
"""simple docstring"""







lowerCamelCase__           =	'''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def 					__lowerCAmelCase       (_UpperCamelCase	):

 if not isinstance(UpperCAmelCase__ ,						UpperCAmelCase__	):
  __lowerCAmelCase    :		Optional[int]		    =   F"a bytes-like object is required, not '{data.__class__.__name__}'"
  raise TypeError(UpperCAmelCase__	)

 __lowerCAmelCase    :		Dict		    =   ''.join(bin(UpperCAmelCase__	)[2:].zfill(8	) for byte in data	)

 __lowerCAmelCase    :		int		    =   len(UpperCAmelCase__	) % 6 != 0

 if padding_needed:
  # The padding that will be added later
  __lowerCAmelCase    :		List[str]		    =   b'=' * ((6 - len(UpperCAmelCase__	) % 6) // 2)

  # Append binary_stream with arbitrary binary digits (0's by default) to make its
  # length a multiple of 6.
  binary_stream += "0" * (6 - len(UpperCAmelCase__	) % 6)
 else:
  __lowerCAmelCase    :		Optional[int]		    =   b''

 # Encode every 6 binary digits to their corresponding Base64 character
 return (
     "".join(
         B64_CHARSET[int(binary_stream[index : index + 6] ,						2	)]
         for index in range(0 ,						len(UpperCAmelCase__	) ,						6	)	).encode()
     + padding
 )
def 					__lowerCAmelCase       (_UpperCamelCase	):

 if not isinstance(UpperCAmelCase__ ,						UpperCAmelCase__	) and not isinstance(UpperCAmelCase__ ,						UpperCAmelCase__	):
  __lowerCAmelCase    :		List[Any]		    =   (
      'argument should be a bytes-like object or ASCII string, '
      F"not '{encoded_data.__class__.__name__}'"
  )
  raise TypeError(UpperCAmelCase__	)

 # In case encoded_data is a bytes-like object, make sure it contains only
 # ASCII characters so we convert it to a string object
 if isinstance(UpperCAmelCase__ ,						UpperCAmelCase__	):
  try:
   __lowerCAmelCase    :		Optional[Any]		    =   encoded_data.decode('utf-8'	)
  except UnicodeDecodeError:
   raise ValueError('base64 encoded data should only contain ASCII characters'	)

 __lowerCAmelCase    :		Dict		    =   encoded_data.count('='	)

 # Check if the encoded string contains non base64 characters
 if padding:
  assert all(
      char in B64_CHARSET for char in encoded_data[:-padding]	), "Invalid base64 character(s) found."
 else:
  assert all(
      char in B64_CHARSET for char in encoded_data	), "Invalid base64 character(s) found."

 # Check the padding
 assert len(UpperCAmelCase__	) % 4 == 0 and padding < 3, "Incorrect padding"

 if padding:
  # Remove padding if there is one
  __lowerCAmelCase    :		str		    =   encoded_data[:-padding]

  __lowerCAmelCase    :		List[str]		    =   ''.join(
      bin(B64_CHARSET.index(UpperCAmelCase__	)	)[2:].zfill(6	) for char in encoded_data	)[: -padding * 2]
 else:
  __lowerCAmelCase    :		Dict		    =   ''.join(
      bin(B64_CHARSET.index(UpperCAmelCase__	)	)[2:].zfill(6	) for char in encoded_data	)

 __lowerCAmelCase    :		Dict		    =   [
     int(binary_stream[index : index + 8] ,						2	)
     for index in range(0 ,						len(UpperCAmelCase__	) ,						8	)
 ]

 return bytes(UpperCAmelCase__	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

"""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()
lowerCamelCase__           =	logging.get_logger(__name__)
def 					__lowerCAmelCase       (_UpperCamelCase	):
	# initialize config
	if "resnet-50" in model_name:
		__lowerCAmelCase    :		int		    =   ResNetConfig.from_pretrained('microsoft/resnet-50'	)
	elif "resnet-101" in model_name:
		__lowerCAmelCase    :		Union[str, Any]		    =   ResNetConfig.from_pretrained('microsoft/resnet-101'	)
	else:
		raise ValueError('Model name should include either resnet50 or resnet101'	)

	__lowerCAmelCase    :		Union[str, Any]		    =   DetrConfig(use_timm_backbone=_UpperCamelCase ,						backbone_config=_UpperCamelCase	)

	# set label attributes
	__lowerCAmelCase    :		Optional[int]		    =   'panoptic' in model_name
	if is_panoptic:
		__lowerCAmelCase    :		Dict		    =   250
	else:
		__lowerCAmelCase    :		List[str]		    =   91
		__lowerCAmelCase    :		Tuple		    =   'huggingface/label-files'
		__lowerCAmelCase    :		str		    =   'coco-detection-id2label.json'
		__lowerCAmelCase    :		Optional[int]		    =   json.load(open(hf_hub_download(_UpperCamelCase ,						_UpperCamelCase ,						repo_type='dataset'	) ,						'r'	)	)
		__lowerCAmelCase    :		List[Any]		    =   {int(_UpperCamelCase	): v for k, v in idalabel.items()}
		__lowerCAmelCase    :		List[Any]		    =   idalabel
		__lowerCAmelCase    :		Dict		    =   {v: k for k, v in idalabel.items()}

	return config, is_panoptic
def 					__lowerCAmelCase       (_UpperCamelCase	):
	# here we list all keys to be renamed (original name on the left, our name on the right)
	__lowerCAmelCase    :		Tuple		    =   []

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

	return im
@torch.no_grad()
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=None ,						_UpperCamelCase=False	):
	__lowerCAmelCase    ,     __lowerCAmelCase    :		int		    =   get_detr_config(_UpperCamelCase	)

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

    # finally, create HuggingFace model and load state dict
	__lowerCAmelCase    :		Tuple		    =   DetrForSegmentation(_UpperCamelCase	) if is_panoptic else DetrForObjectDetection(_UpperCamelCase	)
	model.load_state_dict(_UpperCamelCase	)
	model.eval()

	# verify our conversion on an image
	__lowerCAmelCase    :		Optional[Any]		    =   'coco_panoptic' if is_panoptic else 'coco_detection'
	__lowerCAmelCase    :		List[str]		    =   DetrImageProcessor(format=_UpperCamelCase	)

	__lowerCAmelCase    :		Union[str, Any]		    =   processor(images=prepare_img() ,						return_tensors='pt'	)
	__lowerCAmelCase    :		Optional[int]		    =   encoding['pixel_values']

	__lowerCAmelCase    :		Optional[Any]		    =   detr(_UpperCamelCase	)
	__lowerCAmelCase    :		Union[str, Any]		    =   model(_UpperCamelCase	)

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

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