code: 
  
  
  
  
  import math
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  from transformers import CLIPTextModelWithProjection, CLIPTokenizer
  
  from ...models import PriorTransformer
  from ...pipelines import DiffusionPipeline
  from ...schedulers import HeunDiscreteScheduler
  from ...utils import (
      BaseOutput,
      is_accelerate_available,
      is_accelerate_version,
      logging,
      randn_tensor,
      replace_example_docstring,
  )
  from .renderer import ShapERenderer
  
  
  _A							    =	logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  _A							    =	"""
      Examples:
          ```py
          >>> import torch
          >>> from diffusers import DiffusionPipeline
          >>> from diffusers.utils import export_to_gif
  
          >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")
  
          >>> repo = \"openai/shap-e\"
          >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
          >>> pipe = pipe.to(device)
  
          >>> guidance_scale = 15.0
          >>> prompt = \"a shark\"
  
          >>> images = pipe(
          ...     prompt,
          ...     guidance_scale=guidance_scale,
          ...     num_inference_steps=64,
          ...     frame_size=256,
          ... ).images
  
          >>> gif_path = export_to_gif(images[0], \"shark_3d.gif\")
          ```
  """
  
  
  @dataclass
  class 							_lowerCAmelCase		(     UpperCamelCase__	):
  				lowerCamelCase__									=    42
  
  
  
  
  
  
  
  class 							_lowerCAmelCase		(     UpperCamelCase__	):
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		)    ->      int:
  											super().__init__()
  
  											self.register_modules(
  											    prior=snake_case_		,		text_encoder=snake_case_		,		tokenizer=snake_case_		,		scheduler=snake_case_		,		renderer=snake_case_		,		)
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      List[str]:
  											if latents is None:
  																		SCREAMING_SNAKE_CASE			: Optional[Any]	     =randn_tensor(snake_case_		,		generator=snake_case_		,		device=snake_case_		,		dtype=snake_case_							)
  											else:
  																		if latents.shape != shape:
  																									raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'							)
  																		SCREAMING_SNAKE_CASE			: Any	     =latents.to(snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =latents * scheduler.init_noise_sigma
  											return latents
  
  
  
  
  				def     __a		( self		,		snake_case_=0							)    ->      str:
  											if is_accelerate_available():
  																		from accelerate import cpu_offload
  											else:
  																		raise ImportError('''Please install accelerate via `pip install accelerate`'''							)
  
  											SCREAMING_SNAKE_CASE			: int	     =torch.device(F'cuda:{gpu_id}'							)
  
  											SCREAMING_SNAKE_CASE			: str	     =[self.text_encoder, self.prior]
  											for cpu_offloaded_model in models:
  																		if cpu_offloaded_model is not None:
  																									cpu_offload(snake_case_		,		snake_case_							)
  
  
  
  
  				def     __a		( self		,		snake_case_=0							)    ->      Any:
  											if is_accelerate_available() and is_accelerate_version('''>='''		,		'''0.17.0.dev0'''							):
  																		from accelerate import cpu_offload_with_hook
  											else:
  																		raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''							)
  
  											SCREAMING_SNAKE_CASE			: List[Any]	     =torch.device(F'cuda:{gpu_id}'							)
  
  											if self.device.type != "cpu":
  																		self.to('''cpu'''		,		silence_dtype_warnings=snake_case_							)
  																		torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
  											SCREAMING_SNAKE_CASE			: Any	     =None
  											for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
  																		SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: List[Any]	     =cpu_offload_with_hook(snake_case_		,		snake_case_		,		prev_module_hook=snake_case_							)
  
  											if self.safety_checker is not None:
  																		SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =cpu_offload_with_hook(self.safety_checker		,		snake_case_		,		prev_module_hook=snake_case_							)
  
  											# We'll offload the last model manually.
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =hook
  
  
  
  
  				@property
  				def     __a		( self							)    ->      Optional[int]:
  											if self.device != torch.device('''meta'''							) or not hasattr(self.text_encoder		,		'''_hf_hook'''							):
  																		return self.device
  											for module in self.text_encoder.modules():
  																		if (
  																		    hasattr(snake_case_		,		'''_hf_hook'''							)
  																		    and hasattr(module._hf_hook		,		'''execution_device'''							)
  																		    and module._hf_hook.execution_device is not None
  																		):
  																									return torch.device(module._hf_hook.execution_device							)
  											return self.device
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		)    ->      List[Any]:
  											len(snake_case_							) if isinstance(snake_case_		,		snake_case_							) else 1
  
  											# YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
  											SCREAMING_SNAKE_CASE			: List[str]	     =0
  											# get prompt text embeddings
  											SCREAMING_SNAKE_CASE			: Dict	     =self.tokenizer(
  											    snake_case_		,		padding='''max_length'''		,		max_length=self.tokenizer.model_max_length		,		truncation=snake_case_		,		return_tensors='''pt'''		,		)
  											SCREAMING_SNAKE_CASE			: List[Any]	     =text_inputs.input_ids
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.tokenizer(snake_case_		,		padding='''longest'''		,		return_tensors='''pt'''							).input_ids
  
  											if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(snake_case_		,		snake_case_							):
  																		SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]							)
  																		logger.warning(
  																		    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
  																		    F' {self.tokenizer.model_max_length} tokens: {removed_text}'							)
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.text_encoder(text_input_ids.to(snake_case_							)							)
  											SCREAMING_SNAKE_CASE			: int	     =text_encoder_output.text_embeds
  
  											SCREAMING_SNAKE_CASE			: List[str]	     =prompt_embeds.repeat_interleave(snake_case_		,		dim=0							)
  											# in Shap-E it normalize the prompt_embeds and then later rescale it
  											SCREAMING_SNAKE_CASE			: Dict	     =prompt_embeds / torch.linalg.norm(snake_case_		,		dim=-1		,		keepdim=snake_case_							)
  
  											if do_classifier_free_guidance:
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =torch.zeros_like(snake_case_							)
  
  																		# For classifier free guidance, we need to do two forward passes.
  																		# Here we concatenate the unconditional and text embeddings into a single batch
  																		# to avoid doing two forward passes
  																		SCREAMING_SNAKE_CASE			: List[Any]	     =torch.cat([negative_prompt_embeds, prompt_embeds]							)
  
  											# Rescale the features to have unit variance
  											SCREAMING_SNAKE_CASE			: Dict	     =math.sqrt(prompt_embeds.shape[1]							) * prompt_embeds
  
  											return prompt_embeds
  
  
  
  
  
  
  
  				@torch.no_grad()
  				@replace_example_docstring(snake_case_							)
  				def __call__( self		,		snake_case_		,		snake_case_ = 1		,		snake_case_ = 25		,		snake_case_ = None		,		snake_case_ = None		,		snake_case_ = 4.0		,		snake_case_ = 64		,		snake_case_ = "pil"		,		snake_case_ = True		,		)    ->      Dict:
  
  											if isinstance(snake_case_		,		snake_case_							):
  																		SCREAMING_SNAKE_CASE			: int	     =1
  											elif isinstance(snake_case_		,		snake_case_							):
  																		SCREAMING_SNAKE_CASE			: List[str]	     =len(snake_case_							)
  											else:
  																		raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(snake_case_							)}'							)
  
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self._execution_device
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =batch_size * num_images_per_prompt
  
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =guidance_scale > 1.0
  											SCREAMING_SNAKE_CASE			: Tuple	     =self._encode_prompt(snake_case_		,		snake_case_		,		snake_case_		,		snake_case_							)
  
  											# prior
  
  											self.scheduler.set_timesteps(snake_case_		,		device=snake_case_							)
  											SCREAMING_SNAKE_CASE			: Any	     =self.scheduler.timesteps
  
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.prior.config.num_embeddings
  											SCREAMING_SNAKE_CASE			: List[Any]	     =self.prior.config.embedding_dim
  
  											SCREAMING_SNAKE_CASE			: Any	     =self.prepare_latents(
  											    (batch_size, num_embeddings * embedding_dim)		,		prompt_embeds.dtype		,		snake_case_		,		snake_case_		,		snake_case_		,		self.scheduler		,		)
  
  											# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
  											SCREAMING_SNAKE_CASE			: List[str]	     =latents.reshape(latents.shape[0]		,		snake_case_		,		snake_case_							)
  
  											for i, t in enumerate(self.progress_bar(snake_case_							)							):
  																		# expand the latents if we are doing classifier free guidance
  																		SCREAMING_SNAKE_CASE			: Optional[Any]	     =torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =self.scheduler.scale_model_input(snake_case_		,		snake_case_							)
  
  																		SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.prior(
  																		    snake_case_		,		timestep=snake_case_		,		proj_embedding=snake_case_		,		).predicted_image_embedding
  
  																		# remove the variance
  																		SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Optional[Any]	     =noise_pred.split(
  																		    scaled_model_input.shape[2]		,		dim=2							)  # batch_size, num_embeddings, embedding_dim
  
  																		if do_classifier_free_guidance is not None:
  																									SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Tuple	     =noise_pred.chunk(2							)
  																									SCREAMING_SNAKE_CASE			: int	     =noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
  
  																		SCREAMING_SNAKE_CASE			: int	     =self.scheduler.step(
  																		    snake_case_		,		timestep=snake_case_		,		sample=snake_case_		,		).prev_sample
  
  											if output_type == "latent":
  																		return ShapEPipelineOutput(images=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: List[str]	     =[]
  											for i, latent in enumerate(snake_case_							):
  																		SCREAMING_SNAKE_CASE			: Tuple	     =self.renderer.decode(
  																		    latent[None, :]		,		snake_case_		,		size=snake_case_		,		ray_batch_size=4_096		,		n_coarse_samples=64		,		n_fine_samples=128		,		)
  																		images.append(snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: List[str]	     =torch.stack(snake_case_							)
  
  											if output_type not in ["np", "pil"]:
  																		raise ValueError(F'Only the output types `pil` and `np` are supported not output_type={output_type}'							)
  
  											SCREAMING_SNAKE_CASE			: List[str]	     =images.cpu().numpy()
  
  											if output_type == "pil":
  																		SCREAMING_SNAKE_CASE			: Dict	     =[self.numpy_to_pil(snake_case_							) for image in images]
  
  											# Offload last model to CPU
  											if hasattr(self		,		'''final_offload_hook'''							) and self.final_offload_hook is not None:
  																		self.final_offload_hook.offload()
  
  											if not return_dict:
  																		return (images,)
  
  											return ShapEPipelineOutput(images=snake_case_							)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  import argparse
  import json
  from pathlib import Path
  
  import requests
  import torch
  from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
  from PIL import Image
  
  from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  _A							    =	logging.get_logger(__name__)
  def 						lowerCAmelCase_       (	)       ->			Any:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Dict	     =DetaConfig(
  							    num_queries=900	,	encoder_ffn_dim=2048	,	decoder_ffn_dim=2048	,	num_feature_levels=5	,	assign_first_stage=__a	,	with_box_refine=__a	,	two_stage=__a	,	)
  
  							# set labels
  							SCREAMING_SNAKE_CASE			: Dict	     =91
  							SCREAMING_SNAKE_CASE			: Dict	     ='''huggingface/label-files'''
  							SCREAMING_SNAKE_CASE			: List[str]	     ='''coco-detection-id2label.json'''
  							SCREAMING_SNAKE_CASE			: Tuple	     =json.load(open(cached_download(hf_hub_url(__a	,	__a	,	repo_type='''dataset''' ) )	,	'''r''' ) )
  							SCREAMING_SNAKE_CASE			: int	     ={int(__a ): v for k, v in idalabel.items()}
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =idalabel
  							SCREAMING_SNAKE_CASE			: Any	     ={v: k for k, v in idalabel.items()}
  
  							return config
  def 						lowerCAmelCase_       (	__a )       ->			Union[str, Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: int	     =[]
  
  							# stem
  							# fmt: off
  							rename_keys.append(('''backbone.0.body.conv1.weight''', '''model.backbone.model.embedder.embedder.convolution.weight''') )
  							rename_keys.append(('''backbone.0.body.bn1.weight''', '''model.backbone.model.embedder.embedder.normalization.weight''') )
  							rename_keys.append(('''backbone.0.body.bn1.bias''', '''model.backbone.model.embedder.embedder.normalization.bias''') )
  							rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''model.backbone.model.embedder.embedder.normalization.running_mean''') )
  							rename_keys.append(('''backbone.0.body.bn1.running_var''', '''model.backbone.model.embedder.embedder.normalization.running_var''') )
  							# stages
  							for stage_idx in range(len(config.backbone_config.depths ) ):
  														for layer_idx in range(config.backbone_config.depths[stage_idx] ):
  																					# shortcut
  																					if layer_idx == 0:
  																												rename_keys.append(
  																												    (
  																												        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
  																												        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
  																												    ) )
  																												rename_keys.append(
  																												    (
  																												        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
  																												        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
  																												    ) )
  																												rename_keys.append(
  																												    (
  																												        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
  																												        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
  																												    ) )
  																												rename_keys.append(
  																												    (
  																												        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
  																												        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
  																												    ) )
  																												rename_keys.append(
  																												    (
  																												        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
  																												        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
  																												    ) )
  																					# 3 convs
  																					for i in range(3 ):
  																												rename_keys.append(
  																												    (
  																												        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
  																												        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
  																												    ) )
  																												rename_keys.append(
  																												    (
  																												        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
  																												        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
  																												    ) )
  																												rename_keys.append(
  																												    (
  																												        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
  																												        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
  																												    ) )
  																												rename_keys.append(
  																												    (
  																												        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
  																												        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
  																												    ) )
  																												rename_keys.append(
  																												    (
  																												        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
  																												        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
  																												    ) )
      # transformer encoder
  							for i in range(config.encoder_layers ):
  														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', f'model.encoder.layers.{i}.self_attn.sampling_offsets.weight') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', f'model.encoder.layers.{i}.self_attn.sampling_offsets.bias') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', f'model.encoder.layers.{i}.self_attn.attention_weights.weight') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', f'model.encoder.layers.{i}.self_attn.attention_weights.bias') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.weight', f'model.encoder.layers.{i}.self_attn.value_proj.weight') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.bias', f'model.encoder.layers.{i}.self_attn.value_proj.bias') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.weight', f'model.encoder.layers.{i}.self_attn.output_proj.weight') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.bias', f'model.encoder.layers.{i}.self_attn.output_proj.bias') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.norm1.weight', f'model.encoder.layers.{i}.self_attn_layer_norm.weight') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'model.encoder.layers.{i}.self_attn_layer_norm.bias') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'model.encoder.layers.{i}.fc1.weight') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'model.encoder.layers.{i}.fc1.bias') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'model.encoder.layers.{i}.fc2.weight') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'model.encoder.layers.{i}.fc2.bias') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'model.encoder.layers.{i}.final_layer_norm.weight') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'model.encoder.layers.{i}.final_layer_norm.bias') )
  
  							# transformer decoder
  							for i in range(config.decoder_layers ):
  														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', f'model.decoder.layers.{i}.encoder_attn.attention_weights.weight') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', f'model.decoder.layers.{i}.encoder_attn.attention_weights.bias') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', f'model.decoder.layers.{i}.encoder_attn.value_proj.weight') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', f'model.decoder.layers.{i}.encoder_attn.value_proj.bias') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', f'model.decoder.layers.{i}.encoder_attn.output_proj.weight') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', f'model.decoder.layers.{i}.encoder_attn.output_proj.bias') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.norm1.weight', f'model.decoder.layers.{i}.encoder_attn_layer_norm.weight') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'model.decoder.layers.{i}.encoder_attn_layer_norm.bias') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'model.decoder.layers.{i}.self_attn.out_proj.weight') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'model.decoder.layers.{i}.self_attn.out_proj.bias') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.norm2.weight', f'model.decoder.layers.{i}.self_attn_layer_norm.weight') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.norm2.bias', f'model.decoder.layers.{i}.self_attn_layer_norm.bias') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'model.decoder.layers.{i}.fc1.weight') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'model.decoder.layers.{i}.fc1.bias') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'model.decoder.layers.{i}.fc2.weight') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'model.decoder.layers.{i}.fc2.bias') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'model.decoder.layers.{i}.final_layer_norm.weight') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'model.decoder.layers.{i}.final_layer_norm.bias') )
  
  							# fmt: on
  
  							return rename_keys
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Union[str, Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =dct.pop(__a )
  							SCREAMING_SNAKE_CASE			: List[Any]	     =val
  def 						lowerCAmelCase_       (	__a	,	__a )       ->			Dict:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Tuple	     =config.d_model
  							for i in range(config.decoder_layers ):
  														# read in weights + bias of input projection layer of self-attention
  														SCREAMING_SNAKE_CASE			: List[str]	     =state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
  														SCREAMING_SNAKE_CASE			: Optional[Any]	     =state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_bias' )
  														# next, add query, keys and values (in that order) to the state dict
  														SCREAMING_SNAKE_CASE			: Dict	     =in_proj_weight[:hidden_size, :]
  														SCREAMING_SNAKE_CASE			: List[Any]	     =in_proj_bias[:hidden_size]
  														SCREAMING_SNAKE_CASE			: int	     =in_proj_weight[
  														    hidden_size : hidden_size * 2, :
  														]
  														SCREAMING_SNAKE_CASE			: Any	     =in_proj_bias[hidden_size : hidden_size * 2]
  														SCREAMING_SNAKE_CASE			: List[str]	     =in_proj_weight[-hidden_size:, :]
  														SCREAMING_SNAKE_CASE			: Dict	     =in_proj_bias[-hidden_size:]
  def 						lowerCAmelCase_       (	)       ->			int:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Tuple	     ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =Image.open(requests.get(__a	,	stream=__a ).raw )
  
  							return im
  @torch.no_grad()
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Optional[Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: str	     =get_deta_config()
  
  							# load original state dict
  							if model_name == "deta-resnet-50":
  														SCREAMING_SNAKE_CASE			: int	     ='''adet_checkpoint0011.pth'''
  							elif model_name == "deta-resnet-50-24-epochs":
  														SCREAMING_SNAKE_CASE			: str	     ='''adet_2x_checkpoint0023.pth'''
  							else:
  														raise ValueError(f'Model name {model_name} not supported' )
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =hf_hub_download(repo_id='''nielsr/deta-checkpoints'''	,	filename=__a )
  							SCREAMING_SNAKE_CASE			: str	     =torch.load(__a	,	map_location='''cpu''' )['''model''']
  
  							# rename keys
  							SCREAMING_SNAKE_CASE			: int	     =create_rename_keys(__a )
  							for src, dest in rename_keys:
  														rename_key(__a	,	__a	,	__a )
  							read_in_decoder_q_k_v(__a	,	__a )
  
  							# fix some prefixes
  							for key in state_dict.copy().keys():
  														if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
  																					SCREAMING_SNAKE_CASE			: str	     =state_dict.pop(__a )
  																					SCREAMING_SNAKE_CASE			: List[str]	     =val
  														if "input_proj" in key:
  																					SCREAMING_SNAKE_CASE			: Tuple	     =state_dict.pop(__a )
  																					SCREAMING_SNAKE_CASE			: Tuple	     =val
  														if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
  																					SCREAMING_SNAKE_CASE			: Dict	     =state_dict.pop(__a )
  																					SCREAMING_SNAKE_CASE			: List[str]	     =val
  
      # finally, create HuggingFace model and load state dict
  							SCREAMING_SNAKE_CASE			: str	     =DetaForObjectDetection(__a )
  							model.load_state_dict(__a )
  							model.eval()
  
  							SCREAMING_SNAKE_CASE			: int	     ='''cuda''' if torch.cuda.is_available() else '''cpu'''
  							model.to(__a )
  
  							# load image processor
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =DetaImageProcessor(format='''coco_detection''' )
  
  							# verify our conversion on image
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =prepare_img()
  							SCREAMING_SNAKE_CASE			: Any	     =processor(images=__a	,	return_tensors='''pt''' )
  							SCREAMING_SNAKE_CASE			: List[Any]	     =encoding['''pixel_values''']
  							SCREAMING_SNAKE_CASE			: Dict	     =model(pixel_values.to(__a ) )
  
  							# verify logits
  							if model_name == "deta-resnet-50":
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =torch.tensor(
  														    [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]] )
  														SCREAMING_SNAKE_CASE			: Tuple	     =torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]] )
  							elif model_name == "deta-resnet-50-24-epochs":
  														SCREAMING_SNAKE_CASE			: Any	     =torch.tensor(
  														    [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]] )
  														SCREAMING_SNAKE_CASE			: List[Any]	     =torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]] )
  
  							assert torch.allclose(outputs.logits[0, :3, :3]	,	expected_logits.to(__a )	,	atol=1E-4 )
  							assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,	expected_boxes.to(__a )	,	atol=1E-4 )
  							print('''Everything ok!''' )
  
  							if pytorch_dump_folder_path:
  														# Save model and processor
  														logger.info(f'Saving PyTorch model and processor to {pytorch_dump_folder_path}...' )
  														Path(__a ).mkdir(exist_ok=__a )
  														model.save_pretrained(__a )
  														processor.save_pretrained(__a )
  
  							# Push to hub
  							if push_to_hub:
  														print('''Pushing model and processor to hub...''' )
  														model.push_to_hub(f'jozhang97/{model_name}' )
  														processor.push_to_hub(f'jozhang97/{model_name}' )
  
  
  if __name__ == "__main__":
  							_A							    =	argparse.ArgumentParser()
  
  							parser.add_argument(
  							    """--model_name""",
  							    type=str,
  							    default="""deta-resnet-50""",
  							    choices=["""deta-resnet-50""", """deta-resnet-50-24-epochs"""],
  							    help="""Name of the 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.""",
  							)
  							parser.add_argument(
  							    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
  							)
  							_A							    =	parser.parse_args()
  							convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
  
  
  
  
  
  
  