"""simple docstring"""


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase__			=    logging.get_logger(__name__)

UpperCAmelCase__			=    {"""vocab_file""": """spm_char.model"""}

UpperCAmelCase__			=    {
    """vocab_file""": {
        """microsoft/speecht5_asr""": """https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model""",
        """microsoft/speecht5_tts""": """https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model""",
        """microsoft/speecht5_vc""": """https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model""",
    }
}

UpperCAmelCase__			=    {
    """microsoft/speecht5_asr""": 1_0_2_4,
    """microsoft/speecht5_tts""": 1_0_2_4,
    """microsoft/speecht5_vc""": 1_0_2_4,
}


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	str  				=				VOCAB_FILES_NAMES
    _snake_case  :	Optional[Any]  				=				PRETRAINED_VOCAB_FILES_MAP
    _snake_case  :	Dict  				=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _snake_case  :	List[Any]  				=				['input_ids', 'attention_mask']


    def __init__(       self						:	List[Any] ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	List[str]="<s>" ,					__lowerCAmelCase						:	Tuple="</s>" ,					__lowerCAmelCase						:	Tuple="<unk>" ,					__lowerCAmelCase						:	int="<pad>" ,					__lowerCAmelCase						:	Optional[Dict[str, Any]] = None ,					**__lowerCAmelCase						:	List[str] ,					):
      _UpperCAmelCase    			=   {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=__lowerCAmelCase ,					eos_token=__lowerCAmelCase ,					unk_token=__lowerCAmelCase ,					pad_token=__lowerCAmelCase ,					sp_model_kwargs=self.sp_model_kwargs ,					**__lowerCAmelCase ,					)

      _UpperCAmelCase    			=   vocab_file

      _UpperCAmelCase    			=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
      self.sp_model.Load(__lowerCAmelCase       )


    @property
    def   lowerCAmelCase_	(       self						:	List[str]       ):
      return self.sp_model.get_piece_size()


    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   {self.convert_ids_to_tokens(__lowerCAmelCase       ): i for i in range(self.vocab_size       )}
      vocab.update(self.added_tokens_encoder       )
      return vocab


    def __getstate__(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   self.__dict__.copy()
      _UpperCAmelCase    			=   None
      return state


    def __setstate__(       self						:	Any ,					__lowerCAmelCase						:	List[str]       ):
      _UpperCAmelCase    			=   d

      # for backward compatibility
      if not hasattr(self ,					"""sp_model_kwargs"""       ):
        _UpperCAmelCase    			=   {}

      _UpperCAmelCase    			=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
      self.sp_model.Load(self.vocab_file       )


    def   lowerCAmelCase_	(       self						:	int ,					__lowerCAmelCase						:	str       ):
      return self.sp_model.encode(__lowerCAmelCase ,					out_type=__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[Any] ,					__lowerCAmelCase						:	Tuple       ):
      return self.sp_model.piece_to_id(__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Optional[int] ,					__lowerCAmelCase						:	str       ):
      _UpperCAmelCase    			=   self.sp_model.IdToPiece(__lowerCAmelCase       )
      return token


    def   lowerCAmelCase_	(       self						:	Dict ,					__lowerCAmelCase						:	Optional[Any]       ):
      _UpperCAmelCase    			=   []
      _UpperCAmelCase    			=   """"""
      for token in tokens:
        # make sure that special tokens are not decoded using sentencepiece model
        if token in self.all_special_tokens:
          out_string += self.sp_model.decode(__lowerCAmelCase       ) + token
          _UpperCAmelCase    			=   []
        else:
          current_sub_tokens.append(__lowerCAmelCase       )
      out_string += self.sp_model.decode(__lowerCAmelCase       )
      return out_string.strip()


    def   lowerCAmelCase_	(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	int=None       ):
      if token_ids_a is None:
        return token_ids_a + [self.eos_token_id]
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return token_ids_a + token_ids_a + [self.eos_token_id]


    def   lowerCAmelCase_	(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	List[int] ,					__lowerCAmelCase						:	Optional[List[int]] = None ,					__lowerCAmelCase						:	bool = False       ):
      if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=__lowerCAmelCase ,					token_ids_a=__lowerCAmelCase ,					already_has_special_tokens=__lowerCAmelCase       )

      _UpperCAmelCase    			=   [1]
      if token_ids_a is None:
        return ([0] * len(__lowerCAmelCase       )) + suffix_ones
      return ([0] * len(__lowerCAmelCase       )) + ([0] * len(__lowerCAmelCase       )) + suffix_ones


    def   lowerCAmelCase_	(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	Optional[str] = None       ):
      if not os.path.isdir(__lowerCAmelCase       ):
        logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
        return
      _UpperCAmelCase    			=   os.path.join(
          __lowerCAmelCase ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

      if os.path.abspath(self.vocab_file       ) != os.path.abspath(__lowerCAmelCase       ) and os.path.isfile(self.vocab_file       ):
        copyfile(self.vocab_file ,					__lowerCAmelCase       )
      elif not os.path.isfile(self.vocab_file       ):
        with open(__lowerCAmelCase ,					"""wb"""       ) as fi:
          _UpperCAmelCase    			=   self.sp_model.serialized_model_proto()
          fi.write(__lowerCAmelCase       )

      return (out_vocab_file,)

"""simple docstring"""


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


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

UpperCAmelCase__			=    """
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")

        >>> repo = \"openai/shap-e-img2img\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
        >>> image = load_image(image_url).convert(\"RGB\")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
        ```
"""


@dataclass
class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	Union[PIL.Image.Image, np.ndarray]



class 							a					(		lowerCAmelCase_ ):


    def __init__(       self						:	Dict ,					__lowerCAmelCase						:	PriorTransformer ,					__lowerCAmelCase						:	CLIPVisionModel ,					__lowerCAmelCase						:	CLIPImageProcessor ,					__lowerCAmelCase						:	HeunDiscreteScheduler ,					__lowerCAmelCase						:	ShapERenderer ,					):
      super().__init__()

      self.register_modules(
          prior=__lowerCAmelCase ,					image_encoder=__lowerCAmelCase ,					image_processor=__lowerCAmelCase ,					scheduler=__lowerCAmelCase ,					renderer=__lowerCAmelCase ,					)


    def   lowerCAmelCase_	(       self						:	List[Any] ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	List[Any]       ):
      if latents is None:
        _UpperCAmelCase    			=   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}'''       )
        _UpperCAmelCase    			=   latents.to(__lowerCAmelCase       )

      _UpperCAmelCase    			=   latents * scheduler.init_noise_sigma
      return latents


    def   lowerCAmelCase_	(       self						:	str ,					__lowerCAmelCase						:	List[str]=0       ):
      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(__lowerCAmelCase ,					__lowerCAmelCase       )


    @property
    def   lowerCAmelCase_	(       self						:	List[str]       ):
      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(__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   lowerCAmelCase_	(       self						:	int ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Any ,					):
      if isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ) and isinstance(image[0] ,					torch.Tensor       ):
        _UpperCAmelCase    			=   torch.cat(__lowerCAmelCase ,					axis=0       ) if image[0].ndim == 4 else torch.stack(__lowerCAmelCase ,					axis=0       )

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

      _UpperCAmelCase    			=   image.to(dtype=self.image_encoder.dtype ,					device=__lowerCAmelCase       )

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

      _UpperCAmelCase    			=   image_embeds.repeat_interleave(__lowerCAmelCase ,					dim=0       )

      if do_classifier_free_guidance:
        _UpperCAmelCase    			=   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
        _UpperCAmelCase    			=   torch.cat([negative_image_embeds, image_embeds]       )

      return image_embeds


    @torch.no_grad()
    @replace_example_docstring(__lowerCAmelCase       )
    def __call__(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	Union[PIL.Image.Image, List[PIL.Image.Image]] ,					__lowerCAmelCase						:	int = 1 ,					__lowerCAmelCase						:	int = 25 ,					__lowerCAmelCase						:	Optional[Union[torch.Generator, List[torch.Generator]]] = None ,					__lowerCAmelCase						:	Optional[torch.FloatTensor] = None ,					__lowerCAmelCase						:	float = 4.0 ,					__lowerCAmelCase						:	int = 64 ,					__lowerCAmelCase						:	Optional[str] = "pil" ,					__lowerCAmelCase						:	bool = True ,					):

      if isinstance(__lowerCAmelCase ,					PIL.Image.Image       ):
        _UpperCAmelCase    			=   1
      elif isinstance(__lowerCAmelCase ,					torch.Tensor       ):
        _UpperCAmelCase    			=   image.shape[0]
      elif isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ) and isinstance(image[0] ,					(torch.Tensor, PIL.Image.Image)       ):
        _UpperCAmelCase    			=   len(__lowerCAmelCase       )
      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(__lowerCAmelCase       )}'''       )

      _UpperCAmelCase    			=   self._execution_device

      _UpperCAmelCase    			=   batch_size * num_images_per_prompt

      _UpperCAmelCase    			=   guidance_scale > 1.0
      _UpperCAmelCase    			=   self._encode_image(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase       )

      # prior

      self.scheduler.set_timesteps(__lowerCAmelCase ,					device=__lowerCAmelCase       )
      _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 ,					__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
      _UpperCAmelCase    			=   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
        _UpperCAmelCase    			=   torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents
        _UpperCAmelCase    			=   self.scheduler.scale_model_input(__lowerCAmelCase ,					__lowerCAmelCase       )

        _UpperCAmelCase    			=   self.prior(
            __lowerCAmelCase ,					timestep=__lowerCAmelCase ,					proj_embedding=__lowerCAmelCase ,					).predicted_image_embedding

        # remove the variance
        _UpperCAmelCase			,					_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			,					_UpperCAmelCase    			=   noise_pred.chunk(2       )
          _UpperCAmelCase    			=   noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

        _UpperCAmelCase    			=   self.scheduler.step(
            __lowerCAmelCase ,					timestep=__lowerCAmelCase ,					sample=__lowerCAmelCase ,					).prev_sample

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

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

        images.append(__lowerCAmelCase       )

      _UpperCAmelCase    			=   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}'''       )

      _UpperCAmelCase    			=   images.cpu().numpy()

      if output_type == "pil":
        _UpperCAmelCase    			=   [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       )
