
'''simple docstring'''
import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel


class 			SCREAMING_SNAKE_CASE__      :







				def __init__(       self						,				A_						,				A_=13						,				A_=7						,				A_=True						,				A_=True						,				A_=False						,				A_=True						,				A_=99						,				A_=32						,				A_=5						,				A_=4						,				A_=37						,				A_="gelu"						,				A_=0.1						,				A_=0.1						,				A_=512						,				A_=16						,				A_=2						,				A_=0.02						,				A_=3						,				A_=4						,				A_=None						,				)->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     parent
											UpperCamelCase   			=     batch_size
											UpperCamelCase   			=     seq_length
											UpperCamelCase   			=     is_training
											UpperCamelCase   			=     use_input_mask
											UpperCamelCase   			=     use_token_type_ids
											UpperCamelCase   			=     use_labels
											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     hidden_size
											UpperCamelCase   			=     num_hidden_layers
											UpperCamelCase   			=     num_attention_heads
											UpperCamelCase   			=     intermediate_size
											UpperCamelCase   			=     hidden_act
											UpperCamelCase   			=     hidden_dropout_prob
											UpperCamelCase   			=     attention_probs_dropout_prob
											UpperCamelCase   			=     max_position_embeddings
											UpperCamelCase   			=     type_vocab_size
											UpperCamelCase   			=     type_sequence_label_size
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     num_labels
											UpperCamelCase   			=     num_choices
											UpperCamelCase   			=     scope







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											UpperCamelCase   			=     ids_tensor([self.batch_size, self.seq_length]						,				self.vocab_size     )

											UpperCamelCase   			=     None
											if self.use_input_mask:
																		UpperCamelCase   			=     random_attention_mask([self.batch_size, self.seq_length]     )

											UpperCamelCase   			=     None
											if self.use_token_type_ids:
																		UpperCamelCase   			=     ids_tensor([self.batch_size, self.seq_length]						,				self.type_vocab_size     )

											UpperCamelCase   			=     None
											UpperCamelCase   			=     None
											UpperCamelCase   			=     None
											if self.use_labels:
																		UpperCamelCase   			=     ids_tensor([self.batch_size]						,				self.type_sequence_label_size     )
																		UpperCamelCase   			=     ids_tensor([self.batch_size, self.seq_length]						,				self.num_labels     )
																		UpperCamelCase   			=     ids_tensor([self.batch_size]						,				self.num_choices     )

											UpperCamelCase   			=     self.get_config()

											return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											return OpenLlamaConfig(
											    vocab_size=self.vocab_size						,				hidden_size=self.hidden_size						,				num_hidden_layers=self.num_hidden_layers						,				num_attention_heads=self.num_attention_heads						,				intermediate_size=self.intermediate_size						,				hidden_act=self.hidden_act						,				hidden_dropout_prob=self.hidden_dropout_prob						,				attention_probs_dropout_prob=self.attention_probs_dropout_prob						,				max_position_embeddings=self.max_position_embeddings						,				type_vocab_size=self.type_vocab_size						,				is_decoder=A_						,				initializer_range=self.initializer_range						,				use_stable_embedding=A_						,				)







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







											'''simple docstring'''






											UpperCamelCase   			=     OpenLlamaModel(config=A_     )
											model.to(A_     )
											model.eval()
											UpperCamelCase   			=     model(A_						,				attention_mask=A_     )
											UpperCamelCase   			=     model(A_     )
											self.parent.assertEqual(result.last_hidden_state.shape						,				(self.batch_size, self.seq_length, self.hidden_size)     )







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







											'''simple docstring'''






											UpperCamelCase   			=     True
											UpperCamelCase   			=     OpenLlamaModel(A_     )
											model.to(A_     )
											model.eval()
											UpperCamelCase   			=     model(
											    A_						,				attention_mask=A_						,				encoder_hidden_states=A_						,				encoder_attention_mask=A_						,				)
											UpperCamelCase   			=     model(
											    A_						,				attention_mask=A_						,				encoder_hidden_states=A_						,				)
											UpperCamelCase   			=     model(A_						,				attention_mask=A_     )
											self.parent.assertEqual(result.last_hidden_state.shape						,				(self.batch_size, self.seq_length, self.hidden_size)     )







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







											'''simple docstring'''






											UpperCamelCase   			=     OpenLlamaForCausalLM(config=A_     )
											model.to(A_     )
											model.eval()
											UpperCamelCase   			=     model(A_						,				attention_mask=A_						,				labels=A_     )
											self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.seq_length, self.vocab_size)     )







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







											'''simple docstring'''






											UpperCamelCase   			=     True
											UpperCamelCase   			=     True
											UpperCamelCase   			=     OpenLlamaForCausalLM(config=A_     )
											model.to(A_     )
											model.eval()

											# first forward pass
											UpperCamelCase   			=     model(
											    A_						,				attention_mask=A_						,				encoder_hidden_states=A_						,				encoder_attention_mask=A_						,				use_cache=A_						,				)
											UpperCamelCase   			=     outputs.past_key_values

											# create hypothetical multiple next token and extent to next_input_ids
											UpperCamelCase   			=     ids_tensor((self.batch_size, 3)						,				config.vocab_size     )
											UpperCamelCase   			=     ids_tensor((self.batch_size, 3)						,				vocab_size=2     )

											# append to next input_ids and
											UpperCamelCase   			=     torch.cat([input_ids, next_tokens]						,				dim=-1     )
											UpperCamelCase   			=     torch.cat([input_mask, next_mask]						,				dim=-1     )

											UpperCamelCase   			=     model(
											    A_						,				attention_mask=A_						,				encoder_hidden_states=A_						,				encoder_attention_mask=A_						,				output_hidden_states=A_						,				)['hidden_states'][0]
											UpperCamelCase   			=     model(
											    A_						,				attention_mask=A_						,				encoder_hidden_states=A_						,				encoder_attention_mask=A_						,				past_key_values=A_						,				output_hidden_states=A_						,				)['hidden_states'][0]

											# select random slice
											UpperCamelCase   			=     ids_tensor((1,)						,				output_from_past.shape[-1]     ).item()
											UpperCamelCase   			=     output_from_no_past[:, -3:, random_slice_idx].detach()
											UpperCamelCase   			=     output_from_past[:, :, random_slice_idx].detach()

											self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]     )

											# test that outputs are equal for slice
											self.parent.assertTrue(torch.allclose(A_						,				A_						,				atol=1e-3     )     )







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.prepare_config_and_inputs()
											(
											    (
											    UpperCamelCase
											)			,	(
											    UpperCamelCase
											)			,	(
											    UpperCamelCase
											)			,	(
											    UpperCamelCase
											)			,	(
											    UpperCamelCase
											)			,	(
											    UpperCamelCase
											)			,	(
											    UpperCamelCase
											)			,	
											)   			=     config_and_inputs
											UpperCamelCase   			=     {'input_ids': input_ids, 'attention_mask': input_mask}
											return config, inputs_dict


@require_torch
class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       snake_case_							,       snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							(
				    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
				)
				lowerCAmelCase_     			=							(OpenLlamaForCausalLM,) if is_torch_available() else ()
				lowerCAmelCase_     			=							(
				    {
				        """feature-extraction""": OpenLlamaModel,
				        """text-classification""": OpenLlamaForSequenceClassification,
				        """text-generation""": OpenLlamaForCausalLM,
				        """zero-shot""": OpenLlamaForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							False







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







											'''simple docstring'''






											UpperCamelCase   			=     OpenLlamaModelTester(self     )
											UpperCamelCase   			=     ConfigTester(self						,				config_class=A_						,				hidden_size=37     )







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											self.config_tester.run_common_tests()







				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*A_     )







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
																		UpperCamelCase   			=     type
																		self.model_tester.create_and_check_model(*A_     )







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase			,	UpperCamelCase   			=     self.model_tester.prepare_config_and_inputs_for_common()
											UpperCamelCase   			=     3
											UpperCamelCase   			=     input_dict['input_ids']
											UpperCamelCase   			=     input_ids.ne(1     ).to(A_     )
											UpperCamelCase   			=     ids_tensor([self.model_tester.batch_size]						,				self.model_tester.type_sequence_label_size     )
											UpperCamelCase   			=     OpenLlamaForSequenceClassification(A_     )
											model.to(A_     )
											model.eval()
											UpperCamelCase   			=     model(A_						,				attention_mask=A_						,				labels=A_     )
											self.assertEqual(result.logits.shape						,				(self.model_tester.batch_size, self.model_tester.num_labels)     )







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase			,	UpperCamelCase   			=     self.model_tester.prepare_config_and_inputs_for_common()
											UpperCamelCase   			=     3
											UpperCamelCase   			=     'single_label_classification'
											UpperCamelCase   			=     input_dict['input_ids']
											UpperCamelCase   			=     input_ids.ne(1     ).to(A_     )
											UpperCamelCase   			=     ids_tensor([self.model_tester.batch_size]						,				self.model_tester.type_sequence_label_size     )
											UpperCamelCase   			=     OpenLlamaForSequenceClassification(A_     )
											model.to(A_     )
											model.eval()
											UpperCamelCase   			=     model(A_						,				attention_mask=A_						,				labels=A_     )
											self.assertEqual(result.logits.shape						,				(self.model_tester.batch_size, self.model_tester.num_labels)     )







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







											'''simple docstring'''






											UpperCamelCase			,	UpperCamelCase   			=     self.model_tester.prepare_config_and_inputs_for_common()
											UpperCamelCase   			=     3
											UpperCamelCase   			=     'multi_label_classification'
											UpperCamelCase   			=     input_dict['input_ids']
											UpperCamelCase   			=     input_ids.ne(1     ).to(A_     )
											UpperCamelCase   			=     ids_tensor(
											    [self.model_tester.batch_size, config.num_labels]						,				self.model_tester.type_sequence_label_size     ).to(torch.float     )
											UpperCamelCase   			=     OpenLlamaForSequenceClassification(A_     )
											model.to(A_     )
											model.eval()
											UpperCamelCase   			=     model(A_						,				attention_mask=A_						,				labels=A_     )
											self.assertEqual(result.logits.shape						,				(self.model_tester.batch_size, self.model_tester.num_labels)     )







				@unittest.skip('Open-Llama buffers include complex numbers, which breaks this test'     )
				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											pass







				@parameterized.expand([('linear',), ('dynamic',)]     )
				def 						UpperCAmelCase_    (       self						,				A_     )->		Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase			,	UpperCamelCase   			=     self.model_tester.prepare_config_and_inputs_for_common()
											UpperCamelCase   			=     ids_tensor([1, 10]						,				config.vocab_size     )
											UpperCamelCase   			=     ids_tensor([1, int(config.max_position_embeddings * 1.5     )]						,				config.vocab_size     )

											set_seed(42     )  # Fixed seed at init time so the two models get the same random weights
											UpperCamelCase   			=     OpenLlamaModel(A_     )
											original_model.to(A_     )
											original_model.eval()
											UpperCamelCase   			=     original_model(A_     ).last_hidden_state
											UpperCamelCase   			=     original_model(A_     ).last_hidden_state

											set_seed(42     )  # Fixed seed at init time so the two models get the same random weights
											UpperCamelCase   			=     {'type': scaling_type, 'factor': 10.0}
											UpperCamelCase   			=     OpenLlamaModel(A_     )
											scaled_model.to(A_     )
											scaled_model.eval()
											UpperCamelCase   			=     scaled_model(A_     ).last_hidden_state
											UpperCamelCase   			=     scaled_model(A_     ).last_hidden_state

											# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
											# maximum sequence length, so the outputs for the short input should match.
											if scaling_type == "dynamic":
																		self.assertTrue(torch.allclose(A_						,				A_						,				atol=1e-5     )     )
											else:
																		self.assertFalse(torch.allclose(A_						,				A_						,				atol=1e-5     )     )

											# The output should be different for long inputs
											self.assertFalse(torch.allclose(A_						,				A_						,				atol=1e-5     )     )






'''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




