

import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


lowercase_       		=  datasets.utils.logging.get_logger(__name__)


@dataclass
class  A						(							datasets.BuilderConfig ):







    """simple docstring"""





    lowerCamelCase			=							None
    lowerCamelCase			=							"utf-8"
    lowerCamelCase			=							None
    lowerCamelCase			=							None
    lowerCamelCase			=							True  # deprecated
    lowerCamelCase			=							None  # deprecated
    lowerCamelCase			=							10 << 20  # 10MB
    lowerCamelCase			=							None




class  A						(							datasets.ArrowBasedBuilder ):







    """simple docstring"""





    lowerCamelCase			=							JsonConfig


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





        '''simple docstring'''







        if self.config.block_size is not None:
            logger.warning('The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'		)
            A__          =   self.config.block_size
        if self.config.use_threads is not True:
            logger.warning(
                'The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'		)
        if self.config.newlines_in_values is not None:
            raise ValueError('The JSON loader parameter `newlines_in_values` is no longer supported'		)
        return datasets.DatasetInfo(features=self.config.features		)


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Any		)->    Tuple:





        '''simple docstring'''







        if not self.config.data_files:
            raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}'		)
        A__          =   dl_manager.download_and_extract(self.config.data_files		)
        if isinstance(lowercase_,(str, list, tuple)		):
            A__          =   data_files
            if isinstance(lowercase_,lowercase_		):
                A__          =   [files]
            A__          =   [dl_manager.iter_files(lowercase_		) for file in files]
            return [datasets.SplitGenerator(name=datasets.Split.TRAIN,gen_kwargs={'files': files}		)]
        A__          =   []
        for split_name, files in data_files.items():
            if isinstance(lowercase_,lowercase_		):
                A__          =   [files]
            A__          =   [dl_manager.iter_files(lowercase_		) for file in files]
            splits.append(datasets.SplitGenerator(name=lowercase_,gen_kwargs={'files': files}		)		)
        return splits


    def 						snake_case__     (      self :       int,lowercase_ :       pa.Table		)->    pa.Table:





        '''simple docstring'''







        if self.config.features is not None:
            # adding missing columns
            for column_name in set(self.config.features		) - set(pa_table.column_names		):
                A__          =   self.config.features.arrow_schema.field(lowercase_		).type
                A__          =   pa_table.append_column(lowercase_,pa.array([None] * len(lowercase_		),type=lowercase_		)		)
            # more expensive cast to support nested structures with keys in a different order
            # allows str <-> int/float or str to Audio for example
            A__          =   table_cast(lowercase_,self.config.features.arrow_schema		)
        return pa_table


    def 						snake_case__     (      self :       Dict,lowercase_ :       Any		)->    Optional[Any]:





        '''simple docstring'''







        for file_idx, file in enumerate(itertools.chain.from_iterable(lowercase_		)		):
            # If the file is one json object and if we need to look at the list of items in one specific field
            if self.config.field is not None:
                with open(lowercase_,encoding=self.config.encoding,errors=self.config.encoding_errors		) as f:
                    A__          =   json.load(lowercase_		)

                # We keep only the field we are interested in
                A__          =   dataset[self.config.field]

                # We accept two format: a list of dicts or a dict of lists
                if isinstance(lowercase_,(list, tuple)		):
                    A__          =   set().union(*[row.keys() for row in dataset]		)
                    A__          =   {col: [row.get(lowercase_		) for row in dataset] for col in keys}
                else:
                    A__          =   dataset
                A__          =   pa.Table.from_pydict(lowercase_		)
                yield file_idx, self._cast_table(lowercase_		)

            # If the file has one json object per line
            else:
                with open(lowercase_,'rb'		) as f:
                    A__          =   0
                    # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                    # Set a default minimum value of 16kB if the chunk size is really small
                    A__          =   max(self.config.chunksize // 3_2,1_6 << 1_0		)
                    A__          =   (
                        self.config.encoding_errors if self.config.encoding_errors is not None else 'strict'
                    )
                    while True:
                        A__          =   f.read(self.config.chunksize		)
                        if not batch:
                            break
                        # Finish current line
                        try:
                            batch += f.readline()
                        except (AttributeError, io.UnsupportedOperation):
                            batch += readline(lowercase_		)
                        # PyArrow only accepts utf-8 encoded bytes
                        if self.config.encoding != "utf-8":
                            A__          =   batch.decode(self.config.encoding,errors=lowercase_		).encode('utf-8'		)
                        try:
                            while True:
                                try:
                                    A__          =   paj.read_json(
                                        io.BytesIO(lowercase_		),read_options=paj.ReadOptions(block_size=lowercase_		)		)
                                    break
                                except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                    if (
                                        isinstance(lowercase_,pa.ArrowInvalid		)
                                        and "straddling" not in str(lowercase_		)
                                        or block_size > len(lowercase_		)
                                    ):
                                        raise
                                    else:
                                        # Increase the block size in case it was too small.
                                        # The block size will be reset for the next file.
                                        logger.debug(
                                            F'Batch of {len(lowercase_		)} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.'		)
                                        block_size *= 2
                        except pa.ArrowInvalid as e:
                            try:
                                with open(
                                    lowercase_,encoding=self.config.encoding,errors=self.config.encoding_errors		) as f:
                                    A__          =   json.load(lowercase_		)
                            except json.JSONDecodeError:
                                logger.error(F'Failed to read file \'{file}\' with error {type(lowercase_		)}: {e}'		)
                                raise e
                            # If possible, parse the file as a list of json objects and exit the loop
                            if isinstance(lowercase_,lowercase_		):  # list is the only sequence type supported in JSON
                                try:
                                    A__          =   set().union(*[row.keys() for row in dataset]		)
                                    A__          =   {col: [row.get(lowercase_		) for row in dataset] for col in keys}
                                    A__          =   pa.Table.from_pydict(lowercase_		)
                                except (pa.ArrowInvalid, AttributeError) as e:
                                    logger.error(F'Failed to read file \'{file}\' with error {type(lowercase_		)}: {e}'		)
                                    raise ValueError(F'Not able to read records in the JSON file at {file}.'		) from None
                                yield file_idx, self._cast_table(lowercase_		)
                                break
                            else:
                                logger.error(F'Failed to read file \'{file}\' with error {type(lowercase_		)}: {e}'		)
                                raise ValueError(
                                    F'Not able to read records in the JSON file at {file}. '
                                    F'You should probably indicate the field of the JSON file containing your records. '
                                    F'This JSON file contain the following fields: {str(list(dataset.keys()		)		)}. '
                                    F'Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. '		) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                        yield (file_idx, batch_idx), self._cast_table(lowercase_		)
                        batch_idx += 1




import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase_       		=  get_tests_dir("fixtures/test_sentencepiece.model")


@require_sentencepiece
@require_tokenizers
class  A						(							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							ReformerTokenizer
    lowerCamelCase			=							ReformerTokenizerFast
    lowerCamelCase			=							True
    lowerCamelCase			=							False
    lowerCamelCase			=							True


    def 						snake_case__     (      self :       Any		)->    str:





        '''simple docstring'''







        super().setUp()

        A__          =   ReformerTokenizer(lowercase_,keep_accents=lowercase_		)
        tokenizer.save_pretrained(self.tmpdirname		)


    def 						snake_case__     (      self :       Optional[int]		)->    Optional[int]:





        '''simple docstring'''







        A__          =   '<s>'
        A__          =   1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_		),lowercase_		)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_		),lowercase_		)


    def 						snake_case__     (      self :       str		)->    Tuple:





        '''simple docstring'''







        A__          =   list(self.get_tokenizer().get_vocab().keys()		)

        self.assertEqual(vocab_keys[0],'<unk>'		)
        self.assertEqual(vocab_keys[1],'<s>'		)
        self.assertEqual(vocab_keys[-1],'j'		)
        self.assertEqual(len(lowercase_		),1_0_0_0		)


    def 						snake_case__     (      self :       Dict		)->    Dict:





        '''simple docstring'''







        self.assertEqual(self.get_tokenizer().vocab_size,1_0_0_0		)


    def 						snake_case__     (      self :       Dict		)->    List[str]:





        '''simple docstring'''







        if not self.test_rust_tokenizer:
            return

        A__          =   self.get_tokenizer()
        A__          =   self.get_rust_tokenizer()

        A__          =   'I was born in 92000, and this is falsé.'

        A__          =   tokenizer.tokenize(lowercase_		)
        A__          =   rust_tokenizer.tokenize(lowercase_		)
        self.assertListEqual(lowercase_,lowercase_		)

        A__          =   tokenizer.encode(lowercase_,add_special_tokens=lowercase_		)
        A__          =   rust_tokenizer.encode(lowercase_,add_special_tokens=lowercase_		)
        self.assertListEqual(lowercase_,lowercase_		)

        A__          =   self.get_rust_tokenizer()
        A__          =   tokenizer.encode(lowercase_		)
        A__          =   rust_tokenizer.encode(lowercase_		)
        self.assertListEqual(lowercase_,lowercase_		)


    def 						snake_case__     (      self :       int,lowercase_ :       Optional[int]=1_5		)->    Optional[Any]:





        '''simple docstring'''







        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'		):
                A__          =   self.rust_tokenizer_class.from_pretrained(lowercase_,**lowercase_		)

                # Simple input
                A__          =   'This is a simple input'
                A__          =   ['This is a simple input 1', 'This is a simple input 2']
                A__          =   ('This is a simple input', 'This is a pair')
                A__          =   [
                    ('This is a simple input 1', 'This is a simple input 2'),
                    ('This is a simple pair 1', 'This is a simple pair 2'),
                ]

                # Simple input tests
                self.assertRaises(lowercase_,tokenizer_r.encode,lowercase_,max_length=lowercase_,padding='max_length'		)

                # Simple input
                self.assertRaises(lowercase_,tokenizer_r.encode_plus,lowercase_,max_length=lowercase_,padding='max_length'		)

                # Simple input
                self.assertRaises(
                    lowercase_,tokenizer_r.batch_encode_plus,lowercase_,max_length=lowercase_,padding='max_length',)

                # Pair input
                self.assertRaises(lowercase_,tokenizer_r.encode,lowercase_,max_length=lowercase_,padding='max_length'		)

                # Pair input
                self.assertRaises(lowercase_,tokenizer_r.encode_plus,lowercase_,max_length=lowercase_,padding='max_length'		)

                # Pair input
                self.assertRaises(
                    lowercase_,tokenizer_r.batch_encode_plus,lowercase_,max_length=lowercase_,padding='max_length',)


    def 						snake_case__     (      self :       List[Any]		)->    Tuple:





        '''simple docstring'''







        pass


    def 						snake_case__     (      self :       Dict		)->    str:





        '''simple docstring'''







        A__          =   ReformerTokenizer(lowercase_,keep_accents=lowercase_		)

        A__          =   tokenizer.tokenize('This is a test'		)
        self.assertListEqual(lowercase_,['▁This', '▁is', '▁a', '▁t', 'est']		)

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(lowercase_		),[2_8_5, 4_6, 1_0, 1_7_0, 3_8_2],)

        A__          =   tokenizer.tokenize('I was born in 92000, and this is falsé.'		)
        self.assertListEqual(
            lowercase_,[
                SPIECE_UNDERLINE + 'I',
                SPIECE_UNDERLINE + 'was',
                SPIECE_UNDERLINE + 'b',
                'or',
                'n',
                SPIECE_UNDERLINE + 'in',
                SPIECE_UNDERLINE + '',
                '9',
                '2',
                '0',
                '0',
                '0',
                ',',
                SPIECE_UNDERLINE + 'and',
                SPIECE_UNDERLINE + 'this',
                SPIECE_UNDERLINE + 'is',
                SPIECE_UNDERLINE + 'f',
                'al',
                's',
                'é',
                '.',
            ],)
        A__          =   tokenizer.convert_tokens_to_ids(lowercase_		)
        self.assertListEqual(
            lowercase_,[8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4],)

        A__          =   tokenizer.convert_ids_to_tokens(lowercase_		)
        self.assertListEqual(
            lowercase_,[
                SPIECE_UNDERLINE + 'I',
                SPIECE_UNDERLINE + 'was',
                SPIECE_UNDERLINE + 'b',
                'or',
                'n',
                SPIECE_UNDERLINE + 'in',
                SPIECE_UNDERLINE + '',
                '<unk>',
                '2',
                '0',
                '0',
                '0',
                ',',
                SPIECE_UNDERLINE + 'and',
                SPIECE_UNDERLINE + 'this',
                SPIECE_UNDERLINE + 'is',
                SPIECE_UNDERLINE + 'f',
                'al',
                's',
                '<unk>',
                '.',
            ],)


    @cached_property
    def 						snake_case__     (      self :       Optional[int]		)->    Any:





        '''simple docstring'''







        return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment'		)


    @slow
    def 						snake_case__     (      self :       str		)->    Tuple:





        '''simple docstring'''







        A__          =   'Hello World!'
        A__          =   [1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

        self.assertListEqual(lowercase_,self.big_tokenizer.encode(lowercase_		)		)


    @slow
    def 						snake_case__     (      self :       Optional[int]		)->    str:





        '''simple docstring'''







        A__          =   (
            'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
            ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
        )
        A__          =   [
            1_0_8,
            2_6_5,
            2_4,
            1_1_1,
            4,
            2_5_8,
            1_5_6,
            3_5,
            2_8,
            2_7_5,
            3,
            2_5_9,
            2_9_7,
            2_6_0,
            8_4,
            4,
            3_5,
            1_1_0,
            4_4,
            8,
            2_5_9,
            9_1,
            2_6_8,
            2_1,
            1_1,
            2_0_9,
            2_7_4,
            1_0_9,
            2_6_6,
            2_7_7,
            1_1_7,
            8_6,
            9_3,
            3_1_5,
            2_5_8,
            2_7_8,
            2_5_8,
            2_7_7,
            2_5_8,
            0,
            2_5_8,
            2_8_8,
            2_5_8,
            3_1_9,
            2_5_8,
            0,
            2_5_8,
            0,
            2_5_8,
            0,
            2_5_8,
            0,
            2_5_8,
            2_8_7,
            2_5_8,
            3_1_5,
            2_5_8,
            2_8_9,
            2_5_8,
            2_7_8,
            9_9,
            2_6_9,
            2_6_6,
            2_6_2,
            8,
            2_5_9,
            2_4_1,
            4,
            2_1_7,
            2_3_0,
            2_6_8,
            2_6_6,
            5_5,
            1_6_8,
            1_0_6,
            7_5,
            1_9_3,
            2_6_6,
            2_2_3,
            2_7,
            4_9,
            2_6,
            2_8_2,
            2_5,
            2_6_4,
            2_9_9,
            1_9,
            2_6,
            0,
            2_5_8,
            2_7_7,
            1_1_7,
            8_6,
            9_3,
            1_7_6,
            1_8_3,
            2_7_0,
            1_1,
            2_6_2,
            4_2,
            6_1,
            2_6_5,
        ]

        self.assertListEqual(lowercase_,self.big_tokenizer.encode(lowercase_		)		)


    @require_torch
    @slow
    def 						snake_case__     (      self :       int		)->    Any:





        '''simple docstring'''







        import torch

        from transformers import ReformerConfig, ReformerModel

        # Build sequence
        A__          =   list(self.big_tokenizer.get_vocab().keys()		)[:1_0]
        A__          =   ' '.join(lowercase_		)
        A__          =   self.big_tokenizer.encode_plus(lowercase_,return_tensors='pt'		)
        A__          =   self.big_tokenizer.batch_encode_plus([sequence, sequence],return_tensors='pt'		)

        A__          =   ReformerConfig()
        # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
        A__          =   encoded_sequence['input_ids'].shape
        A__          =   ReformerModel(lowercase_		)

        # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
        assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

        with torch.no_grad():
            model(**lowercase_		)
            model(**lowercase_		)


    @slow
    def 						snake_case__     (      self :       int		)->    Tuple:





        '''simple docstring'''







        A__          =   {'input_ids': [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
        # fmt: on

        # This tokenizer does not know some characters like ")".
        # That is the reason why we use very simple texts here.
        # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
        A__          =   [
            'This is a very simple sentence.',
            'The quick brown fox jumps over the lazy dog.',
        ]

        self.tokenizer_integration_test_util(
            expected_encoding=lowercase_,model_name='google/reformer-crime-and-punishment',revision='0e6c3decb8211d49bf881013425dc8b0448b3f5a',padding=lowercase_,sequences=lowercase_,)

