






import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						OrderedDict(
    [
        ('align', 'EfficientNetImageProcessor'),
        ('beit', 'BeitImageProcessor'),
        ('bit', 'BitImageProcessor'),
        ('blip', 'BlipImageProcessor'),
        ('blip-2', 'BlipImageProcessor'),
        ('bridgetower', 'BridgeTowerImageProcessor'),
        ('chinese_clip', 'ChineseCLIPImageProcessor'),
        ('clip', 'CLIPImageProcessor'),
        ('clipseg', 'ViTImageProcessor'),
        ('conditional_detr', 'ConditionalDetrImageProcessor'),
        ('convnext', 'ConvNextImageProcessor'),
        ('convnextv2', 'ConvNextImageProcessor'),
        ('cvt', 'ConvNextImageProcessor'),
        ('data2vec-vision', 'BeitImageProcessor'),
        ('deformable_detr', 'DeformableDetrImageProcessor'),
        ('deit', 'DeiTImageProcessor'),
        ('deta', 'DetaImageProcessor'),
        ('detr', 'DetrImageProcessor'),
        ('dinat', 'ViTImageProcessor'),
        ('donut-swin', 'DonutImageProcessor'),
        ('dpt', 'DPTImageProcessor'),
        ('efficientformer', 'EfficientFormerImageProcessor'),
        ('efficientnet', 'EfficientNetImageProcessor'),
        ('flava', 'FlavaImageProcessor'),
        ('focalnet', 'BitImageProcessor'),
        ('git', 'CLIPImageProcessor'),
        ('glpn', 'GLPNImageProcessor'),
        ('groupvit', 'CLIPImageProcessor'),
        ('imagegpt', 'ImageGPTImageProcessor'),
        ('instructblip', 'BlipImageProcessor'),
        ('layoutlmv2', 'LayoutLMv2ImageProcessor'),
        ('layoutlmv3', 'LayoutLMv3ImageProcessor'),
        ('levit', 'LevitImageProcessor'),
        ('mask2former', 'Mask2FormerImageProcessor'),
        ('maskformer', 'MaskFormerImageProcessor'),
        ('mgp-str', 'ViTImageProcessor'),
        ('mobilenet_v1', 'MobileNetV1ImageProcessor'),
        ('mobilenet_v2', 'MobileNetV2ImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevitv2', 'MobileViTImageProcessor'),
        ('nat', 'ViTImageProcessor'),
        ('oneformer', 'OneFormerImageProcessor'),
        ('owlvit', 'OwlViTImageProcessor'),
        ('perceiver', 'PerceiverImageProcessor'),
        ('pix2struct', 'Pix2StructImageProcessor'),
        ('poolformer', 'PoolFormerImageProcessor'),
        ('regnet', 'ConvNextImageProcessor'),
        ('resnet', 'ConvNextImageProcessor'),
        ('sam', 'SamImageProcessor'),
        ('segformer', 'SegformerImageProcessor'),
        ('swiftformer', 'ViTImageProcessor'),
        ('swin', 'ViTImageProcessor'),
        ('swin2sr', 'Swin2SRImageProcessor'),
        ('swinv2', 'ViTImageProcessor'),
        ('table-transformer', 'DetrImageProcessor'),
        ('timesformer', 'VideoMAEImageProcessor'),
        ('tvlt', 'TvltImageProcessor'),
        ('upernet', 'SegformerImageProcessor'),
        ('van', 'ConvNextImageProcessor'),
        ('videomae', 'VideoMAEImageProcessor'),
        ('vilt', 'ViltImageProcessor'),
        ('vit', 'ViTImageProcessor'),
        ('vit_hybrid', 'ViTHybridImageProcessor'),
        ('vit_mae', 'ViTImageProcessor'),
        ('vit_msn', 'ViTImageProcessor'),
        ('xclip', 'CLIPImageProcessor'),
        ('yolos', 'YolosImageProcessor'),
    ]
)

__UpperCAmelCase     							=						_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)




def 					lowercase__				(					__snake_case   :							str  ):


					'''simple docstring'''


					for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
										if class_name in extractors:
															UpperCAmelCase_   :				Optional[Any]						=						model_type_to_module_name(__snake_case  )

															UpperCAmelCase_   :				Optional[Any]						=						importlib.import_module(F".{module_name}"					,	'transformers.models'  )
															try:
																				return getattr(__snake_case					,	__snake_case  )
															except AttributeError:
																				continue

					for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
										if getattr(__snake_case					,	'__name__'					,	__snake_case  ) == class_name:
															return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
					UpperCAmelCase_   :				Optional[Any]						=						importlib.import_module('transformers'  )
					if hasattr(__snake_case					,	__snake_case  ):
										return getattr(__snake_case					,	__snake_case  )

					return None




def 					lowercase__				(					__snake_case   :							Union[str, os.PathLike]					,	__snake_case   :							Optional[Union[str, os.PathLike]] = None					,	__snake_case   :							bool = False					,	__snake_case   :							bool = False					,	__snake_case   :							Optional[Dict[str, str]] = None					,	__snake_case   :							Optional[Union[bool, str]] = None					,	__snake_case   :							Optional[str] = None					,	__snake_case   :							bool = False					,	**__snake_case   :							List[Any]					,	):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[Any]						=						get_file_from_repo(
					    __snake_case					,	__snake_case					,	cache_dir=__snake_case					,	force_download=__snake_case					,	resume_download=__snake_case					,	proxies=__snake_case					,	use_auth_token=__snake_case					,	revision=__snake_case					,	local_files_only=__snake_case					,	)
					if resolved_config_file is None:
										logger.info(
										    'Could not locate the image processor configuration file, will try to use the model config instead.'  )
										return {}

					with open(__snake_case					,	encoding='utf-8'  ) as reader:
										return json.load(__snake_case  )






class   lowerCamelCase				:







		'''simple docstring'''



		def __init__(							self       )     ->							Optional[Any]:
							raise EnvironmentError(
							    'AutoImageProcessor is designed to be instantiated '
							    'using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'       )



		@classmethod
		@replace_list_option_in_docstrings(_UpperCamelCase       )
		def  __UpperCAmelCase      (							cls    ,		_UpperCamelCase    ,		**_UpperCamelCase       )     ->							str:
							UpperCAmelCase_   :				str						=						kwargs.pop('config'    ,		_UpperCamelCase       )
							UpperCAmelCase_   :				List[str]						=						kwargs.pop('trust_remote_code'    ,		_UpperCamelCase       )
							UpperCAmelCase_   :				List[Any]						=						True

							UpperCAmelCase_	,  UpperCAmelCase_   :				Any						=						ImageProcessingMixin.get_image_processor_dict(_UpperCamelCase    ,		**_UpperCamelCase       )
							UpperCAmelCase_   :				Dict						=						config_dict.get('image_processor_type'    ,		_UpperCamelCase       )
							UpperCAmelCase_   :				Union[str, Any]						=						None
							if "AutoImageProcessor" in config_dict.get('auto_map'    ,		{}       ):
												UpperCAmelCase_   :				List[str]						=						config_dict['auto_map']['AutoImageProcessor']

							# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
							# and if so, infer the image processor class from there.
							if image_processor_class is None and image_processor_auto_map is None:
												UpperCAmelCase_   :				Union[str, Any]						=						config_dict.pop('feature_extractor_type'    ,		_UpperCamelCase       )
												if feature_extractor_class is not None:
																	logger.warning(
																	    'Could not find image processor class in the image processor config or the model config. Loading'
																	    ' based on pattern matching with the model\'s feature extractor configuration.'       )
																	UpperCAmelCase_   :				Dict						=						feature_extractor_class.replace('FeatureExtractor'    ,		'ImageProcessor'       )
												if "AutoFeatureExtractor" in config_dict.get('auto_map'    ,		{}       ):
																	UpperCAmelCase_   :				str						=						config_dict['auto_map']['AutoFeatureExtractor']
																	UpperCAmelCase_   :				Optional[Any]						=						feature_extractor_auto_map.replace('FeatureExtractor'    ,		'ImageProcessor'       )
																	logger.warning(
																	    'Could not find image processor auto map in the image processor config or the model config.'
																	    ' Loading based on pattern matching with the model\'s feature extractor configuration.'       )

        # If we don't find the image processor class in the image processor config, let's try the model config.
							if image_processor_class is None and image_processor_auto_map is None:
												if not isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
																	UpperCAmelCase_   :				List[Any]						=						AutoConfig.from_pretrained(_UpperCamelCase    ,		**_UpperCamelCase       )
												# It could be in `config.image_processor_type``
												UpperCAmelCase_   :				Dict						=						getattr(_UpperCamelCase    ,		'image_processor_type'    ,		_UpperCamelCase       )
												if hasattr(_UpperCamelCase    ,		'auto_map'       ) and "AutoImageProcessor" in config.auto_map:
																	UpperCAmelCase_   :				List[str]						=						config.auto_map['AutoImageProcessor']

							if image_processor_class is not None:
												UpperCAmelCase_   :				Union[str, Any]						=						image_processor_class_from_name(_UpperCamelCase       )

							UpperCAmelCase_   :				str						=						image_processor_auto_map is not None
							UpperCAmelCase_   :				Tuple						=						image_processor_class is not None or type(_UpperCamelCase       ) in IMAGE_PROCESSOR_MAPPING
							UpperCAmelCase_   :				Dict						=						resolve_trust_remote_code(
							    _UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )

							if has_remote_code and trust_remote_code:
												UpperCAmelCase_   :				Tuple						=						get_class_from_dynamic_module(
												    _UpperCamelCase    ,		_UpperCamelCase    ,		**_UpperCamelCase       )
												UpperCAmelCase_   :				Optional[Any]						=						kwargs.pop('code_revision'    ,		_UpperCamelCase       )
												if os.path.isdir(_UpperCamelCase       ):
																	image_processor_class.register_for_auto_class()
												return image_processor_class.from_dict(_UpperCamelCase    ,		**_UpperCamelCase       )
							elif image_processor_class is not None:
												return image_processor_class.from_dict(_UpperCamelCase    ,		**_UpperCamelCase       )
							# Last try: we use the IMAGE_PROCESSOR_MAPPING.
							elif type(_UpperCamelCase       ) in IMAGE_PROCESSOR_MAPPING:
												UpperCAmelCase_   :				List[str]						=						IMAGE_PROCESSOR_MAPPING[type(_UpperCamelCase       )]
												return image_processor_class.from_dict(_UpperCamelCase    ,		**_UpperCamelCase       )

							raise ValueError(
							    f"Unrecognized image processor in {pretrained_model_name_or_path}. Should have a "
							    f"`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following "
							    f"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()       )}"       )



		@staticmethod
		def  __UpperCAmelCase      (							_UpperCamelCase    ,		_UpperCamelCase       )     ->							Union[str, Any]:
							IMAGE_PROCESSOR_MAPPING.register(_UpperCamelCase    ,		_UpperCamelCase       )














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


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



class   lowerCamelCase				(_snake_case			,  _snake_case      ):







		'''simple docstring'''



		@register_to_config
		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None       )     ->							int:
							super().__init__()

							UpperCAmelCase_   :				str						=						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"

												UpperCAmelCase_   :				Optional[Any]						=						torch.zeros(_UpperCamelCase    ,		_UpperCamelCase       )
							else:
												UpperCAmelCase_   :				Any						=						None

							UpperCAmelCase_   :				Any						=						torch.nn.Parameter(_UpperCamelCase       )





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   VQModel
		_snake_case   :   CLIPTextModel
		_snake_case   :   CLIPTokenizer
		_snake_case   :   TransformeraDModel
		_snake_case   :   LearnedClassifierFreeSamplingEmbeddings
		_snake_case   :   VQDiffusionScheduler



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		)     ->							List[Any]:
							super().__init__()

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



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							List[str]:
							UpperCAmelCase_   :				Union[str, Any]						=						len(_UpperCamelCase       ) if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ) else 1

							# get prompt text embeddings
							UpperCAmelCase_   :				str						=						self.tokenizer(
							    _UpperCamelCase    ,		padding='max_length'    ,		max_length=self.tokenizer.model_max_length    ,		return_tensors='pt'    ,		)
							UpperCAmelCase_   :				Optional[Any]						=						text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
												UpperCAmelCase_   :				List[Any]						=						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}"       )
												UpperCAmelCase_   :				str						=						text_input_ids[:, : self.tokenizer.model_max_length]
							UpperCAmelCase_   :				str						=						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
							UpperCAmelCase_   :				Dict						=						prompt_embeds / prompt_embeds.norm(dim=-1    ,		keepdim=_UpperCamelCase       )

							# duplicate text embeddings for each generation per prompt
							UpperCAmelCase_   :				Dict						=						prompt_embeds.repeat_interleave(_UpperCamelCase    ,		dim=0       )

							if do_classifier_free_guidance:
												if self.learned_classifier_free_sampling_embeddings.learnable:
																	UpperCAmelCase_   :				List[str]						=						self.learned_classifier_free_sampling_embeddings.embeddings
																	UpperCAmelCase_   :				List[str]						=						negative_prompt_embeds.unsqueeze(0       ).repeat(_UpperCamelCase    ,		1    ,		1       )
												else:
																	UpperCAmelCase_   :				List[Any]						=						[''] * batch_size

																	UpperCAmelCase_   :				List[Any]						=						text_input_ids.shape[-1]
																	UpperCAmelCase_   :				Dict						=						self.tokenizer(
																	    _UpperCamelCase    ,		padding='max_length'    ,		max_length=_UpperCamelCase    ,		truncation=_UpperCamelCase    ,		return_tensors='pt'    ,		)
																	UpperCAmelCase_   :				Tuple						=						self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]
																	# See comment for normalizing text embeddings
																	UpperCAmelCase_   :				Dict						=						negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1    ,		keepdim=_UpperCamelCase       )

												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
												UpperCAmelCase_   :				List[Any]						=						negative_prompt_embeds.shape[1]
												UpperCAmelCase_   :				Dict						=						negative_prompt_embeds.repeat(1    ,		_UpperCamelCase    ,		1       )
												UpperCAmelCase_   :				Any						=						negative_prompt_embeds.view(batch_size * num_images_per_prompt    ,		_UpperCamelCase    ,		-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
												UpperCAmelCase_   :				Union[str, Any]						=						torch.cat([negative_prompt_embeds, prompt_embeds]       )

							return prompt_embeds



		@torch.no_grad()
		def __call__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase = 1_0_0    ,		_UpperCamelCase = 5.0    ,		_UpperCamelCase = 1.0    ,		_UpperCamelCase = 1    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = "pil"    ,		_UpperCamelCase = True    ,		_UpperCamelCase = None    ,		_UpperCamelCase = 1    ,		)     ->							Union[ImagePipelineOutput, Tuple]:
							if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
												UpperCAmelCase_   :				Any						=						1
							elif isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
												UpperCAmelCase_   :				Tuple						=						len(_UpperCamelCase       )
							else:
												raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(_UpperCamelCase       )}"       )

							UpperCAmelCase_   :				Union[str, Any]						=						batch_size * num_images_per_prompt

							UpperCAmelCase_   :				Optional[int]						=						guidance_scale > 1.0

							UpperCAmelCase_   :				Any						=						self._encode_prompt(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )

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

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

							UpperCAmelCase_   :				Optional[int]						=						(batch_size, self.transformer.num_latent_pixels)
							if latents is None:
												UpperCAmelCase_   :				Tuple						=						self.transformer.num_vector_embeds - 1
												UpperCAmelCase_   :				List[Any]						=						torch.full(_UpperCamelCase    ,		_UpperCamelCase       ).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)."       )
												UpperCAmelCase_   :				Any						=						latents.to(self.device       )

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

							UpperCAmelCase_   :				List[str]						=						self.scheduler.timesteps.to(self.device       )

							UpperCAmelCase_   :				Union[str, Any]						=						latents

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

												# predict the un-noised image
												# model_output == `log_p_x_0`
												UpperCAmelCase_   :				Dict						=						self.transformer(_UpperCamelCase    ,		encoder_hidden_states=_UpperCamelCase    ,		timestep=_UpperCamelCase       ).sample

												if do_classifier_free_guidance:
																	UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[Any]						=						model_output.chunk(2       )
																	UpperCAmelCase_   :				Optional[int]						=						model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																	model_output -= torch.logsumexp(_UpperCamelCase    ,		dim=1    ,		keepdim=_UpperCamelCase       )

												UpperCAmelCase_   :				str						=						self.truncate(_UpperCamelCase    ,		_UpperCamelCase       )

												# remove `log(0)`'s (`-inf`s)
												UpperCAmelCase_   :				Optional[int]						=						model_output.clamp(-7_0       )

												# compute the previous noisy sample x_t -> x_t-1
												UpperCAmelCase_   :				Union[str, Any]						=						self.scheduler.step(_UpperCamelCase    ,		timestep=_UpperCamelCase    ,		sample=_UpperCamelCase    ,		generator=_UpperCamelCase       ).prev_sample

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

							UpperCAmelCase_   :				str						=						self.vqvae.config.vq_embed_dim
							UpperCAmelCase_   :				Optional[int]						=						(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
							UpperCAmelCase_   :				int						=						self.vqvae.quantize.get_codebook_entry(_UpperCamelCase    ,		shape=_UpperCamelCase       )
							UpperCAmelCase_   :				Dict						=						self.vqvae.decode(_UpperCamelCase    ,		force_not_quantize=_UpperCamelCase       ).sample

							UpperCAmelCase_   :				List[str]						=						(image / 2 + 0.5).clamp(0    ,		1       )
							UpperCAmelCase_   :				Any						=						image.cpu().permute(0    ,		2    ,		3    ,		1       ).numpy()

							if output_type == "pil":
												UpperCAmelCase_   :				int						=						self.numpy_to_pil(_UpperCamelCase       )

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							torch.FloatTensor:
							UpperCAmelCase_	,  UpperCAmelCase_   :				int						=						torch.sort(_UpperCamelCase    ,		1    ,		descending=_UpperCamelCase       )
							UpperCAmelCase_   :				Dict						=						torch.exp(_UpperCamelCase       )
							UpperCAmelCase_   :				int						=						sorted_p_x_0.cumsum(dim=1       ) < truncation_rate

							# Ensure that at least the largest probability is not zeroed out
							UpperCAmelCase_   :				Tuple						=						torch.full_like(keep_mask[:, 0:1, :]    ,		_UpperCamelCase       )
							UpperCAmelCase_   :				List[str]						=						torch.cat((all_true, keep_mask)    ,		dim=1       )
							UpperCAmelCase_   :				int						=						keep_mask[:, :-1, :]

							UpperCAmelCase_   :				Any						=						keep_mask.gather(1    ,		indices.argsort(1       )       )

							UpperCAmelCase_   :				str						=						log_p_x_0.clone()

							UpperCAmelCase_   :				Any						=						-torch.inf  # -inf = log(0)

							return rv






