


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()
A			:						Union[str, Any]        =  logging.get_logger(__name__)






def 					UpperCamelCase   (      __magic_name__	:				List[str]	)  ->    int:
		"""simple docstring"""




		if "resnet-50" in model_name:
				lowercase__	     =						ResNetConfig.from_pretrained("""microsoft/resnet-50"""	)
		elif "resnet-101" in model_name:
				lowercase__	     =						ResNetConfig.from_pretrained("""microsoft/resnet-101"""	)
		else:
				raise ValueError("""Model name should include either resnet50 or resnet101"""	)

		lowercase__	     =						DetrConfig(use_timm_backbone=__magic_name__       ,       backbone_config=__magic_name__	)

		# set label attributes
		lowercase__	     =						"""panoptic""" in model_name
		if is_panoptic:
				lowercase__	     =						250
		else:
				lowercase__	     =						91
				lowercase__	     =						"""huggingface/label-files"""
				lowercase__	     =						"""coco-detection-id2label.json"""
				lowercase__	     =						json.load(open(hf_hub_download(__magic_name__       ,       __magic_name__       ,       repo_type="""dataset"""	)       ,       """r"""	)	)
				lowercase__	     =						{int(__magic_name__	): v for k, v in idalabel.items()}
				lowercase__	     =						idalabel
				lowercase__	     =						{v: k for k, v in idalabel.items()}

		return config, is_panoptic






def 					UpperCamelCase   (      __magic_name__	:				Dict	)  ->    Dict:
		"""simple docstring"""




		lowercase__	     =						[]

		# 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 					UpperCamelCase   (      __magic_name__	:				Dict       ,       __magic_name__	:				Dict       ,       __magic_name__	:				List[str]	)  ->    Dict:
		"""simple docstring"""




		lowercase__	     =						state_dict.pop(__magic_name__	)
		lowercase__	     =						val






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]       ,       __magic_name__	:				List[Any]=False	)  ->    Union[str, Any]:
		"""simple docstring"""




		lowercase__	     =						""""""
		if is_panoptic:
				lowercase__	     =						"""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)
				lowercase__	     =						state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''	)
				lowercase__	     =						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
				lowercase__	     =						in_proj_weight[:256, :]
				lowercase__	     =						in_proj_bias[:256]
				lowercase__	     =						in_proj_weight[256:512, :]
				lowercase__	     =						in_proj_bias[256:512]
				lowercase__	     =						in_proj_weight[-256:, :]
				lowercase__	     =						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
				lowercase__	     =						state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''	)
				lowercase__	     =						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
				lowercase__	     =						in_proj_weight[:256, :]
				lowercase__	     =						in_proj_bias[:256]
				lowercase__	     =						in_proj_weight[256:512, :]
				lowercase__	     =						in_proj_bias[256:512]
				lowercase__	     =						in_proj_weight[-256:, :]
				lowercase__	     =						in_proj_bias[-256:]
				# read in weights + bias of input projection layer of cross-attention
				lowercase__	     =						state_dict.pop(
				    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''	)
				lowercase__	     =						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
				lowercase__	     =						in_proj_weight_cross_attn[:256, :]
				lowercase__	     =						in_proj_bias_cross_attn[:256]
				lowercase__	     =						in_proj_weight_cross_attn[256:512, :]
				lowercase__	     =						in_proj_bias_cross_attn[256:512]
				lowercase__	     =						in_proj_weight_cross_attn[-256:, :]
				lowercase__	     =						in_proj_bias_cross_attn[-256:]






def 					UpperCamelCase   (      )  ->    List[Any]:
		"""simple docstring"""




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

		return im






@torch.no_grad()
def 					UpperCamelCase   (      __magic_name__	:				List[str]       ,       __magic_name__	:				Optional[Any]=None       ,       __magic_name__	:				List[str]=False	)  ->    Dict:
		"""simple docstring"""




		lowercase__     ,      lowercase__	     =						get_detr_config(__magic_name__	)

		# load original model from torch hub
		lowercase__	     =						{
		    """detr-resnet-50""": """detr_resnet50""",
		    """detr-resnet-101""": """detr_resnet101""",
		}
		logger.info(f'''Converting model {model_name}...'''	)
		lowercase__	     =						torch.hub.load("""facebookresearch/detr"""       ,       model_name_to_original_name[model_name]       ,       pretrained=__magic_name__	).eval()
		lowercase__	     =						detr.state_dict()
		# rename keys
		for src, dest in create_rename_keys(__magic_name__	):
				if is_panoptic:
						lowercase__	     =						"""detr.""" + src
				rename_key(__magic_name__       ,       __magic_name__       ,       __magic_name__	)
		# query, key and value matrices need special treatment
		read_in_q_k_v(__magic_name__       ,       is_panoptic=__magic_name__	)
		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
		lowercase__	     =						"""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"""	)
						):
								lowercase__	     =						state_dict.pop(__magic_name__	)
								lowercase__	     =						val
						elif "class_labels_classifier" in key or "bbox_predictor" in key:
								lowercase__	     =						state_dict.pop(__magic_name__	)
								lowercase__	     =						val
						elif key.startswith("""bbox_attention"""	) or key.startswith("""mask_head"""	):
								continue
						else:
								lowercase__	     =						state_dict.pop(__magic_name__	)
								lowercase__	     =						val
				else:
						if not key.startswith("""class_labels_classifier"""	) and not key.startswith("""bbox_predictor"""	):
								lowercase__	     =						state_dict.pop(__magic_name__	)
								lowercase__	     =						val

    # finally, create HuggingFace model and load state dict
		lowercase__	     =						DetrForSegmentation(__magic_name__	) if is_panoptic else DetrForObjectDetection(__magic_name__	)
		model.load_state_dict(__magic_name__	)
		model.eval()

		# verify our conversion on an image
		lowercase__	     =						"""coco_panoptic""" if is_panoptic else """coco_detection"""
		lowercase__	     =						DetrImageProcessor(format=__magic_name__	)

		lowercase__	     =						processor(images=prepare_img()       ,       return_tensors="""pt"""	)
		lowercase__	     =						encoding["""pixel_values"""]

		lowercase__	     =						detr(__magic_name__	)
		lowercase__	     =						model(__magic_name__	)

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

		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__":
	A			:						Any        =  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.')
	A			:						List[Any]        =  parser.parse_args()
	convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





from __future__ import annotations






def 					UpperCamelCase   (      __magic_name__	:				list[int]	)  ->    list[int]:  # This function is recursive
		"""simple docstring"""




		lowercase__	     =						len(__magic_name__	)
		# If the array contains only one element, we return it (it's the stop condition of
		# recursion)
		if array_length <= 1:
				return array
				# Else
		lowercase__	     =						array[0]
		lowercase__	     =						False
		lowercase__	     =						1
		lowercase__	     =						[]
		while not is_found and i < array_length:
				if array[i] < pivot:
						lowercase__	     =						True
						lowercase__	     =						[element for element in array[i:] if element >= array[i]]
						lowercase__	     =						longest_subsequence(__magic_name__	)
						if len(__magic_name__	) > len(__magic_name__	):
								lowercase__	     =						temp_array
				else:
						i += 1

		lowercase__	     =						[element for element in array[1:] if element >= pivot]
		lowercase__	     =						[pivot, *longest_subsequence(__magic_name__	)]
		if len(__magic_name__	) > len(__magic_name__	):
				return temp_array
		else:
				return longest_subseq


if __name__ == "__main__":
	import doctest

	doctest.testmod()

