





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_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> int:
 assert isinstance(_lowerCAmelCase							,   _lowerCAmelCase     )
 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_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> Dict:
 UpperCamelCase	:					Optional[int]       	=   tmp_path / "cache"
 UpperCamelCase	:					Any       	=   {"text": "string"}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  UpperCamelCase	:					Any       	=   TextDatasetReader(_lowerCAmelCase							,   cache_dir=_lowerCAmelCase							,   keep_in_memory=_lowerCAmelCase     ).read()
 _check_text_dataset(_lowerCAmelCase							,   _lowerCAmelCase     )



@pytest.mark.parametrize(
    "features"							,   [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]							,   )
def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					int       	=   tmp_path / "cache"
 UpperCamelCase	:					int       	=   {"text": "string"}
 UpperCamelCase	:					List[str]       	=   features.copy() if features else default_expected_features
 UpperCamelCase	:					Optional[Any]       	=   (
     Features({feature: Value(_lowerCAmelCase     ) for feature, dtype in features.items()}     ) if features is not None else None
 )
 UpperCamelCase	:					Union[str, Any]       	=   TextDatasetReader(_lowerCAmelCase							,   features=_lowerCAmelCase							,   cache_dir=_lowerCAmelCase     ).read()
 _check_text_dataset(_lowerCAmelCase							,   _lowerCAmelCase     )



@pytest.mark.parametrize("split"							,   [None, NamedSplit("train"     ), "train", "test"]     )
def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> Union[str, Any]:
 UpperCamelCase	:					Union[str, Any]       	=   tmp_path / "cache"
 UpperCamelCase	:					Optional[Any]       	=   {"text": "string"}
 UpperCamelCase	:					List[str]       	=   TextDatasetReader(_lowerCAmelCase							,   cache_dir=_lowerCAmelCase							,   split=_lowerCAmelCase     ).read()
 _check_text_dataset(_lowerCAmelCase							,   _lowerCAmelCase     )
 assert dataset.split == split if split else "train"



@pytest.mark.parametrize("path_type"							,   [str, list]     )
def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> List[Any]:
 if issubclass(_lowerCAmelCase							,   _lowerCAmelCase     ):
  UpperCamelCase	:					str       	=   text_path
 elif issubclass(_lowerCAmelCase							,   _lowerCAmelCase     ):
  UpperCamelCase	:					Tuple       	=   [text_path]
 UpperCamelCase	:					Union[str, Any]       	=   tmp_path / "cache"
 UpperCamelCase	:					Any       	=   {"text": "string"}
 UpperCamelCase	:					Optional[Any]       	=   TextDatasetReader(_lowerCAmelCase							,   cache_dir=_lowerCAmelCase     ).read()
 _check_text_dataset(_lowerCAmelCase							,   _lowerCAmelCase     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase=("train",)     )						-> str:
 assert isinstance(_lowerCAmelCase							,   _lowerCAmelCase     )
 for split in splits:
  UpperCamelCase	:					Optional[int]       	=   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_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> Union[str, Any]:
 UpperCamelCase	:					int       	=   tmp_path / "cache"
 UpperCamelCase	:					int       	=   {"text": "string"}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  UpperCamelCase	:					Union[str, Any]       	=   TextDatasetReader({"train": text_path}							,   cache_dir=_lowerCAmelCase							,   keep_in_memory=_lowerCAmelCase     ).read()
 _check_text_datasetdict(_lowerCAmelCase							,   _lowerCAmelCase     )



@pytest.mark.parametrize(
    "features"							,   [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]							,   )
def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> Optional[Any]:
 UpperCamelCase	:					Tuple       	=   tmp_path / "cache"
 # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
 UpperCamelCase	:					Optional[Any]       	=   {"text": "string"}
 UpperCamelCase	:					Dict       	=   features.copy() if features else default_expected_features
 UpperCamelCase	:					Tuple       	=   (
     Features({feature: Value(_lowerCAmelCase     ) for feature, dtype in features.items()}     ) if features is not None else None
 )
 UpperCamelCase	:					Optional[int]       	=   TextDatasetReader({"train": text_path}							,   features=_lowerCAmelCase							,   cache_dir=_lowerCAmelCase     ).read()
 _check_text_datasetdict(_lowerCAmelCase							,   _lowerCAmelCase     )



@pytest.mark.parametrize("split"							,   [None, NamedSplit("train"     ), "train", "test"]     )
def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> Any:
 if split:
  UpperCamelCase	:					int       	=   {split: text_path}
 else:
  UpperCamelCase	:					Optional[int]       	=   "train"
  UpperCamelCase	:					str       	=   {"train": text_path, "test": text_path}
 UpperCamelCase	:					int       	=   tmp_path / "cache"
 UpperCamelCase	:					int       	=   {"text": "string"}
 UpperCamelCase	:					str       	=   TextDatasetReader(_lowerCAmelCase							,   cache_dir=_lowerCAmelCase     ).read()
 _check_text_datasetdict(_lowerCAmelCase							,   _lowerCAmelCase							,   splits=list(path.keys()     )     )
 assert all(dataset[split].split == split for split in path.keys()     )






import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


_lowerCamelCase	:		List[Any]							       =   """sshleifer/bart-tiny-random"""
_lowerCamelCase	:		List[Any]							       =   """patrickvonplaten/t5-tiny-random"""


@require_torch
class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    @cached_property
    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->int:





           '''simple docstring'''

           return AutoConfig.from_pretrained(UpperCAmelCase__)


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





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=1)
           self.assertEqual(student.config.num_hidden_layers					, 1)


    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Any:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=UpperCAmelCase__)


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





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=UpperCAmelCase__)
           self.assertEqual(student.config.encoder_layers					, 1)
           self.assertEqual(student.config.decoder_layers					, self.teacher_config.encoder_layers)


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->int:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=1)
           self.assertEqual(student.config.encoder_layers					, 1)
           self.assertEqual(student.config.decoder_layers					, 1)





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





           '''simple docstring'''

           with self.assertRaises(UpperCAmelCase__):
                  create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=UpperCAmelCase__					, d=UpperCAmelCase__)


