




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


_lowerCAmelCase  :      List[str]						=      logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowerCAmelCase  :      List[str]						=      "\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        >>> repo = \"openai/shap-e-img2img\"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n        >>> image = load_image(image_url).convert(\"RGB\")\n\n        >>> images = pipe(\n        ...     image,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")\n        ```\n"
@dataclass
class 	_UpperCamelCase					( UpperCAmelCase__						):
      UpperCAmelCase_						=						42



class 	_UpperCamelCase					( UpperCAmelCase__						):
      def __init__(	self      :str       ,					lowerCamelCase      :Any       ,					lowerCamelCase      :Optional[Any]       ,					lowerCamelCase      :Any       ,					lowerCamelCase      :List[str]       ,					lowerCamelCase      :str       ,					)   ->	Optional[Any]:
       super().__init__()

       self.register_modules(
           prior=_SCREAMING_SNAKE_CASE       ,					image_encoder=_SCREAMING_SNAKE_CASE       ,					image_processor=_SCREAMING_SNAKE_CASE       ,					scheduler=_SCREAMING_SNAKE_CASE       ,					renderer=_SCREAMING_SNAKE_CASE       ,					)
      def 		UpperCAmelCase_  (	self      :Optional[int]       ,					lowerCamelCase      :Dict       ,					lowerCamelCase      :List[Any]       ,					lowerCamelCase      :Dict       ,					lowerCamelCase      :str       ,					lowerCamelCase      :Any       ,					lowerCamelCase      :Dict					)   ->	int:




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

       UpperCAmelCase__							       =  latents * scheduler.init_noise_sigma
       return latents
      def 		UpperCAmelCase_  (	self      :List[str]       ,					lowerCamelCase      :Tuple=0					)   ->	List[str]:




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

       UpperCAmelCase__							       =  torch.device(f'''cuda:{gpu_id}'''					)

       UpperCAmelCase__							       =  [self.image_encoder, self.prior]
       for cpu_offloaded_model in models:
        if cpu_offloaded_model is not None:
         cpu_offload(_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE					)
      @property
      def 		UpperCAmelCase_  (	self      :Dict					)   ->	List[Any]:




       if self.device != torch.device("meta"					) or not hasattr(self.image_encoder       ,					"_hf_hook"					):
        return self.device
       for module in self.image_encoder.modules():
        if (
            hasattr(_SCREAMING_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 		UpperCAmelCase_  (	self      :Dict       ,					lowerCamelCase      :Any       ,					lowerCamelCase      :str       ,					lowerCamelCase      :int       ,					lowerCamelCase      :int       ,					)   ->	Any:




       if isinstance(_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE					) and isinstance(image[0]       ,					torch.Tensor					):
        UpperCAmelCase__							       =  torch.cat(_SCREAMING_SNAKE_CASE       ,					axis=0					) if image[0].ndim == 4 else torch.stack(_SCREAMING_SNAKE_CASE       ,					axis=0					)

       if not isinstance(_SCREAMING_SNAKE_CASE       ,					torch.Tensor					):
        UpperCAmelCase__							       =  self.image_processor(_SCREAMING_SNAKE_CASE       ,					return_tensors="pt"					).pixel_values[0].unsqueeze(0					)

       UpperCAmelCase__							       =  image.to(dtype=self.image_encoder.dtype       ,					device=_SCREAMING_SNAKE_CASE					)

       UpperCAmelCase__							       =  self.image_encoder(_SCREAMING_SNAKE_CASE					)["""last_hidden_state"""]
       UpperCAmelCase__							       =  image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

       UpperCAmelCase__							       =  image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE       ,					dim=0					)

       if do_classifier_free_guidance:
        UpperCAmelCase__							       =  torch.zeros_like(_SCREAMING_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
        UpperCAmelCase__							       =  torch.cat([negative_image_embeds, image_embeds]					)

       return image_embeds







      @torch.no_grad()
      @replace_example_docstring(_SCREAMING_SNAKE_CASE					)
      def __call__(	self      :int       ,					lowerCamelCase      :Optional[int]       ,					lowerCamelCase      :Any = 1       ,					lowerCamelCase      :Tuple = 25       ,					lowerCamelCase      :Any = None       ,					lowerCamelCase      :Tuple = None       ,					lowerCamelCase      :str = 4.0       ,					lowerCamelCase      :Dict = 64       ,					lowerCamelCase      :int = "pil"       ,					lowerCamelCase      :Union[str, Any] = True       ,					)   ->	Optional[Any]:




       if isinstance(_SCREAMING_SNAKE_CASE       ,					PIL.Image.Image					):
        UpperCAmelCase__							       =  1
       elif isinstance(_SCREAMING_SNAKE_CASE       ,					torch.Tensor					):
        UpperCAmelCase__							       =  image.shape[0]
       elif isinstance(_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE					) and isinstance(image[0]       ,					(torch.Tensor, PIL.Image.Image)					):
        UpperCAmelCase__							       =  len(_SCREAMING_SNAKE_CASE					)
       else:
        raise ValueError(
            f'''`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(_SCREAMING_SNAKE_CASE					)}'''					)

       UpperCAmelCase__							       =  self._execution_device

       UpperCAmelCase__							       =  batch_size * num_images_per_prompt

       UpperCAmelCase__							       =  guidance_scale > 1.0
       UpperCAmelCase__							       =  self._encode_image(_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE					)

       # prior

       self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE       ,					device=_SCREAMING_SNAKE_CASE					)
       UpperCAmelCase__							       =  self.scheduler.timesteps

       UpperCAmelCase__							       =  self.prior.config.num_embeddings
       UpperCAmelCase__							       =  self.prior.config.embedding_dim

       UpperCAmelCase__							       =  self.prepare_latents(
           (batch_size, num_embeddings * embedding_dim)       ,					image_embeds.dtype       ,					_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE       ,					_SCREAMING_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
       UpperCAmelCase__							       =  latents.reshape(latents.shape[0]       ,					_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE					)

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

        UpperCAmelCase__							       =  self.prior(
            _SCREAMING_SNAKE_CASE       ,					timestep=_SCREAMING_SNAKE_CASE       ,					proj_embedding=_SCREAMING_SNAKE_CASE       ,					).predicted_image_embedding

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

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

        UpperCAmelCase__							       =  self.scheduler.step(
            _SCREAMING_SNAKE_CASE       ,					timestep=_SCREAMING_SNAKE_CASE       ,					sample=_SCREAMING_SNAKE_CASE       ,					).prev_sample

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

       UpperCAmelCase__							       =  []
       for i, latent in enumerate(_SCREAMING_SNAKE_CASE					):
        print()
        UpperCAmelCase__							       =  self.renderer.decode(
            latent[None, :]       ,					_SCREAMING_SNAKE_CASE       ,					size=_SCREAMING_SNAKE_CASE       ,					ray_batch_size=4096       ,					n_coarse_samples=64       ,					n_fine_samples=128       ,					)

        images.append(_SCREAMING_SNAKE_CASE					)

       UpperCAmelCase__							       =  torch.stack(_SCREAMING_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}'''					)

       UpperCAmelCase__							       =  images.cpu().numpy()

       if output_type == "pil":
        UpperCAmelCase__							       =  [self.numpy_to_pil(_SCREAMING_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=_SCREAMING_SNAKE_CASE					)










"""simple docstring"""
import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline

def       _snake_case						(						UpperCamelCase						:		Dataset			,		UpperCamelCase						:		Dict[str, str]						):
 UpperCAmelCase	:					Any	=	args.log_outputs
 UpperCAmelCase	:					Any	=	"""_""".join(args.dataset.split("""/"""						) + [args.config, args.split]						)

 # load metric
 UpperCAmelCase	:					List[Any]	=	load_metric("""wer"""						)
 UpperCAmelCase	:					Any	=	load_metric("""cer"""						)

 # compute metrics
 UpperCAmelCase	:					int	=	wer.compute(references=result["""target"""]			,		predictions=result["""prediction"""]						)
 UpperCAmelCase	:					str	=	cer.compute(references=result["""target"""]			,		predictions=result["""prediction"""]						)

 # print & log results
 UpperCAmelCase	:					Tuple	=	F"WER: {wer_result}\nCER: {cer_result}"
 print(UpperCamelCase						)

 with open(F"{dataset_id}_eval_results.txt"			,		"""w"""						) as f:
  f.write(UpperCamelCase						)

 # log all results in text file. Possibly interesting for analysis
 if log_outputs is not None:
  UpperCAmelCase	:					str	=	F"log_{dataset_id}_predictions.txt"
  UpperCAmelCase	:					Tuple	=	F"log_{dataset_id}_targets.txt"

  with open(UpperCamelCase			,		"""w"""						) as p, open(UpperCamelCase			,		"""w"""						) as t:
   # mapping function to write output
   def write_to_file(UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		List[Any]						):
    p.write(F"{i}" + """\n"""						)
    p.write(batch["""prediction"""] + """\n"""						)
    t.write(F"{i}" + """\n"""						)
    t.write(batch["""target"""] + """\n"""						)

   result.map(UpperCamelCase			,		with_indices=UpperCamelCase						)

def       _snake_case						(						UpperCamelCase						:		str						):
 UpperCAmelCase	:					List[str]	=	"""[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

 UpperCAmelCase	:					Dict	=	re.sub(UpperCamelCase			,		""""""			,		text.lower()						)

 # In addition, we can normalize the target text, e.g. removing new lines characters etc...
 # note that order is important here!
 UpperCAmelCase	:					List[str]	=	["""\n\n""", """\n""", """   """, """  """]

 for t in token_sequences_to_ignore:
  UpperCAmelCase	:					Optional[Any]	=	""" """.join(text.split(UpperCamelCase						)						)

 return text

def       _snake_case						(						UpperCamelCase						:		Tuple						):
 # load dataset
 UpperCAmelCase	:					Union[str, Any]	=	load_dataset(args.dataset			,		args.config			,		split=args.split			,		use_auth_token=UpperCamelCase						)

 # for testing: only process the first two examples as a test
 # dataset = dataset.select(range(10))

 # load processor
 UpperCAmelCase	:					Optional[int]	=	AutoFeatureExtractor.from_pretrained(args.model_id						)
 UpperCAmelCase	:					Any	=	feature_extractor.sampling_rate

 # resample audio
 UpperCAmelCase	:					List[str]	=	dataset.cast_column("""audio"""			,		Audio(sampling_rate=UpperCamelCase						)						)

 # load eval pipeline
 if args.device is None:
  UpperCAmelCase	:					Optional[int]	=	0 if torch.cuda.is_available() else -1
 UpperCAmelCase	:					Tuple	=	pipeline("""automatic-speech-recognition"""			,		model=args.model_id			,		device=args.device						)

 # map function to decode audio
 def map_to_pred(UpperCamelCase						:		Any						):
  UpperCAmelCase	:					Any	=	asr(
      batch["""audio"""]["""array"""]			,		chunk_length_s=args.chunk_length_s			,		stride_length_s=args.stride_length_s						)

  UpperCAmelCase	:					Tuple	=	prediction["""text"""]
  UpperCAmelCase	:					List[str]	=	normalize_text(batch["""sentence"""]						)
  return batch

 # run inference on all examples
 UpperCAmelCase	:					int	=	dataset.map(UpperCamelCase			,		remove_columns=dataset.column_names						)

 # compute and log_results
 # do not change function below
 log_results(UpperCamelCase			,		UpperCamelCase						)


if __name__ == "__main__":
      A:   List[Any]								=		argparse.ArgumentParser()

      parser.add_argument(
          "--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
      )
      parser.add_argument(
          "--dataset",
          type=str,
          required=True,
          help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
      )
      parser.add_argument(
          "--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'`  for Common Voice"
      )
      parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
      parser.add_argument(
          "--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
      )
      parser.add_argument(
          "--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
      )
      parser.add_argument(
          "--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
      )
      parser.add_argument(
          "--device",
          type=int,
          default=None,
          help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
      )
      A:   Union[str, Any]								=		parser.parse_args()

      main(args)
