

import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list





class       __UpperCAmelCase  ( unittest.TestCase		):



   def  __magic_name__					(							self							:  Dict		):
     UpperCAmelCase :							Tuple								=				'''<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'''.split(''' '''		)
     UpperCAmelCase :							List[str]								=				dict(zip(__A,	range(len(__A		)		)		)		)

     UpperCAmelCase :							int								=				{
         '''pad_token''': '''<pad>''',
         '''unk_token''': '''<unk>''',
         '''bos_token''': '''<s>''',
         '''eos_token''': '''</s>''',
     }
     UpperCAmelCase :							str								=				{
         '''feature_size''': 1,
         '''padding_value''': 0.0,
         '''sampling_rate''': 1_6_0_0_0,
         '''return_attention_mask''': False,
         '''do_normalize''': True,
     }

     UpperCAmelCase :							List[Any]								=				tempfile.mkdtemp()
     UpperCAmelCase :							str								=				os.path.join(self.tmpdirname,	VOCAB_FILES_NAMES['''vocab_file''']		)
     UpperCAmelCase :							Tuple								=				os.path.join(self.tmpdirname,	__A		)
     with open(self.vocab_file,	'''w''',	encoding='''utf-8'''		) as fp:
       fp.write(json.dumps(__A		) + '''\n'''		)

     with open(self.feature_extraction_file,	'''w''',	encoding='''utf-8'''		) as fp:
       fp.write(json.dumps(__A		) + '''\n'''		)



   def  __magic_name__					(							self							:  List[Any],	**__A							:  List[Any]		):
     UpperCAmelCase :							str								=				self.add_kwargs_tokens_map.copy()
     kwargs.update(__A		)
     return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname,	**__A		)



   def  __magic_name__					(							self							:  List[Any],	**__A							:  Union[str, Any]		):
     return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname,	**__A		)



   def  __magic_name__					(							self							:  Any		):
     shutil.rmtree(self.tmpdirname		)



   def  __magic_name__					(							self							:  str		):
     UpperCAmelCase :							Any								=				self.get_tokenizer()
     UpperCAmelCase :							Optional[int]								=				self.get_feature_extractor()

     UpperCAmelCase :							Tuple								=				WavaVecaProcessor(tokenizer=__A,	feature_extractor=__A		)

     processor.save_pretrained(self.tmpdirname		)
     UpperCAmelCase :							Union[str, Any]								=				WavaVecaProcessor.from_pretrained(self.tmpdirname		)

     self.assertEqual(processor.tokenizer.get_vocab(),	tokenizer.get_vocab()		)
     self.assertIsInstance(processor.tokenizer,	__A		)

     self.assertEqual(processor.feature_extractor.to_json_string(),	feature_extractor.to_json_string()		)
     self.assertIsInstance(processor.feature_extractor,	__A		)



   def  __magic_name__					(							self							:  Optional[int]		):
     UpperCAmelCase :							int								=				WavaVecaProcessor(tokenizer=self.get_tokenizer(),	feature_extractor=self.get_feature_extractor()		)
     processor.save_pretrained(self.tmpdirname		)

     UpperCAmelCase :							Dict								=				self.get_tokenizer(bos_token='''(BOS)''',	eos_token='''(EOS)'''		)
     UpperCAmelCase :							int								=				self.get_feature_extractor(do_normalize=__A,	padding_value=1.0		)

     UpperCAmelCase :							int								=				WavaVecaProcessor.from_pretrained(
         self.tmpdirname,	bos_token='''(BOS)''',	eos_token='''(EOS)''',	do_normalize=__A,	padding_value=1.0		)

     self.assertEqual(processor.tokenizer.get_vocab(),	tokenizer_add_kwargs.get_vocab()		)
     self.assertIsInstance(processor.tokenizer,	__A		)

     self.assertEqual(processor.feature_extractor.to_json_string(),	feature_extractor_add_kwargs.to_json_string()		)
     self.assertIsInstance(processor.feature_extractor,	__A		)



   def  __magic_name__					(							self							:  Union[str, Any]		):
     UpperCAmelCase :							Optional[Any]								=				self.get_feature_extractor()
     UpperCAmelCase :							Optional[int]								=				self.get_tokenizer()

     UpperCAmelCase :							Any								=				WavaVecaProcessor(tokenizer=__A,	feature_extractor=__A		)

     UpperCAmelCase :							Tuple								=				floats_list((3, 1_0_0_0)		)

     UpperCAmelCase :							Any								=				feature_extractor(__A,	return_tensors='''np'''		)
     UpperCAmelCase :							int								=				processor(__A,	return_tensors='''np'''		)

     for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum(),	input_processor[key].sum(),	delta=1E-2		)



   def  __magic_name__					(							self							:  Union[str, Any]		):
     UpperCAmelCase :							Optional[Any]								=				self.get_feature_extractor()
     UpperCAmelCase :							Union[str, Any]								=				self.get_tokenizer()

     UpperCAmelCase :							List[Any]								=				WavaVecaProcessor(tokenizer=__A,	feature_extractor=__A		)

     UpperCAmelCase :							List[str]								=				'''This is a test string'''

     UpperCAmelCase :							Tuple								=				processor(text=__A		)

     UpperCAmelCase :							Tuple								=				tokenizer(__A		)

     for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key],	encoded_processor[key]		)



   def  __magic_name__					(							self							:  Optional[int]		):
     UpperCAmelCase :							Tuple								=				self.get_feature_extractor()
     UpperCAmelCase :							int								=				self.get_tokenizer()

     UpperCAmelCase :							List[str]								=				WavaVecaProcessor(tokenizer=__A,	feature_extractor=__A		)

     UpperCAmelCase :							Any								=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     UpperCAmelCase :							str								=				processor.batch_decode(__A		)
     UpperCAmelCase :							List[str]								=				tokenizer.batch_decode(__A		)

     self.assertListEqual(__A,	__A		)





   def  __magic_name__					(							self							:  int		):
     UpperCAmelCase :							Optional[int]								=				self.get_feature_extractor()
     UpperCAmelCase :							List[str]								=				self.get_tokenizer()

     UpperCAmelCase :							Any								=				WavaVecaProcessor(tokenizer=__A,	feature_extractor=__A		)

     self.assertListEqual(
         processor.model_input_names,	feature_extractor.model_input_names,	msg='''`processor` and `feature_extractor` model input names do not match''',	)








import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def     a__  (	UpperCAmelCase							:    int       ,				UpperCAmelCase							:    int    )						->		List[str]:
  assert isinstance(UpperCAmelCase       ,				UpperCAmelCase    )
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  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__  (	UpperCAmelCase							:    Any       ,				UpperCAmelCase							:    List[Any]       ,				UpperCAmelCase							:    Optional[Any]    )						->		Optional[int]:
  UpperCAmelCase :							Optional[int]								=				tmp_path / '''cache'''
  UpperCAmelCase :							str								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    UpperCAmelCase :							Union[str, Any]								=				CsvDatasetReader(UpperCAmelCase       ,				cache_dir=UpperCAmelCase       ,				keep_in_memory=UpperCAmelCase    ).read()
  _check_csv_dataset(UpperCAmelCase       ,				UpperCAmelCase    )

@pytest.mark.parametrize(
    '''features'''       ,				[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]       ,				)
def     a__  (	UpperCAmelCase							:    Optional[int]       ,				UpperCAmelCase							:    int       ,				UpperCAmelCase							:    int    )						->		Optional[Any]:
  UpperCAmelCase :							List[str]								=				tmp_path / '''cache'''
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  UpperCAmelCase :							Optional[Any]								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase :							List[Any]								=				features.copy() if features else default_expected_features
  UpperCAmelCase :							List[Any]								=				(
      Features({feature: Value(UpperCAmelCase    ) for feature, dtype in features.items()}    ) if features is not None else None
  )
  UpperCAmelCase :							Any								=				CsvDatasetReader(UpperCAmelCase       ,				features=UpperCAmelCase       ,				cache_dir=UpperCAmelCase    ).read()
  _check_csv_dataset(UpperCAmelCase       ,				UpperCAmelCase    )

@pytest.mark.parametrize('''split'''       ,				[None, NamedSplit('''train'''    ), '''train''', '''test''']    )
def     a__  (	UpperCAmelCase							:    Union[str, Any]       ,				UpperCAmelCase							:    Tuple       ,				UpperCAmelCase							:    str    )						->		Any:
  UpperCAmelCase :							List[str]								=				tmp_path / '''cache'''
  UpperCAmelCase :							Any								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase :							int								=				CsvDatasetReader(UpperCAmelCase       ,				cache_dir=UpperCAmelCase       ,				split=UpperCAmelCase    ).read()
  _check_csv_dataset(UpperCAmelCase       ,				UpperCAmelCase    )
  assert dataset.split == split if split else "train"

@pytest.mark.parametrize('''path_type'''       ,				[str, list]    )
def     a__  (	UpperCAmelCase							:    int       ,				UpperCAmelCase							:    List[str]       ,				UpperCAmelCase							:    Any    )						->		str:
  if issubclass(UpperCAmelCase       ,				UpperCAmelCase    ):
    UpperCAmelCase :							Tuple								=				csv_path
  elif issubclass(UpperCAmelCase       ,				UpperCAmelCase    ):
    UpperCAmelCase :							List[str]								=				[csv_path]
  UpperCAmelCase :							Tuple								=				tmp_path / '''cache'''
  UpperCAmelCase :							str								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase :							Tuple								=				CsvDatasetReader(UpperCAmelCase       ,				cache_dir=UpperCAmelCase    ).read()
  _check_csv_dataset(UpperCAmelCase       ,				UpperCAmelCase    )

def     a__  (	UpperCAmelCase							:    str       ,				UpperCAmelCase							:    Any       ,				UpperCAmelCase							:    int=("train",)    )						->		Optional[int]:
  assert isinstance(UpperCAmelCase       ,				UpperCAmelCase    )
  for split in splits:
    UpperCAmelCase :							Optional[Any]								=				dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    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__  (	UpperCAmelCase							:    Tuple       ,				UpperCAmelCase							:    List[Any]       ,				UpperCAmelCase							:    Tuple    )						->		Tuple:
  UpperCAmelCase :							Any								=				tmp_path / '''cache'''
  UpperCAmelCase :							Dict								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    UpperCAmelCase :							str								=				CsvDatasetReader({'''train''': csv_path}       ,				cache_dir=UpperCAmelCase       ,				keep_in_memory=UpperCAmelCase    ).read()
  _check_csv_datasetdict(UpperCAmelCase       ,				UpperCAmelCase    )

@pytest.mark.parametrize(
    '''features'''       ,				[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]       ,				)
def     a__  (	UpperCAmelCase							:    int       ,				UpperCAmelCase							:    Tuple       ,				UpperCAmelCase							:    Any    )						->		Optional[int]:
  UpperCAmelCase :							Tuple								=				tmp_path / '''cache'''
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  UpperCAmelCase :							List[str]								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase :							Union[str, Any]								=				features.copy() if features else default_expected_features
  UpperCAmelCase :							List[str]								=				(
      Features({feature: Value(UpperCAmelCase    ) for feature, dtype in features.items()}    ) if features is not None else None
  )
  UpperCAmelCase :							List[Any]								=				CsvDatasetReader({'''train''': csv_path}       ,				features=UpperCAmelCase       ,				cache_dir=UpperCAmelCase    ).read()
  _check_csv_datasetdict(UpperCAmelCase       ,				UpperCAmelCase    )

@pytest.mark.parametrize('''split'''       ,				[None, NamedSplit('''train'''    ), '''train''', '''test''']    )
def     a__  (	UpperCAmelCase							:    Union[str, Any]       ,				UpperCAmelCase							:    List[Any]       ,				UpperCAmelCase							:    Optional[Any]    )						->		Dict:
  if split:
    UpperCAmelCase :							Union[str, Any]								=				{split: csv_path}
  else:
    UpperCAmelCase :							List[str]								=				{'''train''': csv_path, '''test''': csv_path}
  UpperCAmelCase :							str								=				tmp_path / '''cache'''
  UpperCAmelCase :							Tuple								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase :							str								=				CsvDatasetReader(UpperCAmelCase       ,				cache_dir=UpperCAmelCase    ).read()
  _check_csv_datasetdict(UpperCAmelCase       ,				UpperCAmelCase       ,				splits=list(path.keys()    )    )
  assert all(dataset[split].split == split for split in path.keys()    )

def     a__  (	UpperCAmelCase							:    Optional[Any]    )						->		int:
  with open(UpperCAmelCase       ,				encoding='''utf-8'''    ) as csvfile:
    yield from csv.reader(UpperCAmelCase    )

def     a__  (	UpperCAmelCase							:    List[str]       ,				UpperCAmelCase							:    Union[str, Any]    )						->		Union[str, Any]:
  UpperCAmelCase :							int								=				tmp_path / '''cache'''
  UpperCAmelCase :							int								=				os.path.join(UpperCAmelCase       ,				'''tmp.csv'''    )
  UpperCAmelCase :							List[str]								=				CsvDatasetReader({'''train''': csv_path}       ,				cache_dir=UpperCAmelCase    ).read()
  CsvDatasetWriter(dataset['''train''']       ,				UpperCAmelCase       ,				num_proc=1    ).write()

  UpperCAmelCase :							List[Any]								=				iter_csv_file(UpperCAmelCase    )
  UpperCAmelCase :							int								=				iter_csv_file(UpperCAmelCase    )

  for rowa, rowa in zip(UpperCAmelCase       ,				UpperCAmelCase    ):
    assert rowa == rowa

def     a__  (	UpperCAmelCase							:    Any       ,				UpperCAmelCase							:    Dict    )						->		Any:
  UpperCAmelCase :							List[Any]								=				tmp_path / '''cache'''
  UpperCAmelCase :							Tuple								=				os.path.join(UpperCAmelCase       ,				'''tmp.csv'''    )
  UpperCAmelCase :							List[str]								=				CsvDatasetReader({'''train''': csv_path}       ,				cache_dir=UpperCAmelCase    ).read()
  CsvDatasetWriter(dataset['''train''']       ,				UpperCAmelCase       ,				num_proc=2    ).write()

  UpperCAmelCase :							List[Any]								=				iter_csv_file(UpperCAmelCase    )
  UpperCAmelCase :							Union[str, Any]								=				iter_csv_file(UpperCAmelCase    )

  for rowa, rowa in zip(UpperCAmelCase       ,				UpperCAmelCase    ):
    assert rowa == rowa






def     a__  (	UpperCAmelCase							:    List[Any]       ,				UpperCAmelCase							:    Optional[int]    )						->		Optional[Any]:
  UpperCAmelCase :							Dict								=				tmp_path / '''cache'''
  UpperCAmelCase :							List[Any]								=				os.path.join(UpperCAmelCase       ,				'''tmp.csv'''    )
  UpperCAmelCase :							List[Any]								=				CsvDatasetReader({'''train''': csv_path}       ,				cache_dir=UpperCAmelCase    ).read()
  with pytest.raises(UpperCAmelCase    ):
    CsvDatasetWriter(dataset['''train''']       ,				UpperCAmelCase       ,				num_proc=0    )





