





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 						UpperCamelCase__					( unittest.TestCase  ):



     '''simple docstring'''

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






     def 							SCREAMING_SNAKE_CASE__    (				self		:       str		,lowerCamelCase__		:       Union[str, Any]=0.9999			)	->						Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		UNetaDConditionModel.from_pretrained(self.model_id		,subfolder="""unet"""			)
           SCREAMING_SNAKE_CASE						=		unet.to(__A			)
           SCREAMING_SNAKE_CASE						=		EMAModel(unet.parameters()		,decay=__A		,model_cls=__A		,model_config=unet.config			)
           return unet, ema_unet






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Union[str, Any]			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		torch.randn(
               self.batch_size		,self.num_in_channels		,self.latent_height		,self.latent_width		,generator=self.generator			).to(__A			)
           SCREAMING_SNAKE_CASE						=		torch.randint(0		,1000		,size=(self.batch_size,)		,generator=self.generator			).to(__A			)
           SCREAMING_SNAKE_CASE						=		torch.randn(
               self.batch_size		,self.prompt_length		,self.text_encoder_hidden_dim		,generator=self.generator			).to(__A			)
           return noisy_latents, timesteps, encoder_hidden_states






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[int]		,lowerCamelCase__		:       Tuple			)	->						List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		{}
           for k, param in unet.state_dict().items():
                 SCREAMING_SNAKE_CASE						=		torch.randn_like(__A			) + (param * torch.randn_like(__A			))
                 updated_state_dict.update({k: updated_param}			)
           unet.load_state_dict(__A			)
           return unet






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[Any]			)	->						Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		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 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		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()			)
           SCREAMING_SNAKE_CASE						=		list(unet.parameters()			)
           for s_param, param in zip(ema_unet.shadow_params		,__A			):
                 assert torch.allclose(__A		,__A			)

           # 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		,__A			):
                 assert torch.allclose(__A		,__A			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		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.
           SCREAMING_SNAKE_CASE						=		self.simulate_backprop(__A			)

           # 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.
           SCREAMING_SNAKE_CASE						=		list(unet_pseudo_updated_step_one.parameters()			)
           for s_param, param in zip(ema_unet.shadow_params		,__A			):
                 assert ~torch.allclose(__A		,__A			)

           # 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		,__A			):
                 assert ~torch.allclose(__A		,__A			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]			)	->						str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		self.get_models()

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

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

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






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Any			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		self.get_models(decay=0.0			)
           SCREAMING_SNAKE_CASE						=		self.simulate_backprop(__A			)
           ema_unet.step(unet_step_one.parameters()			)
           SCREAMING_SNAKE_CASE						=		ema_unet.shadow_params

           SCREAMING_SNAKE_CASE						=		self.simulate_backprop(__A			)
           ema_unet.step(unet_step_two.parameters()			)
           SCREAMING_SNAKE_CASE						=		ema_unet.shadow_params

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






     @skip_mps
     def 							SCREAMING_SNAKE_CASE__    (				self		:       str			)	->						int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		self.get_models()
           SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		self.get_dummy_inputs()

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

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

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



'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_  		=			logging.get_logger(__name__)



def 				UpperCamelCase__      (      a__   ):


						'''simple docstring'''






						if "backbone.0.body" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('backbone.0.body'      ,   'backbone.conv_encoder.model'   )
						if "transformer" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('transformer.'      ,   ''   )
						if "norm1" in orig_key:
												if "encoder" in orig_key:
																		_lowerCAmelCase					    =orig_key.replace('norm1'      ,   'self_attn_layer_norm'   )
												else:
																		_lowerCAmelCase					    =orig_key.replace('norm1'      ,   'encoder_attn_layer_norm'   )
						if "norm2" in orig_key:
												if "encoder" in orig_key:
																		_lowerCAmelCase					    =orig_key.replace('norm2'      ,   'final_layer_norm'   )
												else:
																		_lowerCAmelCase					    =orig_key.replace('norm2'      ,   'self_attn_layer_norm'   )
						if "norm3" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('norm3'      ,   'final_layer_norm'   )
						if "linear1" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('linear1'      ,   'fc1'   )
						if "linear2" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('linear2'      ,   'fc2'   )
						if "query_embed" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('query_embed'      ,   'query_position_embeddings'   )
						if "cross_attn" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('cross_attn'      ,   'encoder_attn'   )

						return orig_key



def 				UpperCamelCase__      (      a__   ):


						'''simple docstring'''






						for i in range(6   ):
												# read in weights + bias of input projection layer of self-attention
												_lowerCAmelCase					    =state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''   )
												_lowerCAmelCase					    =state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_bias'''   )
												# next, add query, keys and values (in that order) to the state dict
												_lowerCAmelCase					    =in_proj_weight[:2_5_6, :]
												_lowerCAmelCase					    =in_proj_bias[:2_5_6]
												_lowerCAmelCase					    =in_proj_weight[2_5_6:5_1_2, :]
												_lowerCAmelCase					    =in_proj_bias[2_5_6:5_1_2]
												_lowerCAmelCase					    =in_proj_weight[-2_5_6:, :]
												_lowerCAmelCase					    =in_proj_bias[-2_5_6:]



def 				UpperCamelCase__      (      ):


						'''simple docstring'''






						_lowerCAmelCase					    ='http://images.cocodataset.org/val2017/000000039769.jpg'
						_lowerCAmelCase					    =Image.open(requests.get(a__      ,   stream=a__   ).raw   )

						return im



@torch.no_grad()
def 				UpperCamelCase__      (      a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   ):


						'''simple docstring'''






						_lowerCAmelCase					    =DeformableDetrConfig()
						# set config attributes
						if single_scale:
												_lowerCAmelCase					    =1
						_lowerCAmelCase					    =dilation
						_lowerCAmelCase					    =with_box_refine
						_lowerCAmelCase					    =two_stage
						# set labels
						_lowerCAmelCase					    =9_1
						_lowerCAmelCase					    ='huggingface/label-files'
						_lowerCAmelCase					    ='coco-detection-id2label.json'
						_lowerCAmelCase					    =json.load(open(cached_download(hf_hub_url(a__      ,   a__      ,   repo_type='dataset'   )   )      ,   'r'   )   )
						_lowerCAmelCase					    ={int(a__   ): v for k, v in idalabel.items()}
						_lowerCAmelCase					    =idalabel
						_lowerCAmelCase					    ={v: k for k, v in idalabel.items()}

						# load image processor
						_lowerCAmelCase					    =DeformableDetrImageProcessor(format='coco_detection'   )

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

						logger.info('Converting model...'   )

						# load original state dict
						_lowerCAmelCase					    =torch.load(a__      ,   map_location='cpu'   )['model']
						# rename keys
						for key in state_dict.copy().keys():
												_lowerCAmelCase					    =state_dict.pop(a__   )
												_lowerCAmelCase					    =val
						# query, key and value matrices need special treatment
						read_in_q_k_v(a__   )
						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
						_lowerCAmelCase					    ='model.'
						for key in state_dict.copy().keys():
												if not key.startswith('class_embed'   ) and not key.startswith('bbox_embed'   ):
																		_lowerCAmelCase					    =state_dict.pop(a__   )
																		_lowerCAmelCase					    =val
    # finally, create HuggingFace model and load state dict
						_lowerCAmelCase					    =DeformableDetrForObjectDetection(a__   )
						model.load_state_dict(a__   )
						model.eval()

						_lowerCAmelCase					    ='cuda' if torch.cuda.is_available() else 'cpu'
						model.to(a__   )
						# verify our conversion
						_lowerCAmelCase					    =model(pixel_values.to(a__   )   )

						_lowerCAmelCase					    =torch.tensor(
						    [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]]   )
						_lowerCAmelCase					    =torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]]   )

						if single_scale:
												_lowerCAmelCase					    =torch.tensor(
												    [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]]   )
												_lowerCAmelCase					    =torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]]   )

						if single_scale and dilation:
												_lowerCAmelCase					    =torch.tensor(
												    [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]]   )
												_lowerCAmelCase					    =torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]]   )

						if with_box_refine:
												_lowerCAmelCase					    =torch.tensor(
												    [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]]   )
												_lowerCAmelCase					    =torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]]   )

						if with_box_refine and two_stage:
												_lowerCAmelCase					    =torch.tensor(
												    [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]]   )
												_lowerCAmelCase					    =torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]]   )

						print('Logits:'      ,   outputs.logits[0, :3, :3]   )

						assert torch.allclose(outputs.logits[0, :3, :3]      ,   expected_logits.to(a__   )      ,   atol=1E-4   )
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,   expected_boxes.to(a__   )      ,   atol=1E-4   )

						print('Everything ok!'   )

						# 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__   )

						# Push to hub
						if push_to_hub:
												_lowerCAmelCase					    ='deformable-detr'
												model_name += "-single-scale" if single_scale else ""
												model_name += "-dc5" if dilation else ""
												model_name += "-with-box-refine" if with_box_refine else ""
												model_name += "-two-stage" if two_stage else ""
												print('Pushing model to hub...'   )
												model.push_to_hub(repo_path_or_name=a__      ,   organization='nielsr'      ,   commit_message='Add model'   )


if __name__ == "__main__":
							lowercase_  		=			argparse.ArgumentParser()

							parser.add_argument(
							    '''--checkpoint_path''',
							    type=str,
							    default='''/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth''',
							    help='''Path to Pytorch checkpoint (.pth file) you\'d like to convert.''',
							)
							parser.add_argument('''--single_scale''', action='''store_true''', help='''Whether to set config.num_features_levels = 1.''')
							parser.add_argument('''--dilation''', action='''store_true''', help='''Whether to set config.dilation=True.''')
							parser.add_argument('''--with_box_refine''', action='''store_true''', help='''Whether to set config.with_box_refine=True.''')
							parser.add_argument('''--two_stage''', action='''store_true''', help='''Whether to set config.two_stage=True.''')
							parser.add_argument(
							    '''--pytorch_dump_folder_path''',
							    default=None,
							    type=str,
							    required=True,
							    help='''Path to the folder to output PyTorch model.''',
							)
							parser.add_argument(
							    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
							)
							lowercase_  		=			parser.parse_args()
							convert_deformable_detr_checkpoint(
							    args.checkpoint_path,
							    args.single_scale,
							    args.dilation,
							    args.with_box_refine,
							    args.two_stage,
							    args.pytorch_dump_folder_path,
							    args.push_to_hub,
							)





