'''simple docstring'''





import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
    import onnxruntime as ort




class        __A      ( UpperCamelCase__		,	unittest.TestCase	):
    a__       :						str          =  """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""



    def 					_lowercase						(self    :    List[str]   ,			__a    :    str=0 ):
         UpperCAmelCase_           =   np.random.RandomState(__a )
         UpperCAmelCase_           =   {
             "prompt": "A painting of a squirrel eating a burger",
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 7.5,
             "output_type": "numpy",
         }
         return inputs



    def 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,			provider="CPUExecutionProvider" )
         pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   self.get_dummy_inputs()
         UpperCAmelCase_           =   pipe(**__a ).images
         UpperCAmelCase_           =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =   np.array([0.6_50_72, 0.5_84_92, 0.4_82_19, 0.5_55_21, 0.5_31_80, 0.5_59_39, 0.5_06_97, 0.3_98_00, 0.4_64_55] )

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



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,			provider="CPUExecutionProvider" )
         UpperCAmelCase_           =   PNDMScheduler.from_config(pipe.scheduler.config   ,			skip_prk_steps=__a )
         pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   self.get_dummy_inputs()
         UpperCAmelCase_           =   pipe(**__a ).images
         UpperCAmelCase_           =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =   np.array([0.6_58_63, 0.5_94_25, 0.4_93_26, 0.5_63_13, 0.5_38_75, 0.5_66_27, 0.5_10_65, 0.3_97_77, 0.4_63_30] )

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



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,			provider="CPUExecutionProvider" )
         UpperCAmelCase_           =   LMSDiscreteScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   self.get_dummy_inputs()
         UpperCAmelCase_           =   pipe(**__a ).images
         UpperCAmelCase_           =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =   np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79] )

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



    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,			provider="CPUExecutionProvider" )
         UpperCAmelCase_           =   EulerDiscreteScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   self.get_dummy_inputs()
         UpperCAmelCase_           =   pipe(**__a ).images
         UpperCAmelCase_           =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =   np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79] )

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



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,			provider="CPUExecutionProvider" )
         UpperCAmelCase_           =   EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   self.get_dummy_inputs()
         UpperCAmelCase_           =   pipe(**__a ).images
         UpperCAmelCase_           =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =   np.array([0.5_38_17, 0.6_08_12, 0.4_73_84, 0.4_95_30, 0.5_18_94, 0.4_98_14, 0.4_79_84, 0.3_89_58, 0.4_42_71] )

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



    def 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,			provider="CPUExecutionProvider" )
         UpperCAmelCase_           =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   self.get_dummy_inputs()
         UpperCAmelCase_           =   pipe(**__a ).images
         UpperCAmelCase_           =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =   np.array([0.5_38_95, 0.6_08_08, 0.4_79_33, 0.4_96_08, 0.5_18_86, 0.4_99_50, 0.4_80_53, 0.3_89_57, 0.4_42_00] )

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



    def 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,			provider="CPUExecutionProvider" )
         pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   self.get_dummy_inputs()
         UpperCAmelCase_           =   3 * [inputs["prompt"]]

         # forward
         UpperCAmelCase_           =   pipe(**__a )
         UpperCAmelCase_           =   output.images[0, -3:, -3:, -1]

         UpperCAmelCase_           =   self.get_dummy_inputs()
         UpperCAmelCase_           =   3 * [inputs.pop("prompt" )]

         UpperCAmelCase_           =   pipe.tokenizer(
             __a   ,			padding="max_length"   ,			max_length=pipe.tokenizer.model_max_length   ,			truncation=__a   ,			return_tensors="np"   ,			)
         UpperCAmelCase_           =   text_inputs["input_ids"]

         UpperCAmelCase_           =   pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0]

         UpperCAmelCase_           =   prompt_embeds

         # forward
         UpperCAmelCase_           =   pipe(**__a )
         UpperCAmelCase_           =   output.images[0, -3:, -3:, -1]

         assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4






    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,			provider="CPUExecutionProvider" )
         pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   self.get_dummy_inputs()
         UpperCAmelCase_           =   3 * ["this is a negative prompt"]
         UpperCAmelCase_           =   negative_prompt
         UpperCAmelCase_           =   3 * [inputs["prompt"]]

         # forward
         UpperCAmelCase_           =   pipe(**__a )
         UpperCAmelCase_           =   output.images[0, -3:, -3:, -1]

         UpperCAmelCase_           =   self.get_dummy_inputs()
         UpperCAmelCase_           =   3 * [inputs.pop("prompt" )]

         UpperCAmelCase_           =   []
         for p in [prompt, negative_prompt]:
              UpperCAmelCase_           =   pipe.tokenizer(
                  __a   ,			padding="max_length"   ,			max_length=pipe.tokenizer.model_max_length   ,			truncation=__a   ,			return_tensors="np"   ,			)
              UpperCAmelCase_           =   text_inputs["input_ids"]

              embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0] )

         UpperCAmelCase_    , UpperCAmelCase_           =   embeds

         # forward
         UpperCAmelCase_           =   pipe(**__a )
         UpperCAmelCase_           =   output.images[0, -3:, -3:, -1]

         assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4




@nightly
@require_onnxruntime
@require_torch_gpu
class        __A      ( unittest.TestCase	):



    @property
    def 					_lowercase						(self    :    List[str] ):
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )



    @property
    def 					_lowercase						(self    :    int ):
         UpperCAmelCase_           =   ort.SessionOptions()
         UpperCAmelCase_           =   False
         return options



    def 					_lowercase						(self    :    str ):
         # using the PNDM scheduler by default
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4"   ,			revision="onnx"   ,			safety_checker=__a   ,			feature_extractor=__a   ,			provider=self.gpu_provider   ,			sess_options=self.gpu_options   ,			)
         sd_pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   "A painting of a squirrel eating a burger"
         np.random.seed(0 )
         UpperCAmelCase_           =   sd_pipe([prompt]   ,			guidance_scale=6.0   ,			num_inference_steps=10   ,			output_type="np" )
         UpperCAmelCase_           =   output.images

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

         assert image.shape == (1, 512, 512, 3)
         UpperCAmelCase_           =   np.array([0.04_52, 0.03_90, 0.00_87, 0.03_50, 0.06_17, 0.03_64, 0.05_44, 0.05_23, 0.07_20] )

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



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   DDIMScheduler.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,			subfolder="scheduler"   ,			revision="onnx" )
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,			revision="onnx"   ,			scheduler=__a   ,			safety_checker=__a   ,			feature_extractor=__a   ,			provider=self.gpu_provider   ,			sess_options=self.gpu_options   ,			)
         sd_pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   "open neural network exchange"
         UpperCAmelCase_           =   np.random.RandomState(0 )
         UpperCAmelCase_           =   sd_pipe([prompt]   ,			guidance_scale=7.5   ,			num_inference_steps=10   ,			generator=__a   ,			output_type="np" )
         UpperCAmelCase_           =   output.images
         UpperCAmelCase_           =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         UpperCAmelCase_           =   np.array([0.28_67, 0.19_74, 0.14_81, 0.72_94, 0.72_51, 0.66_67, 0.41_94, 0.56_42, 0.64_86] )

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



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   LMSDiscreteScheduler.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,			subfolder="scheduler"   ,			revision="onnx" )
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,			revision="onnx"   ,			scheduler=__a   ,			safety_checker=__a   ,			feature_extractor=__a   ,			provider=self.gpu_provider   ,			sess_options=self.gpu_options   ,			)
         sd_pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   "open neural network exchange"
         UpperCAmelCase_           =   np.random.RandomState(0 )
         UpperCAmelCase_           =   sd_pipe([prompt]   ,			guidance_scale=7.5   ,			num_inference_steps=10   ,			generator=__a   ,			output_type="np" )
         UpperCAmelCase_           =   output.images
         UpperCAmelCase_           =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         UpperCAmelCase_           =   np.array([0.23_06, 0.19_59, 0.15_93, 0.65_49, 0.63_94, 0.54_08, 0.50_65, 0.60_10, 0.61_61] )

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



    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   0

         def test_callback_fn(__a    :    int   ,			__a    :    int   ,			__a    :    np.ndarray ) -> None:
              UpperCAmelCase_           =   True
              nonlocal number_of_steps
              number_of_steps += 1
              if step == 0:
                   assert latents.shape == (1, 4, 64, 64)
                   UpperCAmelCase_           =   latents[0, -3:, -3:, -1]
                   UpperCAmelCase_           =   np.array(
                       [-0.67_72, -0.38_35, -1.24_56, 0.19_05, -1.09_74, 0.69_67, -1.93_53, 0.01_78, 1.01_67] )

                   assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1E-3
              elif step == 5:
                   assert latents.shape == (1, 4, 64, 64)
                   UpperCAmelCase_           =   latents[0, -3:, -3:, -1]
                   UpperCAmelCase_           =   np.array(
                       [-0.33_51, 0.22_41, -0.18_37, -0.23_25, -0.65_77, 0.33_93, -0.02_41, 0.58_99, 1.38_75] )

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

         UpperCAmelCase_           =   False

         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,			revision="onnx"   ,			safety_checker=__a   ,			feature_extractor=__a   ,			provider=self.gpu_provider   ,			sess_options=self.gpu_options   ,			)
         pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   "Andromeda galaxy in a bottle"

         UpperCAmelCase_           =   np.random.RandomState(0 )
         pipe(
             prompt=__a   ,			num_inference_steps=5   ,			guidance_scale=7.5   ,			generator=__a   ,			callback=__a   ,			callback_steps=1   ,			)
         assert test_callback_fn.has_been_called
         assert number_of_steps == 6






    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,			revision="onnx"   ,			safety_checker=__a   ,			feature_extractor=__a   ,			provider=self.gpu_provider   ,			sess_options=self.gpu_options   ,			)
         assert isinstance(__a   ,			__a )
         assert pipe.safety_checker is None

         UpperCAmelCase_           =   pipe("example prompt"   ,			num_inference_steps=2 ).images[0]
         assert image is not None

         # check that there's no error when saving a pipeline with one of the models being None
         with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(__a )
              UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(__a )

         # sanity check that the pipeline still works
         assert pipe.safety_checker is None
         UpperCAmelCase_           =   pipe("example prompt"   ,			num_inference_steps=2 ).images[0]
         assert image is not None





'''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_:     Optional[int]  =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_:     Tuple  =[]
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_				(					snake_case_       : Dict					,    snake_case_       : Any					,    snake_case_       : Optional[int]			)    ->      Dict:


     '''simple docstring'''




     UpperCAmelCase_           =   state_dict.pop(snake_case_			)
     UpperCAmelCase_           =   val





def 	lowerCAmelCase_				(					snake_case_       : int			)    ->      List[Any]:


     '''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_				(					snake_case_       : List[Any]					,    snake_case_       : Dict=False			)    ->      List[str]:


     '''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_56, :]
          UpperCAmelCase_           =   in_proj_bias[:2_56]
          UpperCAmelCase_           =   in_proj_weight[2_56:5_12, :]
          UpperCAmelCase_           =   in_proj_bias[2_56:5_12]
          UpperCAmelCase_           =   in_proj_weight[-2_56:, :]
          UpperCAmelCase_           =   in_proj_bias[-2_56:]





def 	lowerCAmelCase_				(					)    ->      Dict:


     '''simple docstring'''




     UpperCAmelCase_           =   "http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase_           =   Image.open(requests.get(snake_case_					,    stream=snake_case_			).raw			)

     return im





@torch.no_grad()
def 	lowerCAmelCase_				(					snake_case_       : Optional[int]					,    snake_case_       : Dict			)    ->      Optional[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_50
     else:
          UpperCAmelCase_           =   91
          UpperCAmelCase_           =   "huggingface/label-files"
          UpperCAmelCase_           =   "coco-detection-id2label.json"
          UpperCAmelCase_           =   json.load(open(hf_hub_download(snake_case_					,    snake_case_					,    repo_type="dataset"			)					,    "r"			)			)
          UpperCAmelCase_           =   {int(snake_case_			): 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=snake_case_			)

     # prepare image
     UpperCAmelCase_           =   prepare_img()
     UpperCAmelCase_           =   image_processor(images=snake_case_					,    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"					,    snake_case_					,    pretrained=snake_case_			).eval()
     UpperCAmelCase_           =   conditional_detr.state_dict()
     # rename keys
     for src, dest in rename_keys:
          if is_panoptic:
               UpperCAmelCase_           =   "conditional_detr." + src
          rename_key(snake_case_					,    snake_case_					,    snake_case_			)
     UpperCAmelCase_           =   rename_backbone_keys(snake_case_			)
     # query, key and value matrices need special treatment
     read_in_q_k_v(snake_case_					,    is_panoptic=snake_case_			)
     # 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(snake_case_			)
                    UpperCAmelCase_           =   val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    UpperCAmelCase_           =   state_dict.pop(snake_case_			)
                    UpperCAmelCase_           =   val
               elif key.startswith("bbox_attention"			) or key.startswith("mask_head"			):
                    continue
               else:
                    UpperCAmelCase_           =   state_dict.pop(snake_case_			)
                    UpperCAmelCase_           =   val
          else:
               if not key.startswith("class_labels_classifier"			) and not key.startswith("bbox_predictor"			):
                    UpperCAmelCase_           =   state_dict.pop(snake_case_			)
                    UpperCAmelCase_           =   val
    # finally, create HuggingFace model and load state dict
     UpperCAmelCase_           =   ConditionalDetrForSegmentation(snake_case_			) if is_panoptic else ConditionalDetrForObjectDetection(snake_case_			)
     model.load_state_dict(snake_case_			)
     model.eval()
     model.push_to_hub(repo_id=snake_case_					,    organization="DepuMeng"					,    commit_message="Add model"			)
     # verify our conversion
     UpperCAmelCase_           =   conditional_detr(snake_case_			)
     UpperCAmelCase_           =   model(snake_case_			)
     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(snake_case_			).mkdir(exist_ok=snake_case_			)
     model.save_pretrained(snake_case_			)
     image_processor.save_pretrained(snake_case_			)


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     List[str]  =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_:     int  =parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




