

"""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      _UpperCAmelCase   (							_lowerCAmelCase       ):
	# to overwrite at feature extractactor specific tests
	a__		: Union[str, Any]					=					None
	a__		: Optional[int]					=					None



	@property
	def        a  (  self     :       Dict						):
		return self.feat_extract_tester.prepare_feat_extract_dict()



	def        a  (  self     :       Union[str, Any]						):
		__UpperCAmelCase      		= 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        a  (  self     :       Optional[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(_lowercase						) == len(_lowercase						) for x, y in zip(_lowercase			,    processed_features[input_name]						)						)						)

		__UpperCAmelCase      		= self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowercase						)
		__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        a  (  self     :       Optional[Any]						):
		__UpperCAmelCase      		= self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowercase						)
		__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        a  (  self     :       int						):
		__UpperCAmelCase      		= self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowercase						)
		__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        a  (  self     :       Tuple			,    _lowercase     :       Dict=False						):
		def _inputs_have_equal_length(_lowercase     :       List[str]						):
			__UpperCAmelCase      		= len(input[0]						)
			for input_slice in input[1:]:
				if len(_lowercase						) != length:
					return False
			return True

		def _inputs_are_equal(_lowercase     :       Any			,    _lowercase     :       int						):
			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      		= self.feature_extraction_class(**self.feat_extract_dict						)
		__UpperCAmelCase      		= self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowercase						)
		__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(_lowercase			,    padding=_lowercase						)
		__UpperCAmelCase      		= input_a[input_name]

		__UpperCAmelCase      		= feat_extract.pad(_lowercase			,    padding='''longest'''						)
		__UpperCAmelCase      		= input_a[input_name]

		__UpperCAmelCase      		= feat_extract.pad(_lowercase			,    padding='''max_length'''			,    max_length=len(speech_inputs[-1]						)						)
		__UpperCAmelCase      		= input_a[input_name]

		__UpperCAmelCase      		= feat_extract.pad(_lowercase			,    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(_lowercase						):
			feat_extract.pad(_lowercase			,    padding='''max_length'''						)[input_name]

		__UpperCAmelCase      		= feat_extract.pad(
		    _lowercase			,    padding='''max_length'''			,    max_length=_lowercase			,    return_tensors='''np'''						)
		__UpperCAmelCase      		= 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      		= feat_extract.pad(_lowercase			,    pad_to_multiple_of=10						)
		__UpperCAmelCase      		= input_a[input_name]

		__UpperCAmelCase      		= feat_extract.pad(_lowercase			,    padding='''longest'''			,    pad_to_multiple_of=10						)
		__UpperCAmelCase      		= input_a[input_name]

		__UpperCAmelCase      		= feat_extract.pad(
		    _lowercase			,    padding='''max_length'''			,    pad_to_multiple_of=10			,    max_length=_lowercase						)
		__UpperCAmelCase      		= input_a[input_name]

		__UpperCAmelCase      		= feat_extract.pad(
		    _lowercase			,    padding='''max_length'''			,    pad_to_multiple_of=10			,    max_length=_lowercase			,    return_tensors='''np'''			,    )
		__UpperCAmelCase      		= input_a[input_name]

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

		__UpperCAmelCase      		= 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      		= (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        a  (  self     :       Optional[int]			,    _lowercase     :       List[str]=False						):
		def _inputs_have_equal_length(_lowercase     :       Dict						):
			__UpperCAmelCase      		= len(input[0]						)
			for input_slice in input[1:]:
				if len(_lowercase						) != length:
					return False
			return True

		def _inputs_are_equal(_lowercase     :       Any			,    _lowercase     :       Any						):
			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      		= self.feature_extraction_class(**self.feat_extract_dict						)
		__UpperCAmelCase      		= self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowercase						)
		__UpperCAmelCase      		= feat_extract.model_input_names[0]

		__UpperCAmelCase      		= BatchFeature({input_name: speech_inputs}						)

		# truncate to smallest
		__UpperCAmelCase      		= feat_extract.pad(
		    _lowercase			,    padding='''max_length'''			,    max_length=len(speech_inputs[0]						)			,    truncation=_lowercase						)
		__UpperCAmelCase      		= input_a[input_name]

		__UpperCAmelCase      		= feat_extract.pad(_lowercase			,    padding='''max_length'''			,    max_length=len(speech_inputs[0]						)						)
		__UpperCAmelCase      		= input_a[input_name]

		self.assertTrue(_inputs_have_equal_length(_lowercase						)						)
		self.assertFalse(_inputs_have_equal_length(_lowercase						)						)

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

		__UpperCAmelCase      		= feat_extract.pad(
		    _lowercase			,    padding='''max_length'''			,    max_length=len(speech_inputs[0]						)			,    return_tensors='''np'''						)
		__UpperCAmelCase      		= 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      		= feat_extract.pad(
		    _lowercase			,    padding='''max_length'''			,    max_length=len(speech_inputs[1]						)			,    truncation=_lowercase			,    return_tensors='''np'''			,    )
		__UpperCAmelCase      		= input_a[input_name]

		__UpperCAmelCase      		= feat_extract.pad(
		    _lowercase			,    padding='''max_length'''			,    max_length=len(speech_inputs[1]						)			,    truncation=_lowercase						)
		__UpperCAmelCase      		= input_a[input_name]

		__UpperCAmelCase      		= feat_extract.pad(
		    _lowercase			,    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(_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      		= 12
		__UpperCAmelCase      		= feat_extract.pad(
		    _lowercase			,    padding='''max_length'''			,    max_length=len(speech_inputs[0]						)			,    pad_to_multiple_of=_lowercase			,    truncation=_lowercase			,    )
		__UpperCAmelCase      		= input_a[input_name]

		__UpperCAmelCase      		= feat_extract.pad(
		    _lowercase			,    padding='''max_length'''			,    max_length=len(speech_inputs[0]						)			,    pad_to_multiple_of=_lowercase			,    )
		__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(_lowercase						)						)
		self.assertFalse(_inputs_have_equal_length(_lowercase						)						)



	def        a  (  self     :       str						):
		self._check_padding(numpify=_lowercase						)



	def        a  (  self     :       Optional[int]						):
		self._check_padding(numpify=_lowercase						)



	def        a  (  self     :       List[Any]						):
		self._check_truncation(numpify=_lowercase						)



	def        a  (  self     :       Dict						):
		self._check_truncation(numpify=_lowercase						)



	@require_torch
	def        a  (  self     :       Tuple						):
		__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(_lowercase			,    padding='''longest'''			,    return_tensors='''np'''						)[input_name]
		__UpperCAmelCase      		= 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        a  (  self     :       str						):
		__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(_lowercase			,    padding='''longest'''			,    return_tensors='''np'''						)[input_name]
		__UpperCAmelCase      		= 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        a  (  self     :       Optional[Any]						):
		__UpperCAmelCase      		= self.feat_extract_dict
		__UpperCAmelCase      		= True
		__UpperCAmelCase      		= self.feature_extraction_class(**_lowercase						)
		__UpperCAmelCase      		= self.feat_extract_tester.prepare_inputs_for_common()
		__UpperCAmelCase      		= [len(_lowercase						) for x in speech_inputs]
		__UpperCAmelCase      		= feat_extract.model_input_names[0]

		__UpperCAmelCase      		= BatchFeature({input_name: speech_inputs}						)

		__UpperCAmelCase      		= 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        a  (  self     :       Any						):
		__UpperCAmelCase      		= self.feat_extract_dict
		__UpperCAmelCase      		= True
		__UpperCAmelCase      		= self.feature_extraction_class(**_lowercase						)
		__UpperCAmelCase      		= self.feat_extract_tester.prepare_inputs_for_common()
		__UpperCAmelCase      		= [len(_lowercase						) for x in speech_inputs]
		__UpperCAmelCase      		= feat_extract.model_input_names[0]

		__UpperCAmelCase      		= BatchFeature({input_name: speech_inputs}						)
		__UpperCAmelCase      		= min(_lowercase						)

		__UpperCAmelCase      		= 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 typing import TYPE_CHECKING

from ...utils import _LazyModule


_a					:			Tuple							=    {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}


if TYPE_CHECKING:
  from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
  import sys

  _a					:			int							=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


