




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_							)












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)






