


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


UpperCamelCase								=	logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

UpperCamelCase								=	{
    """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
UpperCamelCase								=	{
    """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
}
UpperCamelCase								=	sorted(arg_to_scheduler.keys())
UpperCamelCase								=	"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""






class   _lowerCamelCase (					pl.LightningModule			):




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE="base"				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       **_SCREAMING_SNAKE_CASE				,       )->Optional[Any]:




         '''simple docstring'''





         super().__init__()
         # TODO: move to self.save_hyperparameters()
         # self.save_hyperparameters()
         # can also expand arguments into trainer signature for easier reading

         self.save_hyperparameters(_SCREAMING_SNAKE_CASE		)
         A_      :				List[str]		     =					0
         A_      :				Tuple		     =					Path(self.hparams.output_dir		)
         A_      :				Any		     =					self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
           A_      :				Optional[int]		     =					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=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE				,       )
         else:
           A_      :				PretrainedConfig		     =					config

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

         if tokenizer is None:
           A_      :				Union[str, Any]		     =					AutoTokenizer.from_pretrained(
               self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path				,       cache_dir=_SCREAMING_SNAKE_CASE				,       )
         else:
           A_      :				PreTrainedTokenizer		     =					tokenizer
         A_      :				List[str]		     =					MODEL_MODES[mode]
         if model is None:
           A_      :				Any		     =					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=_SCREAMING_SNAKE_CASE				,       )
         else:
           A_      :				Dict		     =					model



       def        _snake_case       (      self				,       *_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->str:




         '''simple docstring'''





         A_      :				int		     =					self.model_type.from_pretrained(*_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         A_      :				List[Any]		     =					arg_to_scheduler[self.hparams.lr_scheduler]
         A_      :				str		     =					get_schedule_func(
             self.opt				,       num_warmup_steps=self.hparams.warmup_steps				,       num_training_steps=self.total_steps()		)
         A_      :				Any		     =					{'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
         return scheduler



       def        _snake_case       (      self		)->Optional[Any]:




         '''simple docstring'''





         A_      :				Tuple		     =					self.model
         A_      :				List[Any]		     =					['''bias''', '''LayerNorm.weight''']
         A_      :				List[Any]		     =					[
             {
                 '''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:
           A_      :				Dict		     =					Adafactor(
               _SCREAMING_SNAKE_CASE				,       lr=self.hparams.learning_rate				,       scale_parameter=_SCREAMING_SNAKE_CASE				,       relative_step=_SCREAMING_SNAKE_CASE		)

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

         A_      :				List[str]		     =					self.get_lr_scheduler()

         return [optimizer], [scheduler]



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Optional[Any]:




         '''simple docstring'''





         return self.validation_step(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





         return self.validation_end(_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         A_      :				Tuple		     =					max(1				,       self.hparams.gpus		)  # TODO: consider num_tpu_cores
         A_      :				Any		     =					self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
         return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





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



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = False		)->Optional[Any]:




         '''simple docstring'''





         raise NotImplementedError('''You must implement this for your task'''		)



       def        _snake_case       (      self		)->Optional[Any]:




         '''simple docstring'''





         return self.train_loader



       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         return self.get_dataloader('''dev'''				,       self.hparams.eval_batch_size				,       shuffle=_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         return self.get_dataloader('''test'''				,       self.hparams.eval_batch_size				,       shuffle=_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->int:




         '''simple docstring'''





         return os.path.join(
             self.hparams.data_dir				,       '''cached_{}_{}_{}'''.format(
                 _SCREAMING_SNAKE_CASE				,       list(filter(_SCREAMING_SNAKE_CASE				,       self.hparams.model_name_or_path.split('''/'''		)		)		).pop()				,       str(self.hparams.max_seq_length		)				,       )				,       )



       @pl.utilities.rank_zero_only
       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->None:




         '''simple docstring'''





         A_      :				Optional[Any]		     =					self.output_dir.joinpath('''best_tfmr'''		)
         A_      :				List[str]		     =					self.step_count
         self.model.save_pretrained(_SCREAMING_SNAKE_CASE		)
         self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE		)







       @staticmethod
       def        _snake_case       (      _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Tuple:




         '''simple docstring'''





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






class   _lowerCamelCase (					pl.Callback			):




       """simple docstring"""



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Optional[int]:




         '''simple docstring'''





         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   _lowerCamelCase (					pl.Callback			):




       """simple docstring"""



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->int:




         '''simple docstring'''





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






class   _lowerCamelCase (					pl.Callback			):




       """simple docstring"""



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->int:




         '''simple docstring'''





         A_      :				List[str]		     =					trainer.lr_schedulers[0]['''scheduler''']
         A_      :				List[str]		     =					{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()		)}
         pl_module.logger.log_metrics(_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->List[str]:




         '''simple docstring'''





         rank_zero_info('''***** Validation results *****'''		)
         A_      :				str		     =					trainer.callback_metrics
         # Log results
         for key in sorted(_SCREAMING_SNAKE_CASE		):
           if key not in ["log", "progress_bar"]:
             rank_zero_info('''{} = {}\n'''.format(_SCREAMING_SNAKE_CASE				,       str(metrics[key]		)		)		)







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Any:




         '''simple docstring'''





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




def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  #  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   _SCREAMING_SNAKE_CASE	( 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 ,			):
  pl.seed_everything(args.seed       )

  # init model
  A_      :				Optional[Any]		     =					Path(model.hparams.output_dir       )
  odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE       )

  # add custom checkpoints
  if checkpoint_callback is None:
    A_      :				str		     =					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:
    A_      :				Optional[Any]		     =					LoggingCallback()

  A_      :				Union[str, Any]		     =					{}

  if args.fpaa:
    A_      :				str		     =					16

  if args.gpus > 1:
    A_      :				str		     =					'''auto'''
    A_      :				Optional[int]		     =					'''ddp'''

  A_      :				Optional[int]		     =					args.accumulate_grad_batches
  A_      :				Optional[Any]		     =					None
  A_      :				int		     =					'''auto'''

  A_      :				Optional[int]		     =					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








import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

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


if is_torch_available():
     import torch

     from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel






class   _lowerCamelCase :




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=99				,       _SCREAMING_SNAKE_CASE=13				,       _SCREAMING_SNAKE_CASE=7				,       _SCREAMING_SNAKE_CASE=9				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=32				,       _SCREAMING_SNAKE_CASE=5				,       _SCREAMING_SNAKE_CASE=4				,       _SCREAMING_SNAKE_CASE=37				,       _SCREAMING_SNAKE_CASE=8				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=0.0_0_2				,       _SCREAMING_SNAKE_CASE=1				,       _SCREAMING_SNAKE_CASE=0				,       _SCREAMING_SNAKE_CASE=0				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       )->str:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					parent
         A_      :				Optional[Any]		     =					batch_size
         A_      :				int		     =					encoder_seq_length
         A_      :				List[Any]		     =					decoder_seq_length
         # For common tests
         A_      :				str		     =					self.decoder_seq_length
         A_      :				int		     =					is_training
         A_      :				Tuple		     =					use_attention_mask
         A_      :				Any		     =					use_labels
         A_      :				List[str]		     =					vocab_size
         A_      :				Dict		     =					hidden_size
         A_      :				Optional[Any]		     =					num_hidden_layers
         A_      :				List[Any]		     =					num_attention_heads
         A_      :				Tuple		     =					d_ff
         A_      :				List[str]		     =					relative_attention_num_buckets
         A_      :				Tuple		     =					dropout_rate
         A_      :				Optional[Any]		     =					initializer_factor
         A_      :				List[str]		     =					eos_token_id
         A_      :				Union[str, Any]		     =					pad_token_id
         A_      :				Any		     =					decoder_start_token_id
         A_      :				int		     =					None
         A_      :				Tuple		     =					decoder_layers



       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         return TaConfig.from_pretrained('''google/umt5-base'''		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       )->List[str]:




         '''simple docstring'''





         if attention_mask is None:
           A_      :				Union[str, Any]		     =					input_ids.ne(config.pad_token_id		)
         if decoder_attention_mask is None:
           A_      :				Any		     =					decoder_input_ids.ne(config.pad_token_id		)
         if head_mask is None:
           A_      :				str		     =					torch.ones(config.num_hidden_layers				,       config.num_attention_heads				,       device=_SCREAMING_SNAKE_CASE		)
         if decoder_head_mask is None:
           A_      :				List[str]		     =					torch.ones(config.num_decoder_layers				,       config.num_attention_heads				,       device=_SCREAMING_SNAKE_CASE		)
         if cross_attn_head_mask is None:
           A_      :				Union[str, Any]		     =					torch.ones(
               config.num_decoder_layers				,       config.num_attention_heads				,       device=_SCREAMING_SNAKE_CASE		)
         return {
             "input_ids": input_ids,
             "decoder_input_ids": decoder_input_ids,
             "attention_mask": attention_mask,
             "decoder_attention_mask": decoder_attention_mask,
             "head_mask": head_mask,
             "decoder_head_mask": decoder_head_mask,
             "cross_attn_head_mask": cross_attn_head_mask,
         }



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




         '''simple docstring'''





         A_      :				List[Any]		     =					ids_tensor([self.batch_size, self.encoder_seq_length]				,       self.vocab_size		)
         A_      :				int		     =					ids_tensor([self.batch_size, self.decoder_seq_length]				,       self.vocab_size		)

         # we need to clamp the input ids here to avoid having pad token in between
         # this is because for NllbMoe the position_ids are prepared such that
         # all pad tokens have pos id = 2 and rest are between 2..seq_length
         # and the seq_length here is seq_length - num_pad_tokens
         # but when using past, there is no way of knowing if the past input ids had
         # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
         # position_ids being off by num_pad_tokens in past input
         A_      :				Tuple		     =					input_ids.clamp(self.pad_token_id + 1		)
         A_      :				int		     =					decoder_input_ids.clamp(self.pad_token_id + 1		)

         A_      :				Optional[Any]		     =					self.get_config()
         A_      :				int		     =					config.num_attention_heads
         A_      :				Dict		     =					self.prepare_inputs_dict(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         return config, input_dict



       def        _snake_case       (      self		)->Optional[Any]:




         '''simple docstring'''





         A_     ,       A_      :				str		     =					self.prepare_config_and_inputs()
         return config, inputs_dict



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




         '''simple docstring'''





         return TaConfig(
             vocab_size=166				,       d_model=self.hidden_size				,       d_ff=self.d_ff				,       d_kv=self.hidden_size // self.num_attention_heads				,       num_layers=self.num_hidden_layers				,       num_decoder_layers=self.decoder_layers				,       num_heads=self.num_attention_heads				,       relative_attention_num_buckets=self.relative_attention_num_buckets				,       dropout_rate=self.dropout_rate				,       initializer_factor=self.initializer_factor				,       eos_token_id=self.eos_token_id				,       bos_token_id=self.pad_token_id				,       pad_token_id=self.pad_token_id				,       decoder_start_token_id=self.decoder_start_token_id				,       )



       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         return TaConfig(
             vocab_size=self.vocab_size				,       d_model=self.hidden_size				,       d_ff=self.d_ff				,       d_kv=self.hidden_size // self.num_attention_heads				,       num_layers=self.num_hidden_layers				,       num_decoder_layers=self.decoder_layers				,       num_heads=self.num_attention_heads				,       relative_attention_num_buckets=self.relative_attention_num_buckets				,       dropout_rate=self.dropout_rate				,       initializer_factor=self.initializer_factor				,       eos_token_id=self.eos_token_id				,       bos_token_id=self.pad_token_id				,       pad_token_id=self.pad_token_id				,       decoder_start_token_id=self.decoder_start_token_id				,       )



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       )->Dict:




         '''simple docstring'''





         A_      :				Dict		     =					UMTaModel(config=_SCREAMING_SNAKE_CASE		)
         model.to(_SCREAMING_SNAKE_CASE		)
         model.eval()
         A_      :				Tuple		     =					model(
             input_ids=_SCREAMING_SNAKE_CASE				,       decoder_input_ids=_SCREAMING_SNAKE_CASE				,       attention_mask=_SCREAMING_SNAKE_CASE				,       decoder_attention_mask=_SCREAMING_SNAKE_CASE				,       )
         A_      :				List[Any]		     =					model(input_ids=_SCREAMING_SNAKE_CASE				,       decoder_input_ids=_SCREAMING_SNAKE_CASE		)
         A_      :				str		     =					result.last_hidden_state
         A_      :				List[Any]		     =					result.past_key_values
         A_      :				Any		     =					result.encoder_last_hidden_state

         self.parent.assertEqual(encoder_output.size()				,       (self.batch_size, self.encoder_seq_length, self.hidden_size)		)
         self.parent.assertEqual(decoder_output.size()				,       (self.batch_size, self.decoder_seq_length, self.hidden_size)		)
         # There should be `num_layers` key value embeddings stored in decoder_past
         self.parent.assertEqual(len(_SCREAMING_SNAKE_CASE		)				,       config.num_layers		)
         # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
         self.parent.assertEqual(len(decoder_past[0]		)				,       4		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       )->Optional[Any]:




         '''simple docstring'''





         A_      :				List[Any]		     =					UMTaModel(config=_SCREAMING_SNAKE_CASE		).get_decoder().to(_SCREAMING_SNAKE_CASE		).eval()
         # first forward pass
         A_      :				Union[str, Any]		     =					model(_SCREAMING_SNAKE_CASE				,       use_cache=_SCREAMING_SNAKE_CASE		)
         A_      :				List[str]		     =					model(_SCREAMING_SNAKE_CASE		)
         A_      :				int		     =					model(_SCREAMING_SNAKE_CASE				,       use_cache=_SCREAMING_SNAKE_CASE		)

         self.parent.assertTrue(len(_SCREAMING_SNAKE_CASE		) == len(_SCREAMING_SNAKE_CASE		)		)
         self.parent.assertTrue(len(_SCREAMING_SNAKE_CASE		) == len(_SCREAMING_SNAKE_CASE		) + 1		)

         A_     ,       A_      :				int		     =					outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         A_      :				str		     =					ids_tensor((self.batch_size, 1)				,       config.vocab_size		)

         # append to next input_ids and
         A_      :				Tuple		     =					torch.cat([input_ids, next_tokens]				,       dim=-1		)

         A_      :				Tuple		     =					model(_SCREAMING_SNAKE_CASE		)['''last_hidden_state''']
         A_      :				Union[str, Any]		     =					model(_SCREAMING_SNAKE_CASE				,       past_key_values=_SCREAMING_SNAKE_CASE		)['''last_hidden_state''']

         # select random slice
         A_      :				List[Any]		     =					ids_tensor((1,)				,       output_from_past.shape[-1]		).item()
         A_      :				Union[str, Any]		     =					output_from_no_past[:, -1, random_slice_idx].detach()
         A_      :				List[str]		     =					output_from_past[:, 0, random_slice_idx].detach()

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







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       )->Optional[Any]:




         '''simple docstring'''





         A_      :				List[str]		     =					UMTaModel(config=_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		).half().eval()
         A_      :				Tuple		     =					model(**_SCREAMING_SNAKE_CASE		)['''last_hidden_state''']
         self.parent.assertFalse(torch.isnan(_SCREAMING_SNAKE_CASE		).any().item()		)






@require_torch
class   _lowerCamelCase (					UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							unittest.TestCase			):




       """simple docstring"""
       snake_case      =    (
           (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
       )
       snake_case      =    (UMTaForConditionalGeneration,) if is_torch_available() else ()
       snake_case      =    (
           {
               "conversational": UMTaForConditionalGeneration,
               "feature-extraction": UMTaModel,
               "summarization": UMTaForConditionalGeneration,
               "text2text-generation": UMTaForConditionalGeneration,
               "translation": UMTaForConditionalGeneration,
               "question-answering": UMTaForQuestionAnswering,
           }
           if is_torch_available()
           else {}
       )
       snake_case      =    True
       snake_case      =    False
       snake_case      =    False
       snake_case      =    True
       snake_case      =    True
       # The small UMT5 model needs higher percentages for CPU/MP tests
       snake_case      =    [0.8, 0.9]



       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					UMTaModelTester(self		)



       @unittest.skip('''Test has a segmentation fault on torch 1.8.0'''		)
       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         A_      :				Any		     =					self.model_tester.prepare_config_and_inputs()
         A_      :				str		     =					UMTaModel(config_and_inputs[0]		).to(_SCREAMING_SNAKE_CASE		)
         with tempfile.TemporaryDirectory() as tmpdirname:
           torch.onnx.export(
               _SCREAMING_SNAKE_CASE				,       (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])				,       F'''{tmpdirname}/t5_test.onnx'''				,       export_params=_SCREAMING_SNAKE_CASE				,       opset_version=9				,       input_names=['''input_ids''', '''decoder_input_ids''']				,       )



       @unittest.skipIf(torch_device == '''cpu'''				,       '''Cant do half precision'''		)
       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         A_      :				Dict		     =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model_fpaa_forward(*_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         A_      :				Optional[int]		     =					['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
         A_      :				List[Any]		     =					self.model_tester.prepare_config_and_inputs()
         A_      :				Union[str, Any]		     =					config_and_inputs[0]
         A_      :				int		     =					UMTaForConditionalGeneration(_SCREAMING_SNAKE_CASE		).eval()
         model.to(_SCREAMING_SNAKE_CASE		)

         A_      :				Any		     =					{
             '''head_mask''': torch.zeros(config.num_layers				,       config.num_heads				,       device=_SCREAMING_SNAKE_CASE		),
             '''decoder_head_mask''': torch.zeros(config.num_decoder_layers				,       config.num_heads				,       device=_SCREAMING_SNAKE_CASE		),
             '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers				,       config.num_heads				,       device=_SCREAMING_SNAKE_CASE		),
         }

         for attn_name, (name, mask) in zip(_SCREAMING_SNAKE_CASE				,       head_masking.items()		):
           A_      :				Optional[int]		     =					{name: mask}
           # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
           if name == "head_mask":
             A_      :				Dict		     =					torch.ones(
                 config.num_decoder_layers				,       config.num_heads				,       device=_SCREAMING_SNAKE_CASE		)

           A_      :				Union[str, Any]		     =					model.generate(
               config_and_inputs[1]['''input_ids''']				,       num_beams=1				,       max_length=3				,       output_attentions=_SCREAMING_SNAKE_CASE				,       return_dict_in_generate=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE				,       )
           # We check the state of decoder_attentions and cross_attentions just from the last step
           A_      :				Tuple		     =					out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
           self.assertEqual(sum([w.sum().item() for w in attn_weights]		)				,       0.0		)







       @unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.'''		)
       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         pass






@require_torch
@require_sentencepiece
@require_tokenizers
class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



       @slow
       @unittest.skip(
           '''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'''		)
       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         A_      :				Optional[Any]		     =					UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''				,       return_dict=_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		)
         A_      :				Union[str, Any]		     =					AutoTokenizer.from_pretrained('''google/umt5-small'''				,       use_fast=_SCREAMING_SNAKE_CASE				,       legacy=_SCREAMING_SNAKE_CASE		)
         A_      :				Tuple		     =					[
             '''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
             '''No se como puedo <extra_id_0>.''',
             '''This is the reason why we <extra_id_0> them.''',
             '''The <extra_id_0> walks in <extra_id_1>, seats''',
             '''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
         ]
         A_      :				Union[str, Any]		     =					tokenizer(_SCREAMING_SNAKE_CASE				,       return_tensors='''pt'''				,       padding=_SCREAMING_SNAKE_CASE		).input_ids
         # fmt: off
         A_      :				Dict		     =					torch.tensor(
             [
                 [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                 [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                 [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                 [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                 [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
             ]		)
         # fmt: on
         torch.testing.assert_allclose(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

         A_      :				List[Any]		     =					model.generate(input_ids.to(_SCREAMING_SNAKE_CASE		)		)
         A_      :				str		     =					[
             '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
             '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
             '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
             '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
             '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
         ]
         A_      :				str		     =					tokenizer.batch_decode(_SCREAMING_SNAKE_CASE		)
         self.assertEqual(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)




