"""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						=    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						=    []
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(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





	_lowercase			:     Any      					=				state_dict.pop(__UpperCAmelCase      )
	_lowercase			:     List[str]      					=				val


def   __lowerCAmelCase(		__UpperCAmelCase      ):






	"""simple docstring"""





	_lowercase			:     Optional[Any]      					=				OrderedDict()
	for key, value in state_dict.items():
		if "backbone.0.body" in key:
			_lowercase			:     List[Any]      					=				key.replace('backbone.0.body'  ,'backbone.conv_encoder.model'      )
			_lowercase			:     Any      					=				value
		else:
			_lowercase			:     Optional[Any]      					=				value

	return new_state_dict


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase=False      ):






	"""simple docstring"""





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


def   __lowerCAmelCase(		):






	"""simple docstring"""





	_lowercase			:     Optional[Any]      					=				'http://images.cocodataset.org/val2017/000000039769.jpg'
	_lowercase			:     List[str]      					=				Image.open(requests.get(__UpperCAmelCase  ,stream=__UpperCAmelCase      ).raw      )

	return im


@torch.no_grad()
def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





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

	# load image processor
	_lowercase			:     List[str]      					=				'coco_panoptic' if is_panoptic else 'coco_detection'
	_lowercase			:     List[str]      					=				DetrImageProcessor(format=__UpperCAmelCase      )

	# prepare image
	_lowercase			:     int      					=				prepare_img()
	_lowercase			:     Any      					=				image_processor(images=__UpperCAmelCase  ,return_tensors='pt'      )
	_lowercase			:     str      					=				encoding['pixel_values']

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

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


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









import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()




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



				SCREAMING_SNAKE_CASE_			:				Optional[int]									=   """hf-internal-testing/tiny-stable-diffusion-pipe"""
				SCREAMING_SNAKE_CASE_			:				str									=   1
				SCREAMING_SNAKE_CASE_			:				Dict									=   77
				SCREAMING_SNAKE_CASE_			:				List[str]									=   32
				SCREAMING_SNAKE_CASE_			:				List[str]									=   4
				SCREAMING_SNAKE_CASE_			:				Union[str, Any]									=   64
				SCREAMING_SNAKE_CASE_			:				int									=   torch.manual_seed(0  )





				def 				__UpperCAmelCase				( self							:	List[Any]   , __lowerCamelCase							:	List[Any]=0.9_999				)   ->	Tuple:
							a       =     UNetaDConditionModel.from_pretrained(self.model_id   , subfolder="unet"				)
							a       =     unet.to(__lowerCamelCase				)
							a       =     EMAModel(unet.parameters()   , decay=__lowerCamelCase   , model_cls=__lowerCamelCase   , model_config=unet.config				)
							return unet, ema_unet





				def 				__UpperCAmelCase				( self							:	Tuple				)   ->	Any:
							a       =     torch.randn(
							    self.batch_size   , self.num_in_channels   , self.latent_height   , self.latent_width   , generator=self.generator				).to(__lowerCamelCase				)
							a       =     torch.randint(0   , 10_00   , size=(self.batch_size,)   , generator=self.generator				).to(__lowerCamelCase				)
							a       =     torch.randn(
							    self.batch_size   , self.prompt_length   , self.text_encoder_hidden_dim   , generator=self.generator				).to(__lowerCamelCase				)
							return noisy_latents, timesteps, encoder_hidden_states





				def 				__UpperCAmelCase				( self							:	Optional[int]   , __lowerCamelCase							:	Any				)   ->	Optional[int]:
							a       =     {}
							for k, param in unet.state_dict().items():
										a       =     torch.randn_like(__lowerCamelCase				) + (param * torch.randn_like(__lowerCamelCase				))
										updated_state_dict.update({k: updated_param}				)
							unet.load_state_dict(__lowerCamelCase				)
							return unet





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	Union[str, Any]:
							a       ,						a       =     self.get_models()
							# Take the first (hypothetical) EMA step.
							ema_unet.step(unet.parameters()				)
							assert ema_unet.optimization_step == 1

							# Take two more.
							for _ in range(2				):
										ema_unet.step(unet.parameters()				)
							assert ema_unet.optimization_step == 3





				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	Optional[int]:
							a       ,						a       =     self.get_models()
							# Since the `unet` is not being updated (i.e., backprop'd)
							# there won't be any difference between the `params` of `unet`
							# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
							ema_unet.step(unet.parameters()				)
							a       =     list(unet.parameters()				)
							for s_param, param in zip(ema_unet.shadow_params   , __lowerCamelCase				):
										assert torch.allclose(__lowerCamelCase   , __lowerCamelCase				)

							# The above holds true even if we call `ema.step()` multiple times since
							# `unet` params are still not being updated.
							for _ in range(4				):
										ema_unet.step(unet.parameters()				)
							for s_param, param in zip(ema_unet.shadow_params   , __lowerCamelCase				):
										assert torch.allclose(__lowerCamelCase   , __lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Any				)   ->	Dict:
							a       ,						a       =     self.get_models()
							# Here we simulate the parameter updates for `unet`. Since there might
							# be some parameters which are initialized to zero we take extra care to
							# initialize their values to something non-zero before the multiplication.
							a       =     self.simulate_backprop(__lowerCamelCase				)

							# Take the EMA step.
							ema_unet.step(unet_pseudo_updated_step_one.parameters()				)

							# Now the EMA'd parameters won't be equal to the original model parameters.
							a       =     list(unet_pseudo_updated_step_one.parameters()				)
							for s_param, param in zip(ema_unet.shadow_params   , __lowerCamelCase				):
										assert ~torch.allclose(__lowerCamelCase   , __lowerCamelCase				)

							# Ensure this is the case when we take multiple EMA steps.
							for _ in range(4				):
										ema_unet.step(unet.parameters()				)
							for s_param, param in zip(ema_unet.shadow_params   , __lowerCamelCase				):
										assert ~torch.allclose(__lowerCamelCase   , __lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	Any:
							# If we call EMA step after a backpropagation consecutively for two times,
							# the shadow params from those two steps should be different.
							a       ,						a       =     self.get_models()

							# First backprop + EMA
							a       =     self.simulate_backprop(__lowerCamelCase				)
							ema_unet.step(unet_step_one.parameters()				)
							a       =     ema_unet.shadow_params

							# Second backprop + EMA
							a       =     self.simulate_backprop(__lowerCamelCase				)
							ema_unet.step(unet_step_two.parameters()				)
							a       =     ema_unet.shadow_params

							for step_one, step_two in zip(__lowerCamelCase   , __lowerCamelCase				):
										assert ~torch.allclose(__lowerCamelCase   , __lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Any				)   ->	List[Any]:
							# If there's no decay even if there are backprops, EMA steps
							# won't take any effect i.e., the shadow params would remain the
							# same.
							a       ,						a       =     self.get_models(decay=0.0				)
							a       =     self.simulate_backprop(__lowerCamelCase				)
							ema_unet.step(unet_step_one.parameters()				)
							a       =     ema_unet.shadow_params

							a       =     self.simulate_backprop(__lowerCamelCase				)
							ema_unet.step(unet_step_two.parameters()				)
							a       =     ema_unet.shadow_params

							for step_one, step_two in zip(__lowerCamelCase   , __lowerCamelCase				):
										assert torch.allclose(__lowerCamelCase   , __lowerCamelCase				)





				@skip_mps
				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	Dict:
							a       ,						a       =     self.get_models()
							a       ,						a       ,						a       =     self.get_dummy_inputs()

							with tempfile.TemporaryDirectory() as tmpdir:
										ema_unet.save_pretrained(__lowerCamelCase				)
										a       =     UNetaDConditionModel.from_pretrained(__lowerCamelCase   , model_cls=__lowerCamelCase				)
										a       =     loaded_unet.to(unet.device				)

							# Since no EMA step has been performed the outputs should match.
							a       =     unet(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				).sample
							a       =     loaded_unet(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				).sample

							assert torch.allclose(__lowerCamelCase   , __lowerCamelCase   , atol=1e-4				)






