"""simple docstring"""





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase     :		str											=					logging.get_logger(__name__)

_UpperCamelCase     :		List[Any]											=					"▁"

_UpperCamelCase     :		List[str]											=					{"vocab_file": "sentencepiece.bpe.model"}

_UpperCamelCase     :		int											=					{
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

_UpperCamelCase     :		List[str]											=					{
    "xlm-roberta-base": 5_12,
    "xlm-roberta-large": 5_12,
    "xlm-roberta-large-finetuned-conll02-dutch": 5_12,
    "xlm-roberta-large-finetuned-conll02-spanish": 5_12,
    "xlm-roberta-large-finetuned-conll03-english": 5_12,
    "xlm-roberta-large-finetuned-conll03-german": 5_12,
}


class   UpperCAmelCase_			(					_a):
      lowerCamelCase__	:					Tuple										=						VOCAB_FILES_NAMES
      lowerCamelCase__	:					List[str]										=						PRETRAINED_VOCAB_FILES_MAP
      lowerCamelCase__	:					str										=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCamelCase__	:					Any										=						["input_ids", "attention_mask"]


      def __init__(     self	,    a	,    a="<s>"	,    a="</s>"	,    a="</s>"	,    a="<s>"	,    a="<unk>"	,    a="<pad>"	,    a="<mask>"	,    a = None	,    **a	,    ) ->      None:
             # Mask token behave like a normal word, i.e. include the space before it
             lowercase__    :	Optional[int]     					=			AddedToken(a	,    lstrip=a	,    rstrip=a							) if isinstance(a	,    a							) else mask_token

             lowercase__    :	Any     					=			{} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 bos_token=a	,    eos_token=a	,    unk_token=a	,    sep_token=a	,    cls_token=a	,    pad_token=a	,    mask_token=a	,    sp_model_kwargs=self.sp_model_kwargs	,    **a	,    )

             lowercase__    :	int     					=			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
             self.sp_model.Load(str(a							)							)
             lowercase__    :	int     					=			vocab_file

             # Original fairseq vocab and spm vocab must be "aligned":
             # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
             # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
             # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
             # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

             # Mimic fairseq token-to-id alignment for the first 4 token
             lowercase__    :	int     					=			{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

             # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
             lowercase__    :	Optional[int]     					=			1

             lowercase__    :	List[str]     					=			len(self.sp_model							) + self.fairseq_offset
             lowercase__    :	Any     					=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}


      def __getstate__(     self							) ->      List[Any]:
             lowercase__    :	List[str]     					=			self.__dict__.copy()
             lowercase__    :	int     					=			None
             lowercase__    :	List[str]     					=			self.sp_model.serialized_model_proto()
             return state


      def __setstate__(     self	,    a							) ->      List[Any]:
             lowercase__    :	List[Any]     					=			d

             # for backward compatibility
             if not hasattr(self	,    'sp_model_kwargs'							):
                    lowercase__    :	Tuple     					=			{}

             lowercase__    :	Tuple     					=			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
             self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)


      def 	_UpperCAmelCase (     self	,    a	,    a = None							) ->      List[int]:

             if token_ids_a is None:
                    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             lowercase__    :	Optional[int]     					=			[self.cls_token_id]
             lowercase__    :	str     					=			[self.sep_token_id]
             return cls + token_ids_a + sep + sep + token_ids_a + sep


      def 	_UpperCAmelCase (     self	,    a	,    a = None	,    a = False							) ->      List[int]:

             if already_has_special_tokens:
                    return super().get_special_tokens_mask(
                        token_ids_a=a	,    token_ids_a=a	,    already_has_special_tokens=a							)

             if token_ids_a is None:
                    return [1] + ([0] * len(a							)) + [1]
             return [1] + ([0] * len(a							)) + [1, 1] + ([0] * len(a							)) + [1]


      def 	_UpperCAmelCase (     self	,    a	,    a = None							) ->      List[int]:
             lowercase__    :	List[Any]     					=			[self.sep_token_id]
             lowercase__    :	List[Any]     					=			[self.cls_token_id]

             if token_ids_a is None:
                    return len(cls + token_ids_a + sep							) * [0]
             return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]


      @property
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             return len(self.sp_model							) + self.fairseq_offset + 1  # Add the <mask> token


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	List[str]     					=			{self.convert_ids_to_tokens(a							): i for i in range(self.vocab_size							)}
             vocab.update(self.added_tokens_encoder							)
             return vocab


      def 	_UpperCAmelCase (     self	,    a							) ->      List[str]:
             return self.sp_model.encode(a	,    out_type=a							)


      def 	_UpperCAmelCase (     self	,    a							) ->      Dict:
             if token in self.fairseq_tokens_to_ids:
                    return self.fairseq_tokens_to_ids[token]
             lowercase__    :	Union[str, Any]     					=			self.sp_model.PieceToId(a							)

             # Need to return unknown token if the SP model returned 0
             return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


      def 	_UpperCAmelCase (     self	,    a							) ->      int:
             if index in self.fairseq_ids_to_tokens:
                    return self.fairseq_ids_to_tokens[index]
             return self.sp_model.IdToPiece(index - self.fairseq_offset							)


      def 	_UpperCAmelCase (     self	,    a							) ->      str:
             lowercase__    :	List[Any]     					=			''.join(a							).replace(a	,    ' '							).strip()
             return out_string


      def 	_UpperCAmelCase (     self	,    a	,    a = None							) ->      Tuple[str]:
             if not os.path.isdir(a							):
                    logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
                    return
             lowercase__    :	Optional[Any]     					=			os.path.join(
                 a	,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

             if os.path.abspath(self.vocab_file							) != os.path.abspath(a							) and os.path.isfile(self.vocab_file							):
                    copyfile(self.vocab_file	,    a							)
             elif not os.path.isfile(self.vocab_file							):
                    with open(a	,    'wb'							) as fi:
                           lowercase__    :	Any     					=			self.sp_model.serialized_model_proto()
                           fi.write(a							)

             return (out_vocab_file,)


"""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_			(					_a):


      def __init__(     self							) ->      Any:
             lowercase__    :	Tuple     					=			[]


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Any:
             self.events.append('on_init_end'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Optional[int]:
             self.events.append('on_train_begin'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      List[str]:
             self.events.append('on_train_end'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      int:
             self.events.append('on_epoch_begin'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Optional[Any]:
             self.events.append('on_epoch_end'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      int:
             self.events.append('on_step_begin'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      str:
             self.events.append('on_step_end'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      int:
             self.events.append('on_evaluate'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Tuple:
             self.events.append('on_predict'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Union[str, Any]:
             self.events.append('on_save'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      List[str]:
             self.events.append('on_log'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Any:
             self.events.append('on_prediction_step'							)





@require_torch
class   UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	str     					=			tempfile.mkdtemp()


      def 	_UpperCAmelCase (     self							) ->      Dict:
             shutil.rmtree(self.output_dir							)


      def 	_UpperCAmelCase (     self	,    a=0	,    a=0	,    a=6_4	,    a=6_4	,    a=None	,    a=False	,    **a							) ->      int:
             # 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.
             lowercase__    :	str     					=			RegressionDataset(length=a							)
             lowercase__    :	Any     					=			RegressionDataset(length=a							)
             lowercase__    :	Optional[Any]     					=			RegressionModelConfig(a=a	,    b=a							)
             lowercase__    :	Union[str, Any]     					=			RegressionPreTrainedModel(a							)

             lowercase__    :	Tuple     					=			TrainingArguments(self.output_dir	,    disable_tqdm=a	,    report_to=[]	,    **a							)
             return Trainer(
                 a	,    a	,    train_dataset=a	,    eval_dataset=a	,    callbacks=a	,    )


      def 	_UpperCAmelCase (     self	,    a	,    a							) ->      Union[str, Any]:
             self.assertEqual(len(a							)	,    len(a							)							)

             # Order doesn't matter
             lowercase__    :	Optional[int]     					=			sorted(a	,    key=lambda a							: cb.__name__ if isinstance(a	,    a							) else cb.__class__.__name__							)
             lowercase__    :	Tuple     					=			sorted(a	,    key=lambda a							: cb.__name__ if isinstance(a	,    a							) else cb.__class__.__name__							)

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


      def 	_UpperCAmelCase (     self	,    a							) ->      Optional[Any]:
             lowercase__    :	Dict     					=			['on_init_end', 'on_train_begin']
             lowercase__    :	List[Any]     					=			0
             lowercase__    :	Optional[int]     					=			len(trainer.get_eval_dataloader()							)
             lowercase__    :	Tuple     					=			['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(a							):
                           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 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	int     					=			self.get_trainer()
             lowercase__    :	str     					=			DEFAULT_CALLBACKS.copy() + [ProgressCallback]
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)

             # Callbacks passed at init are added to the default callbacks
             lowercase__    :	str     					=			self.get_trainer(callbacks=[MyTestTrainerCallback]							)
             expected_callbacks.append(a							)
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)

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


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	int     					=			DEFAULT_CALLBACKS.copy() + [ProgressCallback]
             lowercase__    :	List[str]     					=			self.get_trainer()

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

             lowercase__    :	Optional[Any]     					=			self.get_trainer()
             lowercase__    :	List[Any]     					=			trainer.pop_callback(a							)
             self.assertEqual(cb.__class__	,    a							)
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)

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

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

             lowercase__    :	Tuple     					=			self.get_trainer()
             lowercase__    :	Dict     					=			trainer.callback_handler.callbacks[0]
             lowercase__    :	Union[str, Any]     					=			trainer.pop_callback(a							)
             self.assertEqual(a	,    a							)
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)

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


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             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=a							)

             lowercase__    :	Optional[Any]     					=			self.get_trainer(callbacks=[MyTestTrainerCallback]							)
             trainer.train()
             lowercase__    :	Any     					=			trainer.callback_handler.callbacks[-2].events
             self.assertEqual(a	,    self.get_expected_events(a							)							)

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

             lowercase__    :	Union[str, Any]     					=			self.get_trainer(callbacks=[MyTestTrainerCallback]	,    save_steps=5							)
             trainer.train()
             lowercase__    :	Union[str, Any]     					=			trainer.callback_handler.callbacks[-2].events
             self.assertEqual(a	,    self.get_expected_events(a							)							)

             lowercase__    :	List[str]     					=			self.get_trainer(callbacks=[MyTestTrainerCallback]	,    eval_steps=5	,    evaluation_strategy='steps'							)
             trainer.train()
             lowercase__    :	Optional[int]     					=			trainer.callback_handler.callbacks[-2].events
             self.assertEqual(a	,    self.get_expected_events(a							)							)

             lowercase__    :	int     					=			self.get_trainer(callbacks=[MyTestTrainerCallback]	,    evaluation_strategy='epoch'							)
             trainer.train()
             lowercase__    :	str     					=			trainer.callback_handler.callbacks[-2].events
             self.assertEqual(a	,    self.get_expected_events(a							)							)

             # A bit of everything
             lowercase__    :	Any     					=			self.get_trainer(
                 callbacks=[MyTestTrainerCallback]	,    logging_steps=3	,    save_steps=1_0	,    eval_steps=5	,    evaluation_strategy='steps'	,    )
             trainer.train()
             lowercase__    :	Any     					=			trainer.callback_handler.callbacks[-2].events
             self.assertEqual(a	,    self.get_expected_events(a							)							)

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

