



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 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 def lowercase_						(  self		,      a		,      a=False   )		->							Optional[Any]:




   """simple docstring"""







   _A      ,				_A      ,				_A          =      (
       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(a   )		,      '''wb'''   ).close()
   _A          =      pa.Table.from_pydict({'''filename''': [Path(a   ).name] * 1_0_0}   )
   if take == -1:
     _A          =      len(a   ) - skip
   if skip is not None and take is not None:
     _A          =      pa_table.slice(a		,      a   )
   return pa_table







class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 def lowercase_						(  self   )		->							int:




   """simple docstring"""







   _A          =      '''my_name'''
   _A          =      SplitInfo(name='''train'''		,      num_examples=1_0_0   )
   _A          =      SplitInfo(name='''test'''		,      num_examples=1_0_0   )
   _A          =      [train_info, test_info]
   _A          =      SplitDict()
   split_dict.add(a   )
   split_dict.add(a   )
   _A          =      DatasetInfo(splits=a   )

   with tempfile.TemporaryDirectory() as tmp_dir:
     _A          =      ReaderTest(a		,      a   )

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

     _A          =      ['''train''', '''test[:33%]''']
     _A          =      [Split.TRAIN, ReadInstruction.from_spec('''test[:33%]'''   )]
     for instructions in [instructionsa, instructionsa]:
       _A          =      [reader.read(a		,      a		,      a   ) for instr in instructions]
       _A      ,				_A          =      (Dataset(**a   ) 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		,      a   )
       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		,      a   )
       self.assertEqual(str(test_dset.split   )		,      '''test[:33%]'''   )
       del train_dset, test_dset
 def lowercase_						(  self   )		->							Optional[Any]:




   """simple docstring"""







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

   with tempfile.TemporaryDirectory() as tmp_dir:
     _A          =      ReaderTest(a		,      a   )

     _A          =      '''train[:33%]'''
     _A          =      Dataset(**reader.read(a		,      a		,      a   )   )
     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 lowercase_						(  self   )		->							int:




   """simple docstring"""







   _A          =      SplitInfo(name='''train'''		,      num_examples=1_0_0   )
   _A          =      SplitInfo(name='''test'''		,      num_examples=1_0_0   )
   _A          =      SplitDict()
   split_dict.add(a   )
   split_dict.add(a   )
   _A          =      DatasetInfo(splits=a   )

   with tempfile.TemporaryDirectory() as tmp_dir:
     _A          =      ReaderTest(a		,      a   )

     _A          =      [
         {'''filename''': os.path.join(a		,      '''train'''   )},
         {'''filename''': os.path.join(a		,      '''test'''   ), '''skip''': 1_0, '''take''': 1_0},
     ]
     _A          =      Dataset(**reader.read_files(a		,      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__	(							__snake_case  ,			__snake_case  ,			__snake_case							)				->	str:
  _A          =      arrow_file
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    _A          =      ArrowReader.read_table(__snake_case  ,			in_memory=__snake_case							)
  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__	(							__snake_case  ,			__snake_case  ,			__snake_case							)				->	int:
  _A          =      arrow_file
  _A          =      ArrowReader(''''''  ,			__snake_case							)
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    _A          =      reader.read_files([{'''filename''': filename}]  ,			in_memory=__snake_case							)
  assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
  _A          =      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__	(							)				->	List[Any]:
  assert ReadInstruction('''train'''  ,			to=10  ,			unit='''abs'''							).to_spec() == "train[:10]"
  assert ReadInstruction('''train'''  ,			from_=-80  ,			to=10  ,			unit='''%'''							).to_spec() == "train[-80%:10%]"

  _A          =      '''train+test'''
  assert ReadInstruction.from_spec(__snake_case							).to_spec() == spec_train_test

  _A          =      '''train[2:10]'''
  assert ReadInstruction.from_spec(__snake_case							).to_spec() == spec_train_abs

  _A          =      '''train[15%:-20%]'''
  assert ReadInstruction.from_spec(__snake_case							).to_spec() == spec_train_pct

  _A          =      '''train[:10%](closest)'''
  assert ReadInstruction.from_spec(__snake_case							).to_spec() == "train[:10%]"

  _A          =      '''train[:10%](pct1_dropremainder)'''
  assert ReadInstruction.from_spec(__snake_case							).to_spec() == spec_train_pct_rounding

  _A          =      '''train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'''
  assert ReadInstruction.from_spec(__snake_case							).to_spec() == spec_train_test_pct_rounding




def       UpperCAmelCase__	(							)				->	Tuple:
  _A          =      '''dummy'''
  _A          =      [SplitInfo(name='''train'''  ,			num_examples=100							)]
  _A          =      '''train[:33%]'''
  _A          =      '''arrow'''
  _A          =      '''prefix'''

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

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




@pytest.mark.parametrize('''name, expected_exception'''  ,			[(None, TypeError), ('''''', ValueError)]							)
def       UpperCAmelCase__	(							__snake_case  ,			__snake_case							)				->	Tuple:
  _A          =      [SplitInfo(name='''train'''  ,			num_examples=100							)]
  _A          =      '''train'''
  _A          =      '''arrow'''
  _A          =      '''prefix_path'''
  with pytest.raises(__snake_case							):
    _A          =      make_file_instructions(__snake_case  ,			__snake_case  ,			__snake_case  ,			__snake_case  ,			__snake_case							)




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


__lowerCamelCase								=   logging.get_logger(__name__)

__lowerCamelCase								=   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase								=   {
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

__lowerCamelCase								=   {
    """microsoft/deberta-base""": 5_1_2,
    """microsoft/deberta-large""": 5_1_2,
    """microsoft/deberta-xlarge""": 5_1_2,
    """microsoft/deberta-base-mnli""": 5_1_2,
    """microsoft/deberta-large-mnli""": 5_1_2,
    """microsoft/deberta-xlarge-mnli""": 5_1_2,
}

__lowerCamelCase								=   {
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 lowerCamelCase_       =     VOCAB_FILES_NAMES
 lowerCamelCase_       =     PRETRAINED_VOCAB_FILES_MAP
 lowerCamelCase_       =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCamelCase_       =     ['''input_ids''', '''attention_mask''', '''token_type_ids''']
 lowerCamelCase_       =     DebertaTokenizer
 def __init__(  self		,      a=None		,      a=None		,      a=None		,      a="replace"		,      a="[CLS]"		,      a="[SEP]"		,      a="[SEP]"		,      a="[CLS]"		,      a="[UNK]"		,      a="[PAD]"		,      a="[MASK]"		,      a=False		,      **a		,      )		->							Any:




   """simple docstring"""







   super().__init__(
       a		,      a		,      tokenizer_file=a		,      errors=a		,      bos_token=a		,      eos_token=a		,      unk_token=a		,      sep_token=a		,      cls_token=a		,      pad_token=a		,      mask_token=a		,      add_prefix_space=a		,      **a		,      )
   _A          =      kwargs.pop('''add_bos_token'''		,      a   )

   _A          =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
   if pre_tok_state.get('''add_prefix_space'''		,      a   ) != add_prefix_space:
     _A          =      getattr(a		,      pre_tok_state.pop('''type'''   )   )
     _A          =      add_prefix_space
     _A          =      pre_tok_class(**a   )

   _A          =      add_prefix_space
 @property
 def lowercase_						(  self   )		->							str:




   """simple docstring"""







   if self._mask_token is None:
     if self.verbose:
       logger.error('''Using mask_token, but it is not set yet.'''   )
     return None
   return str(self._mask_token   )
 @mask_token.setter
 def lowercase_						(  self		,      a   )		->							Dict:




   """simple docstring"""







   _A          =      AddedToken(a		,      lstrip=a		,      rstrip=a   ) if isinstance(a		,      a   ) else value
   _A          =      value
 def lowercase_						(  self		,      a		,      a = None   )		->							List[int]:




   """simple docstring"""







   if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   _A          =      [self.cls_token_id]
   _A          =      [self.sep_token_id]
   return cls + token_ids_a + sep + token_ids_a + sep
 def lowercase_						(  self		,      a		,      a = None   )		->							List[int]:




   """simple docstring"""







   _A          =      [self.sep_token_id]
   _A          =      [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   ) * [0] + len(token_ids_a + sep   ) * [1]
 def lowercase_						(  self		,      *a		,      **a   )		->							BatchEncoding:




   """simple docstring"""







   _A          =      kwargs.get('''is_split_into_words'''		,      a   )
   assert self.add_prefix_space or not is_split_into_words, (
       f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
       "to use it with pretokenized inputs."
   )

   return super()._batch_encode_plus(*a		,      **a   )
 def lowercase_						(  self		,      *a		,      **a   )		->							BatchEncoding:




   """simple docstring"""







   _A          =      kwargs.get('''is_split_into_words'''		,      a   )

   assert self.add_prefix_space or not is_split_into_words, (
       f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
       "to use it with pretokenized inputs."
   )

   return super()._encode_plus(*a		,      **a   )
 def lowercase_						(  self		,      a		,      a = None   )		->							Tuple[str]:




   """simple docstring"""







   _A          =      self._tokenizer.model.save(a		,      name=a   )
   return tuple(a   )



 def lowercase_						(  self		,      a   )		->							List[int]:




   """simple docstring"""







   _A          =      []
   for is_user, text in conversation.iter_texts():
     input_ids.extend(self.encode(a		,      add_special_tokens=a   ) + [self.eos_token_id]   )

   if len(a   ) > self.model_max_length:
     _A          =      input_ids[-self.model_max_length :]
   return input_ids