
'''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__						(       A ):
    # to overwrite at feature extractactor specific tests
    lowerCAmelCase_				    =		None
    lowerCAmelCase_				    =		None



    @property
    def   lowerCamelCase_  (	self      :       List[Any]     ):
          return self.feat_extract_tester.prepare_feat_extract_dict()



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

          _lowerCamelCase				:   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]     )     )     )

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

          _lowerCamelCase				:   Tuple								=   processed_features[input_name]

          if len(batch_features_input.shape     ) < 3:
                _lowerCamelCase				:   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      :       List[Any]     ):
          _lowerCamelCase				:   str								=   self.feat_extract_tester.prepare_inputs_for_common(equal_length=__A     )
          _lowerCamelCase				:   List[Any]								=   self.feature_extraction_class(**self.feat_extract_dict     )
          _lowerCamelCase				:   Union[str, Any]								=   feat_extract.model_input_names[0]

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

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

          if len(batch_features_input.shape     ) < 3:
                _lowerCamelCase				:   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      :       Dict     ):
          _lowerCamelCase				:   List[str]								=   self.feat_extract_tester.prepare_inputs_for_common(equal_length=__A     )
          _lowerCamelCase				:   Any								=   self.feature_extraction_class(**self.feat_extract_dict     )
          _lowerCamelCase				:   str								=   feat_extract.model_input_names[0]

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

          _lowerCamelCase				:   Dict								=   processed_features[input_name]

          if len(batch_features_input.shape     ) < 3:
                _lowerCamelCase				:   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      :       int,__A      :       List[Any]=False     ):
          def _inputs_have_equal_length(__A      :       Tuple     ):
                _lowerCamelCase				:   Optional[int]								=   len(input[0]     )
                for input_slice in input[1:]:
                      if len(__A     ) != length:
                            return False
                return True

          def _inputs_are_equal(__A      :       Union[str, Any],__A      :       int     ):
                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

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

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

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

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

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

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

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

          _lowerCamelCase				:   Dict								=   feat_extract.pad(
              __A,padding="max_length",max_length=__A,return_tensors="np"     )
          _lowerCamelCase				:   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
          _lowerCamelCase				:   str								=   feat_extract.pad(__A,pad_to_multiple_of=1_0     )
          _lowerCamelCase				:   Union[str, Any]								=   input_a[input_name]

          _lowerCamelCase				:   str								=   feat_extract.pad(__A,padding="longest",pad_to_multiple_of=1_0     )
          _lowerCamelCase				:   Dict								=   input_a[input_name]

          _lowerCamelCase				:   Optional[Any]								=   feat_extract.pad(
              __A,padding="max_length",pad_to_multiple_of=1_0,max_length=__A     )
          _lowerCamelCase				:   Tuple								=   input_a[input_name]

          _lowerCamelCase				:   List[Any]								=   feat_extract.pad(
              __A,padding="max_length",pad_to_multiple_of=1_0,max_length=__A,return_tensors="np",)
          _lowerCamelCase				:   List[str]								=   input_a[input_name]

          self.assertTrue(all(len(__A     ) % 1_0 == 0 for x in input_a     )     )
          self.assertTrue(_inputs_are_equal(__A,__A     )     )

          _lowerCamelCase				:   Any								=   pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
          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
          _lowerCamelCase				:   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      :       int,__A      :       Dict=False     ):
          def _inputs_have_equal_length(__A      :       int     ):
                _lowerCamelCase				:   Tuple								=   len(input[0]     )
                for input_slice in input[1:]:
                      if len(__A     ) != length:
                            return False
                return True

          def _inputs_are_equal(__A      :       Dict,__A      :       Optional[int]     ):
                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

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

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

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

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

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

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

          _lowerCamelCase				:   Union[str, Any]								=   feat_extract.pad(
              __A,padding="max_length",max_length=len(speech_inputs[0]     ),return_tensors="np"     )
          _lowerCamelCase				:   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
          _lowerCamelCase				:   List[Any]								=   feat_extract.pad(
              __A,padding="max_length",max_length=len(speech_inputs[1]     ),truncation=__A,return_tensors="np",)
          _lowerCamelCase				:   Tuple								=   input_a[input_name]

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

          _lowerCamelCase				:   List[str]								=   feat_extract.pad(
              __A,padding="max_length",max_length=len(speech_inputs[1]     ),return_tensors="np"     )
          _lowerCamelCase				:   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
          _lowerCamelCase				:   Optional[int]								=   1_2
          _lowerCamelCase				:   Dict								=   feat_extract.pad(
              __A,padding="max_length",max_length=len(speech_inputs[0]     ),pad_to_multiple_of=__A,truncation=__A,)
          _lowerCamelCase				:   str								=   input_a[input_name]

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

          # retrieve expected_length as multiple of pad_to_multiple_of
          _lowerCamelCase				:   Union[str, Any]								=   len(speech_inputs[0]     )
          if expected_length % pad_to_multiple_of != 0:
                _lowerCamelCase				:   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      :       Any     ):
          self._check_padding(numpify=__A     )



    def   lowerCamelCase_  (	self      :       str     ):
          self._check_padding(numpify=__A     )



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          self._check_truncation(numpify=__A     )



    def   lowerCamelCase_  (	self      :       Any     ):
          self._check_truncation(numpify=__A     )



    @require_torch
    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          _lowerCamelCase				:   List[str]								=   self.feature_extraction_class(**self.feat_extract_dict     )
          _lowerCamelCase				:   Any								=   self.feat_extract_tester.prepare_inputs_for_common()
          _lowerCamelCase				:   Tuple								=   feat_extract.model_input_names[0]

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

          _lowerCamelCase				:   int								=   feat_extract.pad(__A,padding="longest",return_tensors="np"     )[input_name]
          _lowerCamelCase				:   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      :       Optional[int]     ):
          _lowerCamelCase				:   Tuple								=   self.feature_extraction_class(**self.feat_extract_dict     )
          _lowerCamelCase				:   Any								=   self.feat_extract_tester.prepare_inputs_for_common()
          _lowerCamelCase				:   List[Any]								=   feat_extract.model_input_names[0]

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

          _lowerCamelCase				:   Optional[Any]								=   feat_extract.pad(__A,padding="longest",return_tensors="np"     )[input_name]
          _lowerCamelCase				:   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      :       int     ):
          _lowerCamelCase				:   List[Any]								=   self.feat_extract_dict
          _lowerCamelCase				:   Optional[Any]								=   True
          _lowerCamelCase				:   str								=   self.feature_extraction_class(**__A     )
          _lowerCamelCase				:   int								=   self.feat_extract_tester.prepare_inputs_for_common()
          _lowerCamelCase				:   Optional[int]								=   [len(__A     ) for x in speech_inputs]
          _lowerCamelCase				:   str								=   feat_extract.model_input_names[0]

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

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

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

          _lowerCamelCase				:   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 typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCAmelCase_       :   Tuple   =       {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}


if TYPE_CHECKING:
 from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
 import sys

 UpperCAmelCase_       :   Tuple   =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)