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


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

# here we list all keys to be renamed (original name on the left, our name on the right)
__A 		= []
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 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase )       ->				Union[str, Any]:
	_lowerCAmelCase			=state_dict.pop(__UpperCamelCase )
	_lowerCAmelCase			=val



def 					_lowerCamelCase(__UpperCamelCase )       ->				str:
	_lowerCAmelCase			=OrderedDict()
	for key, value in state_dict.items():
		if "backbone.0.body" in key:
			_lowerCAmelCase			=key.replace("""backbone.0.body"""					,				"""backbone.conv_encoder.model""" )
			_lowerCAmelCase			=value
		else:
			_lowerCAmelCase			=value

	return new_state_dict



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase=False )       ->				List[Any]:
	_lowerCAmelCase			=""""""
	if is_panoptic:
		_lowerCAmelCase			="""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			=state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
		_lowerCAmelCase			=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			=in_proj_weight[:256, :]
		_lowerCAmelCase			=in_proj_bias[:256]
		_lowerCAmelCase			=in_proj_weight[256:512, :]
		_lowerCAmelCase			=in_proj_bias[256:512]
		_lowerCAmelCase			=in_proj_weight[-256:, :]
		_lowerCAmelCase			=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
		_lowerCAmelCase			=state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
		_lowerCAmelCase			=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
		_lowerCAmelCase			=in_proj_weight[:256, :]
		_lowerCAmelCase			=in_proj_bias[:256]
		_lowerCAmelCase			=in_proj_weight[256:512, :]
		_lowerCAmelCase			=in_proj_bias[256:512]
		_lowerCAmelCase			=in_proj_weight[-256:, :]
		_lowerCAmelCase			=in_proj_bias[-256:]
		# read in weights + bias of input projection layer of cross-attention
		_lowerCAmelCase			=state_dict.pop(
		    F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
		_lowerCAmelCase			=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
		_lowerCAmelCase			=in_proj_weight_cross_attn[:256, :]
		_lowerCAmelCase			=in_proj_bias_cross_attn[:256]
		_lowerCAmelCase			=in_proj_weight_cross_attn[256:512, :]
		_lowerCAmelCase			=in_proj_bias_cross_attn[256:512]
		_lowerCAmelCase			=in_proj_weight_cross_attn[-256:, :]
		_lowerCAmelCase			=in_proj_bias_cross_attn[-256:]



def 					_lowerCamelCase()       ->				List[str]:
	_lowerCAmelCase			="""http://images.cocodataset.org/val2017/000000039769.jpg"""
	_lowerCAmelCase			=Image.open(requests.get(__UpperCamelCase					,				stream=__UpperCamelCase ).raw )

	return im



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

	# load image processor
	_lowerCAmelCase			="""coco_panoptic""" if is_panoptic else """coco_detection"""
	_lowerCAmelCase			=DetrImageProcessor(format=__UpperCamelCase )

	# prepare image
	_lowerCAmelCase			=prepare_img()
	_lowerCAmelCase			=image_processor(images=__UpperCamelCase					,				return_tensors="""pt""" )
	_lowerCAmelCase			=encoding["""pixel_values"""]

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

	# load original model from torch hub
	_lowerCAmelCase			=torch.hub.load("""facebookresearch/detr"""					,				__UpperCamelCase					,				pretrained=__UpperCamelCase ).eval()
	_lowerCAmelCase			=detr.state_dict()
	# rename keys
	for src, dest in rename_keys:
		if is_panoptic:
			_lowerCAmelCase			="""detr.""" + src
		rename_key(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase )
	_lowerCAmelCase			=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			="""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""" )
			):
				_lowerCAmelCase			=state_dict.pop(__UpperCamelCase )
				_lowerCAmelCase			=val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				_lowerCAmelCase			=state_dict.pop(__UpperCamelCase )
				_lowerCAmelCase			=val
			elif key.startswith("""bbox_attention""" ) or key.startswith("""mask_head""" ):
				continue
			else:
				_lowerCAmelCase			=state_dict.pop(__UpperCamelCase )
				_lowerCAmelCase			=val
		else:
			if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
				_lowerCAmelCase			=state_dict.pop(__UpperCamelCase )
				_lowerCAmelCase			=val
    # finally, create HuggingFace model and load state dict
	_lowerCAmelCase			=DetrForSegmentation(__UpperCamelCase ) if is_panoptic else DetrForObjectDetection(__UpperCamelCase )
	model.load_state_dict(__UpperCamelCase )
	model.eval()
	# verify our conversion
	_lowerCAmelCase			=detr(__UpperCamelCase )
	_lowerCAmelCase			=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 		= 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.'
			)
			__A 		= parser.parse_args()
			convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

"""simple docstring"""


from typing import Dict, Optional, Tuple

import torch



def 					_lowerCamelCase(__UpperCamelCase )       ->				torch.Tensor:
	_lowerCAmelCase			=boundaries[1] - boundaries[0]
	_lowerCAmelCase			=boundaries + step / 2
	_lowerCAmelCase			=torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1 )]					,				dim=0 )
	return bin_centers



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				)       ->				Tuple[torch.Tensor, torch.Tensor]:
	_lowerCAmelCase			=_calculate_bin_centers(__UpperCamelCase )
	return (
	    torch.sum(aligned_distance_error_probs * bin_centers					,				dim=-1 ),
	    bin_centers[-1],
	)



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase = 31					,				__UpperCamelCase = 64					,				**__UpperCamelCase					,				)       ->				Dict[str, torch.Tensor]:
	_lowerCAmelCase			=torch.linspace(0					,				__UpperCamelCase					,				steps=(no_bins - 1)					,				device=logits.device )

	_lowerCAmelCase			=torch.nn.functional.softmax(__UpperCamelCase					,				dim=-1 )
	_lowerCAmelCase		,							_lowerCAmelCase			=_calculate_expected_aligned_error(
	    alignment_confidence_breaks=__UpperCamelCase					,				aligned_distance_error_probs=__UpperCamelCase					,				)

	return {
	    "aligned_confidence_probs": aligned_confidence_probs,
	    "predicted_aligned_error": predicted_aligned_error,
	    "max_predicted_aligned_error": max_predicted_aligned_error,
	}



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase = None					,				__UpperCamelCase = 31					,				__UpperCamelCase = 64					,				__UpperCamelCase = 1E-8					,				**__UpperCamelCase					,				)       ->				torch.Tensor:
	if residue_weights is None:
		_lowerCAmelCase			=logits.new_ones(logits.shape[-2] )

	_lowerCAmelCase			=torch.linspace(0					,				__UpperCamelCase					,				steps=(no_bins - 1)					,				device=logits.device )

	_lowerCAmelCase			=_calculate_bin_centers(__UpperCamelCase )
	torch.sum(__UpperCamelCase )
	_lowerCAmelCase			=logits.shape[-2]
	_lowerCAmelCase			=max(__UpperCamelCase					,				19 )

	_lowerCAmelCase			=1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

	_lowerCAmelCase			=torch.nn.functional.softmax(__UpperCamelCase					,				dim=-1 )

	_lowerCAmelCase			=1.0 / (1 + (bin_centers**2) / (da**2))
	_lowerCAmelCase			=torch.sum(probs * tm_per_bin					,				dim=-1 )

	_lowerCAmelCase			=residue_weights / (eps + residue_weights.sum())
	_lowerCAmelCase			=torch.sum(predicted_tm_term * normed_residue_mask					,				dim=-1 )

	_lowerCAmelCase			=per_alignment * residue_weights

	_lowerCAmelCase			=(weighted == torch.max(__UpperCamelCase )).nonzero()[0]
	return per_alignment[tuple(__UpperCamelCase )]



