code: 
  
  
  import math
  from collections import defaultdict
  from typing import List, Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import randn_tensor
  from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=0.999							,     _UpperCAmelCase="cosine"							,     )    ->  List[str]:
  						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}"""				)
  
  						_a     =			[]
  						for i in range(lowercase__				):
  												_a     =			i / num_diffusion_timesteps
  												_a     =			(i + 1) / num_diffusion_timesteps
  												betas.append(min(1 - alpha_bar_fn(lowercase__				) / alpha_bar_fn(lowercase__				)							,     lowercase__				)				)
  						return torch.tensor(lowercase__							,     dtype=torch.floataa				)
  
  
  
  
  
  
  class 					_UpperCamelCase (      lowerCamelCase__			,							lowerCamelCase__	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  [e.name for e in KarrasDiffusionSchedulers]
  							_A  				=  2
  
  
  
  
  
  
  
  							@register_to_config
  							def __init__(		self  :				Optional[int]		,				SCREAMING_SNAKE_CASE_  :				int = 1_0_0_0		,				SCREAMING_SNAKE_CASE_  :				float = 0.00085		,				SCREAMING_SNAKE_CASE_  :				float = 0.012		,				SCREAMING_SNAKE_CASE_  :				str = "linear"		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[np.ndarray, List[float]]] = None		,				SCREAMING_SNAKE_CASE_  :				str = "epsilon"		,				SCREAMING_SNAKE_CASE_  :				str = "linspace"		,				SCREAMING_SNAKE_CASE_  :				int = 0		,				):
  													if trained_betas is not None:
  																			_a     =			torch.tensor(A_		,				dtype=torch.floataa		)
  													elif beta_schedule == "linear":
  																			_a     =			torch.linspace(A_		,				A_		,				A_		,				dtype=torch.floataa		)
  													elif beta_schedule == "scaled_linear":
  																			# this schedule is very specific to the latent diffusion model.
  																			_a     =			(
  																			    torch.linspace(beta_start**0.5		,				beta_end**0.5		,				A_		,				dtype=torch.floataa		) ** 2
  																			)
  													elif beta_schedule == "squaredcos_cap_v2":
  																			# Glide cosine schedule
  																			_a     =			betas_for_alpha_bar(A_		)
  													else:
  																			raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""		)
  
  													_a     =			1.0 - self.betas
  													_a     =			torch.cumprod(self.alphas		,				dim=0		)
  
  													#  set all values
  													self.set_timesteps(A_		,				A_		,				A_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				Any		,				SCREAMING_SNAKE_CASE_  :				List[str]=None		):
  													if schedule_timesteps is None:
  																			_a     =			self.timesteps
  
  													_a     =			(schedule_timesteps == timestep).nonzero()
  
  													# The sigma index that is taken for the **very** first `step`
  													# is always the second index (or the last index if there is only 1)
  													# This way we can ensure we don't accidentally skip a sigma in
  													# case we start in the middle of the denoising schedule (e.g. for image-to-image)
  													if len(self._index_counter		) == 0:
  																			_a     =			1 if len(A_		) > 1 else 0
  													else:
  																			_a     =			timestep.cpu().item() if torch.is_tensor(A_		) else timestep
  																			_a     =			self._index_counter[timestep_int]
  
  													return indices[pos].item()
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				List[str]		):
  													# standard deviation of the initial noise distribution
  													if self.config.timestep_spacing in ["linspace", "trailing"]:
  																			return self.sigmas.max()
  
  													return (self.sigmas.max() ** 2 + 1) ** 0.5
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				torch.FloatTensor		,				SCREAMING_SNAKE_CASE_  :				Union[float, torch.FloatTensor]		,				):
  													_a     =			self.index_for_timestep(A_		)
  
  													if self.state_in_first_order:
  																			_a     =			self.sigmas[step_index]
  													else:
  																			_a     =			self.sigmas_interpol[step_index - 1]
  
  													_a     =			sample / ((sigma**2 + 1) ** 0.5)
  													return sample
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Dict		,				SCREAMING_SNAKE_CASE_  :				int		,				SCREAMING_SNAKE_CASE_  :				Union[str, torch.device] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[int] = None		,				):
  													_a     =			num_inference_steps
  
  													_a     =			num_train_timesteps or self.config.num_train_timesteps
  
  													# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
  													if self.config.timestep_spacing == "linspace":
  																			_a     =			np.linspace(0		,				num_train_timesteps - 1		,				A_		,				dtype=A_		)[::-1].copy()
  													elif self.config.timestep_spacing == "leading":
  																			_a     =			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
  																			_a     =			(np.arange(0		,				A_		) * step_ratio).round()[::-1].copy().astype(A_		)
  																			timesteps += self.config.steps_offset
  													elif self.config.timestep_spacing == "trailing":
  																			_a     =			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
  																			_a     =			(np.arange(A_		,				0		,				-step_ratio		)).round().copy().astype(A_		)
  																			timesteps -= 1
  													else:
  																			raise ValueError(
  																			    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'."""		)
  
  													_a     =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
  													_a     =			torch.from_numpy(np.log(A_		)		).to(A_		)
  
  													_a     =			np.interp(A_		,				np.arange(0		,				len(A_		)		)		,				A_		)
  													_a     =			np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
  													_a     =			torch.from_numpy(A_		).to(device=A_		)
  
  													# compute up and down sigmas
  													_a     =			sigmas.roll(-1		)
  													_a     =			0.0
  													_a     =			(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
  													_a     =			(sigmas_next**2 - sigmas_up**2) ** 0.5
  													_a     =			0.0
  
  													# compute interpolated sigmas
  													_a     =			sigmas.log().lerp(sigmas_down.log()		,				0.5		).exp()
  													_a     =			0.0
  
  													# set sigmas
  													_a     =			torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2		), sigmas[-1:]]		)
  													_a     =			torch.cat(
  													    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2		), sigmas_interpol[-1:]]		)
  													_a     =			torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2		), sigmas_up[-1:]]		)
  													_a     =			torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2		), sigmas_down[-1:]]		)
  
  													if str(A_		).startswith('mps'		):
  																			# mps does not support float64
  																			_a     =			torch.from_numpy(A_		).to(A_		,				dtype=torch.floataa		)
  													else:
  																			_a     =			torch.from_numpy(A_		).to(A_		)
  
  													_a     =			self.sigma_to_t(A_		).to(A_		,				dtype=timesteps.dtype		)
  													_a     =			torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])		,				dim=-1		).flatten()
  
  													_a     =			torch.cat([timesteps[:1], interleaved_timesteps]		)
  
  													_a     =			None
  
  													# for exp beta schedules, such as the one for `pipeline_shap_e.py`
  													# we need an index counter
  													_a     =			defaultdict(A_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				List[str]		):
  													# get log sigma
  													_a     =			sigma.log()
  
  													# get distribution
  													_a     =			log_sigma - self.log_sigmas[:, None]
  
  													# get sigmas range
  													_a     =			dists.ge(0		).cumsum(dim=0		).argmax(dim=0		).clamp(max=self.log_sigmas.shape[0] - 2		)
  													_a     =			low_idx + 1
  
  													_a     =			self.log_sigmas[low_idx]
  													_a     =			self.log_sigmas[high_idx]
  
  													# interpolate sigmas
  													_a     =			(low - log_sigma) / (low - high)
  													_a     =			w.clamp(0		,				1		)
  
  													# transform interpolation to time range
  													_a     =			(1 - w) * low_idx + w * high_idx
  													_a     =			t.view(sigma.shape		)
  													return t
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				List[str]		):
  													return self.sample is None
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Dict		,				SCREAMING_SNAKE_CASE_  :				Union[torch.FloatTensor, np.ndarray]		,				SCREAMING_SNAKE_CASE_  :				Union[float, torch.FloatTensor]		,				SCREAMING_SNAKE_CASE_  :				Union[torch.FloatTensor, np.ndarray]		,				SCREAMING_SNAKE_CASE_  :				Optional[torch.Generator] = None		,				SCREAMING_SNAKE_CASE_  :				bool = True		,				):
  													_a     =			self.index_for_timestep(A_		)
  
  													# advance index counter by 1
  													_a     =			timestep.cpu().item() if torch.is_tensor(A_		) else timestep
  													self._index_counter[timestep_int] += 1
  
  													if self.state_in_first_order:
  																			_a     =			self.sigmas[step_index]
  																			_a     =			self.sigmas_interpol[step_index]
  																			_a     =			self.sigmas_up[step_index]
  																			_a     =			self.sigmas_down[step_index - 1]
  													else:
  																			# 2nd order / KPDM2's method
  																			_a     =			self.sigmas[step_index - 1]
  																			_a     =			self.sigmas_interpol[step_index - 1]
  																			_a     =			self.sigmas_up[step_index - 1]
  																			_a     =			self.sigmas_down[step_index - 1]
  
  													# currently only gamma=0 is supported. This usually works best anyways.
  													# We can support gamma in the future but then need to scale the timestep before
  													# passing it to the model which requires a change in API
  													_a     =			0
  													_a     =			sigma * (gamma + 1)  # Note: sigma_hat == sigma for now
  
  													_a     =			model_output.device
  													_a     =			randn_tensor(model_output.shape		,				dtype=model_output.dtype		,				device=A_		,				generator=A_		)
  
  													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
  													if self.config.prediction_type == "epsilon":
  																			_a     =			sigma_hat if self.state_in_first_order else sigma_interpol
  																			_a     =			sample - sigma_input * model_output
  													elif self.config.prediction_type == "v_prediction":
  																			_a     =			sigma_hat if self.state_in_first_order else sigma_interpol
  																			_a     =			model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
  																			    sample / (sigma_input**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`"""		)
  
  													if self.state_in_first_order:
  																			# 2. Convert to an ODE derivative for 1st order
  																			_a     =			(sample - pred_original_sample) / sigma_hat
  																			# 3. delta timestep
  																			_a     =			sigma_interpol - sigma_hat
  
  																			# store for 2nd order step
  																			_a     =			sample
  																			_a     =			dt
  																			_a     =			sample + derivative * dt
  													else:
  																			# DPM-Solver-2
  																			# 2. Convert to an ODE derivative for 2nd order
  																			_a     =			(sample - pred_original_sample) / sigma_interpol
  																			# 3. delta timestep
  																			_a     =			sigma_down - sigma_hat
  
  																			_a     =			self.sample
  																			_a     =			None
  
  																			_a     =			sample + derivative * dt
  																			_a     =			prev_sample + noise * sigma_up
  
  													if not return_dict:
  																			return (prev_sample,)
  
  													return SchedulerOutput(prev_sample=A_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				int		,				SCREAMING_SNAKE_CASE_  :				torch.FloatTensor		,				SCREAMING_SNAKE_CASE_  :				torch.FloatTensor		,				SCREAMING_SNAKE_CASE_  :				torch.FloatTensor		,				):
  													# Make sure sigmas and timesteps have the same device and dtype as original_samples
  													_a     =			self.sigmas.to(device=original_samples.device		,				dtype=original_samples.dtype		)
  													if original_samples.device.type == "mps" and torch.is_floating_point(A_		):
  																			# mps does not support float64
  																			_a     =			self.timesteps.to(original_samples.device		,				dtype=torch.floataa		)
  																			_a     =			timesteps.to(original_samples.device		,				dtype=torch.floataa		)
  													else:
  																			_a     =			self.timesteps.to(original_samples.device		)
  																			_a     =			timesteps.to(original_samples.device		)
  
  													_a     =			[self.index_for_timestep(A_		,				A_		) for t in timesteps]
  
  													_a     =			sigmas[step_indices].flatten()
  													while len(sigma.shape		) < len(original_samples.shape		):
  																			_a     =			sigma.unsqueeze(-1		)
  
  													_a     =			original_samples + noise * sigma
  													return noisy_samples
  
  
  
  
  
  
  
  							def __len__(		self  :				int		):
  													return self.config.num_train_timesteps
  
  
  
style_context: 
  
  
  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()
  lowercase_								=    logging.get_logger(__name__)
  
  # here we list all keys to be renamed (original name on the left, our name on the right)
  lowercase_								=    []
  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 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)    ->  Union[str, Any]:
  						_a     =			state_dict.pop(_UpperCAmelCase				)
  						_a     =			val
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  List[str]:
  						_a     =			OrderedDict()
  						for key, value in state_dict.items():
  												if "backbone.0.body" in key:
  																		_a     =			key.replace('backbone.0.body'							,     'backbone.conv_encoder.model'				)
  																		_a     =			value
  												else:
  																		_a     =			value
  
  						return new_state_dict
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=False				)    ->  Tuple:
  						_a     =			''
  						if is_panoptic:
  												_a     =			'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)
  												_a     =			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""				)
  												_a     =			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
  												_a     =			in_proj_weight[:256, :]
  												_a     =			in_proj_bias[:256]
  												_a     =			in_proj_weight[256:512, :]
  												_a     =			in_proj_bias[256:512]
  												_a     =			in_proj_weight[-256:, :]
  												_a     =			in_proj_bias[-256:]
  						# 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
  												_a     =			state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""				)
  												_a     =			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
  												_a     =			in_proj_weight[:256, :]
  												_a     =			in_proj_bias[:256]
  												_a     =			in_proj_weight[256:512, :]
  												_a     =			in_proj_bias[256:512]
  												_a     =			in_proj_weight[-256:, :]
  												_a     =			in_proj_bias[-256:]
  												# read in weights + bias of input projection layer of cross-attention
  												_a     =			state_dict.pop(
  												    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""				)
  												_a     =			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
  												_a     =			in_proj_weight_cross_attn[:256, :]
  												_a     =			in_proj_bias_cross_attn[:256]
  												_a     =			in_proj_weight_cross_attn[256:512, :]
  												_a     =			in_proj_bias_cross_attn[256:512]
  												_a     =			in_proj_weight_cross_attn[-256:, :]
  												_a     =			in_proj_bias_cross_attn[-256:]
  
  
  def 							SCREAMING_SNAKE_CASE					(  )    ->  Union[str, Any]:
  						_a     =			'http://images.cocodataset.org/val2017/000000039769.jpg'
  						_a     =			Image.open(requests.get(_UpperCAmelCase							,     stream=_UpperCAmelCase				).raw				)
  
  						return im
  
  
  @torch.no_grad()
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase				)    ->  List[str]:
  						_a     =			DetrConfig()
  						# set backbone and dilation attributes
  						if "resnet101" in model_name:
  												_a     =			'resnet101'
  						if "dc5" in model_name:
  												_a     =			True
  						_a     =			'panoptic' in model_name
  						if is_panoptic:
  												_a     =			250
  						else:
  												_a     =			91
  												_a     =			'huggingface/label-files'
  												_a     =			'coco-detection-id2label.json'
  												_a     =			json.load(open(hf_hub_download(_UpperCAmelCase							,     _UpperCAmelCase							,     repo_type='dataset'				)							,     'r'				)				)
  												_a     =			{int(_UpperCAmelCase				): v for k, v in idalabel.items()}
  												_a     =			idalabel
  												_a     =			{v: k for k, v in idalabel.items()}
  
  						# load image processor
  						_a     =			'coco_panoptic' if is_panoptic else 'coco_detection'
  						_a     =			DetrImageProcessor(format=_UpperCAmelCase				)
  
  						# prepare image
  						_a     =			prepare_img()
  						_a     =			image_processor(images=_UpperCAmelCase							,     return_tensors='pt'				)
  						_a     =			encoding['pixel_values']
  
  						logger.info(f"""Converting model {model_name}..."""				)
  
  						# load original model from torch hub
  						_a     =			torch.hub.load('facebookresearch/detr'							,     _UpperCAmelCase							,     pretrained=_UpperCAmelCase				).eval()
  						_a     =			detr.state_dict()
  						# rename keys
  						for src, dest in rename_keys:
  												if is_panoptic:
  																		_a     =			'detr.' + src
  												rename_key(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)
  						_a     =			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
  						_a     =			'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'				)
  																		):
  																								_a     =			state_dict.pop(_UpperCAmelCase				)
  																								_a     =			val
  																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
  																								_a     =			state_dict.pop(_UpperCAmelCase				)
  																								_a     =			val
  																		elif key.startswith('bbox_attention'				) or key.startswith('mask_head'				):
  																								continue
  																		else:
  																								_a     =			state_dict.pop(_UpperCAmelCase				)
  																								_a     =			val
  												else:
  																		if not key.startswith('class_labels_classifier'				) and not key.startswith('bbox_predictor'				):
  																								_a     =			state_dict.pop(_UpperCAmelCase				)
  																								_a     =			val
      # finally, create HuggingFace model and load state dict
  						_a     =			DetrForSegmentation(_UpperCAmelCase				) if is_panoptic else DetrForObjectDetection(_UpperCAmelCase				)
  						model.load_state_dict(_UpperCAmelCase				)
  						model.eval()
  						# verify our conversion
  						_a     =			detr(_UpperCAmelCase				)
  						_a     =			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__":
  		lowercase_								=    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.'
  		)
  		lowercase_								=    parser.parse_args()
  		convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
  
  
  