


"""simple docstring"""


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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_nllb import NllbTokenizer
else:
   _snake_case          =							None


_snake_case          =							logging.get_logger(__name__)


_snake_case          =							{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_snake_case          =							{
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
        ),
    },
}

_snake_case          =							{
    "facebook/nllb-large-en-ro": 1024,
    "facebook/nllb-200-distilled-600M": 1024,
}

# fmt: off
_snake_case          =							["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]



class   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				Any             =    VOCAB_FILES_NAMES
    a_    :				Optional[Any]             =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a_    :				Optional[Any]             =    PRETRAINED_VOCAB_FILES_MAP
    a_    :				List[str]             =    ['input_ids', 'attention_mask']
    a_    :				Union[str, Any]             =    NllbTokenizer

    a_    :				List[int]             =    []
    a_    :				List[int]             =    []
    def __init__( self							:					int							,					SCREAMING_SNAKE_CASE__							:					List[Any]=None							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=None							,					SCREAMING_SNAKE_CASE__							:					List[Any]="<s>"							,					SCREAMING_SNAKE_CASE__							:					List[str]="</s>"							,					SCREAMING_SNAKE_CASE__							:					Any="</s>"							,					SCREAMING_SNAKE_CASE__							:					List[str]="<s>"							,					SCREAMING_SNAKE_CASE__							:					Tuple="<unk>"							,					SCREAMING_SNAKE_CASE__							:					Optional[int]="<pad>"							,					SCREAMING_SNAKE_CASE__							:					Any="<mask>"							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=None							,					SCREAMING_SNAKE_CASE__							:					int=None							,					SCREAMING_SNAKE_CASE__							:					Dict=None							,					SCREAMING_SNAKE_CASE__							:					Tuple=False							,					**SCREAMING_SNAKE_CASE__							:					str							,					):
          # Mask token behave like a normal word, i.e. include the space before it
          lowerCamelCase__           =		AddedToken(SCREAMING_SNAKE_CASE__							,					lstrip=SCREAMING_SNAKE_CASE__							,					rstrip=SCREAMING_SNAKE_CASE__				) if isinstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				) else mask_token
          lowerCamelCase__           =		legacy_behaviour
          super().__init__(
              vocab_file=SCREAMING_SNAKE_CASE__							,					tokenizer_file=SCREAMING_SNAKE_CASE__							,					bos_token=SCREAMING_SNAKE_CASE__							,					eos_token=SCREAMING_SNAKE_CASE__							,					sep_token=SCREAMING_SNAKE_CASE__							,					cls_token=SCREAMING_SNAKE_CASE__							,					unk_token=SCREAMING_SNAKE_CASE__							,					pad_token=SCREAMING_SNAKE_CASE__							,					mask_token=SCREAMING_SNAKE_CASE__							,					src_lang=SCREAMING_SNAKE_CASE__							,					tgt_lang=SCREAMING_SNAKE_CASE__							,					additional_special_tokens=SCREAMING_SNAKE_CASE__							,					legacy_behaviour=SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__							,					)

          lowerCamelCase__           =		vocab_file
          lowerCamelCase__           =		False if not self.vocab_file else True

          lowerCamelCase__           =		FAIRSEQ_LANGUAGE_CODES.copy()

          if additional_special_tokens is not None:
                # Only add those special tokens if they are not already there.
                _additional_special_tokens.extend(
                    [t for t in additional_special_tokens if t not in _additional_special_tokens]				)

          self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}				)
          lowerCamelCase__           =		{
              lang_code: self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__				) for lang_code in FAIRSEQ_LANGUAGE_CODES
          }

          lowerCamelCase__           =		src_lang if src_lang is not None else 'eng_Latn'
          lowerCamelCase__           =		self.convert_tokens_to_ids(self._src_lang				)
          lowerCamelCase__           =		tgt_lang
          self.set_src_lang_special_tokens(self._src_lang				)
    @property
    def      _UpperCamelCase       ( self							:					str				):
          return self._src_lang
    @src_lang.setter
    def      _UpperCamelCase       ( self							:					int							,					SCREAMING_SNAKE_CASE__							:					str				):
          lowerCamelCase__           =		new_src_lang
          self.set_src_lang_special_tokens(self._src_lang				)
    def      _UpperCamelCase       ( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					List[int]							,					SCREAMING_SNAKE_CASE__							:					Optional[List[int]] = None				):
          if token_ids_a is None:
                return self.prefix_tokens + token_ids_a + self.suffix_tokens
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
    def      _UpperCamelCase       ( self							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					List[int]							,					SCREAMING_SNAKE_CASE__							:					Optional[List[int]] = None				):
          lowerCamelCase__           =		[self.sep_token_id]
          lowerCamelCase__           =		[self.cls_token_id]

          if token_ids_a is None:
                return len(cls + token_ids_a + sep				) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]
    def      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Optional[str]							,					SCREAMING_SNAKE_CASE__							:					Optional[str]							,					**SCREAMING_SNAKE_CASE__							:					Optional[int]				):
          if src_lang is None or tgt_lang is None:
                raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'				)
          lowerCamelCase__           =		src_lang
          lowerCamelCase__           =		self(SCREAMING_SNAKE_CASE__							,					add_special_tokens=SCREAMING_SNAKE_CASE__							,					return_tensors=SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		tgt_lang_id
          return inputs
    def      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					str = "eng_Latn"							,					SCREAMING_SNAKE_CASE__							:					Optional[List[str]] = None							,					SCREAMING_SNAKE_CASE__							:					str = "fra_Latn"							,					**SCREAMING_SNAKE_CASE__							:					Dict							,					):
          lowerCamelCase__           =		src_lang
          lowerCamelCase__           =		tgt_lang
          return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					List[str]				):
          return self.set_src_lang_special_tokens(self.src_lang				)
    def      _UpperCamelCase       ( self							:					List[Any]				):
          return self.set_tgt_lang_special_tokens(self.tgt_lang				)
    def      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]				):
          lowerCamelCase__           =		self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__				)

          if self.legacy_behaviour:
                lowerCamelCase__           =		[]
                lowerCamelCase__           =		[self.eos_token_id, self.cur_lang_code]
          else:
                lowerCamelCase__           =		[self.cur_lang_code]
                lowerCamelCase__           =		[self.eos_token_id]

          lowerCamelCase__           =		self.convert_ids_to_tokens(self.prefix_tokens				)
          lowerCamelCase__           =		self.convert_ids_to_tokens(self.suffix_tokens				)

          lowerCamelCase__           =		processors.TemplateProcessing(
              single=prefix_tokens_str + ['$A'] + suffix_tokens_str							,					pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str							,					special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str							,					self.prefix_tokens + self.suffix_tokens				)				)							,					)
    def      _UpperCamelCase       ( self							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					str				):
          lowerCamelCase__           =		self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__				)
          if self.legacy_behaviour:
                lowerCamelCase__           =		[]
                lowerCamelCase__           =		[self.eos_token_id, self.cur_lang_code]
          else:
                lowerCamelCase__           =		[self.cur_lang_code]
                lowerCamelCase__           =		[self.eos_token_id]

          lowerCamelCase__           =		self.convert_ids_to_tokens(self.prefix_tokens				)
          lowerCamelCase__           =		self.convert_ids_to_tokens(self.suffix_tokens				)

          lowerCamelCase__           =		processors.TemplateProcessing(
              single=prefix_tokens_str + ['$A'] + suffix_tokens_str							,					pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str							,					special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str							,					self.prefix_tokens + self.suffix_tokens				)				)							,					)


    def      _UpperCamelCase       ( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Optional[str] = None				):
          if not self.can_save_slow_tokenizer:
                raise ValueError(
                    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                    'tokenizer.'				)

          if not os.path.isdir(SCREAMING_SNAKE_CASE__				):
                logger.error(F'Vocabulary path ({save_directory}) should be a directory.'				)
                return
          lowerCamelCase__           =		os.path.join(
              SCREAMING_SNAKE_CASE__							,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

          if os.path.abspath(self.vocab_file				) != os.path.abspath(SCREAMING_SNAKE_CASE__				):
                copyfile(self.vocab_file							,					SCREAMING_SNAKE_CASE__				)

          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


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

_snake_case          =							"\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   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				Union[PIL.Image.Image, np.ndarray]





class   _a      (      SCREAMING_SNAKE_CASE_ ):
    def __init__( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					PriorTransformer							,					SCREAMING_SNAKE_CASE__							:					CLIPVisionModel							,					SCREAMING_SNAKE_CASE__							:					CLIPImageProcessor							,					SCREAMING_SNAKE_CASE__							:					HeunDiscreteScheduler							,					SCREAMING_SNAKE_CASE__							:					ShapERenderer							,					):
          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							:					str							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					List[str]				):
          if latents is None:
                lowerCamelCase__           =		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}'				)
                lowerCamelCase__           =		latents.to(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		latents * scheduler.init_noise_sigma
          return latents
    def      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					List[str]=0				):
          if is_accelerate_available():
                from accelerate import cpu_offload
          else:
                raise ImportError('Please install accelerate via `pip install accelerate`'				)

          lowerCamelCase__           =		torch.device(F'cuda:{gpu_id}'				)

          lowerCamelCase__           =		[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				):
          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							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					int							,					):
          if isinstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				) and isinstance(image[0]							,					torch.Tensor				):
                lowerCamelCase__           =		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				):
                lowerCamelCase__           =		self.image_processor(SCREAMING_SNAKE_CASE__							,					return_tensors='pt'				).pixel_values[0].unsqueeze(0				)

          lowerCamelCase__           =		image.to(dtype=self.image_encoder.dtype							,					device=SCREAMING_SNAKE_CASE__				)

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

          lowerCamelCase__           =		image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__							,					dim=0				)

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

          return image_embeds


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

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

          lowerCamelCase__           =		self._execution_device

          lowerCamelCase__           =		batch_size * num_images_per_prompt

          lowerCamelCase__           =		guidance_scale > 1.0
          lowerCamelCase__           =		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__				)
          lowerCamelCase__           =		self.scheduler.timesteps

          lowerCamelCase__           =		self.prior.config.num_embeddings
          lowerCamelCase__           =		self.prior.config.embedding_dim

          lowerCamelCase__           =		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
          lowerCamelCase__           =		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
                lowerCamelCase__           =		torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
                lowerCamelCase__           =		self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		self.prior(
                    SCREAMING_SNAKE_CASE__							,					timestep=SCREAMING_SNAKE_CASE__							,					proj_embedding=SCREAMING_SNAKE_CASE__							,					).predicted_image_embedding

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

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

                lowerCamelCase__           =		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__				)

          lowerCamelCase__           =		[]
          for i, latent in enumerate(SCREAMING_SNAKE_CASE__				):
                print()
                lowerCamelCase__           =		self.renderer.decode(
                    latent[None, :]							,					SCREAMING_SNAKE_CASE__							,					size=SCREAMING_SNAKE_CASE__							,					ray_batch_size=40_96							,					n_coarse_samples=64							,					n_fine_samples=1_28							,					)

                images.append(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		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}'				)

          lowerCamelCase__           =		images.cpu().numpy()

          if output_type == "pil":
                lowerCamelCase__           =		[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__				)



