




import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

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


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation

lowerCamelCase_  						=       logging.get_logger(__name__)

lowerCamelCase_  						=       {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

lowerCamelCase_  						=       {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
}

lowerCamelCase_  						=       {
    "gpt2": 1024,
    "gpt2-medium": 1024,
    "gpt2-large": 1024,
    "gpt2-xl": 1024,
    "distilgpt2": 1024,
}



@lru_cache()
def      UpperCAmelCase_   (     ):
    SCREAMING_SNAKE_CASE__       =(
        list(range(ord("""!"""     ),    ord("""~"""     ) + 1     )     ) + list(range(ord("""¡"""     ),    ord("""¬"""     ) + 1     )     ) + list(range(ord("""®"""     ),    ord("""ÿ"""     ) + 1     )     )
    )
    SCREAMING_SNAKE_CASE__       =bs[:]
    SCREAMING_SNAKE_CASE__       =0
    for b in range(2**8     ):
        if b not in bs:
            bs.append(__UpperCamelCase     )
            cs.append(2**8 + n     )
            n += 1
    SCREAMING_SNAKE_CASE__       =[chr(__UpperCamelCase     ) for n in cs]
    return dict(zip(__UpperCamelCase,    __UpperCamelCase     )     )



def      UpperCAmelCase_   (     __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =set()
    SCREAMING_SNAKE_CASE__       =word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)     )
        SCREAMING_SNAKE_CASE__       =char
    return pairs




class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     _A    :   Dict     =  VOCAB_FILES_NAMES
     _A    :   Any     =  PRETRAINED_VOCAB_FILES_MAP
     _A    :   Optional[Any]     =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A    :   List[Any]     =  ["input_ids", "attention_mask"]




     def __init__(     self      :			str    ,_UpperCamelCase      :			Any    ,_UpperCamelCase      :			Union[str, Any]    ,_UpperCamelCase      :			Optional[int]="replace"    ,_UpperCamelCase      :			List[str]="<|endoftext|>"    ,_UpperCamelCase      :			Optional[Any]="<|endoftext|>"    ,_UpperCamelCase      :			Union[str, Any]="<|endoftext|>"    ,_UpperCamelCase      :			List[Any]=None    ,_UpperCamelCase      :			List[Any]=False    ,_UpperCamelCase      :			int=False    ,**_UpperCamelCase      :			List[str]    ,)		->       Any:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =AddedToken(_UpperCamelCase    ,lstrip=_UpperCamelCase    ,rstrip=_UpperCamelCase						) if isinstance(_UpperCamelCase    ,_UpperCamelCase						) else bos_token
         SCREAMING_SNAKE_CASE__       =AddedToken(_UpperCamelCase    ,lstrip=_UpperCamelCase    ,rstrip=_UpperCamelCase						) if isinstance(_UpperCamelCase    ,_UpperCamelCase						) else eos_token
         SCREAMING_SNAKE_CASE__       =AddedToken(_UpperCamelCase    ,lstrip=_UpperCamelCase    ,rstrip=_UpperCamelCase						) if isinstance(_UpperCamelCase    ,_UpperCamelCase						) else unk_token
         SCREAMING_SNAKE_CASE__       =AddedToken(_UpperCamelCase    ,lstrip=_UpperCamelCase    ,rstrip=_UpperCamelCase						) if isinstance(_UpperCamelCase    ,_UpperCamelCase						) else pad_token
         super().__init__(
             errors=_UpperCamelCase    ,unk_token=_UpperCamelCase    ,bos_token=_UpperCamelCase    ,eos_token=_UpperCamelCase    ,pad_token=_UpperCamelCase    ,add_prefix_space=_UpperCamelCase    ,add_bos_token=_UpperCamelCase    ,**_UpperCamelCase    ,)
         SCREAMING_SNAKE_CASE__       =add_bos_token

         with open(_UpperCamelCase    ,encoding="""utf-8"""						) as vocab_handle:
             SCREAMING_SNAKE_CASE__       =json.load(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       ={v: k for k, v in self.encoder.items()}
         SCREAMING_SNAKE_CASE__       =errors  # how to handle errors in decoding
         SCREAMING_SNAKE_CASE__       =bytes_to_unicode()
         SCREAMING_SNAKE_CASE__       ={v: k for k, v in self.byte_encoder.items()}
         with open(_UpperCamelCase    ,encoding="""utf-8"""						) as merges_handle:
             SCREAMING_SNAKE_CASE__       =merges_handle.read().split("""\n"""						)[1:-1]
         SCREAMING_SNAKE_CASE__       =[tuple(merge.split()						) for merge in bpe_merges]
         SCREAMING_SNAKE_CASE__       =dict(zip(_UpperCamelCase    ,range(len(_UpperCamelCase						)						)						)						)
         SCREAMING_SNAKE_CASE__       ={}
         SCREAMING_SNAKE_CASE__       =add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         SCREAMING_SNAKE_CASE__       =re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""						)




     @property
     def   __A    (     self      :			Tuple						)		->       Union[str, Any]:


         '''simple docstring'''





         return len(self.encoder						)




     def   __A    (     self      :			List[Any]						)		->       Optional[Any]:


         '''simple docstring'''





         return dict(self.encoder    ,**self.added_tokens_encoder						)




     def   __A    (     self      :			Tuple    ,_UpperCamelCase      :			List[Any]						)		->       Dict:


         '''simple docstring'''





         if token in self.cache:
             return self.cache[token]
         SCREAMING_SNAKE_CASE__       =tuple(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =get_pairs(_UpperCamelCase						)

         if not pairs:
             return token

         while True:
             SCREAMING_SNAKE_CASE__       =min(_UpperCamelCase    ,key=lambda _UpperCamelCase						: self.bpe_ranks.get(_UpperCamelCase    ,float("""inf"""						)						)						)
             if bigram not in self.bpe_ranks:
                 break
             SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =bigram
             SCREAMING_SNAKE_CASE__       =[]
             SCREAMING_SNAKE_CASE__       =0
             while i < len(_UpperCamelCase						):
                 try:
                     SCREAMING_SNAKE_CASE__       =word.index(_UpperCamelCase    ,_UpperCamelCase						)
                 except ValueError:
                     new_word.extend(word[i:]						)
                     break
                 else:
                     new_word.extend(word[i:j]						)
                     SCREAMING_SNAKE_CASE__       =j

                 if word[i] == first and i < len(_UpperCamelCase						) - 1 and word[i + 1] == second:
                     new_word.append(first + second						)
                     i += 2
                 else:
                     new_word.append(word[i]						)
                     i += 1
             SCREAMING_SNAKE_CASE__       =tuple(_UpperCamelCase						)
             SCREAMING_SNAKE_CASE__       =new_word
             if len(_UpperCamelCase						) == 1:
                 break
             else:
                 SCREAMING_SNAKE_CASE__       =get_pairs(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =""" """.join(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =word
         return word




     def   __A    (     self      :			List[str]    ,_UpperCamelCase      :			List[str]    ,_UpperCamelCase      :			Optional[int]=None						)		->       Any:


         '''simple docstring'''





         if self.add_bos_token:
             SCREAMING_SNAKE_CASE__       =[self.bos_token_id]
         else:
             SCREAMING_SNAKE_CASE__       =[]

         SCREAMING_SNAKE_CASE__       =bos_token_ids + token_ids_a

         if token_ids_a is None:
             return output

         return output + bos_token_ids + token_ids_a




     def   __A    (     self      :			Union[str, Any]    ,_UpperCamelCase      :			List[int]    ,_UpperCamelCase      :			Optional[List[int]] = None    ,_UpperCamelCase      :			bool = False						)		->       List[int]:


         '''simple docstring'''





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

         if not self.add_bos_token:
             return super().get_special_tokens_mask(
                 token_ids_a=_UpperCamelCase    ,token_ids_a=_UpperCamelCase    ,already_has_special_tokens=_UpperCamelCase						)

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




     def   __A    (     self      :			Tuple    ,_UpperCamelCase      :			str						)		->       Optional[int]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =[]
         for token in re.findall(self.pat    ,_UpperCamelCase						):
             SCREAMING_SNAKE_CASE__       ="""""".join(
                 self.byte_encoder[b] for b in token.encode("""utf-8"""						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
             bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_UpperCamelCase						).split(""" """						)						)
         return bpe_tokens




     def   __A    (     self      :			Tuple    ,_UpperCamelCase      :			Dict						)		->       List[Any]:


         '''simple docstring'''





         return self.encoder.get(_UpperCamelCase    ,self.encoder.get(self.unk_token						)						)




     def   __A    (     self      :			Dict    ,_UpperCamelCase      :			Tuple						)		->       Optional[int]:


         '''simple docstring'''





         return self.decoder.get(_UpperCamelCase						)




     def   __A    (     self      :			Dict    ,_UpperCamelCase      :			Union[str, Any]						)		->       int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       ="""""".join(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =bytearray([self.byte_decoder[c] for c in text]						).decode("""utf-8"""    ,errors=self.errors						)
         return text




     def   __A    (     self      :			str    ,_UpperCamelCase      :			str    ,_UpperCamelCase      :			Optional[str] = None						)		->       Tuple[str]:


         '''simple docstring'''





         if not os.path.isdir(_UpperCamelCase						):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
             return
         SCREAMING_SNAKE_CASE__       =os.path.join(
             _UpperCamelCase    ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)
         SCREAMING_SNAKE_CASE__       =os.path.join(
             _UpperCamelCase    ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]						)

         with open(_UpperCamelCase    ,"""w"""    ,encoding="""utf-8"""						) as f:
             f.write(json.dumps(self.encoder    ,indent=2    ,sort_keys=_UpperCamelCase    ,ensure_ascii=_UpperCamelCase						) + """\n"""						)

         SCREAMING_SNAKE_CASE__       =0
         with open(_UpperCamelCase    ,"""w"""    ,encoding="""utf-8"""						) as writer:
             writer.write("""#version: 0.2\n"""						)
             for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,key=lambda _UpperCamelCase						: kv[1]						):
                 if index != token_index:
                     logger.warning(
                         f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                         """ Please check that the tokenizer is not corrupted!"""						)
                     SCREAMING_SNAKE_CASE__       =token_index
                 writer.write(""" """.join(_UpperCamelCase						) + """\n"""						)
                 index += 1

         return vocab_file, merge_file




     def   __A    (     self      :			Optional[Any]    ,_UpperCamelCase      :			List[Any]    ,_UpperCamelCase      :			str=False    ,**_UpperCamelCase      :			List[str]						)		->       Optional[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =kwargs.pop("""add_prefix_space"""    ,self.add_prefix_space						)
         if is_split_into_words or add_prefix_space:
             SCREAMING_SNAKE_CASE__       =""" """ + text
         return (text, kwargs)




     def   __A    (     self      :			Optional[int]    ,_UpperCamelCase      :			"Conversation"						)		->       List[int]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =[]
         for is_user, text in conversation.iter_texts():
             input_ids.extend(self.encode(_UpperCamelCase    ,add_special_tokens=_UpperCamelCase						) + [self.eos_token_id]						)
         if len(_UpperCamelCase						) > self.model_max_length:
             SCREAMING_SNAKE_CASE__       =input_ids[-self.model_max_length :]
         return input_ids












import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     def   __A    (     self      :			Optional[Any]    ,_UpperCamelCase      :			Optional[int]    ,_UpperCamelCase      :			Optional[Any]=False						)		->       Tuple:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =(
             filename_skip_take["""filename"""],
             filename_skip_take["""skip"""] if """skip""" in filename_skip_take else None,
             filename_skip_take["""take"""] if """take""" in filename_skip_take else None,
         )
         open(os.path.join(_UpperCamelCase						)    ,"""wb"""						).close()
         SCREAMING_SNAKE_CASE__       =pa.Table.from_pydict({"""filename""": [Path(_UpperCamelCase						).name] * 1_0_0}						)
         if take == -1:
             SCREAMING_SNAKE_CASE__       =len(_UpperCamelCase						) - skip
         if skip is not None and take is not None:
             SCREAMING_SNAKE_CASE__       =pa_table.slice(_UpperCamelCase    ,_UpperCamelCase						)
         return pa_table






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     def   __A    (     self      :			List[str]						)		->       Any:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       ="""my_name"""
         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""train"""    ,num_examples=1_0_0						)
         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""test"""    ,num_examples=1_0_0						)
         SCREAMING_SNAKE_CASE__       =[train_info, test_info]
         SCREAMING_SNAKE_CASE__       =SplitDict()
         split_dict.add(_UpperCamelCase						)
         split_dict.add(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =DatasetInfo(splits=_UpperCamelCase						)

         with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE__       =ReaderTest(_UpperCamelCase    ,_UpperCamelCase						)

             SCREAMING_SNAKE_CASE__       ="""test[:33%]"""
             SCREAMING_SNAKE_CASE__       =Dataset(**reader.read(_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase						)						)
             self.assertEqual(dset["""filename"""][0]    ,f"""{name}-test"""						)
             self.assertEqual(dset.num_rows    ,3_3						)
             self.assertEqual(dset.num_columns    ,1						)

             SCREAMING_SNAKE_CASE__       =["""train""", """test[:33%]"""]
             SCREAMING_SNAKE_CASE__       =[Split.TRAIN, ReadInstruction.from_spec("""test[:33%]"""						)]
             for instructions in [instructionsa, instructionsa]:
                 SCREAMING_SNAKE_CASE__       =[reader.read(_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase						) for instr in instructions]
                 SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =(Dataset(**_UpperCamelCase						) for dataset_kwargs in datasets_kwargs)
                 self.assertEqual(train_dset["""filename"""][0]    ,f"""{name}-train"""						)
                 self.assertEqual(train_dset.num_rows    ,1_0_0						)
                 self.assertEqual(train_dset.num_columns    ,1						)
                 self.assertIsInstance(train_dset.split    ,_UpperCamelCase						)
                 self.assertEqual(str(train_dset.split						)    ,"""train"""						)
                 self.assertEqual(test_dset["""filename"""][0]    ,f"""{name}-test"""						)
                 self.assertEqual(test_dset.num_rows    ,3_3						)
                 self.assertEqual(test_dset.num_columns    ,1						)
                 self.assertIsInstance(test_dset.split    ,_UpperCamelCase						)
                 self.assertEqual(str(test_dset.split						)    ,"""test[:33%]"""						)
                 del train_dset, test_dset




     def   __A    (     self      :			Tuple						)		->       str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       ="""my_name"""
         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""train"""    ,num_examples=1_0_0_0    ,shard_lengths=[1_0_0] * 1_0						)
         SCREAMING_SNAKE_CASE__       =[train_info]
         SCREAMING_SNAKE_CASE__       =SplitDict()
         split_dict.add(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =DatasetInfo(splits=_UpperCamelCase						)

         with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE__       =ReaderTest(_UpperCamelCase    ,_UpperCamelCase						)

             SCREAMING_SNAKE_CASE__       ="""train[:33%]"""
             SCREAMING_SNAKE_CASE__       =Dataset(**reader.read(_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase						)						)
             self.assertEqual(dset["""filename"""][0]    ,f"""{name}-train-00000-of-00010"""						)
             self.assertEqual(dset["""filename"""][-1]    ,f"""{name}-train-00003-of-00010"""						)
             self.assertEqual(dset.num_rows    ,3_3_0						)
             self.assertEqual(dset.num_columns    ,1						)




     def   __A    (     self      :			Dict						)		->       Union[str, Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""train"""    ,num_examples=1_0_0						)
         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""test"""    ,num_examples=1_0_0						)
         SCREAMING_SNAKE_CASE__       =SplitDict()
         split_dict.add(_UpperCamelCase						)
         split_dict.add(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =DatasetInfo(splits=_UpperCamelCase						)

         with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE__       =ReaderTest(_UpperCamelCase    ,_UpperCamelCase						)

             SCREAMING_SNAKE_CASE__       =[
                 {"""filename""": os.path.join(_UpperCamelCase    ,"""train"""						)},
                 {"""filename""": os.path.join(_UpperCamelCase    ,"""test"""						), """skip""": 1_0, """take""": 1_0},
             ]
             SCREAMING_SNAKE_CASE__       =Dataset(**reader.read_files(_UpperCamelCase    ,original_instructions="""train+test[10:20]"""						)						)
             self.assertEqual(dset.num_rows    ,1_1_0						)
             self.assertEqual(dset.num_columns    ,1						)
             del dset






@pytest.mark.parametrize("""in_memory""",    [False, True]     )
def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =arrow_file
    with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
        SCREAMING_SNAKE_CASE__       =ArrowReader.read_table(__UpperCamelCase,    in_memory=__UpperCamelCase     )
    assert table.shape == dataset.data.shape
    assert set(table.column_names     ) == set(dataset.data.column_names     )
    assert dict(table.to_pydict()     ) == dict(dataset.data.to_pydict()     )  # to_pydict returns OrderedDict



@pytest.mark.parametrize("""in_memory""",    [False, True]     )
def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =arrow_file
    SCREAMING_SNAKE_CASE__       =ArrowReader("""""",    __UpperCamelCase     )
    with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
        SCREAMING_SNAKE_CASE__       =reader.read_files([{"""filename""": filename}],    in_memory=__UpperCamelCase     )
    assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
    SCREAMING_SNAKE_CASE__       =dataset_kwargs["""arrow_table"""]
    assert table.shape == dataset.data.shape
    assert set(table.column_names     ) == set(dataset.data.column_names     )
    assert dict(table.to_pydict()     ) == dict(dataset.data.to_pydict()     )  # to_pydict returns OrderedDict



def      UpperCAmelCase_   (     ):
    assert ReadInstruction("""train""",    to=10,    unit="""abs"""     ).to_spec() == "train[:10]"
    assert ReadInstruction("""train""",    from_=-80,    to=10,    unit="""%"""     ).to_spec() == "train[-80%:10%]"

    SCREAMING_SNAKE_CASE__       ="""train+test"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_test

    SCREAMING_SNAKE_CASE__       ="""train[2:10]"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_abs

    SCREAMING_SNAKE_CASE__       ="""train[15%:-20%]"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_pct

    SCREAMING_SNAKE_CASE__       ="""train[:10%](closest)"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == "train[:10%]"

    SCREAMING_SNAKE_CASE__       ="""train[:10%](pct1_dropremainder)"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_pct_rounding

    SCREAMING_SNAKE_CASE__       ="""train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_test_pct_rounding



def      UpperCAmelCase_   (     ):
    SCREAMING_SNAKE_CASE__       ="""dummy"""
    SCREAMING_SNAKE_CASE__       =[SplitInfo(name="""train""",    num_examples=100     )]
    SCREAMING_SNAKE_CASE__       ="""train[:33%]"""
    SCREAMING_SNAKE_CASE__       ="""arrow"""
    SCREAMING_SNAKE_CASE__       ="""prefix"""

    SCREAMING_SNAKE_CASE__       =make_file_instructions(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     )
    assert isinstance(__UpperCamelCase,    __UpperCamelCase     )
    assert file_instructions.num_examples == 33
    assert file_instructions.file_instructions == [
        {"filename": os.path.join(__UpperCamelCase,    f"""{name}-train.arrow"""     ), "skip": 0, "take": 33}
    ]

    SCREAMING_SNAKE_CASE__       =[SplitInfo(name="""train""",    num_examples=100,    shard_lengths=[10] * 10     )]
    SCREAMING_SNAKE_CASE__       =make_file_instructions(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     )
    assert isinstance(__UpperCamelCase,    __UpperCamelCase     )
    assert file_instructions.num_examples == 33
    assert file_instructions.file_instructions == [
        {"filename": os.path.join(__UpperCamelCase,    f"""{name}-train-00000-of-00010.arrow"""     ), "skip": 0, "take": -1},
        {"filename": os.path.join(__UpperCamelCase,    f"""{name}-train-00001-of-00010.arrow"""     ), "skip": 0, "take": -1},
        {"filename": os.path.join(__UpperCamelCase,    f"""{name}-train-00002-of-00010.arrow"""     ), "skip": 0, "take": -1},
        {"filename": os.path.join(__UpperCamelCase,    f"""{name}-train-00003-of-00010.arrow"""     ), "skip": 0, "take": 3},
    ]




@pytest.mark.parametrize("""name, expected_exception""",    [(None, TypeError), ("""""", ValueError)]     )
def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =[SplitInfo(name="""train""",    num_examples=100     )]
    SCREAMING_SNAKE_CASE__       ="""train"""
    SCREAMING_SNAKE_CASE__       ="""arrow"""
    SCREAMING_SNAKE_CASE__       ="""prefix_path"""
    with pytest.raises(__UpperCamelCase     ):
        SCREAMING_SNAKE_CASE__       =make_file_instructions(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     )






