
import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean

import pandas as pd
import torch
from tqdm import tqdm

import transformers


_lowercase    =		float("""nan""")




class   UpperCAmelCase_					:






						'''simple docstring'''






						def __init__( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			sys.stdout
												_lowerCAmelCase  						=			open(_lowercase   ,       """a"""				)




						def __getattr__( self   ,       _lowercase				):

												"""simple docstring"""



												return getattr(self.stdout   ,       _lowercase				)




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												self.stdout.write(_lowercase				)
												# strip tqdm codes
												self.file.write(re.sub(R"""^.*\r"""   ,       """"""   ,       _lowercase   ,       0   ,       re.M				)				)






def A				(__lowerCamelCase				:List[Any]=80			,     __lowerCamelCase				:Optional[int]=False		):
						_lowerCAmelCase  						=			[]

						# deal with critical env vars
						_lowerCAmelCase  						=			["""CUDA_VISIBLE_DEVICES"""]
						for key in env_keys:
												_lowerCAmelCase  						=			os.environ.get(__lowerCamelCase			,     __lowerCamelCase		)
												if val is not None:
																		cmd.append(f'{key}={val}'		)

    # python executable (not always needed if the script is executable)
						_lowerCAmelCase  						=			sys.executable if full_python_path else sys.executable.split("""/"""		)[-1]
						cmd.append(__lowerCamelCase		)

						# now the normal args
						cmd += list(map(shlex.quote			,     sys.argv		)		)

						# split up into up to MAX_WIDTH lines with shell multi-line escapes
						_lowerCAmelCase  						=			[]
						_lowerCAmelCase  						=			""""""
						while len(__lowerCamelCase		) > 0:
												current_line += f'{cmd.pop(0		)} '
												if len(__lowerCamelCase		) == 0 or len(__lowerCamelCase		) + len(cmd[0]		) + 1 > max_width - 1:
																		lines.append(__lowerCamelCase		)
																		_lowerCAmelCase  						=			""""""
						return "\\\n".join(__lowerCamelCase		)


def A				(__lowerCamelCase				:Dict			,     __lowerCamelCase				:Dict		):
						# unwrap multi-line input
						_lowerCAmelCase  						=			re.sub(r"""[\\\n]+"""			,     """ """			,     args.base_cmd		)

						# remove --output_dir if any and set our own
						_lowerCAmelCase  						=			re.sub("""--output_dir\s+[^\s]+"""			,     """"""			,     args.base_cmd		)
						args.base_cmd += f' --output_dir {output_dir}'

						# ensure we have --overwrite_output_dir
						_lowerCAmelCase  						=			re.sub("""--overwrite_output_dir\s+"""			,     """"""			,     args.base_cmd		)
						args.base_cmd += " --overwrite_output_dir"

						return [sys.executable] + shlex.split(args.base_cmd		)


def A				(__lowerCamelCase				:List[str]			,     __lowerCamelCase				:List[Any]			,     __lowerCamelCase				:List[Any]			,     __lowerCamelCase				:Tuple			,     __lowerCamelCase				:Optional[int]			,     __lowerCamelCase				:Tuple			,     __lowerCamelCase				:Dict		):
						# Enable to debug everything but the run itself, to do it fast and see the progress.
						# This is useful for debugging the output formatting quickly - we can remove it later once
						# everybody is happy with the output
						if 0:
												import random
												from time import sleep

												sleep(0		)
												return dict(
												    {k: random.uniform(0			,     100		) for k in metric_keys}			,     **{target_metric_key: random.choice([nan, 10.31, 100.2, 55.6_666, 222.22_222_222]		)}			,     )

						_lowerCAmelCase  						=			subprocess.run(__lowerCamelCase			,     capture_output=__lowerCamelCase			,     text=__lowerCamelCase		)

						if verbose:
												print("""STDOUT"""			,     result.stdout		)
												print("""STDERR"""			,     result.stderr		)

						# save the streams
						_lowerCAmelCase  						=			variation.replace(""" """			,     """-"""		)
						with open(Path(__lowerCamelCase		) / f'log.{prefix}.stdout.txt'			,     """w"""		) as f:
												f.write(result.stdout		)
						with open(Path(__lowerCamelCase		) / f'log.{prefix}.stderr.txt'			,     """w"""		) as f:
												f.write(result.stderr		)

						if result.returncode != 0:
												if verbose:
																		print("""failed"""		)
												return {target_metric_key: nan}

						with io.open(f'{output_dir}/all_results.json'			,     """r"""			,     encoding="""utf-8"""		) as f:
												_lowerCAmelCase  						=			json.load(__lowerCamelCase		)

						# filter out just the keys we want
						return {k: v for k, v in metrics.items() if k in metric_keys}


def A				(__lowerCamelCase				:int			,     __lowerCamelCase				:Any			,     __lowerCamelCase				:Any			,     __lowerCamelCase				:List[str]			,     __lowerCamelCase				:int			,     __lowerCamelCase				:Union[str, Any]			,     __lowerCamelCase				:Tuple			,     __lowerCamelCase				:int			,     __lowerCamelCase				:Optional[int]			,     __lowerCamelCase				:int			,     ):
						_lowerCAmelCase  						=			[]
						_lowerCAmelCase  						=			[]
						_lowerCAmelCase  						=			f'{id}: {variation:<{longest_variation_len}}'
						_lowerCAmelCase  						=			f'{preamble}: '
						_lowerCAmelCase  						=			set(report_metric_keys + [target_metric_key]		)
						for i in tqdm(range(__lowerCamelCase		)			,     desc=__lowerCamelCase			,     leave=__lowerCamelCase		):
												_lowerCAmelCase  						=			process_run_single(
												    __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase		)
												_lowerCAmelCase  						=			single_run_metrics[target_metric_key]
												if not math.isnan(__lowerCamelCase		):
																		metrics.append(__lowerCamelCase		)
																		results.append(__lowerCamelCase		)
																		outcome += "✓"
												else:
																		outcome += "✘"
						_lowerCAmelCase  						=			f'\33[2K\r{outcome}'
						if len(__lowerCamelCase		) > 0:
												_lowerCAmelCase  						=			{k: fmean([x[k] for x in metrics]		) for k in metrics[0].keys()}
												_lowerCAmelCase  						=			round(mean_metrics[target_metric_key]			,     2		)
												_lowerCAmelCase  						=			f'{outcome} {mean_target}'
												if len(__lowerCamelCase		) > 1:
																		results_str += f' {tuple(round(__lowerCamelCase			,     2		) for x in results		)}'
												print(__lowerCamelCase		)
												_lowerCAmelCase  						=			variation
												return mean_metrics
						else:
												print(__lowerCamelCase		)
												return {variation_key: variation, target_metric_key: nan}


def A				():
						_lowerCAmelCase  						=			torch.cuda.get_device_properties(torch.device("""cuda"""		)		)
						return f'\nDatetime    : {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"		)}\n\nSoftware:\ntransformers: {transformers.__version__}\ntorch       : {torch.__version__}\ncuda        : {torch.version.cuda}\npython      : {platform.python_version()}\n\nHardware:\n{torch.cuda.device_count()} GPUs      : {properties.name}, {properties.total_memory/2**30:0.2f}GB\n'


def A				(__lowerCamelCase				:Any			,     __lowerCamelCase				:Tuple			,     __lowerCamelCase				:int			,     __lowerCamelCase				:Union[str, Any]			,     __lowerCamelCase				:Any		):
						_lowerCAmelCase  						=			pd.DataFrame(__lowerCamelCase		)
						_lowerCAmelCase  						=			"""variation"""
						_lowerCAmelCase  						=			"""diff_%"""

						_lowerCAmelCase  						=			nan
						if base_variation is not None and len(df[df[variation_key] == base_variation]		):
												# this may still return nan
												_lowerCAmelCase  						=			df.loc[df[variation_key] == base_variation][target_metric_key].item()
						if math.isnan(__lowerCamelCase		):
												# as a fallback, use the minimal value as the sentinel
												_lowerCAmelCase  						=			df.loc[df[target_metric_key] != nan][target_metric_key].min()

						# create diff column if possible
						if not math.isnan(__lowerCamelCase		):
												_lowerCAmelCase  						=			df.apply(
												    lambda __lowerCamelCase		: round(100 * (r[target_metric_key] - sentinel_value) / sentinel_value		)
												    if not math.isnan(r[target_metric_key]		)
												    else 0			,     axis="""columns"""			,     )

						# re-order columns
						_lowerCAmelCase  						=			[variation_key, target_metric_key, diff_key, *report_metric_keys]
						_lowerCAmelCase  						=			df.reindex(__lowerCamelCase			,     axis="""columns"""		)  # reorder cols

						# capitalize
						_lowerCAmelCase  						=			df.rename(str.capitalize			,     axis="""columns"""		)

						# make the cols as narrow as possible
						_lowerCAmelCase  						=			df.rename(lambda __lowerCamelCase		: c.replace("""_"""			,     """<br>"""		)			,     axis="""columns"""		)
						_lowerCAmelCase  						=			df.rename(lambda __lowerCamelCase		: c.replace("""_"""			,     """\n"""		)			,     axis="""columns"""		)

						_lowerCAmelCase  						=			["""""", """Copy between the cut-here-lines and paste as is to github or a forum"""]
						report += ["----------8<-----------------8<--------"]
						report += ["*** Results:", df_github.to_markdown(index=__lowerCamelCase			,     floatfmt=""".2f"""		)]
						report += ["```"]
						report += ["*** Setup:", get_versions()]
						report += ["*** The benchmark command line was:", get_original_command()]
						report += ["```"]
						report += ["----------8<-----------------8<--------"]
						report += ["*** Results (console):", df_console.to_markdown(index=__lowerCamelCase			,     floatfmt=""".2f"""		)]

						print("""\n\n""".join(__lowerCamelCase		)		)


def A				():
						_lowerCAmelCase  						=			argparse.ArgumentParser()
						parser.add_argument(
						    """--base-cmd"""			,     default=__lowerCamelCase			,     type=__lowerCamelCase			,     required=__lowerCamelCase			,     help="""Base cmd"""			,     )
						parser.add_argument(
						    """--variations"""			,     default=__lowerCamelCase			,     type=__lowerCamelCase			,     nargs="""+"""			,     required=__lowerCamelCase			,     help="""Multi-dimensional variations, example: '|--fp16|--bf16' '|--tf32'"""			,     )
						parser.add_argument(
						    """--base-variation"""			,     default=__lowerCamelCase			,     type=__lowerCamelCase			,     help="""Baseline variation to compare to. if None the minimal target value will be used to compare against"""			,     )
						parser.add_argument(
						    """--target-metric-key"""			,     default=__lowerCamelCase			,     type=__lowerCamelCase			,     required=__lowerCamelCase			,     help="""Target metric key in output_dir/all_results.json, e.g., train_samples_per_second"""			,     )
						parser.add_argument(
						    """--report-metric-keys"""			,     default=""""""			,     type=__lowerCamelCase			,     help="""Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., 'train_loss train_samples"""			,     )
						parser.add_argument(
						    """--repeat-times"""			,     default=1			,     type=__lowerCamelCase			,     help="""How many times to re-run each variation - an average will be reported"""			,     )
						parser.add_argument(
						    """--output_dir"""			,     default="""output_benchmark"""			,     type=__lowerCamelCase			,     help="""The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked"""			,     )
						parser.add_argument(
						    """--verbose"""			,     default=__lowerCamelCase			,     action="""store_true"""			,     help="""Whether to show the outputs of each run or just the benchmark progress"""			,     )
						_lowerCAmelCase  						=			parser.parse_args()

						_lowerCAmelCase  						=			args.output_dir
						Path(__lowerCamelCase		).mkdir(exist_ok=__lowerCamelCase		)
						_lowerCAmelCase  						=			get_base_command(__lowerCamelCase			,     __lowerCamelCase		)

						# split each dimension into its --foo variations
						_lowerCAmelCase  						=			[list(map(str.strip			,     re.split(r"""\|"""			,     __lowerCamelCase		)		)		) for x in args.variations]
						# build a cartesian product of dimensions and convert those back into cmd-line arg strings,
						# while stripping white space for inputs that were empty
						_lowerCAmelCase  						=			list(map(str.strip			,     map(""" """.join			,     itertools.product(*__lowerCamelCase		)		)		)		)
						_lowerCAmelCase  						=			max(len(__lowerCamelCase		) for x in variations		)

						# split wanted keys
						_lowerCAmelCase  						=			args.report_metric_keys.split()

						# capture prints into a log file for convenience
						_lowerCAmelCase  						=			f'benchmark-report-{datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S"		)}.txt'
						print(f'\nNote: each run\'s output is also logged under {output_dir}/log.*.std*.txt'		)
						print(f'and this script\'s output is also piped into {report_fn}'		)

						_lowerCAmelCase  						=			Tee(__lowerCamelCase		)

						print(f'\n*** Running {len(__lowerCamelCase		)} benchmarks:'		)
						print(f'Base command: {" ".join(__lowerCamelCase		)}'		)

						_lowerCAmelCase  						=			"""variation"""
						_lowerCAmelCase  						=			[]
						for id, variation in enumerate(tqdm(__lowerCamelCase			,     desc="""Total completion: """			,     leave=__lowerCamelCase		)		):
												_lowerCAmelCase  						=			base_cmd + variation.split()
												results.append(
												    process_run(
												        id + 1			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     args.target_metric_key			,     __lowerCamelCase			,     args.repeat_times			,     __lowerCamelCase			,     args.verbose			,     )		)

						process_results(__lowerCamelCase			,     args.target_metric_key			,     __lowerCamelCase			,     args.base_variation			,     __lowerCamelCase		)


if __name__ == "__main__":
						main()


'''simple docstring'''



from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						@register_to_config
						def __init__( self   ,       _lowercase   ,       _lowercase = None   ,       _lowercase = None				):

												"""simple docstring"""



												super().__init__()

												_lowerCAmelCase  						=			learnable

												if self.learnable:
																		assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
																		assert length is not None, "learnable=True requires `length` to be set"

																		_lowerCAmelCase  						=			torch.zeros(_lowercase   ,       _lowercase				)
												else:
																		_lowerCAmelCase  						=			None

												_lowerCAmelCase  						=			torch.nn.Parameter(_lowercase				)






class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				VQModel
						_lowercase		:				CLIPTextModel
						_lowercase		:				CLIPTokenizer
						_lowercase		:				TransformeraDModel
						_lowercase		:				LearnedClassifierFreeSamplingEmbeddings
						_lowercase		:				VQDiffusionScheduler




						def __init__( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       ):

												"""simple docstring"""



												super().__init__()

												self.register_modules(
												    vqvae=_lowercase   ,       transformer=_lowercase   ,       text_encoder=_lowercase   ,       tokenizer=_lowercase   ,       scheduler=_lowercase   ,       learned_classifier_free_sampling_embeddings=_lowercase   ,       )




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			len(_lowercase				) if isinstance(_lowercase   ,       _lowercase				) else 1

												# get prompt text embeddings
												_lowerCAmelCase  						=			self.tokenizer(
												    _lowercase   ,       padding="""max_length"""   ,       max_length=self.tokenizer.model_max_length   ,       return_tensors="""pt"""   ,       )
												_lowerCAmelCase  						=			text_inputs.input_ids

												if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																		_lowerCAmelCase  						=			self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]				)
																		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  						=			text_input_ids[:, : self.tokenizer.model_max_length]
												_lowerCAmelCase  						=			self.text_encoder(text_input_ids.to(self.device				)				)[0]

												# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
												# While CLIP does normalize the pooled output of the text transformer when combining
												# the image and text embeddings, CLIP does not directly normalize the last hidden state.
												#
												# CLIP normalizing the pooled output.
												# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
												_lowerCAmelCase  						=			prompt_embeds / prompt_embeds.norm(dim=-1   ,       keepdim=_lowercase				)

												# duplicate text embeddings for each generation per prompt
												_lowerCAmelCase  						=			prompt_embeds.repeat_interleave(_lowercase   ,       dim=0				)

												if do_classifier_free_guidance:
																		if self.learned_classifier_free_sampling_embeddings.learnable:
																								_lowerCAmelCase  						=			self.learned_classifier_free_sampling_embeddings.embeddings
																								_lowerCAmelCase  						=			negative_prompt_embeds.unsqueeze(0				).repeat(_lowercase   ,       1   ,       1				)
																		else:
																								_lowerCAmelCase  						=			[""""""] * batch_size

																								_lowerCAmelCase  						=			text_input_ids.shape[-1]
																								_lowerCAmelCase  						=			self.tokenizer(
																								    _lowercase   ,       padding="""max_length"""   ,       max_length=_lowercase   ,       truncation=_lowercase   ,       return_tensors="""pt"""   ,       )
																								_lowerCAmelCase  						=			self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]
																								# See comment for normalizing text embeddings
																								_lowerCAmelCase  						=			negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1   ,       keepdim=_lowercase				)

																		# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																		_lowerCAmelCase  						=			negative_prompt_embeds.shape[1]
																		_lowerCAmelCase  						=			negative_prompt_embeds.repeat(1   ,       _lowercase   ,       1				)
																		_lowerCAmelCase  						=			negative_prompt_embeds.view(batch_size * num_images_per_prompt   ,       _lowercase   ,       -1				)

																		# 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_prompt_embeds, prompt_embeds]				)

												return prompt_embeds




						@torch.no_grad()
						def __call__( self   ,       _lowercase   ,       _lowercase = 100   ,       _lowercase = 5.0   ,       _lowercase = 1.0   ,       _lowercase = 1   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = "pil"   ,       _lowercase = True   ,       _lowercase = None   ,       _lowercase = 1   ,       ):

												"""simple docstring"""



												if isinstance(_lowercase   ,       _lowercase				):
																		_lowerCAmelCase  						=			1
												elif isinstance(_lowercase   ,       _lowercase				):
																		_lowerCAmelCase  						=			len(_lowercase				)
												else:
																		raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(_lowercase				)}'				)

												_lowerCAmelCase  						=			batch_size * num_images_per_prompt

												_lowerCAmelCase  						=			guidance_scale > 1.0

												_lowerCAmelCase  						=			self._encode_prompt(_lowercase   ,       _lowercase   ,       _lowercase				)

												if (callback_steps is None) or (
												    callback_steps is not None and (not isinstance(_lowercase   ,       _lowercase				) or callback_steps <= 0)
												):
																		raise ValueError(
																		    F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
																		    F' {type(_lowercase				)}.'				)

												# get the initial completely masked latents unless the user supplied it

												_lowerCAmelCase  						=			(batch_size, self.transformer.num_latent_pixels)
												if latents is None:
																		_lowerCAmelCase  						=			self.transformer.num_vector_embeds - 1
																		_lowerCAmelCase  						=			torch.full(_lowercase   ,       _lowercase				).to(self.device				)
												else:
																		if latents.shape != latents_shape:
																								raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'				)
																		if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
																								raise ValueError(
																								    """Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"""
																								    F' {self.transformer.num_vector_embeds - 1} (inclusive).'				)
																		_lowerCAmelCase  						=			latents.to(self.device				)

												# set timesteps
												self.scheduler.set_timesteps(_lowercase   ,       device=self.device				)

												_lowerCAmelCase  						=			self.scheduler.timesteps.to(self.device				)

												_lowerCAmelCase  						=			latents

												for i, t in enumerate(self.progress_bar(_lowercase				)				):
																		# expand the sample if we are doing classifier free guidance
																		_lowerCAmelCase  						=			torch.cat([sample] * 2				) if do_classifier_free_guidance else sample

																		# predict the un-noised image
																		# model_output == `log_p_x_0`
																		_lowerCAmelCase  						=			self.transformer(_lowercase   ,       encoder_hidden_states=_lowercase   ,       timestep=_lowercase				).sample

																		if do_classifier_free_guidance:
																								_lowerCAmelCase						,   _lowerCAmelCase  						=			model_output.chunk(2				)
																								_lowerCAmelCase  						=			model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																								model_output -= torch.logsumexp(_lowercase   ,       dim=1   ,       keepdim=_lowercase				)

																		_lowerCAmelCase  						=			self.truncate(_lowercase   ,       _lowercase				)

																		# remove `log(0)`'s (`-inf`s)
																		_lowerCAmelCase  						=			model_output.clamp(-70				)

																		# compute the previous noisy sample x_t -> x_t-1
																		_lowerCAmelCase  						=			self.scheduler.step(_lowercase   ,       timestep=_lowercase   ,       sample=_lowercase   ,       generator=_lowercase				).prev_sample

																		# call the callback, if provided
																		if callback is not None and i % callback_steps == 0:
																								callback(_lowercase   ,       _lowercase   ,       _lowercase				)

												_lowerCAmelCase  						=			self.vqvae.config.vq_embed_dim
												_lowerCAmelCase  						=			(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
												_lowerCAmelCase  						=			self.vqvae.quantize.get_codebook_entry(_lowercase   ,       shape=_lowercase				)
												_lowerCAmelCase  						=			self.vqvae.decode(_lowercase   ,       force_not_quantize=_lowercase				).sample

												_lowerCAmelCase  						=			(image / 2 + 0.5).clamp(0   ,       1				)
												_lowerCAmelCase  						=			image.cpu().permute(0   ,       2   ,       3   ,       1				).numpy()

												if output_type == "pil":
																		_lowerCAmelCase  						=			self.numpy_to_pil(_lowercase				)

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=_lowercase				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase  						=			torch.sort(_lowercase   ,       1   ,       descending=_lowercase				)
												_lowerCAmelCase  						=			torch.exp(_lowercase				)
												_lowerCAmelCase  						=			sorted_p_x_0.cumsum(dim=1				) < truncation_rate

												# Ensure that at least the largest probability is not zeroed out
												_lowerCAmelCase  						=			torch.full_like(keep_mask[:, 0:1, :]   ,       _lowercase				)
												_lowerCAmelCase  						=			torch.cat((all_true, keep_mask)   ,       dim=1				)
												_lowerCAmelCase  						=			keep_mask[:, :-1, :]

												_lowerCAmelCase  						=			keep_mask.gather(1   ,       indices.argsort(1				)				)

												_lowerCAmelCase  						=			log_p_x_0.clone()

												_lowerCAmelCase  						=			-torch.inf  # -inf = log(0)

												return rv
