'''simple docstring'''





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, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




class        __A      ( unittest.TestCase	):



    def 					_lowercase						(self    :    Tuple ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()



    @property
    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   1
         UpperCAmelCase_           =   3
         UpperCAmelCase_           =   (32, 32)

         UpperCAmelCase_           =   floats_tensor((batch_size, num_channels) + sizes   ,			rng=random.Random(0 ) ).to(__a )
         return image



    @property
    def 					_lowercase						(self    :    int ):
         torch.manual_seed(0 )
         UpperCAmelCase_           =   UNetaDConditionModel(
             block_out_channels=(32, 32, 64)   ,			layers_per_block=2   ,			sample_size=32   ,			in_channels=7   ,			out_channels=4   ,			down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D")   ,			up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D")   ,			cross_attention_dim=32   ,			attention_head_dim=8   ,			use_linear_projection=__a   ,			only_cross_attention=(True, True, False)   ,			num_class_embeds=100   ,			)
         return model



    @property
    def 					_lowercase						(self    :    Any ):
         torch.manual_seed(0 )
         UpperCAmelCase_           =   AutoencoderKL(
             block_out_channels=[32, 32, 64]   ,			in_channels=3   ,			out_channels=3   ,			down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"]   ,			up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]   ,			latent_channels=4   ,			)
         return model



    @property
    def 					_lowercase						(self    :    Optional[Any] ):
         torch.manual_seed(0 )
         UpperCAmelCase_           =   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   ,			hidden_act="gelu"   ,			projection_dim=512   ,			)
         return CLIPTextModel(__a )



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   "cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_           =   self.dummy_cond_unet_upscale
         UpperCAmelCase_           =   DDPMScheduler()
         UpperCAmelCase_           =   DDIMScheduler(prediction_type="v_prediction" )
         UpperCAmelCase_           =   self.dummy_vae
         UpperCAmelCase_           =   self.dummy_text_encoder
         UpperCAmelCase_           =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

         UpperCAmelCase_           =   self.dummy_image.cpu().permute(0   ,			2   ,			3   ,			1 )[0]
         UpperCAmelCase_           =   Image.fromarray(np.uinta(__a ) ).convert("RGB" ).resize((64, 64) )

         # make sure here that pndm scheduler skips prk
         UpperCAmelCase_           =   StableDiffusionUpscalePipeline(
             unet=__a   ,			low_res_scheduler=__a   ,			scheduler=__a   ,			vae=__a   ,			text_encoder=__a   ,			tokenizer=__a   ,			max_noise_level=350   ,			)
         UpperCAmelCase_           =   sd_pipe.to(__a )
         sd_pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   "A painting of a squirrel eating a burger"
         UpperCAmelCase_           =   torch.Generator(device=__a ).manual_seed(0 )
         UpperCAmelCase_           =   sd_pipe(
             [prompt]   ,			image=__a   ,			generator=__a   ,			guidance_scale=6.0   ,			noise_level=20   ,			num_inference_steps=2   ,			output_type="np"   ,			)

         UpperCAmelCase_           =   output.images

         UpperCAmelCase_           =   torch.Generator(device=__a ).manual_seed(0 )
         UpperCAmelCase_           =   sd_pipe(
             [prompt]   ,			image=__a   ,			generator=__a   ,			guidance_scale=6.0   ,			noise_level=20   ,			num_inference_steps=2   ,			output_type="np"   ,			return_dict=__a   ,			)[0]

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

         UpperCAmelCase_           =   low_res_image.size[0] * 4
         assert image.shape == (1, expected_height_width, expected_height_width, 3)
         UpperCAmelCase_           =   np.array([0.31_13, 0.39_10, 0.42_72, 0.48_59, 0.50_61, 0.46_52, 0.53_62, 0.57_15, 0.56_61] )

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



    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   "cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_           =   self.dummy_cond_unet_upscale
         UpperCAmelCase_           =   DDPMScheduler()
         UpperCAmelCase_           =   DDIMScheduler(prediction_type="v_prediction" )
         UpperCAmelCase_           =   self.dummy_vae
         UpperCAmelCase_           =   self.dummy_text_encoder
         UpperCAmelCase_           =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

         UpperCAmelCase_           =   self.dummy_image.cpu().permute(0   ,			2   ,			3   ,			1 )[0]
         UpperCAmelCase_           =   Image.fromarray(np.uinta(__a ) ).convert("RGB" ).resize((64, 64) )

         # make sure here that pndm scheduler skips prk
         UpperCAmelCase_           =   StableDiffusionUpscalePipeline(
             unet=__a   ,			low_res_scheduler=__a   ,			scheduler=__a   ,			vae=__a   ,			text_encoder=__a   ,			tokenizer=__a   ,			max_noise_level=350   ,			)
         UpperCAmelCase_           =   sd_pipe.to(__a )
         sd_pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   "A painting of a squirrel eating a burger"
         UpperCAmelCase_           =   sd_pipe(
             2 * [prompt]   ,			image=2 * [low_res_image]   ,			guidance_scale=6.0   ,			noise_level=20   ,			num_inference_steps=2   ,			output_type="np"   ,			)
         UpperCAmelCase_           =   output.images
         assert image.shape[0] == 2

         UpperCAmelCase_           =   torch.Generator(device=__a ).manual_seed(0 )
         UpperCAmelCase_           =   sd_pipe(
             [prompt]   ,			image=__a   ,			generator=__a   ,			num_images_per_prompt=2   ,			guidance_scale=6.0   ,			noise_level=20   ,			num_inference_steps=2   ,			output_type="np"   ,			)
         UpperCAmelCase_           =   output.images
         assert image.shape[0] == 2






    @unittest.skipIf(torch_device != "cuda"   ,			"This test requires a GPU" )
    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   self.dummy_cond_unet_upscale
         UpperCAmelCase_           =   DDPMScheduler()
         UpperCAmelCase_           =   DDIMScheduler(prediction_type="v_prediction" )
         UpperCAmelCase_           =   self.dummy_vae
         UpperCAmelCase_           =   self.dummy_text_encoder
         UpperCAmelCase_           =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

         UpperCAmelCase_           =   self.dummy_image.cpu().permute(0   ,			2   ,			3   ,			1 )[0]
         UpperCAmelCase_           =   Image.fromarray(np.uinta(__a ) ).convert("RGB" ).resize((64, 64) )

         # put models in fp16, except vae as it overflows in fp16
         UpperCAmelCase_           =   unet.half()
         UpperCAmelCase_           =   text_encoder.half()

         # make sure here that pndm scheduler skips prk
         UpperCAmelCase_           =   StableDiffusionUpscalePipeline(
             unet=__a   ,			low_res_scheduler=__a   ,			scheduler=__a   ,			vae=__a   ,			text_encoder=__a   ,			tokenizer=__a   ,			max_noise_level=350   ,			)
         UpperCAmelCase_           =   sd_pipe.to(__a )
         sd_pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   "A painting of a squirrel eating a burger"
         UpperCAmelCase_           =   torch.manual_seed(0 )
         UpperCAmelCase_           =   sd_pipe(
             [prompt]   ,			image=__a   ,			generator=__a   ,			num_inference_steps=2   ,			output_type="np"   ,			).images

         UpperCAmelCase_           =   low_res_image.size[0] * 4
         assert image.shape == (1, expected_height_width, expected_height_width, 3)




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



    def 					_lowercase						(self    :    List[str] ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/sd2-upscale/low_res_cat.png" )
         UpperCAmelCase_           =   load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
             "/upsampled_cat.npy" )

         UpperCAmelCase_           =   "stabilityai/stable-diffusion-x4-upscaler"
         UpperCAmelCase_           =   StableDiffusionUpscalePipeline.from_pretrained(__a )
         pipe.to(__a )
         pipe.set_progress_bar_config(disable=__a )
         pipe.enable_attention_slicing()

         UpperCAmelCase_           =   "a cat sitting on a park bench"

         UpperCAmelCase_           =   torch.manual_seed(0 )
         UpperCAmelCase_           =   pipe(
             prompt=__a   ,			image=__a   ,			generator=__a   ,			output_type="np"   ,			)
         UpperCAmelCase_           =   output.images[0]

         assert image.shape == (512, 512, 3)
         assert np.abs(expected_image - image ).max() < 1E-3



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/sd2-upscale/low_res_cat.png" )
         UpperCAmelCase_           =   load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
             "/upsampled_cat_fp16.npy" )

         UpperCAmelCase_           =   "stabilityai/stable-diffusion-x4-upscaler"
         UpperCAmelCase_           =   StableDiffusionUpscalePipeline.from_pretrained(
             __a   ,			torch_dtype=torch.floataa   ,			)
         pipe.to(__a )
         pipe.set_progress_bar_config(disable=__a )
         pipe.enable_attention_slicing()

         UpperCAmelCase_           =   "a cat sitting on a park bench"

         UpperCAmelCase_           =   torch.manual_seed(0 )
         UpperCAmelCase_           =   pipe(
             prompt=__a   ,			image=__a   ,			generator=__a   ,			output_type="np"   ,			)
         UpperCAmelCase_           =   output.images[0]

         assert image.shape == (512, 512, 3)
         assert np.abs(expected_image - image ).max() < 5E-1






    def 					_lowercase						(self    :    List[Any] ):
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         UpperCAmelCase_           =   load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/sd2-upscale/low_res_cat.png" )

         UpperCAmelCase_           =   "stabilityai/stable-diffusion-x4-upscaler"
         UpperCAmelCase_           =   StableDiffusionUpscalePipeline.from_pretrained(
             __a   ,			torch_dtype=torch.floataa   ,			)
         pipe.to(__a )
         pipe.set_progress_bar_config(disable=__a )
         pipe.enable_attention_slicing(1 )
         pipe.enable_sequential_cpu_offload()

         UpperCAmelCase_           =   "a cat sitting on a park bench"

         UpperCAmelCase_           =   torch.manual_seed(0 )
         UpperCAmelCase_           =   pipe(
             prompt=__a   ,			image=__a   ,			generator=__a   ,			num_inference_steps=5   ,			output_type="np"   ,			)

         UpperCAmelCase_           =   torch.cuda.max_memory_allocated()
         # make sure that less than 2.9 GB is allocated
         assert mem_bytes < 2.9 * 10**9





'''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)




