



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 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: Union[PIL.Image.Image, np.ndarray]


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					int			,						__lowerCAmelCase					:					PriorTransformer			,						__lowerCAmelCase					:					CLIPVisionModel			,						__lowerCAmelCase					:					CLIPImageProcessor			,						__lowerCAmelCase					:					HeunDiscreteScheduler			,						__lowerCAmelCase					:					ShapERenderer			,						)   ->	List[str]:
    super().__init__()

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

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					Optional[Any]			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Tuple      )   ->	int:
    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     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					List[Any]=0      )   ->	Optional[Any]:
    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.image_encoder, self.prior]
    for cpu_offloaded_model in models:
       if cpu_offloaded_model is not None:
          cpu_offload(__lowerCAmelCase			,						__lowerCAmelCase      )

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

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

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

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

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

    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_image_embeds, image_embeds]      )

    return image_embeds

 @torch.no_grad()
 @replace_example_docstring(__lowerCAmelCase      )
 def __call__(    self					:					str			,						__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			,						)   ->	Tuple:

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

    _A      =       self._execution_device

    _A      =       batch_size * num_images_per_prompt

    _A      =       guidance_scale > 1.0
    _A      =       self._encode_image(__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)			,						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
    _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      ):
       print()
       _A      =       self.renderer.decode(
           latent[None, :]			,						__lowerCAmelCase			,						size=__lowerCAmelCase			,						ray_batch_size=40_96			,						n_coarse_samples=64			,						n_fine_samples=1_28			,						)

       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      )








import json
import os
from typing import Dict, List, Optional, Tuple

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


UpperCAmelCase_		 =					logging.get_logger(__name__)


UpperCAmelCase_		 =					{
    """vocab_file""": """vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
    """merges_file""": """merges.txt""",
}

UpperCAmelCase_		 =					{
    """vocab_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"""
        ),
    },
    """tokenizer_config_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"""
        ),
    },
    """merges_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"""
        ),
    },
}

UpperCAmelCase_		 =					"""</w>"""
UpperCAmelCase_		 =					"""@@ """

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]				)  ->     List[str]:
   _A      =       set()
   _A      =       word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)				)
      _A      =       char
   return pairs


# Speech2Text2 has no max input length
UpperCAmelCase_		 =					{"""facebook/s2t-wav2vec2-large-en-de""": 1_0_2_4}


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: Dict =  VOCAB_FILES_NAMES
 a__					: str =  PRETRAINED_VOCAB_FILES_MAP
 a__					: Optional[Any] =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 a__					: List[Any] =  ["input_ids", "attention_mask"]

 def __init__(    self					:					Optional[Any]			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					List[str]="<s>"			,						__lowerCAmelCase					:					Tuple="<pad>"			,						__lowerCAmelCase					:					Optional[Any]="</s>"			,						__lowerCAmelCase					:					Dict="<unk>"			,						__lowerCAmelCase					:					Union[str, Any]=False			,						__lowerCAmelCase					:					Optional[int]=None			,						**__lowerCAmelCase					:					str			,						)   ->	Dict:
    super().__init__(
        unk_token=__lowerCAmelCase			,						bos_token=__lowerCAmelCase			,						eos_token=__lowerCAmelCase			,						pad_token=__lowerCAmelCase			,						do_lower_case=__lowerCAmelCase			,						**__lowerCAmelCase			,						)

    _A      =       do_lower_case

    with open(__lowerCAmelCase			,						encoding='''utf-8'''      ) as vocab_handle:
       _A      =       json.load(__lowerCAmelCase      )
    _A      =       {v: k for k, v in self.encoder.items()}

    if merges_file is None:
       logger.info(f'''No merges files provided. {self.__class__.__name__} can only be used for decoding.'''      )

       _A      =       None
       _A      =       None
    else:
       with open(__lowerCAmelCase			,						encoding='''utf-8'''      ) as merges_handle:
          _A      =       merges_handle.read().split('''\n'''      )[:-1]

       _A      =       [tuple(merge.split()[:2]      ) for merge in merges]
       _A      =       dict(zip(__lowerCAmelCase			,						range(len(__lowerCAmelCase      )      )      )      )
       _A      =       {}

 @property
 def     snake_case_      (    self					:					List[str]      )   ->	int:
    return len(self.decoder      )

 def     snake_case_      (    self					:					Dict      )   ->	Dict:
    return dict(self.encoder			,						**self.added_tokens_encoder      )

 def     snake_case_      (    self					:					Union[str, Any]			,						__lowerCAmelCase					:					Any      )   ->	Union[str, Any]:
    _A      =       tuple(token[:-1]      ) + (token[-1] + BPE_TOKEN_MERGES,)
    if token in self.cache:
       return self.cache[token]
    _A      =       get_pairs(__lowerCAmelCase      )

    if not pairs:
       return token

    while True:
       _A      =       min(__lowerCAmelCase			,						key=lambda __lowerCAmelCase      : self.bpe_ranks.get(__lowerCAmelCase			,						float('''inf'''      )      )      )
       if bigram not in self.bpe_ranks:
          break
       _A  ,    _A      =       bigram
       _A      =       []
       _A      =       0
       while i < len(__lowerCAmelCase      ):
          try:
             _A      =       word.index(__lowerCAmelCase			,						__lowerCAmelCase      )
          except ValueError:
             new_word.extend(word[i:]      )
             break
          else:
             new_word.extend(word[i:j]      )
             _A      =       j

          if word[i] == first and i < len(__lowerCAmelCase      ) - 1 and word[i + 1] == second:
             new_word.append(first + second      )
             i += 2
          else:
             new_word.append(word[i]      )
             i += 1
       _A      =       tuple(__lowerCAmelCase      )
       _A      =       new_word
       if len(__lowerCAmelCase      ) == 1:
          break
       else:
          _A      =       get_pairs(__lowerCAmelCase      )
    _A      =       ''' '''.join(__lowerCAmelCase      )
    if word == "\n  " + BPE_TOKEN_MERGES:
       _A      =       '''\n''' + BPE_TOKEN_MERGES

    if word.endswith(__lowerCAmelCase      ):
       _A      =       word.replace(__lowerCAmelCase			,						''''''      )

    _A      =       word.replace(''' '''			,						__lowerCAmelCase      )
    _A      =       word
    return word

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					Tuple      )   ->	Optional[int]:

    if self.bpe_ranks is None:
       raise ValueError(
           '''This tokenizer was instantiated without a `merges.txt` file, so'''
           ''' that it can only be used for decoding, not for encoding.'''
           '''Make sure to provide `merges.txt` file at instantiation to enable '''
           '''encoding.'''      )

    if self.do_lower_case:
       _A      =       text.lower()

    _A      =       text.split()

    _A      =       []
    for token in text:
       if token:
          split_tokens.extend(list(self.bpe(__lowerCAmelCase      ).split(''' '''      )      )      )

    return split_tokens

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					str      )   ->	int:
    return self.encoder.get(__lowerCAmelCase			,						self.encoder.get(self.unk_token      )      )

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					int      )   ->	str:
    _A      =       self.decoder.get(__lowerCAmelCase			,						self.unk_token      )
    return result

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					List[str]      )   ->	str:
    _A      =       ''' '''.join(__lowerCAmelCase      )

    # make sure @@ tokens are concatenated
    _A      =       ''''''.join(string.split(__lowerCAmelCase      )      )

    return string

 def     snake_case_      (    self					:					Tuple			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Optional[str] = None      )   ->	Tuple[str]:
    if not os.path.isdir(__lowerCAmelCase      ):
       logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
       return
    _A      =       os.path.join(
        __lowerCAmelCase			,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
    _A      =       os.path.join(
        __lowerCAmelCase			,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']      )

    with open(__lowerCAmelCase			,						'''w'''			,						encoding='''utf-8'''      ) as f:
       f.write(json.dumps(self.encoder			,						indent=2			,						sort_keys=__lowerCAmelCase			,						ensure_ascii=__lowerCAmelCase      ) + '''\n'''      )

    _A      =       0
    if self.bpe_ranks is None:
       return (vocab_file,)

    with open(__lowerCAmelCase			,						'''w'''			,						encoding='''utf-8'''      ) as writer:
       for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,						key=lambda __lowerCAmelCase      : kv[1]      ):
          if index != token_index:
             logger.warning(
                 f'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
                 ''' Please check that the tokenizer is not corrupted!'''      )
             _A      =       token_index
          writer.write(''' '''.join(__lowerCAmelCase      ) + '''\n'''      )
          index += 1

    return (vocab_file, merges_file)



