




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 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[Any]						,							_lowerCAmelCase							:   Tuple	):




 """simple docstring"""







 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 						lowerCAmelCase		(		_lowerCAmelCase							:   Union[str, Any]						,							_lowerCAmelCase							:   List[Any]						,							_lowerCAmelCase							:   Union[str, Any]	):




 """simple docstring"""







 UpperCAmelCase__							 =						tmp_path / "cache"
 UpperCAmelCase__							 =						{"text": "string"}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  UpperCAmelCase__							 =						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 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[int]						,							_lowerCAmelCase							:   Union[str, Any]						,							_lowerCAmelCase							:   Dict	):




 """simple docstring"""







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



@pytest.mark.parametrize("split"						,							[None, NamedSplit("train"	), "train", "test"]	)
def 						lowerCAmelCase		(		_lowerCAmelCase							:   int						,							_lowerCAmelCase							:   Tuple						,							_lowerCAmelCase							:   List[str]	):




 """simple docstring"""







 UpperCAmelCase__							 =						tmp_path / "cache"
 UpperCAmelCase__							 =						{"text": "string"}
 UpperCAmelCase__							 =						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 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[Any]						,							_lowerCAmelCase							:   Tuple						,							_lowerCAmelCase							:   Tuple	):




 """simple docstring"""







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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[Any]						,							_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   Optional[Any]=("train",)	):




 """simple docstring"""







 assert isinstance(_lowerCAmelCase						,							_lowerCAmelCase	)
 for split in splits:
  UpperCAmelCase__							 =						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 						lowerCAmelCase		(		_lowerCAmelCase							:   Dict						,							_lowerCAmelCase							:   Union[str, Any]						,							_lowerCAmelCase							:   Dict	):




 """simple docstring"""







 UpperCAmelCase__							 =						tmp_path / "cache"
 UpperCAmelCase__							 =						{"text": "string"}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  UpperCAmelCase__							 =						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 						lowerCAmelCase		(		_lowerCAmelCase							:   int						,							_lowerCAmelCase							:   Optional[int]						,							_lowerCAmelCase							:   Union[str, Any]	):




 """simple docstring"""







 UpperCAmelCase__							 =						tmp_path / "cache"
 # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
 UpperCAmelCase__							 =						{"text": "string"}
 UpperCAmelCase__							 =						features.copy() if features else default_expected_features
 UpperCAmelCase__							 =						(
     Features({feature: Value(_lowerCAmelCase	) for feature, dtype in features.items()}	) if features is not None else None
 )
 UpperCAmelCase__							 =						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 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   Any						,							_lowerCAmelCase							:   Tuple	):




 """simple docstring"""







 if split:
  UpperCAmelCase__							 =						{split: text_path}
 else:
  UpperCAmelCase__							 =						"train"
  UpperCAmelCase__							 =						{"train": text_path, "test": text_path}
 UpperCAmelCase__							 =						tmp_path / "cache"
 UpperCAmelCase__							 =						{"text": "string"}
 UpperCAmelCase__							 =						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 numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class 			_UpperCamelCase      (				lowerCAmelCase		):
      # to overwrite at feature extractactor specific tests
      UpperCAmelCase_        =		None
      UpperCAmelCase_        =		None
      @property
      def 	UpperCAmelCase_						(			self   :int      )     ->			int:
       return self.feat_extract_tester.prepare_feat_extract_dict()
      def 	UpperCAmelCase_						(			self   :Any      )     ->			str:
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       self.assertTrue(hasattr(lowerCamelCase ,      "feature_size"      )      )
       self.assertTrue(hasattr(lowerCamelCase ,      "sampling_rate"      )      )
       self.assertTrue(hasattr(lowerCamelCase ,      "padding_value"      )      )
      def 	UpperCAmelCase_						(			self   :str      )     ->			int:
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       self.assertTrue(all(len(lowerCamelCase      ) == len(lowerCamelCase      ) for x, y in zip(lowerCamelCase ,      processed_features[input_name]      )      )      )

       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase      )
       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs} ,      tensor_type="np"      )

       UpperCAmelCase__							 =						processed_features[input_name]

       if len(batch_features_input.shape      ) < 3:
        UpperCAmelCase__							 =						batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]      ), self.feat_extract_tester.feature_size)      )
      @require_torch
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase      )
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs} ,      tensor_type="pt"      )

       UpperCAmelCase__							 =						processed_features[input_name]

       if len(batch_features_input.shape      ) < 3:
        UpperCAmelCase__							 =						batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]      ), self.feat_extract_tester.feature_size)      )
      @require_tf
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Dict:
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase      )
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs} ,      tensor_type="tf"      )

       UpperCAmelCase__							 =						processed_features[input_name]

       if len(batch_features_input.shape      ) < 3:
        UpperCAmelCase__							 =						batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]      ), self.feat_extract_tester.feature_size)      )
      def 	UpperCAmelCase_						(			self   :int ,      lowerCamelCase   :int=False      )     ->			str:
       def _inputs_have_equal_length(lowerCamelCase   :Union[str, Any]      ):
        UpperCAmelCase__							 =						len(input[0]      )
        for input_slice in input[1:]:
         if len(lowerCamelCase      ) != length:
          return False
        return True

       def _inputs_are_equal(lowerCamelCase   :Dict ,      lowerCamelCase   :Optional[Any]      ):
        if len(lowerCamelCase      ) != len(lowerCamelCase      ):
         return False

        for input_slice_a, input_slice_a in zip(lowerCamelCase ,      lowerCamelCase      ):
         if not np.allclose(np.asarray(lowerCamelCase      ) ,      np.asarray(lowerCamelCase      ) ,      atol=1e-3      ):
          return False
        return True

       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCamelCase      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       UpperCAmelCase__							 =						self.feat_extract_tester.seq_length_diff
       UpperCAmelCase__							 =						self.feat_extract_tester.max_seq_length + pad_diff
       UpperCAmelCase__							 =						self.feat_extract_tester.min_seq_length
       UpperCAmelCase__							 =						self.feat_extract_tester.batch_size
       UpperCAmelCase__							 =						self.feat_extract_tester.feature_size

       # test padding for List[int] + numpy
       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding=lowerCamelCase      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest"      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[-1]      )      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="np"      )
       UpperCAmelCase__							 =						input_a[input_name]

       # max_length parameter has to be provided when setting `padding="max_length"`
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      padding="max_length"      )[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=lowerCamelCase ,      return_tensors="np"      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_are_equal(lowerCamelCase ,      lowerCamelCase      )      )
       self.assertTrue(len(input_a[0]      ) == pad_min_length      )
       self.assertTrue(len(input_a[1]      ) == pad_min_length + pad_diff      )
       self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]      ))      )
       self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)      )

       if feature_size > 1:
        self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size      )

       # test padding for `pad_to_multiple_of` for List[int] + numpy
       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      pad_to_multiple_of=10      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      pad_to_multiple_of=10      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      pad_to_multiple_of=10 ,      max_length=lowerCamelCase      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      pad_to_multiple_of=10 ,      max_length=lowerCamelCase ,      return_tensors="np" ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertTrue(all(len(lowerCamelCase      ) % 10 == 0 for x in input_a      )      )
       self.assertTrue(_inputs_are_equal(lowerCamelCase ,      lowerCamelCase      )      )

       UpperCAmelCase__							 =						pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
       self.assertTrue(all(len(lowerCamelCase      ) == expected_mult_pad_length for x in input_a      )      )
       self.assertEqual(input_a.shape[:2] ,      (batch_size, expected_mult_pad_length)      )

       if feature_size > 1:
        self.assertTrue(input_a.shape[2] == feature_size      )

       # Check padding value is correct
       UpperCAmelCase__							 =						(np.ones(self.feat_extract_tester.feature_size      ) * feat_extract.padding_value).sum()
       self.assertTrue(
           abs(np.asarray(input_a[0]      )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)      )
           < 1e-3      )
       self.assertTrue(
           abs(
               np.asarray(input_a[1]      )[pad_min_length + pad_diff :].sum()
               - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)      )
           < 1e-3      )
       self.assertTrue(
           abs(
               np.asarray(input_a[2]      )[pad_min_length + 2 * pad_diff :].sum()
               - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)      )
           < 1e-3      )
       self.assertTrue(
           abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)      ) < 1e-3      )
       self.assertTrue(
           abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)      )
           < 1e-3      )
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :int=False      )     ->			str:
       def _inputs_have_equal_length(lowerCamelCase   :Any      ):
        UpperCAmelCase__							 =						len(input[0]      )
        for input_slice in input[1:]:
         if len(lowerCamelCase      ) != length:
          return False
        return True

       def _inputs_are_equal(lowerCamelCase   :Optional[int] ,      lowerCamelCase   :str      ):
        if len(lowerCamelCase      ) != len(lowerCamelCase      ):
         return False

        for input_slice_a, input_slice_a in zip(lowerCamelCase ,      lowerCamelCase      ):
         if not np.allclose(np.asarray(lowerCamelCase      ) ,      np.asarray(lowerCamelCase      ) ,      atol=1e-3      ):
          return False
        return True

       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCamelCase      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       # truncate to smallest
       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      truncation=lowerCamelCase      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      )      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )

       # truncate to smallest with np
       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      return_tensors="np" ,      truncation=lowerCamelCase ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      return_tensors="np"      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(input_a.shape[1] == len(speech_inputs[0]      )      )

       # since truncation forces padding to be smaller than longest input
       # function can't return `np.ndarray`, but has to return list
       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )

       # truncate to middle
       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[1]      ) ,      truncation=lowerCamelCase ,      return_tensors="np" ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[1]      ) ,      truncation=lowerCamelCase      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[1]      ) ,      return_tensors="np"      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertTrue(input_a.shape[1] == len(speech_inputs[1]      )      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_are_equal(lowerCamelCase ,      lowerCamelCase      )      )

       # since truncation forces padding to be smaller than longest input
       # function can't return `np.ndarray`, but has to return list
       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(len(input_a[-1]      ) == len(speech_inputs[-1]      )      )

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      truncation=lowerCamelCase      )[input_name]

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      padding="longest" ,      truncation=lowerCamelCase      )[input_name]

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      padding="longest" ,      truncation=lowerCamelCase      )[input_name]

       # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      padding="max_length" ,      truncation=lowerCamelCase      )[input_name]

       # test truncation for `pad_to_multiple_of` for List[int] + numpy
       UpperCAmelCase__							 =						12
       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      pad_to_multiple_of=lowerCamelCase ,      truncation=lowerCamelCase ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      pad_to_multiple_of=lowerCamelCase ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       # retrieve expected_length as multiple of pad_to_multiple_of
       UpperCAmelCase__							 =						len(speech_inputs[0]      )
       if expected_length % pad_to_multiple_of != 0:
        UpperCAmelCase__							 =						((len(speech_inputs[0]      ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

       self.assertTrue(len(input_a[0]      ) == expected_length      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )
      def 	UpperCAmelCase_						(			self   :int      )     ->			List[str]:
       self._check_padding(numpify=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			int:
       self._check_padding(numpify=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :str      )     ->			str:
       self._check_truncation(numpify=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			str:
       self._check_truncation(numpify=lowerCamelCase      )
      @require_torch
      def 	UpperCAmelCase_						(			self   :int      )     ->			Any:
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="np"      )[input_name]
       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="pt"      )[input_name]

       self.assertTrue(abs(input_np.astype(np.floataa      ).sum() - input_pt.numpy().astype(np.floataa      ).sum()      ) < 1e-2      )
      @require_tf
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="np"      )[input_name]
       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="tf"      )[input_name]

       self.assertTrue(abs(input_np.astype(np.floataa      ).sum() - input_tf.numpy().astype(np.floataa      ).sum()      ) < 1e-2      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			str:
       UpperCAmelCase__							 =						self.feat_extract_dict
       UpperCAmelCase__							 =						True
       UpperCAmelCase__							 =						self.feature_extraction_class(**lowerCamelCase      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						[len(lowerCamelCase      ) for x in speech_inputs]
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="np"      )
       self.assertIn("attention_mask" ,      lowerCamelCase      )
       self.assertListEqual(list(processed.attention_mask.shape      ) ,      list(processed[input_name].shape[:2]      )      )
       self.assertListEqual(processed.attention_mask.sum(-1      ).tolist() ,      lowerCamelCase      )







      def 	UpperCAmelCase_						(			self   :int      )     ->			int:
       UpperCAmelCase__							 =						self.feat_extract_dict
       UpperCAmelCase__							 =						True
       UpperCAmelCase__							 =						self.feature_extraction_class(**lowerCamelCase      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						[len(lowerCamelCase      ) for x in speech_inputs]
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )
       UpperCAmelCase__							 =						min(lowerCamelCase      )

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=lowerCamelCase ,      truncation=lowerCamelCase ,      return_tensors="np"      )
       self.assertIn("attention_mask" ,      lowerCamelCase      )
       self.assertListEqual(
           list(processed_pad.attention_mask.shape      ) ,      [processed_pad[input_name].shape[0], max_length]      )
       self.assertListEqual(
           processed_pad.attention_mask[:, :max_length].sum(-1      ).tolist() ,      [max_length for x in speech_inputs]      )





