






import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
					import torch

					from ..models.auto.modeling_auto import (
					    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
					    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
					)

if is_tf_available() and is_tensorflow_probability_available():
					import tensorflow as tf
					import tensorflow_probability as tfp

					from ..models.auto.modeling_tf_auto import (
					    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
					    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
					)





class        a__      (	lowerCAmelCase__		):






		def __call__(      self							, lowercase__=None							, lowercase__=None							, **lowercase__						)				->				Tuple:
				# Returns tqa_pipeline_inputs of shape:
				# [
				#   {"table": pd.DataFrame, "query": List[str]},
				#   ...,
				#   {"table": pd.DataFrame, "query" : List[str]}
				# ]
				requires_backends(self							, "pandas"						)
				import pandas as pd

				if table is None:
						raise ValueError("Keyword argument `table` cannot be None."						)
				elif query is None:
						if isinstance(lowercase__							, lowercase__						) and table.get("query"						) is not None and table.get("table"						) is not None:
								__A    =							[table]
						elif isinstance(lowercase__							, lowercase__						) and len(lowercase__						) > 0:
								if not all(isinstance(lowercase__							, lowercase__						) for d in table						):
										raise ValueError(
										    F"""Keyword argument `table` should be a list of dict, but is {(type(lowercase__						) for d in table)}"""						)

								if table[0].get("query"						) is not None and table[0].get("table"						) is not None:
										__A    =							table
								else:
										raise ValueError(
										    "If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`"
										    F""" and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys."""						)
						elif Dataset is not None and isinstance(lowercase__							, lowercase__						) or isinstance(lowercase__							, types.GeneratorType						):
								return table
						else:
								raise ValueError(
								    "Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but "
								    F"""is {type(lowercase__						)})"""						)
				else:
						__A    =							[{"table": table, "query": query}]

				for tqa_pipeline_input in tqa_pipeline_inputs:
						if not isinstance(tqa_pipeline_input["table"]							, pd.DataFrame						):
								if tqa_pipeline_input["table"] is None:
										raise ValueError("Table cannot be None."						)

								__A    =							pd.DataFrame(tqa_pipeline_input["table"]						)

				return tqa_pipeline_inputs





@add_end_docstrings(lowerCAmelCase__		)
class        a__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       List[Any] 					= 'table,query'






		def __init__(      self							, lowercase__=TableQuestionAnsweringArgumentHandler()							, *lowercase__							, **lowercase__						)				->				Optional[int]:
				super().__init__(*lowercase__							, **lowercase__						)
				__A    =							args_parser

				self.check_model_type(
				    dict(
				        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
				        + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()						)
				    if self.framework == "tf"
				    else dict(
				        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()						)						)

				__A    =							bool(getattr(self.model.config							, "aggregation_labels"							, lowercase__						)						) and bool(
				    getattr(self.model.config							, "num_aggregation_labels"							, lowercase__						)						)
				__A    =							"tapas" if hasattr(self.model.config							, "aggregation_labels"						) else None






		def 					_lowerCamelCase					(      self							, **lowercase__						)				->				str:
				return self.model(**lowercase__						)






		def 					_lowerCamelCase					(      self							, **lowercase__						)				->				Tuple:
				if self.framework == "pt":
						__A    =							[]
						__A    =							[]
						__A    =							None
						__A    =							inputs["input_ids"].shape[0]

						__A    =							inputs["input_ids"].to(self.device						)
						__A    =							inputs["attention_mask"].to(self.device						)
						__A    =							inputs["token_type_ids"].to(self.device						)
						__A    =							None

						for index in range(lowercase__						):
								# If sequences have already been processed, the token type IDs will be created according to the previous
								# answer.
								if prev_answers is not None:
										__A    =							token_type_ids_example[:, 3]  # shape (seq_len,)
										__A    =							np.zeros_like(prev_labels_example.cpu().numpy()						)  # shape (seq_len,)

										__A    =							token_type_ids[index]  # shape (seq_len, 7)
										for i in range(model_labels.shape[0]						):
												__A    =							token_type_ids_example[:, 0].tolist()[i]
												__A    =							token_type_ids_example[:, 1].tolist()[i] - 1
												__A    =							token_type_ids_example[:, 2].tolist()[i] - 1

												if row_id >= 0 and col_id >= 0 and segment_id == 1:
														__A    =							int(prev_answers[(col_id, row_id)]						)

										__A    =							torch.from_numpy(lowercase__						).type(torch.long						).to(self.device						)

								__A    =							input_ids[index]
								__A    =							attention_mask[index]  # shape (seq_len,)
								__A    =							token_type_ids[index]  # shape (seq_len, 7)
								__A    =							self.model(
								    input_ids=input_ids_example.unsqueeze(0						)							, attention_mask=attention_mask_example.unsqueeze(0						)							, token_type_ids=token_type_ids_example.unsqueeze(0						)							, )
								__A    =							outputs.logits

								if self.aggregate:
										all_aggregations.append(outputs.logits_aggregation						)

								all_logits.append(lowercase__						)

								__A    =							torch.distributions.Bernoulli(logits=lowercase__						)
								__A    =							dist_per_token.probs * attention_mask_example.type(torch.floataa						).to(
								    dist_per_token.probs.device						)

								__A    =							collections.defaultdict(lowercase__						)
								for i, p in enumerate(probabilities.squeeze().tolist()						):
										__A    =							token_type_ids_example[:, 0].tolist()[i]
										__A    =							token_type_ids_example[:, 1].tolist()[i] - 1
										__A    =							token_type_ids_example[:, 2].tolist()[i] - 1
										if col >= 0 and row >= 0 and segment_id == 1:
												coords_to_probs[(col, row)].append(lowercase__						)

								__A    =							{key: np.array(coords_to_probs[key]						).mean() > 0.5 for key in coords_to_probs}

						__A    =							torch.cat(tuple(lowercase__						)							, 0						)

						return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(lowercase__						)							, 0						))
				else:
						__A    =							[]
						__A    =							[]
						__A    =							None
						__A    =							inputs["input_ids"].shape[0]

						__A    =							inputs["input_ids"]
						__A    =							inputs["attention_mask"]
						__A    =							inputs["token_type_ids"].numpy()
						__A    =							None

						for index in range(lowercase__						):
								# If sequences have already been processed, the token type IDs will be created according to the previous
								# answer.
								if prev_answers is not None:
										__A    =							token_type_ids_example[:, 3]  # shape (seq_len,)
										__A    =							np.zeros_like(lowercase__							, dtype=np.intaa						)  # shape (seq_len,)

										__A    =							token_type_ids[index]  # shape (seq_len, 7)
										for i in range(model_labels.shape[0]						):
												__A    =							token_type_ids_example[:, 0].tolist()[i]
												__A    =							token_type_ids_example[:, 1].tolist()[i] - 1
												__A    =							token_type_ids_example[:, 2].tolist()[i] - 1

												if row_id >= 0 and col_id >= 0 and segment_id == 1:
														__A    =							int(prev_answers[(col_id, row_id)]						)

										__A    =							model_labels

								__A    =							input_ids[index]
								__A    =							attention_mask[index]  # shape (seq_len,)
								__A    =							token_type_ids[index]  # shape (seq_len, 7)
								__A    =							self.model(
								    input_ids=np.expand_dims(lowercase__							, axis=0						)							, attention_mask=np.expand_dims(lowercase__							, axis=0						)							, token_type_ids=np.expand_dims(lowercase__							, axis=0						)							, )
								__A    =							outputs.logits

								if self.aggregate:
										all_aggregations.append(outputs.logits_aggregation						)

								all_logits.append(lowercase__						)

								__A    =							tfp.distributions.Bernoulli(logits=lowercase__						)
								__A    =							dist_per_token.probs_parameter() * tf.cast(lowercase__							, tf.floataa						)

								__A    =							collections.defaultdict(lowercase__						)
								__A    =							token_type_ids_example
								for i, p in enumerate(tf.squeeze(lowercase__						).numpy().tolist()						):
										__A    =							token_type_ids_example[:, 0].tolist()[i]
										__A    =							token_type_ids_example[:, 1].tolist()[i] - 1
										__A    =							token_type_ids_example[:, 2].tolist()[i] - 1
										if col >= 0 and row >= 0 and segment_id == 1:
												coords_to_probs[(col, row)].append(lowercase__						)

								__A    =							{key: np.array(coords_to_probs[key]						).mean() > 0.5 for key in coords_to_probs}

						__A    =							tf.concat(tuple(lowercase__						)							, 0						)

						return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(lowercase__						)							, 0						))






		def __call__(      self							, *lowercase__							, **lowercase__						)				->				Union[str, Any]:
				__A    =							self._args_parser(*lowercase__							, **lowercase__						)

				__A    =							super().__call__(lowercase__							, **lowercase__						)
				if len(lowercase__						) == 1:
						return results[0]
				return results






		def 					_lowerCamelCase					(      self							, lowercase__=None							, lowercase__=None							, lowercase__=None							, **lowercase__						)				->				Optional[Any]:
				__A    =							{}
				if padding is not None:
						__A    =							padding
				if truncation is not None:
						__A    =							truncation

				__A    =							{}
				if sequential is not None:
						__A    =							sequential
				return preprocess_params, forward_params, {}






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__=None							, lowercase__=True							, lowercase__=None						)				->				List[str]:
				if truncation is None:
						if self.type == "tapas":
								__A    =							"drop_rows_to_fit"
						else:
								__A    =							"do_not_truncate"

				__A ,   __A    =							pipeline_input["table"], pipeline_input["query"]
				if table.empty:
						raise ValueError("table is empty"						)
				if query is None or query == "":
						raise ValueError("query is empty"						)
				__A    =							self.tokenizer(lowercase__							, lowercase__							, return_tensors=self.framework							, truncation=lowercase__							, padding=lowercase__						)
				__A    =							table
				return inputs






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__=False						)				->				str:
				__A    =							model_inputs.pop("table"						)

				if self.type == "tapas":
						if sequential:
								__A    =							self.sequential_inference(**lowercase__						)
						else:
								__A    =							self.batch_inference(**lowercase__						)
				else:
						__A    =							self.model.generate(**lowercase__						)
				__A    =							{"model_inputs": model_inputs, "table": table, "outputs": outputs}
				return model_outputs






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				Optional[int]:
				__A    =							model_outputs["model_inputs"]
				__A    =							model_outputs["table"]
				__A    =							model_outputs["outputs"]
				if self.type == "tapas":
						if self.aggregate:
								__A ,   __A    =							outputs[:2]
								__A    =							self.tokenizer.convert_logits_to_predictions(lowercase__							, lowercase__							, lowercase__						)
								__A ,   __A    =							predictions
								__A    =							{i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(lowercase__						)}

								__A    =							self.model.config.no_aggregation_label_index
								__A    =							{
								    i: aggregators[i] + " > " for i, pred in enumerate(lowercase__						) if pred != no_agg_label_index
								}
						else:
								__A    =							outputs[0]
								__A    =							self.tokenizer.convert_logits_to_predictions(lowercase__							, lowercase__						)
								__A    =							predictions[0]
								__A    =							{}
								__A    =							{}
						__A    =							[]
						for index, coordinates in enumerate(lowercase__						):
								__A    =							[table.iat[coordinate] for coordinate in coordinates]
								__A    =							aggregators.get(lowercase__							, ""						)
								__A    =							aggregators_prefix.get(lowercase__							, ""						)
								__A    =							{
								    "answer": aggregator_prefix + ", ".join(lowercase__						),
								    "coordinates": coordinates,
								    "cells": [table.iat[coordinate] for coordinate in coordinates],
								}
								if aggregator:
										__A    =							aggregator

								answers.append(lowercase__						)
						if len(lowercase__						) == 0:
								raise PipelineException("Empty answer"						)
				else:
						__A    =							[{"answer": answer} for answer in self.tokenizer.batch_decode(lowercase__							, skip_special_tokens=lowercase__						)]

				return answers if len(lowercase__						) > 1 else answers[0]


"""simple docstring"""




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

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

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


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

UpperCAmelCase       :    List[str]									=			"\n    Examples:\n        ```py\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif\n\n        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        >>> repo = \"openai/shap-e\"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 15.0\n        >>> prompt = \"a shark\"\n\n        >>> images = pipe(\n        ...     prompt,\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], \"shark_3d.gif\")\n        ```\n"

@dataclass
class        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase							):
				UpperCAmelCase	=      42





class        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase							):







				def __init__(    self    :Optional[Any]      ,__UpperCAmelCase    :PriorTransformer      ,__UpperCAmelCase    :CLIPTextModelWithProjection      ,__UpperCAmelCase    :CLIPTokenizer      ,__UpperCAmelCase    :HeunDiscreteScheduler      ,__UpperCAmelCase    :ShapERenderer      ,)      ->					List[Any]:




									"""simple docstring"""



									super().__init__()

									self.register_modules(
									    prior=__UpperCAmelCase      ,text_encoder=__UpperCAmelCase      ,tokenizer=__UpperCAmelCase      ,scheduler=__UpperCAmelCase      ,renderer=__UpperCAmelCase      ,)







				def   lowercase_		(    self    :Any      ,__UpperCAmelCase    :Tuple      ,__UpperCAmelCase    :Dict      ,__UpperCAmelCase    :Optional[int]      ,__UpperCAmelCase    :Dict      ,__UpperCAmelCase    :Any      ,__UpperCAmelCase    :Dict  )      ->					Optional[Any]:




									"""simple docstring"""



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

									lowerCamelCase__		:   Dict  				=					latents * scheduler.init_noise_sigma
									return latents







				def   lowercase_		(    self    :Dict      ,__UpperCAmelCase    :List[Any]=0  )      ->					str:




									"""simple docstring"""



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

									lowerCamelCase__		:   List[str]  				=					torch.device(F"""cuda:{gpu_id}"""  )

									lowerCamelCase__		:   Optional[int]  				=					[self.text_encoder, self.prior]
									for cpu_offloaded_model in models:
														if cpu_offloaded_model is not None:
																			cpu_offload(__UpperCAmelCase      ,__UpperCAmelCase  )







				def   lowercase_		(    self    :int      ,__UpperCAmelCase    :Dict=0  )      ->					Dict:




									"""simple docstring"""



									if is_accelerate_available() and is_accelerate_version('''>='''      ,'''0.17.0.dev0'''  ):
														from accelerate import cpu_offload_with_hook
									else:
														raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''  )

									lowerCamelCase__		:   List[Any]  				=					torch.device(F"""cuda:{gpu_id}"""  )

									if self.device.type != "cpu":
														self.to('''cpu'''      ,silence_dtype_warnings=__UpperCAmelCase  )
														torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

									lowerCamelCase__		:   List[Any]  				=					None
									for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
														lowerCamelCase__				,						lowerCamelCase__		:   Tuple  				=					cpu_offload_with_hook(__UpperCAmelCase      ,__UpperCAmelCase      ,prev_module_hook=__UpperCAmelCase  )

									if self.safety_checker is not None:
														lowerCamelCase__				,						lowerCamelCase__		:   Any  				=					cpu_offload_with_hook(self.safety_checker      ,__UpperCAmelCase      ,prev_module_hook=__UpperCAmelCase  )

									# We'll offload the last model manually.
									lowerCamelCase__		:   Dict  				=					hook







				@property
				def   lowercase_		(    self    :Dict  )      ->					Optional[Any]:




									"""simple docstring"""



									if self.device != torch.device('''meta'''  ) or not hasattr(self.text_encoder      ,'''_hf_hook'''  ):
														return self.device
									for module in self.text_encoder.modules():
														if (
														    hasattr(__UpperCAmelCase      ,'''_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   lowercase_		(    self    :Tuple      ,__UpperCAmelCase    :Optional[int]      ,__UpperCAmelCase    :Optional[Any]      ,__UpperCAmelCase    :Any      ,__UpperCAmelCase    :Optional[Any]      ,)      ->					str:




									"""simple docstring"""



									len(__UpperCAmelCase  ) if isinstance(__UpperCAmelCase      ,__UpperCAmelCase  ) else 1

									# YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
									lowerCamelCase__		:   Optional[int]  				=					0
									# get prompt text embeddings
									lowerCamelCase__		:   Union[str, Any]  				=					self.tokenizer(
									    __UpperCAmelCase      ,padding='''max_length'''      ,max_length=self.tokenizer.model_max_length      ,truncation=__UpperCAmelCase      ,return_tensors='''pt'''      ,)
									lowerCamelCase__		:   Union[str, Any]  				=					text_inputs.input_ids
									lowerCamelCase__		:   Any  				=					self.tokenizer(__UpperCAmelCase      ,padding='''longest'''      ,return_tensors='''pt'''  ).input_ids

									if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(__UpperCAmelCase      ,__UpperCAmelCase  ):
														lowerCamelCase__		:   List[str]  				=					self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]  )
														logger.warning(
														    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
														    F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""  )

									lowerCamelCase__		:   Optional[int]  				=					self.text_encoder(text_input_ids.to(__UpperCAmelCase  )  )
									lowerCamelCase__		:   List[Any]  				=					text_encoder_output.text_embeds

									lowerCamelCase__		:   Optional[Any]  				=					prompt_embeds.repeat_interleave(__UpperCAmelCase      ,dim=0  )
									# in Shap-E it normalize the prompt_embeds and then later rescale it
									lowerCamelCase__		:   Union[str, Any]  				=					prompt_embeds / torch.linalg.norm(__UpperCAmelCase      ,dim=-1      ,keepdim=__UpperCAmelCase  )

									if do_classifier_free_guidance:
														lowerCamelCase__		:   Any  				=					torch.zeros_like(__UpperCAmelCase  )

														# 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__		:   Optional[int]  				=					torch.cat([negative_prompt_embeds, prompt_embeds]  )

									# Rescale the features to have unit variance
									lowerCamelCase__		:   Optional[int]  				=					math.sqrt(prompt_embeds.shape[1]  ) * prompt_embeds

									return prompt_embeds







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




									"""simple docstring"""



									if isinstance(__UpperCAmelCase      ,__UpperCAmelCase  ):
														lowerCamelCase__		:   Union[str, Any]  				=					1
									elif isinstance(__UpperCAmelCase      ,__UpperCAmelCase  ):
														lowerCamelCase__		:   Any  				=					len(__UpperCAmelCase  )
									else:
														raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(__UpperCAmelCase  )}"""  )

									lowerCamelCase__		:   Optional[Any]  				=					self._execution_device

									lowerCamelCase__		:   List[Any]  				=					batch_size * num_images_per_prompt

									lowerCamelCase__		:   Optional[int]  				=					guidance_scale > 1.0
									lowerCamelCase__		:   Tuple  				=					self._encode_prompt(__UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase  )

									# prior

									self.scheduler.set_timesteps(__UpperCAmelCase      ,device=__UpperCAmelCase  )
									lowerCamelCase__		:   str  				=					self.scheduler.timesteps

									lowerCamelCase__		:   Dict  				=					self.prior.config.num_embeddings
									lowerCamelCase__		:   Optional[Any]  				=					self.prior.config.embedding_dim

									lowerCamelCase__		:   int  				=					self.prepare_latents(
									    (batch_size, num_embeddings * embedding_dim)      ,prompt_embeds.dtype      ,__UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase      ,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__		:   Tuple  				=					latents.reshape(latents.shape[0]      ,__UpperCAmelCase      ,__UpperCAmelCase  )

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

														lowerCamelCase__		:   Dict  				=					self.prior(
														    __UpperCAmelCase      ,timestep=__UpperCAmelCase      ,proj_embedding=__UpperCAmelCase      ,).predicted_image_embedding

														# remove the variance
														lowerCamelCase__				,						lowerCamelCase__		:   Optional[Any]  				=					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__		:   Dict  				=					noise_pred.chunk(2  )
																			lowerCamelCase__		:   List[str]  				=					noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

														lowerCamelCase__		:   Union[str, Any]  				=					self.scheduler.step(
														    __UpperCAmelCase      ,timestep=__UpperCAmelCase      ,sample=__UpperCAmelCase      ,).prev_sample

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

									lowerCamelCase__		:   List[Any]  				=					[]
									for i, latent in enumerate(__UpperCAmelCase  ):
														lowerCamelCase__		:   Tuple  				=					self.renderer.decode(
														    latent[None, :]      ,__UpperCAmelCase      ,size=__UpperCAmelCase      ,ray_batch_size=40_96      ,n_coarse_samples=64      ,n_fine_samples=1_28      ,)
														images.append(__UpperCAmelCase  )

									lowerCamelCase__		:   Union[str, Any]  				=					torch.stack(__UpperCAmelCase  )

									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__		:   Optional[int]  				=					images.cpu().numpy()

									if output_type == "pil":
														lowerCamelCase__		:   Optional[Any]  				=					[self.numpy_to_pil(__UpperCAmelCase  ) 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=__UpperCAmelCase  )






