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()
_UpperCAmelCase            =		logging.get_logger(__name__)




def     UpperCamelCase						(	__lowercase						:			Dict     ):
			'''simple docstring'''




			if "resnet-50" in model_name:
						A_		:    Any         =				ResNetConfig.from_pretrained('microsoft/resnet-50'     )
			elif "resnet-101" in model_name:
						A_		:    str         =				ResNetConfig.from_pretrained('microsoft/resnet-101'     )
			else:
						raise ValueError('Model name should include either resnet50 or resnet101'     )

			A_		:    Union[str, Any]         =				DetrConfig(use_timm_backbone=__lowercase						,backbone_config=__lowercase     )

			# set label attributes
			A_		:    Tuple         =				"""panoptic""" in model_name
			if is_panoptic:
						A_		:    List[Any]         =				2_50
			else:
						A_		:    Tuple         =				91
						A_		:    str         =				"""huggingface/label-files"""
						A_		:    Dict         =				"""coco-detection-id2label.json"""
						A_		:    str         =				json.load(open(hf_hub_download(__lowercase						,__lowercase						,repo_type='dataset'     )						,'r'     )     )
						A_		:    Optional[Any]         =				{int(__lowercase     ): v for k, v in idalabel.items()}
						A_		:    Optional[Any]         =				idalabel
						A_		:    Dict         =				{v: k for k, v in idalabel.items()}

			return config, is_panoptic




def     UpperCamelCase						(	__lowercase						:			Union[str, Any]     ):
			'''simple docstring'''




			A_		:    Dict         =				[]

			# 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						(	__lowercase						:			Union[str, Any]						,__lowercase						:			Any						,__lowercase						:			List[str]     ):
			'''simple docstring'''




			A_		:    Union[str, Any]         =				state_dict.pop(__lowercase     )
			A_		:    Tuple         =				val




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			Optional[int]=False     ):
			'''simple docstring'''




			A_		:    Optional[int]         =				""""""
			if is_panoptic:
						A_		:    Tuple         =				"""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_		:    List[str]         =				state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''     )
						A_		:    Tuple         =				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_		:    int         =				in_proj_weight[:2_56, :]
						A_		:    Optional[int]         =				in_proj_bias[:2_56]
						A_		:    Optional[Any]         =				in_proj_weight[2_56:5_12, :]
						A_		:    Any         =				in_proj_bias[2_56:5_12]
						A_		:    List[Any]         =				in_proj_weight[-2_56:, :]
						A_		:    Optional[Any]         =				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
						A_		:    Union[str, Any]         =				state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''     )
						A_		:    List[str]         =				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_		:    str         =				in_proj_weight[:2_56, :]
						A_		:    Optional[int]         =				in_proj_bias[:2_56]
						A_		:    List[Any]         =				in_proj_weight[2_56:5_12, :]
						A_		:    List[Any]         =				in_proj_bias[2_56:5_12]
						A_		:    str         =				in_proj_weight[-2_56:, :]
						A_		:    List[Any]         =				in_proj_bias[-2_56:]
						# read in weights + bias of input projection layer of cross-attention
						A_		:    List[str]         =				state_dict.pop(
						    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''     )
						A_		:    str         =				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_		:    Optional[int]         =				in_proj_weight_cross_attn[:2_56, :]
						A_		:    Dict         =				in_proj_bias_cross_attn[:2_56]
						A_		:    int         =				in_proj_weight_cross_attn[2_56:5_12, :]
						A_		:    Tuple         =				in_proj_bias_cross_attn[2_56:5_12]
						A_		:    List[Any]         =				in_proj_weight_cross_attn[-2_56:, :]
						A_		:    Tuple         =				in_proj_bias_cross_attn[-2_56:]




def     UpperCamelCase						(	):
			'''simple docstring'''




			A_		:    Optional[int]         =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
			A_		:    Dict         =				Image.open(requests.get(__lowercase						,stream=__lowercase     ).raw     )

			return im




@torch.no_grad()
def     UpperCamelCase						(	__lowercase						:			Optional[int]						,__lowercase						:			str=None						,__lowercase						:			int=False     ):
			'''simple docstring'''




			A_		:    Any         =				get_detr_config(__lowercase     )

			# load original model from torch hub
			A_		:    Dict         =				{
			    """detr-resnet-50""": """detr_resnet50""",
			    """detr-resnet-101""": """detr_resnet101""",
			}
			logger.info(f'''Converting model {model_name}...'''     )
			A_		:    int         =				torch.hub.load('facebookresearch/detr'						,model_name_to_original_name[model_name]						,pretrained=__lowercase     ).eval()
			A_		:    Union[str, Any]         =				detr.state_dict()
			# rename keys
			for src, dest in create_rename_keys(__lowercase     ):
						if is_panoptic:
									A_		:    List[str]         =				"""detr.""" + src
						rename_key(__lowercase						,__lowercase						,__lowercase     )
			# query, key and value matrices need special treatment
			read_in_q_k_v(__lowercase						,is_panoptic=__lowercase     )
			# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
			A_		:    Dict         =				"""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_		:    Optional[Any]         =				state_dict.pop(__lowercase     )
												A_		:    Optional[Any]         =				val
									elif "class_labels_classifier" in key or "bbox_predictor" in key:
												A_		:    Optional[int]         =				state_dict.pop(__lowercase     )
												A_		:    Optional[int]         =				val
									elif key.startswith('bbox_attention'     ) or key.startswith('mask_head'     ):
												continue
									else:
												A_		:    int         =				state_dict.pop(__lowercase     )
												A_		:    List[Any]         =				val
						else:
									if not key.startswith('class_labels_classifier'     ) and not key.startswith('bbox_predictor'     ):
												A_		:    int         =				state_dict.pop(__lowercase     )
												A_		:    int         =				val

												# finally, create HuggingFace model and load state dict
			A_		:    Dict         =				DetrForSegmentation(__lowercase     ) if is_panoptic else DetrForObjectDetection(__lowercase     )
			model.load_state_dict(__lowercase     )
			model.eval()

			# verify our conversion on an image
			A_		:    Union[str, Any]         =				"""coco_panoptic""" if is_panoptic else """coco_detection"""
			A_		:    Any         =				DetrImageProcessor(format=__lowercase     )

			A_		:    Any         =				processor(images=prepare_img()						,return_tensors='pt'     )
			A_		:    Union[str, Any]         =				encoding["""pixel_values"""]

			A_		:    Optional[int]         =				detr(__lowercase     )
			A_		:    Any         =				model(__lowercase     )

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

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







from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase            =		{
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase            =		[
	    """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """AutoformerForPrediction""",
	    """AutoformerModel""",
	    """AutoformerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_autoformer import (
	    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    AutoformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_autoformer import (
		    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AutoformerForPrediction,
		    AutoformerModel,
		    AutoformerPreTrainedModel,
		)

else:
	import sys

	_UpperCAmelCase            =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






