import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


lowerCAmelCase__      		=					logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 							a_					(		lowercase__		):






 '''simple docstring'''






 UpperCAmelCase_      					=   42
 UpperCAmelCase_      					=   None






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__=0.9_9_9      ,    lowerCAmelCase__="cosine"      ,    ):
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(lowerCAmelCase__      ):
                     return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2      ) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(lowerCAmelCase__      ):
                     return math.exp(t * -1_2.0      )

       else:
              raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""      )

       lowerCAmelCase__	      =   []
       for i in range(_snake_case      ):
              lowerCAmelCase__	      =   i / num_diffusion_timesteps
              lowerCAmelCase__	      =   (i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(_snake_case      ) / alpha_bar_fn(_snake_case      )      ,    _snake_case      )      )
       return torch.tensor(_snake_case      ,    dtype=torch.floataa      )



class 							a_					(		lowercase__			,	lowercase__		):






 '''simple docstring'''






 UpperCAmelCase_      					=   [e.name for e in KarrasDiffusionSchedulers]
 UpperCAmelCase_      					=   1




 @register_to_config
 def __init__(     self   :    List[Any]     ,       lowercase__   :    int = 1_000     ,       lowercase__   :    float = 0.0_001     ,       lowercase__   :    float = 0.02     ,       lowercase__   :    str = "linear"     ,       lowercase__   :    Optional[Union[np.ndarray, List[float]]] = None     ,       lowercase__   :    str = "epsilon"     ,       lowercase__   :    str = "linspace"     ,       lowercase__   :    int = 0     ,       ):







        '''simple docstring'''



        if trained_betas is not None:
               lowerCAmelCase__	      =   torch.tensor(_lowercase     ,       dtype=torch.floataa)
        elif beta_schedule == "linear":
               lowerCAmelCase__	      =   torch.linspace(_lowercase     ,       _lowercase     ,       _lowercase     ,       dtype=torch.floataa)
        elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               lowerCAmelCase__	      =   (
                   torch.linspace(beta_start**0.5     ,       beta_end**0.5     ,       _lowercase     ,       dtype=torch.floataa) ** 2
               )
        elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               lowerCAmelCase__	      =   betas_for_alpha_bar(_lowercase)
        else:
               raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""")

        lowerCAmelCase__	      =   1.0 - self.betas
        lowerCAmelCase__	      =   torch.cumprod(self.alphas     ,       dim=0)

        lowerCAmelCase__	      =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
        lowerCAmelCase__	      =   np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
        lowerCAmelCase__	      =   torch.from_numpy(_lowercase)

        # setable values
        lowerCAmelCase__	      =   None
        lowerCAmelCase__	      =   np.linspace(0     ,       num_train_timesteps - 1     ,       _lowercase     ,       dtype=_lowercase)[::-1].copy()
        lowerCAmelCase__	      =   torch.from_numpy(_lowercase)
        lowerCAmelCase__	      =   False




 @property
 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

        return (self.sigmas.max() ** 2 + 1) ** 0.5




 def 						__snake_case		(     self   :    Dict     ,       lowercase__   :    torch.FloatTensor     ,       lowercase__   :    Union[float, torch.FloatTensor]):







        '''simple docstring'''



        if isinstance(_lowercase     ,       torch.Tensor):
               lowerCAmelCase__	      =   timestep.to(self.timesteps.device)
        lowerCAmelCase__	      =   (self.timesteps == timestep).nonzero().item()
        lowerCAmelCase__	      =   self.sigmas[step_index]
        lowerCAmelCase__	      =   sample / ((sigma**2 + 1) ** 0.5)
        lowerCAmelCase__	      =   True
        return sample




 def 						__snake_case		(     self   :    List[str]     ,       lowercase__   :    int     ,       lowercase__   :    Union[str, torch.device] = None):







        '''simple docstring'''



        lowerCAmelCase__	      =   num_inference_steps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
               lowerCAmelCase__	      =   np.linspace(0     ,       self.config.num_train_timesteps - 1     ,       _lowercase     ,       dtype=_lowercase)[
                   ::-1
               ].copy()
        elif self.config.timestep_spacing == "leading":
               lowerCAmelCase__	      =   self.config.num_train_timesteps // self.num_inference_steps
               # creates integer timesteps by multiplying by ratio
               # casting to int to avoid issues when num_inference_step is power of 3
               lowerCAmelCase__	      =   (np.arange(0     ,       _lowercase) * step_ratio).round()[::-1].copy().astype(_lowercase)
               timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
               lowerCAmelCase__	      =   self.config.num_train_timesteps / self.num_inference_steps
               # creates integer timesteps by multiplying by ratio
               # casting to int to avoid issues when num_inference_step is power of 3
               lowerCAmelCase__	      =   (np.arange(self.config.num_train_timesteps     ,       0     ,       -step_ratio)).round().copy().astype(_lowercase)
               timesteps -= 1
        else:
               raise ValueError(
                   F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""")

        lowerCAmelCase__	      =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
        lowerCAmelCase__	      =   np.interp(_lowercase     ,       np.arange(0     ,       len(_lowercase))     ,       _lowercase)
        lowerCAmelCase__	      =   np.concatenate([sigmas, [0.0]]).astype(np.floataa)
        lowerCAmelCase__	      =   torch.from_numpy(_lowercase).to(device=_lowercase)
        if str(_lowercase).startswith('mps'):
               # mps does not support float64
               lowerCAmelCase__	      =   torch.from_numpy(_lowercase).to(_lowercase     ,       dtype=torch.floataa)
        else:
               lowerCAmelCase__	      =   torch.from_numpy(_lowercase).to(device=_lowercase)




 def 						__snake_case		(     self   :    List[str]     ,       lowercase__   :    torch.FloatTensor     ,       lowercase__   :    Union[float, torch.FloatTensor]     ,       lowercase__   :    torch.FloatTensor     ,       lowercase__   :    Optional[torch.Generator] = None     ,       lowercase__   :    bool = True     ,       ):







        '''simple docstring'''



        if (
            isinstance(_lowercase     ,       _lowercase)
            or isinstance(_lowercase     ,       torch.IntTensor)
            or isinstance(_lowercase     ,       torch.LongTensor)
        ):
               raise ValueError(
                   (
                       'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                       ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
                       ' one of the `scheduler.timesteps` as a timestep.'
                   )     ,       )

        if not self.is_scale_input_called:
               logger.warning(
                   'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
                   'See `StableDiffusionPipeline` for a usage example.')

        if isinstance(_lowercase     ,       torch.Tensor):
               lowerCAmelCase__	      =   timestep.to(self.timesteps.device)

        lowerCAmelCase__	      =   (self.timesteps == timestep).nonzero().item()
        lowerCAmelCase__	      =   self.sigmas[step_index]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
               lowerCAmelCase__	      =   sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               lowerCAmelCase__	      =   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        elif self.config.prediction_type == "sample":
               raise NotImplementedError('prediction_type not implemented yet: sample')
        else:
               raise ValueError(
                   F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""")

        lowerCAmelCase__	      =   self.sigmas[step_index]
        lowerCAmelCase__	      =   self.sigmas[step_index + 1]
        lowerCAmelCase__	      =   (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
        lowerCAmelCase__	      =   (sigma_to**2 - sigma_up**2) ** 0.5

        # 2. Convert to an ODE derivative
        lowerCAmelCase__	      =   (sample - pred_original_sample) / sigma

        lowerCAmelCase__	      =   sigma_down - sigma

        lowerCAmelCase__	      =   sample + derivative * dt

        lowerCAmelCase__	      =   model_output.device
        lowerCAmelCase__	      =   randn_tensor(model_output.shape     ,       dtype=model_output.dtype     ,       device=_lowercase     ,       generator=_lowercase)

        lowerCAmelCase__	      =   prev_sample + noise * sigma_up

        if not return_dict:
               return (prev_sample,)

        return EulerAncestralDiscreteSchedulerOutput(
            prev_sample=_lowercase     ,       pred_original_sample=_lowercase)




 def 						__snake_case		(     self   :    Tuple     ,       lowercase__   :    torch.FloatTensor     ,       lowercase__   :    torch.FloatTensor     ,       lowercase__   :    torch.FloatTensor     ,       ):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.sigmas.to(device=original_samples.device     ,       dtype=original_samples.dtype)
        if original_samples.device.type == "mps" and torch.is_floating_point(_lowercase):
               # mps does not support float64
               lowerCAmelCase__	      =   self.timesteps.to(original_samples.device     ,       dtype=torch.floataa)
               lowerCAmelCase__	      =   timesteps.to(original_samples.device     ,       dtype=torch.floataa)
        else:
               lowerCAmelCase__	      =   self.timesteps.to(original_samples.device)
               lowerCAmelCase__	      =   timesteps.to(original_samples.device)

        lowerCAmelCase__	      =   [(schedule_timesteps == t).nonzero().item() for t in timesteps]

        lowerCAmelCase__	      =   sigmas[step_indices].flatten()
        while len(sigma.shape) < len(original_samples.shape):
               lowerCAmelCase__	      =   sigma.unsqueeze(-1)

        lowerCAmelCase__	      =   original_samples + noise * sigma
        return noisy_samples




 def __len__(     self   :    Optional[int]):







        '''simple docstring'''



        return self.config.num_train_timesteps

"""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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase       :    Union[str, Any]         =		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCamelCase       :    Optional[Any]         =		[]
for i in range(6):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
  )
  rename_keys.append(
      (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
  )
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
  rename_keys.append(
      (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
  )
  rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
  # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
  )
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
  )
  rename_keys.append(
      (
          f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
          f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
      )
  )
  rename_keys.append(
      (
          f'''transformer.decoder.layers.{i}.multihead_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'''))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
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"),
    ]
)


def   __UpperCAmelCase     (	_snake_case		:       List[str],				_snake_case		:       Tuple,				_snake_case		:       Any       ):
   _lowercase							     =       state_dict.pop(_snake_case       )
   _lowercase							     =       val


def   __UpperCAmelCase     (	_snake_case		:       Optional[int]       ):
   _lowercase							     =       OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         _lowercase							     =       key.replace("backbone.0.body",				"backbone.conv_encoder.model"       )
         _lowercase							     =       value
      else:
         _lowercase							     =       value

   return new_state_dict


def   __UpperCAmelCase     (	_snake_case		:       Optional[Any],				_snake_case		:       int=False       ):
   _lowercase							     =       ""
   if is_panoptic:
      _lowercase							     =       "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)
      _lowercase							     =       state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
      _lowercase							     =       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
      _lowercase							     =       in_proj_weight[:2_5_6, :]
      _lowercase							     =       in_proj_bias[:2_5_6]
      _lowercase							     =       in_proj_weight[2_5_6:5_1_2, :]
      _lowercase							     =       in_proj_bias[2_5_6:5_1_2]
      _lowercase							     =       in_proj_weight[-2_5_6:, :]
      _lowercase							     =       in_proj_bias[-2_5_6:]
   # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
   for i in range(6       ):
      # read in weights + bias of input projection layer of self-attention
      _lowercase							     =       state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""       )
      _lowercase							     =       state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""       )
      # next, add query, keys and values (in that order) to the state dict
      _lowercase							     =       in_proj_weight[:2_5_6, :]
      _lowercase							     =       in_proj_bias[:2_5_6]
      _lowercase							     =       in_proj_weight[2_5_6:5_1_2, :]
      _lowercase							     =       in_proj_bias[2_5_6:5_1_2]
      _lowercase							     =       in_proj_weight[-2_5_6:, :]
      _lowercase							     =       in_proj_bias[-2_5_6:]
      # read in weights + bias of input projection layer of cross-attention
      _lowercase							     =       state_dict.pop(
          f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""       )
      _lowercase							     =       state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""       )
      # next, add query, keys and values (in that order) of cross-attention to the state dict
      _lowercase							     =       in_proj_weight_cross_attn[:2_5_6, :]
      _lowercase							     =       in_proj_bias_cross_attn[:2_5_6]
      _lowercase							     =       in_proj_weight_cross_attn[2_5_6:5_1_2, :]
      _lowercase							     =       in_proj_bias_cross_attn[2_5_6:5_1_2]
      _lowercase							     =       in_proj_weight_cross_attn[-2_5_6:, :]
      _lowercase							     =       in_proj_bias_cross_attn[-2_5_6:]


def   __UpperCAmelCase     (	):
   _lowercase							     =       "http://images.cocodataset.org/val2017/000000039769.jpg"
   _lowercase							     =       Image.open(requests.get(_snake_case,				stream=_snake_case       ).raw       )

   return im


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

   # load image processor
   _lowercase							     =       "coco_panoptic" if is_panoptic else "coco_detection"
   _lowercase							     =       DetrImageProcessor(format=_snake_case       )

   # prepare image
   _lowercase							     =       prepare_img()
   _lowercase							     =       image_processor(images=_snake_case,				return_tensors="pt"       )
   _lowercase							     =       encoding["pixel_values"]

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

   # load original model from torch hub
   _lowercase							     =       torch.hub.load("facebookresearch/detr",				_snake_case,				pretrained=_snake_case       ).eval()
   _lowercase							     =       detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         _lowercase							     =       "detr." + src
      rename_key(_snake_case,				_snake_case,				_snake_case       )
   _lowercase							     =       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
   _lowercase							     =       "detr.model." if is_panoptic else "model."
   for key in state_dict.copy().keys():
      if is_panoptic:
         if (
             key.startswith("detr"       )
             and not key.startswith("class_labels_classifier"       )
             and not key.startswith("bbox_predictor"       )
         ):
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
         elif key.startswith("bbox_attention"       ) or key.startswith("mask_head"       ):
            continue
         else:
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
      else:
         if not key.startswith("class_labels_classifier"       ) and not key.startswith("bbox_predictor"       ):
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
    # finally, create HuggingFace model and load state dict
   _lowercase							     =       DetrForSegmentation(_snake_case       ) if is_panoptic else DetrForObjectDetection(_snake_case       )
   model.load_state_dict(_snake_case       )
   model.eval()
   # verify our conversion
   _lowercase							     =       detr(_snake_case       )
   _lowercase							     =       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__":
  __UpperCamelCase       :    int         =		argparse.ArgumentParser()

  parser.add_argument(
      "--model_name", default="detr_resnet50", type=str, help="Name of the 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       :    str         =		parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)