'''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()
__lowerCAmelCase	:       List[str]						       =	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCAmelCase	:       List[Any]						       =	[]
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__				:   str    ,					UpperCamelCase__				:   List[str]    ,					UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			state_dict.pop(UpperCamelCase__       )
	__UpperCAmelCase							=			val





def       lowerCAmelCase					(		UpperCamelCase__				:   str       ):


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

	return new_state_dict





def       lowerCAmelCase					(		UpperCamelCase__				:   Any    ,					UpperCamelCase__				:   int=False       ):


	"""simple docstring"""
	__UpperCAmelCase							=			''''''
	if is_panoptic:
		__UpperCAmelCase							=			'''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)
		__UpperCAmelCase							=			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
		__UpperCAmelCase							=			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
		__UpperCAmelCase							=			in_proj_weight[:2_5_6, :]
		__UpperCAmelCase							=			in_proj_bias[:2_5_6]
		__UpperCAmelCase							=			in_proj_weight[2_5_6:5_1_2, :]
		__UpperCAmelCase							=			in_proj_bias[2_5_6:5_1_2]
		__UpperCAmelCase							=			in_proj_weight[-2_5_6:, :]
		__UpperCAmelCase							=			in_proj_bias[-2_5_6:]





def       lowerCAmelCase					(		):


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

	return im





@torch.no_grad()
def       lowerCAmelCase					(		UpperCamelCase__				:   Any    ,					UpperCamelCase__				:   int       ):


	"""simple docstring"""
	__UpperCAmelCase							=			ConditionalDetrConfig()
	# set backbone and dilation attributes
	if "resnet101" in model_name:
		__UpperCAmelCase							=			'''resnet101'''
	if "dc5" in model_name:
		__UpperCAmelCase							=			True
	__UpperCAmelCase							=			'''panoptic''' in model_name
	if is_panoptic:
		__UpperCAmelCase							=			2_5_0
	else:
		__UpperCAmelCase							=			9_1
		__UpperCAmelCase							=			'''huggingface/label-files'''
		__UpperCAmelCase							=			'''coco-detection-id2label.json'''
		__UpperCAmelCase							=			json.load(open(hf_hub_download(UpperCamelCase__    ,					UpperCamelCase__    ,					repo_type='''dataset'''       )    ,					'''r'''       )       )
		__UpperCAmelCase							=			{int(UpperCamelCase__       ): v for k, v in idalabel.items()}
		__UpperCAmelCase							=			idalabel
		__UpperCAmelCase							=			{v: k for k, v in idalabel.items()}

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

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

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

	# load original model from torch hub
	__UpperCAmelCase							=			torch.hub.load('''DeppMeng/ConditionalDETR'''    ,					UpperCamelCase__    ,					pretrained=UpperCamelCase__       ).eval()
	__UpperCAmelCase							=			conditional_detr.state_dict()
	# rename keys
	for src, dest in rename_keys:
		if is_panoptic:
			__UpperCAmelCase							=			'''conditional_detr.''' + src
		rename_key(UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__       )
	__UpperCAmelCase							=			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
	__UpperCAmelCase							=			'''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'''       )
			):
				__UpperCAmelCase							=			state_dict.pop(UpperCamelCase__       )
				__UpperCAmelCase							=			val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				__UpperCAmelCase							=			state_dict.pop(UpperCamelCase__       )
				__UpperCAmelCase							=			val
			elif key.startswith('''bbox_attention'''       ) or key.startswith('''mask_head'''       ):
				continue
			else:
				__UpperCAmelCase							=			state_dict.pop(UpperCamelCase__       )
				__UpperCAmelCase							=			val
		else:
			if not key.startswith('''class_labels_classifier'''       ) and not key.startswith('''bbox_predictor'''       ):
				__UpperCAmelCase							=			state_dict.pop(UpperCamelCase__       )
				__UpperCAmelCase							=			val
    # finally, create HuggingFace model and load state dict
	__UpperCAmelCase							=			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
	__UpperCAmelCase							=			conditional_detr(UpperCamelCase__       )
	__UpperCAmelCase							=			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__":
					__lowerCAmelCase	:       Tuple						       =	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."
					)
					__lowerCAmelCase	:       Optional[Any]						       =	parser.parse_args()
					convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

'''simple docstring'''


import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    "The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionImg2ImgPipeline` instead."
)
