




'''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()
SCREAMING_SNAKE_CASE:    List[Any]											=   logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
SCREAMING_SNAKE_CASE:    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 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )->  Tuple:
		SCREAMING_SNAKE_CASE_							=       state_dict.pop(lowercase_   )
		SCREAMING_SNAKE_CASE_							=       val


def 				_a       (				lowerCAmelCase   )->  Tuple:
		SCREAMING_SNAKE_CASE_							=       OrderedDict()
		for key, value in state_dict.items():
				if "backbone.0.body" in key:
						SCREAMING_SNAKE_CASE_							=       key.replace('backbone.0.body'						,	'backbone.conv_encoder.model'   )
						SCREAMING_SNAKE_CASE_							=       value
				else:
						SCREAMING_SNAKE_CASE_							=       value

		return new_state_dict


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase=False   )->  Tuple:
		SCREAMING_SNAKE_CASE_							=       ''
		if is_panoptic:
				SCREAMING_SNAKE_CASE_							=       '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)
				SCREAMING_SNAKE_CASE_							=       state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''   )
				SCREAMING_SNAKE_CASE_							=       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
				SCREAMING_SNAKE_CASE_							=       in_proj_weight[:256, :]
				SCREAMING_SNAKE_CASE_							=       in_proj_bias[:256]
				SCREAMING_SNAKE_CASE_							=       in_proj_weight[256:512, :]
				SCREAMING_SNAKE_CASE_							=       in_proj_bias[256:512]
				SCREAMING_SNAKE_CASE_							=       in_proj_weight[-256:, :]
				SCREAMING_SNAKE_CASE_							=       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
				SCREAMING_SNAKE_CASE_							=       state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''   )
				SCREAMING_SNAKE_CASE_							=       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
				SCREAMING_SNAKE_CASE_							=       in_proj_weight[:256, :]
				SCREAMING_SNAKE_CASE_							=       in_proj_bias[:256]
				SCREAMING_SNAKE_CASE_							=       in_proj_weight[256:512, :]
				SCREAMING_SNAKE_CASE_							=       in_proj_bias[256:512]
				SCREAMING_SNAKE_CASE_							=       in_proj_weight[-256:, :]
				SCREAMING_SNAKE_CASE_							=       in_proj_bias[-256:]
				# read in weights + bias of input projection layer of cross-attention
				SCREAMING_SNAKE_CASE_							=       state_dict.pop(
				    F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''   )
				SCREAMING_SNAKE_CASE_							=       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
				SCREAMING_SNAKE_CASE_							=       in_proj_weight_cross_attn[:256, :]
				SCREAMING_SNAKE_CASE_							=       in_proj_bias_cross_attn[:256]
				SCREAMING_SNAKE_CASE_							=       in_proj_weight_cross_attn[256:512, :]
				SCREAMING_SNAKE_CASE_							=       in_proj_bias_cross_attn[256:512]
				SCREAMING_SNAKE_CASE_							=       in_proj_weight_cross_attn[-256:, :]
				SCREAMING_SNAKE_CASE_							=       in_proj_bias_cross_attn[-256:]


def 				_a       (				)->  List[Any]:
		SCREAMING_SNAKE_CASE_							=       'http://images.cocodataset.org/val2017/000000039769.jpg'
		SCREAMING_SNAKE_CASE_							=       Image.open(requests.get(lowercase_						,	stream=lowercase_   ).raw   )

		return im


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

		# load image processor
		SCREAMING_SNAKE_CASE_							=       'coco_panoptic' if is_panoptic else 'coco_detection'
		SCREAMING_SNAKE_CASE_							=       DetrImageProcessor(format=lowercase_   )

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

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

		# load original model from torch hub
		SCREAMING_SNAKE_CASE_							=       torch.hub.load('facebookresearch/detr'						,	lowercase_						,	pretrained=lowercase_   ).eval()
		SCREAMING_SNAKE_CASE_							=       detr.state_dict()
		# rename keys
		for src, dest in rename_keys:
				if is_panoptic:
						SCREAMING_SNAKE_CASE_							=       'detr.' + src
				rename_key(lowercase_						,	lowercase_						,	lowercase_   )
		SCREAMING_SNAKE_CASE_							=       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
		SCREAMING_SNAKE_CASE_							=       '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'   )
						):
								SCREAMING_SNAKE_CASE_							=       state_dict.pop(lowercase_   )
								SCREAMING_SNAKE_CASE_							=       val
						elif "class_labels_classifier" in key or "bbox_predictor" in key:
								SCREAMING_SNAKE_CASE_							=       state_dict.pop(lowercase_   )
								SCREAMING_SNAKE_CASE_							=       val
						elif key.startswith('bbox_attention'   ) or key.startswith('mask_head'   ):
								continue
						else:
								SCREAMING_SNAKE_CASE_							=       state_dict.pop(lowercase_   )
								SCREAMING_SNAKE_CASE_							=       val
				else:
						if not key.startswith('class_labels_classifier'   ) and not key.startswith('bbox_predictor'   ):
								SCREAMING_SNAKE_CASE_							=       state_dict.pop(lowercase_   )
								SCREAMING_SNAKE_CASE_							=       val
    # finally, create HuggingFace model and load state dict
		SCREAMING_SNAKE_CASE_							=       DetrForSegmentation(lowercase_   ) if is_panoptic else DetrForObjectDetection(lowercase_   )
		model.load_state_dict(lowercase_   )
		model.eval()
		# verify our conversion
		SCREAMING_SNAKE_CASE_							=       detr(lowercase_   )
		SCREAMING_SNAKE_CASE_							=       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__":
						SCREAMING_SNAKE_CASE:    int											=   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.'''
						)
						SCREAMING_SNAKE_CASE:    Union[str, Any]											=   parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





from __future__ import annotations


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  int:
		SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       0, len(lowerCAmelCase   ) - 1
		while start_ind <= end_ind:
				if array[start_ind] == search_item:
						return start_ind
				elif array[end_ind] == search_item:
						return end_ind
				else:
						start_ind += 1
						end_ind -= 1
    # returns -1 if search_item is not found in array
		return -1


if __name__ == "__main__":
						print(double_linear_search(list(range(1_0_0)), 4_0))