




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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
__A        =    []
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}.cross_attn.out_proj.weight''',
					        f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
					    )
					)
					rename_keys.append(
					    (
					        f'''transformer.decoder.layers.{i}.cross_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'''))

					# q, k, v projections in self/cross-attention in decoder for conditional DETR
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', f'''decoder.layers.{i}.sa_qcontent_proj.weight''')
					)
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', f'''decoder.layers.{i}.sa_kcontent_proj.weight''')
					)
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', f'''decoder.layers.{i}.sa_qpos_proj.weight''')
					)
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', f'''decoder.layers.{i}.sa_kpos_proj.weight''')
					)
					rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.weight''', f'''decoder.layers.{i}.sa_v_proj.weight'''))
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', f'''decoder.layers.{i}.ca_qcontent_proj.weight''')
					)
					# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', f'''decoder.layers.{i}.ca_kcontent_proj.weight''')
					)
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', f'''decoder.layers.{i}.ca_kpos_proj.weight''')
					)
					rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.weight''', f'''decoder.layers.{i}.ca_v_proj.weight'''))
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', f'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
					)

					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', f'''decoder.layers.{i}.sa_qcontent_proj.bias''')
					)
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', f'''decoder.layers.{i}.sa_kcontent_proj.bias''')
					)
					rename_keys.append((f'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', f'''decoder.layers.{i}.sa_qpos_proj.bias'''))
					rename_keys.append((f'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', f'''decoder.layers.{i}.sa_kpos_proj.bias'''))
					rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.bias''', f'''decoder.layers.{i}.sa_v_proj.bias'''))
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', f'''decoder.layers.{i}.ca_qcontent_proj.bias''')
					)
					# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', f'''decoder.layers.{i}.ca_kcontent_proj.bias''')
					)
					rename_keys.append((f'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', f'''decoder.layers.{i}.ca_kpos_proj.bias'''))
					rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.bias''', f'''decoder.layers.{i}.ca_v_proj.bias'''))
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', f'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
					)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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"),
        ("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
        ("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
        ("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
        ("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
        ("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
        ("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
        ("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
        ("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
        ("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
        ("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
    ]
)







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      int ,     UpperCamelCase__				:      List[Any]      )     ->       Dict:





				"""simple docstring"""


				__lowerCamelCase		       =    state_dict.pop(UpperCamelCase__      )
				__lowerCamelCase		       =    val







def 			lowerCamelCase_		(       UpperCamelCase__				:      int      )     ->       Union[str, Any]:





				"""simple docstring"""


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

				return new_state_dict







def 			lowerCamelCase_		(       UpperCamelCase__				:      int ,     UpperCamelCase__				:      Dict=False      )     ->       Union[str, Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    ''
				if is_panoptic:
								__lowerCamelCase		       =    'conditional_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)
								__lowerCamelCase		       =    state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""      )
								__lowerCamelCase		       =    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
								__lowerCamelCase		       =    in_proj_weight[:256, :]
								__lowerCamelCase		       =    in_proj_bias[:256]
								__lowerCamelCase		       =    in_proj_weight[256:512, :]
								__lowerCamelCase		       =    in_proj_bias[256:512]
								__lowerCamelCase		       =    in_proj_weight[-256:, :]
								__lowerCamelCase		       =    in_proj_bias[-256:]







def 			lowerCamelCase_		(       )     ->       Dict:





				"""simple docstring"""


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

				return im







@torch.no_grad()
def 			lowerCamelCase_		(       UpperCamelCase__				:      Union[str, Any] ,     UpperCamelCase__				:      str      )     ->       List[str]:





				"""simple docstring"""


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

				# load image processor
				__lowerCamelCase		       =    'coco_panoptic' if is_panoptic else 'coco_detection'
				__lowerCamelCase		       =    ConditionalDetrImageProcessor(format=UpperCamelCase__      )

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

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

				# load original model from torch hub
				__lowerCamelCase		       =    torch.hub.load('DeppMeng/ConditionalDETR' ,     UpperCamelCase__ ,     pretrained=UpperCamelCase__      ).eval()
				__lowerCamelCase		       =    conditional_detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												__lowerCamelCase		       =    'conditional_detr.' + src
								rename_key(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
				__lowerCamelCase		       =    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
				__lowerCamelCase		       =    'conditional_detr.model.' if is_panoptic else 'model.'
				for key in state_dict.copy().keys():
								if is_panoptic:
												if (
												    key.startswith('conditional_detr'      )
												    and not key.startswith('class_labels_classifier'      )
												    and not key.startswith('bbox_predictor'      )
												):
																__lowerCamelCase		       =    state_dict.pop(UpperCamelCase__      )
																__lowerCamelCase		       =    val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																__lowerCamelCase		       =    state_dict.pop(UpperCamelCase__      )
																__lowerCamelCase		       =    val
												elif key.startswith('bbox_attention'      ) or key.startswith('mask_head'      ):
																continue
												else:
																__lowerCamelCase		       =    state_dict.pop(UpperCamelCase__      )
																__lowerCamelCase		       =    val
								else:
												if not key.startswith('class_labels_classifier'      ) and not key.startswith('bbox_predictor'      ):
																__lowerCamelCase		       =    state_dict.pop(UpperCamelCase__      )
																__lowerCamelCase		       =    val
    # finally, create HuggingFace model and load state dict
				__lowerCamelCase		       =    ConditionalDetrForSegmentation(UpperCamelCase__      ) if is_panoptic else ConditionalDetrForObjectDetection(UpperCamelCase__      )
				model.load_state_dict(UpperCamelCase__      )
				model.eval()
				model.push_to_hub(repo_id=UpperCamelCase__ ,     organization='DepuMeng' ,     commit_message='Add model'      )
				# verify our conversion
				__lowerCamelCase		       =    conditional_detr(UpperCamelCase__      )
				__lowerCamelCase		       =    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__":
					__A        =    argparse.ArgumentParser()

					parser.add_argument(
					    "--model_name",
					    default="conditional_detr_resnet50",
					    type=str,
					    help="Name of the CONDITIONAL_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."
					)
					__A        =    parser.parse_args()
					convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


@slow
@require_torch_gpu
class 				lowercase					(		unittest.TestCase   ):



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_euler'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe([prompt]				,      generator=A_				,      guidance_scale=9.0				,      num_inference_steps=20				,      output_type='np'       )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_euler'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe([prompt]				,      generator=A_				,      guidance_scale=9.0				,      num_inference_steps=20				,      output_type='np'       )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5e-1



		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_dpmpp_2m'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe(
				    [prompt]				,      generator=A_				,      guidance_scale=7.5				,      num_inference_steps=15				,      output_type='np'				,      use_karras_sigmas=A_				,      )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array(
				    [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
