(
														    F'The following files have auto mappings that need sorting: {", ".join(snake_case  )}. Run `make style` to fix'
														    ' this.'  )


if __name__ == "__main__":
				_lowercase:	Any       			=		argparse.ArgumentParser()
				parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
				_lowercase:	Tuple       			=		parser.parse_args()

				sort_all_auto_mappings(not args.check_only)






