



"""simple docstring"""

import shutil
import tempfile
import unittest
from unittest.mock import patch

from transformers import (
    DefaultFlowCallback,
    IntervalStrategy,
    PrinterCallback,
    ProgressCallback,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    is_torch_available,
)
from transformers.testing_utils import require_torch


if is_torch_available():
     from transformers.trainer import DEFAULT_CALLBACKS

     from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel




class        _UpperCAmelCase       (			lowerCAmelCase__):

 def __init__(     self		:       Optional[int]				):
     snake_case_ :     str				=	[]

 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       Any  ,     lowercase_		:       Union[str, Any]  ,     lowercase_		:       List[str]  ,     **lowercase_		:       Tuple				):
     self.events.append('''on_init_end'''				)

 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       str  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       List[str]  ,     **lowercase_		:       List[str]				):
     self.events.append('''on_train_begin'''				)

 def 						_snake_case					(     self		:       Any  ,     lowercase_		:       List[str]  ,     lowercase_		:       Tuple  ,     lowercase_		:       List[Any]  ,     **lowercase_		:       Optional[int]				):
     self.events.append('''on_train_end'''				)

 def 						_snake_case					(     self		:       str  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       int  ,     lowercase_		:       Optional[Any]  ,     **lowercase_		:       List[Any]				):
     self.events.append('''on_epoch_begin'''				)

 def 						_snake_case					(     self		:       Tuple  ,     lowercase_		:       List[str]  ,     lowercase_		:       Dict  ,     lowercase_		:       Union[str, Any]  ,     **lowercase_		:       Optional[Any]				):
     self.events.append('''on_epoch_end'''				)

 def 						_snake_case					(     self		:       List[str]  ,     lowercase_		:       Optional[Any]  ,     lowercase_		:       Optional[Any]  ,     lowercase_		:       int  ,     **lowercase_		:       Optional[Any]				):
     self.events.append('''on_step_begin'''				)

 def 						_snake_case					(     self		:       int  ,     lowercase_		:       int  ,     lowercase_		:       Union[str, Any]  ,     lowercase_		:       List[Any]  ,     **lowercase_		:       List[str]				):
     self.events.append('''on_step_end'''				)

 def 						_snake_case					(     self		:       str  ,     lowercase_		:       int  ,     lowercase_		:       Dict  ,     lowercase_		:       List[str]  ,     **lowercase_		:       List[str]				):
     self.events.append('''on_evaluate'''				)

 def 						_snake_case					(     self		:       Dict  ,     lowercase_		:       Union[str, Any]  ,     lowercase_		:       Any  ,     lowercase_		:       List[Any]  ,     **lowercase_		:       str				):
     self.events.append('''on_predict'''				)

 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       Union[str, Any]  ,     lowercase_		:       List[Any]  ,     lowercase_		:       int  ,     **lowercase_		:       Union[str, Any]				):
     self.events.append('''on_save'''				)

 def 						_snake_case					(     self		:       str  ,     lowercase_		:       Tuple  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       List[str]  ,     **lowercase_		:       Any				):
     self.events.append('''on_log'''				)






 def 						_snake_case					(     self		:       Dict  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       List[str]  ,     lowercase_		:       Union[str, Any]  ,     **lowercase_		:       Optional[int]				):
     self.events.append('''on_prediction_step'''				)





@require_torch
class        _UpperCAmelCase       (			unittest.TestCase):

 def 						_snake_case					(     self		:       List[str]				):
     snake_case_ :     Tuple				=	tempfile.mkdtemp()

 def 						_snake_case					(     self		:       Tuple				):
     shutil.rmtree(self.output_dir				)

 def 						_snake_case					(     self		:       int  ,     lowercase_		:       Union[str, Any]=0  ,     lowercase_		:       Dict=0  ,     lowercase_		:       List[str]=64  ,     lowercase_		:       Union[str, Any]=64  ,     lowercase_		:       Union[str, Any]=None  ,     lowercase_		:       Any=False  ,     **lowercase_		:       List[Any]				):
     # disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
     # its set to False since the tests later on depend on its value.
     snake_case_ :     int				=	RegressionDataset(length=lowercase_				)
     snake_case_ :     Any				=	RegressionDataset(length=lowercase_				)
     snake_case_ :     int				=	RegressionModelConfig(a=lowercase_  ,     b=lowercase_				)
     snake_case_ :     Tuple				=	RegressionPreTrainedModel(lowercase_				)

     snake_case_ :     Any				=	TrainingArguments(self.output_dir  ,     disable_tqdm=lowercase_  ,     report_to=[]  ,     **lowercase_				)
     return Trainer(
         lowercase_  ,     lowercase_  ,     train_dataset=lowercase_  ,     eval_dataset=lowercase_  ,     callbacks=lowercase_  ,     )

 def 						_snake_case					(     self		:       Optional[int]  ,     lowercase_		:       Any  ,     lowercase_		:       List[Any]				):
     self.assertEqual(len(lowercase_				)  ,     len(lowercase_				)				)

     # Order doesn't matter
     snake_case_ :     Any				=	sorted(lowercase_  ,     key=lambda lowercase_				: cb.__name__ if isinstance(lowercase_  ,     lowercase_				) else cb.__class__.__name__				)
     snake_case_ :     List[str]				=	sorted(lowercase_  ,     key=lambda lowercase_				: cb.__name__ if isinstance(lowercase_  ,     lowercase_				) else cb.__class__.__name__				)

     for cba, cba in zip(lowercase_  ,     lowercase_				):
         if isinstance(lowercase_  ,     lowercase_				) and isinstance(lowercase_  ,     lowercase_				):
             self.assertEqual(lowercase_  ,     lowercase_				)
         elif isinstance(lowercase_  ,     lowercase_				) and not isinstance(lowercase_  ,     lowercase_				):
             self.assertEqual(lowercase_  ,     cba.__class__				)
         elif not isinstance(lowercase_  ,     lowercase_				) and isinstance(lowercase_  ,     lowercase_				):
             self.assertEqual(cba.__class__  ,     lowercase_				)
         else:
             self.assertEqual(lowercase_  ,     lowercase_				)

 def 						_snake_case					(     self		:       Optional[Any]  ,     lowercase_		:       Tuple				):
     snake_case_ :     Tuple				=	['''on_init_end''', '''on_train_begin''']
     snake_case_ :     List[Any]				=	0
     snake_case_ :     Union[str, Any]				=	len(trainer.get_eval_dataloader()				)
     snake_case_ :     List[Any]				=	['''on_prediction_step'''] * len(trainer.get_eval_dataloader()				) + ['''on_log''', '''on_evaluate''']
     for _ in range(trainer.state.num_train_epochs				):
         expected_events.append('''on_epoch_begin'''				)
         for _ in range(lowercase_				):
             step += 1
             expected_events += ["on_step_begin", "on_step_end"]
             if step % trainer.args.logging_steps == 0:
                 expected_events.append('''on_log'''				)
             if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
                 expected_events += evaluation_events.copy()
             if step % trainer.args.save_steps == 0:
                 expected_events.append('''on_save'''				)
         expected_events.append('''on_epoch_end'''				)
         if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
             expected_events += evaluation_events.copy()
     expected_events += ["on_log", "on_train_end"]
     return expected_events

 def 						_snake_case					(     self		:       List[str]				):
     snake_case_ :     Union[str, Any]				=	self.get_trainer()
     snake_case_ :     Dict				=	DEFAULT_CALLBACKS.copy() + [ProgressCallback]
     self.check_callbacks_equality(trainer.callback_handler.callbacks  ,     lowercase_				)

     # Callbacks passed at init are added to the default callbacks
     snake_case_ :     Optional[Any]				=	self.get_trainer(callbacks=[MyTestTrainerCallback]				)
     expected_callbacks.append(lowercase_				)
     self.check_callbacks_equality(trainer.callback_handler.callbacks  ,     lowercase_				)

     # TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
     snake_case_ :     Optional[int]				=	self.get_trainer(disable_tqdm=lowercase_				)
     snake_case_ :     List[Any]				=	DEFAULT_CALLBACKS.copy() + [PrinterCallback]
     self.check_callbacks_equality(trainer.callback_handler.callbacks  ,     lowercase_				)

 def 						_snake_case					(     self		:       int				):
     snake_case_ :     int				=	DEFAULT_CALLBACKS.copy() + [ProgressCallback]
     snake_case_ :     List[Any]				=	self.get_trainer()

     # We can add, pop, or remove by class name
     trainer.remove_callback(lowercase_				)
     expected_callbacks.remove(lowercase_				)
     self.check_callbacks_equality(trainer.callback_handler.callbacks  ,     lowercase_				)

     snake_case_ :     Dict				=	self.get_trainer()
     snake_case_ :     Optional[int]				=	trainer.pop_callback(lowercase_				)
     self.assertEqual(cb.__class__  ,     lowercase_				)
     self.check_callbacks_equality(trainer.callback_handler.callbacks  ,     lowercase_				)

     trainer.add_callback(lowercase_				)
     expected_callbacks.insert(0  ,     lowercase_				)
     self.check_callbacks_equality(trainer.callback_handler.callbacks  ,     lowercase_				)

     # We can also add, pop, or remove by instance
     snake_case_ :     Optional[int]				=	self.get_trainer()
     snake_case_ :     List[Any]				=	trainer.callback_handler.callbacks[0]
     trainer.remove_callback(lowercase_				)
     expected_callbacks.remove(lowercase_				)
     self.check_callbacks_equality(trainer.callback_handler.callbacks  ,     lowercase_				)

     snake_case_ :     List[Any]				=	self.get_trainer()
     snake_case_ :     Optional[int]				=	trainer.callback_handler.callbacks[0]
     snake_case_ :     Optional[Any]				=	trainer.pop_callback(lowercase_				)
     self.assertEqual(lowercase_  ,     lowercase_				)
     self.check_callbacks_equality(trainer.callback_handler.callbacks  ,     lowercase_				)

     trainer.add_callback(lowercase_				)
     expected_callbacks.insert(0  ,     lowercase_				)
     self.check_callbacks_equality(trainer.callback_handler.callbacks  ,     lowercase_				)






 def 						_snake_case					(     self		:       List[Any]				):
     import warnings

     # XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
     warnings.simplefilter(action='''ignore'''  ,     category=lowercase_				)

     snake_case_ :     int				=	self.get_trainer(callbacks=[MyTestTrainerCallback]				)
     trainer.train()
     snake_case_ :     Union[str, Any]				=	trainer.callback_handler.callbacks[-2].events
     self.assertEqual(lowercase_  ,     self.get_expected_events(lowercase_				)				)

     # Independent log/save/eval
     snake_case_ :     int				=	self.get_trainer(callbacks=[MyTestTrainerCallback]  ,     logging_steps=5				)
     trainer.train()
     snake_case_ :     str				=	trainer.callback_handler.callbacks[-2].events
     self.assertEqual(lowercase_  ,     self.get_expected_events(lowercase_				)				)

     snake_case_ :     List[Any]				=	self.get_trainer(callbacks=[MyTestTrainerCallback]  ,     save_steps=5				)
     trainer.train()
     snake_case_ :     int				=	trainer.callback_handler.callbacks[-2].events
     self.assertEqual(lowercase_  ,     self.get_expected_events(lowercase_				)				)

     snake_case_ :     List[Any]				=	self.get_trainer(callbacks=[MyTestTrainerCallback]  ,     eval_steps=5  ,     evaluation_strategy='''steps'''				)
     trainer.train()
     snake_case_ :     Union[str, Any]				=	trainer.callback_handler.callbacks[-2].events
     self.assertEqual(lowercase_  ,     self.get_expected_events(lowercase_				)				)

     snake_case_ :     Union[str, Any]				=	self.get_trainer(callbacks=[MyTestTrainerCallback]  ,     evaluation_strategy='''epoch'''				)
     trainer.train()
     snake_case_ :     Dict				=	trainer.callback_handler.callbacks[-2].events
     self.assertEqual(lowercase_  ,     self.get_expected_events(lowercase_				)				)

     # A bit of everything
     snake_case_ :     str				=	self.get_trainer(
         callbacks=[MyTestTrainerCallback]  ,     logging_steps=3  ,     save_steps=10  ,     eval_steps=5  ,     evaluation_strategy='''steps'''  ,     )
     trainer.train()
     snake_case_ :     str				=	trainer.callback_handler.callbacks[-2].events
     self.assertEqual(lowercase_  ,     self.get_expected_events(lowercase_				)				)

     # warning should be emitted for duplicated callbacks
     with patch('''transformers.trainer_callback.logger.warning'''				) as warn_mock:
         snake_case_ :     Dict				=	self.get_trainer(
             callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]  ,     )
         assert str(lowercase_				) in warn_mock.call_args[0][0]










"""simple docstring"""

import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger




class        _UpperCAmelCase       (			lowerCAmelCase__):

 def __init__(     self		:       Optional[int]  ,     lowercase_		:       str  ,     lowercase_		:       int				):
     snake_case_ :     Dict				=	params

     snake_case_ :     Union[str, Any]				=	np.array(lowercase_				)
     snake_case_ :     str				=	np.array([len(lowercase_				) for t in data]				)

     self.check()
     self.remove_long_sequences()
     self.remove_empty_sequences()
     self.remove_unknown_sequences()
     self.check()
     self.print_statistics()

 def __getitem__(     self		:       Dict  ,     lowercase_		:       Union[str, Any]				):
     return (self.token_ids[index], self.lengths[index])

 def __len__(     self		:       List[Any]				):
     return len(self.lengths				)

 def 						_snake_case					(     self		:       Tuple				):
     assert len(self.token_ids				) == len(self.lengths				)
     assert all(self.lengths[i] == len(self.token_ids[i]				) for i in range(len(self.lengths				)				)				)

 def 						_snake_case					(     self		:       Tuple				):
     snake_case_ :     str				=	self.params.max_model_input_size
     snake_case_ :     Dict				=	self.lengths > max_len
     logger.info(f"Splitting {sum(lowercase_				)} too long sequences."				)

     def divide_chunks(lowercase_		:       Tuple  ,     lowercase_		:       Optional[Any]				):
         return [l[i : i + n] for i in range(0  ,     len(lowercase_				)  ,     lowercase_				)]

     snake_case_ :     Tuple				=	[]
     snake_case_ :     Any				=	[]
     if self.params.mlm:
         snake_case_,	snake_case_ :     Union[str, Any]				=	self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
     else:
         snake_case_,	snake_case_ :     Dict				=	self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']

     for seq_, len_ in zip(self.token_ids  ,     self.lengths				):
         assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
         if len_ <= max_len:
             new_tok_ids.append(seq_				)
             new_lengths.append(len_				)
         else:
             snake_case_ :     Any				=	[]
             for sub_s in divide_chunks(seq_  ,     max_len - 2				):
                 if sub_s[0] != cls_id:
                     snake_case_ :     Dict				=	np.insert(lowercase_  ,     0  ,     lowercase_				)
                 if sub_s[-1] != sep_id:
                     snake_case_ :     Tuple				=	np.insert(lowercase_  ,     len(lowercase_				)  ,     lowercase_				)
                 assert len(lowercase_				) <= max_len
                 assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                 sub_seqs.append(lowercase_				)

             new_tok_ids.extend(lowercase_				)
             new_lengths.extend([len(lowercase_				) for l in sub_seqs]				)

     snake_case_ :     List[str]				=	np.array(lowercase_				)
     snake_case_ :     Optional[Any]				=	np.array(lowercase_				)

 def 						_snake_case					(     self		:       Optional[int]				):
     snake_case_ :     List[Any]				=	len(self				)
     snake_case_ :     List[str]				=	self.lengths > 11
     snake_case_ :     Dict				=	self.token_ids[indices]
     snake_case_ :     Dict				=	self.lengths[indices]
     snake_case_ :     str				=	len(self				)
     logger.info(f"Remove {init_size - new_size} too short (<=11 tokens) sequences."				)

 def 						_snake_case					(     self		:       Tuple				):
     if "unk_token" not in self.params.special_tok_ids:
         return
     else:
         snake_case_ :     str				=	self.params.special_tok_ids['''unk_token''']
     snake_case_ :     str				=	len(self				)
     snake_case_ :     int				=	np.array([np.count_nonzero(a == unk_token_id				) for a in self.token_ids]				)
     snake_case_ :     str				=	(unk_occs / self.lengths) < 0.5
     snake_case_ :     Optional[Any]				=	self.token_ids[indices]
     snake_case_ :     Optional[int]				=	self.lengths[indices]
     snake_case_ :     Dict				=	len(self				)
     logger.info(f"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."				)

 def 						_snake_case					(     self		:       Dict				):
     if not self.params.is_master:
         return
     logger.info(f"{len(self				)} sequences"				)
     # data_len = sum(self.lengths)
     # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
     # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

     # unk_idx = self.params.special_tok_ids['unk_token']
     # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
     # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')






 def 						_snake_case					(     self		:       List[str]  ,     lowercase_		:       Dict				):
     snake_case_ :     Optional[int]				=	[t[0] for t in batch]
     snake_case_ :     str				=	[t[1] for t in batch]
     assert len(lowercase_				) == len(lowercase_				)

     # Max for paddings
     snake_case_ :     str				=	max(lowercase_				)

     # Pad token ids
     if self.params.mlm:
         snake_case_ :     Tuple				=	self.params.special_tok_ids['''pad_token''']
     else:
         snake_case_ :     Dict				=	self.params.special_tok_ids['''unk_token''']
     snake_case_ :     Any				=	[list(t.astype(lowercase_				)				) + [pad_idx] * (max_seq_len_ - len(lowercase_				)) for t in token_ids]
     assert len(tk_				) == len(lowercase_				)
     assert all(len(lowercase_				) == max_seq_len_ for t in tk_				)

     snake_case_ :     str				=	torch.tensor(tk_				)  # (bs, max_seq_len_)
     snake_case_ :     Optional[int]				=	torch.tensor(lowercase_				)  # (bs)
     return tk_t, lg_t





