





"""simple docstring"""





import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowercase__				:Optional[int]				   =       logging.getLogger(__name__)


lowercase__				:str				   =       list(MODEL_WITH_LM_HEAD_MAPPING.keys())
lowercase__				:int				   =       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class 							snake_case							:

      '''simple docstring'''



      _A     :       Optional[str]         =       field(
          default=__UpperCAmelCase      ,  metadata={
              'help': (
                  'The model checkpoint for weights initialization. Leave None if you want to train a model from'
                  ' scratch.'
              )
          }      ,  )
      _A     :       Optional[str]         =       field(
          default=__UpperCAmelCase      ,  metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(__UpperCAmelCase )}      ,  )
      _A     :       Optional[str]         =       field(
          default=__UpperCAmelCase      ,  metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
      _A     :       Optional[str]         =       field(
          default=__UpperCAmelCase      ,  metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
      _A     :       Optional[str]         =       field(
          default=__UpperCAmelCase      ,  metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}      ,  )




@dataclass
class 							snake_case							:

      '''simple docstring'''



      _A     :       Optional[str]         =       field(
          default=__UpperCAmelCase      ,  metadata={'help': 'The input training data file (a text file).'} )
      _A     :       Optional[str]         =       field(
          default=__UpperCAmelCase      ,  metadata={
              'help': (
                  'The input training data files (multiple files in glob format). '
                  'Very often splitting large files to smaller files can prevent tokenizer going out of memory'
              )
          }      ,  )
      _A     :       Optional[str]         =       field(
          default=__UpperCAmelCase      ,  metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}      ,  )
      _A     :       Optional[str]         =       field(
          default=__UpperCAmelCase      ,  metadata={'help': 'An optional input train ref data file for whole word mask in Chinese.'}      ,  )
      _A     :       Optional[str]         =       field(
          default=__UpperCAmelCase      ,  metadata={'help': 'An optional input eval ref data file for whole word mask in Chinese.'}      ,  )
      _A     :       bool         =       field(
          default=__UpperCAmelCase      ,  metadata={'help': 'Whether distinct lines of text in the dataset are to be handled as distinct sequences.'}      ,  )

      _A     :       bool         =       field(
          default=__UpperCAmelCase      ,  metadata={'help': 'Train with masked-language modeling loss instead of language modeling.'} )
      _A     :       bool         =       field(default=__UpperCAmelCase      ,  metadata={'help': 'Whether ot not to use whole word mask.'} )
      _A     :       float         =       field(
          default=0.15      ,  metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'} )
      _A     :       float         =       field(
          default=1 / 6      ,  metadata={
              'help': (
                  'Ratio of length of a span of masked tokens to surrounding context length for permutation language'
                  ' modeling.'
              )
          }      ,  )
      _A     :       int         =       field(
          default=5      ,  metadata={'help': 'Maximum length of a span of masked tokens for permutation language modeling.'} )

      _A     :       int         =       field(
          default=-1      ,  metadata={
              'help': (
                  'Optional input sequence length after tokenization.'
                  'The training dataset will be truncated in block of this size for training.'
                  'Default to the model max input length for single sentence inputs (take into account special tokens).'
              )
          }      ,  )
      _A     :       bool         =       field(
          default=__UpperCAmelCase      ,  metadata={'help': 'Overwrite the cached training and evaluation sets'} )


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_ = False  ,			UpperCAmelCase_ = None  ,			)						->Optional[int]:





  """simple docstring"""


  def _dataset(UpperCAmelCase_  ,			UpperCAmelCase_=None  ):
    if args.line_by_line:
      if ref_path is not None:
        if not args.whole_word_mask or not args.mlm:
          raise ValueError('''You need to set world whole masking and mlm to True for Chinese Whole Word Mask'''  )
        return LineByLineWithRefDataset(
            tokenizer=UpperCAmelCase_  ,			file_path=UpperCAmelCase_  ,			block_size=args.block_size  ,			ref_path=UpperCAmelCase_  ,			)

      return LineByLineTextDataset(tokenizer=UpperCAmelCase_  ,			file_path=UpperCAmelCase_  ,			block_size=args.block_size  )
    else:
      return TextDataset(
          tokenizer=UpperCAmelCase_  ,			file_path=UpperCAmelCase_  ,			block_size=args.block_size  ,			overwrite_cache=args.overwrite_cache  ,			cache_dir=UpperCAmelCase_  ,			)

  if evaluate:
    return _dataset(args.eval_data_file  ,			args.eval_ref_file  )
  elif args.train_data_files:
    return ConcatDataset([_dataset(UpperCAmelCase_  ) for f in glob(args.train_data_files  )]  )
  else:
    return _dataset(args.train_data_file  ,			args.train_ref_file  )


def       lowerCamelCase_       (   )						->Tuple:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[Any]       =						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
  __UpperCAmelCase    :						Union[str, Any]       =						parser.parse_args_into_dataclasses()

  if data_args.eval_data_file is None and training_args.do_eval:
    raise ValueError(
        '''Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file '''
        '''or remove the --do_eval argument.'''  )
  if (
      os.path.exists(training_args.output_dir  )
      and os.listdir(training_args.output_dir  )
      and training_args.do_train
      and not training_args.overwrite_output_dir
  ):
    raise ValueError(
        f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
        ''' --overwrite_output_dir to overcome.'''  )

  # Setup logging
  logging.basicConfig(
      format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''  ,			datefmt='''%m/%d/%Y %H:%M:%S'''  ,			level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN  ,			)
  logger.warning(
      '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''  ,			training_args.local_rank  ,			training_args.device  ,			training_args.n_gpu  ,			bool(training_args.local_rank != -1  )  ,			training_args.fpaa  ,			)
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank  ):
    transformers.utils.logging.set_verbosity_info()
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
  logger.info('''Training/evaluation parameters %s'''  ,			UpperCAmelCase_  )

  # Set seed
  set_seed(training_args.seed  )

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  if model_args.config_name:
    __UpperCAmelCase    :						Tuple       =						AutoConfig.from_pretrained(model_args.config_name  ,			cache_dir=model_args.cache_dir  )
  elif model_args.model_name_or_path:
    __UpperCAmelCase    :						Dict       =						AutoConfig.from_pretrained(model_args.model_name_or_path  ,			cache_dir=model_args.cache_dir  )
  else:
    __UpperCAmelCase    :						int       =						CONFIG_MAPPING[model_args.model_type]()
    logger.warning('''You are instantiating a new config instance from scratch.'''  )

  if model_args.tokenizer_name:
    __UpperCAmelCase    :						int       =						AutoTokenizer.from_pretrained(model_args.tokenizer_name  ,			cache_dir=model_args.cache_dir  )
  elif model_args.model_name_or_path:
    __UpperCAmelCase    :						str       =						AutoTokenizer.from_pretrained(model_args.model_name_or_path  ,			cache_dir=model_args.cache_dir  )
  else:
    raise ValueError(
        '''You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another'''
        ''' script, save it,and load it from here, using --tokenizer_name'''  )

  if model_args.model_name_or_path:
    __UpperCAmelCase    :						Dict       =						AutoModelWithLMHead.from_pretrained(
        model_args.model_name_or_path  ,			from_tf=bool('''.ckpt''' in model_args.model_name_or_path  )  ,			config=UpperCAmelCase_  ,			cache_dir=model_args.cache_dir  ,			)
  else:
    logger.info('''Training new model from scratch'''  )
    __UpperCAmelCase    :						Union[str, Any]       =						AutoModelWithLMHead.from_config(UpperCAmelCase_  )

  model.resize_token_embeddings(len(UpperCAmelCase_  )  )

  if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
    raise ValueError(
        '''BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the'''
        '''--mlm flag (masked language modeling).'''  )

  if data_args.block_size <= 0:
    __UpperCAmelCase    :						str       =						tokenizer.max_len
    # Our input block size will be the max possible for the model
  else:
    __UpperCAmelCase    :						Dict       =						min(data_args.block_size  ,			tokenizer.max_len  )

  # Get datasets

  __UpperCAmelCase    :						Tuple       =						(
      get_dataset(UpperCAmelCase_  ,			tokenizer=UpperCAmelCase_  ,			cache_dir=model_args.cache_dir  ) if training_args.do_train else None
  )
  __UpperCAmelCase    :						Tuple       =						(
      get_dataset(UpperCAmelCase_  ,			tokenizer=UpperCAmelCase_  ,			evaluate=UpperCAmelCase_  ,			cache_dir=model_args.cache_dir  )
      if training_args.do_eval
      else None
  )
  if config.model_type == "xlnet":
    __UpperCAmelCase    :						int       =						DataCollatorForPermutationLanguageModeling(
        tokenizer=UpperCAmelCase_  ,			plm_probability=data_args.plm_probability  ,			max_span_length=data_args.max_span_length  ,			)
  else:
    if data_args.mlm and data_args.whole_word_mask:
      __UpperCAmelCase    :						Tuple       =						DataCollatorForWholeWordMask(
          tokenizer=UpperCAmelCase_  ,			mlm_probability=data_args.mlm_probability  )
    else:
      __UpperCAmelCase    :						List[Any]       =						DataCollatorForLanguageModeling(
          tokenizer=UpperCAmelCase_  ,			mlm=data_args.mlm  ,			mlm_probability=data_args.mlm_probability  )

    # Initialize our Trainer
  __UpperCAmelCase    :						int       =						Trainer(
      model=UpperCAmelCase_  ,			args=UpperCAmelCase_  ,			data_collator=UpperCAmelCase_  ,			train_dataset=UpperCAmelCase_  ,			eval_dataset=UpperCAmelCase_  ,			prediction_loss_only=UpperCAmelCase_  ,			)

  # Training
  if training_args.do_train:
    __UpperCAmelCase    :						str       =						(
        model_args.model_name_or_path
        if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path  )
        else None
    )
    trainer.train(model_path=UpperCAmelCase_  )
    trainer.save_model()
    # For convenience, we also re-save the tokenizer to the same directory,
    # so that you can share your model easily on huggingface.co/models =)
    if trainer.is_world_master():
      tokenizer.save_pretrained(training_args.output_dir  )

    # Evaluation
  __UpperCAmelCase    :						Tuple       =						{}
  if training_args.do_eval:
    logger.info('''*** Evaluate ***'''  )

    __UpperCAmelCase    :						List[Any]       =						trainer.evaluate()

    __UpperCAmelCase    :						List[Any]       =						math.exp(eval_output['''eval_loss''']  )
    __UpperCAmelCase    :						Tuple       =						{'''perplexity''': perplexity}

    __UpperCAmelCase    :						Dict       =						os.path.join(training_args.output_dir  ,			'''eval_results_lm.txt'''  )
    if trainer.is_world_master():
      with open(UpperCAmelCase_  ,			'''w'''  ) as writer:
        logger.info('''***** Eval results *****'''  )
        for key in sorted(result.keys()  ):
          logger.info('''  %s = %s'''  ,			UpperCAmelCase_  ,			str(result[key]  )  )
          writer.write('''%s = %s\n''' % (key, str(result[key]  ))  )

    results.update(UpperCAmelCase_  )

  return results


def       lowerCamelCase_       (   UpperCAmelCase_  )						->int:





  """simple docstring"""


  main()


if __name__ == "__main__":
  main()






"""simple docstring"""





from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin




@dataclass
class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       torch.FloatTensor




class 							snake_case							(			__UpperCAmelCase      ,  __UpperCAmelCase ):

      '''simple docstring'''



      @register_to_config
      def __init__(							self       :      Union[str, Any]						,				__lowercase       :      int = 32						,				__lowercase       :      int = 64						,				__lowercase       :      int = 20						,				__lowercase       :      int = 768						,				__lowercase       :      Optional[int]=77						,				__lowercase       :      Union[str, Any]=4						,				__lowercase       :      float = 0.0						,				__lowercase       :      str = "silu"						,				__lowercase       :      Optional[str] = None						,				__lowercase       :      Optional[str] = None						,				__lowercase       :      Optional[str] = "linear"						,				__lowercase       :      Optional[str] = "prd"						,				__lowercase       :      Optional[int] = None						,				__lowercase       :      Optional[int] = None						,				__lowercase       :      Optional[int] = None						,				):


        '''simple docstring'''




        super().__init__()
        __UpperCAmelCase    :						int       =						num_attention_heads
        __UpperCAmelCase    :						List[Any]       =						attention_head_dim
        __UpperCAmelCase    :						int       =						num_attention_heads * attention_head_dim
        __UpperCAmelCase    :						List[str]       =						additional_embeddings

        __UpperCAmelCase    :						Optional[int]       =						time_embed_dim or inner_dim
        __UpperCAmelCase    :						Tuple       =						embedding_proj_dim or embedding_dim
        __UpperCAmelCase    :						Dict       =						clip_embed_dim or embedding_dim

        __UpperCAmelCase    :						Dict       =						Timesteps(__lowercase						,				__lowercase						,				0 )
        __UpperCAmelCase    :						List[str]       =						TimestepEmbedding(__lowercase						,				__lowercase						,				out_dim=__lowercase						,				act_fn=__lowercase )

        __UpperCAmelCase    :						Any       =						nn.Linear(__lowercase						,				__lowercase )

        if embedding_proj_norm_type is None:
          __UpperCAmelCase    :						Dict       =						None
        elif embedding_proj_norm_type == "layer":
          __UpperCAmelCase    :						Any       =						nn.LayerNorm(__lowercase )
        else:
          raise ValueError(f'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}''' )

        __UpperCAmelCase    :						List[Any]       =						nn.Linear(__lowercase						,				__lowercase )

        if encoder_hid_proj_type is None:
          __UpperCAmelCase    :						int       =						None
        elif encoder_hid_proj_type == "linear":
          __UpperCAmelCase    :						int       =						nn.Linear(__lowercase						,				__lowercase )
        else:
          raise ValueError(f'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}''' )

        __UpperCAmelCase    :						str       =						nn.Parameter(torch.zeros(1						,				num_embeddings + additional_embeddings						,				__lowercase ) )

        if added_emb_type == "prd":
          __UpperCAmelCase    :						Optional[int]       =						nn.Parameter(torch.zeros(1						,				1						,				__lowercase ) )
        elif added_emb_type is None:
          __UpperCAmelCase    :						str       =						None
        else:
          raise ValueError(
              f'''`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.''' )

        __UpperCAmelCase    :						Union[str, Any]       =						nn.ModuleList(
            [
                BasicTransformerBlock(
                    __lowercase						,				__lowercase						,				__lowercase						,				dropout=__lowercase						,				activation_fn='''gelu'''						,				attention_bias=__lowercase						,				)
                for d in range(__lowercase )
            ] )

        if norm_in_type == "layer":
          __UpperCAmelCase    :						Optional[Any]       =						nn.LayerNorm(__lowercase )
        elif norm_in_type is None:
          __UpperCAmelCase    :						List[Any]       =						None
        else:
          raise ValueError(f'''Unsupported norm_in_type: {norm_in_type}.''' )

        __UpperCAmelCase    :						str       =						nn.LayerNorm(__lowercase )

        __UpperCAmelCase    :						List[Any]       =						nn.Linear(__lowercase						,				__lowercase )

        __UpperCAmelCase    :						List[Any]       =						torch.full(
            [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]						,				-1_0_0_0_0.0 )
        causal_attention_mask.triu_(1 )
        __UpperCAmelCase    :						Optional[Any]       =						causal_attention_mask[None, ...]
        self.register_buffer('''causal_attention_mask'''						,				__lowercase						,				persistent=__lowercase )

        __UpperCAmelCase    :						Any       =						nn.Parameter(torch.zeros(1						,				__lowercase ) )
        __UpperCAmelCase    :						List[str]       =						nn.Parameter(torch.zeros(1						,				__lowercase ) )

      @property
      # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						{}

        def fn_recursive_add_processors(__lowercase       :      str						,				__lowercase       :      torch.nn.Module						,				__lowercase       :      Dict[str, AttentionProcessor] ):
          if hasattr(__lowercase						,				'''set_processor''' ):
            __UpperCAmelCase    :						Optional[Any]       =						module.processor

          for sub_name, child in module.named_children():
            fn_recursive_add_processors(f'''{name}.{sub_name}'''						,				__lowercase						,				__lowercase )

          return processors

        for name, module in self.named_children():
          fn_recursive_add_processors(__lowercase						,				__lowercase						,				__lowercase )

        return processors

      def       A_					(							self       :      Any						,				__lowercase       :      Union[AttentionProcessor, Dict[str, AttentionProcessor]] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						len(self.attn_processors.keys() )

        if isinstance(__lowercase						,				__lowercase ) and len(__lowercase ) != count:
          raise ValueError(
              f'''A dict of processors was passed, but the number of processors {len(__lowercase )} does not match the'''
              f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.''' )

        def fn_recursive_attn_processor(__lowercase       :      str						,				__lowercase       :      torch.nn.Module						,				__lowercase       :      int ):
          if hasattr(__lowercase						,				'''set_processor''' ):
            if not isinstance(__lowercase						,				__lowercase ):
              module.set_processor(__lowercase )
            else:
              module.set_processor(processor.pop(f'''{name}.processor''' ) )

          for sub_name, child in module.named_children():
            fn_recursive_attn_processor(f'''{name}.{sub_name}'''						,				__lowercase						,				__lowercase )

        for name, module in self.named_children():
          fn_recursive_attn_processor(__lowercase						,				__lowercase						,				__lowercase )

      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        self.set_attn_processor(AttnProcessor() )

      def       A_					(							self       :      Optional[int]						,				__lowercase       :      Dict						,				__lowercase       :      Union[torch.Tensor, float, int]						,				__lowercase       :      torch.FloatTensor						,				__lowercase       :      Optional[torch.FloatTensor] = None						,				__lowercase       :      Optional[torch.BoolTensor] = None						,				__lowercase       :      bool = True						,				):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						hidden_states.shape[0]

        __UpperCAmelCase    :						Any       =						timestep
        if not torch.is_tensor(__lowercase ):
          __UpperCAmelCase    :						Union[str, Any]       =						torch.tensor([timesteps]						,				dtype=torch.long						,				device=hidden_states.device )
        elif torch.is_tensor(__lowercase ) and len(timesteps.shape ) == 0:
          __UpperCAmelCase    :						Union[str, Any]       =						timesteps[None].to(hidden_states.device )

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        __UpperCAmelCase    :						List[str]       =						timesteps * torch.ones(__lowercase						,				dtype=timesteps.dtype						,				device=timesteps.device )

        __UpperCAmelCase    :						List[str]       =						self.time_proj(__lowercase )

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might be fp16, so we need to cast here.
        __UpperCAmelCase    :						Any       =						timesteps_projected.to(dtype=self.dtype )
        __UpperCAmelCase    :						List[str]       =						self.time_embedding(__lowercase )

        if self.embedding_proj_norm is not None:
          __UpperCAmelCase    :						Dict       =						self.embedding_proj_norm(__lowercase )

        __UpperCAmelCase    :						Optional[int]       =						self.embedding_proj(__lowercase )
        if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
          __UpperCAmelCase    :						Dict       =						self.encoder_hidden_states_proj(__lowercase )
        elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
          raise ValueError('''`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set''' )

        __UpperCAmelCase    :						int       =						self.proj_in(__lowercase )

        __UpperCAmelCase    :						Tuple       =						self.positional_embedding.to(hidden_states.dtype )

        __UpperCAmelCase    :						Optional[Any]       =						[]
        __UpperCAmelCase    :						str       =						0

        if encoder_hidden_states is not None:
          additional_embeds.append(__lowercase )
          additional_embeddings_len += encoder_hidden_states.shape[1]

        if len(proj_embeddings.shape ) == 2:
          __UpperCAmelCase    :						str       =						proj_embeddings[:, None, :]

        if len(hidden_states.shape ) == 2:
          __UpperCAmelCase    :						Optional[int]       =						hidden_states[:, None, :]

        __UpperCAmelCase    :						str       =						additional_embeds + [
            proj_embeddings,
            time_embeddings[:, None, :],
            hidden_states,
        ]

        if self.prd_embedding is not None:
          __UpperCAmelCase    :						Dict       =						self.prd_embedding.to(hidden_states.dtype ).expand(__lowercase						,				-1						,				-1 )
          additional_embeds.append(__lowercase )

        __UpperCAmelCase    :						Optional[Any]       =						torch.cat(
            __lowercase						,				dim=1						,				)

        # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
        __UpperCAmelCase    :						Optional[int]       =						additional_embeddings_len + proj_embeddings.shape[1] + 1
        if positional_embeddings.shape[1] < hidden_states.shape[1]:
          __UpperCAmelCase    :						List[str]       =						F.pad(
              __lowercase						,				(
                  0,
                  0,
                  additional_embeddings_len,
                  self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
              )						,				value=0.0						,				)

        __UpperCAmelCase    :						Dict       =						hidden_states + positional_embeddings

        if attention_mask is not None:
          __UpperCAmelCase    :						Optional[Any]       =						(1 - attention_mask.to(hidden_states.dtype )) * -1_0_0_0_0.0
          __UpperCAmelCase    :						Optional[Any]       =						F.pad(__lowercase						,				(0, self.additional_embeddings)						,				value=0.0 )
          __UpperCAmelCase    :						Dict       =						(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype )
          __UpperCAmelCase    :						Tuple       =						attention_mask.repeat_interleave(self.config.num_attention_heads						,				dim=0 )

        if self.norm_in is not None:
          __UpperCAmelCase    :						str       =						self.norm_in(__lowercase )

        for block in self.transformer_blocks:
          __UpperCAmelCase    :						Dict       =						block(__lowercase						,				attention_mask=__lowercase )

        __UpperCAmelCase    :						Any       =						self.norm_out(__lowercase )

        if self.prd_embedding is not None:
          __UpperCAmelCase    :						List[Any]       =						hidden_states[:, -1]
        else:
          __UpperCAmelCase    :						List[str]       =						hidden_states[:, additional_embeddings_len:]

        __UpperCAmelCase    :						Optional[int]       =						self.proj_to_clip_embeddings(__lowercase )

        if not return_dict:
          return (predicted_image_embedding,)

        return PriorTransformerOutput(predicted_image_embedding=__lowercase )







      def       A_					(							self       :      List[Any]						,				__lowercase       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[str]       =						(prior_latents * self.clip_std) + self.clip_mean
        return prior_latents