






'''simple docstring'''







from datetime import datetime as dt
import os

from github import Github


_A	:			List[str]         =[
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]





def  SCREAMING_SNAKE_CASE_       ()      ->       Any:
							lowerCamelCase__					:	Tuple         =       Github(os.environ["""GITHUB_TOKEN"""]      )
							lowerCamelCase__					:	Tuple         =       g.get_repo("""huggingface/transformers"""      )
							lowerCamelCase__					:	Union[str, Any]         =       repo.get_issues(state="""open"""      )

							for issue in open_issues:
														lowerCamelCase__					:	List[str]         =       sorted([comment for comment in issue.get_comments()]				,			key=lambda UpperCamelCase      : i.created_at				,			reverse=UpperCamelCase      )
														lowerCamelCase__					:	Union[str, Any]         =       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 >= 30
														    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 > 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()      )
														):
																					# 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()









'''simple docstring'''







import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_A	:			List[str]         =logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_A	:			Tuple         =[]
for i in range(6):
			# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
			rename_keys.append(
			    (F'transformer.encoder.layers.{i}.self_attn.out_proj.weight', F'encoder.layers.{i}.self_attn.out_proj.weight')
			)
			rename_keys.append(
			    (F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias')
			)
			rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight'))
			rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias'))
			rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight'))
			rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias'))
			rename_keys.append(
			    (F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight')
			)
			rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias'))
			rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight'))
			rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias'))
			# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'decoder.layers.{i}.self_attn.out_proj.weight')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias')
			)
			rename_keys.append(
			    (
			        F'transformer.decoder.layers.{i}.cross_attn.out_proj.weight',
			        F'decoder.layers.{i}.encoder_attn.out_proj.weight',
			    )
			)
			rename_keys.append(
			    (
			        F'transformer.decoder.layers.{i}.cross_attn.out_proj.bias',
			        F'decoder.layers.{i}.encoder_attn.out_proj.bias',
			    )
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight'))
			rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias'))
			rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight'))
			rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias'))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight')
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias'))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias')
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight'))
			rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias'))

			# q, k, v projections in self/cross-attention in decoder for conditional DETR
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.sa_qcontent_proj.weight', F'decoder.layers.{i}.sa_qcontent_proj.weight')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.sa_kcontent_proj.weight', F'decoder.layers.{i}.sa_kcontent_proj.weight')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.sa_qpos_proj.weight', F'decoder.layers.{i}.sa_qpos_proj.weight')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.sa_kpos_proj.weight', F'decoder.layers.{i}.sa_kpos_proj.weight')
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.weight', F'decoder.layers.{i}.sa_v_proj.weight'))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.ca_qcontent_proj.weight', F'decoder.layers.{i}.ca_qcontent_proj.weight')
			)
			# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.ca_kcontent_proj.weight', F'decoder.layers.{i}.ca_kcontent_proj.weight')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.ca_kpos_proj.weight', F'decoder.layers.{i}.ca_kpos_proj.weight')
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.weight', F'decoder.layers.{i}.ca_v_proj.weight'))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight', F'decoder.layers.{i}.ca_qpos_sine_proj.weight')
			)

			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.sa_qcontent_proj.bias', F'decoder.layers.{i}.sa_qcontent_proj.bias')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.sa_kcontent_proj.bias', F'decoder.layers.{i}.sa_kcontent_proj.bias')
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.sa_qpos_proj.bias', F'decoder.layers.{i}.sa_qpos_proj.bias'))
			rename_keys.append((F'transformer.decoder.layers.{i}.sa_kpos_proj.bias', F'decoder.layers.{i}.sa_kpos_proj.bias'))
			rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.bias', F'decoder.layers.{i}.sa_v_proj.bias'))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.ca_qcontent_proj.bias', F'decoder.layers.{i}.ca_qcontent_proj.bias')
			)
			# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.ca_kcontent_proj.bias', F'decoder.layers.{i}.ca_kcontent_proj.bias')
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.ca_kpos_proj.bias', F'decoder.layers.{i}.ca_kpos_proj.bias'))
			rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.bias', F'decoder.layers.{i}.ca_v_proj.bias'))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias', F'decoder.layers.{i}.ca_qpos_sine_proj.bias')
			)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       List[str]:
							lowerCamelCase__					:	List[Any]         =       state_dict.pop(UpperCamelCase      )
							lowerCamelCase__					:	Any         =       val





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       Dict:
							lowerCamelCase__					:	Tuple         =       OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					lowerCamelCase__					:	List[str]         =       key.replace("""backbone.0.body"""				,			"""backbone.conv_encoder.model"""      )
																					lowerCamelCase__					:	Optional[int]         =       value
														else:
																					lowerCamelCase__					:	Any         =       value

							return new_state_dict





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase=False      )      ->       Dict:
							lowerCamelCase__					:	Optional[int]         =       """"""
							if is_panoptic:
														lowerCamelCase__					:	Dict         =       """conditional_detr."""

							# first: transformer encoder
							for i in range(6      ):
														# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
														lowerCamelCase__					:	List[Any]         =       state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''      )
														lowerCamelCase__					:	Union[str, Any]         =       state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''      )
														# next, add query, keys and values (in that order) to the state dict
														lowerCamelCase__					:	int         =       in_proj_weight[:256, :]
														lowerCamelCase__					:	Any         =       in_proj_bias[:256]
														lowerCamelCase__					:	str         =       in_proj_weight[256:512, :]
														lowerCamelCase__					:	Optional[int]         =       in_proj_bias[256:512]
														lowerCamelCase__					:	Dict         =       in_proj_weight[-256:, :]
														lowerCamelCase__					:	str         =       in_proj_bias[-256:]





def  SCREAMING_SNAKE_CASE_       ()      ->       List[Any]:
							lowerCamelCase__					:	Optional[int]         =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCamelCase__					:	Optional[Any]         =       Image.open(requests.get(UpperCamelCase				,			stream=UpperCamelCase      ).raw      )

							return im





@torch.no_grad()
def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       int:
							lowerCamelCase__					:	Optional[Any]         =       ConditionalDetrConfig()
							# set backbone and dilation attributes
							if "resnet101" in model_name:
														lowerCamelCase__					:	Any         =       """resnet101"""
							if "dc5" in model_name:
														lowerCamelCase__					:	Optional[int]         =       True
							lowerCamelCase__					:	int         =       """panoptic""" in model_name
							if is_panoptic:
														lowerCamelCase__					:	List[str]         =       250
							else:
														lowerCamelCase__					:	int         =       91
														lowerCamelCase__					:	int         =       """huggingface/label-files"""
														lowerCamelCase__					:	List[str]         =       """coco-detection-id2label.json"""
														lowerCamelCase__					:	Optional[Any]         =       json.load(open(hf_hub_download(UpperCamelCase				,			UpperCamelCase				,			repo_type="""dataset"""      )				,			"""r"""      )      )
														lowerCamelCase__					:	Any         =       {int(UpperCamelCase      ): v for k, v in idalabel.items()}
														lowerCamelCase__					:	str         =       idalabel
														lowerCamelCase__					:	List[str]         =       {v: k for k, v in idalabel.items()}

							# load image processor
							lowerCamelCase__					:	Optional[int]         =       """coco_panoptic""" if is_panoptic else """coco_detection"""
							lowerCamelCase__					:	int         =       ConditionalDetrImageProcessor(format=UpperCamelCase      )

							# prepare image
							lowerCamelCase__					:	List[str]         =       prepare_img()
							lowerCamelCase__					:	int         =       image_processor(images=UpperCamelCase				,			return_tensors="""pt"""      )
							lowerCamelCase__					:	Optional[Any]         =       encoding["""pixel_values"""]

							logger.info(f'''Converting model {model_name}...'''      )

							# load original model from torch hub
							lowerCamelCase__					:	List[Any]         =       torch.hub.load("""DeppMeng/ConditionalDETR"""				,			UpperCamelCase				,			pretrained=UpperCamelCase      ).eval()
							lowerCamelCase__					:	Dict         =       conditional_detr.state_dict()
							# rename keys
							for src, dest in rename_keys:
														if is_panoptic:
																					lowerCamelCase__					:	Optional[Any]         =       """conditional_detr.""" + src
														rename_key(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )
							lowerCamelCase__					:	Dict         =       rename_backbone_keys(UpperCamelCase      )
							# query, key and value matrices need special treatment
							read_in_q_k_v(UpperCamelCase				,			is_panoptic=UpperCamelCase      )
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							lowerCamelCase__					:	Dict         =       """conditional_detr.model.""" if is_panoptic else """model."""
							for key in state_dict.copy().keys():
														if is_panoptic:
																					if (
																					    key.startswith("""conditional_detr"""      )
																					    and not key.startswith("""class_labels_classifier"""      )
																					    and not key.startswith("""bbox_predictor"""      )
																					):
																												lowerCamelCase__					:	int         =       state_dict.pop(UpperCamelCase      )
																												lowerCamelCase__					:	Union[str, Any]         =       val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												lowerCamelCase__					:	List[str]         =       state_dict.pop(UpperCamelCase      )
																												lowerCamelCase__					:	Any         =       val
																					elif key.startswith("""bbox_attention"""      ) or key.startswith("""mask_head"""      ):
																												continue
																					else:
																												lowerCamelCase__					:	int         =       state_dict.pop(UpperCamelCase      )
																												lowerCamelCase__					:	Tuple         =       val
														else:
																					if not key.startswith("""class_labels_classifier"""      ) and not key.startswith("""bbox_predictor"""      ):
																												lowerCamelCase__					:	Union[str, Any]         =       state_dict.pop(UpperCamelCase      )
																												lowerCamelCase__					:	Dict         =       val
    # finally, create HuggingFace model and load state dict
							lowerCamelCase__					:	Tuple         =       ConditionalDetrForSegmentation(UpperCamelCase      ) if is_panoptic else ConditionalDetrForObjectDetection(UpperCamelCase      )
							model.load_state_dict(UpperCamelCase      )
							model.eval()
							model.push_to_hub(repo_id=UpperCamelCase				,			organization="""DepuMeng"""				,			commit_message="""Add model"""      )
							# verify our conversion
							lowerCamelCase__					:	Optional[Any]         =       conditional_detr(UpperCamelCase      )
							lowerCamelCase__					:	Optional[Any]         =       model(UpperCamelCase      )
							assert torch.allclose(outputs.logits				,			original_outputs["""pred_logits"""]				,			atol=1E-4      )
							assert torch.allclose(outputs.pred_boxes				,			original_outputs["""pred_boxes"""]				,			atol=1E-4      )
							if is_panoptic:
														assert torch.allclose(outputs.pred_masks				,			original_outputs["""pred_masks"""]				,			atol=1E-4      )

							# Save model and image processor
							logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''      )
							Path(UpperCamelCase      ).mkdir(exist_ok=UpperCamelCase      )
							model.save_pretrained(UpperCamelCase      )
							image_processor.save_pretrained(UpperCamelCase      )


if __name__ == "__main__":
			_A	:			List[Any]         =argparse.ArgumentParser()

			parser.add_argument(
			    '''--model_name''',
			    default='''conditional_detr_resnet50''',
			    type=str,
			    help='''Name of the CONDITIONAL_DETR model you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
			)
			_A	:			Optional[Any]         =parser.parse_args()
			convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

