





import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 					lowercase__ (			__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase    ,       unittest.TestCase       ):

   '''simple docstring'''


   a		:							Union[str, Any]     	=	StableDiffusionInstructPixaPixPipeline
   a		:							int     	=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
   a		:							Dict     	=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   a		:							List[str]     	=	IMAGE_TO_IMAGE_IMAGE_PARAMS
   a		:							Optional[int]     	=	IMAGE_TO_IMAGE_IMAGE_PARAMS




   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       torch.manual_seed(0							)
       UpperCamelCase__							:       Union[str, Any]      =   UNetaDConditionModel(
           block_out_channels=(32, 64),	layers_per_block=2,	sample_size=32,	in_channels=8,	out_channels=4,	down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),	up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),	cross_attention_dim=32,	)
       UpperCamelCase__							:       Optional[Any]      =   PNDMScheduler(skip_prk_steps=__magic_name__							)
       torch.manual_seed(0							)
       UpperCamelCase__							:       List[str]      =   AutoencoderKL(
           block_out_channels=[32, 64],	in_channels=3,	out_channels=3,	down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],	up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],	latent_channels=4,	)
       torch.manual_seed(0							)
       UpperCamelCase__							:       Dict      =   CLIPTextConfig(
           bos_token_id=0,	eos_token_id=2,	hidden_size=32,	intermediate_size=37,	layer_norm_eps=1E-05,	num_attention_heads=4,	num_hidden_layers=5,	pad_token_id=1,	vocab_size=1000,	)
       UpperCamelCase__							:       Tuple      =   CLIPTextModel(__magic_name__							)
       UpperCamelCase__							:       int      =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

       UpperCamelCase__							:       Optional[Any]      =   {
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__=0							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   floats_tensor((1, 3, 32, 32),	rng=random.Random(__magic_name__							)							).to(__magic_name__							)
       UpperCamelCase__							:       int      =   image.cpu().permute(0,	2,	3,	1							)[0]
       UpperCamelCase__							:       Optional[int]      =   Image.fromarray(np.uinta(__magic_name__							)							).convert('''RGB'''							)
       if str(__magic_name__							).startswith('''mps'''							):
           UpperCamelCase__							:       Optional[Any]      =   torch.manual_seed(__magic_name__							)
       else:
           UpperCamelCase__							:       Optional[int]      =   torch.Generator(device=__magic_name__							).manual_seed(__magic_name__							)
       UpperCamelCase__							:       Union[str, Any]      =   {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 6.0,
           '''image_guidance_scale''': 1,
           '''output_type''': '''numpy''',
       }
       return inputs




   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       UpperCamelCase__							:       Union[str, Any]      =   self.get_dummy_components()
       UpperCamelCase__							:       str      =   StableDiffusionInstructPixaPixPipeline(**__magic_name__							)
       UpperCamelCase__							:       str      =   sd_pipe.to(__magic_name__							)
       sd_pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       Union[str, Any]      =   self.get_dummy_inputs(__magic_name__							)
       UpperCamelCase__							:       str      =   sd_pipe(**__magic_name__							).images
       UpperCamelCase__							:       List[Any]      =   image[0, -3:, -3:, -1]
       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase__							:       Tuple      =   np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]							)

       assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3




   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       UpperCamelCase__							:       Union[str, Any]      =   self.get_dummy_components()
       UpperCamelCase__							:       Optional[int]      =   StableDiffusionInstructPixaPixPipeline(**__magic_name__							)
       UpperCamelCase__							:       List[Any]      =   sd_pipe.to(__magic_name__							)
       sd_pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       List[str]      =   self.get_dummy_inputs(__magic_name__							)
       UpperCamelCase__							:       Optional[int]      =   '''french fries'''
       UpperCamelCase__							:       Union[str, Any]      =   sd_pipe(**__magic_name__,	negative_prompt=__magic_name__							)
       UpperCamelCase__							:       Tuple      =   output.images
       UpperCamelCase__							:       Optional[Any]      =   image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase__							:       Optional[int]      =   np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]							)

       assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       UpperCamelCase__							:       Optional[Any]      =   self.get_dummy_components()
       UpperCamelCase__							:       int      =   StableDiffusionInstructPixaPixPipeline(**__magic_name__							)
       UpperCamelCase__							:       Union[str, Any]      =   sd_pipe.to(__magic_name__							)
       sd_pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       int      =   self.get_dummy_inputs(__magic_name__							)
       UpperCamelCase__							:       List[str]      =   [inputs['''prompt''']] * 2

       UpperCamelCase__							:       Union[str, Any]      =   np.array(inputs['''image''']							).astype(np.floataa							) / 255.0
       UpperCamelCase__							:       Dict      =   torch.from_numpy(__magic_name__							).unsqueeze(0							).to(__magic_name__							)
       UpperCamelCase__							:       Any      =   image / 2 + 0.5
       UpperCamelCase__							:       Optional[Any]      =   image.permute(0,	3,	1,	2							)
       UpperCamelCase__							:       List[Any]      =   image.repeat(2,	1,	1,	1							)

       UpperCamelCase__							:       Any      =   sd_pipe(**__magic_name__							).images
       UpperCamelCase__							:       Union[str, Any]      =   image[-1, -3:, -3:, -1]

       assert image.shape == (2, 32, 32, 3)
       UpperCamelCase__							:       int      =   np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]							)

       assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3




   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       UpperCamelCase__							:       Tuple      =   self.get_dummy_components()
       UpperCamelCase__							:       Optional[int]      =   EulerAncestralDiscreteScheduler(
           beta_start=0.0_0085,	beta_end=0.012,	beta_schedule='''scaled_linear'''							)
       UpperCamelCase__							:       Tuple      =   StableDiffusionInstructPixaPixPipeline(**__magic_name__							)
       UpperCamelCase__							:       Optional[Any]      =   sd_pipe.to(__magic_name__							)
       sd_pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       Union[str, Any]      =   self.get_dummy_inputs(__magic_name__							)
       UpperCamelCase__							:       Optional[Any]      =   sd_pipe(**__magic_name__							).images
       UpperCamelCase__							:       Tuple      =   image[0, -3:, -3:, -1]

       UpperCamelCase__							:       str      =   [round(__magic_name__,	4							) for x in image_slice.flatten().tolist()]
       print(''','''.join([str(__magic_name__							) for x in slice]							)							)

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase__							:       Optional[Any]      =   np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]							)

       assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       super().test_inference_batch_single_identical(expected_max_diff=3E-3							)




   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   self.get_dummy_components()
       UpperCamelCase__							:       Any      =   StableDiffusionInstructPixaPixPipeline(**__magic_name__							)
       UpperCamelCase__							:       int      =   VaeImageProcessor(do_resize=__magic_name__,	do_normalize=__magic_name__							)
       UpperCamelCase__							:       List[str]      =   pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       Any      =   pipe(**self.get_dummy_inputs_by_type(__magic_name__,	input_image_type='''pt'''							)							)[0]

       UpperCamelCase__							:       int      =   components['''vae''']
       UpperCamelCase__							:       Dict      =   self.get_dummy_inputs_by_type(__magic_name__,	input_image_type='''pt'''							)

       for image_param in self.image_latents_params:
           if image_param in inputs.keys():
               UpperCamelCase__							:       Optional[Any]      =   vae.encode(inputs[image_param]							).latent_dist.mode()

       UpperCamelCase__							:       str      =   pipe(**__magic_name__							)[0]

       UpperCamelCase__							:       Optional[Any]      =   np.abs(out - out_latents_inputs							).max()
       self.assertLess(__magic_name__,	1E-4,	'''passing latents as image input generate different result from passing image'''							)






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

   '''simple docstring'''


   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




   def    UpperCamelCase__						(					self,	__magic_name__=0							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   torch.manual_seed(__magic_name__							)
       UpperCamelCase__							:       str      =   load_image(
           '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''							)
       UpperCamelCase__							:       int      =   {
           '''prompt''': '''turn him into a cyborg''',
           '''image''': image,
           '''generator''': generator,
           '''num_inference_steps''': 3,
           '''guidance_scale''': 7.5,
           '''image_guidance_scale''': 1.0,
           '''output_type''': '''numpy''',
       }
       return inputs




   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__							:       int      =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
           '''timbrooks/instruct-pix2pix''',	safety_checker=__magic_name__							)
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)
       pipe.enable_attention_slicing()

       UpperCamelCase__							:       str      =   self.get_inputs()
       UpperCamelCase__							:       List[str]      =   pipe(**__magic_name__							).images
       UpperCamelCase__							:       Tuple      =   image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       UpperCamelCase__							:       Any      =   np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]							)

       assert np.abs(expected_slice - image_slice							).max() < 1E-3




   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
           '''timbrooks/instruct-pix2pix''',	safety_checker=__magic_name__							)
       UpperCamelCase__							:       str      =   LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)
       pipe.enable_attention_slicing()

       UpperCamelCase__							:       Union[str, Any]      =   self.get_inputs()
       UpperCamelCase__							:       int      =   pipe(**__magic_name__							).images
       UpperCamelCase__							:       List[str]      =   image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       UpperCamelCase__							:       Dict      =   np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]							)

       assert np.abs(expected_slice - image_slice							).max() < 1E-3




   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__							:       str      =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
           '''timbrooks/instruct-pix2pix''',	safety_checker=__magic_name__							)
       UpperCamelCase__							:       Any      =   DDIMScheduler.from_config(pipe.scheduler.config							)
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)
       pipe.enable_attention_slicing()

       UpperCamelCase__							:       List[Any]      =   self.get_inputs()
       UpperCamelCase__							:       str      =   pipe(**__magic_name__							).images
       UpperCamelCase__							:       Union[str, Any]      =   image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       UpperCamelCase__							:       Union[str, Any]      =   np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]							)

       assert np.abs(expected_slice - image_slice							).max() < 1E-3




   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       str      =   0

       def callback_fn(__magic_name__,	__magic_name__,	__magic_name__							) -> None:
           UpperCamelCase__							:       Tuple      =   True
           nonlocal number_of_steps
           number_of_steps += 1
           if step == 1:
               UpperCamelCase__							:       Optional[int]      =   latents.detach().cpu().numpy()
               assert latents.shape == (1, 4, 64, 64)
               UpperCamelCase__							:       int      =   latents[0, -3:, -3:, -1]
               UpperCamelCase__							:       List[str]      =   np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]							)

               assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5E-2
           elif step == 2:
               UpperCamelCase__							:       Optional[int]      =   latents.detach().cpu().numpy()
               assert latents.shape == (1, 4, 64, 64)
               UpperCamelCase__							:       Optional[int]      =   latents[0, -3:, -3:, -1]
               UpperCamelCase__							:       List[Any]      =   np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]							)

               assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5E-2

       UpperCamelCase__							:       Dict      =   False

       UpperCamelCase__							:       Optional[int]      =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
           '''timbrooks/instruct-pix2pix''',	safety_checker=__magic_name__,	torch_dtype=torch.floataa							)
       UpperCamelCase__							:       List[Any]      =   pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)
       pipe.enable_attention_slicing()

       UpperCamelCase__							:       int      =   self.get_inputs()
       pipe(**__magic_name__,	callback=__magic_name__,	callback_steps=1							)
       assert callback_fn.has_been_called
       assert number_of_steps == 3




   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       UpperCamelCase__							:       Union[str, Any]      =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
           '''timbrooks/instruct-pix2pix''',	safety_checker=__magic_name__,	torch_dtype=torch.floataa							)
       UpperCamelCase__							:       Optional[int]      =   pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)
       pipe.enable_attention_slicing(1							)
       pipe.enable_sequential_cpu_offload()

       UpperCamelCase__							:       Any      =   self.get_inputs()
       UpperCamelCase__							:       Tuple      =   pipe(**__magic_name__							)

       UpperCamelCase__							:       Dict      =   torch.cuda.max_memory_allocated()
       # make sure that less than 2.2 GB is allocated
       assert mem_bytes < 2.2 * 10**9




   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   self.get_inputs()
       # resize to resolution that is divisible by 8 but not 16 or 32
       UpperCamelCase__							:       str      =   inputs['''image'''].resize((504, 504)							)

       UpperCamelCase__							:       Tuple      =   '''timbrooks/instruct-pix2pix'''
       UpperCamelCase__							:       Tuple      =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
           __magic_name__,	safety_checker=__magic_name__,	)
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)
       pipe.enable_attention_slicing()

       UpperCamelCase__							:       Optional[int]      =   pipe(**__magic_name__							)
       UpperCamelCase__							:       Union[str, Any]      =   output.images[0]

       UpperCamelCase__							:       List[Any]      =   image[255:258, 383:386, -1]

       assert image.shape == (504, 504, 3)
       UpperCamelCase__							:       int      =   np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]							)

       assert np.abs(image_slice.flatten() - expected_slice							).max() < 5E-3








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()
UpperCAmelCase_  							=				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase_  							=				[]
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:    Tuple	,     __UpperCAmelCase:    List[Any]	,     __UpperCAmelCase:    Dict   )   ->			Dict:
    UpperCamelCase__							:       List[str]      =   state_dict.pop(__UpperCAmelCase   )
    UpperCamelCase__							:       Union[str, Any]      =   val




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Dict   )   ->			List[str]:
    UpperCamelCase__							:       List[str]      =   OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            UpperCamelCase__							:       Optional[Any]      =   key.replace('''backbone.0.body'''	,     '''backbone.conv_encoder.model'''   )
            UpperCamelCase__							:       List[str]      =   value
        else:
            UpperCamelCase__							:       Optional[int]      =   value

    return new_state_dict




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    Optional[int]=False   )   ->			Optional[int]:
    UpperCamelCase__							:       List[str]      =   ''''''
    if is_panoptic:
        UpperCamelCase__							:       List[Any]      =   '''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__							:       int      =   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__							:       Any      =   in_proj_weight[:256, :]
        UpperCamelCase__							:       List[str]      =   in_proj_bias[:256]
        UpperCamelCase__							:       Optional[int]      =   in_proj_weight[256:512, :]
        UpperCamelCase__							:       List[str]      =   in_proj_bias[256:512]
        UpperCamelCase__							:       Tuple      =   in_proj_weight[-256:, :]
        UpperCamelCase__							:       List[Any]      =   in_proj_bias[-256:]




def 	lowerCAmelCase_					(						)   ->			int:
    UpperCamelCase__							:       Any      =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase__							:       Optional[int]      =   Image.open(requests.get(__UpperCAmelCase	,     stream=__UpperCAmelCase   ).raw   )

    return im




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

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

    # prepare image
    UpperCamelCase__							:       Tuple      =   prepare_img()
    UpperCamelCase__							:       List[str]      =   image_processor(images=__UpperCAmelCase	,     return_tensors='''pt'''   )
    UpperCamelCase__							:       Union[str, Any]      =   encoding['''pixel_values''']

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

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

