



import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


a_   :Any        =      logging.getLogger(__name__)




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""summarization"""
     _SCREAMING_SNAKE_CASE						=	["""loss"""]
     _SCREAMING_SNAKE_CASE						=	ROUGE_KEYS
     _SCREAMING_SNAKE_CASE						=	"""rouge2"""






     def __init__(       self  :  int, _snake_case  :  Union[str, Any], **_snake_case  :  Dict					)   ->Optional[int]:







            if hparams.sortish_sampler and hparams.gpus > 1:
                   snake_case__							:	List[Any]   	=	False
            elif hparams.max_tokens_per_batch is not None:
                   if hparams.gpus > 1:
                          raise NotImplementedError('Dynamic Batch size does not work for multi-gpu training'					)
                   if hparams.sortish_sampler:
                          raise ValueError('--sortish_sampler and --max_tokens_per_batch may not be used simultaneously'					)

            super().__init__(__lowerCAmelCase, num_labels=__lowerCAmelCase, mode=self.mode, **__lowerCAmelCase					)
            use_task_specific_params(self.model, 'summarization'					)
            save_git_info(self.hparams.output_dir					)
            snake_case__							:	int   	=	Path(self.output_dir					) / 'metrics.json'
            snake_case__							:	int   	=	Path(self.output_dir					) / 'hparams.pkl'
            pickle_save(self.hparams, self.hparams_save_path					)
            snake_case__							:	Union[str, Any]   	=	0
            snake_case__							:	Tuple   	=	defaultdict(__lowerCAmelCase					)
            snake_case__							:	List[str]   	=	self.config.model_type
            snake_case__							:	Dict   	=	self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

            snake_case__							:	int   	=	{
                'data_dir': self.hparams.data_dir,
                'max_source_length': self.hparams.max_source_length,
                'prefix': self.model.config.prefix or '',
            }
            snake_case__							:	Union[str, Any]   	=	{
                'train': self.hparams.n_train,
                'val': self.hparams.n_val,
                'test': self.hparams.n_test,
            }
            snake_case__							:	Dict   	=	{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

            snake_case__							:	List[str]   	=	{
                'train': self.hparams.max_target_length,
                'val': self.hparams.val_max_target_length,
                'test': self.hparams.test_max_target_length,
            }
            assert self.target_lens["train"] <= self.target_lens["val"], F'''target_lens: {self.target_lens}'''
            assert self.target_lens["train"] <= self.target_lens["test"], F'''target_lens: {self.target_lens}'''
            if self.hparams.freeze_embeds:
                   freeze_embeds(self.model					)
            if self.hparams.freeze_encoder:
                   freeze_params(self.model.get_encoder()					)
                   assert_all_frozen(self.model.get_encoder()					)

            snake_case__							:	Tuple   	=	get_git_info()['repo_sha']
            snake_case__							:	List[Any]   	=	hparams.num_workers
            snake_case__							:	List[Any]   	=	None  # default to config
            if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer, __lowerCAmelCase					):
                   snake_case__							:	Tuple   	=	self.tokenizer.lang_code_to_id[hparams.tgt_lang]
                   snake_case__							:	Union[str, Any]   	=	self.decoder_start_token_id
            snake_case__							:	List[Any]   	=	(
                SeqaSeqDataset if hasattr(self.tokenizer, 'prepare_seq2seq_batch'					) else LegacySeqaSeqDataset
            )
            snake_case__							:	List[Any]   	=	False
            snake_case__							:	Optional[Any]   	=	self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
            if self.hparams.eval_max_gen_length is not None:
                   snake_case__							:	str   	=	self.hparams.eval_max_gen_length
            else:
                   snake_case__							:	List[str]   	=	self.model.config.max_length
            snake_case__							:	Optional[Any]   	=	self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric






     def    lowercase_   (       self  :  Tuple, _snake_case  :  Optional[Any]					)   ->Optional[int]:
            snake_case__							:	List[Any]   	=	{
                k: self.tokenizer.batch_decode(v.tolist()					) if 'mask' not in k else v.shape for k, v in batch.items()
            }
            save_json(__lowerCAmelCase, Path(self.output_dir					) / 'text_batch.json'					)
            save_json({k: v.tolist() for k, v in batch.items()}, Path(self.output_dir					) / 'tok_batch.json'					)

            snake_case__							:	Union[str, Any]   	=	True
            return readable_batch






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  Tuple, **_snake_case  :  List[str]					)   ->List[Any]:
            return self.model(__lowerCAmelCase, **__lowerCAmelCase					)






     def    lowercase_   (       self  :  Optional[Any], _snake_case  :  Tuple					)   ->Tuple:
            snake_case__							:	Optional[int]   	=	self.tokenizer.batch_decode(
                __lowerCAmelCase, skip_special_tokens=__lowerCAmelCase, clean_up_tokenization_spaces=__lowerCAmelCase					)
            return lmap(str.strip, __lowerCAmelCase					)






     def    lowercase_   (       self  :  str, _snake_case  :  Tuple					)   ->int:
            snake_case__							:	Dict   	=	self.tokenizer.pad_token_id
            snake_case__	,				snake_case__							:	Any   	=	batch['input_ids'], batch['attention_mask']
            snake_case__							:	List[Any]   	=	batch['labels']
            if isinstance(self.model, __lowerCAmelCase					):
                   snake_case__							:	Dict   	=	self.model._shift_right(__lowerCAmelCase					)
            else:
                   snake_case__							:	Optional[Any]   	=	shift_tokens_right(__lowerCAmelCase, __lowerCAmelCase					)
            if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
                   snake_case__							:	List[str]   	=	decoder_input_ids
                   self.save_readable_batch(__lowerCAmelCase					)

            snake_case__							:	Optional[int]   	=	self(__lowerCAmelCase, attention_mask=__lowerCAmelCase, decoder_input_ids=__lowerCAmelCase, use_cache=__lowerCAmelCase					)
            snake_case__							:	Any   	=	outputs['logits']
            if self.hparams.label_smoothing == 0:
                   # Same behavior as modeling_bart.py, besides ignoring pad_token_id
                   snake_case__							:	int   	=	nn.CrossEntropyLoss(ignore_index=__lowerCAmelCase					)

                   assert lm_logits.shape[-1] == self.vocab_size
                   snake_case__							:	List[Any]   	=	ce_loss_fct(lm_logits.view(-1, lm_logits.shape[-1]					), tgt_ids.view(-1					)					)
            else:
                   snake_case__							:	int   	=	nn.functional.log_softmax(__lowerCAmelCase, dim=-1					)
                   snake_case__	,				snake_case__							:	List[str]   	=	label_smoothed_nll_loss(
                       __lowerCAmelCase, __lowerCAmelCase, self.hparams.label_smoothing, ignore_index=__lowerCAmelCase					)
            return (loss,)






     @property
     def    lowercase_   (       self  :  List[str]					)   ->int:
            return self.tokenizer.pad_token_id






     def    lowercase_   (       self  :  Any, _snake_case  :  str, _snake_case  :  Tuple					)   ->str:
            snake_case__							:	int   	=	self._step(__lowerCAmelCase					)

            snake_case__							:	Dict   	=	dict(zip(self.loss_names, __lowerCAmelCase					)					)
            # tokens per batch
            snake_case__							:	Tuple   	=	batch['input_ids'].ne(self.pad					).sum() + batch['labels'].ne(self.pad					).sum()
            snake_case__							:	Union[str, Any]   	=	batch['input_ids'].shape[0]
            snake_case__							:	List[str]   	=	batch['input_ids'].eq(self.pad					).sum()
            snake_case__							:	List[str]   	=	batch['input_ids'].eq(self.pad					).float().mean()
            # TODO(SS): make a wandb summary metric for this
            return {"loss": loss_tensors[0], "log": logs}






     def    lowercase_   (       self  :  Any, _snake_case  :  List[Any], _snake_case  :  Tuple					)   ->Dict:
            return self._generative_step(__lowerCAmelCase					)






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  List[Any], _snake_case  :  Tuple="val"					)   ->List[Any]:
            self.step_count += 1
            snake_case__							:	Union[str, Any]   	=	{k: torch.stack([x[k] for x in outputs]					).mean() for k in self.loss_names}
            snake_case__							:	int   	=	losses['loss']
            snake_case__							:	Union[str, Any]   	=	{
                k: np.array([x[k] for x in outputs]					).mean() for k in self.metric_names + ['gen_time', 'gen_len']
            }
            snake_case__							:	Any   	=	(
                generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
            )
            snake_case__							:	Any   	=	torch.tensor(__lowerCAmelCase					).type_as(__lowerCAmelCase					)
            generative_metrics.update({k: v.item() for k, v in losses.items()}					)
            losses.update(__lowerCAmelCase					)
            snake_case__							:	List[Any]   	=	{F'''{prefix}_avg_{k}''': x for k, x in losses.items()}
            snake_case__							:	Union[str, Any]   	=	self.step_count
            self.metrics[prefix].append(__lowerCAmelCase					)  # callback writes this to self.metrics_save_path
            snake_case__							:	Dict   	=	flatten_list([x['preds'] for x in outputs]					)
            return {
                "log": all_metrics,
                "preds": preds,
                F'''{prefix}_loss''': loss,
                F'''{prefix}_{self.val_metric}''': metric_tensor,
            }






     def    lowercase_   (       self  :  List[Any], _snake_case  :  Optional[Any], _snake_case  :  Optional[int]					)   ->Dict:
            return calculate_rouge(__lowerCAmelCase, __lowerCAmelCase					)






     def    lowercase_   (       self  :  Any, _snake_case  :  Dict					)   ->Union[str, Any]:
            snake_case__							:	str   	=	time.time()

            # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
            snake_case__							:	Dict   	=	self.model.generate(
                batch['input_ids'], attention_mask=batch['attention_mask'], use_cache=__lowerCAmelCase, decoder_start_token_id=self.decoder_start_token_id, num_beams=self.eval_beams, max_length=self.eval_max_length, )
            snake_case__							:	List[Any]   	=	(time.time() - ta) / batch['input_ids'].shape[0]
            snake_case__							:	Union[str, Any]   	=	self.ids_to_clean_text(__lowerCAmelCase					)
            snake_case__							:	Optional[Any]   	=	self.ids_to_clean_text(batch['labels']					)
            snake_case__							:	Any   	=	self._step(__lowerCAmelCase					)
            snake_case__							:	str   	=	dict(zip(self.loss_names, __lowerCAmelCase					)					)
            snake_case__							:	int   	=	self.calc_generative_metrics(__lowerCAmelCase, __lowerCAmelCase					)
            snake_case__							:	Dict   	=	np.mean(lmap(__lowerCAmelCase, __lowerCAmelCase					)					)
            base_metrics.update(gen_time=__lowerCAmelCase, gen_len=__lowerCAmelCase, preds=__lowerCAmelCase, target=__lowerCAmelCase, **__lowerCAmelCase					)
            return base_metrics






     def    lowercase_   (       self  :  Tuple, _snake_case  :  Dict, _snake_case  :  Optional[Any]					)   ->List[Any]:
            return self._generative_step(__lowerCAmelCase					)






     def    lowercase_   (       self  :  int, _snake_case  :  Any					)   ->int:
            return self.validation_epoch_end(__lowerCAmelCase, prefix='test'					)






     def    lowercase_   (       self  :  List[str], _snake_case  :  Tuple					)   ->Optional[int]:
            snake_case__							:	Optional[Any]   	=	self.n_obs[type_path]
            snake_case__							:	List[str]   	=	self.target_lens[type_path]
            snake_case__							:	Optional[Any]   	=	self.dataset_class(
                self.tokenizer, type_path=__lowerCAmelCase, n_obs=__lowerCAmelCase, max_target_length=__lowerCAmelCase, **self.dataset_kwargs, )
            return dataset






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  str, _snake_case  :  int, _snake_case  :  Tuple = False					)   ->Tuple:
            snake_case__							:	Optional[int]   	=	self.get_dataset(__lowerCAmelCase					)

            if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
                   snake_case__							:	Union[str, Any]   	=	dataset.make_sortish_sampler(__lowerCAmelCase, distributed=self.hparams.gpus > 1					)
                   return DataLoader(
                       __lowerCAmelCase, batch_size=__lowerCAmelCase, collate_fn=dataset.collate_fn, shuffle=__lowerCAmelCase, num_workers=self.num_workers, sampler=__lowerCAmelCase, )

            elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
                   snake_case__							:	Optional[int]   	=	dataset.make_dynamic_sampler(
                       self.hparams.max_tokens_per_batch, distributed=self.hparams.gpus > 1					)
                   return DataLoader(
                       __lowerCAmelCase, batch_sampler=__lowerCAmelCase, collate_fn=dataset.collate_fn, num_workers=self.num_workers, )
            else:
                   return DataLoader(
                       __lowerCAmelCase, batch_size=__lowerCAmelCase, collate_fn=dataset.collate_fn, shuffle=__lowerCAmelCase, num_workers=self.num_workers, sampler=__lowerCAmelCase, )






     def    lowercase_   (       self  :  int					)   ->Tuple:
            snake_case__							:	Dict   	=	self.get_dataloader('train', batch_size=self.hparams.train_batch_size, shuffle=__lowerCAmelCase					)
            return dataloader






     def    lowercase_   (       self  :  Tuple					)   ->Any:
            return self.get_dataloader('val', batch_size=self.hparams.eval_batch_size					)






     def    lowercase_   (       self  :  Tuple					)   ->Tuple:
            return self.get_dataloader('test', batch_size=self.hparams.eval_batch_size					)






     @staticmethod
     def    lowercase_   (       _snake_case  :  List[Any], _snake_case  :  Dict					)   ->Dict:
            BaseTransformer.add_model_specific_args(__lowerCAmelCase, __lowerCAmelCase					)
            add_generic_args(__lowerCAmelCase, __lowerCAmelCase					)
            parser.add_argument(
                '--max_source_length', default=1_0_2_4, type=__lowerCAmelCase, help=(
                    'The maximum total input sequence length after tokenization. Sequences longer '
                    'than this will be truncated, sequences shorter will be padded.'
                ), )
            parser.add_argument(
                '--max_target_length', default=5_6, type=__lowerCAmelCase, help=(
                    'The maximum total input sequence length after tokenization. Sequences longer '
                    'than this will be truncated, sequences shorter will be padded.'
                ), )
            parser.add_argument(
                '--val_max_target_length', default=1_4_2, type=__lowerCAmelCase, help=(
                    'The maximum total input sequence length after tokenization. Sequences longer '
                    'than this will be truncated, sequences shorter will be padded.'
                ), )
            parser.add_argument(
                '--test_max_target_length', default=1_4_2, type=__lowerCAmelCase, help=(
                    'The maximum total input sequence length after tokenization. Sequences longer '
                    'than this will be truncated, sequences shorter will be padded.'
                ), )
            parser.add_argument('--freeze_encoder', action='store_true'					)
            parser.add_argument('--freeze_embeds', action='store_true'					)
            parser.add_argument('--sortish_sampler', action='store_true', default=__lowerCAmelCase					)
            parser.add_argument('--overwrite_output_dir', action='store_true', default=__lowerCAmelCase					)
            parser.add_argument('--max_tokens_per_batch', type=__lowerCAmelCase, default=__lowerCAmelCase					)
            parser.add_argument('--logger_name', type=__lowerCAmelCase, choices=['default', 'wandb', 'wandb_shared'], default='default'					)
            parser.add_argument('--n_train', type=__lowerCAmelCase, default=-1, required=__lowerCAmelCase, help='# examples. -1 means use all.'					)
            parser.add_argument('--n_val', type=__lowerCAmelCase, default=5_0_0, required=__lowerCAmelCase, help='# examples. -1 means use all.'					)
            parser.add_argument('--n_test', type=__lowerCAmelCase, default=-1, required=__lowerCAmelCase, help='# examples. -1 means use all.'					)
            parser.add_argument(
                '--task', type=__lowerCAmelCase, default='summarization', required=__lowerCAmelCase, help='# examples. -1 means use all.'					)
            parser.add_argument('--label_smoothing', type=__lowerCAmelCase, default=0.0, required=__lowerCAmelCase					)
            parser.add_argument('--src_lang', type=__lowerCAmelCase, default='', required=__lowerCAmelCase					)
            parser.add_argument('--tgt_lang', type=__lowerCAmelCase, default='', required=__lowerCAmelCase					)
            parser.add_argument('--eval_beams', type=__lowerCAmelCase, default=__lowerCAmelCase, required=__lowerCAmelCase					)
            parser.add_argument(
                '--val_metric', type=__lowerCAmelCase, default=__lowerCAmelCase, required=__lowerCAmelCase, choices=['bleu', 'rouge2', 'loss', None]					)
            parser.add_argument('--eval_max_gen_length', type=__lowerCAmelCase, default=__lowerCAmelCase, help='never generate more than n tokens'					)
            parser.add_argument('--save_top_k', type=__lowerCAmelCase, default=1, required=__lowerCAmelCase, help='How many checkpoints to save'					)
            parser.add_argument(
                '--early_stopping_patience', type=__lowerCAmelCase, default=-1, required=__lowerCAmelCase, help=(
                    '-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'
                    ' val_check_interval will effect it.'
                ), )
            return parser




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""translation"""
     _SCREAMING_SNAKE_CASE						=	["""loss"""]
     _SCREAMING_SNAKE_CASE						=	["""bleu"""]
     _SCREAMING_SNAKE_CASE						=	"""bleu"""






     def __init__(       self  :  Dict, _snake_case  :  List[str], **_snake_case  :  int					)   ->Tuple:
            super().__init__(__lowerCAmelCase, **__lowerCAmelCase					)
            snake_case__							:	str   	=	hparams.src_lang
            snake_case__							:	Any   	=	hparams.tgt_lang






     def    lowercase_   (       self  :  Tuple, _snake_case  :  Any, _snake_case  :  Optional[Any]					)   ->Tuple:
            return calculate_bleu(__lowerCAmelCase, __lowerCAmelCase					)


def        lowercase_							(A : str		,					A : Dict=None      ):
       Path(args.output_dir      ).mkdir(exist_ok=__snake_case      )
       check_output_dir(__snake_case		,					expected_items=3      )

       if model is None:
              if "summarization" in args.task:
                     snake_case__							:	Dict   	=	SummarizationModule(__snake_case      )
              else:
                     snake_case__							:	List[str]   	=	TranslationModule(__snake_case      )
       snake_case__							:	Optional[Any]   	=	Path(args.data_dir      ).name
       if (
           args.logger_name == "default"
           or args.fast_dev_run
           or str(args.output_dir      ).startswith('/tmp'      )
           or str(args.output_dir      ).startswith('/var'      )
       ):
              snake_case__							:	List[str]   	=	True  # don't pollute wandb logs unnecessarily
       elif args.logger_name == "wandb":
              from pytorch_lightning.loggers import WandbLogger

              snake_case__							:	Union[str, Any]   	=	os.environ.get('WANDB_PROJECT'		,					__snake_case      )
              snake_case__							:	Dict   	=	WandbLogger(name=model.output_dir.name		,					project=__snake_case      )

       elif args.logger_name == "wandb_shared":
              from pytorch_lightning.loggers import WandbLogger

              snake_case__							:	Optional[int]   	=	WandbLogger(name=model.output_dir.name		,					project=F'''hf_{dataset}'''      )

       if args.early_stopping_patience >= 0:
              snake_case__							:	Dict   	=	get_early_stopping_callback(model.val_metric		,					args.early_stopping_patience      )
       else:
              snake_case__							:	Dict   	=	False

       snake_case__							:	Dict   	=	args.val_metric == 'loss'
       snake_case__							:	Dict   	=	generic_train(
           __snake_case		,					__snake_case		,					logging_callback=SeqaSeqLoggingCallback()		,					checkpoint_callback=get_checkpoint_callback(
               args.output_dir		,					model.val_metric		,					args.save_top_k		,					__snake_case      )		,					early_stopping_callback=__snake_case		,					logger=__snake_case		,					)
       pickle_save(model.hparams		,					model.output_dir / 'hparams.pkl'      )
       if not args.do_predict:
              return model

       snake_case__							:	Optional[int]   	=	''
       snake_case__							:	Tuple   	=	sorted(glob.glob(os.path.join(args.output_dir		,					'*.ckpt'      )		,					recursive=__snake_case      )      )
       if checkpoints:
              snake_case__							:	str   	=	checkpoints[-1]
              snake_case__							:	Any   	=	checkpoints[-1]
       trainer.logger.log_hyperparams(model.hparams      )

       # test() without a model tests using the best checkpoint automatically
       trainer.test()
       return model


if __name__ == "__main__":
 a_   :List[Any]        =      argparse.ArgumentParser()
 a_   :Tuple        =      pl.Trainer.add_argparse_args(parser)
 a_   :List[Any]        =      SummarizationModule.add_model_specific_args(parser, os.getcwd())

 a_   :Any        =      parser.parse_args()

 main(args)













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 					__A    :

			'''simple docstring'''







			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=9_9       ,  __lowerCAmelCase=1_3       ,  __lowerCAmelCase=7       ,  __lowerCAmelCase=9       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=False       ,  __lowerCAmelCase=3_2       ,  __lowerCAmelCase=5       ,  __lowerCAmelCase=4       ,  __lowerCAmelCase=3_7       ,  __lowerCAmelCase=8       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=0.002       ,  __lowerCAmelCase=1       ,  __lowerCAmelCase=0       ,  __lowerCAmelCase=0       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		parent
				lowerCamelCase__								=		batch_size
				lowerCamelCase__								=		encoder_seq_length
				lowerCamelCase__								=		decoder_seq_length
				# For common tests
				lowerCamelCase__								=		self.decoder_seq_length
				lowerCamelCase__								=		is_training
				lowerCamelCase__								=		use_attention_mask
				lowerCamelCase__								=		use_labels
				lowerCamelCase__								=		vocab_size
				lowerCamelCase__								=		hidden_size
				lowerCamelCase__								=		num_hidden_layers
				lowerCamelCase__								=		num_attention_heads
				lowerCamelCase__								=		d_ff
				lowerCamelCase__								=		relative_attention_num_buckets
				lowerCamelCase__								=		dropout_rate
				lowerCamelCase__								=		initializer_factor
				lowerCamelCase__								=		eos_token_id
				lowerCamelCase__								=		pad_token_id
				lowerCamelCase__								=		decoder_start_token_id
				lowerCamelCase__								=		None
				lowerCamelCase__								=		decoder_layers





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  ):



				'''simple docstring'''







				if attention_mask is None:
					lowerCamelCase__								=		input_ids.ne(config.pad_token_id							)
				if decoder_attention_mask is None:
					lowerCamelCase__								=		decoder_input_ids.ne(config.pad_token_id							)
				if head_mask is None:
					lowerCamelCase__								=		torch.ones(config.num_hidden_layers       ,  config.num_attention_heads       ,  device=__lowerCAmelCase							)
				if decoder_head_mask is None:
					lowerCamelCase__								=		torch.ones(config.num_decoder_layers       ,  config.num_attention_heads       ,  device=__lowerCAmelCase							)
				if cross_attn_head_mask is None:
					lowerCamelCase__								=		torch.ones(
					    config.num_decoder_layers       ,  config.num_attention_heads       ,  device=__lowerCAmelCase							)
				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 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		ids_tensor([self.batch_size, self.encoder_seq_length]       ,  self.vocab_size							)
				lowerCamelCase__								=		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
				lowerCamelCase__								=		input_ids.clamp(self.pad_token_id + 1							)
				lowerCamelCase__								=		decoder_input_ids.clamp(self.pad_token_id + 1							)

				lowerCamelCase__								=		self.get_config()
				lowerCamelCase__								=		config.num_attention_heads
				lowerCamelCase__								=		self.prepare_inputs_dict(__lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							)
				return config, input_dict





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__				,	lowerCamelCase__								=		self.prepare_config_and_inputs()
				return config, inputs_dict





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return TaConfig(
				    vocab_size=1_6_6       ,  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 		__lowerCamelCase				(    self							):



				'''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 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		UMTaModel(config=__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()
				lowerCamelCase__								=		model(
				    input_ids=__lowerCAmelCase       ,  decoder_input_ids=__lowerCAmelCase       ,  attention_mask=__lowerCAmelCase       ,  decoder_attention_mask=__lowerCAmelCase       ,  )
				lowerCamelCase__								=		model(input_ids=__lowerCAmelCase       ,  decoder_input_ids=__lowerCAmelCase							)
				lowerCamelCase__								=		result.last_hidden_state
				lowerCamelCase__								=		result.past_key_values
				lowerCamelCase__								=		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(__lowerCAmelCase							)       ,  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 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		UMTaModel(config=__lowerCAmelCase							).get_decoder().to(__lowerCAmelCase							).eval()
				# first forward pass
				lowerCamelCase__								=		model(__lowerCAmelCase       ,  use_cache=__lowerCAmelCase							)
				lowerCamelCase__								=		model(__lowerCAmelCase							)
				lowerCamelCase__								=		model(__lowerCAmelCase       ,  use_cache=__lowerCAmelCase							)

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

				lowerCamelCase__				,	lowerCamelCase__								=		outputs.to_tuple()

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

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

				lowerCamelCase__								=		model(__lowerCAmelCase							)['''last_hidden_state''']
				lowerCamelCase__								=		model(__lowerCAmelCase       ,  past_key_values=__lowerCAmelCase							)['''last_hidden_state''']

				# select random slice
				lowerCamelCase__								=		ids_tensor((1,)       ,  output_from_past.shape[-1]							).item()
				lowerCamelCase__								=		output_from_no_past[:, -1, random_slice_idx].detach()
				lowerCamelCase__								=		output_from_past[:, 0, random_slice_idx].detach()

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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		UMTaModel(config=__lowerCAmelCase							).to(__lowerCAmelCase							).half().eval()
				lowerCamelCase__								=		model(**__lowerCAmelCase							)['''last_hidden_state''']
				self.parent.assertFalse(torch.isnan(__lowerCAmelCase							).any().item()							)






@require_torch
class 					__A    (    lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				unittest.TestCase ):

			'''simple docstring'''







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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		UMTaModelTester(self							)





			@unittest.skip('''Test has a segmentation fault on torch 1.8.0'''							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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





			@unittest.skipIf(torch_device == '''cpu'''       ,  '''Cant do half precision'''							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model_fpaa_forward(*__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				lowerCamelCase__								=		config_and_inputs[0]
				lowerCamelCase__								=		UMTaForConditionalGeneration(__lowerCAmelCase							).eval()
				model.to(__lowerCAmelCase							)

				lowerCamelCase__								=		{
				    '''head_mask''': torch.zeros(config.num_layers       ,  config.num_heads       ,  device=__lowerCAmelCase							),
				    '''decoder_head_mask''': torch.zeros(config.num_decoder_layers       ,  config.num_heads       ,  device=__lowerCAmelCase							),
				    '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers       ,  config.num_heads       ,  device=__lowerCAmelCase							),
				}

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

					lowerCamelCase__								=		model.generate(
					    config_and_inputs[1]['''input_ids''']       ,  num_beams=1       ,  max_length=3       ,  output_attentions=__lowerCAmelCase       ,  return_dict_in_generate=__lowerCAmelCase       ,  **__lowerCAmelCase       ,  )
					# We check the state of decoder_attentions and cross_attentions just from the last step
					lowerCamelCase__								=		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 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass






@require_torch
@require_sentencepiece
@require_tokenizers
class 					__A    (    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 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''       ,  return_dict=__lowerCAmelCase							).to(__lowerCAmelCase							)
				lowerCamelCase__								=		AutoTokenizer.from_pretrained('''google/umt5-small'''       ,  use_fast=__lowerCAmelCase       ,  legacy=__lowerCAmelCase							)
				lowerCamelCase__								=		[
				    '''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>.''',
				]
				lowerCamelCase__								=		tokenizer(__lowerCAmelCase       ,  return_tensors='''pt'''       ,  padding=__lowerCAmelCase							).input_ids
				# fmt: off
				lowerCamelCase__								=		torch.tensor(
				    [
				        [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
				        [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
				        [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
				        [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
				        [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
				    ]							)
				# fmt: on
				torch.testing.assert_allclose(__lowerCAmelCase       ,  __lowerCAmelCase							)

				lowerCamelCase__								=		model.generate(input_ids.to(__lowerCAmelCase							)							)
				lowerCamelCase__								=		[
				    '''<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>''',
				]
				lowerCamelCase__								=		tokenizer.batch_decode(__lowerCAmelCase							)
				self.assertEqual(__lowerCAmelCase       ,  __lowerCAmelCase							)



