
'''simple docstring'''
import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


lowerCAmelCase					:    Optional[Any]           = get_logger(__name__)


lowerCAmelCase					:    List[str]           = r'\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n'


class 			SCREAMING_SNAKE_CASE__      :







				@add_start_docstrings(A_     )
				def __call__(       self						,				A_						,				A_     )->		jnp.ndarray:







											'''simple docstring'''






											raise NotImplementedError(
											    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''     )


class 			SCREAMING_SNAKE_CASE__      :







				@add_start_docstrings(A_     )
				def __call__(       self						,				A_						,				A_     )->		jnp.ndarray:







											'''simple docstring'''






											raise NotImplementedError(
											    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''     )


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				@add_start_docstrings(A_     )
				def __call__(       self						,				A_						,				A_						,				A_						,				**A_     )->		jnp.ndarray:







											'''simple docstring'''






											for processor in self:
																		UpperCamelCase   			=     inspect.signature(processor.__call__     ).parameters
																		if len(A_     ) > 3:
																									if not all(arg in kwargs for arg in list(function_args.keys()     )[2:]     ):
																																raise ValueError(
																																    F'''Make sure that all the required parameters: {list(function_args.keys()     )} for '''
																																    F'''{processor.__class__} are passed to the logits processor.'''     )
																									UpperCamelCase   			=     processor(A_						,				A_						,				A_						,				**A_     )
																		else:
																									UpperCamelCase   			=     processor(A_						,				A_						,				A_     )
											return scores


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_     )->		int:







											'''simple docstring'''






											if not isinstance(A_						,				A_     ) or not (temperature > 0):
																		raise ValueError(F'''`temperature` has to be a strictly positive float, but is {temperature}'''     )

											UpperCamelCase   			=     temperature







				def __call__(       self						,				A_						,				A_						,				A_     )->		jnp.ndarray:







											'''simple docstring'''






											UpperCamelCase   			=     scores / self.temperature
											return scores


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_						,				A_ = -float('Inf'     )						,				A_ = 1     )->		Optional[int]:







											'''simple docstring'''






											if not isinstance(A_						,				A_     ) or (top_p < 0 or top_p > 1.0):
																		raise ValueError(F'''`top_p` has to be a float > 0 and < 1, but is {top_p}'''     )
											if not isinstance(A_						,				A_     ) or (min_tokens_to_keep < 1):
																		raise ValueError(F'''`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}'''     )

											UpperCamelCase   			=     top_p
											UpperCamelCase   			=     filter_value
											UpperCamelCase   			=     min_tokens_to_keep







				def __call__(       self						,				A_						,				A_						,				A_     )->		jnp.ndarray:







											'''simple docstring'''






											UpperCamelCase			,	UpperCamelCase   			=     lax.top_k(A_						,				scores.shape[-1]     )

											UpperCamelCase   			=     jnp.full_like(A_						,				self.filter_value     )
											UpperCamelCase   			=     jax.nn.softmax(A_						,				axis=-1     ).cumsum(axis=-1     )
											UpperCamelCase   			=     cumulative_probs < self.top_p

											# include the token that is higher than top_p as well
											UpperCamelCase   			=     jnp.roll(A_						,				1     )
											score_mask |= score_mask.at[:, 0].set(A_     )

											# min tokens to keep
											UpperCamelCase   			=     score_mask.at[:, : self.min_tokens_to_keep].set(A_     )

											UpperCamelCase   			=     jnp.where(A_						,				A_						,				A_     )
											UpperCamelCase   			=     jax.lax.sort_key_val(A_						,				A_     )[-1]

											return next_scores


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_						,				A_ = -float('Inf'     )						,				A_ = 1     )->		Dict:







											'''simple docstring'''






											if not isinstance(A_						,				A_     ) or top_k <= 0:
																		raise ValueError(F'''`top_k` has to be a strictly positive integer, but is {top_k}'''     )

											UpperCamelCase   			=     max(A_						,				A_     )
											UpperCamelCase   			=     filter_value







				def __call__(       self						,				A_						,				A_						,				A_     )->		jnp.ndarray:







											'''simple docstring'''






											UpperCamelCase			,	UpperCamelCase   			=     scores.shape
											UpperCamelCase   			=     jnp.full(batch_size * vocab_size						,				self.filter_value     )

											UpperCamelCase   			=     min(self.top_k						,				scores.shape[-1]     )  # Safety check
											UpperCamelCase			,	UpperCamelCase   			=     lax.top_k(A_						,				A_     )
											UpperCamelCase   			=     jnp.broadcast_to((jnp.arange(A_     ) * vocab_size)[:, None]						,				(batch_size, topk)     ).flatten()
											UpperCamelCase   			=     topk_scores.flatten()
											UpperCamelCase   			=     topk_indices.flatten() + shift

											UpperCamelCase   			=     next_scores_flat.at[topk_indices_flat].set(A_     )
											UpperCamelCase   			=     next_scores_flat.reshape(A_						,				A_     )
											return next_scores


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     bos_token_id







				def __call__(       self						,				A_						,				A_						,				A_     )->		jnp.ndarray:







											'''simple docstring'''






											UpperCamelCase   			=     jnp.full(scores.shape						,				-float('inf'     )     )

											UpperCamelCase   			=     1 - jnp.bool_(cur_len - 1     )

											UpperCamelCase   			=     jnp.where(A_						,				new_scores.at[:, self.bos_token_id].set(0     )						,				A_     )

											return scores


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_						,				A_     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     max_length
											UpperCamelCase   			=     eos_token_id







				def __call__(       self						,				A_						,				A_						,				A_     )->		jnp.ndarray:







											'''simple docstring'''






											UpperCamelCase   			=     jnp.full(scores.shape						,				-float('inf'     )     )

											UpperCamelCase   			=     1 - jnp.bool_(cur_len - self.max_length + 1     )

											UpperCamelCase   			=     jnp.where(A_						,				new_scores.at[:, self.eos_token_id].set(0     )						,				A_     )

											return scores


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_						,				A_     )->		Tuple:







											'''simple docstring'''






											if not isinstance(A_						,				A_     ) or min_length < 0:
																		raise ValueError(F'''`min_length` has to be a positive integer, but is {min_length}'''     )

											if not isinstance(A_						,				A_     ) or eos_token_id < 0:
																		raise ValueError(F'''`eos_token_id` has to be a positive integer, but is {eos_token_id}'''     )

											UpperCamelCase   			=     min_length
											UpperCamelCase   			=     eos_token_id







				def __call__(       self						,				A_						,				A_						,				A_     )->		jnp.ndarray:







											'''simple docstring'''






											UpperCamelCase   			=     1 - jnp.clip(cur_len - self.min_length						,				0						,				1     )

											UpperCamelCase   			=     jnp.where(A_						,				scores.at[:, self.eos_token_id].set(-float('inf'     )     )						,				A_     )

											return scores


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_						,				A_     )->		int:







											'''simple docstring'''






											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     begin_index







				def __call__(       self						,				A_						,				A_						,				A_     )->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     1 - jnp.bool_(cur_len - self.begin_index     )

											UpperCamelCase   			=     jnp.where(A_						,				scores.at[:, self.begin_suppress_tokens].set(-float('inf'     )     )						,				A_     )

											return scores


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_     )->		int:







											'''simple docstring'''






											UpperCamelCase   			=     list(A_     )







				def __call__(       self						,				A_						,				A_						,				A_     )->		jnp.ndarray:







											'''simple docstring'''






											UpperCamelCase   			=     scores.at[..., self.suppress_tokens].set(-float('inf'     )     )

											return scores


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     dict(A_     )
											# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
											# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
											# Indexes without forced tokens will have a negative value.
											UpperCamelCase   			=     jnp.ones((max(force_token_map.keys()     ) + 1)						,				dtype=jnp.intaa     ) * -1
											for index, token in force_token_map.items():
																		if token is not None:
																									UpperCamelCase   			=     force_token_array.at[index].set(A_     )
											UpperCamelCase   			=     jnp.intaa(A_     )







				def __call__(       self						,				A_						,				A_						,				A_     )->		jnp.ndarray:







											'''simple docstring'''






											def _force_token(A_     ):
																		UpperCamelCase   			=     scores.shape[0]
																		UpperCamelCase   			=     self.force_token_array[generation_idx]

																		UpperCamelCase   			=     jnp.ones_like(A_						,				dtype=scores.dtype     ) * -float('inf'     )
																		UpperCamelCase   			=     jnp.zeros((batch_size, 1)						,				dtype=scores.dtype     )
																		UpperCamelCase   			=     lax.dynamic_update_slice(A_						,				A_						,				(0, current_token)     )
																		return new_scores

											UpperCamelCase   			=     lax.cond(
											    cur_len >= self.force_token_array.shape[0]						,				lambda: scores						,				lambda: lax.cond(
											        self.force_token_array[cur_len] >= 0						,				lambda: _force_token(A_     )						,				lambda: scores						,				)						,				)
											return scores


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_						,				A_						,				A_     )->		int:







											'''simple docstring'''






											UpperCamelCase   			=     generate_config.eos_token_id
											UpperCamelCase   			=     generate_config.no_timestamps_token_id
											UpperCamelCase   			=     generate_config.no_timestamps_token_id + 1

											UpperCamelCase   			=     decoder_input_length + 1

											if generate_config.is_multilingual:
																		# room for language token and task token
																		self.begin_index += 2
											if hasattr(A_						,				'max_initial_timestamp_index'     ):
																		UpperCamelCase   			=     generate_config.max_initial_timestamp_index
											else:
																		UpperCamelCase   			=     model_config.vocab_size
											if self.max_initial_timestamp_index is None:
																		UpperCamelCase   			=     model_config.vocab_size







				def __call__(       self						,				A_						,				A_						,				A_     )->		int:







											'''simple docstring'''






											UpperCamelCase   			=     scores.at[:, self.no_timestamps_token_id].set(-float('inf'     )     )

											def handle_pairs(A_						,				A_     ):
																		UpperCamelCase   			=     jnp.where((cur_len - self.begin_index) >= 1						,				A_						,				A_     )
																		UpperCamelCase   			=     jnp.where(
																		    input_ids_k[cur_len - 1] >= self.timestamp_begin						,				True and last_was_timestamp						,				A_						,				)

																		UpperCamelCase   			=     jnp.where((cur_len - self.begin_index) < 2						,				A_						,				A_     )
																		UpperCamelCase   			=     jnp.where(
																		    input_ids_k[cur_len - 2] >= self.timestamp_begin						,				A_						,				A_						,				)

																		return jnp.where(
																		    A_						,				jnp.where(
																		        penultimate_was_timestamp > 0						,				scores_k.at[self.timestamp_begin :].set(-float('inf'     )     )						,				scores_k.at[: self.eos_token_id].set(-float('inf'     )     )						,				)						,				A_						,				)

											UpperCamelCase   			=     jax.vmap(A_     )(A_						,				A_     )

											UpperCamelCase   			=     jnp.where(cur_len == self.begin_index						,				A_						,				A_     )
											UpperCamelCase   			=     jnp.where(
											    self.max_initial_timestamp_index is not None						,				True and apply_max_initial_timestamp						,				A_						,				)

											UpperCamelCase   			=     self.timestamp_begin + self.max_initial_timestamp_index

											UpperCamelCase   			=     jnp.where(
											    A_						,				scores.at[:, last_allowed + 1 :].set(-float('inf'     )     )						,				A_						,				)

											# if sum of probability over timestamps is above any other token, sample timestamp
											UpperCamelCase   			=     jax.nn.log_softmax(A_						,				axis=-1     )

											def handle_cumulative_probs(A_						,				A_     ):
																		UpperCamelCase   			=     jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]						,				axis=-1     )
																		UpperCamelCase   			=     jnp.max(logprobs_k[: self.timestamp_begin]     )
																		return jnp.where(
																		    timestamp_logprob > max_text_token_logprob						,				scores_k.at[: self.timestamp_begin].set(-float('inf'     )     )						,				A_						,				)

											UpperCamelCase   			=     jax.vmap(A_     )(A_						,				A_     )

											return scores






'''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


lowerCAmelCase					:    Optional[Any]           = logging.get_logger(__name__)  # pylint: disable=invalid-name


class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       snake_case_):







				@register_to_config
				def __init__(       self						,				A_						,				A_ = None						,				A_ = None     )->		Tuple:







											'''simple docstring'''






											super().__init__()

											UpperCamelCase   			=     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   			=     torch.zeros(A_						,				A_     )
											else:
																		UpperCamelCase   			=     None

											UpperCamelCase   			=     torch.nn.Parameter(A_     )


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							42
				lowerCAmelCase_     			=							42
				lowerCAmelCase_     			=							42
				lowerCAmelCase_     			=							42
				lowerCAmelCase_     			=							42
				lowerCAmelCase_     			=							42







				def __init__(       self						,				A_						,				A_						,				A_						,				A_						,				A_						,				A_						,				)->		Union[str, Any]:







											'''simple docstring'''






											super().__init__()

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







				def 						UpperCAmelCase_    (       self						,				A_						,				A_						,				A_     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     len(A_     ) if isinstance(A_						,				A_     ) else 1

											# get prompt text embeddings
											UpperCamelCase   			=     self.tokenizer(
											    A_						,				padding='max_length'						,				max_length=self.tokenizer.model_max_length						,				return_tensors='pt'						,				)
											UpperCamelCase   			=     text_inputs.input_ids

											if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																		UpperCamelCase   			=     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   			=     text_input_ids[:, : self.tokenizer.model_max_length]
											UpperCamelCase   			=     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   			=     prompt_embeds / prompt_embeds.norm(dim=-1						,				keepdim=A_     )

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

											if do_classifier_free_guidance:
																		if self.learned_classifier_free_sampling_embeddings.learnable:
																									UpperCamelCase   			=     self.learned_classifier_free_sampling_embeddings.embeddings
																									UpperCamelCase   			=     negative_prompt_embeds.unsqueeze(0     ).repeat(A_						,				1						,				1     )
																		else:
																									UpperCamelCase   			=     [''] * batch_size

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

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

											return prompt_embeds







				@torch.no_grad()
				def __call__(       self						,				A_						,				A_ = 100						,				A_ = 5.0						,				A_ = 1.0						,				A_ = 1						,				A_ = None						,				A_ = None						,				A_ = "pil"						,				A_ = True						,				A_ = None						,				A_ = 1						,				)->		Union[ImagePipelineOutput, Tuple]:







											'''simple docstring'''






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

											UpperCamelCase   			=     batch_size * num_images_per_prompt

											UpperCamelCase   			=     guidance_scale > 1.0

											UpperCamelCase   			=     self._encode_prompt(A_						,				A_						,				A_     )

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

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

											UpperCamelCase   			=     (batch_size, self.transformer.num_latent_pixels)
											if latents is None:
																		UpperCamelCase   			=     self.transformer.num_vector_embeds - 1
																		UpperCamelCase   			=     torch.full(A_						,				A_     ).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   			=     latents.to(self.device     )

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

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

											UpperCamelCase   			=     latents

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

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

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

																		UpperCamelCase   			=     self.truncate(A_						,				A_     )

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

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

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

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

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

											if output_type == "pil":
																		UpperCamelCase   			=     self.numpy_to_pil(A_     )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=A_     )







				def 						UpperCAmelCase_    (       self						,				A_						,				A_     )->		torch.FloatTensor:







											'''simple docstring'''






											UpperCamelCase			,	UpperCamelCase   			=     torch.sort(A_						,				1						,				descending=A_     )
											UpperCamelCase   			=     torch.exp(A_     )
											UpperCamelCase   			=     sorted_p_x_0.cumsum(dim=1     ) < truncation_rate

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

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

											UpperCamelCase   			=     log_p_x_0.clone()

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

											return rv




