





"""simple docstring"""


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

import numpy as np
import torch

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



@dataclass
class    lowercase_ (   __lowerCAmelCase							):
       snake_case__ :							torch.FloatTensor
       snake_case__ :							torch.FloatTensor





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase=0.999		,   _UpperCAmelCase="cosine"		,   )			->     str:

 '''simple docstring'''






 if alpha_transform_type == "cosine":

  def alpha_bar_fn(_UpperCAmelCase		):
   return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(_UpperCAmelCase		):
   return math.exp(t * -12.0		)

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

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



class    lowercase_ (   __lowerCAmelCase ,__lowerCAmelCase							):
       snake_case__ :							List[Any] 							=   1

       @register_to_config
       def __init__(	self      ,    __SCREAMING_SNAKE_CASE = 1000      ,    __SCREAMING_SNAKE_CASE = 0.0001      ,    __SCREAMING_SNAKE_CASE = 0.02      ,    __SCREAMING_SNAKE_CASE = "linear"      ,    __SCREAMING_SNAKE_CASE = 0.0      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = True      ,    ):






        '''simple docstring'''

        if trained_betas is not None:
         snake_case__										= torch.from_numpy(__SCREAMING_SNAKE_CASE   )
        elif beta_schedule == "linear":
         snake_case__										= torch.linspace(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    dtype=torch.floataa   )
        elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         snake_case__										= (
             torch.linspace(beta_start**0.5      ,    beta_end**0.5      ,    __SCREAMING_SNAKE_CASE      ,    dtype=torch.floataa   ) ** 2
         )
        elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         snake_case__										= betas_for_alpha_bar(__SCREAMING_SNAKE_CASE   )
        elif beta_schedule == "sigmoid":
         # GeoDiff sigmoid schedule
         snake_case__										= torch.linspace(-6      ,    6      ,    __SCREAMING_SNAKE_CASE   )
         snake_case__										= torch.sigmoid(__SCREAMING_SNAKE_CASE   ) * (beta_end - beta_start) + beta_start
        else:
         raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"   )

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

        snake_case__										= torch.tensor(1.0   )

        # standard deviation of the initial noise distribution
        snake_case__										= 1.0

        # setable values
        snake_case__										= None
        snake_case__										= torch.from_numpy(np.arange(0      ,    __SCREAMING_SNAKE_CASE   )[::-1].copy()   )

        snake_case__										= eta

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None   ):






        '''simple docstring'''

        return sample

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = 10      ,    __SCREAMING_SNAKE_CASE = 10      ,    __SCREAMING_SNAKE_CASE = None      ,    ):






        '''simple docstring'''

        snake_case__										= min(self.config.num_train_timesteps      ,    __SCREAMING_SNAKE_CASE   )
        snake_case__										= num_inference_steps

        snake_case__										= []

        snake_case__										= {}
        for j in range(0      ,    num_inference_steps - jump_length      ,    __SCREAMING_SNAKE_CASE   ):
         snake_case__										= jump_n_sample - 1

        snake_case__										= num_inference_steps
        while t >= 1:
         snake_case__										= t - 1
         timesteps.append(__SCREAMING_SNAKE_CASE   )

         if jumps.get(__SCREAMING_SNAKE_CASE      ,    0   ) > 0:
          snake_case__										= jumps[t] - 1
          for _ in range(__SCREAMING_SNAKE_CASE   ):
           snake_case__										= t + 1
           timesteps.append(__SCREAMING_SNAKE_CASE   )

        snake_case__										= np.array(__SCREAMING_SNAKE_CASE   ) * (self.config.num_train_timesteps // self.num_inference_steps)
        snake_case__										= torch.from_numpy(__SCREAMING_SNAKE_CASE   ).to(__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= t - self.config.num_train_timesteps // self.num_inference_steps

        snake_case__										= self.alphas_cumprod[t]
        snake_case__										= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        snake_case__										= 1 - alpha_prod_t
        snake_case__										= 1 - alpha_prod_t_prev

        # For t > 0, compute predicted variance βt (see formula (6) and (7) from
        # https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
        # previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
        # variance to pred_sample
        # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
        # without eta.
        # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
        snake_case__										= (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

        return variance

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = True      ,    ):






        '''simple docstring'''

        snake_case__										= timestep
        snake_case__										= timestep - self.config.num_train_timesteps // self.num_inference_steps

        # 1. compute alphas, betas
        snake_case__										= self.alphas_cumprod[t]
        snake_case__										= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        snake_case__										= 1 - alpha_prod_t

        # 2. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
        snake_case__										= (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
         snake_case__										= torch.clamp(__SCREAMING_SNAKE_CASE      ,    -1      ,    1   )

        # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
        # substitute formula (7) in the algorithm coming from DDPM paper
        # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
        # DDIM schedule gives the same results as DDPM with eta = 1.0
        # Noise is being reused in 7. and 8., but no impact on quality has
        # been observed.

        # 5. Add noise
        snake_case__										= model_output.device
        snake_case__										= randn_tensor(model_output.shape      ,    generator=__SCREAMING_SNAKE_CASE      ,    device=__SCREAMING_SNAKE_CASE      ,    dtype=model_output.dtype   )
        snake_case__										= self.eta * self._get_variance(__SCREAMING_SNAKE_CASE   ) ** 0.5

        snake_case__										= 0
        if t > 0 and self.eta > 0:
         snake_case__										= std_dev_t * noise

        # 6. compute "direction pointing to x_t" of formula (12)
        # from https://arxiv.org/pdf/2010.02502.pdf
        snake_case__										= (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

        # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        snake_case__										= alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

        # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
        snake_case__										= (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

        # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
        snake_case__										= mask * prev_known_part + (1.0 - mask) * prev_unknown_part

        if not return_dict:
         return (
             pred_prev_sample,
             pred_original_sample,
         )

        return RePaintSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE      ,    pred_original_sample=__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE=None   ):






        '''simple docstring'''

        snake_case__										= self.config.num_train_timesteps // self.num_inference_steps

        for i in range(__SCREAMING_SNAKE_CASE   ):
         snake_case__										= self.betas[timestep + i]
         if sample.device.type == "mps":
          # randn does not work reproducibly on mps
          snake_case__										= randn_tensor(sample.shape      ,    dtype=sample.dtype      ,    generator=__SCREAMING_SNAKE_CASE   )
          snake_case__										= noise.to(sample.device   )
         else:
          snake_case__										= randn_tensor(sample.shape      ,    generator=__SCREAMING_SNAKE_CASE      ,    device=sample.device      ,    dtype=sample.dtype   )

         # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
         snake_case__										= (1 - beta) ** 0.5 * sample + beta**0.5 * noise

        return sample

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."   )

       def __len__(	self   ):






        '''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()
a__    :   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)
a__    :   List[str]    						= []
for i in range(6):
   # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
   rename_keys.append(
       (f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
   )
   rename_keys.append(
       (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
   )
   rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
   rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
   rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
   rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
   rename_keys.append(
       (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
   )
   rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
   rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
   rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
   # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
   rename_keys.append(
       (f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
   )
   rename_keys.append(
       (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
   )
   rename_keys.append(
       (
           f'''transformer.decoder.layers.{i}.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 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)			->     Any:

 '''simple docstring'''






 snake_case__										= state_dict.pop(_UpperCAmelCase		)
 snake_case__										= val





def 							a__	(     _UpperCAmelCase		)			->     Any:

 '''simple docstring'''






 snake_case__										= OrderedDict()
 for key, value in state_dict.items():
  if "backbone.0.body" in key:
   snake_case__										= key.replace("backbone.0.body"		,   "backbone.conv_encoder.model"		)
   snake_case__										= value
  else:
   snake_case__										= value

 return new_state_dict





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase=False		)			->     List[Any]:

 '''simple docstring'''






 snake_case__										= ""
 if is_panoptic:
  snake_case__										= "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)
  snake_case__										= state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"		)
  snake_case__										= 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
  snake_case__										= in_proj_weight[:2_5_6, :]
  snake_case__										= in_proj_bias[:2_5_6]
  snake_case__										= in_proj_weight[2_5_6:5_1_2, :]
  snake_case__										= in_proj_bias[2_5_6:5_1_2]
  snake_case__										= in_proj_weight[-2_5_6:, :]
  snake_case__										= 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
  snake_case__										= state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"		)
  snake_case__										= 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
  snake_case__										= in_proj_weight[:2_5_6, :]
  snake_case__										= in_proj_bias[:2_5_6]
  snake_case__										= in_proj_weight[2_5_6:5_1_2, :]
  snake_case__										= in_proj_bias[2_5_6:5_1_2]
  snake_case__										= in_proj_weight[-2_5_6:, :]
  snake_case__										= in_proj_bias[-2_5_6:]
  # read in weights + bias of input projection layer of cross-attention
  snake_case__										= state_dict.pop(
      f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"		)
  snake_case__										= 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
  snake_case__										= in_proj_weight_cross_attn[:2_5_6, :]
  snake_case__										= in_proj_bias_cross_attn[:2_5_6]
  snake_case__										= in_proj_weight_cross_attn[2_5_6:5_1_2, :]
  snake_case__										= in_proj_bias_cross_attn[2_5_6:5_1_2]
  snake_case__										= in_proj_weight_cross_attn[-2_5_6:, :]
  snake_case__										= in_proj_bias_cross_attn[-2_5_6:]





def 							a__	(     )			->     Optional[int]:

 '''simple docstring'''






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

 return im





@torch.no_grad()
def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     Any:

 '''simple docstring'''






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

 # load image processor
 snake_case__										= "coco_panoptic" if is_panoptic else "coco_detection"
 snake_case__										= DetrImageProcessor(format=_UpperCAmelCase		)

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

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

 # load original model from torch hub
 snake_case__										= torch.hub.load("facebookresearch/detr"		,   _UpperCAmelCase		,   pretrained=_UpperCAmelCase		).eval()
 snake_case__										= detr.state_dict()
 # rename keys
 for src, dest in rename_keys:
  if is_panoptic:
   snake_case__										= "detr." + src
  rename_key(_UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)
 snake_case__										= 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
 snake_case__										= "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"		)
   ):
    snake_case__										= state_dict.pop(_UpperCAmelCase		)
    snake_case__										= val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    snake_case__										= state_dict.pop(_UpperCAmelCase		)
    snake_case__										= val
   elif key.startswith("bbox_attention"		) or key.startswith("mask_head"		):
    continue
   else:
    snake_case__										= state_dict.pop(_UpperCAmelCase		)
    snake_case__										= val
  else:
   if not key.startswith("class_labels_classifier"		) and not key.startswith("bbox_predictor"		):
    snake_case__										= state_dict.pop(_UpperCAmelCase		)
    snake_case__										= val
    # finally, create HuggingFace model and load state dict
 snake_case__										= DetrForSegmentation(_UpperCAmelCase		) if is_panoptic else DetrForObjectDetection(_UpperCAmelCase		)
 model.load_state_dict(_UpperCAmelCase		)
 model.eval()
 # verify our conversion
 snake_case__										= detr(_UpperCAmelCase		)
 snake_case__										= 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__":
   a__    :   List[Any]    						= 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."""
   )
   a__    :   Optional[int]    						= parser.parse_args()
   convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

