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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase            =		logging.get_logger(__name__)

_UpperCAmelCase            =		"""▁"""

_UpperCAmelCase            =		{"""vocab_file""": """sentencepiece.bpe.model"""}

_UpperCAmelCase            =		{
    """vocab_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"""
        ),
    }
}

_UpperCAmelCase            =		{
    """facebook/nllb-200-distilled-600M""": 1024,
}

# fmt: off
_UpperCAmelCase            =		["""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 UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = VOCAB_FILES_NAMES
				lowerCamelCase_    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_    = PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_    = ['''input_ids''', '''attention_mask''']

				lowerCamelCase_    = []
				lowerCamelCase_    = []
				def __init__(						self		,     lowercase		,     lowercase="<s>"		,     lowercase="</s>"		,     lowercase="</s>"		,     lowercase="<s>"		,     lowercase="<unk>"		,     lowercase="<pad>"		,     lowercase="<mask>"		,     lowercase=None		,     lowercase=None		,     lowercase=None		,     lowercase = None		,     lowercase=None		,     lowercase=False		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else mask_token

						A_		:    Dict         =				{} if sp_model_kwargs is None else sp_model_kwargs
						A_		:    List[Any]         =				legacy_behaviour

						super().__init__(
						    bos_token=lowercase		,     eos_token=lowercase		,     unk_token=lowercase		,     sep_token=lowercase		,     cls_token=lowercase		,     pad_token=lowercase		,     mask_token=lowercase		,     tokenizer_file=lowercase		,     src_lang=lowercase		,     tgt_lang=lowercase		,     additional_special_tokens=lowercase		,     sp_model_kwargs=self.sp_model_kwargs		,     legacy_behaviour=lowercase		,     **lowercase		,     )

						A_		:    Tuple         =				spm.SentencePieceProcessor(**self.sp_model_kwargs )
						self.sp_model.Load(str(lowercase ) )
						A_		:    Dict         =				vocab_file
						# Original fairseq vocab and spm vocab must be "aligned":
						# Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
						# -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
						# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
						# spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

						# Mimic fairseq token-to-id alignment for the first 4 token
						A_		:    Union[str, Any]         =				{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

						# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
						A_		:    List[str]         =				1

						A_		:    str         =				len(self.sp_model )
						A_		:    List[Any]         =				{
						    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase )
						}
						A_		:    Optional[Any]         =				{v: k for k, v in self.lang_code_to_id.items()}
						A_		:    Any         =				len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset

						self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
						A_		:    Optional[Any]         =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}
						A_		:    Optional[Any]         =				list(self.lang_code_to_id.keys() )

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

						A_		:    Optional[Any]         =				src_lang if src_lang is not None else 'eng_Latn'
						A_		:    Union[str, Any]         =				self.lang_code_to_id[self._src_lang]
						A_		:    Union[str, Any]         =				tgt_lang
						self.set_src_lang_special_tokens(self._src_lang )
				def __getstate__(						self ):



						"""simple docstring"""





						A_		:    Tuple         =				self.__dict__.copy()
						A_		:    Optional[Any]         =				None
						A_		:    Optional[Any]         =				self.sp_model.serialized_model_proto()
						return state
				def __setstate__(						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				d

						# for backward compatibility
						if not hasattr(self		,     'sp_model_kwargs' ):
								A_		:    Tuple         =				{}

						A_		:    Union[str, Any]         =				spm.SentencePieceProcessor(**self.sp_model_kwargs )
						self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1  # Plus 1 for the mask token
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return self._src_lang
				@src_lang.setter
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				new_src_lang
						self.set_src_lang_special_tokens(self._src_lang )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None		,     lowercase = False ):



						"""simple docstring"""





						if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=lowercase		,     token_ids_a=lowercase		,     already_has_special_tokens=lowercase )

						A_		:    List[str]         =				[1] * len(self.prefix_tokens )
						A_		:    Dict         =				[1] * len(self.suffix_tokens )
						if token_ids_a is None:
								return prefix_ones + ([0] * len(lowercase )) + suffix_ones
						return prefix_ones + ([0] * len(lowercase )) + ([0] * len(lowercase )) + suffix_ones
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						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   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						A_		:    List[Any]         =				[self.sep_token_id]
						A_		:    Optional[Any]         =				[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   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     **lowercase ):



						"""simple docstring"""





						if src_lang is None or tgt_lang is None:
								raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model' )
						A_		:    Optional[Any]         =				src_lang
						A_		:    Union[str, Any]         =				self(lowercase		,     add_special_tokens=lowercase		,     return_tensors=lowercase		,     **lowercase )
						A_		:    Union[str, Any]         =				self.convert_tokens_to_ids(lowercase )
						A_		:    Dict         =				tgt_lang_id
						return inputs
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





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



						"""simple docstring"""





						return self.sp_model.encode(lowercase		,     out_type=lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						if token in self.fairseq_tokens_to_ids:
								return self.fairseq_tokens_to_ids[token]
						A_		:    Dict         =				self.sp_model.PieceToId(lowercase )

						# Need to return unknown token if the SP model returned 0
						return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						if index in self.fairseq_ids_to_tokens:
								return self.fairseq_ids_to_tokens[index]
						return self.sp_model.IdToPiece(index - self.fairseq_offset )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				''.join(lowercase ).replace(lowercase		,     ' ' ).strip()
						return out_string
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





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

						if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase ) and os.path.isfile(self.vocab_file ):
								copyfile(self.vocab_file		,     lowercase )
						elif not os.path.isfile(self.vocab_file ):
								with open(lowercase		,     'wb' ) as fi:
										A_		:    List[str]         =				self.sp_model.serialized_model_proto()
										fi.write(lowercase )

						return (out_vocab_file,)
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = "eng_Latn"		,     lowercase = None		,     lowercase = "fra_Latn"		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    List[str]         =				src_lang
						A_		:    int         =				tgt_lang
						return super().prepare_seqaseq_batch(lowercase		,     lowercase		,     **lowercase )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return self.set_src_lang_special_tokens(self.src_lang )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return self.set_tgt_lang_special_tokens(self.tgt_lang )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Any         =				self.lang_code_to_id[src_lang]
						if self.legacy_behaviour:
								A_		:    Optional[Any]         =				[]
								A_		:    int         =				[self.eos_token_id, self.cur_lang_code]
						else:
								A_		:    Optional[int]         =				[self.cur_lang_code]
								A_		:    Any         =				[self.eos_token_id]







				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				self.lang_code_to_id[lang]
						if self.legacy_behaviour:
								A_		:    List[str]         =				[]
								A_		:    Optional[int]         =				[self.eos_token_id, self.cur_lang_code]
						else:
								A_		:    Optional[Any]         =				[self.cur_lang_code]
								A_		:    List[Any]         =				[self.eos_token_id]







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 UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = 42





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				def __init__(						self		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     ):



						"""simple docstring"""





						super().__init__()

						self.register_modules(
						    prior=lowercase		,     image_encoder=lowercase		,     image_processor=lowercase		,     scheduler=lowercase		,     renderer=lowercase		,     )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase ):



						"""simple docstring"""





						if latents is None:
								A_		:    Optional[Any]         =				randn_tensor(lowercase		,     generator=lowercase		,     device=lowercase		,     dtype=lowercase )
						else:
								if latents.shape != shape:
										raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
								A_		:    Optional[int]         =				latents.to(lowercase )

						A_		:    List[Any]         =				latents * scheduler.init_noise_sigma
						return latents
				def   lowerCAmelCase_       (						self		,     lowercase=0 ):



						"""simple docstring"""





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

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

						A_		:    Dict         =				[self.image_encoder, self.prior]
						for cpu_offloaded_model in models:
								if cpu_offloaded_model is not None:
										cpu_offload(lowercase		,     lowercase )
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						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(lowercase		,     '_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		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     ):



						"""simple docstring"""





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

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

						A_		:    List[str]         =				image.to(dtype=self.image_encoder.dtype		,     device=lowercase )

						A_		:    Tuple         =				self.image_encoder(lowercase )['last_hidden_state']
						A_		:    Dict         =				image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

						A_		:    List[str]         =				image_embeds.repeat_interleave(lowercase		,     dim=0 )

						if do_classifier_free_guidance:
								A_		:    str         =				torch.zeros_like(lowercase )

								# 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_		:    Optional[Any]         =				torch.cat([negative_image_embeds, image_embeds] )

						return image_embeds







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



						"""simple docstring"""





						if isinstance(lowercase		,     PIL.Image.Image ):
								A_		:    int         =				1
						elif isinstance(lowercase		,     torch.Tensor ):
								A_		:    int         =				image.shape[0]
						elif isinstance(lowercase		,     lowercase ) and isinstance(image[0]		,     (torch.Tensor, PIL.Image.Image) ):
								A_		:    List[str]         =				len(lowercase )
						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(lowercase )}''' )

						A_		:    Any         =				self._execution_device

						A_		:    List[Any]         =				batch_size * num_images_per_prompt

						A_		:    int         =				guidance_scale > 1.0
						A_		:    Optional[int]         =				self._encode_image(lowercase		,     lowercase		,     lowercase		,     lowercase )

						# prior

						self.scheduler.set_timesteps(lowercase		,     device=lowercase )
						A_		:    Dict         =				self.scheduler.timesteps

						A_		:    int         =				self.prior.config.num_embeddings
						A_		:    int         =				self.prior.config.embedding_dim

						A_		:    Dict         =				self.prepare_latents(
						    (batch_size, num_embeddings * embedding_dim)		,     image_embeds.dtype		,     lowercase		,     lowercase		,     lowercase		,     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_		:    Union[str, Any]         =				latents.reshape(latents.shape[0]		,     lowercase		,     lowercase )

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

								A_		:    Any         =				self.prior(
								    lowercase		,     timestep=lowercase		,     proj_embedding=lowercase		,     ).predicted_image_embedding

								# remove the variance
								A_       ,      A_		:    int         =				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_		:    List[Any]         =				noise_pred.chunk(2 )
										A_		:    str         =				noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

								A_		:    Optional[int]         =				self.scheduler.step(
								    lowercase		,     timestep=lowercase		,     sample=lowercase		,     ).prev_sample

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

						A_		:    str         =				[]
						for i, latent in enumerate(lowercase ):
								print()
								A_		:    Optional[Any]         =				self.renderer.decode(
								    latent[None, :]		,     lowercase		,     size=lowercase		,     ray_batch_size=4_0_9_6		,     n_coarse_samples=6_4		,     n_fine_samples=1_2_8		,     )

								images.append(lowercase )

						A_		:    Dict         =				torch.stack(lowercase )

						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_		:    Dict         =				images.cpu().numpy()

						if output_type == "pil":
								A_		:    str         =				[self.numpy_to_pil(lowercase ) 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=lowercase )






