



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


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__     :						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)
SCREAMING_SNAKE_CASE__     :						List[str]	=  []
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 				a  (     UpperCamelCase_    :    List[Any]      ,					UpperCamelCase_    :    Union[str, Any]      ,					UpperCamelCase_    :    Optional[int]			)       ->	Optional[Any]:
		snake_case__	     =state_dict.pop(lowercase_			)
		snake_case__	     =val






def 				a  (     UpperCamelCase_    :    str			)       ->	Union[str, Any]:
		snake_case__	     =OrderedDict()
		for key, value in state_dict.items():
				if "backbone.0.body" in key:
						snake_case__	     =key.replace('backbone.0.body'      ,					'backbone.conv_encoder.model'			)
						snake_case__	     =value
				else:
						snake_case__	     =value

		return new_state_dict






def 				a  (     UpperCamelCase_    :    List[str]      ,					UpperCamelCase_    :    str=False			)       ->	Dict:
		snake_case__	     =''''''
		if is_panoptic:
				snake_case__	     ='''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)
				snake_case__	     =state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""			)
				snake_case__	     =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
				snake_case__	     =in_proj_weight[:256, :]
				snake_case__	     =in_proj_bias[:256]
				snake_case__	     =in_proj_weight[256:512, :]
				snake_case__	     =in_proj_bias[256:512]
				snake_case__	     =in_proj_weight[-256:, :]
				snake_case__	     =in_proj_bias[-256:]






def 				a  (     )       ->	List[str]:
		snake_case__	     ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
		snake_case__	     =Image.open(requests.get(lowercase_      ,					stream=lowercase_			).raw			)

		return im






@torch.no_grad()
def 				a  (     UpperCamelCase_    :    List[str]      ,					UpperCamelCase_    :    List[str]			)       ->	Any:
		snake_case__	     =ConditionalDetrConfig()
		# set backbone and dilation attributes
		if "resnet101" in model_name:
				snake_case__	     ='''resnet101'''
		if "dc5" in model_name:
				snake_case__	     =True
		snake_case__	     ='''panoptic''' in model_name
		if is_panoptic:
				snake_case__	     =250
		else:
				snake_case__	     =91
				snake_case__	     ='''huggingface/label-files'''
				snake_case__	     ='''coco-detection-id2label.json'''
				snake_case__	     =json.load(open(hf_hub_download(lowercase_      ,					lowercase_      ,					repo_type='dataset'			)      ,					'r'			)			)
				snake_case__	     ={int(lowercase_			): v for k, v in idalabel.items()}
				snake_case__	     =idalabel
				snake_case__	     ={v: k for k, v in idalabel.items()}

		# load image processor
		snake_case__	     ='''coco_panoptic''' if is_panoptic else '''coco_detection'''
		snake_case__	     =ConditionalDetrImageProcessor(format=lowercase_			)

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

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

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


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE__     :						int	=  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.'''
			)
			SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  parser.parse_args()
			convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		UNetaDModel
	_lowerCAmelCase     :		ScoreSdeVeScheduler


	def __init__(    self				,     lowercase__				,     lowercase__):
							super().__init__()
							self.register_modules(unet=lowercase__				,     scheduler=lowercase__)


	@torch.no_grad()
	def __call__(    self				,     lowercase__ = 1				,     lowercase__ = 2_0_0_0				,     lowercase__ = None				,     lowercase__ = "pil"				,     lowercase__ = True				,     **lowercase__				,     ):
							__UpperCAmelCase			:							int	    =	self.unet.config.sample_size
							__UpperCAmelCase			:							Optional[int]	    =	(batch_size, 3, img_size, img_size)

							__UpperCAmelCase			:							int	    =	self.unet

							__UpperCAmelCase			:							Tuple	    =	randn_tensor(lowercase__				,     generator=lowercase__) * self.scheduler.init_noise_sigma
							__UpperCAmelCase			:							List[str]	    =	sample.to(self.device)

							self.scheduler.set_timesteps(lowercase__)
							self.scheduler.set_sigmas(lowercase__)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
													__UpperCAmelCase			:							List[str]	    =	self.scheduler.sigmas[i] * torch.ones(shape[0]				,     device=self.device)

													# correction step
													for _ in range(self.scheduler.config.correct_steps):
																			__UpperCAmelCase			:							int	    =	self.unet(lowercase__				,     lowercase__).sample
																			__UpperCAmelCase			:							str	    =	self.scheduler.step_correct(lowercase__				,     lowercase__				,     generator=lowercase__).prev_sample

													# prediction step
													__UpperCAmelCase			:							Tuple	    =	model(lowercase__				,     lowercase__).sample
													__UpperCAmelCase			:							Dict	    =	self.scheduler.step_pred(lowercase__				,     lowercase__				,     lowercase__				,     generator=lowercase__)

													__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[Any]	    =	output.prev_sample, output.prev_sample_mean

							__UpperCAmelCase			:							List[str]	    =	sample_mean.clamp(0				,     1)
							__UpperCAmelCase			:							Union[str, Any]	    =	sample.cpu().permute(0				,     2				,     3				,     1).numpy()
							if output_type == "pil":
													__UpperCAmelCase			:							List[Any]	    =	self.numpy_to_pil(lowercase__)

							if not return_dict:
													return (sample,)

							return ImagePipelineOutput(images=lowercase__)

