


from manim import *


class        __lowerCamelCase      (				lowerCamelCase_ ):


				"""simple docstring"""







				def        lowerCAmelCase__			(				self     :    Dict					):
								_lowerCAmelCase  						=Rectangle(height=0.5	,							width=0.5					)
								_lowerCAmelCase  						=Rectangle(height=0.46	,							width=0.46					).set_stroke(width=0					)
								_lowerCAmelCase  						=Rectangle(height=0.25	,							width=0.25					)

								_lowerCAmelCase  						=[mem.copy() for i in range(6					)]
								_lowerCAmelCase  						=[mem.copy() for i in range(6					)]
								_lowerCAmelCase  						=VGroup(*UpperCAmelCase_					).arrange(UpperCAmelCase_	,							buff=0					)
								_lowerCAmelCase  						=VGroup(*UpperCAmelCase_					).arrange(UpperCAmelCase_	,							buff=0					)
								_lowerCAmelCase  						=VGroup(UpperCAmelCase_	,							UpperCAmelCase_					).arrange(UpperCAmelCase_	,							buff=0					)
								_lowerCAmelCase  						=Text("""CPU"""	,							font_size=24					)
								_lowerCAmelCase  						=Group(UpperCAmelCase_	,							UpperCAmelCase_					).arrange(UpperCAmelCase_	,							buff=0.5	,							aligned_edge=UpperCAmelCase_					)
								cpu.move_to([-2.5, -0.5, 0]					)
								self.add(UpperCAmelCase_					)

								_lowerCAmelCase  						=[mem.copy() for i in range(4					)]
								_lowerCAmelCase  						=VGroup(*UpperCAmelCase_					).arrange(UpperCAmelCase_	,							buff=0					)
								_lowerCAmelCase  						=Text("""GPU"""	,							font_size=24					)
								_lowerCAmelCase  						=Group(UpperCAmelCase_	,							UpperCAmelCase_					).arrange(UpperCAmelCase_	,							buff=0.5	,							aligned_edge=UpperCAmelCase_					)
								gpu.move_to([-1, -1, 0]					)
								self.add(UpperCAmelCase_					)

								_lowerCAmelCase  						=[mem.copy() for i in range(6					)]
								_lowerCAmelCase  						=VGroup(*UpperCAmelCase_					).arrange(UpperCAmelCase_	,							buff=0					)

								_lowerCAmelCase  						=Text("""Model"""	,							font_size=24					)
								_lowerCAmelCase  						=Group(UpperCAmelCase_	,							UpperCAmelCase_					).arrange(UpperCAmelCase_	,							buff=0.5	,							aligned_edge=UpperCAmelCase_					)
								model.move_to([3, -1.0, 0]					)
								self.add(UpperCAmelCase_					)

								_lowerCAmelCase  						=[]
								_lowerCAmelCase  						=[]

								for i, rect in enumerate(UpperCAmelCase_					):
												rect.set_stroke(UpperCAmelCase_					)

												_lowerCAmelCase  						=Rectangle(height=0.46 / 4	,							width=0.46 / 3					).set_stroke(width=0.0					).set_fill(UpperCAmelCase_	,							opacity=0.7					)

												if i == 0:
																cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT					)	,							buff=0.02	,							direction=UpperCAmelCase_					)
																cpu_target.set_x(cpu_target.get_x() + 0.1					)
												elif i == 3:
																cpu_target.next_to(model_cpu_arr[0]	,							direction=UpperCAmelCase_	,							buff=0.0					)
												else:
																cpu_target.next_to(model_cpu_arr[i - 1]	,							direction=UpperCAmelCase_	,							buff=0.0					)
												self.add(UpperCAmelCase_					)
												model_cpu_arr.append(UpperCAmelCase_					)

								self.add(*UpperCAmelCase_	,							*UpperCAmelCase_					)

								_lowerCAmelCase  						=[meta_mem.copy() for i in range(6					)]
								_lowerCAmelCase  						=[meta_mem.copy() for i in range(6					)]
								_lowerCAmelCase  						=VGroup(*UpperCAmelCase_					).arrange(UpperCAmelCase_	,							buff=0					)
								_lowerCAmelCase  						=VGroup(*UpperCAmelCase_					).arrange(UpperCAmelCase_	,							buff=0					)
								_lowerCAmelCase  						=VGroup(UpperCAmelCase_	,							UpperCAmelCase_					).arrange(UpperCAmelCase_	,							buff=0					)
								_lowerCAmelCase  						=Text("""Disk"""	,							font_size=24					)
								_lowerCAmelCase  						=Group(UpperCAmelCase_	,							UpperCAmelCase_					).arrange(UpperCAmelCase_	,							buff=0.5	,							aligned_edge=UpperCAmelCase_					)
								disk.move_to([-4.0, -1.25, 0]					)
								self.add(UpperCAmelCase_	,							UpperCAmelCase_					)

								_lowerCAmelCase  						=[]

								for i in range(6					):
												_lowerCAmelCase  						=fill.copy().set_fill(UpperCAmelCase_	,							opacity=0.8					)
												target.move_to(disk_left_col_base[i]					).scale(0.5					)
												cpu_disk_arr.append(UpperCAmelCase_					)

								self.add(*UpperCAmelCase_					)

								_lowerCAmelCase  						=Square(side_length=2.2					)
								key.move_to([-5, 2, 0]					)

								_lowerCAmelCase  						=MarkupText(
								    F"<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model"	,							font_size=18	,							)

								key_text.move_to([-5, 2.4, 0]					)

								self.add(UpperCAmelCase_	,							UpperCAmelCase_					)

								_lowerCAmelCase  						=MarkupText(
								    F"<span fgcolor=\'{BLUE}\'>●</span> Checkpoint"	,							font_size=18	,							)

								blue_text.next_to(UpperCAmelCase_	,							DOWN * 2.4	,							aligned_edge=key_text.get_left()					)
								self.add(UpperCAmelCase_					)

								_lowerCAmelCase  						=MarkupText(F"The offloaded weights are all sent to the CPU."	,							font_size=24					)
								step_a.move_to([2, 2, 0]					)

								self.play(Write(UpperCAmelCase_	,							run_time=3					)					)

								for i in range(6					):
												_lowerCAmelCase  						=cpu_disk_arr[i]
												_lowerCAmelCase  						=rect.copy().set_fill(UpperCAmelCase_	,							opacity=0.8					).scale(2.0					)
												cpa.generate_target()
												cpa.target.move_to(model_base[i]					)

												if i == 0:
																rect.set_fill(UpperCAmelCase_	,							opacity=0.8					)
																rect.generate_target()
																rect.target.move_to(cpu_left_col_base[0]					).scale(2.0					)

																self.remove(
																    *UpperCAmelCase_	,							*UpperCAmelCase_	,							)

												else:
																rect.generate_target()
																rect.target.move_to(cpu_left_col_base[i]					).scale(2.0					)
												self.play(MoveToTarget(UpperCAmelCase_					)	,							MoveToTarget(UpperCAmelCase_					)	,							model_base[i].animate.set_stroke(UpperCAmelCase_					)					)
								self.play(FadeOut(UpperCAmelCase_					)					)

								_lowerCAmelCase  						=MarkupText(
								    F"Finally, hooks are added to each weight in the model\nto transfer the weights from CPU to GPU\n\t\tand back when needed."	,							font_size=24	,							)
								step_a.move_to([2, 2, 0]					)

								self.play(Write(UpperCAmelCase_	,							run_time=3					)					)

								_lowerCAmelCase  						=[]
								_lowerCAmelCase  						=[]
								for i in range(6					):
												_lowerCAmelCase  						=Arrow(start=UpperCAmelCase_	,							end=UpperCAmelCase_	,							color=UpperCAmelCase_	,							buff=0.5					)
												a.next_to(model_base[i].get_left()	,							UpperCAmelCase_	,							buff=0.2					)
												arrows.append(UpperCAmelCase_					)
												animations.append(Write(UpperCAmelCase_					)					)
								self.play(*UpperCAmelCase_					)
								self.wait()




"""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()
a__			:						Optional[int]							=			logging.get_logger(__name__)





def 					__UpperCamelCase (     a__:			str      )					->      List[str]:




					"""simple docstring"""



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

					return orig_key





def 					__UpperCamelCase (     a__:			Union[str, Any]      )					->      Tuple:




					"""simple docstring"""



					for i in range(6      ):
										# read in weights + bias of input projection layer of self-attention
										__lowercase        =      state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''      )
										__lowercase        =      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
										__lowercase        =      in_proj_weight[:2_56, :]
										__lowercase        =      in_proj_bias[:2_56]
										__lowercase        =      in_proj_weight[2_56:5_12, :]
										__lowercase        =      in_proj_bias[2_56:5_12]
										__lowercase        =      in_proj_weight[-2_56:, :]
										__lowercase        =      in_proj_bias[-2_56:]





def 					__UpperCamelCase (     )					->      str:




					"""simple docstring"""



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

					return im





@torch.no_grad()
def 					__UpperCamelCase (     a__:			List[Any]			,						a__:			Union[str, Any]			,						a__:			Optional[int]			,						a__:			Optional[int]			,						a__:			List[Any]			,						a__:			Optional[Any]			,						a__:			str			,						)					->      List[str]:




					"""simple docstring"""



					__lowercase        =      DeformableDetrConfig()
					# set config attributes
					if single_scale:
										__lowercase        =      1
					__lowercase        =      dilation
					__lowercase        =      with_box_refine
					__lowercase        =      two_stage
					# set labels
					__lowercase        =      91
					__lowercase        =      '''huggingface/label-files'''
					__lowercase        =      '''coco-detection-id2label.json'''
					__lowercase        =      json.load(open(cached_download(hf_hub_url(a__			,						a__			,						repo_type='''dataset'''      )      )			,						'''r'''      )      )
					__lowercase        =      {int(a__      ): v for k, v in idalabel.items()}
					__lowercase        =      idalabel
					__lowercase        =      {v: k for k, v in idalabel.items()}

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

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

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

					# load original state dict
					__lowercase        =      torch.load(a__			,						map_location='''cpu'''      )['''model''']
					# rename keys
					for key in state_dict.copy().keys():
										__lowercase        =      state_dict.pop(a__      )
										__lowercase        =      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
					__lowercase        =      '''model.'''
					for key in state_dict.copy().keys():
										if not key.startswith('''class_embed'''      ) and not key.startswith('''bbox_embed'''      ):
															__lowercase        =      state_dict.pop(a__      )
															__lowercase        =      val
    # finally, create HuggingFace model and load state dict
					__lowercase        =      DeformableDetrForObjectDetection(a__      )
					model.load_state_dict(a__      )
					model.eval()

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

					__lowercase        =      torch.tensor(
					    [[-9.66_45, -4.34_49, -5.87_05], [-9.70_35, -3.85_04, -5.07_24], [-10.56_34, -5.33_79, -7.51_16]]      )
					__lowercase        =      torch.tensor([[0.86_93, 0.22_89, 0.24_92], [0.31_50, 0.54_89, 0.58_45], [0.55_63, 0.75_80, 0.85_18]]      )

					if single_scale:
										__lowercase        =      torch.tensor(
										    [[-9.90_51, -4.25_41, -6.48_52], [-9.69_47, -4.08_54, -6.80_33], [-10.06_65, -5.84_70, -7.70_03]]      )
										__lowercase        =      torch.tensor([[0.72_92, 0.49_91, 0.55_32], [0.79_59, 0.24_26, 0.42_36], [0.75_82, 0.35_18, 0.44_51]]      )

					if single_scale and dilation:
										__lowercase        =      torch.tensor(
										    [[-8.96_52, -4.10_74, -5.66_35], [-9.05_96, -4.94_47, -6.60_75], [-10.11_78, -4.52_75, -6.26_71]]      )
										__lowercase        =      torch.tensor([[0.76_65, 0.41_30, 0.47_69], [0.83_64, 0.18_41, 0.33_91], [0.62_61, 0.38_95, 0.79_78]]      )

					if with_box_refine:
										__lowercase        =      torch.tensor(
										    [[-8.88_95, -5.41_87, -6.81_53], [-8.47_06, -6.16_68, -7.61_84], [-9.00_42, -5.53_59, -6.91_41]]      )
										__lowercase        =      torch.tensor([[0.78_28, 0.22_08, 0.43_23], [0.08_92, 0.59_96, 0.13_19], [0.55_24, 0.63_89, 0.89_14]]      )

					if with_box_refine and two_stage:
										__lowercase        =      torch.tensor(
										    [[-6.71_08, -4.32_13, -6.37_77], [-8.90_14, -6.17_99, -6.72_40], [-6.93_15, -4.47_35, -6.22_98]]      )
										__lowercase        =      torch.tensor([[0.25_83, 0.54_99, 0.46_83], [0.76_52, 0.90_68, 0.48_82], [0.54_90, 0.27_63, 0.05_64]]      )

					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:
										__lowercase        =      '''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__":
							a__			:						str							=			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."""
							)
							a__			:						Union[str, Any]							=			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,
							)


