


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()
UpperCamelCase			:					Tuple         =							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCamelCase			:					List[Any]         =							[]
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_	(					__snake_case      :    List[Any]						,      __snake_case      :    Any						,      __snake_case      :    int  )->   Optional[Any]:




						'''simple docstring'''
						__magic_name__					=      state_dict.pop(lowercase__  )
						__magic_name__					=      val
def 				UpperCamelCase_	(					__snake_case      :    str  )->   List[Any]:




						'''simple docstring'''
						__magic_name__					=      OrderedDict()
						for key, value in state_dict.items():
												if "backbone.0.body" in key:
																		__magic_name__					=      key.replace('backbone.0.body'						,      'backbone.conv_encoder.model'  )
																		__magic_name__					=      value
												else:
																		__magic_name__					=      value

						return new_state_dict
def 				UpperCamelCase_	(					__snake_case      :    Optional[int]						,      __snake_case      :    Tuple=False  )->   Any:




						'''simple docstring'''
						__magic_name__					=      ''
						if is_panoptic:
												__magic_name__					=      '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)
												__magic_name__					=      state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''  )
												__magic_name__					=      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
												__magic_name__					=      in_proj_weight[:2_56, :]
												__magic_name__					=      in_proj_bias[:2_56]
												__magic_name__					=      in_proj_weight[2_56:5_12, :]
												__magic_name__					=      in_proj_bias[2_56:5_12]
												__magic_name__					=      in_proj_weight[-2_56:, :]
												__magic_name__					=      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
												__magic_name__					=      state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''  )
												__magic_name__					=      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
												__magic_name__					=      in_proj_weight[:2_56, :]
												__magic_name__					=      in_proj_bias[:2_56]
												__magic_name__					=      in_proj_weight[2_56:5_12, :]
												__magic_name__					=      in_proj_bias[2_56:5_12]
												__magic_name__					=      in_proj_weight[-2_56:, :]
												__magic_name__					=      in_proj_bias[-2_56:]
												# read in weights + bias of input projection layer of cross-attention
												__magic_name__					=      state_dict.pop(
												    F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''  )
												__magic_name__					=      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
												__magic_name__					=      in_proj_weight_cross_attn[:2_56, :]
												__magic_name__					=      in_proj_bias_cross_attn[:2_56]
												__magic_name__					=      in_proj_weight_cross_attn[2_56:5_12, :]
												__magic_name__					=      in_proj_bias_cross_attn[2_56:5_12]
												__magic_name__					=      in_proj_weight_cross_attn[-2_56:, :]
												__magic_name__					=      in_proj_bias_cross_attn[-2_56:]
def 				UpperCamelCase_	(					)->   Dict:




						'''simple docstring'''
						__magic_name__					=      'http://images.cocodataset.org/val2017/000000039769.jpg'
						__magic_name__					=      Image.open(requests.get(lowercase__						,      stream=lowercase__  ).raw  )

						return im
@torch.no_grad()
def 				UpperCamelCase_	(					__snake_case      :    Union[str, Any]						,      __snake_case      :    Dict  )->   Union[str, Any]:




						'''simple docstring'''
						__magic_name__					=      DetrConfig()
						# set backbone and dilation attributes
						if "resnet101" in model_name:
												__magic_name__					=      'resnet101'
						if "dc5" in model_name:
												__magic_name__					=      True
						__magic_name__					=      'panoptic' in model_name
						if is_panoptic:
												__magic_name__					=      2_50
						else:
												__magic_name__					=      91
												__magic_name__					=      'huggingface/label-files'
												__magic_name__					=      'coco-detection-id2label.json'
												__magic_name__					=      json.load(open(hf_hub_download(lowercase__						,      lowercase__						,      repo_type='dataset'  )						,      'r'  )  )
												__magic_name__					=      {int(lowercase__  ): v for k, v in idalabel.items()}
												__magic_name__					=      idalabel
												__magic_name__					=      {v: k for k, v in idalabel.items()}

						# load image processor
						__magic_name__					=      'coco_panoptic' if is_panoptic else 'coco_detection'
						__magic_name__					=      DetrImageProcessor(format=lowercase__  )

						# prepare image
						__magic_name__					=      prepare_img()
						__magic_name__					=      image_processor(images=lowercase__						,      return_tensors='pt'  )
						__magic_name__					=      encoding['pixel_values']

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

						# load original model from torch hub
						__magic_name__					=      torch.hub.load('facebookresearch/detr'						,      lowercase__						,      pretrained=lowercase__  ).eval()
						__magic_name__					=      detr.state_dict()
						# rename keys
						for src, dest in rename_keys:
												if is_panoptic:
																		__magic_name__					=      'detr.' + src
												rename_key(lowercase__						,      lowercase__						,      lowercase__  )
						__magic_name__					=      rename_backbone_keys(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
						__magic_name__					=      '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'  )
																		):
																								__magic_name__					=      state_dict.pop(lowercase__  )
																								__magic_name__					=      val
																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
																								__magic_name__					=      state_dict.pop(lowercase__  )
																								__magic_name__					=      val
																		elif key.startswith('bbox_attention'  ) or key.startswith('mask_head'  ):
																								continue
																		else:
																								__magic_name__					=      state_dict.pop(lowercase__  )
																								__magic_name__					=      val
												else:
																		if not key.startswith('class_labels_classifier'  ) and not key.startswith('bbox_predictor'  ):
																								__magic_name__					=      state_dict.pop(lowercase__  )
																								__magic_name__					=      val
    # finally, create HuggingFace model and load state dict
						__magic_name__					=      DetrForSegmentation(lowercase__  ) if is_panoptic else DetrForObjectDetection(lowercase__  )
						model.load_state_dict(lowercase__  )
						model.eval()
						# verify our conversion
						__magic_name__					=      detr(lowercase__  )
						__magic_name__					=      model(lowercase__  )
						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(lowercase__  ).mkdir(exist_ok=lowercase__  )
						model.save_pretrained(lowercase__  )
						image_processor.save_pretrained(lowercase__  )


if __name__ == "__main__":
					UpperCamelCase			:					Tuple         =							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.'''
					)
					UpperCamelCase			:					List[str]         =							parser.parse_args()
					convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)






import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()




def    UpperCAmelCase					(  lowercase__						:					int				,					lowercase__						:					str				,					lowercase__						:					Union[str, Any]				,					lowercase__						:					Optional[int]				,					lowercase__						:					Optional[Any]	):



			'''simple docstring'''





			a__					=  BertConfig.from_pretrained(
			    """bert-large-cased"""				,					vocab_size=lowercase__				,					max_position_embeddings=512				,					is_decoder=lowercase__				,					add_cross_attention=lowercase__				,					)
			a__					=  bert_config.to_dict()
			del bert_config_dict["type_vocab_size"]
			a__					=  BertGenerationConfig(**lowercase__	)
			if is_encoder:
						a__					=  BertGenerationEncoder(lowercase__	)
			else:
						a__					=  BertGenerationDecoder(lowercase__	)
			print(f'Building PyTorch model from configuration: {config}'	)

			# Load weights from tf checkpoint
			load_tf_weights_in_bert_generation(
			    lowercase__				,					lowercase__				,					model_class="""bert"""				,					is_encoder_named_decoder=lowercase__				,					is_encoder=lowercase__				,					)

			# Save pytorch-model
			print(f'Save PyTorch model and config to {pytorch_dump_path}'	)
			model.save_pretrained(lowercase__	)


if __name__ == "__main__":
	_lowercase				:					str     					=argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--tf_hub_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
	)
	parser.add_argument(
	    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	parser.add_argument(
	    """--is_encoder_named_decoder""",
	    action="""store_true""",
	    help="""If decoder has to be renamed to encoder in PyTorch model.""",
	)
	parser.add_argument("""--is_encoder""", action="""store_true""", help="""If model is an encoder.""")
	parser.add_argument("""--vocab_size""", default=5_03_58, type=int, help="""Vocab size of model""")
	_lowercase				:					List[str]     					=parser.parse_args()
	convert_tf_checkpoint_to_pytorch(
	    args.tf_hub_path,
	    args.pytorch_dump_path,
	    args.is_encoder_named_decoder,
	    args.vocab_size,
	    is_encoder=args.is_encoder,
	)

