"""simple docstring"""
import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
     from transformers import SpeechaTextFeatureExtractor

SCREAMING_SNAKE_CASE__      						=  random.Random()





def    lowerCAmelCase__    (			_UpperCamelCase       :				Union[str, Any] ,			_UpperCamelCase       :				Tuple=1.0 ,			_UpperCamelCase       :				Tuple=None ,			_UpperCamelCase       :				List[Any]=None					)					->				Tuple:





      """simple docstring"""






      if rng is None:
            snake_case								=			global_rng

      snake_case								=			[]
      for batch_idx in range(shape[0]					):
            values.append([]					)
            for _ in range(shape[1]					):
                  values[-1].append(rng.random() * scale					)

      return values



@require_torch
@require_torchaudio
class   lowerCAmelCase_    (			unittest.TestCase					):



    """simple docstring"""

    def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase=7		,    lowerCAmelCase=4_00		,    lowerCAmelCase=20_00		,    lowerCAmelCase=24		,    lowerCAmelCase=24		,    lowerCAmelCase=0.0		,    lowerCAmelCase=1_60_00		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    ):






          """simple docstring"""

          snake_case								=			parent
          snake_case								=			batch_size
          snake_case								=			min_seq_length
          snake_case								=			max_seq_length
          snake_case								=			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
          snake_case								=			feature_size
          snake_case								=			num_mel_bins
          snake_case								=			padding_value
          snake_case								=			sampling_rate
          snake_case								=			return_attention_mask
          snake_case								=			do_normalize

    def 		snake_case (				self    ):






          """simple docstring"""

          return {
              "feature_size": self.feature_size,
              "num_mel_bins": self.num_mel_bins,
              "padding_value": self.padding_value,
              "sampling_rate": self.sampling_rate,
              "return_attention_mask": self.return_attention_mask,
              "do_normalize": self.do_normalize,
          }

    def 		snake_case (				self		,    lowerCAmelCase=False		,    lowerCAmelCase=False    ):






          """simple docstring"""

          def _flatten(lowerCAmelCase    ):
                return list(itertools.chain(*lowerCAmelCase    )    )

          if equal_length:
                snake_case								=			[floats_list((self.max_seq_length, self.feature_size)    ) for _ in range(self.batch_size    )]
          else:
                # make sure that inputs increase in size
                snake_case								=			[
                    floats_list((x, self.feature_size)    )
                    for x in range(self.min_seq_length		,    self.max_seq_length		,    self.seq_length_diff    )
                ]
          if numpify:
                snake_case								=			[np.asarray(lowerCAmelCase    ) for x in speech_inputs]
          return speech_inputs





@require_torch
@require_torchaudio
class   lowerCAmelCase_    (			lowerCAmelCase     ,    unittest.TestCase					):



    """simple docstring"""
    _lowerCAmelCase     :    Any			  =						SpeechaTextFeatureExtractor if is_speech_available() else None

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			SpeechaTextFeatureExtractionTester(self    )

    def 		snake_case (				self		,    lowerCAmelCase    ):






          """simple docstring"""

          self.assertTrue(np.all(np.mean(lowerCAmelCase		,    axis=0    ) < 1E-3    )    )
          self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase		,    axis=0    ) - 1    ) < 1E-3    )    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
          # create three inputs of length 800, 1000, and 1200
          snake_case								=			[floats_list((1, x)    )[0] for x in range(8_00		,    14_00		,    2_00    )]
          snake_case								=			[np.asarray(lowerCAmelCase    ) for speech_input in speech_inputs]

          # Test feature size
          snake_case								=			feature_extractor(lowerCAmelCase		,    padding=lowerCAmelCase		,    return_tensors='np'    ).input_features
          self.assertTrue(input_features.ndim == 3    )
          self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size    )

          # Test not batched input
          snake_case								=			feature_extractor(speech_inputs[0]		,    return_tensors='np'    ).input_features
          snake_case								=			feature_extractor(np_speech_inputs[0]		,    return_tensors='np'    ).input_features
          self.assertTrue(np.allclose(lowerCAmelCase		,    lowerCAmelCase		,    atol=1E-3    )    )

          # Test batched
          snake_case								=			feature_extractor(lowerCAmelCase		,    return_tensors='np'    ).input_features
          snake_case								=			feature_extractor(lowerCAmelCase		,    return_tensors='np'    ).input_features
          for enc_seq_a, enc_seq_a in zip(lowerCAmelCase		,    lowerCAmelCase    ):
                self.assertTrue(np.allclose(lowerCAmelCase		,    lowerCAmelCase		,    atol=1E-3    )    )

          # Test 2-D numpy arrays are batched.
          snake_case								=			[floats_list((1, x)    )[0] for x in (8_00, 8_00, 8_00)]
          snake_case								=			np.asarray(lowerCAmelCase    )
          snake_case								=			feature_extractor(lowerCAmelCase		,    return_tensors='np'    ).input_features
          snake_case								=			feature_extractor(lowerCAmelCase		,    return_tensors='np'    ).input_features
          for enc_seq_a, enc_seq_a in zip(lowerCAmelCase		,    lowerCAmelCase    ):
                self.assertTrue(np.allclose(lowerCAmelCase		,    lowerCAmelCase		,    atol=1E-3    )    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
          snake_case								=			[floats_list((1, x)    )[0] for x in range(8_00		,    14_00		,    2_00    )]

          snake_case								=			['longest', 'max_length', 'do_not_pad']
          snake_case								=			[None, 16, None]
          for max_length, padding in zip(lowerCAmelCase		,    lowerCAmelCase    ):
                snake_case								=			feature_extractor(
                    lowerCAmelCase		,    padding=lowerCAmelCase		,    max_length=lowerCAmelCase		,    return_attention_mask=lowerCAmelCase    )
                snake_case								=			inputs.input_features
                snake_case								=			inputs.attention_mask
                snake_case								=			[np.sum(lowerCAmelCase    ) for x in attention_mask]

                self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]    )
                self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]    )
                self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
          snake_case								=			[floats_list((1, x)    )[0] for x in range(8_00		,    14_00		,    2_00    )]

          snake_case								=			['longest', 'max_length', 'do_not_pad']
          snake_case								=			[None, 16, None]
          for max_length, padding in zip(lowerCAmelCase		,    lowerCAmelCase    ):
                snake_case								=			feature_extractor(
                    lowerCAmelCase		,    max_length=lowerCAmelCase		,    padding=lowerCAmelCase		,    return_tensors='np'		,    return_attention_mask=lowerCAmelCase    )
                snake_case								=			inputs.input_features
                snake_case								=			inputs.attention_mask
                snake_case								=			[np.sum(lowerCAmelCase    ) for x in attention_mask]

                self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]    )
                self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6    )
                self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]    )
                self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6    )
                self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
          snake_case								=			[floats_list((1, x)    )[0] for x in range(8_00		,    14_00		,    2_00    )]
          snake_case								=			feature_extractor(
              lowerCAmelCase		,    padding='max_length'		,    max_length=4		,    truncation=lowerCAmelCase		,    return_tensors='np'		,    return_attention_mask=lowerCAmelCase		,    )
          snake_case								=			inputs.input_features
          snake_case								=			inputs.attention_mask
          snake_case								=			np.sum(attention_mask == 1		,    axis=1    )

          self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]    )
          self._check_zero_mean_unit_variance(input_features[1]    )
          self._check_zero_mean_unit_variance(input_features[2]    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
          snake_case								=			[floats_list((1, x)    )[0] for x in range(8_00		,    14_00		,    2_00    )]
          snake_case								=			feature_extractor(
              lowerCAmelCase		,    padding='longest'		,    max_length=4		,    truncation=lowerCAmelCase		,    return_tensors='np'		,    return_attention_mask=lowerCAmelCase		,    )
          snake_case								=			inputs.input_features
          snake_case								=			inputs.attention_mask
          snake_case								=			np.sum(attention_mask == 1		,    axis=1    )

          self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]    )
          self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]    )
          self._check_zero_mean_unit_variance(input_features[2]    )

          # make sure that if max_length < longest -> then pad to max_length
          self.assertEqual(input_features.shape		,    (3, 4, 24)    )

          snake_case								=			[floats_list((1, x)    )[0] for x in range(8_00		,    14_00		,    2_00    )]
          snake_case								=			feature_extractor(
              lowerCAmelCase		,    padding='longest'		,    max_length=16		,    truncation=lowerCAmelCase		,    return_tensors='np'		,    return_attention_mask=lowerCAmelCase		,    )
          snake_case								=			inputs.input_features
          snake_case								=			inputs.attention_mask
          snake_case								=			np.sum(attention_mask == 1		,    axis=1    )

          self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]    )
          self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]    )
          self._check_zero_mean_unit_variance(input_features[2]    )

          # make sure that if max_length < longest -> then pad to max_length
          self.assertEqual(input_features.shape		,    (3, 6, 24)    )

    def 		snake_case (				self    ):






          """simple docstring"""

          import torch

          snake_case								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
          snake_case								=			np.random.rand(1_00		,    32    ).astype(np.floataa    )
          snake_case								=			np_speech_inputs.tolist()

          for inputs in [py_speech_inputs, np_speech_inputs]:
                snake_case								=			feature_extractor.pad([{'input_features': inputs}]		,    return_tensors='np'    )
                self.assertTrue(np_processed.input_features.dtype == np.floataa    )
                snake_case								=			feature_extractor.pad([{'input_features': inputs}]		,    return_tensors='pt'    )
                self.assertTrue(pt_processed.input_features.dtype == torch.floataa    )

    def 		snake_case (				self		,    lowerCAmelCase    ):






          """simple docstring"""

          from datasets import load_dataset

          snake_case								=			load_dataset('hf-internal-testing/librispeech_asr_dummy'		,    'clean'		,    split='validation'    )
          # automatic decoding with librispeech
          snake_case								=			ds.sort('id'    ).select(range(lowerCAmelCase    )    )[:num_samples]['audio']

          return [x["array"] for x in speech_samples]

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			np.array([
              -1.57_45, -1.77_13, -1.70_20, -1.60_69, -1.22_50, -1.11_05, -0.90_72, -0.82_41,
              -1.23_10, -0.80_98, -0.33_20, -0.41_01, -0.79_85, -0.49_96, -0.82_13, -0.91_28,
              -1.04_20, -1.12_86, -1.04_40, -0.79_99, -0.84_05, -1.22_75, -1.54_43, -1.46_25,
          ]    )
          # fmt: on

          snake_case								=			self._load_datasamples(1    )
          snake_case								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
          snake_case								=			feature_extractor(lowerCAmelCase		,    return_tensors='pt'    ).input_features
          self.assertEquals(input_features.shape		,    (1, 5_84, 24)    )
          self.assertTrue(np.allclose(input_features[0, 0, :30]		,    lowerCAmelCase		,    atol=1E-4    )    )



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__      						=  {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__      						=  [
					    "SEW_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "SEWForCTC",
					    "SEWForSequenceClassification",
					    "SEWModel",
					    "SEWPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sew import (
										    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SEWForCTC,
										    SEWForSequenceClassification,
										    SEWModel,
										    SEWPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE__      						=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


