
'''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()
__lowercase	=    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowercase	=    []
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 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       int    							=state_dict.pop(_SCREAMING_SNAKE_CASE  )
		lowerCAmelCase_   :       Union[str, Any]    							=val







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       Dict    							=OrderedDict()
		for key, value in state_dict.items():
				if "backbone.0.body" in key:
						lowerCAmelCase_   :       List[Any]    							=key.replace('''backbone.0.body'''  ,					'''backbone.conv_encoder.model'''  )
						lowerCAmelCase_   :       int    							=value
				else:
						lowerCAmelCase_   :       List[str]    							=value

		return new_state_dict







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE=False  ):
		lowerCAmelCase_   :       List[str]    							=""""""
		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_   :       Union[str, Any]    							=state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'  )
				lowerCAmelCase_   :       Union[str, 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_   :       Dict    							=in_proj_weight[:256, :]
				lowerCAmelCase_   :       List[Any]    							=in_proj_bias[:256]
				lowerCAmelCase_   :       List[str]    							=in_proj_weight[256:512, :]
				lowerCAmelCase_   :       Dict    							=in_proj_bias[256:512]
				lowerCAmelCase_   :       Any    							=in_proj_weight[-256:, :]
				lowerCAmelCase_   :       List[Any]    							=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_   :       List[Any]    							=state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'  )
				lowerCAmelCase_   :       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
				lowerCAmelCase_   :       Optional[Any]    							=in_proj_weight[:256, :]
				lowerCAmelCase_   :       str    							=in_proj_bias[:256]
				lowerCAmelCase_   :       Tuple    							=in_proj_weight[256:512, :]
				lowerCAmelCase_   :       Tuple    							=in_proj_bias[256:512]
				lowerCAmelCase_   :       Dict    							=in_proj_weight[-256:, :]
				lowerCAmelCase_   :       str    							=in_proj_bias[-256:]
				# read in weights + bias of input projection layer of cross-attention
				lowerCAmelCase_   :       str    							=state_dict.pop(
				    f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'  )
				lowerCAmelCase_   :       Dict    							=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_   :       Dict    							=in_proj_weight_cross_attn[:256, :]
				lowerCAmelCase_   :       int    							=in_proj_bias_cross_attn[:256]
				lowerCAmelCase_   :       List[str]    							=in_proj_weight_cross_attn[256:512, :]
				lowerCAmelCase_   :       Dict    							=in_proj_bias_cross_attn[256:512]
				lowerCAmelCase_   :       int    							=in_proj_weight_cross_attn[-256:, :]
				lowerCAmelCase_   :       str    							=in_proj_bias_cross_attn[-256:]







def 		SCREAMING_SNAKE_CASE__					(							):
		lowerCAmelCase_   :       str    							="""http://images.cocodataset.org/val2017/000000039769.jpg"""
		lowerCAmelCase_   :       Optional[int]    							=Image.open(requests.get(_SCREAMING_SNAKE_CASE  ,					stream=_SCREAMING_SNAKE_CASE  ).raw  )

		return im







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

		# load image processor
		lowerCAmelCase_   :       List[Any]    							="""coco_panoptic""" if is_panoptic else """coco_detection"""
		lowerCAmelCase_   :       Union[str, Any]    							=DetrImageProcessor(format=_SCREAMING_SNAKE_CASE  )

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

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

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


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














'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_A      :     Optional[Any]       	={
    '''configuration_realm''': ['''REALM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RealmConfig'''],
    '''tokenization_realm''': ['''RealmTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     str       	=['''RealmTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     List[Any]       	=[
			    '''REALM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''RealmEmbedder''',
			    '''RealmForOpenQA''',
			    '''RealmKnowledgeAugEncoder''',
			    '''RealmPreTrainedModel''',
			    '''RealmReader''',
			    '''RealmScorer''',
			    '''load_tf_weights_in_realm''',
			]
			_A      :     Union[str, Any]       	=['''RealmRetriever''']


if TYPE_CHECKING:
			from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
			from .tokenization_realm import RealmTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_realm import RealmTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_realm import (
						    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RealmEmbedder,
						    RealmForOpenQA,
						    RealmKnowledgeAugEncoder,
						    RealmPreTrainedModel,
						    RealmReader,
						    RealmScorer,
						    load_tf_weights_in_realm,
						)
						from .retrieval_realm import RealmRetriever


else:
			import sys

			_A      :     int       	=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

