




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

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCAmelCase						:							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      __magic_name__							(					A						:   List[Any],    A						:   str,    A						:   Union[str, Any]  ):






			'''simple docstring'''







			a       =     state_dict.pop(A  )
			a       =     val




def      __magic_name__							(					A						:   Optional[int]  ):






			'''simple docstring'''







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

			return new_state_dict




def      __magic_name__							(					A						:   Optional[Any],    A						:   Union[str, Any]=False  ):






			'''simple docstring'''







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




def      __magic_name__							(					):






			'''simple docstring'''







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

			return im




@torch.no_grad()
def      __magic_name__							(					A						:   List[str],    A						:   Dict  ):






			'''simple docstring'''







			a       =     DetrConfig()
			# set backbone and dilation attributes
			if "resnet101" in model_name:
						a       =     "resnet101"
			if "dc5" in model_name:
						a       =     True
			a       =     "panoptic" in model_name
			if is_panoptic:
						a       =     250
			else:
						a       =     91
						a       =     "huggingface/label-files"
						a       =     "coco-detection-id2label.json"
						a       =     json.load(open(hf_hub_download(A,    A,    repo_type="dataset"  ),    "r"  )  )
						a       =     {int(A  ): v for k, v in idalabel.items()}
						a       =     idalabel
						a       =     {v: k for k, v in idalabel.items()}

			# load image processor
			a       =     "coco_panoptic" if is_panoptic else "coco_detection"
			a       =     DetrImageProcessor(format=A  )

			# prepare image
			a       =     prepare_img()
			a       =     image_processor(images=A,    return_tensors="pt"  )
			a       =     encoding["pixel_values"]

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

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


if __name__ == "__main__":
		__lowerCAmelCase						:							List[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.'
		)
		__lowerCAmelCase						:							Optional[Any]			     =						parser.parse_args()
		convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)












import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import SwinaSRImageProcessor
		from transformers.image_transforms import get_image_size




class        snake_case__				(unittest.TestCase  ):
				"""simple docstring"""



				def __init__( self							:	Union[str, Any]   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	List[str]=7   , __lowerCamelCase							:	int=3   , __lowerCamelCase							:	Any=18   , __lowerCamelCase							:	Optional[int]=30   , __lowerCamelCase							:	Union[str, Any]=4_00   , __lowerCamelCase							:	Optional[int]=True   , __lowerCamelCase							:	List[str]=1 / 2_55   , __lowerCamelCase							:	Any=True   , __lowerCamelCase							:	Optional[int]=8   , )   ->	Optional[Any]:
							a       =     parent
							a       =     batch_size
							a       =     num_channels
							a       =     image_size
							a       =     min_resolution
							a       =     max_resolution
							a       =     do_rescale
							a       =     rescale_factor
							a       =     do_pad
							a       =     pad_size





				def 				__UpperCAmelCase				( self							:	List[Any]				)   ->	Optional[int]:
							return {
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							    "pad_size": self.pad_size,
							}





				def 				__UpperCAmelCase				( self							:	List[Any]   , __lowerCamelCase							:	Optional[int]=False   , __lowerCamelCase							:	Any=False   , __lowerCamelCase							:	List[Any]=False				)   ->	List[Any]:
							assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

							if equal_resolution:
										a       =     []
										for i in range(self.batch_size				):
													image_inputs.append(
													    np.random.randint(
													        2_55   , size=(self.num_channels, self.max_resolution, self.max_resolution)   , dtype=np.uinta				)				)
							else:
										a       =     []
										for i in range(self.batch_size				):
													a       ,						a       =     np.random.choice(np.arange(self.min_resolution   , self.max_resolution				)   , 2				)
													image_inputs.append(np.random.randint(2_55   , size=(self.num_channels, width, height)   , dtype=np.uinta				)				)

							if not numpify and not torchify:
										# PIL expects the channel dimension as last dimension
										a       =     [Image.fromarray(np.moveaxis(__lowerCamelCase   , 0   , -1				)				) for x in image_inputs]

							if torchify:
										a       =     [torch.from_numpy(__lowerCamelCase				) for x in image_inputs]

							return image_inputs




@require_torch
@require_vision
class        snake_case__				(_UpperCamelCase       ,			unittest.TestCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Optional[Any]									=   SwinaSRImageProcessor if is_vision_available() else None





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Tuple:
							a       =     SwinaSRImageProcessingTester(self				)





				@property
				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	List[Any]:
							return self.image_processor_tester.prepare_image_processor_dict()





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Tuple:
							a       =     self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(__lowerCamelCase   , "do_rescale"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "rescale_factor"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "do_pad"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "pad_size"				)				)





				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	Union[str, Any]:
							pass





				def 				__UpperCAmelCase				( self							:	int   , __lowerCamelCase							:	int				)   ->	Dict:
							a       ,						a       =     get_image_size(__lowerCamelCase				)
							a       =     self.image_processor_tester.pad_size

							a       =     (old_height // size + 1) * size - old_height
							a       =     (old_width // size + 1) * size - old_width
							return old_height + pad_height, old_width + pad_width





				def 				__UpperCAmelCase				( self							:	Any				)   ->	Dict:
							# Initialize image_processor
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							a       =     self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase				)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase   , Image.Image				)

							# Test not batched input
							a       =     image_processor(image_inputs[0]   , return_tensors="pt"				).pixel_values
							a       ,						a       =     self.calculate_expected_size(np.array(image_inputs[0]				)				)
							self.assertEqual(
							    encoded_images.shape   , (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   , )





				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	Union[str, Any]:
							# Initialize image_processor
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							a       =     self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase   , numpify=__lowerCamelCase				)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase   , np.ndarray				)

							# Test not batched input
							a       =     image_processor(image_inputs[0]   , return_tensors="pt"				).pixel_values
							a       ,						a       =     self.calculate_expected_size(image_inputs[0]				)
							self.assertEqual(
							    encoded_images.shape   , (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   , )





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	int:
							# Initialize image_processor
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							a       =     self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase   , torchify=__lowerCamelCase				)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase   , torch.Tensor				)

							# Test not batched input
							a       =     image_processor(image_inputs[0]   , return_tensors="pt"				).pixel_values
							a       ,						a       =     self.calculate_expected_size(image_inputs[0]				)
							self.assertEqual(
							    encoded_images.shape   , (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   , )






