
"""simple docstring"""





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 A_	(lowercase__      ):




  '''simple docstring'''
  # to overwrite at feature extractactor specific tests
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		None
  SCREAMING_SNAKE_CASE__			:						List[str]       =		None







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return self.feat_extract_tester.prepare_feat_extract_dict()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    self.feature_extraction_class(**self.feat_extract_dict  )
     self.assertTrue(hasattr(lowercase_				,   "feature_size"  )  )
     self.assertTrue(hasattr(lowercase_				,   "sampling_rate"  )  )
     self.assertTrue(hasattr(lowercase_				,   "padding_value"  )  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[int]			     =    self.feat_extract_tester.prepare_inputs_for_common()
     UpperCAmelCase_					:					Dict			     =    self.feature_extraction_class(**self.feat_extract_dict  )
     UpperCAmelCase_					:					Union[str, Any]			     =    feat_extract.model_input_names[0]

     UpperCAmelCase_					:					Tuple			     =    BatchFeature({input_name: speech_inputs}  )

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

     UpperCAmelCase_					:					Any			     =    self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowercase_  )
     UpperCAmelCase_					:					List[Any]			     =    BatchFeature({input_name: speech_inputs}				,   tensor_type="np"  )

     UpperCAmelCase_					:					int			     =    processed_features[input_name]

     if len(batch_features_input.shape  ) < 3:
        UpperCAmelCase_					:					Optional[int]			     =    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  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowercase_  )
     UpperCAmelCase_					:					Optional[int]			     =    self.feature_extraction_class(**self.feat_extract_dict  )
     UpperCAmelCase_					:					Tuple			     =    feat_extract.model_input_names[0]

     UpperCAmelCase_					:					Optional[int]			     =    BatchFeature({input_name: speech_inputs}				,   tensor_type="pt"  )

     UpperCAmelCase_					:					List[str]			     =    processed_features[input_name]

     if len(batch_features_input.shape  ) < 3:
        UpperCAmelCase_					:					int			     =    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  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowercase_  )
     UpperCAmelCase_					:					Tuple			     =    self.feature_extraction_class(**self.feat_extract_dict  )
     UpperCAmelCase_					:					int			     =    feat_extract.model_input_names[0]

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

     UpperCAmelCase_					:					List[str]			     =    processed_features[input_name]

     if len(batch_features_input.shape  ) < 3:
        UpperCAmelCase_					:					Tuple			     =    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				,   lowercase_=False  ):

     """simple docstring"""
     def _inputs_have_equal_length(lowercase_  ):
        UpperCAmelCase_					:					str			     =    len(input[0]  )
        for input_slice in input[1:]:
           if len(lowercase_  ) != length:
              return False
        return True

     def _inputs_are_equal(lowercase_				,   lowercase_  ):
        if len(lowercase_  ) != len(lowercase_  ):
           return False

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

     UpperCAmelCase_					:					int			     =    self.feature_extraction_class(**self.feat_extract_dict  )
     UpperCAmelCase_					:					Any			     =    self.feat_extract_tester.prepare_inputs_for_common(numpify=lowercase_  )
     UpperCAmelCase_					:					Union[str, Any]			     =    feat_extract.model_input_names[0]

     UpperCAmelCase_					:					Optional[Any]			     =    BatchFeature({input_name: speech_inputs}  )

     UpperCAmelCase_					:					Union[str, Any]			     =    self.feat_extract_tester.seq_length_diff
     UpperCAmelCase_					:					List[str]			     =    self.feat_extract_tester.max_seq_length + pad_diff
     UpperCAmelCase_					:					Optional[Any]			     =    self.feat_extract_tester.min_seq_length
     UpperCAmelCase_					:					Optional[Any]			     =    self.feat_extract_tester.batch_size
     UpperCAmelCase_					:					Dict			     =    self.feat_extract_tester.feature_size

     # test padding for List[int] + numpy
     UpperCAmelCase_					:					int			     =    feat_extract.pad(lowercase_				,   padding=lowercase_  )
     UpperCAmelCase_					:					Optional[Any]			     =    input_a[input_name]

     UpperCAmelCase_					:					List[str]			     =    feat_extract.pad(lowercase_				,   padding="longest"  )
     UpperCAmelCase_					:					Dict			     =    input_a[input_name]

     UpperCAmelCase_					:					Optional[Any]			     =    feat_extract.pad(lowercase_				,   padding="max_length"				,   max_length=len(speech_inputs[-1]  )  )
     UpperCAmelCase_					:					Optional[Any]			     =    input_a[input_name]

     UpperCAmelCase_					:					Optional[int]			     =    feat_extract.pad(lowercase_				,   padding="longest"				,   return_tensors="np"  )
     UpperCAmelCase_					:					Any			     =    input_a[input_name]

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

     UpperCAmelCase_					:					List[Any]			     =    feat_extract.pad(
         lowercase_				,   padding="max_length"				,   max_length=lowercase_				,   return_tensors="np"  )
     UpperCAmelCase_					:					List[str]			     =    input_a[input_name]

     self.assertFalse(_inputs_have_equal_length(lowercase_  )  )
     self.assertTrue(_inputs_have_equal_length(lowercase_  )  )
     self.assertTrue(_inputs_have_equal_length(lowercase_  )  )
     self.assertTrue(_inputs_are_equal(lowercase_				,   lowercase_  )  )
     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_					:					List[str]			     =    feat_extract.pad(lowercase_				,   pad_to_multiple_of=10  )
     UpperCAmelCase_					:					str			     =    input_a[input_name]

     UpperCAmelCase_					:					Union[str, Any]			     =    feat_extract.pad(lowercase_				,   padding="longest"				,   pad_to_multiple_of=10  )
     UpperCAmelCase_					:					Optional[Any]			     =    input_a[input_name]

     UpperCAmelCase_					:					Optional[Any]			     =    feat_extract.pad(
         lowercase_				,   padding="max_length"				,   pad_to_multiple_of=10				,   max_length=lowercase_  )
     UpperCAmelCase_					:					List[Any]			     =    input_a[input_name]

     UpperCAmelCase_					:					List[Any]			     =    feat_extract.pad(
         lowercase_				,   padding="max_length"				,   pad_to_multiple_of=10				,   max_length=lowercase_				,   return_tensors="np"				,   )
     UpperCAmelCase_					:					str			     =    input_a[input_name]

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

     UpperCAmelCase_					:					Any			     =    pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
     self.assertTrue(all(len(lowercase_  ) == 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_					:					str			     =    (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				,   lowercase_=False  ):

     """simple docstring"""
     def _inputs_have_equal_length(lowercase_  ):
        UpperCAmelCase_					:					Union[str, Any]			     =    len(input[0]  )
        for input_slice in input[1:]:
           if len(lowercase_  ) != length:
              return False
        return True

     def _inputs_are_equal(lowercase_				,   lowercase_  ):
        if len(lowercase_  ) != len(lowercase_  ):
           return False

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

     UpperCAmelCase_					:					List[Any]			     =    self.feature_extraction_class(**self.feat_extract_dict  )
     UpperCAmelCase_					:					Any			     =    self.feat_extract_tester.prepare_inputs_for_common(numpify=lowercase_  )
     UpperCAmelCase_					:					str			     =    feat_extract.model_input_names[0]

     UpperCAmelCase_					:					Dict			     =    BatchFeature({input_name: speech_inputs}  )

     # truncate to smallest
     UpperCAmelCase_					:					Optional[int]			     =    feat_extract.pad(
         lowercase_				,   padding="max_length"				,   max_length=len(speech_inputs[0]  )				,   truncation=lowercase_  )
     UpperCAmelCase_					:					Any			     =    input_a[input_name]

     UpperCAmelCase_					:					Optional[int]			     =    feat_extract.pad(lowercase_				,   padding="max_length"				,   max_length=len(speech_inputs[0]  )  )
     UpperCAmelCase_					:					Optional[int]			     =    input_a[input_name]

     self.assertTrue(_inputs_have_equal_length(lowercase_  )  )
     self.assertFalse(_inputs_have_equal_length(lowercase_  )  )

     # truncate to smallest with np
     UpperCAmelCase_					:					Dict			     =    feat_extract.pad(
         lowercase_				,   padding="max_length"				,   max_length=len(speech_inputs[0]  )				,   return_tensors="np"				,   truncation=lowercase_				,   )
     UpperCAmelCase_					:					int			     =    input_a[input_name]

     UpperCAmelCase_					:					List[str]			     =    feat_extract.pad(
         lowercase_				,   padding="max_length"				,   max_length=len(speech_inputs[0]  )				,   return_tensors="np"  )
     UpperCAmelCase_					:					List[str]			     =    input_a[input_name]

     self.assertTrue(_inputs_have_equal_length(lowercase_  )  )
     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(lowercase_  )  )

     # truncate to middle
     UpperCAmelCase_					:					int			     =    feat_extract.pad(
         lowercase_				,   padding="max_length"				,   max_length=len(speech_inputs[1]  )				,   truncation=lowercase_				,   return_tensors="np"				,   )
     UpperCAmelCase_					:					Tuple			     =    input_a[input_name]

     UpperCAmelCase_					:					Optional[Any]			     =    feat_extract.pad(
         lowercase_				,   padding="max_length"				,   max_length=len(speech_inputs[1]  )				,   truncation=lowercase_  )
     UpperCAmelCase_					:					Dict			     =    input_a[input_name]

     UpperCAmelCase_					:					str			     =    feat_extract.pad(
         lowercase_				,   padding="max_length"				,   max_length=len(speech_inputs[1]  )				,   return_tensors="np"  )
     UpperCAmelCase_					:					List[str]			     =    input_a[input_name]

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

     # 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(lowercase_  )  )
     self.assertTrue(len(input_a[-1]  ) == len(speech_inputs[-1]  )  )

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

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

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

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

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

     UpperCAmelCase_					:					List[str]			     =    feat_extract.pad(
         lowercase_				,   padding="max_length"				,   max_length=len(speech_inputs[0]  )				,   pad_to_multiple_of=lowercase_				,   )
     UpperCAmelCase_					:					List[Any]			     =    input_a[input_name]

     # retrieve expected_length as multiple of pad_to_multiple_of
     UpperCAmelCase_					:					List[str]			     =    len(speech_inputs[0]  )
     if expected_length % pad_to_multiple_of != 0:
        UpperCAmelCase_					:					Optional[int]			     =    ((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(lowercase_  )  )
     self.assertFalse(_inputs_have_equal_length(lowercase_  )  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     self._check_padding(numpify=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     self._check_padding(numpify=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     self._check_truncation(numpify=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     self._check_truncation(numpify=lowercase_  )







  @require_torch
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.feature_extraction_class(**self.feat_extract_dict  )
     UpperCAmelCase_					:					List[Any]			     =    self.feat_extract_tester.prepare_inputs_for_common()
     UpperCAmelCase_					:					Any			     =    feat_extract.model_input_names[0]

     UpperCAmelCase_					:					Dict			     =    BatchFeature({input_name: speech_inputs}  )

     UpperCAmelCase_					:					List[str]			     =    feat_extract.pad(lowercase_				,   padding="longest"				,   return_tensors="np"  )[input_name]
     UpperCAmelCase_					:					Any			     =    feat_extract.pad(lowercase_				,   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  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    self.feature_extraction_class(**self.feat_extract_dict  )
     UpperCAmelCase_					:					Dict			     =    self.feat_extract_tester.prepare_inputs_for_common()
     UpperCAmelCase_					:					int			     =    feat_extract.model_input_names[0]

     UpperCAmelCase_					:					List[str]			     =    BatchFeature({input_name: speech_inputs}  )

     UpperCAmelCase_					:					List[str]			     =    feat_extract.pad(lowercase_				,   padding="longest"				,   return_tensors="np"  )[input_name]
     UpperCAmelCase_					:					Union[str, Any]			     =    feat_extract.pad(lowercase_				,   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  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    self.feat_extract_dict
     UpperCAmelCase_					:					List[str]			     =    True
     UpperCAmelCase_					:					Optional[int]			     =    self.feature_extraction_class(**lowercase_  )
     UpperCAmelCase_					:					Any			     =    self.feat_extract_tester.prepare_inputs_for_common()
     UpperCAmelCase_					:					Optional[int]			     =    [len(lowercase_  ) for x in speech_inputs]
     UpperCAmelCase_					:					Optional[Any]			     =    feat_extract.model_input_names[0]

     UpperCAmelCase_					:					str			     =    BatchFeature({input_name: speech_inputs}  )

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    self.feat_extract_dict
     UpperCAmelCase_					:					int			     =    True
     UpperCAmelCase_					:					Tuple			     =    self.feature_extraction_class(**lowercase_  )
     UpperCAmelCase_					:					Dict			     =    self.feat_extract_tester.prepare_inputs_for_common()
     UpperCAmelCase_					:					Optional[int]			     =    [len(lowercase_  ) for x in speech_inputs]
     UpperCAmelCase_					:					int			     =    feat_extract.model_input_names[0]

     UpperCAmelCase_					:					List[str]			     =    BatchFeature({input_name: speech_inputs}  )
     UpperCAmelCase_					:					Optional[Any]			     =    min(lowercase_  )

     UpperCAmelCase_					:					Tuple			     =    feat_extract.pad(
         lowercase_				,   padding="max_length"				,   max_length=lowercase_				,   truncation=lowercase_				,   return_tensors="np"  )
     self.assertIn("attention_mask"				,   lowercase_  )
     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]  )




"""simple docstring"""





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
    from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


