






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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase__			 =	[]
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}.multihead_attn.out_proj.weight",
			        F"decoder.layers.{i}.encoder_attn.out_proj.weight",
			    )
			)
			rename_keys.append(
			    (
			        F"transformer.decoder.layers.{i}.multihead_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"))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
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'''),
    ]
)







def 						a_      (__A							,					__A							,					__A			)							->	Optional[Any]:



					"""simple docstring"""
					__a				:	Any			=				state_dict.pop(__A			)
					__a				:	List[Any]			=				val







def 						a_      (__A			)							->	int:



					"""simple docstring"""
					__a				:	Any			=				OrderedDict()
					for key, value in state_dict.items():
										if "backbone.0.body" in key:
															__a				:	List[Any]			=				key.replace("backbone.0.body"							,					"backbone.conv_encoder.model"			)
															__a				:	Any			=				value
										else:
															__a				:	List[str]			=				value

					return new_state_dict







def 						a_      (__A							,					__A=False			)							->	str:



					"""simple docstring"""
					__a				:	Any			=				""
					if is_panoptic:
										__a				:	List[Any]			=				"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)
										__a				:	str			=				state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'			)
										__a				:	Dict			=				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
										__a				:	str			=				in_proj_weight[:256, :]
										__a				:	Tuple			=				in_proj_bias[:256]
										__a				:	Union[str, Any]			=				in_proj_weight[256:512, :]
										__a				:	List[str]			=				in_proj_bias[256:512]
										__a				:	Any			=				in_proj_weight[-256:, :]
										__a				:	Any			=				in_proj_bias[-256:]
					# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
					for i in range(6			):
										# read in weights + bias of input projection layer of self-attention
										__a				:	Tuple			=				state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'			)
										__a				:	Optional[Any]			=				state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'			)
										# next, add query, keys and values (in that order) to the state dict
										__a				:	Union[str, Any]			=				in_proj_weight[:256, :]
										__a				:	Optional[int]			=				in_proj_bias[:256]
										__a				:	List[str]			=				in_proj_weight[256:512, :]
										__a				:	List[Any]			=				in_proj_bias[256:512]
										__a				:	int			=				in_proj_weight[-256:, :]
										__a				:	Tuple			=				in_proj_bias[-256:]
										# read in weights + bias of input projection layer of cross-attention
										__a				:	Any			=				state_dict.pop(
										    f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'			)
										__a				:	Optional[int]			=				state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'			)
										# next, add query, keys and values (in that order) of cross-attention to the state dict
										__a				:	Dict			=				in_proj_weight_cross_attn[:256, :]
										__a				:	Tuple			=				in_proj_bias_cross_attn[:256]
										__a				:	Union[str, Any]			=				in_proj_weight_cross_attn[256:512, :]
										__a				:	str			=				in_proj_bias_cross_attn[256:512]
										__a				:	Any			=				in_proj_weight_cross_attn[-256:, :]
										__a				:	Union[str, Any]			=				in_proj_bias_cross_attn[-256:]







def 						a_      ()							->	int:



					"""simple docstring"""
					__a				:	int			=				"http://images.cocodataset.org/val2017/000000039769.jpg"
					__a				:	Tuple			=				Image.open(requests.get(__A							,					stream=__A			).raw			)

					return im







@torch.no_grad()
def 						a_      (__A							,					__A			)							->	Optional[int]:



					"""simple docstring"""
					__a				:	List[Any]			=				DetrConfig()
					# set backbone and dilation attributes
					if "resnet101" in model_name:
										__a				:	str			=				"resnet101"
					if "dc5" in model_name:
										__a				:	Dict			=				True
					__a				:	List[str]			=				"panoptic" in model_name
					if is_panoptic:
										__a				:	Any			=				250
					else:
										__a				:	Optional[Any]			=				91
										__a				:	Optional[int]			=				"huggingface/label-files"
										__a				:	List[Any]			=				"coco-detection-id2label.json"
										__a				:	List[Any]			=				json.load(open(hf_hub_download(__A							,					__A							,					repo_type="dataset"			)							,					"r"			)			)
										__a				:	Any			=				{int(__A			): v for k, v in idalabel.items()}
										__a				:	int			=				idalabel
										__a				:	List[str]			=				{v: k for k, v in idalabel.items()}

					# load image processor
					__a				:	Any			=				"coco_panoptic" if is_panoptic else "coco_detection"
					__a				:	Dict			=				DetrImageProcessor(format=__A			)

					# prepare image
					__a				:	Union[str, Any]			=				prepare_img()
					__a				:	Dict			=				image_processor(images=__A							,					return_tensors="pt"			)
					__a				:	Optional[Any]			=				encoding["pixel_values"]

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

					# load original model from torch hub
					__a				:	List[Any]			=				torch.hub.load("facebookresearch/detr"							,					__A							,					pretrained=__A			).eval()
					__a				:	int			=				detr.state_dict()
					# rename keys
					for src, dest in rename_keys:
										if is_panoptic:
															__a				:	str			=				"detr." + src
										rename_key(__A							,					__A							,					__A			)
					__a				:	Dict			=				rename_backbone_keys(__A			)
					# query, key and value matrices need special treatment
					read_in_q_k_v(__A							,					is_panoptic=__A			)
					# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
					__a				:	int			=				"detr.model." if is_panoptic else "model."
					for key in state_dict.copy().keys():
										if is_panoptic:
															if (
															    key.startswith("detr"			)
															    and not key.startswith("class_labels_classifier"			)
															    and not key.startswith("bbox_predictor"			)
															):
																				__a				:	Union[str, Any]			=				state_dict.pop(__A			)
																				__a				:	List[str]			=				val
															elif "class_labels_classifier" in key or "bbox_predictor" in key:
																				__a				:	int			=				state_dict.pop(__A			)
																				__a				:	List[str]			=				val
															elif key.startswith("bbox_attention"			) or key.startswith("mask_head"			):
																				continue
															else:
																				__a				:	Optional[int]			=				state_dict.pop(__A			)
																				__a				:	List[str]			=				val
										else:
															if not key.startswith("class_labels_classifier"			) and not key.startswith("bbox_predictor"			):
																				__a				:	Dict			=				state_dict.pop(__A			)
																				__a				:	int			=				val
    # finally, create HuggingFace model and load state dict
					__a				:	Union[str, Any]			=				DetrForSegmentation(__A			) if is_panoptic else DetrForObjectDetection(__A			)
					model.load_state_dict(__A			)
					model.eval()
					# verify our conversion
					__a				:	int			=				detr(__A			)
					__a				:	Optional[int]			=				model(__A			)
					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(__A			).mkdir(exist_ok=__A			)
					model.save_pretrained(__A			)
					image_processor.save_pretrained(__A			)


if __name__ == "__main__":
			UpperCAmelCase__			 =	argparse.ArgumentParser()

			parser.add_argument(
			    '''--model_name''', default='''detr_resnet50''', type=str, help='''Name of the 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.'''
			)
			UpperCAmelCase__			 =	parser.parse_args()
			convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)













import argparse
import json
import re
import string
import sys
from collections import Counter







def 						a_      (__A			)							->	Optional[Any]:



					"""simple docstring"""

					def remove_articles(__A			):
										return re.sub(r"\b(a|an|the)\b"							,					" "							,					__A			)

					def white_space_fix(__A			):
										return " ".join(text.split()			)

					def remove_punc(__A			):
										__a				:	Union[str, Any]			=				set(string.punctuation			)
										return "".join(ch for ch in text if ch not in exclude			)

					def lower(__A			):
										return text.lower()

					return white_space_fix(remove_articles(remove_punc(lower(__A			)			)			)			)







def 						a_      (__A							,					__A			)							->	Union[str, Any]:



					"""simple docstring"""
					__a				:	str			=				normalize_answer(__A			).split()
					__a				:	Dict			=				normalize_answer(__A			).split()
					__a				:	List[Any]			=				Counter(__A			) & Counter(__A			)
					__a				:	List[Any]			=				sum(common.values()			)
					if num_same == 0:
										return 0
					__a				:	Optional[Any]			=				1.0 * num_same / len(__A			)
					__a				:	int			=				1.0 * num_same / len(__A			)
					__a				:	List[str]			=				(2 * precision * recall) / (precision + recall)
					return fa







def 						a_      (__A							,					__A			)							->	Dict:



					"""simple docstring"""
					return normalize_answer(__A			) == normalize_answer(__A			)







def 						a_      (__A							,					__A							,					__A			)							->	Optional[Any]:



					"""simple docstring"""
					__a				:	str			=				[]
					for ground_truth in ground_truths:
										__a				:	Tuple			=				metric_fn(__A							,					__A			)
										scores_for_ground_truths.append(__A			)
					return max(__A			)







def 						a_      (__A							,					__A			)							->	Union[str, Any]:



					"""simple docstring"""
					__a				:	str			=				0
					for article in dataset:
										for paragraph in article["paragraphs"]:
															for qa in paragraph["qas"]:
																				total += 1
																				if qa["id"] not in predictions:
																									__a				:	str			=				"Unanswered question " + qa["id"] + " will receive score 0."
																									print(__A							,					file=sys.stderr			)
																									continue
																				__a				:	Any			=				[x["text"] for x in qa["answers"]]
																				__a				:	List[Any]			=				predictions[qa["id"]]
																				exact_match += metric_max_over_ground_truths(__A							,					__A							,					__A			)
																				fa += metric_max_over_ground_truths(__A							,					__A							,					__A			)

					__a				:	Any			=				100.0 * exact_match / total
					__a				:	List[Any]			=				100.0 * fa / total

					return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
			UpperCAmelCase__			 =	'''1.1'''
			UpperCAmelCase__			 =	argparse.ArgumentParser(description='''Evaluation for SQuAD ''' + expected_version)
			parser.add_argument('''dataset_file''', help='''Dataset file''')
			parser.add_argument('''prediction_file''', help='''Prediction File''')
			UpperCAmelCase__			 =	parser.parse_args()
			with open(args.dataset_file) as dataset_file:
						UpperCAmelCase__			 =	json.load(dataset_file)
						if dataset_json["version"] != expected_version:
									print(
									    '''Evaluation expects v-''' + expected_version + ''', but got dataset with v-''' + dataset_json['''version'''],
									    file=sys.stderr,
									)
						UpperCAmelCase__			 =	dataset_json['''data''']
			with open(args.prediction_file) as prediction_file:
						UpperCAmelCase__			 =	json.load(prediction_file)
			print(json.dumps(evaluate(dataset, predictions)))





