



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


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







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Any			):



					"""simple docstring"""





					if "resnet-50" in model_name:
										snake_case_					:		Union[str, Any] 					=					ResNetConfig.from_pretrained("""microsoft/resnet-50"""			)
					elif "resnet-101" in model_name:
										snake_case_					:		Optional[int] 					=					ResNetConfig.from_pretrained("""microsoft/resnet-101"""			)
					else:
										raise ValueError("""Model name should include either resnet50 or resnet101"""			)

					snake_case_					:		List[str] 					=					DetrConfig(use_timm_backbone=SCREAMING_SNAKE_CASE__				,    backbone_config=SCREAMING_SNAKE_CASE__			)

					# set label attributes
					snake_case_					:		Union[str, Any] 					=					"""panoptic""" in model_name
					if is_panoptic:
										snake_case_					:		Tuple 					=					2_5_0
					else:
										snake_case_					:		Optional[Any] 					=					9_1
										snake_case_					:		Union[str, Any] 					=					"""huggingface/label-files"""
										snake_case_					:		List[Any] 					=					"""coco-detection-id2label.json"""
										snake_case_					:		Any 					=					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    repo_type="""dataset"""			)				,    """r"""			)			)
										snake_case_					:		Tuple 					=					{int(SCREAMING_SNAKE_CASE__			): v for k, v in idalabel.items()}
										snake_case_					:		List[Any] 					=					idalabel
										snake_case_					:		int 					=					{v: k for k, v in idalabel.items()}

					return config, is_panoptic







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[Any]			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					[]

					# 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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





					snake_case_					:		Optional[int] 					=					state_dict.pop(SCREAMING_SNAKE_CASE__			)
					snake_case_					:		Any 					=					val







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  str=False			):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					""""""
					if is_panoptic:
										snake_case_					:		str 					=					"""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_					:		List[Any] 					=					state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'			)
										snake_case_					:		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
										snake_case_					:		List[Any] 					=					in_proj_weight[:2_5_6, :]
										snake_case_					:		Dict 					=					in_proj_bias[:2_5_6]
										snake_case_					:		List[Any] 					=					in_proj_weight[2_5_6:5_1_2, :]
										snake_case_					:		Optional[int] 					=					in_proj_bias[2_5_6:5_1_2]
										snake_case_					:		Dict 					=					in_proj_weight[-2_5_6:, :]
										snake_case_					:		Dict 					=					in_proj_bias[-2_5_6:]
					# 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_					:		Optional[Any] 					=					state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'			)
										snake_case_					:		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
										snake_case_					:		Any 					=					in_proj_weight[:2_5_6, :]
										snake_case_					:		Tuple 					=					in_proj_bias[:2_5_6]
										snake_case_					:		str 					=					in_proj_weight[2_5_6:5_1_2, :]
										snake_case_					:		Optional[int] 					=					in_proj_bias[2_5_6:5_1_2]
										snake_case_					:		Optional[int] 					=					in_proj_weight[-2_5_6:, :]
										snake_case_					:		str 					=					in_proj_bias[-2_5_6:]
										# read in weights + bias of input projection layer of cross-attention
										snake_case_					:		List[str] 					=					state_dict.pop(
										    f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'			)
										snake_case_					:		Any 					=					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_					:		Dict 					=					in_proj_weight_cross_attn[:2_5_6, :]
										snake_case_					:		int 					=					in_proj_bias_cross_attn[:2_5_6]
										snake_case_					:		Dict 					=					in_proj_weight_cross_attn[2_5_6:5_1_2, :]
										snake_case_					:		Any 					=					in_proj_bias_cross_attn[2_5_6:5_1_2]
										snake_case_					:		Tuple 					=					in_proj_weight_cross_attn[-2_5_6:, :]
										snake_case_					:		int 					=					in_proj_bias_cross_attn[-2_5_6:]







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		str 					=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
					snake_case_					:		int 					=					Image.open(requests.get(SCREAMING_SNAKE_CASE__				,    stream=SCREAMING_SNAKE_CASE__			).raw			)

					return im







@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]=None				,    SCREAMING_SNAKE_CASE__ :  List[Any]=False			):



					"""simple docstring"""





					snake_case_					:		Any 					=					get_detr_config(SCREAMING_SNAKE_CASE__			)

					# load original model from torch hub
					snake_case_					:		str 					=					{
					    """detr-resnet-50""": """detr_resnet50""",
					    """detr-resnet-101""": """detr_resnet101""",
					}
					logger.info(f'Converting model {model_name}...'			)
					snake_case_					:		List[str] 					=					torch.hub.load("""facebookresearch/detr"""				,    model_name_to_original_name[model_name]				,    pretrained=SCREAMING_SNAKE_CASE__			).eval()
					snake_case_					:		Dict 					=					detr.state_dict()
					# rename keys
					for src, dest in create_rename_keys(SCREAMING_SNAKE_CASE__			):
										if is_panoptic:
															snake_case_					:		Union[str, Any] 					=					"""detr.""" + src
										rename_key(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					# query, key and value matrices need special treatment
					read_in_q_k_v(SCREAMING_SNAKE_CASE__				,    is_panoptic=SCREAMING_SNAKE_CASE__			)
					# 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_					:		Union[str, Any] 					=					"""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_					:		Optional[int] 					=					state_dict.pop(SCREAMING_SNAKE_CASE__			)
																				snake_case_					:		Optional[Any] 					=					val
															elif "class_labels_classifier" in key or "bbox_predictor" in key:
																				snake_case_					:		int 					=					state_dict.pop(SCREAMING_SNAKE_CASE__			)
																				snake_case_					:		Union[str, Any] 					=					val
															elif key.startswith("""bbox_attention"""			) or key.startswith("""mask_head"""			):
																				continue
															else:
																				snake_case_					:		Any 					=					state_dict.pop(SCREAMING_SNAKE_CASE__			)
																				snake_case_					:		Optional[Any] 					=					val
										else:
															if not key.startswith("""class_labels_classifier"""			) and not key.startswith("""bbox_predictor"""			):
																				snake_case_					:		Dict 					=					state_dict.pop(SCREAMING_SNAKE_CASE__			)
																				snake_case_					:		Dict 					=					val

    # finally, create HuggingFace model and load state dict
					snake_case_					:		Optional[Any] 					=					DetrForSegmentation(SCREAMING_SNAKE_CASE__			) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE__			)
					model.load_state_dict(SCREAMING_SNAKE_CASE__			)
					model.eval()

					# verify our conversion on an image
					snake_case_					:		int 					=					"""coco_panoptic""" if is_panoptic else """coco_detection"""
					snake_case_					:		List[Any] 					=					DetrImageProcessor(format=SCREAMING_SNAKE_CASE__			)

					snake_case_					:		int 					=					processor(images=prepare_img()				,    return_tensors="""pt"""			)
					snake_case_					:		Optional[Any] 					=					encoding["""pixel_values"""]

					snake_case_					:		Optional[Any] 					=					detr(SCREAMING_SNAKE_CASE__			)
					snake_case_					:		Optional[Any] 					=					model(SCREAMING_SNAKE_CASE__			)

					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(SCREAMING_SNAKE_CASE__			).mkdir(exist_ok=SCREAMING_SNAKE_CASE__			)
										model.save_pretrained(SCREAMING_SNAKE_CASE__			)
										processor.save_pretrained(SCREAMING_SNAKE_CASE__			)

					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__":
			a_			   =					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.''')
			a_			   =					parser.parse_args()
			convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_			   =					logging.get_logger(__name__)

a_			   =					{
    '''facebook/dpr-ctx_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-ctx_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'''
    ),
}







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Optional[int]							       =		"""dpr"""







						def __init__(self   ,		lowercase__=3_05_22   ,		lowercase__=7_68   ,		lowercase__=12   ,		lowercase__=12   ,		lowercase__=30_72   ,		lowercase__="gelu"   ,		lowercase__=0.1   ,		lowercase__=0.1   ,		lowercase__=5_12   ,		lowercase__=2   ,		lowercase__=0.02   ,		lowercase__=1e-12   ,		lowercase__=0   ,		lowercase__="absolute"   ,		lowercase__ = 0   ,		**lowercase__   ,		):
											super().__init__(pad_token_id=lowercase__   ,		**lowercase__       )

											snake_case_					:		List[Any] 					=					vocab_size
											snake_case_					:		List[str] 					=					hidden_size
											snake_case_					:		Tuple 					=					num_hidden_layers
											snake_case_					:		List[Any] 					=					num_attention_heads
											snake_case_					:		int 					=					hidden_act
											snake_case_					:		Dict 					=					intermediate_size
											snake_case_					:		int 					=					hidden_dropout_prob
											snake_case_					:		Any 					=					attention_probs_dropout_prob
											snake_case_					:		str 					=					max_position_embeddings
											snake_case_					:		List[str] 					=					type_vocab_size
											snake_case_					:		List[str] 					=					initializer_range
											snake_case_					:		Optional[int] 					=					layer_norm_eps
											snake_case_					:		Union[str, Any] 					=					projection_dim
											snake_case_					:		str 					=					position_embedding_type




