




'''simple docstring'''






import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


A_			      =		logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

A_			      =		{
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
A_			      =		{
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
A_			      =		sorted(arg_to_scheduler.keys())
A_			      =		'{' + ', '.join(arg_to_scheduler_choices) + '}'






class        lowercase_      (						pl.LightningModule							):





     def __init__(     self     :					Tuple     ,							__lowerCamelCase     :					Optional[Any]     ,							__lowerCamelCase     :					str=None     ,							__lowerCamelCase     :					Tuple="base"     ,							__lowerCamelCase     :					Tuple=None     ,							__lowerCamelCase     :					List[str]=None     ,							__lowerCamelCase     :					Optional[Any]=None     ,							**__lowerCamelCase     :					Union[str, Any]     ,							):
           super().__init__()
           # TODO: move to self.save_hyperparameters()
           # self.save_hyperparameters()
           # can also expand arguments into trainer signature for easier reading

           self.save_hyperparameters(A_			)
           snake_case__       :   Optional[int]	      =    0
           snake_case__       :   List[Any]	      =    Path(self.hparams.output_dir			)
           snake_case__       :   int	      =    self.hparams.cache_dir if self.hparams.cache_dir else None
           if config is None:
                 snake_case__       :   str	      =    AutoConfig.from_pretrained(
                     self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path     ,							**({'num_labels': num_labels} if num_labels is not None else {})     ,							cache_dir=A_     ,							**A_     ,							)
           else:
                 snake_case__       :   Any	      =    config

           snake_case__       :   str	      =    ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
           for p in extra_model_params:
                 if getattr(self.hparams     ,							A_     ,							A_			):
                       assert hasattr(self.config     ,							A_			), F"model config doesn\'t have a `{p}` attribute"
                       setattr(self.config     ,							A_     ,							getattr(self.hparams     ,							A_			)			)

           if tokenizer is None:
                 snake_case__       :   int	      =    AutoTokenizer.from_pretrained(
                     self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,							cache_dir=A_     ,							)
           else:
                 snake_case__       :   Tuple	      =    tokenizer
           snake_case__       :   List[str]	      =    MODEL_MODES[mode]
           if model is None:
                 snake_case__       :   List[str]	      =    self.model_type.from_pretrained(
                     self.hparams.model_name_or_path     ,							from_tf=bool('.ckpt' in self.hparams.model_name_or_path			)     ,							config=self.config     ,							cache_dir=A_     ,							)
           else:
                 snake_case__       :   Union[str, Any]	      =    model





     def 			_lowerCAmelCase      (     self     :					str     ,							*__lowerCamelCase     :					List[str]     ,							**__lowerCamelCase     :					Optional[int]			):
           snake_case__       :   str	      =    self.model_type.from_pretrained(*A_     ,							**A_			)





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   Optional[int]	      =    arg_to_scheduler[self.hparams.lr_scheduler]
           snake_case__       :   int	      =    get_schedule_func(
               self.opt     ,							num_warmup_steps=self.hparams.warmup_steps     ,							num_training_steps=self.total_steps()			)
           snake_case__       :   int	      =    {'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
           return scheduler





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           snake_case__       :   Any	      =    self.model
           snake_case__       :   Optional[Any]	      =    ['bias', 'LayerNorm.weight']
           snake_case__       :   int	      =    [
               {
                   'params': [
                       p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay			)
                   ],  # check this named paramters
                   'weight_decay': self.hparams.weight_decay,
               },
               {
                   'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay			)],
                   'weight_decay': 0.0,
               },
           ]
           if self.hparams.adafactor:
                 snake_case__       :   List[str]	      =    Adafactor(
                     A_     ,							lr=self.hparams.learning_rate     ,							scale_parameter=A_     ,							relative_step=A_			)

           else:
                 snake_case__       :   List[Any]	      =    AdamW(
                     A_     ,							lr=self.hparams.learning_rate     ,							eps=self.hparams.adam_epsilon			)
           snake_case__       :   Optional[int]	      =    optimizer

           snake_case__       :   str	      =    self.get_lr_scheduler()

           return [optimizer], [scheduler]





     def 			_lowerCAmelCase      (     self     :					int     ,							__lowerCamelCase     :					List[Any]     ,							__lowerCamelCase     :					Tuple			):
           return self.validation_step(A_     ,							A_			)





     def 			_lowerCAmelCase      (     self     :					str     ,							__lowerCamelCase     :					Optional[Any]			):
           return self.validation_end(A_			)





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           snake_case__       :   int	      =    max(1     ,							self.hparams.gpus			)  # TODO: consider num_tpu_cores
           snake_case__       :   List[str]	      =    self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
           return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs





     def 			_lowerCAmelCase      (     self     :					List[Any]     ,							__lowerCamelCase     :					Optional[Any]			):






           if stage == "test":
                 snake_case__       :   Optional[Any]	      =    len(self.test_dataloader().dataset			)
           else:
                 snake_case__       :   List[Any]	      =    self.get_dataloader('train'     ,							self.hparams.train_batch_size     ,							shuffle=A_			)
                 snake_case__       :   Optional[int]	      =    len(self.train_dataloader().dataset			)





     def 			_lowerCAmelCase      (     self     :					int     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					Any     ,							__lowerCamelCase     :					Any = False			):
           raise NotImplementedError('You must implement this for your task'			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           return self.train_loader





     def 			_lowerCAmelCase      (     self     :					Dict			):
           return self.get_dataloader('dev'     ,							self.hparams.eval_batch_size     ,							shuffle=A_			)





     def 			_lowerCAmelCase      (     self     :					Dict			):
           return self.get_dataloader('test'     ,							self.hparams.eval_batch_size     ,							shuffle=A_			)





     def 			_lowerCAmelCase      (     self     :					List[Any]     ,							__lowerCamelCase     :					Dict			):
           return os.path.join(
               self.hparams.data_dir     ,							'cached_{}_{}_{}'.format(
                   A_     ,							list(filter(A_     ,							self.hparams.model_name_or_path.split('/'			)			)			).pop()     ,							str(self.hparams.max_seq_length			)     ,							)     ,							)





     @pl.utilities.rank_zero_only
     def 			_lowerCAmelCase      (     self     :					Any     ,							__lowerCamelCase     :					Dict			):
           snake_case__       :   Any	      =    self.output_dir.joinpath('best_tfmr'			)
           snake_case__       :   Optional[int]	      =    self.step_count
           self.model.save_pretrained(A_			)
           self.tokenizer.save_pretrained(A_			)





     @staticmethod
     def 			_lowerCAmelCase      (     __lowerCamelCase     :					str     ,							__lowerCamelCase     :					Optional[int]			):
           parser.add_argument(
               '--model_name_or_path'     ,							default=A_     ,							type=A_     ,							required=A_     ,							help='Path to pretrained model or model identifier from huggingface.co/models'     ,							)
           parser.add_argument(
               '--config_name'     ,							default=''     ,							type=A_     ,							help='Pretrained config name or path if not the same as model_name'			)
           parser.add_argument(
               '--tokenizer_name'     ,							default=A_     ,							type=A_     ,							help='Pretrained tokenizer name or path if not the same as model_name'     ,							)
           parser.add_argument(
               '--cache_dir'     ,							default=str(Path(A_			).parent / 'test_run' / 'cache'			)     ,							type=A_     ,							help='Where do you want to store the pre-trained models downloaded from huggingface.co'     ,							)
           parser.add_argument(
               '--encoder_layerdrop'     ,							type=A_     ,							help='Encoder layer dropout probability (Optional). Goes into model.config'     ,							)
           parser.add_argument(
               '--decoder_layerdrop'     ,							type=A_     ,							help='Decoder layer dropout probability (Optional). Goes into model.config'     ,							)
           parser.add_argument(
               '--dropout'     ,							type=A_     ,							help='Dropout probability (Optional). Goes into model.config'     ,							)
           parser.add_argument(
               '--attention_dropout'     ,							type=A_     ,							help='Attention dropout probability (Optional). Goes into model.config'     ,							)
           parser.add_argument('--learning_rate'     ,							default=5E-5     ,							type=A_     ,							help='The initial learning rate for Adam.'			)
           parser.add_argument(
               '--lr_scheduler'     ,							default='linear'     ,							choices=A_     ,							metavar=A_     ,							type=A_     ,							help='Learning rate scheduler'     ,							)
           parser.add_argument('--weight_decay'     ,							default=0.0     ,							type=A_     ,							help='Weight decay if we apply some.'			)
           parser.add_argument('--adam_epsilon'     ,							default=1E-8     ,							type=A_     ,							help='Epsilon for Adam optimizer.'			)
           parser.add_argument('--warmup_steps'     ,							default=0     ,							type=A_     ,							help='Linear warmup over warmup_steps.'			)
           parser.add_argument('--num_workers'     ,							default=4     ,							type=A_     ,							help='kwarg passed to DataLoader'			)
           parser.add_argument('--num_train_epochs'     ,							dest='max_epochs'     ,							default=3     ,							type=A_			)
           parser.add_argument('--train_batch_size'     ,							default=32     ,							type=A_			)
           parser.add_argument('--eval_batch_size'     ,							default=32     ,							type=A_			)
           parser.add_argument('--adafactor'     ,							action='store_true'			)






class        lowercase_      (						pl.Callback							):





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					List[Any]     ,							__lowerCamelCase     :					List[str]			):






           if (
               trainer.is_global_zero and trainer.global_rank == 0
           ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
                 pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class        lowercase_      (						pl.Callback							):





     def 			_lowerCAmelCase      (     self     :					Any     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					List[str]			):






           for name, param in pl_module.model.rag.named_parameters():
                 if param.grad is None:
                       print(A_			)






class        lowercase_      (						pl.Callback							):





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					List[Any]     ,							__lowerCamelCase     :					List[str]			):
           snake_case__       :   Optional[Any]	      =    trainer.lr_schedulers[0]['scheduler']
           snake_case__       :   int	      =    {F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()			)}
           pl_module.logger.log_metrics(A_			)





     def 			_lowerCAmelCase      (     self     :					str     ,							__lowerCamelCase     :					Union[str, Any]     ,							__lowerCamelCase     :					Any			):
           rank_zero_info('***** Validation results *****'			)
           snake_case__       :   List[str]	      =    trainer.callback_metrics
           # Log results
           for key in sorted(A_			):
                 if key not in ["log", "progress_bar"]:
                       rank_zero_info('{} = {}\n'.format(A_     ,							str(metrics[key]			)			)			)





     def 			_lowerCAmelCase      (     self     :					Tuple     ,							__lowerCamelCase     :					Any     ,							__lowerCamelCase     :					Optional[Any]			):
           rank_zero_info('***** Test results *****'			)
           snake_case__       :   Optional[Any]	      =    trainer.callback_metrics
           # Log and save results to file
           snake_case__       :   List[Any]	      =    os.path.join(pl_module.hparams.output_dir     ,							'test_results.txt'			)
           with open(A_     ,							'w'			) as writer:
                 for key in sorted(A_			):
                       if key not in ["log", "progress_bar"]:
                             rank_zero_info('{} = {}\n'.format(A_     ,							str(metrics[key]			)			)			)
                             writer.write('{} = {}\n'.format(A_     ,							str(metrics[key]			)			)			)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   List[Any]:
      #  To allow all pl args uncomment the following line
      #  parser = pl.Trainer.add_argparse_args(parser)
      parser.add_argument(
          '--output_dir'				,      default=str(Path(__SCREAMING_SNAKE_CASE		).parent / 'test_run' / 'model_checkpoints'		)				,      type=__SCREAMING_SNAKE_CASE				,      help='The output directory where the model predictions and checkpoints will be written.'				,      )
      parser.add_argument(
          '--fp16'				,      action='store_true'				,      help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'				,      )

      parser.add_argument(
          '--fp16_opt_level'				,      type=__SCREAMING_SNAKE_CASE				,      default='O2'				,      help=(
              'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
              'See details at https://nvidia.github.io/apex/amp.html'
          )				,      )
      parser.add_argument('--n_tpu_cores'				,      dest='tpu_cores'				,      type=__SCREAMING_SNAKE_CASE		)
      parser.add_argument('--max_grad_norm'				,      dest='gradient_clip_val'				,      default=1.0				,      type=__SCREAMING_SNAKE_CASE				,      help='Max gradient norm'		)
      parser.add_argument('--do_train'				,      action='store_true'				,      help='Whether to run training.'		)
      parser.add_argument('--do_predict'				,      action='store_true'				,      help='Whether to run predictions on the test set.'		)
      parser.add_argument(
          '--gradient_accumulation_steps'				,      dest='accumulate_grad_batches'				,      type=__SCREAMING_SNAKE_CASE				,      default=1				,      help='Number of updates steps to accumulate before performing a backward/update pass.'				,      )
      parser.add_argument('--seed'				,      type=__SCREAMING_SNAKE_CASE				,      default=42				,      help='random seed for initialization'		)
      parser.add_argument(
          '--data_dir'				,      default=str(Path(__SCREAMING_SNAKE_CASE		).parent / 'test_run' / 'dummy-train-data'		)				,      type=__SCREAMING_SNAKE_CASE				,      help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'				,      )






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE=None				,      __SCREAMING_SNAKE_CASE=True				,      __SCREAMING_SNAKE_CASE=[]				,      __SCREAMING_SNAKE_CASE=None				,      __SCREAMING_SNAKE_CASE=None				,      **__SCREAMING_SNAKE_CASE				,      )    ->   Optional[Any]:
      pl.seed_everything(args.seed		)

      # init model
      snake_case__       :   Optional[int]	      =    Path(model.hparams.output_dir		)
      odir.mkdir(exist_ok=__SCREAMING_SNAKE_CASE		)

      # add custom checkpoints
      if checkpoint_callback is None:
            snake_case__       :   List[Any]	      =    pl.callbacks.ModelCheckpoint(
                filepath=args.output_dir				,      prefix='checkpoint'				,      monitor='val_loss'				,      mode='min'				,      save_top_k=1		)
      if early_stopping_callback:
            extra_callbacks.append(__SCREAMING_SNAKE_CASE		)
      if logging_callback is None:
            snake_case__       :   Optional[int]	      =    LoggingCallback()

      snake_case__       :   Union[str, Any]	      =    {}

      if args.fpaa:
            snake_case__       :   Any	      =    16

      if args.gpus > 1:
            snake_case__       :   Any	      =    'auto'
            snake_case__       :   Optional[int]	      =    'ddp'

      snake_case__       :   Any	      =    args.accumulate_grad_batches
      snake_case__       :   Any	      =    None
      snake_case__       :   Any	      =    'auto'

      snake_case__       :   str	      =    pl.Trainer.from_argparse_args(
          __SCREAMING_SNAKE_CASE				,      weights_summary=__SCREAMING_SNAKE_CASE				,      callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]				,      logger=__SCREAMING_SNAKE_CASE				,      val_check_interval=1				,      num_sanity_val_steps=2				,      **__SCREAMING_SNAKE_CASE				,      )

      if args.do_train:
            trainer.fit(__SCREAMING_SNAKE_CASE		)

      else:
            print('RAG modeling tests with new set functions successfuly executed!'		)
      return trainer


'''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




