



'''simple docstring'''


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


lowercase											=      logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase											=      '''
    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 						UpperCAmelCase						(		__a):


 '''simple docstring'''




 __magic_name__       :			Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]







class 						UpperCAmelCase						(		__a):


 '''simple docstring'''




 def __init__( self				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	)			->   List[Any]:



      """simple docstring"""







      super().__init__()

      self.register_modules(
          prior=lowerCAmelCase_				,	text_encoder=lowerCAmelCase_				,	tokenizer=lowerCAmelCase_				,	scheduler=lowerCAmelCase_				,	renderer=lowerCAmelCase_				,	)




 def    lowercase_	( self				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_)			->   Optional[Any]:



      """simple docstring"""







      if latents is None:
           a_									=randn_tensor(lowerCAmelCase_				,	generator=lowerCAmelCase_				,	device=lowerCAmelCase_				,	dtype=lowerCAmelCase_)
      else:
           if latents.shape != shape:
                raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}""")
           a_									=latents.to(lowerCAmelCase_)

      a_									=latents * scheduler.init_noise_sigma
      return latents




 def    lowercase_	( self				,	lowerCAmelCase_=0)			->   Optional[Any]:



      """simple docstring"""







      if is_accelerate_available():
           from accelerate import cpu_offload
      else:
           raise ImportError("Please install accelerate via `pip install accelerate`")

      a_									=torch.device(f"""cuda:{gpu_id}""")

      a_									=[self.text_encoder, self.prior]
      for cpu_offloaded_model in models:
           if cpu_offloaded_model is not None:
                cpu_offload(lowerCAmelCase_				,	lowerCAmelCase_)




 def    lowercase_	( self				,	lowerCAmelCase_=0)			->   Optional[Any]:



      """simple docstring"""







      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.")

      a_									=torch.device(f"""cuda:{gpu_id}""")

      if self.device.type != "cpu":
           self.to("cpu"				,	silence_dtype_warnings=lowerCAmelCase_)
           torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

      a_									=None
      for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
           a_		,    a_									=cpu_offload_with_hook(lowerCAmelCase_				,	lowerCAmelCase_				,	prev_module_hook=lowerCAmelCase_)

      if self.safety_checker is not None:
           a_		,    a_									=cpu_offload_with_hook(self.safety_checker				,	lowerCAmelCase_				,	prev_module_hook=lowerCAmelCase_)

      # We'll offload the last model manually.
      a_									=hook




 @property
 def    lowercase_	( self)			->   Optional[Any]:



      """simple docstring"""







      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(lowerCAmelCase_				,	"_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    lowercase_	( self				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	)			->   List[Any]:



      """simple docstring"""







      len(lowerCAmelCase_) if isinstance(lowerCAmelCase_				,	lowerCAmelCase_) else 1

      # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
      a_									=0
      # get prompt text embeddings
      a_									=self.tokenizer(
          lowerCAmelCase_				,	padding="max_length"				,	max_length=self.tokenizer.model_max_length				,	truncation=lowerCAmelCase_				,	return_tensors="pt"				,	)
      a_									=text_inputs.input_ids
      a_									=self.tokenizer(lowerCAmelCase_				,	padding="longest"				,	return_tensors="pt").input_ids

      if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(lowerCAmelCase_				,	lowerCAmelCase_):
           a_									=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}""")

      a_									=self.text_encoder(text_input_ids.to(lowerCAmelCase_))
      a_									=text_encoder_output.text_embeds

      a_									=prompt_embeds.repeat_interleave(lowerCAmelCase_				,	dim=0)
      # in Shap-E it normalize the prompt_embeds and then later rescale it
      a_									=prompt_embeds / torch.linalg.norm(lowerCAmelCase_				,	dim=-1				,	keepdim=lowerCAmelCase_)

      if do_classifier_free_guidance:
           a_									=torch.zeros_like(lowerCAmelCase_)

           # 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
           a_									=torch.cat([negative_prompt_embeds, prompt_embeds])

      # Rescale the features to have unit variance
      a_									=math.sqrt(prompt_embeds.shape[1]) * prompt_embeds

      return prompt_embeds




 @torch.no_grad()
 @replace_example_docstring(lowerCAmelCase_)
 def __call__( self				,	lowerCAmelCase_				,	lowerCAmelCase_ = 1				,	lowerCAmelCase_ = 2_5				,	lowerCAmelCase_ = None				,	lowerCAmelCase_ = None				,	lowerCAmelCase_ = 4.0				,	lowerCAmelCase_ = 6_4				,	lowerCAmelCase_ = "pil"				,	lowerCAmelCase_ = True				,	)			->   Tuple:



      """simple docstring"""







      if isinstance(lowerCAmelCase_				,	lowerCAmelCase_):
           a_									=1
      elif isinstance(lowerCAmelCase_				,	lowerCAmelCase_):
           a_									=len(lowerCAmelCase_)
      else:
           raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase_)}""")

      a_									=self._execution_device

      a_									=batch_size * num_images_per_prompt

      a_									=guidance_scale > 1.0
      a_									=self._encode_prompt(lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_)

      # prior

      self.scheduler.set_timesteps(lowerCAmelCase_				,	device=lowerCAmelCase_)
      a_									=self.scheduler.timesteps

      a_									=self.prior.config.num_embeddings
      a_									=self.prior.config.embedding_dim

      a_									=self.prepare_latents(
          (batch_size, num_embeddings * embedding_dim)				,	prompt_embeds.dtype				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	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
      a_									=latents.reshape(latents.shape[0]				,	lowerCAmelCase_				,	lowerCAmelCase_)

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

           a_									=self.prior(
               lowerCAmelCase_				,	timestep=lowerCAmelCase_				,	proj_embedding=lowerCAmelCase_				,	).predicted_image_embedding

           # remove the variance
           a_		,    a_									=noise_pred.split(
               scaled_model_input.shape[2]				,	dim=2)  # batch_size, num_embeddings, embedding_dim

           if do_classifier_free_guidance is not None:
                a_		,    a_									=noise_pred.chunk(2)
                a_									=noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

           a_									=self.scheduler.step(
               lowerCAmelCase_				,	timestep=lowerCAmelCase_				,	sample=lowerCAmelCase_				,	).prev_sample

      if output_type == "latent":
           return ShapEPipelineOutput(images=lowerCAmelCase_)

      a_									=[]
      for i, latent in enumerate(lowerCAmelCase_):
           a_									=self.renderer.decode(
               latent[None, :]				,	lowerCAmelCase_				,	size=lowerCAmelCase_				,	ray_batch_size=4_0_9_6				,	n_coarse_samples=6_4				,	n_fine_samples=1_2_8				,	)
           images.append(lowerCAmelCase_)

      a_									=torch.stack(lowerCAmelCase_)

      if output_type not in ["np", "pil"]:
           raise ValueError(f"""Only the output types `pil` and `np` are supported not output_type={output_type}""")

      a_									=images.cpu().numpy()

      if output_type == "pil":
           a_									=[self.numpy_to_pil(lowerCAmelCase_) 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=lowerCAmelCase_)







'''simple docstring'''


import argparse
import json
from pathlib import Path

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

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase											=      logging.get_logger(__name__)






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=ConvNextConfig()

     if "tiny" in checkpoint_url:
          a_									=[3, 3, 9, 3]
          a_									=[9_6, 1_9_2, 3_8_4, 7_6_8]
     if "small" in checkpoint_url:
          a_									=[3, 3, 2_7, 3]
          a_									=[9_6, 1_9_2, 3_8_4, 7_6_8]
     if "base" in checkpoint_url:
          a_									=[3, 3, 2_7, 3]
          a_									=[1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
     if "large" in checkpoint_url:
          a_									=[3, 3, 2_7, 3]
          a_									=[1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
     if "xlarge" in checkpoint_url:
          a_									=[3, 3, 2_7, 3]
          a_									=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]

     if "1k" in checkpoint_url:
          a_									=1_0_0_0
          a_									="imagenet-1k-id2label.json"
          a_									=(1, 1_0_0_0)
     else:
          a_									=2_1_8_4_1
          a_									="imagenet-22k-id2label.json"
          a_									=(1, 2_1_8_4_1)

     a_									="huggingface/label-files"
     a_									=num_labels
     a_									=json.load(open(hf_hub_download(lowercase__							,						lowercase__							,						repo_type="dataset"							)							,						"r"							)							)
     a_									={int(lowercase__							): v for k, v in idalabel.items()}
     if "1k" not in checkpoint_url:
          # this dataset contains 21843 labels but the model only has 21841
          # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
          del idalabel[9_2_0_5]
          del idalabel[1_5_0_2_7]
     a_									=idalabel
     a_									={v: k for k, v in idalabel.items()}
     a_									=hidden_sizes
     a_									=depths

     return config, expected_shape






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     if "downsample_layers.0.0" in name:
          a_									=name.replace("downsample_layers.0.0"							,						"embeddings.patch_embeddings"							)
     if "downsample_layers.0.1" in name:
          a_									=name.replace("downsample_layers.0.1"							,						"embeddings.norm"							)  # we rename to layernorm later on
     if "downsample_layers.1.0" in name:
          a_									=name.replace("downsample_layers.1.0"							,						"stages.1.downsampling_layer.0"							)
     if "downsample_layers.1.1" in name:
          a_									=name.replace("downsample_layers.1.1"							,						"stages.1.downsampling_layer.1"							)
     if "downsample_layers.2.0" in name:
          a_									=name.replace("downsample_layers.2.0"							,						"stages.2.downsampling_layer.0"							)
     if "downsample_layers.2.1" in name:
          a_									=name.replace("downsample_layers.2.1"							,						"stages.2.downsampling_layer.1"							)
     if "downsample_layers.3.0" in name:
          a_									=name.replace("downsample_layers.3.0"							,						"stages.3.downsampling_layer.0"							)
     if "downsample_layers.3.1" in name:
          a_									=name.replace("downsample_layers.3.1"							,						"stages.3.downsampling_layer.1"							)
     if "stages" in name and "downsampling_layer" not in name:
          # stages.0.0. for instance should be renamed to stages.0.layers.0.
          a_									=name[: len("stages.0"							)] + ".layers" + name[len("stages.0"							) :]
     if "stages" in name:
          a_									=name.replace("stages"							,						"encoder.stages"							)
     if "norm" in name:
          a_									=name.replace("norm"							,						"layernorm"							)
     if "gamma" in name:
          a_									=name.replace("gamma"							,						"layer_scale_parameter"							)
     if "head" in name:
          a_									=name.replace("head"							,						"classifier"							)

     return name






def 			UpperCAmelCase_							(		):


     '''simple docstring'''







     a_									="http://images.cocodataset.org/val2017/000000039769.jpg"
     a_									=Image.open(requests.get(lowercase__							,						stream=lowercase__							).raw							)
     return im






@torch.no_grad()
def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							):


     '''simple docstring'''







     a_		,    a_									=get_convnext_config(lowercase__							)
     # load original state_dict from URL
     a_									=torch.hub.load_state_dict_from_url(lowercase__							)["model"]
     # rename keys
     for key in state_dict.copy().keys():
          a_									=state_dict.pop(lowercase__							)
          a_									=val
     # add prefix to all keys expect classifier head
     for key in state_dict.copy().keys():
          a_									=state_dict.pop(lowercase__							)
          if not key.startswith("classifier"							):
               a_									="convnext." + key
          a_									=val

     # load HuggingFace model
     a_									=ConvNextForImageClassification(lowercase__							)
     model.load_state_dict(lowercase__							)
     model.eval()

     # Check outputs on an image, prepared by ConvNextImageProcessor
     a_									=2_2_4 if "224" in checkpoint_url else 3_8_4
     a_									=ConvNextImageProcessor(size=lowercase__							)
     a_									=image_processor(images=prepare_img()							,						return_tensors="pt"							).pixel_values

     a_									=model(lowercase__							).logits

     # note: the logits below were obtained without center cropping
     if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
          a_									=torch.tensor([-0.1210, -0.6605, 0.1918]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
          a_									=torch.tensor([-0.4473, -0.1847, -0.6365]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
          a_									=torch.tensor([0.4525, 0.7539, 0.0308]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
          a_									=torch.tensor([0.3561, 0.6350, -0.0384]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
          a_									=torch.tensor([0.4174, -0.0989, 0.1489]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
          a_									=torch.tensor([0.2513, -0.1349, -0.1613]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
          a_									=torch.tensor([1.2980, 0.3631, -0.1198]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
          a_									=torch.tensor([1.2963, 0.1227, 0.1723]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
          a_									=torch.tensor([1.7956, 0.8390, 0.2820]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
          a_									=torch.tensor([-0.2822, -0.0502, -0.0878]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
          a_									=torch.tensor([-0.5672, -0.0730, -0.4348]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
          a_									=torch.tensor([0.2681, 0.2365, 0.6246]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
          a_									=torch.tensor([-0.2642, 0.3931, 0.5116]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
          a_									=torch.tensor([-0.6677, -0.1873, -0.8379]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
          a_									=torch.tensor([-0.7749, -0.2967, -0.6444]							)
     else:
          raise ValueError(F"""Unknown URL: {checkpoint_url}"""							)

     assert torch.allclose(logits[0, :3]							,						lowercase__							,						atol=1E-3							)
     assert logits.shape == expected_shape

     Path(lowercase__							).mkdir(exist_ok=lowercase__							)
     print(F"""Saving model to {pytorch_dump_folder_path}"""							)
     model.save_pretrained(lowercase__							)
     print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
     image_processor.save_pretrained(lowercase__							)

     print("Pushing model to the hub..."							)
     a_									="convnext"
     if "tiny" in checkpoint_url:
          model_name += "-tiny"
     elif "small" in checkpoint_url:
          model_name += "-small"
     elif "base" in checkpoint_url:
          model_name += "-base"
     elif "xlarge" in checkpoint_url:
          model_name += "-xlarge"
     elif "large" in checkpoint_url:
          model_name += "-large"
     if "224" in checkpoint_url:
          model_name += "-224"
     elif "384" in checkpoint_url:
          model_name += "-384"
     if "22k" in checkpoint_url and "1k" not in checkpoint_url:
          model_name += "-22k"
     if "22k" in checkpoint_url and "1k" in checkpoint_url:
          model_name += "-22k-1k"

     model.push_to_hub(
         repo_path_or_name=Path(lowercase__							,						lowercase__							)							,						organization="nielsr"							,						commit_message="Add model"							,						)


if __name__ == "__main__":
  lowercase											=      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--checkpoint_url''',
      default='''https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth''',
      type=str,
      help='''URL of the original ConvNeXT checkpoint you\'d like to convert.''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''',
      default=None,
      type=str,
      required=True,
      help='''Path to the output PyTorch model directory.''',
  )

  lowercase											=      parser.parse_args()
  convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


