



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async






def      __lowercase       (			a__=None       )    ->			Tuple:
							if subparsers is not None:
														__SCREAMING_SNAKE_CASE									= subparsers.add_parser('test'       )
							else:
														__SCREAMING_SNAKE_CASE									= argparse.ArgumentParser('Accelerate test command'       )

							parser.add_argument(
							    '--config_file'							,       default=a__							,       help=(
							        'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
							        'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
							        'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
							        'with \'huggingface\'.'
							    )							,       )

							if subparsers is not None:
														parser.set_defaults(func=a__       )
							return parser






def      __lowercase       (			a__       )    ->			Dict:
							__SCREAMING_SNAKE_CASE									= os.path.sep.join(__file__.split(os.path.sep       )[:-2] + ['test_utils', 'scripts', 'test_script.py']       )

							if args.config_file is None:
														__SCREAMING_SNAKE_CASE									= script_name
							else:
														__SCREAMING_SNAKE_CASE									= f"""--config_file={args.config_file} {script_name}"""

							__SCREAMING_SNAKE_CASE									= ['accelerate-launch'] + test_args.split()
							__SCREAMING_SNAKE_CASE									= execute_subprocess_async(a__							,       env=os.environ.copy()       )
							if result.returncode == 0:
														print('Test is a success! You are ready for your distributed training!'       )






def      __lowercase       (			)    ->			Optional[int]:
							__SCREAMING_SNAKE_CASE									= test_command_parser()
							__SCREAMING_SNAKE_CASE									= parser.parse_args()
							test_command(a__       )


if __name__ == "__main__":
			main()











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      __lowercase       (			a__       )    ->			Optional[Any]:
							__SCREAMING_SNAKE_CASE									= [2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
							__SCREAMING_SNAKE_CASE									= True if 'large' in model_name or 'huge' in model_name else False
							__SCREAMING_SNAKE_CASE									= True if 'large' in model_name or 'huge' in model_name else False
							__SCREAMING_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:
																					__SCREAMING_SNAKE_CASE									= [3, 3, 3, 3]
																					__SCREAMING_SNAKE_CASE									= [5, 5, 5, 5]
														elif "fl4" in model_name:
																					__SCREAMING_SNAKE_CASE									= [4, 4, 4, 4]
																					__SCREAMING_SNAKE_CASE									= [3, 3, 3, 3]

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

							if "tiny" in model_name:
														__SCREAMING_SNAKE_CASE									= 96
							elif "small" in model_name:
														__SCREAMING_SNAKE_CASE									= 96
							elif "base" in model_name:
														__SCREAMING_SNAKE_CASE									= 1_28
							elif "large" in model_name:
														__SCREAMING_SNAKE_CASE									= 1_92
							elif "xlarge" in model_name:
														__SCREAMING_SNAKE_CASE									= 2_56
							elif "huge" in model_name:
														__SCREAMING_SNAKE_CASE									= 3_52

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

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

							__SCREAMING_SNAKE_CASE									= FocalNetConfig(
							    embed_dim=a__							,       depths=a__							,       focal_levels=a__							,       focal_windows=a__							,       use_conv_embed=a__							,       idalabel=a__							,       labelaid=a__							,       use_post_layernorm=a__							,       use_layerscale=a__							,       )

							return config






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

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

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

							return name






def      __lowercase       (			a__							,       a__							,       a__=False       )    ->			Dict:
							# fmt: off
							__SCREAMING_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

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

							# rename keys
							for key in state_dict.copy().keys():
														__SCREAMING_SNAKE_CASE									= state_dict.pop(a__       )
														__SCREAMING_SNAKE_CASE									= val

							__SCREAMING_SNAKE_CASE									= get_focalnet_config(a__       )
							__SCREAMING_SNAKE_CASE									= FocalNetForImageClassification(a__       )
							model.eval()

							# load state dict
							model.load_state_dict(a__       )

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

							__SCREAMING_SNAKE_CASE									= BitImageProcessor(
							    do_resize=a__							,       size={'shortest_edge': 2_56}							,       resample=PILImageResampling.BILINEAR							,       do_center_crop=a__							,       crop_size=2_24							,       do_normalize=a__							,       image_mean=a__							,       image_std=a__							,       )
							__SCREAMING_SNAKE_CASE									= Image.open(requests.get(a__							,       stream=a__       ).raw       )
							__SCREAMING_SNAKE_CASE									= processor(images=a__							,       return_tensors='pt'       )

							__SCREAMING_SNAKE_CASE									= transforms.Compose(
							    [
							        transforms.Resize(2_56       ),
							        transforms.CenterCrop(2_24       ),
							        transforms.ToTensor(),
							        transforms.Normalize(mean=[0.485, 0.456, 0.406]							,       std=[0.229, 0.224, 0.225]       ),
							    ]       )

							__SCREAMING_SNAKE_CASE									= image_transforms(a__       ).unsqueeze(0       )

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

							__SCREAMING_SNAKE_CASE									= model(**a__       )

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

							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__		:					int							=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__		:					List[Any]							=parser.parse_args()
			convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






