



'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_			=     logging.get_logger(__name__)






def 		__lowercase  (_SCREAMING_SNAKE_CASE      :Dict       ):
				SCREAMING_SNAKE_CASE  :						List[str]      					= '''huggingface/label-files'''
				SCREAMING_SNAKE_CASE  :						str      					= '''imagenet-1k-id2label.json'''
				SCREAMING_SNAKE_CASE  :						List[Any]      					= json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	repo_type='''dataset'''       )    ,	'''r'''       )       )
				SCREAMING_SNAKE_CASE  :						List[str]      					= {int(_SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
				SCREAMING_SNAKE_CASE  :						str      					= {v: k for k, v in idalabel.items()}

				SCREAMING_SNAKE_CASE  :						Optional[int]      					= '''std_conv''' if '''bit''' in model_name else False

				# note that when using BiT as backbone for ViT-hybrid checkpoints,
				# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
				# config.conv_layer = "std_conv_same"
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= BitConfig(
				    conv_layer=_SCREAMING_SNAKE_CASE    ,	num_labels=10_00    ,	idalabel=_SCREAMING_SNAKE_CASE    ,	labelaid=_SCREAMING_SNAKE_CASE    ,	)

				return config






def 		__lowercase  (_SCREAMING_SNAKE_CASE      :int       ):
				if "stem.conv" in name:
								SCREAMING_SNAKE_CASE  :						Tuple      					= name.replace('''stem.conv'''    ,	'''bit.embedder.convolution'''       )
				if "blocks" in name:
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= name.replace('''blocks'''    ,	'''layers'''       )
				if "head.fc" in name:
								SCREAMING_SNAKE_CASE  :						List[str]      					= name.replace('''head.fc'''    ,	'''classifier.1'''       )
				if name.startswith('''norm'''       ):
								SCREAMING_SNAKE_CASE  :						List[str]      					= '''bit.''' + name
				if "bit" not in name and "classifier" not in name:
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''bit.encoder.''' + name

				return name






def 		__lowercase  ():
				SCREAMING_SNAKE_CASE  :						Tuple      					= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				SCREAMING_SNAKE_CASE  :						int      					= Image.open(requests.get(_SCREAMING_SNAKE_CASE    ,	stream=_SCREAMING_SNAKE_CASE       ).raw       )
				return im






@torch.no_grad()
def 		__lowercase  (_SCREAMING_SNAKE_CASE      :Optional[int]    ,	_SCREAMING_SNAKE_CASE      :List[Any]    ,	_SCREAMING_SNAKE_CASE      :Tuple=False       ):
				SCREAMING_SNAKE_CASE  :						Any      					= get_config(_SCREAMING_SNAKE_CASE       )

				# load original model from timm
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= create_model(_SCREAMING_SNAKE_CASE    ,	pretrained=_SCREAMING_SNAKE_CASE       )
				timm_model.eval()

				# load state_dict of original model
				SCREAMING_SNAKE_CASE  :						Tuple      					= timm_model.state_dict()
				for key in state_dict.copy().keys():
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= state_dict.pop(_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						Tuple      					= val.squeeze() if '''head''' in key else val

				# load HuggingFace model
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= BitForImageClassification(_SCREAMING_SNAKE_CASE       )
				model.eval()
				model.load_state_dict(_SCREAMING_SNAKE_CASE       )

				# create image processor
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= create_transform(**resolve_data_config({}    ,	model=_SCREAMING_SNAKE_CASE       )       )
				SCREAMING_SNAKE_CASE  :						Dict      					= transform.transforms

				SCREAMING_SNAKE_CASE  :						Tuple      					= {
				    '''bilinear''': PILImageResampling.BILINEAR,
				    '''bicubic''': PILImageResampling.BICUBIC,
				    '''nearest''': PILImageResampling.NEAREST,
				}

				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= BitImageProcessor(
				    do_resize=_SCREAMING_SNAKE_CASE    ,	size={'''shortest_edge''': timm_transforms[0].size}    ,	resample=pillow_resamplings[timm_transforms[0].interpolation.value]    ,	do_center_crop=_SCREAMING_SNAKE_CASE    ,	crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}    ,	do_normalize=_SCREAMING_SNAKE_CASE    ,	image_mean=timm_transforms[-1].mean.tolist()    ,	image_std=timm_transforms[-1].std.tolist()    ,	)

				SCREAMING_SNAKE_CASE  :						Optional[int]      					= prepare_img()
				SCREAMING_SNAKE_CASE  :						int      					= transform(_SCREAMING_SNAKE_CASE       ).unsqueeze(0       )
				SCREAMING_SNAKE_CASE  :						str      					= processor(_SCREAMING_SNAKE_CASE    ,	return_tensors='''pt'''       ).pixel_values

				# verify pixel values
				assert torch.allclose(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

				# verify logits
				with torch.no_grad():
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model(_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						List[str]      					= outputs.logits

				print('''Logits:'''    ,	logits[0, :3]       )
				print('''Predicted class:'''    ,	model.config.idalabel[logits.argmax(-1       ).item()]       )
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= timm_model(_SCREAMING_SNAKE_CASE       )
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(_SCREAMING_SNAKE_CASE    ,	outputs.logits    ,	atol=1E-3       )
				print('''Looks ok!'''       )

				if pytorch_dump_folder_path is not None:
								Path(_SCREAMING_SNAKE_CASE       ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE       )
								print(F'''Saving model {model_name} and processor to {pytorch_dump_folder_path}'''       )
								model.save_pretrained(_SCREAMING_SNAKE_CASE       )
								processor.save_pretrained(_SCREAMING_SNAKE_CASE       )

				if push_to_hub:
								print(F'''Pushing model {model_name} and processor to the hub'''       )
								model.push_to_hub(F'''ybelkada/{model_name}'''       )
								processor.push_to_hub(F'''ybelkada/{model_name}'''       )


if __name__ == "__main__":
	snake_case_			=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model_name""",
	    default="""resnetv2_50x1_bitm""",
	    type=str,
	    help="""Name of the BiT timm 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 to the hub.""",
	)

	snake_case_			=     parser.parse_args()
	convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''

import os
from datetime import datetime as dt

from github import Github


snake_case_			=     [
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """enhancement""",
    """new pipeline/model""",
    """new scheduler""",
    """wip""",
]






def       __lowercase  ():
				SCREAMING_SNAKE_CASE  :						List[Any]      					= Github(os.environ['''GITHUB_TOKEN''']       )
				SCREAMING_SNAKE_CASE  :						List[str]      					= g.get_repo('''huggingface/diffusers'''       )
				SCREAMING_SNAKE_CASE  :						List[str]      					= repo.get_issues(state='''open'''       )

				for issue in open_issues:
								SCREAMING_SNAKE_CASE  :						List[Any]      					= sorted(issue.get_comments()    ,	key=lambda _SCREAMING_SNAKE_CASE       : i.created_at    ,	reverse=_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= comments[0] if len(_SCREAMING_SNAKE_CASE       ) > 0 else None
								if (
								    last_comment is not None
								    and last_comment.user.login == "github-actions[bot]"
								    and (dt.utcnow() - issue.updated_at).days > 7
								    and (dt.utcnow() - issue.created_at).days >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()       )
								):
												# Closes the issue after 7 days of inactivity since the Stalebot notification.
												issue.edit(state='''closed'''       )
								elif (
								    "stale" in issue.get_labels()
								    and last_comment is not None
								    and last_comment.user.login != "github-actions[bot]"
								):
												# Opens the issue if someone other than Stalebot commented.
												issue.edit(state='''open'''       )
												issue.remove_from_labels('''stale'''       )
								elif (
								    (dt.utcnow() - issue.updated_at).days > 23
								    and (dt.utcnow() - issue.created_at).days >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()       )
								):
												# Post a Stalebot notification after 23 days of inactivity.
												issue.create_comment(
												    '''This issue has been automatically marked as stale because it has not had '''
												    '''recent activity. If you think this still needs to be addressed '''
												    '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
												    '''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
												    '''are likely to be ignored.'''       )
												issue.add_to_labels('''stale'''       )


if __name__ == "__main__":
	main()




