



'''simple docstring'''




import math
from typing import Callable, List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase=[]	)   ->    int:
							UpperCAmelCase  :	Union[str, Any]							  =	size[0] - overlap_pixels * 2
							UpperCAmelCase  :	Optional[Any]							  =	size[1] - overlap_pixels * 2
							for letter in ["l", "r"]:
														if letter in remove_borders:
																					size_x += overlap_pixels
							for letter in ["t", "b"]:
														if letter in remove_borders:
																					size_y += overlap_pixels
							UpperCAmelCase  :	Any							  =	np.ones((size_y, size_x)			,       dtype=np.uinta	) * 2_5_5
							UpperCAmelCase  :	Dict							  =	np.pad(_lowercase			,       mode="""linear_ramp"""			,       pad_width=_lowercase			,       end_values=0	)

							if "l" in remove_borders:
														UpperCAmelCase  :	Dict							  =	mask[:, overlap_pixels : mask.shape[1]]
							if "r" in remove_borders:
														UpperCAmelCase  :	int							  =	mask[:, 0 : mask.shape[1] - overlap_pixels]
							if "t" in remove_borders:
														UpperCAmelCase  :	Optional[Any]							  =	mask[overlap_pixels : mask.shape[0], :]
							if "b" in remove_borders:
														UpperCAmelCase  :	Any							  =	mask[0 : mask.shape[0] - overlap_pixels, :]
							return mask



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    List[Any]:
							return max(_lowercase			,       min(_lowercase			,       _lowercase	)	)



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    List[str]:
							return (
							    clamp(rect[0]			,       min[0]			,       max[0]	),
							    clamp(rect[1]			,       min[1]			,       max[1]	),
							    clamp(rect[2]			,       min[0]			,       max[0]	),
							    clamp(rect[3]			,       min[1]			,       max[1]	),
							)



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    Union[str, Any]:
							UpperCAmelCase  :	Optional[Any]							  =	list(_lowercase	)
							rect[0] -= overlap
							rect[1] -= overlap
							rect[2] += overlap
							rect[3] += overlap
							UpperCAmelCase  :	str							  =	clamp_rect(_lowercase			,       [0, 0]			,       [image_size[0], image_size[1]]	)
							return rect



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase	)   ->    List[Any]:
							UpperCAmelCase  :	str							  =	Image.new("""RGB"""			,       (tile.size[0] + original_slice, tile.size[1])	)
							result.paste(
							    original_image.resize((tile.size[0], tile.size[1])			,       Image.BICUBIC	).crop(
							        (slice_x, 0, slice_x + original_slice, tile.size[1])	)			,       (0, 0)			,       )
							result.paste(_lowercase			,       (original_slice, 0)	)
							return result



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    str:
							UpperCAmelCase  :	Optional[int]							  =	(original_image_slice * 4, 0, tile.size[0], tile.size[1])
							UpperCAmelCase  :	Dict							  =	tile.crop(_lowercase	)
							return tile



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    Optional[Any]:
							UpperCAmelCase  :	Dict							  =	n % d
							return n - divisor

class     UpperCamelCase_				(							__magic_name__    ):





	def __init__(						self	,	A	,	A	,	A	,	A	,	A	,	A	,	A = 350	,	)    ->    List[Any]:
								super().__init__(
								    vae=A	,	text_encoder=A	,	tokenizer=A	,	unet=A	,	low_res_scheduler=A	,	scheduler=A	,	max_noise_level=A	,	)





	def 				_lowercase(						self	,	A	,	A	,	A	,	A	,	A	,	A	,	A	,	**A					)    ->    Tuple:
								torch.manual_seed(0					)
								UpperCAmelCase  :	List[Any]							  =	(
								    min(image.size[0] - (tile_size + original_image_slice)	,	x * tile_size					),
								    min(image.size[1] - (tile_size + original_image_slice)	,	y * tile_size					),
								    min(image.size[0]	,	(x + 1) * tile_size					),
								    min(image.size[1]	,	(y + 1) * tile_size					),
								)
								UpperCAmelCase  :	Optional[int]							  =	add_overlap_rect(A	,	A	,	image.size					)
								UpperCAmelCase  :	Any							  =	image.crop(A					)
								UpperCAmelCase  :	int							  =	((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
								UpperCAmelCase  :	int							  =	translated_slice_x - (original_image_slice / 2)
								UpperCAmelCase  :	List[Any]							  =	max(0	,	A					)
								UpperCAmelCase  :	str							  =	squeeze_tile(A	,	A	,	A	,	A					)
								UpperCAmelCase  :	Optional[int]							  =	to_input.size
								UpperCAmelCase  :	Optional[int]							  =	to_input.resize((tile_size, tile_size)	,	Image.BICUBIC					)
								UpperCAmelCase  :	Dict							  =	super(A	,	self					).__call__(image=A	,	**A					).images[0]
								UpperCAmelCase  :	Optional[Any]							  =	upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)	,	Image.BICUBIC					)
								UpperCAmelCase  :	str							  =	unsqueeze_tile(A	,	A					)
								UpperCAmelCase  :	List[Any]							  =	upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)	,	Image.BICUBIC					)
								UpperCAmelCase  :	List[Any]							  =	[]
								if x == 0:
															remove_borders.append("""l"""					)
								elif crop_rect[2] == image.size[0]:
															remove_borders.append("""r"""					)
								if y == 0:
															remove_borders.append("""t"""					)
								elif crop_rect[3] == image.size[1]:
															remove_borders.append("""b"""					)
								UpperCAmelCase  :	Optional[int]							  =	Image.fromarray(
								    make_transparency_mask(
								        (upscaled_tile.size[0], upscaled_tile.size[1])	,	tile_border * 4	,	remove_borders=A					)	,	mode="""L"""	,	)
								final_image.paste(
								    A	,	(crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)	,	A					)






	@torch.no_grad()
	def __call__(						self	,	A	,	A	,	A = 75	,	A = 9.0	,	A = 50	,	A = None	,	A = 1	,	A = 0.0	,	A = None	,	A = None	,	A = None	,	A = 1	,	A = 128	,	A = 32	,	A = 32	,	)    ->    List[str]:
								UpperCAmelCase  :	List[Any]							  =	Image.new("""RGB"""	,	(image.size[0] * 4, image.size[1] * 4)					)
								UpperCAmelCase  :	Any							  =	math.ceil(image.size[0] / tile_size					)
								UpperCAmelCase  :	Any							  =	math.ceil(image.size[1] / tile_size					)
								UpperCAmelCase  :	Tuple							  =	tcx * tcy
								UpperCAmelCase  :	Optional[int]							  =	0
								for y in range(A					):
															for x in range(A					):
																						self._process_tile(
																						    A	,	A	,	A	,	A	,	A	,	A	,	A	,	prompt=A	,	num_inference_steps=A	,	guidance_scale=A	,	noise_level=A	,	negative_prompt=A	,	num_images_per_prompt=A	,	eta=A	,	generator=A	,	latents=A	,	)
																						current_count += 1
																						if callback is not None:
																													callback({"""progress""": current_count / total_tile_count, """image""": final_image}					)
								return final_image




def    __lowerCamelCase						(      )   ->    int:
							# Run a demo
							UpperCAmelCase  :	int							  =	"""stabilityai/stable-diffusion-x4-upscaler"""
							UpperCAmelCase  :	str							  =	StableDiffusionTiledUpscalePipeline.from_pretrained(_lowercase			,       revision="""fp16"""			,       torch_dtype=torch.floataa	)
							UpperCAmelCase  :	Dict							  =	pipe.to("""cuda"""	)
							UpperCAmelCase  :	Dict							  =	Image.open("""../../docs/source/imgs/diffusers_library.jpg"""	)

							def callback(_lowercase	):
														print(F'''progress: {obj['progress']:.4f}'''	)
														obj["image"].save("""diffusers_library_progress.jpg"""	)

							UpperCAmelCase  :	int							  =	pipe(image=_lowercase			,       prompt="""Black font, white background, vector"""			,       noise_level=4_0			,       callback=_lowercase	)
							final_image.save("""diffusers_library.jpg"""	)


if __name__ == "__main__":
							main()






'''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()
a     :    List[Any]      						=					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a     :    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    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    Optional[Any]:
							UpperCAmelCase  :	List[str]							  =	state_dict.pop(_lowercase	)
							UpperCAmelCase  :	List[str]							  =	val



def    __lowerCamelCase						(      _lowercase	)   ->    Any:
							UpperCAmelCase  :	Union[str, Any]							  =	OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					UpperCAmelCase  :	List[str]							  =	key.replace("""backbone.0.body"""			,       """backbone.conv_encoder.model"""	)
																					UpperCAmelCase  :	Dict							  =	value
														else:
																					UpperCAmelCase  :	List[Any]							  =	value

							return new_state_dict



def    __lowerCamelCase						(      _lowercase			,       _lowercase=False	)   ->    Optional[int]:
							UpperCAmelCase  :	Dict							  =	""""""
							if is_panoptic:
														UpperCAmelCase  :	Tuple							  =	"""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  :	List[Any]							  =	state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''	)
														UpperCAmelCase  :	List[Any]							  =	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  :	Dict							  =	in_proj_weight[:2_5_6, :]
														UpperCAmelCase  :	Optional[Any]							  =	in_proj_bias[:2_5_6]
														UpperCAmelCase  :	List[Any]							  =	in_proj_weight[2_5_6:5_1_2, :]
														UpperCAmelCase  :	Tuple							  =	in_proj_bias[2_5_6:5_1_2]
														UpperCAmelCase  :	List[str]							  =	in_proj_weight[-2_5_6:, :]
														UpperCAmelCase  :	List[str]							  =	in_proj_bias[-2_5_6:]



def    __lowerCamelCase						(      )   ->    Dict:
							UpperCAmelCase  :	List[Any]							  =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCAmelCase  :	Tuple							  =	Image.open(requests.get(_lowercase			,       stream=_lowercase	).raw	)

							return im



@torch.no_grad()
def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    str:
							UpperCAmelCase  :	str							  =	ConditionalDetrConfig()
							# set backbone and dilation attributes
							if "resnet101" in model_name:
														UpperCAmelCase  :	List[Any]							  =	"""resnet101"""
							if "dc5" in model_name:
														UpperCAmelCase  :	Optional[int]							  =	True
							UpperCAmelCase  :	List[Any]							  =	"""panoptic""" in model_name
							if is_panoptic:
														UpperCAmelCase  :	Union[str, Any]							  =	2_5_0
							else:
														UpperCAmelCase  :	int							  =	9_1
														UpperCAmelCase  :	Tuple							  =	"""huggingface/label-files"""
														UpperCAmelCase  :	List[Any]							  =	"""coco-detection-id2label.json"""
														UpperCAmelCase  :	Optional[int]							  =	json.load(open(hf_hub_download(_lowercase			,       _lowercase			,       repo_type="""dataset"""	)			,       """r"""	)	)
														UpperCAmelCase  :	Dict							  =	{int(_lowercase	): v for k, v in idalabel.items()}
														UpperCAmelCase  :	Optional[Any]							  =	idalabel
														UpperCAmelCase  :	List[Any]							  =	{v: k for k, v in idalabel.items()}

							# load image processor
							UpperCAmelCase  :	List[str]							  =	"""coco_panoptic""" if is_panoptic else """coco_detection"""
							UpperCAmelCase  :	List[Any]							  =	ConditionalDetrImageProcessor(format=_lowercase	)

							# prepare image
							UpperCAmelCase  :	Union[str, Any]							  =	prepare_img()
							UpperCAmelCase  :	Dict							  =	image_processor(images=_lowercase			,       return_tensors="""pt"""	)
							UpperCAmelCase  :	List[Any]							  =	encoding["""pixel_values"""]

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

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

