


'''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			:		Any =			logging.get_logger(__name__)

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






def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Any  ):
					__a    : Tuple      					=						OrderedDict()
					for key, value in state_dict.items():
										if "backbone.0.body" in key:
															__a    : List[str]      					=						key.replace('backbone.0.body'		,   'backbone.conv_encoder.model'  )
															__a    : Union[str, Any]      					=						value
										else:
															__a    : Optional[Any]      					=						value

					return new_state_dict






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






def 						lowerCamelCase							():
					__a    : str      					=						'http://images.cocodataset.org/val2017/000000039769.jpg'
					__a    : Optional[Any]      					=						Image.open(requests.get(_SCREAMING_SNAKE_CASE		,   stream=_SCREAMING_SNAKE_CASE  ).raw  )

					return im






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

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

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

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

					# load original model from torch hub
					__a    : List[Any]      					=						torch.hub.load('facebookresearch/detr'		,   _SCREAMING_SNAKE_CASE		,   pretrained=_SCREAMING_SNAKE_CASE  ).eval()
					__a    : Tuple      					=						detr.state_dict()
					# rename keys
					for src, dest in rename_keys:
										if is_panoptic:
															__a    : Dict      					=						'detr.' + src
										rename_key(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE  )
					__a    : int      					=						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
					__a    : 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'  )
															):
																				__a    : int      					=						state_dict.pop(_SCREAMING_SNAKE_CASE  )
																				__a    : Tuple      					=						val
															elif "class_labels_classifier" in key or "bbox_predictor" in key:
																				__a    : Any      					=						state_dict.pop(_SCREAMING_SNAKE_CASE  )
																				__a    : Union[str, Any]      					=						val
															elif key.startswith('bbox_attention'  ) or key.startswith('mask_head'  ):
																				continue
															else:
																				__a    : Dict      					=						state_dict.pop(_SCREAMING_SNAKE_CASE  )
																				__a    : int      					=						val
										else:
															if not key.startswith('class_labels_classifier'  ) and not key.startswith('bbox_predictor'  ):
																				__a    : int      					=						state_dict.pop(_SCREAMING_SNAKE_CASE  )
																				__a    : Optional[Any]      					=						val
    # finally, create HuggingFace model and load state dict
					__a    : Union[str, Any]      					=						DetrForSegmentation(_SCREAMING_SNAKE_CASE  ) if is_panoptic else DetrForObjectDetection(_SCREAMING_SNAKE_CASE  )
					model.load_state_dict(_SCREAMING_SNAKE_CASE  )
					model.eval()
					# verify our conversion
					__a    : Union[str, Any]      					=						detr(_SCREAMING_SNAKE_CASE  )
					__a    : int      					=						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			:		str =			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			:		Union[str, Any] =			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_torch_available


__lowercase			:		Any =			{'configuration_visual_bert': ['VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VisualBertConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase			:		Optional[int] =			[
	    'VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'VisualBertForMultipleChoice',
	    'VisualBertForPreTraining',
	    'VisualBertForQuestionAnswering',
	    'VisualBertForRegionToPhraseAlignment',
	    'VisualBertForVisualReasoning',
	    'VisualBertLayer',
	    'VisualBertModel',
	    'VisualBertPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_visual_bert import (
		    VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    VisualBertForMultipleChoice,
		    VisualBertForPreTraining,
		    VisualBertForQuestionAnswering,
		    VisualBertForRegionToPhraseAlignment,
		    VisualBertForVisualReasoning,
		    VisualBertLayer,
		    VisualBertModel,
		    VisualBertPreTrainedModel,
		)


else:
	import sys

	__lowercase			:		Tuple =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



