






import argparse
import json
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, ResNetConfig
from transformers.utils import logging


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





def 		__UpperCAmelCase     (	a_):
							# initialize config
							if "resnet-50" in model_name:
														snake_case_           =				ResNetConfig.from_pretrained('microsoft/resnet-50')
							elif "resnet-101" in model_name:
														snake_case_           =				ResNetConfig.from_pretrained('microsoft/resnet-101')
							else:
														raise ValueError('Model name should include either resnet50 or resnet101')

							snake_case_           =				DetrConfig(use_timm_backbone=a_				,	backbone_config=a_)

							# set label attributes
							snake_case_           =				'panoptic' in model_name
							if is_panoptic:
														snake_case_           =				2_50
							else:
														snake_case_           =				91
														snake_case_           =				'huggingface/label-files'
														snake_case_           =				'coco-detection-id2label.json'
														snake_case_           =				json.load(open(hf_hub_download(a_				,	a_				,	repo_type='dataset')				,	'r'))
														snake_case_           =				{int(a_): v for k, v in idalabel.items()}
														snake_case_           =				idalabel
														snake_case_           =				{v: k for k, v in idalabel.items()}

							return config, is_panoptic





def 		__UpperCAmelCase     (	a_):
							# here we list all keys to be renamed (original name on the left, our name on the right)
							snake_case_           =				[]

							# stem
							# fmt: off
							rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight'))
							rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight'))
							rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias'))
							rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean'))
							rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var'))
							# stages
							for stage_idx in range(len(config.backbone_config.depths)):
														for layer_idx in range(config.backbone_config.depths[stage_idx]):
																					# shortcut
																					if layer_idx == 0:
																												rename_keys.append(
																												    (
																												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
																												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
																												    ))
																												rename_keys.append(
																												    (
																												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
																												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
																												    ))
																												rename_keys.append(
																												    (
																												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
																												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
																												    ))
																												rename_keys.append(
																												    (
																												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
																												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
																												    ))
																												rename_keys.append(
																												    (
																												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
																												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
																												    ))
																					# 3 convs
																					for i in range(3):
																												rename_keys.append(
																												    (
																												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
																												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
																												    ))
																												rename_keys.append(
																												    (
																												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
																												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
																												    ))
																												rename_keys.append(
																												    (
																												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
																												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
																												    ))
																												rename_keys.append(
																												    (
																												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
																												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
																												    ))
																												rename_keys.append(
																												    (
																												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
																												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
																												    ))
    # fmt: on

							for i in range(config.encoder_layers):
														# 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'),
							    ])

							return rename_keys





def 		__UpperCAmelCase     (	a_				,	a_				,	a_):
							snake_case_           =				state_dict.pop(a_)
							snake_case_           =				val





def 		__UpperCAmelCase     (	a_				,	a_=False):
							snake_case_           =				''
							if is_panoptic:
														snake_case_           =				'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)
														snake_case_           =				state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''')
														snake_case_           =				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
														snake_case_           =				in_proj_weight[:2_56, :]
														snake_case_           =				in_proj_bias[:2_56]
														snake_case_           =				in_proj_weight[2_56:5_12, :]
														snake_case_           =				in_proj_bias[2_56:5_12]
														snake_case_           =				in_proj_weight[-2_56:, :]
														snake_case_           =				in_proj_bias[-2_56:]
							# 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
														snake_case_           =				state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''')
														snake_case_           =				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
														snake_case_           =				in_proj_weight[:2_56, :]
														snake_case_           =				in_proj_bias[:2_56]
														snake_case_           =				in_proj_weight[2_56:5_12, :]
														snake_case_           =				in_proj_bias[2_56:5_12]
														snake_case_           =				in_proj_weight[-2_56:, :]
														snake_case_           =				in_proj_bias[-2_56:]
														# read in weights + bias of input projection layer of cross-attention
														snake_case_           =				state_dict.pop(
														    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''')
														snake_case_           =				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
														snake_case_           =				in_proj_weight_cross_attn[:2_56, :]
														snake_case_           =				in_proj_bias_cross_attn[:2_56]
														snake_case_           =				in_proj_weight_cross_attn[2_56:5_12, :]
														snake_case_           =				in_proj_bias_cross_attn[2_56:5_12]
														snake_case_           =				in_proj_weight_cross_attn[-2_56:, :]
														snake_case_           =				in_proj_bias_cross_attn[-2_56:]





def 		__UpperCAmelCase     (	):
							snake_case_           =				'http://images.cocodataset.org/val2017/000000039769.jpg'
							snake_case_           =				Image.open(requests.get(a_				,	stream=a_).raw)

							return im





@torch.no_grad()
def 		__UpperCAmelCase     (	a_				,	a_=None				,	a_=False):
							snake_case_     ,						snake_case_           =				get_detr_config(a_)

							# load original model from torch hub
							snake_case_           =				{
							    'detr-resnet-50': 'detr_resnet50',
							    'detr-resnet-101': 'detr_resnet101',
							}
							logger.info(f'''Converting model {model_name}...''')
							snake_case_           =				torch.hub.load('facebookresearch/detr'				,	model_name_to_original_name[model_name]				,	pretrained=a_).eval()
							snake_case_           =				detr.state_dict()
							# rename keys
							for src, dest in create_rename_keys(a_):
														if is_panoptic:
																					snake_case_           =				'detr.' + src
														rename_key(a_				,	a_				,	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
							snake_case_           =				'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')
																					):
																												snake_case_           =				state_dict.pop(a_)
																												snake_case_           =				val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												snake_case_           =				state_dict.pop(a_)
																												snake_case_           =				val
																					elif key.startswith('bbox_attention') or key.startswith('mask_head'):
																												continue
																					else:
																												snake_case_           =				state_dict.pop(a_)
																												snake_case_           =				val
														else:
																					if not key.startswith('class_labels_classifier') and not key.startswith('bbox_predictor'):
																												snake_case_           =				state_dict.pop(a_)
																												snake_case_           =				val

    # finally, create HuggingFace model and load state dict
							snake_case_           =				DetrForSegmentation(a_) if is_panoptic else DetrForObjectDetection(a_)
							model.load_state_dict(a_)
							model.eval()

							# verify our conversion on an image
							snake_case_           =				'coco_panoptic' if is_panoptic else 'coco_detection'
							snake_case_           =				DetrImageProcessor(format=a_)

							snake_case_           =				processor(images=prepare_img()				,	return_tensors='pt')
							snake_case_           =				encoding['pixel_values']

							snake_case_           =				detr(a_)
							snake_case_           =				model(a_)

							assert torch.allclose(outputs.logits				,	original_outputs['pred_logits']				,	atol=1E-3)
							assert torch.allclose(outputs.pred_boxes				,	original_outputs['pred_boxes']				,	atol=1E-3)
							if is_panoptic:
														assert torch.allclose(outputs.pred_masks				,	original_outputs['pred_masks']				,	atol=1E-4)
							print('Looks ok!')

							if pytorch_dump_folder_path is not None:
														# 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_)
														processor.save_pretrained(a_)

							if push_to_hub:
														# Upload model and image processor to the hub
														logger.info('Uploading PyTorch model and image processor to the hub...')
														model.push_to_hub(f'''nielsr/{model_name}''')
														processor.push_to_hub(f'''nielsr/{model_name}''')


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

	parser.add_argument(
	    "--model_name",
	    default="detr-resnet-50",
	    type=str,
	    choices=["detr-resnet-50", "detr-resnet-101"],
	    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."
	)
	parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub or not.")
	lowercase    				=   parser.parse_args()
	convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)













from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate




@dataclass(frozen=snake_case_							)
class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					field(default='''language-modeling'''			,   metadata={'''include_in_asdict_even_if_is_default''': True}							)

	lowerCAmelCase						=					Features({'''text''': Value('''string'''							)}							)
	lowerCAmelCase						=					Features({}							)
	lowerCAmelCase						=					"text"




	@property
	def 				_UpperCamelCase     (			self   )			->       Dict[str, str]:
								return {self.text_column: "text"}





