
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__    ):

     """simple docstring"""

     a_    	=					None
     a_    	=					None


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return self.feat_extract_tester.prepare_feat_extract_dict()


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							self.feature_extraction_class(**self.feat_extract_dict )
       self.assertTrue(hasattr(__A							,		"""feature_size""" ) )
       self.assertTrue(hasattr(__A							,		"""sampling_rate""" ) )
       self.assertTrue(hasattr(__A							,		"""padding_value""" ) )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.feat_extract_tester.prepare_inputs_for_common()
       a_      :	Optional[int]      =							self.feature_extraction_class(**self.feat_extract_dict )
       a_      :	List[Any]      =							feat_extract.model_input_names[0]

       a_      :	Union[str, Any]      =							BatchFeature({input_name: speech_inputs} )

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

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

       a_      :	Tuple      =							processed_features[input_name]

       if len(batch_features_input.shape ) < 3:
         a_      :	str      =							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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							self.feat_extract_tester.prepare_inputs_for_common(equal_length=__A )
       a_      :	List[Any]      =							self.feature_extraction_class(**self.feat_extract_dict )
       a_      :	Union[str, Any]      =							feat_extract.model_input_names[0]

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

       a_      :	Union[str, Any]      =							processed_features[input_name]

       if len(batch_features_input.shape ) < 3:
         a_      :	List[Any]      =							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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							self.feat_extract_tester.prepare_inputs_for_common(equal_length=__A )
       a_      :	Any      =							self.feature_extraction_class(**self.feat_extract_dict )
       a_      :	str      =							feat_extract.model_input_names[0]

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

       a_      :	Dict      =							processed_features[input_name]

       if len(batch_features_input.shape ) < 3:
         a_      :	Optional[Any]      =							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 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=False ):

       '''simple docstring'''

       def _inputs_have_equal_length(lowerCAmelCase_ ):
         a_      :	Optional[int]      =							len(input[0] )
         for input_slice in input[1:]:
           if len(__A ) != length:
             return False
         return True

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

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

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

       a_      :	Optional[int]      =							BatchFeature({input_name: speech_inputs} )

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

       # test padding for List[int] + numpy
       a_      :	Tuple      =							feat_extract.pad(__A							,		padding=__A )
       a_      :	Dict      =							input_a[input_name]

       a_      :	int      =							feat_extract.pad(__A							,		padding="""longest""" )
       a_      :	int      =							input_a[input_name]

       a_      :	int      =							feat_extract.pad(__A							,		padding="""max_length"""							,		max_length=len(speech_inputs[-1] ) )
       a_      :	Tuple      =							input_a[input_name]

       a_      :	List[Any]      =							feat_extract.pad(__A							,		padding="""longest"""							,		return_tensors="""np""" )
       a_      :	Any      =							input_a[input_name]

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

       a_      :	Dict      =							feat_extract.pad(
           __A							,		padding="""max_length"""							,		max_length=__A							,		return_tensors="""np""" )
       a_      :	Optional[Any]      =							input_a[input_name]

       self.assertFalse(_inputs_have_equal_length(__A ) )
       self.assertTrue(_inputs_have_equal_length(__A ) )
       self.assertTrue(_inputs_have_equal_length(__A ) )
       self.assertTrue(_inputs_are_equal(__A							,		__A ) )
       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
       a_      :	str      =							feat_extract.pad(__A							,		pad_to_multiple_of=10 )
       a_      :	Union[str, Any]      =							input_a[input_name]

       a_      :	str      =							feat_extract.pad(__A							,		padding="""longest"""							,		pad_to_multiple_of=10 )
       a_      :	Dict      =							input_a[input_name]

       a_      :	Optional[Any]      =							feat_extract.pad(
           __A							,		padding="""max_length"""							,		pad_to_multiple_of=10							,		max_length=__A )
       a_      :	Tuple      =							input_a[input_name]

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

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

       a_      :	Any      =							pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
       self.assertTrue(all(len(__A ) == 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
       a_      :	List[Any]      =							(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 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=False ):

       '''simple docstring'''

       def _inputs_have_equal_length(lowerCAmelCase_ ):
         a_      :	Tuple      =							len(input[0] )
         for input_slice in input[1:]:
           if len(__A ) != length:
             return False
         return True

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

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

       a_      :	Optional[Any]      =							self.feature_extraction_class(**self.feat_extract_dict )
       a_      :	Optional[int]      =							self.feat_extract_tester.prepare_inputs_for_common(numpify=__A )
       a_      :	List[Any]      =							feat_extract.model_input_names[0]

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

       # truncate to smallest
       a_      :	Union[str, Any]      =							feat_extract.pad(
           __A							,		padding="""max_length"""							,		max_length=len(speech_inputs[0] )							,		truncation=__A )
       a_      :	List[Any]      =							input_a[input_name]

       a_      :	List[str]      =							feat_extract.pad(__A							,		padding="""max_length"""							,		max_length=len(speech_inputs[0] ) )
       a_      :	int      =							input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(__A ) )
       self.assertFalse(_inputs_have_equal_length(__A ) )

       # truncate to smallest with np
       a_      :	Dict      =							feat_extract.pad(
           __A							,		padding="""max_length"""							,		max_length=len(speech_inputs[0] )							,		return_tensors="""np"""							,		truncation=__A							,		)
       a_      :	Optional[Any]      =							input_a[input_name]

       a_      :	Union[str, Any]      =							feat_extract.pad(
           __A							,		padding="""max_length"""							,		max_length=len(speech_inputs[0] )							,		return_tensors="""np""" )
       a_      :	Optional[int]      =							input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(__A ) )
       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(__A ) )

       # truncate to middle
       a_      :	List[Any]      =							feat_extract.pad(
           __A							,		padding="""max_length"""							,		max_length=len(speech_inputs[1] )							,		truncation=__A							,		return_tensors="""np"""							,		)
       a_      :	Tuple      =							input_a[input_name]

       a_      :	Union[str, Any]      =							feat_extract.pad(
           __A							,		padding="""max_length"""							,		max_length=len(speech_inputs[1] )							,		truncation=__A )
       a_      :	str      =							input_a[input_name]

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

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

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

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

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

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

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

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

       a_      :	Any      =							feat_extract.pad(
           __A							,		padding="""max_length"""							,		max_length=len(speech_inputs[0] )							,		pad_to_multiple_of=__A							,		)
       a_      :	int      =							input_a[input_name]

       # retrieve expected_length as multiple of pad_to_multiple_of
       a_      :	Union[str, Any]      =							len(speech_inputs[0] )
       if expected_length % pad_to_multiple_of != 0:
         a_      :	Tuple      =							((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(__A ) )
       self.assertFalse(_inputs_have_equal_length(__A ) )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       self._check_padding(numpify=__A )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       self._check_padding(numpify=__A )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       self._check_truncation(numpify=__A )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       self._check_truncation(numpify=__A )


     @require_torch
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							self.feature_extraction_class(**self.feat_extract_dict )
       a_      :	Any      =							self.feat_extract_tester.prepare_inputs_for_common()
       a_      :	Tuple      =							feat_extract.model_input_names[0]

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

       a_      :	int      =							feat_extract.pad(__A							,		padding="""longest"""							,		return_tensors="""np""" )[input_name]
       a_      :	str      =							feat_extract.pad(__A							,		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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							self.feature_extraction_class(**self.feat_extract_dict )
       a_      :	Any      =							self.feat_extract_tester.prepare_inputs_for_common()
       a_      :	List[Any]      =							feat_extract.model_input_names[0]

       a_      :	List[Any]      =							BatchFeature({input_name: speech_inputs} )

       a_      :	Optional[Any]      =							feat_extract.pad(__A							,		padding="""longest"""							,		return_tensors="""np""" )[input_name]
       a_      :	Tuple      =							feat_extract.pad(__A							,		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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							self.feat_extract_dict
       a_      :	Optional[Any]      =							True
       a_      :	str      =							self.feature_extraction_class(**__A )
       a_      :	int      =							self.feat_extract_tester.prepare_inputs_for_common()
       a_      :	Optional[int]      =							[len(__A ) for x in speech_inputs]
       a_      :	str      =							feat_extract.model_input_names[0]

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

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






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							self.feat_extract_dict
       a_      :	Any      =							True
       a_      :	Union[str, Any]      =							self.feature_extraction_class(**__A )
       a_      :	Optional[int]      =							self.feat_extract_tester.prepare_inputs_for_common()
       a_      :	List[str]      =							[len(__A ) for x in speech_inputs]
       a_      :	Optional[Any]      =							feat_extract.model_input_names[0]

       a_      :	List[Any]      =							BatchFeature({input_name: speech_inputs} )
       a_      :	Any      =							min(__A )

       a_      :	Any      =							feat_extract.pad(
           __A							,		padding="""max_length"""							,		max_length=__A							,		truncation=__A							,		return_tensors="""np""" )
       self.assertIn("""attention_mask"""							,		__A )
       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 torch import nn






def 	_snake_case						(      A_       :    Dict   ):
  """simple docstring"""







  if act_fn in ["swish", "silu"]:
    return nn.SiLU()
  elif act_fn == "mish":
    return nn.Mish()
  elif act_fn == "gelu":
    return nn.GELU()
  else:
    raise ValueError(f'''Unsupported activation function: {act_fn}'''   )


