"""simple docstring"""


import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     assert isinstance(A    ,				A      )
     assert dataset.num_rows == 4
     assert dataset.num_columns == 1
     assert dataset.column_names == ["text"]
     for feature, expected_dtype in expected_features.items():
          assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("keep_in_memory"    ,				[False, True]      )
def 					__a   (		A    ,				A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							tmp_path / "cache"
     lowercase__				 =							{"text": "string"}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          lowercase__				 =							TextDatasetReader(A    ,				cache_dir=A    ,				keep_in_memory=A      ).read()
     _check_text_dataset(A    ,				A      )







@pytest.mark.parametrize(
    "features"    ,				[
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]    ,				)
def 					__a   (		A    ,				A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							tmp_path / "cache"
     lowercase__				 =							{"text": "string"}
     lowercase__				 =							features.copy() if features else default_expected_features
     lowercase__				 =							(
         Features({feature: Value(A      ) for feature, dtype in features.items()}      ) if features is not None else None
     )
     lowercase__				 =							TextDatasetReader(A    ,				features=A    ,				cache_dir=A      ).read()
     _check_text_dataset(A    ,				A      )







@pytest.mark.parametrize("split"    ,				[None, NamedSplit("train"      ), "train", "test"]      )
def 					__a   (		A    ,				A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							tmp_path / "cache"
     lowercase__				 =							{"text": "string"}
     lowercase__				 =							TextDatasetReader(A    ,				cache_dir=A    ,				split=A      ).read()
     _check_text_dataset(A    ,				A      )
     assert dataset.split == split if split else "train"







@pytest.mark.parametrize("path_type"    ,				[str, list]      )
def 					__a   (		A    ,				A    ,				A      ):






     '''simple docstring'''



     if issubclass(A    ,				A      ):
          lowercase__				 =							text_path
     elif issubclass(A    ,				A      ):
          lowercase__				 =							[text_path]
     lowercase__				 =							tmp_path / "cache"
     lowercase__				 =							{"text": "string"}
     lowercase__				 =							TextDatasetReader(A    ,				cache_dir=A      ).read()
     _check_text_dataset(A    ,				A      )







def 					__a   (		A    ,				A    ,				A=("train",)      ):






     '''simple docstring'''



     assert isinstance(A    ,				A      )
     for split in splits:
          lowercase__				 =							dataset_dict[split]
          assert dataset.num_rows == 4
          assert dataset.num_columns == 1
          assert dataset.column_names == ["text"]
          for feature, expected_dtype in expected_features.items():
               assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("keep_in_memory"    ,				[False, True]      )
def 					__a   (		A    ,				A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							tmp_path / "cache"
     lowercase__				 =							{"text": "string"}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          lowercase__				 =							TextDatasetReader({"train": text_path}    ,				cache_dir=A    ,				keep_in_memory=A      ).read()
     _check_text_datasetdict(A    ,				A      )







@pytest.mark.parametrize(
    "features"    ,				[
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]    ,				)
def 					__a   (		A    ,				A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							tmp_path / "cache"
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     lowercase__				 =							{"text": "string"}
     lowercase__				 =							features.copy() if features else default_expected_features
     lowercase__				 =							(
         Features({feature: Value(A      ) for feature, dtype in features.items()}      ) if features is not None else None
     )
     lowercase__				 =							TextDatasetReader({"train": text_path}    ,				features=A    ,				cache_dir=A      ).read()
     _check_text_datasetdict(A    ,				A      )







@pytest.mark.parametrize("split"    ,				[None, NamedSplit("train"      ), "train", "test"]      )
def 					__a   (		A    ,				A    ,				A      ):






     '''simple docstring'''



     if split:
          lowercase__				 =							{split: text_path}
     else:
          lowercase__				 =							"train"
          lowercase__				 =							{"train": text_path, "test": text_path}
     lowercase__				 =							tmp_path / "cache"
     lowercase__				 =							{"text": "string"}
     lowercase__				 =							TextDatasetReader(A    ,				cache_dir=A      ).read()
     _check_text_datasetdict(A    ,				A    ,				splits=list(path.keys()      )      )
     assert all(dataset[split].split == split for split in path.keys()      )





"""simple docstring"""


import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class    a__    (   _a					,					unittest.TestCase	):
   snake_case_							=      MgpstrTokenizer
   snake_case_							=      False
   snake_case_							=      {}
   snake_case_							=      False


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        super().setUp()

        # fmt: off
        lowercase__				 =							["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
        # fmt: on
        lowercase__				 =							dict(zip(_UpperCAmelCase,				range(len(_UpperCAmelCase  )  )  )  )

        lowercase__				 =							os.path.join(self.tmpdirname,				VOCAB_FILES_NAMES["vocab_file"]  )
        with open(self.vocab_file,				"w",				encoding="utf-8"  ) as fp:
             fp.write(json.dumps(_UpperCAmelCase  ) + "\n"  )


   def 	snake_case__	(     self,				**_UpperCAmelCase  ):
        '''simple docstring'''



        return MgpstrTokenizer.from_pretrained(self.tmpdirname,				**_UpperCAmelCase  )


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							"tester"
        lowercase__				 =							"tester"
        return input_text, output_text


   @unittest.skip("MGP-STR always lower cases letters."  )
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.get_tokenizers(do_lower_case=_UpperCAmelCase  )
        for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
                  lowercase__				 =							"[SPECIAL_TOKEN]"

                  tokenizer.add_special_tokens({"cls_token": special_token}  )
                  lowercase__				 =							tokenizer.encode([special_token],				add_special_tokens=_UpperCAmelCase  )
                  self.assertEqual(len(_UpperCAmelCase  ),				1  )

                  lowercase__				 =							tokenizer.decode(_UpperCAmelCase,				skip_special_tokens=_UpperCAmelCase  )
                  self.assertTrue(special_token not in decoded  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.get_tokenizers()
        for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
                  lowercase__ ,						lowercase__				 =							self.get_input_output_texts(_UpperCAmelCase  )

                  lowercase__				 =							tokenizer.tokenize(_UpperCAmelCase  )
                  lowercase__				 =							tokenizer.convert_tokens_to_ids(_UpperCAmelCase  )
                  lowercase__				 =							tokenizer.encode(_UpperCAmelCase,				add_special_tokens=_UpperCAmelCase  )
                  self.assertListEqual(_UpperCAmelCase,				_UpperCAmelCase  )

                  lowercase__				 =							tokenizer.convert_ids_to_tokens(_UpperCAmelCase  )
                  self.assertNotEqual(len(_UpperCAmelCase  ),				0  )
                  lowercase__				 =							tokenizer.decode(_UpperCAmelCase  )
                  self.assertIsInstance(_UpperCAmelCase,				_UpperCAmelCase  )

                  self.assertEqual(text_a.replace(" ",				""  ),				_UpperCAmelCase  )


   @unittest.skip("MGP-STR tokenizer only handles one sequence."  )
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   @unittest.skip("inputs cannot be pretokenized in MgpstrTokenizer"  )
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass




