

"""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()
lowerCamelCase__				:  str													=  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCamelCase__				:  int													=  []
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       UpperCamelCase					(			_lowerCAmelCase     :						List[Any],  _lowerCAmelCase     :						Optional[Any],  _lowerCAmelCase     :						Union[str, Any] )				->			Union[str, Any]:
	_UpperCAmelCase    :							int									=					state_dict.pop(_lowerCAmelCase )
	_UpperCAmelCase    :							Union[str, Any]									=					val
def       UpperCamelCase					(			_lowerCAmelCase     :						Dict )				->			Dict:
	_UpperCAmelCase    :							Dict									=					OrderedDict()
	for key, value in state_dict.items():
		if "backbone.0.body" in key:
			_UpperCAmelCase    :							Union[str, Any]									=					key.replace("""backbone.0.body""",  """backbone.conv_encoder.model""" )
			_UpperCAmelCase    :							Dict									=					value
		else:
			_UpperCAmelCase    :							List[str]									=					value

	return new_state_dict
def       UpperCamelCase					(			_lowerCAmelCase     :						List[Any],  _lowerCAmelCase     :						Dict=False )				->			List[str]:
	_UpperCAmelCase    :							int									=					""""""
	if is_panoptic:
		_UpperCAmelCase    :							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)
		_UpperCAmelCase    :							Union[str, Any]									=					state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
		_UpperCAmelCase    :							List[str]									=					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
		_UpperCAmelCase    :							Union[str, Any]									=					in_proj_weight[:256, :]
		_UpperCAmelCase    :							List[Any]									=					in_proj_bias[:256]
		_UpperCAmelCase    :							Any									=					in_proj_weight[256:512, :]
		_UpperCAmelCase    :							List[Any]									=					in_proj_bias[256:512]
		_UpperCAmelCase    :							List[Any]									=					in_proj_weight[-256:, :]
		_UpperCAmelCase    :							Tuple									=					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
		_UpperCAmelCase    :							List[Any]									=					state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
		_UpperCAmelCase    :							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
		_UpperCAmelCase    :							Optional[int]									=					in_proj_weight[:256, :]
		_UpperCAmelCase    :							Any									=					in_proj_bias[:256]
		_UpperCAmelCase    :							Any									=					in_proj_weight[256:512, :]
		_UpperCAmelCase    :							Tuple									=					in_proj_bias[256:512]
		_UpperCAmelCase    :							Any									=					in_proj_weight[-256:, :]
		_UpperCAmelCase    :							Dict									=					in_proj_bias[-256:]
		# read in weights + bias of input projection layer of cross-attention
		_UpperCAmelCase    :							str									=					state_dict.pop(
		    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
		_UpperCAmelCase    :							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
		_UpperCAmelCase    :							str									=					in_proj_weight_cross_attn[:256, :]
		_UpperCAmelCase    :							Optional[int]									=					in_proj_bias_cross_attn[:256]
		_UpperCAmelCase    :							List[str]									=					in_proj_weight_cross_attn[256:512, :]
		_UpperCAmelCase    :							str									=					in_proj_bias_cross_attn[256:512]
		_UpperCAmelCase    :							Dict									=					in_proj_weight_cross_attn[-256:, :]
		_UpperCAmelCase    :							List[Any]									=					in_proj_bias_cross_attn[-256:]
def       UpperCamelCase					(			)				->			Optional[Any]:
	_UpperCAmelCase    :							str									=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	_UpperCAmelCase    :							Optional[int]									=					Image.open(requests.get(_lowerCAmelCase,  stream=_lowerCAmelCase ).raw )

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

	# load image processor
	_UpperCAmelCase    :							List[Any]									=					"""coco_panoptic""" if is_panoptic else """coco_detection"""
	_UpperCAmelCase    :							int									=					DetrImageProcessor(format=_lowerCAmelCase )

	# prepare image
	_UpperCAmelCase    :							List[Any]									=					prepare_img()
	_UpperCAmelCase    :							List[str]									=					image_processor(images=_lowerCAmelCase,  return_tensors="""pt""" )
	_UpperCAmelCase    :							str									=					encoding["""pixel_values"""]

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

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


if __name__ == "__main__":
						lowerCamelCase__				:  Dict													=  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.'''
						)
						lowerCamelCase__				:  Dict													=  parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








"""simple docstring"""






def       UpperCamelCase					(			_lowerCAmelCase     :						int )				->			str:
	_UpperCAmelCase    :							Any									=					1
	_UpperCAmelCase    :							List[str]									=					0
	_UpperCAmelCase    :							str									=					1
	_UpperCAmelCase    :							Union[str, Any]									=					1
	_UpperCAmelCase    :							int									=					3
	_UpperCAmelCase    :							int									=					3
	_UpperCAmelCase    :							List[str]									=					limit
	_UpperCAmelCase    :							Optional[Any]									=					0

	_UpperCAmelCase    :							Any									=					""""""
	while counter != decimal + 1:
		if 4 * q + r - t < n * t:
			result += str(_lowerCAmelCase )
			if counter == 0:
				result += "."

			if decimal == counter:
				break

			counter += 1
			_UpperCAmelCase    :							str									=					10 * (r - n * t)
			_UpperCAmelCase    :							List[Any]									=					((10 * (3 * q + r)) // t) - 10 * n
			q *= 10
			_UpperCAmelCase    :							int									=					nr
		else:
			_UpperCAmelCase    :							Any									=					(2 * q + r) * l
			_UpperCAmelCase    :							str									=					(q * (7 * k) + 2 + (r * l)) // (t * l)
			q *= k
			t *= l
			l += 2
			k += 1
			_UpperCAmelCase    :							List[Any]									=					nn
			_UpperCAmelCase    :							Optional[int]									=					nr
	return result
def       UpperCamelCase					(			)				->			None:
	print(f'''{calculate_pi(50 ) = }''' )
	import doctest

	doctest.testmod()


if __name__ == "__main__":
						main()





