

import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
    lowerCAmelCase   :      Tuple     =     "pt"
elif is_tf_available():
    lowerCAmelCase   :      Optional[int]     =     "tf"
else:
    lowerCAmelCase   :      Tuple     =     "jax"
class        _A						(     A__      ,   unittest.TestCase):
  SCREAMING_SNAKE_CASE			:					Any				=     ByTaTokenizer
  SCREAMING_SNAKE_CASE			:					Union[str, Any]				=     False

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        super().setUp()
        SCREAMING_SNAKE_CASE_ :     Tuple							=   ByTaTokenizer()
        tokenizer.save_pretrained(self.tmpdirname    )

  @cached_property
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        return ByTaTokenizer.from_pretrained('google/byt5-small'    )

  def        UpperCAmelCase			( self       ,    **_SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        return self.tokenizer_class.from_pretrained(self.tmpdirname       ,    **_a    )

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE=False       ,    _SCREAMING_SNAKE_CASE=20       ,    _SCREAMING_SNAKE_CASE=5    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Dict							=   []
        for i in range(len(_a    )    ):
              try:
                    SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   tokenizer.decode([i]       ,    clean_up_tokenization_spaces=_a    )
              except UnicodeDecodeError:
                    pass
              toks.append((i, tok)    )

        SCREAMING_SNAKE_CASE_ :     int							=   list(filter(lambda _SCREAMING_SNAKE_CASE    : re.match(r'^[ a-zA-Z]+$'       ,    t[1]    )       ,    _a    )    )
        SCREAMING_SNAKE_CASE_ :     str							=   list(filter(lambda _SCREAMING_SNAKE_CASE    : [t[0]] == tokenizer.encode(t[1]       ,    add_special_tokens=_a    )       ,    _a    )    )
        if max_length is not None and len(_a    ) > max_length:
              SCREAMING_SNAKE_CASE_ :     Optional[int]							=   toks[:max_length]
        if min_length is not None and len(_a    ) < min_length and len(_a    ) > 0:
              while len(_a    ) < min_length:
                    SCREAMING_SNAKE_CASE_ :     int							=   toks + toks
        # toks_str = [t[1] for t in toks]
        SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   [t[0] for t in toks]

        # Ensure consistency
        SCREAMING_SNAKE_CASE_ :     str							=   tokenizer.decode(_a       ,    clean_up_tokenization_spaces=_a    )
        if " " not in output_txt and len(_a    ) > 1:
              SCREAMING_SNAKE_CASE_ :     Tuple							=   (
                  tokenizer.decode([toks_ids[0]]       ,    clean_up_tokenization_spaces=_a    )
                  + ' '
                  + tokenizer.decode(toks_ids[1:]       ,    clean_up_tokenization_spaces=_a    )
              )
        if with_prefix_space:
              SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   ' ' + output_txt
        SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   tokenizer.encode(_a       ,    add_special_tokens=_a    )
        return output_txt, output_ids

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   self.ta_base_tokenizer
        SCREAMING_SNAKE_CASE_ :     str							=   tokenizer(['hi</s>', 'I went to the gym</s>', '</s>']    )
        SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   tokenizer(['hi', 'I went to the gym', '']    )
        self.assertListEqual(batch_with_eos_added['input_ids']       ,    batch_without_eos_added['input_ids']    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Dict							=   self.ta_base_tokenizer
        SCREAMING_SNAKE_CASE_ :     str							=   'Unicode €.'
        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   tokenizer(_a    )
        SCREAMING_SNAKE_CASE_ :     Tuple							=   [88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
        self.assertEqual(encoded['input_ids']       ,    _a    )

        # decoding
        SCREAMING_SNAKE_CASE_ :     int							=   tokenizer.decode(_a    )
        self.assertEqual(_a       ,    'Unicode €.</s>'    )

        SCREAMING_SNAKE_CASE_ :     List[Any]							=   tokenizer('e è é ê ë'    )
        SCREAMING_SNAKE_CASE_ :     Any							=   [104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
        self.assertEqual(encoded['input_ids']       ,    _a    )
        # decoding
        SCREAMING_SNAKE_CASE_ :     int							=   tokenizer.decode(_a    )
        self.assertEqual(_a       ,    'e è é ê ë</s>'    )

        # encode/decode, but with `encode` instead of `__call__`
        self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'    )    )       ,    'e è é ê ë</s>'    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   self.ta_base_tokenizer
        SCREAMING_SNAKE_CASE_ :     Any							=   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
        # fmt: off
        SCREAMING_SNAKE_CASE_ :     int							=   [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
        # fmt: on
        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   tokenizer(_a       ,    padding=_a       ,    return_tensors=_a    )
        self.assertIsInstance(_a       ,    _a    )

        if FRAMEWORK != "jax":
              SCREAMING_SNAKE_CASE_ :     Dict							=   list(batch.input_ids.numpy()[0]    )
        else:
              SCREAMING_SNAKE_CASE_ :     str							=   list(batch.input_ids.tolist()[0]    )

        self.assertListEqual(_a       ,    _a    )

        self.assertEqual((2, 37)       ,    batch.input_ids.shape    )
        self.assertEqual((2, 37)       ,    batch.attention_mask.shape    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     int							=   self.ta_base_tokenizer
        SCREAMING_SNAKE_CASE_ :     int							=   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
        SCREAMING_SNAKE_CASE_ :     Dict							=   tokenizer(_a       ,    padding=_a       ,    return_tensors=_a    )
        # check if input_ids are returned and no decoder_input_ids
        self.assertIn('input_ids'       ,    _a    )
        self.assertIn('attention_mask'       ,    _a    )
        self.assertNotIn('decoder_input_ids'       ,    _a    )
        self.assertNotIn('decoder_attention_mask'       ,    _a    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     int							=   self.ta_base_tokenizer
        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   [
            'Summary of the text.',
            'Another summary.',
        ]
        SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   tokenizer(
            text_target=_a       ,    max_length=32       ,    padding='max_length'       ,    truncation=_a       ,    return_tensors=_a    )
        self.assertEqual(32       ,    targets['input_ids'].shape[1]    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     str							=   self.ta_base_tokenizer
        SCREAMING_SNAKE_CASE_ :     Tuple							=   ['A long paragraph for summarization. </s>']
        SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   ['Summary of the text. </s>']
        # fmt: off
        SCREAMING_SNAKE_CASE_ :     Tuple							=   [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   [86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
        # fmt: on

        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   tokenizer(_a       ,    text_target=_a    )

        self.assertEqual(_a       ,    batch['input_ids'][0]    )
        self.assertEqual(_a       ,    batch['labels'][0]    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   self.get_tokenizers()
        for tokenizer in tokenizers:
              with self.subTest(f"{tokenizer.__class__.__name__}"    ):
                    self.assertNotEqual(tokenizer.model_max_length       ,    42    )

        # Now let's start the test
        SCREAMING_SNAKE_CASE_ :     str							=   self.get_tokenizers()
        for tokenizer in tokenizers:
              with self.subTest(f"{tokenizer.__class__.__name__}"    ):
                    # Isolate this from the other tests because we save additional tokens/etc
                    SCREAMING_SNAKE_CASE_ :     List[str]							=   tempfile.mkdtemp()

                    SCREAMING_SNAKE_CASE_ :     Optional[int]							=   ' He is very happy, UNwant\u00E9d,running'
                    SCREAMING_SNAKE_CASE_ :     str							=   tokenizer.encode(_a       ,    add_special_tokens=_a    )
                    tokenizer.save_pretrained(_a    )

                    SCREAMING_SNAKE_CASE_ :     Dict							=   tokenizer.__class__.from_pretrained(_a    )
                    SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   after_tokenizer.encode(_a       ,    add_special_tokens=_a    )
                    self.assertListEqual(_a       ,    _a    )

                    shutil.rmtree(_a    )

        SCREAMING_SNAKE_CASE_ :     List[Any]							=   self.get_tokenizers(model_max_length=42    )
        for tokenizer in tokenizers:
              with self.subTest(f"{tokenizer.__class__.__name__}"    ):
                    # Isolate this from the other tests because we save additional tokens/etc
                    SCREAMING_SNAKE_CASE_ :     Tuple							=   tempfile.mkdtemp()

                    SCREAMING_SNAKE_CASE_ :     List[str]							=   ' He is very happy, UNwant\u00E9d,running'
                    tokenizer.add_tokens(['bim', 'bambam']    )
                    SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   tokenizer.additional_special_tokens
                    additional_special_tokens.append('new_additional_special_token'    )
                    tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}    )
                    SCREAMING_SNAKE_CASE_ :     str							=   tokenizer.encode(_a       ,    add_special_tokens=_a    )
                    tokenizer.save_pretrained(_a    )

                    SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   tokenizer.__class__.from_pretrained(_a    )
                    SCREAMING_SNAKE_CASE_ :     int							=   after_tokenizer.encode(_a       ,    add_special_tokens=_a    )
                    self.assertListEqual(_a       ,    _a    )
                    self.assertIn('new_additional_special_token'       ,    after_tokenizer.additional_special_tokens    )
                    self.assertEqual(after_tokenizer.model_max_length       ,    42    )

                    SCREAMING_SNAKE_CASE_ :     Tuple							=   tokenizer.__class__.from_pretrained(_a       ,    model_max_length=43    )
                    self.assertEqual(tokenizer.model_max_length       ,    43    )

                    shutil.rmtree(_a    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Any							=   []
        if self.test_slow_tokenizer:
              tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())    )

        if self.test_rust_tokenizer:
              tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())    )

        for tokenizer_class, tokenizer_utils in tokenizer_list:
              with tempfile.TemporaryDirectory() as tmp_dir:
                    tokenizer_utils.save_pretrained(_a    )

                    with open(os.path.join(_a       ,    'special_tokens_map.json'    )       ,    encoding='utf-8'    ) as json_file:
                          SCREAMING_SNAKE_CASE_ :     Optional[int]							=   json.load(_a    )

                    with open(os.path.join(_a       ,    'tokenizer_config.json'    )       ,    encoding='utf-8'    ) as json_file:
                          SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   json.load(_a    )

                    SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   [f"<extra_id_{i}>" for i in range(125    )]

                    SCREAMING_SNAKE_CASE_ :     Tuple							=   added_tokens_extra_ids + [
                        'an_additional_special_token'
                    ]
                    SCREAMING_SNAKE_CASE_ :     Any							=   added_tokens_extra_ids + [
                        'an_additional_special_token'
                    ]

                    with open(os.path.join(_a       ,    'special_tokens_map.json'    )       ,    'w'       ,    encoding='utf-8'    ) as outfile:
                          json.dump(_a       ,    _a    )
                    with open(os.path.join(_a       ,    'tokenizer_config.json'    )       ,    'w'       ,    encoding='utf-8'    ) as outfile:
                          json.dump(_a       ,    _a    )

                    # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                    # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                    # "special_tokens_map.json" files
                    SCREAMING_SNAKE_CASE_ :     Optional[int]							=   tokenizer_class.from_pretrained(
                        _a       ,    )
                    self.assertIn(
                        'an_additional_special_token'       ,    tokenizer_without_change_in_init.additional_special_tokens    )
                    # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                    self.assertEqual(
                        ['an_additional_special_token']       ,    tokenizer_without_change_in_init.convert_ids_to_tokens(
                            tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token']    )    )       ,    )

                    # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                    SCREAMING_SNAKE_CASE_ :     Tuple							=   added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'       ,    lstrip=_a    )]
                    SCREAMING_SNAKE_CASE_ :     Tuple							=   tokenizer_class.from_pretrained(
                        _a       ,    additional_special_tokens=_a       ,    )

                    self.assertIn('a_new_additional_special_token'       ,    tokenizer.additional_special_tokens    )
                    self.assertEqual(
                        ['a_new_additional_special_token']       ,    tokenizer.convert_ids_to_tokens(
                            tokenizer.convert_tokens_to_ids(['a_new_additional_special_token']    )    )       ,    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Dict							=   []
        if self.test_slow_tokenizer:
              tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())    )

        if self.test_rust_tokenizer:
              tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())    )

        for tokenizer_class, tokenizer_utils in tokenizer_list:
              with tempfile.TemporaryDirectory() as tmp_dir:
                    tokenizer_utils.save_pretrained(_a    )

                    SCREAMING_SNAKE_CASE_ :     Any							=   tokenizer_class.from_pretrained(_a    )

                    self.assertTrue(tokenizer.decode([255]    ) == ''    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        pass

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        pass

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        pass

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        pass

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   self.get_tokenizers(fast=_a       ,    do_lower_case=_a    )
        for tokenizer in tokenizers:
              with self.subTest(f"{tokenizer.__class__.__name__}"    ):
                    SCREAMING_SNAKE_CASE_ :     str							=   ['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 'x', 't', '</s>']
                    SCREAMING_SNAKE_CASE_ :     int							=   tokenizer.convert_tokens_to_string(_a    )

                    self.assertIsInstance(_a       ,    _a    )



  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     int							=   self.get_tokenizers()
        for tokenizer in tokenizers:
              with self.subTest(f"{tokenizer.__class__.__name__}"    ):
                    SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   [
                        'bos_token',
                        'eos_token',
                        'unk_token',
                        'sep_token',
                        'pad_token',
                        'cls_token',
                        'mask_token',
                    ]

                    SCREAMING_SNAKE_CASE_ :     List[Any]							=   0
                    SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   tokenizer.convert_ids_to_tokens(
                        _a       ,    skip_special_tokens=_a    )

                    for attr in attributes_list:
                          setattr(_a       ,    attr + '_id'       ,    _a    )
                          self.assertEqual(getattr(_a       ,    _a    )       ,    _a    )
                          self.assertEqual(getattr(_a       ,    attr + '_id'    )       ,    _a    )

                          setattr(_a       ,    attr + '_id'       ,    _a    )
                          self.assertEqual(getattr(_a       ,    _a    )       ,    _a    )
                          self.assertEqual(getattr(_a       ,    attr + '_id'    )       ,    _a    )

                    setattr(_a       ,    'additional_special_tokens_ids'       ,    []    )
                    self.assertListEqual(getattr(_a       ,    'additional_special_tokens'    )       ,    []    )
                    self.assertListEqual(getattr(_a       ,    'additional_special_tokens_ids'    )       ,    []    )

                    setattr(_a       ,    'additional_special_tokens_ids'       ,    [token_id_to_test_setters]    )
                    self.assertListEqual(getattr(_a       ,    'additional_special_tokens'    )       ,    [token_to_test_setters]    )
                    self.assertListEqual(getattr(_a       ,    'additional_special_tokens_ids'    )       ,    [token_id_to_test_setters]    )









'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def        _lowerCAmelCase					(						)      ->			Any:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =ArgumentParser(
         description=(
             'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
         )				)

     # Optional arguments for the launch helper
     parser.add_argument('--num_cores'							, type=_UpperCamelCase							, default=1							, help='Number of TPU cores to use (1 or 8).'				)

     # positional
     parser.add_argument(
         'training_script'							, type=_UpperCamelCase							, help=(
             'The full path to the single TPU training '
             'program/script to be launched in parallel, '
             'followed by all the arguments for the '
             'training script'
         )							, )

     # rest from the training program
     parser.add_argument('training_script_args'							, nargs=_UpperCamelCase				)

     return parser.parse_args()


def        _lowerCAmelCase					(						)      ->			Optional[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =parse_args()

     # Import training_script as a module.
     _SCREAMING_SNAKE_CASE							    =Path(args.training_script				)
     sys.path.append(str(script_fpath.parent.resolve()				)				)
     _SCREAMING_SNAKE_CASE							    =script_fpath.stem
     _SCREAMING_SNAKE_CASE							    =importlib.import_module(_UpperCamelCase				)

     # Patch sys.argv
     _SCREAMING_SNAKE_CASE							    =[args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores				)]

     xmp.spawn(mod._mp_fn							, args=()							, nprocs=args.num_cores				)


if __name__ == "__main__":
     main()
