



"""simple docstring"""







import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput







class  UpperCamelCase__  (      nn.Module    ):




      """simple docstring"""




      def __init__( self							: Optional[int]       ,     SCREAMING_SNAKE_CASE_							: Any       ,     SCREAMING_SNAKE_CASE_							: int=1.0			):
        super().__init__()

        lowerCAmelCase_  :							Optional[int]								=					cut_size
        lowerCAmelCase_  :							int								=					cut_power





      def 	SCREAMING_SNAKE_CASE__					( self							: str       ,     SCREAMING_SNAKE_CASE_							: Any       ,     SCREAMING_SNAKE_CASE_							: Optional[int]			):
        lowerCAmelCase_		,lowerCAmelCase_  :							Tuple								=					pixel_values.shape[2:4]
        lowerCAmelCase_  :							Tuple								=					min(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Optional[int]								=					min(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     self.cut_size			)
        lowerCAmelCase_  :							List[str]								=					[]
        for _ in range(SCREAMING_SNAKE_CASE_			):
          lowerCAmelCase_  :							Union[str, Any]								=					int(torch.rand([]			) ** self.cut_power * (max_size - min_size) + min_size			)
          lowerCAmelCase_  :							List[Any]								=					torch.randint(0       ,     sideX - size + 1       ,     ()			)
          lowerCAmelCase_  :							Optional[Any]								=					torch.randint(0       ,     sideY - size + 1       ,     ()			)
          lowerCAmelCase_  :							List[Any]								=					pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
          cutouts.append(F.adaptive_avg_poolad(SCREAMING_SNAKE_CASE_       ,     self.cut_size			)			)
        return torch.cat(SCREAMING_SNAKE_CASE_			)




def 							UpperCamelCase_       (       lowerCAmelCase__ :							Optional[int]       ,  lowerCAmelCase__ :							List[str]		)		->  List[Any]:

  """simple docstring"""




  lowerCAmelCase_  :							Tuple								=					F.normalize(lowerCAmelCase__       ,  dim=-1		)
  lowerCAmelCase_  :							Dict								=					F.normalize(lowerCAmelCase__       ,  dim=-1		)
  return (x - y).norm(dim=-1		).div(2		).arcsin().pow(2		).mul(2		)

def 							UpperCamelCase_       (       lowerCAmelCase__ :							str       ,  lowerCAmelCase__ :							int		)		->  Union[str, Any]:

  """simple docstring"""




  for param in model.parameters():
    lowerCAmelCase_  :							List[Any]								=					value







class  UpperCamelCase__  (      lowercase_    ):




      """simple docstring"""




      def __init__( self							: Dict       ,     SCREAMING_SNAKE_CASE_							: AutoencoderKL       ,     SCREAMING_SNAKE_CASE_							: CLIPTextModel       ,     SCREAMING_SNAKE_CASE_							: CLIPModel       ,     SCREAMING_SNAKE_CASE_							: CLIPTokenizer       ,     SCREAMING_SNAKE_CASE_							: UNetaDConditionModel       ,     SCREAMING_SNAKE_CASE_							: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]       ,     SCREAMING_SNAKE_CASE_							: CLIPImageProcessor       ,     ):
        super().__init__()
        self.register_modules(
            vae=SCREAMING_SNAKE_CASE_       ,     text_encoder=SCREAMING_SNAKE_CASE_       ,     clip_model=SCREAMING_SNAKE_CASE_       ,     tokenizer=SCREAMING_SNAKE_CASE_       ,     unet=SCREAMING_SNAKE_CASE_       ,     scheduler=SCREAMING_SNAKE_CASE_       ,     feature_extractor=SCREAMING_SNAKE_CASE_       ,     )

        lowerCAmelCase_  :							Any								=					transforms.Normalize(mean=feature_extractor.image_mean       ,     std=feature_extractor.image_std			)
        lowerCAmelCase_  :							Optional[Any]								=					(
            feature_extractor.size
            if isinstance(feature_extractor.size       ,     SCREAMING_SNAKE_CASE_			)
            else feature_extractor.size['shortest_edge']
        )
        lowerCAmelCase_  :							Optional[Any]								=					MakeCutouts(self.cut_out_size			)

        set_requires_grad(self.text_encoder       ,     SCREAMING_SNAKE_CASE_			)
        set_requires_grad(self.clip_model       ,     SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Dict       ,     SCREAMING_SNAKE_CASE_							: Optional[Union[str, int]] = "auto"			):
        if slice_size == "auto":
          # half the attention head size is usually a good trade-off between
          # speed and memory
          lowerCAmelCase_  :							Union[str, Any]								=					self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: List[Any]			):
        self.enable_attention_slicing(SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: str			):
        set_requires_grad(self.vae       ,     SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Any			):
        set_requires_grad(self.vae       ,     SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[int]			):
        set_requires_grad(self.unet       ,     SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: List[Any]			):
        set_requires_grad(self.unet       ,     SCREAMING_SNAKE_CASE_			)



      @torch.enable_grad()
      def 	SCREAMING_SNAKE_CASE__					( self							: int       ,     SCREAMING_SNAKE_CASE_							: Any       ,     SCREAMING_SNAKE_CASE_							: Union[str, Any]       ,     SCREAMING_SNAKE_CASE_							: Optional[int]       ,     SCREAMING_SNAKE_CASE_							: Optional[Any]       ,     SCREAMING_SNAKE_CASE_							: Any       ,     SCREAMING_SNAKE_CASE_							: List[str]       ,     SCREAMING_SNAKE_CASE_							: Tuple       ,     SCREAMING_SNAKE_CASE_							: Union[str, Any]       ,     SCREAMING_SNAKE_CASE_							: str=True       ,     ):
        lowerCAmelCase_  :							Optional[Any]								=					latents.detach().requires_grad_()

        lowerCAmelCase_  :							Optional[Any]								=					self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)

        # predict the noise residual
        lowerCAmelCase_  :							Any								=					self.unet(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     encoder_hidden_states=SCREAMING_SNAKE_CASE_			).sample

        if isinstance(self.scheduler       ,     (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)			):
          lowerCAmelCase_  :							str								=					self.scheduler.alphas_cumprod[timestep]
          lowerCAmelCase_  :							str								=					1 - alpha_prod_t
          # compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          lowerCAmelCase_  :							List[Any]								=					(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

          lowerCAmelCase_  :							str								=					torch.sqrt(SCREAMING_SNAKE_CASE_			)
          lowerCAmelCase_  :							Union[str, Any]								=					pred_original_sample * (fac) + latents * (1 - fac)
        elif isinstance(self.scheduler       ,     SCREAMING_SNAKE_CASE_			):
          lowerCAmelCase_  :							Optional[Any]								=					self.scheduler.sigmas[index]
          lowerCAmelCase_  :							Dict								=					latents - sigma * noise_pred
        else:
          raise ValueError(F"scheduler type {type(self.scheduler			)} not supported"			)

        lowerCAmelCase_  :							Union[str, Any]								=					1 / self.vae.config.scaling_factor * sample
        lowerCAmelCase_  :							Tuple								=					self.vae.decode(SCREAMING_SNAKE_CASE_			).sample
        lowerCAmelCase_  :							Tuple								=					(image / 2 + 0.5).clamp(0       ,     1			)

        if use_cutouts:
          lowerCAmelCase_  :							List[Any]								=					self.make_cutouts(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)
        else:
          lowerCAmelCase_  :							Optional[Any]								=					transforms.Resize(self.cut_out_size			)(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							str								=					self.normalize(SCREAMING_SNAKE_CASE_			).to(latents.dtype			)

        lowerCAmelCase_  :							int								=					self.clip_model.get_image_features(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							List[str]								=					image_embeddings_clip / image_embeddings_clip.norm(p=2       ,     dim=-1       ,     keepdim=SCREAMING_SNAKE_CASE_			)

        if use_cutouts:
          lowerCAmelCase_  :							Any								=					spherical_dist_loss(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)
          lowerCAmelCase_  :							List[Any]								=					dists.view([num_cutouts, sample.shape[0], -1]			)
          lowerCAmelCase_  :							Tuple								=					dists.sum(2			).mean(0			).sum() * clip_guidance_scale
        else:
          lowerCAmelCase_  :							int								=					spherical_dist_loss(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			).mean() * clip_guidance_scale

        lowerCAmelCase_  :							Dict								=					-torch.autograd.grad(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)[0]

        if isinstance(self.scheduler       ,     SCREAMING_SNAKE_CASE_			):
          lowerCAmelCase_  :							Optional[Any]								=					latents.detach() + grads * (sigma**2)
          lowerCAmelCase_  :							Tuple								=					noise_pred_original
        else:
          lowerCAmelCase_  :							List[Any]								=					noise_pred_original - torch.sqrt(SCREAMING_SNAKE_CASE_			) * grads
        return noise_pred, latents





      @torch.no_grad()
      def __call__( self							: List[str]       ,     SCREAMING_SNAKE_CASE_							: Union[str, List[str]]       ,     SCREAMING_SNAKE_CASE_							: Optional[int] = 5_1_2       ,     SCREAMING_SNAKE_CASE_							: Optional[int] = 5_1_2       ,     SCREAMING_SNAKE_CASE_							: Optional[int] = 5_0       ,     SCREAMING_SNAKE_CASE_							: Optional[float] = 7.5       ,     SCREAMING_SNAKE_CASE_							: Optional[int] = 1       ,     SCREAMING_SNAKE_CASE_							: float = 0.0       ,     SCREAMING_SNAKE_CASE_							: Optional[float] = 1_0_0       ,     SCREAMING_SNAKE_CASE_							: Optional[Union[str, List[str]]] = None       ,     SCREAMING_SNAKE_CASE_							: Optional[int] = 4       ,     SCREAMING_SNAKE_CASE_							: Optional[bool] = True       ,     SCREAMING_SNAKE_CASE_							: Optional[torch.Generator] = None       ,     SCREAMING_SNAKE_CASE_							: Optional[torch.FloatTensor] = None       ,     SCREAMING_SNAKE_CASE_							: Optional[str] = "pil"       ,     SCREAMING_SNAKE_CASE_							: bool = True       ,     ):
        if isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			):
          lowerCAmelCase_  :							List[str]								=					1
        elif isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			):
          lowerCAmelCase_  :							Optional[int]								=					len(SCREAMING_SNAKE_CASE_			)
        else:
          raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_			)}"			)

        if height % 8 != 0 or width % 8 != 0:
          raise ValueError(F"`height` and `width` have to be divisible by 8 but are {height} and {width}."			)

        # get prompt text embeddings
        lowerCAmelCase_  :							int								=					self.tokenizer(
            SCREAMING_SNAKE_CASE_       ,     padding='max_length'       ,     max_length=self.tokenizer.model_max_length       ,     truncation=SCREAMING_SNAKE_CASE_       ,     return_tensors='pt'       ,     )
        lowerCAmelCase_  :							Union[str, Any]								=					self.text_encoder(text_input.input_ids.to(self.device			)			)[0]
        # duplicate text embeddings for each generation per prompt
        lowerCAmelCase_  :							str								=					text_embeddings.repeat_interleave(SCREAMING_SNAKE_CASE_       ,     dim=0			)

        if clip_guidance_scale > 0:
          if clip_prompt is not None:
            lowerCAmelCase_  :							Tuple								=					self.tokenizer(
                SCREAMING_SNAKE_CASE_       ,     padding='max_length'       ,     max_length=self.tokenizer.model_max_length       ,     truncation=SCREAMING_SNAKE_CASE_       ,     return_tensors='pt'       ,     ).input_ids.to(self.device			)
          else:
            lowerCAmelCase_  :							List[Any]								=					text_input.input_ids.to(self.device			)
          lowerCAmelCase_  :							Optional[int]								=					self.clip_model.get_text_features(SCREAMING_SNAKE_CASE_			)
          lowerCAmelCase_  :							Union[str, Any]								=					text_embeddings_clip / text_embeddings_clip.norm(p=2       ,     dim=-1       ,     keepdim=SCREAMING_SNAKE_CASE_			)
          # duplicate text embeddings clip for each generation per prompt
          lowerCAmelCase_  :							int								=					text_embeddings_clip.repeat_interleave(SCREAMING_SNAKE_CASE_       ,     dim=0			)

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        lowerCAmelCase_  :							Optional[int]								=					guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
          lowerCAmelCase_  :							List[Any]								=					text_input.input_ids.shape[-1]
          lowerCAmelCase_  :							Optional[Any]								=					self.tokenizer(['']       ,     padding='max_length'       ,     max_length=SCREAMING_SNAKE_CASE_       ,     return_tensors='pt'			)
          lowerCAmelCase_  :							int								=					self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]
          # duplicate unconditional embeddings for each generation per prompt
          lowerCAmelCase_  :							Union[str, Any]								=					uncond_embeddings.repeat_interleave(SCREAMING_SNAKE_CASE_       ,     dim=0			)

          # 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
          lowerCAmelCase_  :							List[Any]								=					torch.cat([uncond_embeddings, text_embeddings]			)

        # get the initial random noise unless the user supplied it

        # Unlike in other pipelines, latents need to be generated in the target device
        # for 1-to-1 results reproducibility with the CompVis implementation.
        # However this currently doesn't work in `mps`.
        lowerCAmelCase_  :							Any								=					(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
        lowerCAmelCase_  :							Union[str, Any]								=					text_embeddings.dtype
        if latents is None:
          if self.device.type == "mps":
            # randn does not work reproducibly on mps
            lowerCAmelCase_  :							Optional[Any]								=					torch.randn(SCREAMING_SNAKE_CASE_       ,     generator=SCREAMING_SNAKE_CASE_       ,     device='cpu'       ,     dtype=SCREAMING_SNAKE_CASE_			).to(
                self.device			)
          else:
            lowerCAmelCase_  :							Union[str, Any]								=					torch.randn(SCREAMING_SNAKE_CASE_       ,     generator=SCREAMING_SNAKE_CASE_       ,     device=self.device       ,     dtype=SCREAMING_SNAKE_CASE_			)
        else:
          if latents.shape != latents_shape:
            raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"			)
          lowerCAmelCase_  :							Optional[int]								=					latents.to(self.device			)

        # set timesteps
        lowerCAmelCase_  :							List[str]								=					'offset' in set(inspect.signature(self.scheduler.set_timesteps			).parameters.keys()			)
        lowerCAmelCase_  :							List[Any]								=					{}
        if accepts_offset:
          lowerCAmelCase_  :							Tuple								=					1

        self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_       ,     **SCREAMING_SNAKE_CASE_			)

        # Some schedulers like PNDM have timesteps as arrays
        # It's more optimized to move all timesteps to correct device beforehand
        lowerCAmelCase_  :							Dict								=					self.scheduler.timesteps.to(self.device			)

        # scale the initial noise by the standard deviation required by the scheduler
        lowerCAmelCase_  :							Union[str, Any]								=					latents * self.scheduler.init_noise_sigma

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]
        lowerCAmelCase_  :							Optional[Any]								=					'eta' in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
        lowerCAmelCase_  :							List[Any]								=					{}
        if accepts_eta:
          lowerCAmelCase_  :							int								=					eta

        # check if the scheduler accepts generator
        lowerCAmelCase_  :							List[str]								=					'generator' in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
        if accepts_generator:
          lowerCAmelCase_  :							int								=					generator

        for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_			)			):
          # expand the latents if we are doing classifier free guidance
          lowerCAmelCase_  :							Optional[int]								=					torch.cat([latents] * 2			) if do_classifier_free_guidance else latents
          lowerCAmelCase_  :							str								=					self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)

          # predict the noise residual
          lowerCAmelCase_  :							str								=					self.unet(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     encoder_hidden_states=SCREAMING_SNAKE_CASE_			).sample

          # perform classifier free guidance
          if do_classifier_free_guidance:
            lowerCAmelCase_		,lowerCAmelCase_  :							str								=					noise_pred.chunk(2			)
            lowerCAmelCase_  :							Any								=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

          # perform clip guidance
          if clip_guidance_scale > 0:
            lowerCAmelCase_  :							Union[str, Any]								=					(
                text_embeddings.chunk(2			)[1] if do_classifier_free_guidance else text_embeddings
            )
            lowerCAmelCase_		,lowerCAmelCase_  :							List[Any]								=					self.cond_fn(
                SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     )

          # compute the previous noisy sample x_t -> x_t-1
          lowerCAmelCase_  :							List[Any]								=					self.scheduler.step(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     **SCREAMING_SNAKE_CASE_			).prev_sample

        # scale and decode the image latents with vae
        lowerCAmelCase_  :							Union[str, Any]								=					1 / self.vae.config.scaling_factor * latents
        lowerCAmelCase_  :							Optional[int]								=					self.vae.decode(SCREAMING_SNAKE_CASE_			).sample

        lowerCAmelCase_  :							Dict								=					(image / 2 + 0.5).clamp(0       ,     1			)
        lowerCAmelCase_  :							Union[str, Any]								=					image.cpu().permute(0       ,     2       ,     3       ,     1			).numpy()

        if output_type == "pil":
          lowerCAmelCase_  :							int								=					self.numpy_to_pil(SCREAMING_SNAKE_CASE_			)

        if not return_dict:
          return (image, None)

        return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE_       ,     nsfw_content_detected=SCREAMING_SNAKE_CASE_			)










"""simple docstring"""







import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

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


logging.set_verbosity_info()
lowercase__						:							Dict        =      logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase__						:							Union[str, Any]        =      []
for i in range(6):
     # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
     rename_keys.append(
         (f'transformer.encoder.layers.{i}.self_attn.out_proj.weight', f'encoder.layers.{i}.self_attn.out_proj.weight')
     )
     rename_keys.append(
         (f'transformer.encoder.layers.{i}.self_attn.out_proj.bias', f'encoder.layers.{i}.self_attn.out_proj.bias')
     )
     rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'encoder.layers.{i}.fc1.weight'))
     rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'encoder.layers.{i}.fc1.bias'))
     rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'encoder.layers.{i}.fc2.weight'))
     rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'encoder.layers.{i}.fc2.bias'))
     rename_keys.append(
         (f'transformer.encoder.layers.{i}.norm1.weight', f'encoder.layers.{i}.self_attn_layer_norm.weight')
     )
     rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'encoder.layers.{i}.self_attn_layer_norm.bias'))
     rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'encoder.layers.{i}.final_layer_norm.weight'))
     rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'encoder.layers.{i}.final_layer_norm.bias'))
     # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
     rename_keys.append(
         (f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'decoder.layers.{i}.self_attn.out_proj.weight')
     )
     rename_keys.append(
         (f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'decoder.layers.{i}.self_attn.out_proj.bias')
     )
     rename_keys.append(
         (
             f'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
             f'decoder.layers.{i}.encoder_attn.out_proj.weight',
         )
     )
     rename_keys.append(
         (
             f'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
             f'decoder.layers.{i}.encoder_attn.out_proj.bias',
         )
     )
     rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'decoder.layers.{i}.fc1.weight'))
     rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'decoder.layers.{i}.fc1.bias'))
     rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'decoder.layers.{i}.fc2.weight'))
     rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'decoder.layers.{i}.fc2.bias'))
     rename_keys.append(
         (f'transformer.decoder.layers.{i}.norm1.weight', f'decoder.layers.{i}.self_attn_layer_norm.weight')
     )
     rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'decoder.layers.{i}.self_attn_layer_norm.bias'))
     rename_keys.append(
         (f'transformer.decoder.layers.{i}.norm2.weight', f'decoder.layers.{i}.encoder_attn_layer_norm.weight')
     )
     rename_keys.append(
         (f'transformer.decoder.layers.{i}.norm2.bias', f'decoder.layers.{i}.encoder_attn_layer_norm.bias')
     )
     rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'decoder.layers.{i}.final_layer_norm.weight'))
     rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'decoder.layers.{i}.final_layer_norm.bias'))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
    ]
)

def 							UpperCamelCase_       (       lowerCAmelCase__ :							str       ,  lowerCAmelCase__ :							Dict       ,  lowerCAmelCase__ :							Optional[Any]		)		->  List[Any]:

  """simple docstring"""




  lowerCAmelCase_  :							Dict								=					state_dict.pop(lowerCAmelCase__		)
  lowerCAmelCase_  :							Union[str, Any]								=					val

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Union[str, Any]		)		->  Any:

  """simple docstring"""




  lowerCAmelCase_  :							Tuple								=					OrderedDict()
  for key, value in state_dict.items():
    if "backbone.0.body" in key:
      lowerCAmelCase_  :							List[str]								=					key.replace('backbone.0.body'       ,  'backbone.conv_encoder.model'		)
      lowerCAmelCase_  :							Optional[int]								=					value
    else:
      lowerCAmelCase_  :							Optional[Any]								=					value

  return new_state_dict

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Dict       ,  lowerCAmelCase__ :							List[Any]=False		)		->  Optional[int]:

  """simple docstring"""




  lowerCAmelCase_  :							Union[str, Any]								=					''
  if is_panoptic:
    lowerCAmelCase_  :							str								=					'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)
    lowerCAmelCase_  :							Optional[Any]								=					state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"		)
    lowerCAmelCase_  :							Any								=					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
    lowerCAmelCase_  :							Optional[int]								=					in_proj_weight[:256, :]
    lowerCAmelCase_  :							int								=					in_proj_bias[:256]
    lowerCAmelCase_  :							Optional[Any]								=					in_proj_weight[256:512, :]
    lowerCAmelCase_  :							Union[str, Any]								=					in_proj_bias[256:512]
    lowerCAmelCase_  :							List[Any]								=					in_proj_weight[-256:, :]
    lowerCAmelCase_  :							int								=					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
    lowerCAmelCase_  :							List[str]								=					state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"		)
    lowerCAmelCase_  :							str								=					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
    lowerCAmelCase_  :							Any								=					in_proj_weight[:256, :]
    lowerCAmelCase_  :							List[Any]								=					in_proj_bias[:256]
    lowerCAmelCase_  :							List[Any]								=					in_proj_weight[256:512, :]
    lowerCAmelCase_  :							Tuple								=					in_proj_bias[256:512]
    lowerCAmelCase_  :							Any								=					in_proj_weight[-256:, :]
    lowerCAmelCase_  :							Tuple								=					in_proj_bias[-256:]
    # read in weights + bias of input projection layer of cross-attention
    lowerCAmelCase_  :							Optional[Any]								=					state_dict.pop(
        f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"		)
    lowerCAmelCase_  :							Optional[int]								=					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
    lowerCAmelCase_  :							int								=					in_proj_weight_cross_attn[:256, :]
    lowerCAmelCase_  :							Optional[Any]								=					in_proj_bias_cross_attn[:256]
    lowerCAmelCase_  :							Any								=					in_proj_weight_cross_attn[256:512, :]
    lowerCAmelCase_  :							int								=					in_proj_bias_cross_attn[256:512]
    lowerCAmelCase_  :							Dict								=					in_proj_weight_cross_attn[-256:, :]
    lowerCAmelCase_  :							Optional[Any]								=					in_proj_bias_cross_attn[-256:]

def 							UpperCamelCase_       (       )		->  Any:

  """simple docstring"""




  lowerCAmelCase_  :							Optional[Any]								=					'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowerCAmelCase_  :							List[str]								=					Image.open(requests.get(lowerCAmelCase__       ,  stream=lowerCAmelCase__		).raw		)

  return im

@torch.no_grad()
def 							UpperCamelCase_       (       lowerCAmelCase__ :							Any       ,  lowerCAmelCase__ :							Tuple		)		->  str:

  """simple docstring"""




  lowerCAmelCase_  :							int								=					DetrConfig()
  # set backbone and dilation attributes
  if "resnet101" in model_name:
    lowerCAmelCase_  :							int								=					'resnet101'
  if "dc5" in model_name:
    lowerCAmelCase_  :							Any								=					True
  lowerCAmelCase_  :							Tuple								=					'panoptic' in model_name
  if is_panoptic:
    lowerCAmelCase_  :							int								=					250
  else:
    lowerCAmelCase_  :							Optional[int]								=					91
    lowerCAmelCase_  :							int								=					'huggingface/label-files'
    lowerCAmelCase_  :							Dict								=					'coco-detection-id2label.json'
    lowerCAmelCase_  :							List[str]								=					json.load(open(hf_hub_download(lowerCAmelCase__       ,  lowerCAmelCase__       ,  repo_type='dataset'		)       ,  'r'		)		)
    lowerCAmelCase_  :							str								=					{int(lowerCAmelCase__		): v for k, v in idalabel.items()}
    lowerCAmelCase_  :							Union[str, Any]								=					idalabel
    lowerCAmelCase_  :							int								=					{v: k for k, v in idalabel.items()}

  # load image processor
  lowerCAmelCase_  :							Optional[int]								=					'coco_panoptic' if is_panoptic else 'coco_detection'
  lowerCAmelCase_  :							List[Any]								=					DetrImageProcessor(format=lowerCAmelCase__		)

  # prepare image
  lowerCAmelCase_  :							str								=					prepare_img()
  lowerCAmelCase_  :							Tuple								=					image_processor(images=lowerCAmelCase__       ,  return_tensors='pt'		)
  lowerCAmelCase_  :							int								=					encoding['pixel_values']

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

  # load original model from torch hub
  lowerCAmelCase_  :							int								=					torch.hub.load('facebookresearch/detr'       ,  lowerCAmelCase__       ,  pretrained=lowerCAmelCase__		).eval()
  lowerCAmelCase_  :							Optional[Any]								=					detr.state_dict()
  # rename keys
  for src, dest in rename_keys:
    if is_panoptic:
      lowerCAmelCase_  :							List[Any]								=					'detr.' + src
    rename_key(lowerCAmelCase__       ,  lowerCAmelCase__       ,  lowerCAmelCase__		)
  lowerCAmelCase_  :							Optional[int]								=					rename_backbone_keys(lowerCAmelCase__		)
  # query, key and value matrices need special treatment
  read_in_q_k_v(lowerCAmelCase__       ,  is_panoptic=lowerCAmelCase__		)
  # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  lowerCAmelCase_  :							List[str]								=					'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'		)
      ):
        lowerCAmelCase_  :							Optional[int]								=					state_dict.pop(lowerCAmelCase__		)
        lowerCAmelCase_  :							Any								=					val
      elif "class_labels_classifier" in key or "bbox_predictor" in key:
        lowerCAmelCase_  :							str								=					state_dict.pop(lowerCAmelCase__		)
        lowerCAmelCase_  :							str								=					val
      elif key.startswith('bbox_attention'		) or key.startswith('mask_head'		):
        continue
      else:
        lowerCAmelCase_  :							Optional[int]								=					state_dict.pop(lowerCAmelCase__		)
        lowerCAmelCase_  :							Dict								=					val
    else:
      if not key.startswith('class_labels_classifier'		) and not key.startswith('bbox_predictor'		):
        lowerCAmelCase_  :							str								=					state_dict.pop(lowerCAmelCase__		)
        lowerCAmelCase_  :							str								=					val
    # finally, create HuggingFace model and load state dict
  lowerCAmelCase_  :							List[str]								=					DetrForSegmentation(lowerCAmelCase__		) if is_panoptic else DetrForObjectDetection(lowerCAmelCase__		)
  model.load_state_dict(lowerCAmelCase__		)
  model.eval()
  # verify our conversion
  lowerCAmelCase_  :							Tuple								=					detr(lowerCAmelCase__		)
  lowerCAmelCase_  :							List[Any]								=					model(lowerCAmelCase__		)
  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(lowerCAmelCase__		).mkdir(exist_ok=lowerCAmelCase__		)
  model.save_pretrained(lowerCAmelCase__		)
  image_processor.save_pretrained(lowerCAmelCase__		)


if __name__ == "__main__":
     lowercase__						:							Tuple        =      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__						:							Tuple        =      parser.parse_args()
     convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





