






import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


lowerCamelCase	:							int				=logging.getLogger(__name__)
lowerCamelCase	:							Dict				=list(MODEL_FOR_MASKED_LM_MAPPING.keys())
lowerCamelCase	:							Optional[int]				=tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class 			__a  :
 _lowerCAmelCase       :       Optional[str]        = field(
     default=A__       ,   metadata={
         '''help''': (
             '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
         )
     }       ,   )
 _lowerCAmelCase       :       Optional[str]        = field(
     default=A__       ,   metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(A__       )}       ,   )
 _lowerCAmelCase       :       Optional[str]        = field(
     default=A__       ,   metadata={
         '''help''': (
             '''Override some existing default config settings when a model is trained from scratch. Example: '''
             '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
         )
     }       ,   )
 _lowerCAmelCase       :       Optional[str]        = field(
     default=A__       ,   metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}       )
 _lowerCAmelCase       :       Optional[str]        = field(
     default=A__       ,   metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}       )
 _lowerCAmelCase       :       Optional[str]        = field(
     default=A__       ,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}       ,   )
 _lowerCAmelCase       :       bool        = field(
     default=A__       ,   metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}       ,   )
 _lowerCAmelCase       :       str        = field(
     default='''main'''       ,   metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}       ,   )
 _lowerCAmelCase       :       bool        = field(
     default=A__       ,   metadata={
         '''help''': (
             '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
             '''with private models).'''
         )
     }       ,   )







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
           raise ValueError(
               "--config_overrides can't be used in combination with --config_name or --model_name_or_path"						)







@dataclass
class 			__a  :
 _lowerCAmelCase       :       Optional[str]        = field(
     default=A__       ,   metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}       )
 _lowerCAmelCase       :       Optional[str]        = field(
     default=A__       ,   metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}       )
 _lowerCAmelCase       :       Optional[str]        = field(default=A__       ,   metadata={'''help''': '''The input training data file (a text file).'''}       )
 _lowerCAmelCase       :       Optional[str]        = field(
     default=A__       ,   metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}       ,   )
 _lowerCAmelCase       :       Optional[str]        = field(
     default=A__       ,   metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}       ,   )
 _lowerCAmelCase       :       Optional[str]        = field(
     default=A__       ,   metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}       ,   )
 _lowerCAmelCase       :       bool        = field(
     default=A__       ,   metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}       )
 _lowerCAmelCase       :       Optional[int]        = field(
     default=5       ,   metadata={
         '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
     }       ,   )
 _lowerCAmelCase       :       Optional[int]        = field(
     default=A__       ,   metadata={
         '''help''': (
             '''The maximum total input sequence length after tokenization. Sequences longer '''
             '''than this will be truncated. Default to the max input length of the model.'''
         )
     }       ,   )
 _lowerCAmelCase       :       Optional[int]        = field(
     default=A__       ,   metadata={'''help''': '''The number of processes to use for the preprocessing.'''}       ,   )
 _lowerCAmelCase       :       float        = field(
     default=0.1_5       ,   metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}       )
 _lowerCAmelCase       :       bool        = field(
     default=A__       ,   metadata={
         '''help''': (
             '''Whether to pad all samples to `max_seq_length`. '''
             '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
         )
     }       ,   )







 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      if self.train_file is not None:
           UpperCamelCase__			:   Tuple       				=  self.train_file.split("."						)[-1]
           assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
      if self.validation_file is not None:
           UpperCamelCase__			:   Optional[Any]       				=  self.validation_file.split("."						)[-1]
           assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	int:
     with open(__lowerCAmelCase					,    "r"					,    encoding="utf-8"			) as f:
          UpperCamelCase__			:   Dict       				=  [json.loads(__lowerCAmelCase			) for line in f.read().splitlines() if (len(__lowerCAmelCase			) > 0 and not line.isspace())]
     assert len(__lowerCAmelCase			) == len(__lowerCAmelCase			)

     UpperCamelCase__			:   Dict       				=  {c: dataset[c] for c in dataset.column_names}
     UpperCamelCase__			:   List[str]       				=  refs
     return Dataset.from_dict(__lowerCAmelCase			)







def 		SCREAMING_SNAKE_CASE							(					) ->	Dict:
     # See all possible arguments in src/transformers/training_args.py
     # or by passing the --help flag to this script.
     # We now keep distinct sets of args, for a cleaner separation of concerns.

     UpperCamelCase__			:   int       				=  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
     if len(sys.argv			) == 2 and sys.argv[1].endswith(".json"			):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   Dict       				=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
     else:
          UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   List[Any]       				=  parser.parse_args_into_dataclasses()

     # Detecting last checkpoint.
     UpperCamelCase__			:   Union[str, Any]       				=  None
     if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
          UpperCamelCase__			:   Any       				=  get_last_checkpoint(training_args.output_dir			)
          if last_checkpoint is None and len(os.listdir(training_args.output_dir			)			) > 0:
               raise ValueError(
                   f'Output directory ({training_args.output_dir}) already exists and is not empty. '
                   "Use --overwrite_output_dir to overcome."			)
          elif last_checkpoint is not None:
               logger.info(
                   f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
                   "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."			)

    # Setup logging
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"					,    datefmt="%m/%d/%Y %H:%M:%S"					,    handlers=[logging.StreamHandler(sys.stdout			)]					,    )
     logger.setLevel(logging.INFO if is_main_process(training_args.local_rank			) else logging.WARN			)

     # Log on each process the small summary:
     logger.warning(
         f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
         + f'distributed training: {bool(training_args.local_rank != -1			)}, 16-bits training: {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"					,    __lowerCAmelCase			)

     # Set seed before initializing model.
     set_seed(training_args.seed			)

     # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
     # (the dataset will be downloaded automatically from the datasets Hub).
     #
     # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
     # 'text' is found. You can easily tweak this behavior (see below).
     #
     # In distributed training, the load_dataset function guarantee that only one local process can concurrently
     # download the dataset.
     if data_args.dataset_name is not None:
          # Downloading and loading a dataset from the hub.
          UpperCamelCase__			:   Union[str, Any]       				=  load_dataset(data_args.dataset_name					,    data_args.dataset_config_name			)
          if "validation" not in datasets.keys():
               UpperCamelCase__			:   List[Any]       				=  load_dataset(
                   data_args.dataset_name					,    data_args.dataset_config_name					,    split=f'train[:{data_args.validation_split_percentage}%]'					,    )
               UpperCamelCase__			:   Dict       				=  load_dataset(
                   data_args.dataset_name					,    data_args.dataset_config_name					,    split=f'train[{data_args.validation_split_percentage}%:]'					,    )
     else:
          UpperCamelCase__			:   Optional[int]       				=  {}
          if data_args.train_file is not None:
               UpperCamelCase__			:   Optional[Any]       				=  data_args.train_file
          if data_args.validation_file is not None:
               UpperCamelCase__			:   List[Any]       				=  data_args.validation_file
          UpperCamelCase__			:   Dict       				=  data_args.train_file.split("."			)[-1]
          if extension == "txt":
               UpperCamelCase__			:   Union[str, Any]       				=  "text"
          UpperCamelCase__			:   Union[str, Any]       				=  load_dataset(__lowerCAmelCase					,    data_files=__lowerCAmelCase			)
     # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
     # https://huggingface.co/docs/datasets/loading_datasets.html.

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     UpperCamelCase__			:   Dict       				=  {
         "cache_dir": model_args.cache_dir,
         "revision": model_args.model_revision,
         "use_auth_token": True if model_args.use_auth_token else None,
     }
     if model_args.config_name:
          UpperCamelCase__			:   Optional[Any]       				=  AutoConfig.from_pretrained(model_args.config_name					,    **__lowerCAmelCase			)
     elif model_args.model_name_or_path:
          UpperCamelCase__			:   Optional[Any]       				=  AutoConfig.from_pretrained(model_args.model_name_or_path					,    **__lowerCAmelCase			)
     else:
          UpperCamelCase__			:   List[Any]       				=  CONFIG_MAPPING[model_args.model_type]()
          logger.warning("You are instantiating a new config instance from scratch."			)
          if model_args.config_overrides is not None:
               logger.info(f'Overriding config: {model_args.config_overrides}'			)
               config.update_from_string(model_args.config_overrides			)
               logger.info(f'New config: {config}'			)

     UpperCamelCase__			:   Optional[int]       				=  {
         "cache_dir": model_args.cache_dir,
         "use_fast": model_args.use_fast_tokenizer,
         "revision": model_args.model_revision,
         "use_auth_token": True if model_args.use_auth_token else None,
     }
     if model_args.tokenizer_name:
          UpperCamelCase__			:   Dict       				=  AutoTokenizer.from_pretrained(model_args.tokenizer_name					,    **__lowerCAmelCase			)
     elif model_args.model_name_or_path:
          UpperCamelCase__			:   List[Any]       				=  AutoTokenizer.from_pretrained(model_args.model_name_or_path					,    **__lowerCAmelCase			)
     else:
          raise ValueError(
              "You are instantiating a new tokenizer from scratch. This is not supported by this script."
              "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__			:   Union[str, Any]       				=  AutoModelForMaskedLM.from_pretrained(
              model_args.model_name_or_path					,    from_tf=bool(".ckpt" in model_args.model_name_or_path			)					,    config=__lowerCAmelCase					,    cache_dir=model_args.cache_dir					,    revision=model_args.model_revision					,    use_auth_token=True if model_args.use_auth_token else None					,    )
     else:
          logger.info("Training new model from scratch"			)
          UpperCamelCase__			:   List[Any]       				=  AutoModelForMaskedLM.from_config(__lowerCAmelCase			)

     model.resize_token_embeddings(len(__lowerCAmelCase			)			)

     # Preprocessing the datasets.
     # First we tokenize all the texts.
     if training_args.do_train:
          UpperCamelCase__			:   Tuple       				=  datasets["train"].column_names
     else:
          UpperCamelCase__			:   Any       				=  datasets["validation"].column_names
     UpperCamelCase__			:   Any       				=  "text" if "text" in column_names else column_names[0]

     UpperCamelCase__			:   Dict       				=  "max_length" if data_args.pad_to_max_length else False

     def tokenize_function(__lowerCAmelCase			):
          # Remove empty lines
          UpperCamelCase__			:   List[str]       				=  [line for line in examples["text"] if len(__lowerCAmelCase			) > 0 and not line.isspace()]
          return tokenizer(examples["text"]					,    padding=__lowerCAmelCase					,    truncation=__lowerCAmelCase					,    max_length=data_args.max_seq_length			)

     UpperCamelCase__			:   Any       				=  datasets.map(
         __lowerCAmelCase					,    batched=__lowerCAmelCase					,    num_proc=data_args.preprocessing_num_workers					,    remove_columns=[text_column_name]					,    load_from_cache_file=not data_args.overwrite_cache					,    )

     # Add the chinese references if provided
     if data_args.train_ref_file is not None:
          UpperCamelCase__			:   Union[str, Any]       				=  add_chinese_references(tokenized_datasets["train"]					,    data_args.train_ref_file			)
     if data_args.validation_ref_file is not None:
          UpperCamelCase__			:   Dict       				=  add_chinese_references(
              tokenized_datasets["validation"]					,    data_args.validation_ref_file			)
     # If we have ref files, need to avoid it removed by trainer
     UpperCamelCase__			:   Any       				=  data_args.train_ref_file or data_args.validation_ref_file
     if has_ref:
          UpperCamelCase__			:   str       				=  False

     # Data collator
     # This one will take care of randomly masking the tokens.
     UpperCamelCase__			:   List[Any]       				=  DataCollatorForWholeWordMask(tokenizer=__lowerCAmelCase					,    mlm_probability=data_args.mlm_probability			)

     # Initialize our Trainer
     UpperCamelCase__			:   Optional[int]       				=  Trainer(
         model=__lowerCAmelCase					,    args=__lowerCAmelCase					,    train_dataset=tokenized_datasets["train"] if training_args.do_train else None					,    eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None					,    tokenizer=__lowerCAmelCase					,    data_collator=__lowerCAmelCase					,    )

     # Training
     if training_args.do_train:
          if last_checkpoint is not None:
               UpperCamelCase__			:   Any       				=  last_checkpoint
          elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path			):
               UpperCamelCase__			:   Tuple       				=  model_args.model_name_or_path
          else:
               UpperCamelCase__			:   Union[str, Any]       				=  None
          UpperCamelCase__			:   Optional[Any]       				=  trainer.train(resume_from_checkpoint=__lowerCAmelCase			)
          trainer.save_model()  # Saves the tokenizer too for easy upload

          UpperCamelCase__			:   List[str]       				=  os.path.join(training_args.output_dir					,    "train_results.txt"			)
          if trainer.is_world_process_zero():
               with open(__lowerCAmelCase					,    "w"			) as writer:
                    logger.info("***** Train results *****"			)
                    for key, value in sorted(train_result.metrics.items()			):
                         logger.info(f'  {key} = {value}'			)
                         writer.write(f'{key} = {value}\n'			)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
               trainer.state.save_to_json(os.path.join(training_args.output_dir					,    "trainer_state.json"			)			)

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

          UpperCamelCase__			:   str       				=  trainer.evaluate()

          UpperCamelCase__			:   Any       				=  math.exp(eval_output["eval_loss"]			)
          UpperCamelCase__			:   Optional[int]       				=  perplexity

          UpperCamelCase__			:   Union[str, Any]       				=  os.path.join(training_args.output_dir					,    "eval_results_mlm_wwm.txt"			)
          if trainer.is_world_process_zero():
               with open(__lowerCAmelCase					,    "w"			) as writer:
                    logger.info("***** Eval results *****"			)
                    for key, value in sorted(results.items()			):
                         logger.info(f'  {key} = {value}'			)
                         writer.write(f'{key} = {value}\n'			)

     return results







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Union[str, Any]:
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
 main()







import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin





class 			__a  (    A__       ,   A__       ):







 @register_to_config
 def __init__(		self					:   str							,   SCREAMING_SNAKE_CASE					:   int = 1_28							,   SCREAMING_SNAKE_CASE					:   int = 2_56							,   SCREAMING_SNAKE_CASE					:   float = 2_0_0_0.0							,   SCREAMING_SNAKE_CASE					:   int = 7_68							,   SCREAMING_SNAKE_CASE					:   int = 12							,   SCREAMING_SNAKE_CASE					:   int = 12							,   SCREAMING_SNAKE_CASE					:   int = 64							,   SCREAMING_SNAKE_CASE					:   int = 20_48							,   SCREAMING_SNAKE_CASE					:   float = 0.1							,   ):


      '''simple docstring'''






      super().__init__()

      UpperCamelCase__			:   Optional[Any]       				=  nn.Sequential(
          nn.Linear(SCREAMING_SNAKE_CASE							,   d_model * 4							,   bias=SCREAMING_SNAKE_CASE						)							,   nn.SiLU()							,   nn.Linear(d_model * 4							,   d_model * 4							,   bias=SCREAMING_SNAKE_CASE						)							,   nn.SiLU()							,   )

      UpperCamelCase__			:   Optional[int]       				=  nn.Embedding(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[Any]       				=  False

      UpperCamelCase__			:   Union[str, Any]       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   bias=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Optional[Any]       				=  nn.Dropout(p=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Any       				=  nn.ModuleList()
      for lyr_num in range(SCREAMING_SNAKE_CASE						):
           # FiLM conditional T5 decoder
           UpperCamelCase__			:   Optional[int]       				=  DecoderLayer(d_model=SCREAMING_SNAKE_CASE							,   d_kv=SCREAMING_SNAKE_CASE							,   num_heads=SCREAMING_SNAKE_CASE							,   d_ff=SCREAMING_SNAKE_CASE							,   dropout_rate=SCREAMING_SNAKE_CASE						)
           self.decoders.append(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Dict       				=  TaLayerNorm(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Optional[Any]       				=  nn.Dropout(p=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Any       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   bias=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Tuple							,   SCREAMING_SNAKE_CASE					:   List[str]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  torch.mul(query_input.unsqueeze(-1						)							,   key_input.unsqueeze(-2						)						)
      return mask.unsqueeze(-3						)







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   Tuple       				=  decoder_input_tokens.shape
      assert decoder_noise_time.shape == (batch,)

      # decoder_noise_time is in [0, 1), so rescale to expected timing range.
      UpperCamelCase__			:   List[str]       				=  get_timestep_embedding(
          decoder_noise_time * self.config.max_decoder_noise_time							,   embedding_dim=self.config.d_model							,   max_period=self.config.max_decoder_noise_time							,   ).to(dtype=self.dtype						)

      UpperCamelCase__			:   Any       				=  self.conditioning_emb(SCREAMING_SNAKE_CASE						).unsqueeze(1						)

      assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

      UpperCamelCase__			:   Optional[int]       				=  decoder_input_tokens.shape[1]

      # If we want to use relative positions for audio context, we can just offset
      # this sequence by the length of encodings_and_masks.
      UpperCamelCase__			:   Optional[int]       				=  torch.broadcast_to(
          torch.arange(SCREAMING_SNAKE_CASE							,   device=decoder_input_tokens.device						)							,   (batch, seq_length)							,   )

      UpperCamelCase__			:   Dict       				=  self.position_encoding(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Dict       				=  self.continuous_inputs_projection(SCREAMING_SNAKE_CASE						)
      inputs += position_encodings
      UpperCamelCase__			:   Optional[Any]       				=  self.dropout(SCREAMING_SNAKE_CASE						)

      # decoder: No padding present.
      UpperCamelCase__			:   Dict       				=  torch.ones(
          decoder_input_tokens.shape[:2]							,   device=decoder_input_tokens.device							,   dtype=inputs.dtype						)

      # Translate encoding masks to encoder-decoder masks.
      UpperCamelCase__			:   Optional[int]       				=  [(x, self.encoder_decoder_mask(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)) for x, y in encodings_and_masks]

      # cross attend style: concat encodings
      UpperCamelCase__			:   int       				=  torch.cat([x[0] for x in encodings_and_encdec_masks]							,   dim=1						)
      UpperCamelCase__			:   List[Any]       				=  torch.cat([x[1] for x in encodings_and_encdec_masks]							,   dim=-1						)

      for lyr in self.decoders:
           UpperCamelCase__			:   int       				=  lyr(
               SCREAMING_SNAKE_CASE							,   conditioning_emb=SCREAMING_SNAKE_CASE							,   encoder_hidden_states=SCREAMING_SNAKE_CASE							,   encoder_attention_mask=SCREAMING_SNAKE_CASE							,   )[0]

      UpperCamelCase__			:   Tuple       				=  self.decoder_norm(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Any       				=  self.post_dropout(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Tuple       				=  self.spec_out(SCREAMING_SNAKE_CASE						)
      return spec_out







class 			__a  (    nn.Module       ):







 def __init__(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Dict							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   List[Any]=1e-6						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   List[str]       				=  nn.ModuleList()

      # cond self attention: layer 0
      self.layer.append(
          TaLayerSelfAttentionCond(d_model=SCREAMING_SNAKE_CASE							,   d_kv=SCREAMING_SNAKE_CASE							,   num_heads=SCREAMING_SNAKE_CASE							,   dropout_rate=SCREAMING_SNAKE_CASE						)						)

      # cross attention: layer 1
      self.layer.append(
          TaLayerCrossAttention(
              d_model=SCREAMING_SNAKE_CASE							,   d_kv=SCREAMING_SNAKE_CASE							,   num_heads=SCREAMING_SNAKE_CASE							,   dropout_rate=SCREAMING_SNAKE_CASE							,   layer_norm_epsilon=SCREAMING_SNAKE_CASE							,   )						)

      # Film Cond MLP + dropout: last layer
      self.layer.append(
          TaLayerFFCond(d_model=SCREAMING_SNAKE_CASE							,   d_ff=SCREAMING_SNAKE_CASE							,   dropout_rate=SCREAMING_SNAKE_CASE							,   layer_norm_epsilon=SCREAMING_SNAKE_CASE						)						)







 def 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Any=None							,   SCREAMING_SNAKE_CASE					:   List[str]=None							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=None							,   SCREAMING_SNAKE_CASE					:   Any=None							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=None							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  self.layer[0](
          SCREAMING_SNAKE_CASE							,   conditioning_emb=SCREAMING_SNAKE_CASE							,   attention_mask=SCREAMING_SNAKE_CASE							,   )

      if encoder_hidden_states is not None:
           UpperCamelCase__			:   int       				=  torch.where(encoder_attention_mask > 0							,   0							,   -1e1_0						).to(
               encoder_hidden_states.dtype						)

           UpperCamelCase__			:   Tuple       				=  self.layer[1](
               SCREAMING_SNAKE_CASE							,   key_value_states=SCREAMING_SNAKE_CASE							,   attention_mask=SCREAMING_SNAKE_CASE							,   )

      # Apply Film Conditional Feed Forward layer
      UpperCamelCase__			:   Any       				=  self.layer[-1](SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

      return (hidden_states,)







class 			__a  (    nn.Module       ):







 def __init__(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   int							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   List[str]						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   Union[str, Any]       				=  TaLayerNorm(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  TaFiLMLayer(in_features=d_model * 4							,   out_features=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  Attention(query_dim=SCREAMING_SNAKE_CASE							,   heads=SCREAMING_SNAKE_CASE							,   dim_head=SCREAMING_SNAKE_CASE							,   out_bias=SCREAMING_SNAKE_CASE							,   scale_qk=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Any       				=  nn.Dropout(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Any							,   SCREAMING_SNAKE_CASE					:   List[str]=None							,   SCREAMING_SNAKE_CASE					:   List[Any]=None							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  self.layer_norm(SCREAMING_SNAKE_CASE						)

      if conditioning_emb is not None:
           UpperCamelCase__			:   List[Any]       				=  self.FiLMLayer(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

      # Self-attention block
      UpperCamelCase__			:   Optional[Any]       				=  self.attention(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Tuple       				=  hidden_states + self.dropout(SCREAMING_SNAKE_CASE						)

      return hidden_states







class 			__a  (    nn.Module       ):







 def __init__(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Any							,   SCREAMING_SNAKE_CASE					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   Any							,   SCREAMING_SNAKE_CASE					:   List[Any]						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   str       				=  Attention(query_dim=SCREAMING_SNAKE_CASE							,   heads=SCREAMING_SNAKE_CASE							,   dim_head=SCREAMING_SNAKE_CASE							,   out_bias=SCREAMING_SNAKE_CASE							,   scale_qk=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Union[str, Any]       				=  TaLayerNorm(SCREAMING_SNAKE_CASE							,   eps=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[Any]       				=  nn.Dropout(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=None							,   SCREAMING_SNAKE_CASE					:   Optional[int]=None							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  self.layer_norm(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[str]       				=  self.attention(
          SCREAMING_SNAKE_CASE							,   encoder_hidden_states=SCREAMING_SNAKE_CASE							,   attention_mask=attention_mask.squeeze(1						)							,   )
      UpperCamelCase__			:   Optional[Any]       				=  hidden_states + self.dropout(SCREAMING_SNAKE_CASE						)
      return layer_output







class 			__a  (    nn.Module       ):







 def __init__(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   List[str]						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   Any       				=  TaDenseGatedActDense(d_model=SCREAMING_SNAKE_CASE							,   d_ff=SCREAMING_SNAKE_CASE							,   dropout_rate=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  TaFiLMLayer(in_features=d_model * 4							,   out_features=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Dict       				=  TaLayerNorm(SCREAMING_SNAKE_CASE							,   eps=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[str]       				=  nn.Dropout(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Dict=None						):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  self.layer_norm(SCREAMING_SNAKE_CASE						)
      if conditioning_emb is not None:
           UpperCamelCase__			:   Optional[int]       				=  self.film(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Optional[int]       				=  self.DenseReluDense(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[str]       				=  hidden_states + self.dropout(SCREAMING_SNAKE_CASE						)
      return hidden_states







class 			__a  (    nn.Module       ):







 def __init__(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   str						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   Tuple       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   bias=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   bias=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[int]       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   bias=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Dict       				=  nn.Dropout(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Union[str, Any]       				=  NewGELUActivation()







 def 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  self.act(self.wi_a(SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__			:   Dict       				=  self.wi_a(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[Any]       				=  hidden_gelu * hidden_linear
      UpperCamelCase__			:   int       				=  self.dropout(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Tuple       				=  self.wo(SCREAMING_SNAKE_CASE						)
      return hidden_states







class 			__a  (    nn.Module       ):







 def __init__(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   str=1e-6						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   List[str]       				=  nn.Parameter(torch.ones(SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__			:   Any       				=  eps







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


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  hidden_states.to(torch.floataa						).pow(2						).mean(-1							,   keepdim=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   str       				=  hidden_states * torch.rsqrt(variance + self.variance_epsilon						)

      # convert into half-precision if necessary
      if self.weight.dtype in [torch.floataa, torch.bfloataa]:
           UpperCamelCase__			:   Any       				=  hidden_states.to(self.weight.dtype						)

      return self.weight * hidden_states







class 			__a  (    nn.Module       ):







 def 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   torch.Tensor						):


      '''simple docstring'''






      return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi						) * (input + 0.0_4_4_7_1_5 * torch.pow(SCREAMING_SNAKE_CASE							,   3.0						))						))







class 			__a  (    nn.Module       ):







 def __init__(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   int       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   out_features * 2							,   bias=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  self.scale_bias(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__				,     UpperCamelCase__			:   List[str]       				=  torch.chunk(SCREAMING_SNAKE_CASE							,   2							,   -1						)
      UpperCamelCase__			:   Dict       				=  x * (1 + scale) + shift
      return x