






'''simple docstring'''
import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)





def 	_a(					UpperCamelCase__			:  Dict,		UpperCamelCase__			:  Optional[int]							):




						'''simple docstring'''






						SCREAMING_SNAKE_CASE__       :			Optional[Any]													=XCLIPTextConfig()

						# derive patch size from model name
						SCREAMING_SNAKE_CASE__       :			List[Any]													=model_name.find('''patch'''							)
						SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=int(model_name[start_idx + len('''patch'''							) : start_idx + len('''patch'''							) + 2]							)
						SCREAMING_SNAKE_CASE__       :			int													=XCLIPVisionConfig(patch_size=UpperCamelCase__,		num_frames=UpperCamelCase__							)

						if "large" in model_name:
												SCREAMING_SNAKE_CASE__       :			int													=7_6_8
												SCREAMING_SNAKE_CASE__       :			List[str]													=3_0_7_2
												SCREAMING_SNAKE_CASE__       :			Any													=1_2

												SCREAMING_SNAKE_CASE__       :			Dict													=1_0_2_4
												SCREAMING_SNAKE_CASE__       :			Optional[Any]													=4_0_9_6
												SCREAMING_SNAKE_CASE__       :			Any													=1_6
												SCREAMING_SNAKE_CASE__       :			Optional[int]													=2_4
												SCREAMING_SNAKE_CASE__       :			Optional[Any]													=7_6_8
												SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=3_0_7_2

						if model_name == "xclip-large-patch14-16-frames":
												SCREAMING_SNAKE_CASE__       :			List[str]													=3_3_6

						SCREAMING_SNAKE_CASE__       :			Optional[Any]													=XCLIPConfig.from_text_vision_configs(UpperCamelCase__,		UpperCamelCase__							)

						if "large" in model_name:
												SCREAMING_SNAKE_CASE__       :			List[Any]													=7_6_8

						return config





def 	_a(					UpperCamelCase__			:  Dict							):




						'''simple docstring'''






						if name == "token_embedding.weight":
												SCREAMING_SNAKE_CASE__       :			Optional[int]													=name.replace('''token_embedding.weight''',		'''text_model.embeddings.token_embedding.weight'''							)
						if name == "positional_embedding":
												SCREAMING_SNAKE_CASE__       :			str													=name.replace('''positional_embedding''',		'''text_model.embeddings.position_embedding.weight'''							)
						if "ln_1" in name:
												SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=name.replace('''ln_1''',		'''layer_norm1'''							)
						if "ln_2" in name:
												SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=name.replace('''ln_2''',		'''layer_norm2'''							)
						if "c_fc" in name:
												SCREAMING_SNAKE_CASE__       :			List[str]													=name.replace('''c_fc''',		'''fc1'''							)
						if "c_proj" in name:
												SCREAMING_SNAKE_CASE__       :			Tuple													=name.replace('''c_proj''',		'''fc2'''							)
						if name.startswith('''transformer.resblocks'''							):
												SCREAMING_SNAKE_CASE__       :			int													=name.replace('''transformer.resblocks''',		'''text_model.encoder.layers'''							)
						if "attn.out_proj" in name and "message" not in name:
												SCREAMING_SNAKE_CASE__       :			Optional[int]													=name.replace('''attn.out_proj''',		'''self_attn.out_proj'''							)
						if "ln_final" in name:
												SCREAMING_SNAKE_CASE__       :			int													=name.replace('''ln_final''',		'''text_model.final_layer_norm'''							)
						# visual encoder
						if name == "visual.class_embedding":
												SCREAMING_SNAKE_CASE__       :			str													=name.replace('''visual.class_embedding''',		'''vision_model.embeddings.class_embedding'''							)
						if name == "visual.positional_embedding":
												SCREAMING_SNAKE_CASE__       :			List[str]													=name.replace('''visual.positional_embedding''',		'''vision_model.embeddings.position_embedding.weight'''							)
						if name.startswith('''visual.transformer.resblocks'''							):
												SCREAMING_SNAKE_CASE__       :			int													=name.replace('''visual.transformer.resblocks''',		'''vision_model.encoder.layers'''							)
						if "visual.conv1" in name:
												SCREAMING_SNAKE_CASE__       :			int													=name.replace('''visual.conv1''',		'''vision_model.embeddings.patch_embedding'''							)
						if "visual.ln_pre" in name:
												SCREAMING_SNAKE_CASE__       :			Dict													=name.replace('''visual.ln_pre''',		'''vision_model.pre_layernorm'''							)
						if "visual.ln_post" in name:
												SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=name.replace('''visual.ln_post''',		'''vision_model.post_layernorm'''							)
						if "visual.proj" in name:
												SCREAMING_SNAKE_CASE__       :			Any													=name.replace('''visual.proj''',		'''visual_projection.weight'''							)
						if "text_projection" in name:
												SCREAMING_SNAKE_CASE__       :			Tuple													=name.replace('''text_projection''',		'''text_projection.weight'''							)
						# things on top
						if "prompts_visual_proj" in name:
												SCREAMING_SNAKE_CASE__       :			List[str]													=name.replace('''prompts_visual_proj''',		'''prompts_visual_projection'''							)
						if "prompts_visual_ln" in name:
												SCREAMING_SNAKE_CASE__       :			Dict													=name.replace('''prompts_visual_ln''',		'''prompts_visual_layernorm'''							)
						# mit
						if name == "mit.positional_embedding":
												SCREAMING_SNAKE_CASE__       :			Optional[Any]													=name.replace('''positional''',		'''position'''							)
						if name.startswith('''mit.resblocks'''							):
												SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=name.replace('''mit.resblocks''',		'''mit.encoder.layers'''							)
						# prompts generator
						if name.startswith('''prompts_generator.norm'''							):
												SCREAMING_SNAKE_CASE__       :			Any													=name.replace('''prompts_generator.norm''',		'''prompts_generator.layernorm'''							)

						return name





def 	_a(					UpperCamelCase__			:  Any,		UpperCamelCase__			:  List[Any]							):




						'''simple docstring'''






						for key in orig_state_dict.copy().keys():
												SCREAMING_SNAKE_CASE__       :			List[str]													=orig_state_dict.pop(UpperCamelCase__							)

												if "attn.in_proj" in key:
																		SCREAMING_SNAKE_CASE__       :			List[str]													=key.split('''.'''							)
																		if key.startswith('''visual'''							):
																								SCREAMING_SNAKE_CASE__       :			Tuple													=key_split[3]
																								SCREAMING_SNAKE_CASE__       :			Any													=config.vision_config.hidden_size
																								if "message_attn" in key:
																														if "weight" in key:
																																				SCREAMING_SNAKE_CASE__       :			Tuple													=val[
																																				    :dim, :
																																				]
																																				SCREAMING_SNAKE_CASE__       :			str													=val[
																																				    dim : dim * 2, :
																																				]
																																				SCREAMING_SNAKE_CASE__       :			List[Any]													=val[
																																				    -dim:, :
																																				]
																														else:
																																				SCREAMING_SNAKE_CASE__       :			int													=val[
																																				    :dim
																																				]
																																				SCREAMING_SNAKE_CASE__       :			Any													=val[
																																				    dim : dim * 2
																																				]
																																				SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=val[
																																				    -dim:
																																				]
																								else:
																														if "weight" in key:
																																				SCREAMING_SNAKE_CASE__       :			str													=val[
																																				    :dim, :
																																				]
																																				SCREAMING_SNAKE_CASE__       :			List[str]													=val[
																																				    dim : dim * 2, :
																																				]
																																				SCREAMING_SNAKE_CASE__       :			str													=val[
																																				    -dim:, :
																																				]
																														else:
																																				SCREAMING_SNAKE_CASE__       :			Optional[int]													=val[:dim]
																																				SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=val[
																																				    dim : dim * 2
																																				]
																																				SCREAMING_SNAKE_CASE__       :			Optional[int]													=val[-dim:]
																		elif key.startswith('''mit'''							):
																								SCREAMING_SNAKE_CASE__       :			List[str]													=key_split[2]
																								SCREAMING_SNAKE_CASE__       :			str													=config.vision_config.mit_hidden_size
																								if "weight" in key:
																														SCREAMING_SNAKE_CASE__       :			List[str]													=val[:dim, :]
																														SCREAMING_SNAKE_CASE__       :			List[Any]													=val[dim : dim * 2, :]
																														SCREAMING_SNAKE_CASE__       :			Optional[int]													=val[-dim:, :]
																								else:
																														SCREAMING_SNAKE_CASE__       :			int													=val[:dim]
																														SCREAMING_SNAKE_CASE__       :			str													=val[dim : dim * 2]
																														SCREAMING_SNAKE_CASE__       :			Optional[Any]													=val[-dim:]
																		else:
																								SCREAMING_SNAKE_CASE__       :			List[Any]													=key_split[2]
																								SCREAMING_SNAKE_CASE__       :			Optional[Any]													=config.text_config.hidden_size
																								if "weight" in key:
																														SCREAMING_SNAKE_CASE__       :			str													=val[:dim, :]
																														SCREAMING_SNAKE_CASE__       :			List[Any]													=val[
																														    dim : dim * 2, :
																														]
																														SCREAMING_SNAKE_CASE__       :			List[str]													=val[-dim:, :]
																								else:
																														SCREAMING_SNAKE_CASE__       :			Dict													=val[:dim]
																														SCREAMING_SNAKE_CASE__       :			int													=val[
																														    dim : dim * 2
																														]
																														SCREAMING_SNAKE_CASE__       :			Optional[int]													=val[-dim:]
												else:
																		SCREAMING_SNAKE_CASE__       :			int													=rename_key(UpperCamelCase__							)
																		if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
																								SCREAMING_SNAKE_CASE__       :			Tuple													=val.T
																		SCREAMING_SNAKE_CASE__       :			int													=val

						return orig_state_dict





def 	_a(					UpperCamelCase__			:  List[Any]							):




						'''simple docstring'''






						if num_frames == 8:
												SCREAMING_SNAKE_CASE__       :			List[str]													='''eating_spaghetti_8_frames.npy'''
						elif num_frames == 1_6:
												SCREAMING_SNAKE_CASE__       :			Any													='''eating_spaghetti.npy'''
						elif num_frames == 3_2:
												SCREAMING_SNAKE_CASE__       :			Union[str, Any]													='''eating_spaghetti_32_frames.npy'''
						SCREAMING_SNAKE_CASE__       :			List[str]													=hf_hub_download(
						    repo_id='''hf-internal-testing/spaghetti-video''',		filename=UpperCamelCase__,		repo_type='''dataset''',		)
						SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=np.load(UpperCamelCase__							)
						return list(UpperCamelCase__							)





def 	_a(					UpperCamelCase__			:  List[str],		UpperCamelCase__			:  int=None,		UpperCamelCase__			:  Optional[int]=False							):




						'''simple docstring'''






						SCREAMING_SNAKE_CASE__       :			Dict													={
						    # fully supervised kinetics-400 checkpoints
						    '''xclip-base-patch32''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth''',
						    '''xclip-base-patch32-16-frames''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'''
						    ),
						    '''xclip-base-patch16''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth''',
						    '''xclip-base-patch16-16-frames''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'''
						    ),
						    '''xclip-large-patch14''': '''https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb''',
						    '''xclip-large-patch14-16-frames''': '''https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f''',
						    # fully supervised kinetics-600 checkpoints
						    '''xclip-base-patch16-kinetics-600''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'''
						    ),
						    '''xclip-base-patch16-kinetics-600-16-frames''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'''
						    ),
						    '''xclip-large-patch14-kinetics-600''': '''https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be''',
						    # few shot
						    '''xclip-base-patch16-hmdb-2-shot''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'''
						    ),
						    '''xclip-base-patch16-hmdb-4-shot''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'''
						    ),
						    '''xclip-base-patch16-hmdb-8-shot''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'''
						    ),
						    '''xclip-base-patch16-hmdb-16-shot''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'''
						    ),
						    '''xclip-base-patch16-ucf-2-shot''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'''
						    ),
						    '''xclip-base-patch16-ucf-4-shot''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'''
						    ),
						    '''xclip-base-patch16-ucf-8-shot''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'''
						    ),
						    '''xclip-base-patch16-ucf-16-shot''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'''
						    ),
						    # zero shot
						    '''xclip-base-patch16-zero-shot''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth''',
						}

						SCREAMING_SNAKE_CASE__       :			Any													=model_to_url[model_name]
						SCREAMING_SNAKE_CASE__       :			int													=8
						if "16-frames" in model_name:
												SCREAMING_SNAKE_CASE__       :			List[Any]													=1_6
						elif "shot" in model_name:
												SCREAMING_SNAKE_CASE__       :			Tuple													=3_2

						SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=get_xclip_config(UpperCamelCase__,		UpperCamelCase__							)
						SCREAMING_SNAKE_CASE__       :			Any													=XCLIPModel(UpperCamelCase__							)
						model.eval()

						if "drive" in checkpoint_url:
												SCREAMING_SNAKE_CASE__       :			str													='''pytorch_model.bin'''
												gdown.cached_download(UpperCamelCase__,		UpperCamelCase__,		quiet=UpperCamelCase__							)
												SCREAMING_SNAKE_CASE__       :			Optional[int]													=torch.load(UpperCamelCase__,		map_location='''cpu'''							)['''model''']
						else:
												SCREAMING_SNAKE_CASE__       :			List[str]													=torch.hub.load_state_dict_from_url(UpperCamelCase__							)['''model''']

						SCREAMING_SNAKE_CASE__       :			List[Any]													=convert_state_dict(UpperCamelCase__,		UpperCamelCase__							)

						SCREAMING_SNAKE_CASE__       :			Tuple													=XCLIPModel(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=model.load_state_dict(UpperCamelCase__,		strict=UpperCamelCase__							)
						assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
						model.eval()

						SCREAMING_SNAKE_CASE__       :			Optional[Any]													=3_3_6 if model_name == '''xclip-large-patch14-16-frames''' else 2_2_4
						SCREAMING_SNAKE_CASE__       :			List[Any]													=VideoMAEImageProcessor(size=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE__       :			Optional[int]													=CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''							)
						SCREAMING_SNAKE_CASE__       :			List[str]													=CLIPTokenizerFast.from_pretrained('''openai/clip-vit-base-patch32'''							)
						SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=XCLIPProcessor(image_processor=UpperCamelCase__,		tokenizer=UpperCamelCase__							)

						SCREAMING_SNAKE_CASE__       :			Tuple													=prepare_video(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE__       :			Optional[int]													=processor(
						    text=['''playing sports''', '''eating spaghetti''', '''go shopping'''],		videos=UpperCamelCase__,		return_tensors='''pt''',		padding=UpperCamelCase__							)

						print('''Shape of pixel values:''',		inputs.pixel_values.shape							)

						with torch.no_grad():
												SCREAMING_SNAKE_CASE__       :			int													=model(**UpperCamelCase__							)

						# Verify outputs
						SCREAMING_SNAKE_CASE__       :			str													=outputs.logits_per_video
						SCREAMING_SNAKE_CASE__       :			List[str]													=logits_per_video.softmax(dim=1							)
						print('''Probs:''',		UpperCamelCase__							)
						# kinetics-400
						if model_name == "xclip-base-patch32":
												SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.tensor([[0.0_0_1_9, 0.9_9_5_1, 0.0_0_3_0]]							)
						elif model_name == "xclip-base-patch32-16-frames":
												SCREAMING_SNAKE_CASE__       :			int													=torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]]							)
						elif model_name == "xclip-base-patch16":
												SCREAMING_SNAKE_CASE__       :			List[Any]													=torch.tensor([[0.0_0_8_3, 0.9_6_8_1, 0.0_2_3_6]]							)
						elif model_name == "xclip-base-patch16-16-frames":
												SCREAMING_SNAKE_CASE__       :			str													=torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]]							)
						elif model_name == "xclip-large-patch14":
												SCREAMING_SNAKE_CASE__       :			List[Any]													=torch.tensor([[0.0_0_6_2, 0.9_8_6_4, 0.0_0_7_5]]							)
						elif model_name == "xclip-large-patch14-16-frames":
												SCREAMING_SNAKE_CASE__       :			str													=torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]]							)
						# kinetics-600
						elif model_name == "xclip-base-patch16-kinetics-600":
												SCREAMING_SNAKE_CASE__       :			int													=torch.tensor([[0.0_5_5_5, 0.8_9_1_4, 0.0_5_3_1]]							)
						elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
												SCREAMING_SNAKE_CASE__       :			str													=torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]]							)
						elif model_name == "xclip-large-patch14-kinetics-600":
												SCREAMING_SNAKE_CASE__       :			Dict													=torch.tensor([[0.0_0_3_6, 0.9_9_2_0, 0.0_0_4_5]]							)
						# few shot
						elif model_name == "xclip-base-patch16-hmdb-2-shot":
												SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]]							)
						elif model_name == "xclip-base-patch16-hmdb-4-shot":
												SCREAMING_SNAKE_CASE__       :			List[str]													=torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]]							)
						elif model_name == "xclip-base-patch16-hmdb-8-shot":
												SCREAMING_SNAKE_CASE__       :			List[str]													=torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]]							)
						elif model_name == "xclip-base-patch16-hmdb-16-shot":
												SCREAMING_SNAKE_CASE__       :			Dict													=torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]]							)
						elif model_name == "xclip-base-patch16-ucf-2-shot":
												SCREAMING_SNAKE_CASE__       :			Any													=torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]							)
						elif model_name == "xclip-base-patch16-ucf-4-shot":
												SCREAMING_SNAKE_CASE__       :			str													=torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]							)
						elif model_name == "xclip-base-patch16-ucf-8-shot":
												SCREAMING_SNAKE_CASE__       :			str													=torch.tensor([[0.0_0_2_7, 0.9_9_0_4, 0.0_0_7_0]]							)
						elif model_name == "xclip-base-patch16-ucf-16-shot":
												SCREAMING_SNAKE_CASE__       :			List[Any]													=torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]]							)
						# zero shot
						elif model_name == "xclip-base-patch16-zero-shot":
												SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]]							)
						else:
												raise ValueError(f"Model name {model_name} not supported"							)
						assert torch.allclose(UpperCamelCase__,		UpperCamelCase__,		atol=1e-3							)
						print('''Looks ok!'''							)

						if pytorch_dump_folder_path is not None:
												print(f"Saving model {model_name} to {pytorch_dump_folder_path}"							)
												model.save_pretrained(UpperCamelCase__							)

						if push_to_hub:
												print('''Pushing model, processor and slow tokenizer files to the hub...'''							)
												model.push_to_hub(UpperCamelCase__,		organization='''nielsr'''							)
												processor.push_to_hub(UpperCamelCase__,		organization='''nielsr'''							)
												slow_tokenizer.push_to_hub(UpperCamelCase__,		organization='''nielsr'''							)


if __name__ == "__main__":
		a_					=	argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--model_name',
		    default='xclip-base-patch32',
		    type=str,
		    help='Name of the model.',
		)
		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 or not to push the converted model to the 🤗 hub.'
		)

		a_					=	parser.parse_args()
		convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''
from datetime import datetime as dt
import os

from github import Github


a_					=	[
    'good first issue',
    'good second issue',
    'good difficult issue',
    'feature request',
    'new model',
    'wip',
]





def 	_a(					):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			List[str]													=Github(os.environ['''GITHUB_TOKEN''']							)
      SCREAMING_SNAKE_CASE__       :			List[Any]													=g.get_repo('''huggingface/transformers'''							)
      SCREAMING_SNAKE_CASE__       :			List[Any]													=repo.get_issues(state='''open'''							)

      for issue in open_issues:
            SCREAMING_SNAKE_CASE__       :			List[Any]													=sorted([comment for comment in issue.get_comments()],		key=lambda UpperCamelCase__							: i.created_at,		reverse=UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__       :			Dict													=comments[0] if len(UpperCamelCase__							) > 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 >= 3_0
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()							)
            ):
                  # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
                  issue.edit(state='''closed'''							)
            elif (
                (dt.utcnow() - issue.updated_at).days > 2_3
                and (dt.utcnow() - issue.created_at).days >= 3_0
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()							)
            ):
                  # print(f"Would add stale comment to {issue.number}")
                  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/transformers/blob/main/CONTRIBUTING.md) '''
                      '''are likely to be ignored.'''							)


if __name__ == "__main__":
  main()