

"""simple docstring"""






import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


__lowerCamelCase										=		random.Random()





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase=1.0      ,   __UpperCamelCase=None      ,   __UpperCamelCase=None						) ->      Optional[Any]:
 if rng is None:
  __magic_name__     =      global_rng

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

 return values
class        _lowercase					(     unittest.TestCase     ):







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_=7 ,						UpperCamelCase_=400 ,						UpperCamelCase_=2000 ,						UpperCamelCase_=1 ,						UpperCamelCase_=0.0 ,						UpperCamelCase_=1_6000 ,						UpperCamelCase_=True ,						UpperCamelCase_=True ,						):
      __magic_name__     =      parent
      __magic_name__     =      batch_size
      __magic_name__     =      min_seq_length
      __magic_name__     =      max_seq_length
      __magic_name__     =      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      __magic_name__     =      feature_size
      __magic_name__     =      padding_value
      __magic_name__     =      sampling_rate
      __magic_name__     =      return_attention_mask
      __magic_name__     =      do_normalize







     def 	lowerCAmelCase__    (      self       ):
      return {
          "feature_size": self.feature_size,
          "padding_value": self.padding_value,
          "sampling_rate": self.sampling_rate,
          "return_attention_mask": self.return_attention_mask,
          "do_normalize": self.do_normalize,
      }







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_=False ,						UpperCamelCase_=False       ):
      def _flatten(UpperCamelCase_       ):
       return list(itertools.chain(*UpperCamelCase_       )       )

      if equal_length:
       __magic_name__     =      floats_list((self.batch_size, self.max_seq_length)       )
      else:
       # make sure that inputs increase in size
       __magic_name__     =      [
           _flatten(floats_list((x, self.feature_size)       )       )
           for x in range(self.min_seq_length ,						self.max_seq_length ,						self.seq_length_diff       )
       ]

      if numpify:
       __magic_name__     =      [np.asarray(UpperCamelCase_       ) for x in speech_inputs]

      return speech_inputs


class        _lowercase					(     __UpperCAmelCase   , unittest.TestCase     ):
     _lowerCamelCase     =       WavaVecaFeatureExtractor







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      WavaVecaFeatureExtractionTester(self       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      self.assertTrue(np.all(np.mean(UpperCamelCase_ ,						axis=0       ) < 1E-3       )       )
      self.assertTrue(np.all(np.abs(np.var(UpperCamelCase_ ,						axis=0       ) - 1       ) < 1E-3       )       )







     def 	lowerCAmelCase__    (      self       ):
      # Tests that all call wrap to encode_plus and batch_encode_plus
      __magic_name__     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
      # create three inputs of length 800, 1000, and 1200
      __magic_name__     =      [floats_list((1, x)       )[0] for x in range(800 ,						1400 ,						200       )]
      __magic_name__     =      [np.asarray(UpperCamelCase_       ) for speech_input in speech_inputs]

      # Test not batched input
      __magic_name__     =      feat_extract(speech_inputs[0] ,						return_tensors='''np'''       ).input_values
      __magic_name__     =      feat_extract(np_speech_inputs[0] ,						return_tensors='''np'''       ).input_values
      self.assertTrue(np.allclose(UpperCamelCase_ ,						UpperCamelCase_ ,						atol=1E-3       )       )

      # Test batched
      __magic_name__     =      feat_extract(UpperCamelCase_ ,						return_tensors='''np'''       ).input_values
      __magic_name__     =      feat_extract(UpperCamelCase_ ,						return_tensors='''np'''       ).input_values
      for enc_seq_a, enc_seq_a in zip(UpperCamelCase_ ,						UpperCamelCase_       ):
       self.assertTrue(np.allclose(UpperCamelCase_ ,						UpperCamelCase_ ,						atol=1E-3       )       )

      # Test 2-D numpy arrays are batched.
      __magic_name__     =      [floats_list((1, x)       )[0] for x in (800, 800, 800)]
      __magic_name__     =      np.asarray(UpperCamelCase_       )
      __magic_name__     =      feat_extract(UpperCamelCase_ ,						return_tensors='''np'''       ).input_values
      __magic_name__     =      feat_extract(UpperCamelCase_ ,						return_tensors='''np'''       ).input_values
      for enc_seq_a, enc_seq_a in zip(UpperCamelCase_ ,						UpperCamelCase_       ):
       self.assertTrue(np.allclose(UpperCamelCase_ ,						UpperCamelCase_ ,						atol=1E-3       )       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
      __magic_name__     =      [floats_list((1, x)       )[0] for x in range(800 ,						1400 ,						200       )]

      __magic_name__     =      ['''longest''', '''max_length''', '''do_not_pad''']
      __magic_name__     =      [None, 1600, None]
      for max_length, padding in zip(UpperCamelCase_ ,						UpperCamelCase_       ):
       __magic_name__     =      feat_extract(UpperCamelCase_ ,						padding=UpperCamelCase_ ,						max_length=UpperCamelCase_ ,						return_tensors='''np'''       )
       __magic_name__     =      processed.input_values

       self._check_zero_mean_unit_variance(input_values[0][:800]       )
       self.assertTrue(input_values[0][800:].sum() < 1E-6       )
       self._check_zero_mean_unit_variance(input_values[1][:1000]       )
       self.assertTrue(input_values[0][1000:].sum() < 1E-6       )
       self._check_zero_mean_unit_variance(input_values[2][:1200]       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
      __magic_name__     =      range(800 ,						1400 ,						200       )
      __magic_name__     =      [floats_list((1, x)       )[0] for x in lengths]

      __magic_name__     =      ['''longest''', '''max_length''', '''do_not_pad''']
      __magic_name__     =      [None, 1600, None]

      for max_length, padding in zip(UpperCamelCase_ ,						UpperCamelCase_       ):
       __magic_name__     =      feat_extract(UpperCamelCase_ ,						max_length=UpperCamelCase_ ,						padding=UpperCamelCase_       )
       __magic_name__     =      processed.input_values

       self._check_zero_mean_unit_variance(input_values[0][:800]       )
       self._check_zero_mean_unit_variance(input_values[1][:1000]       )
       self._check_zero_mean_unit_variance(input_values[2][:1200]       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
      __magic_name__     =      [floats_list((1, x)       )[0] for x in range(800 ,						1400 ,						200       )]
      __magic_name__     =      feat_extract(
          UpperCamelCase_ ,						truncation=UpperCamelCase_ ,						max_length=1000 ,						padding='''max_length''' ,						return_tensors='''np'''       )
      __magic_name__     =      processed.input_values

      self._check_zero_mean_unit_variance(input_values[0, :800]       )
      self._check_zero_mean_unit_variance(input_values[1]       )
      self._check_zero_mean_unit_variance(input_values[2]       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
      __magic_name__     =      [floats_list((1, x)       )[0] for x in range(800 ,						1400 ,						200       )]
      __magic_name__     =      feat_extract(
          UpperCamelCase_ ,						truncation=UpperCamelCase_ ,						max_length=1000 ,						padding='''longest''' ,						return_tensors='''np'''       )
      __magic_name__     =      processed.input_values

      self._check_zero_mean_unit_variance(input_values[0, :800]       )
      self._check_zero_mean_unit_variance(input_values[1, :1000]       )
      self._check_zero_mean_unit_variance(input_values[2]       )

      # make sure that if max_length < longest -> then pad to max_length
      self.assertTrue(input_values.shape == (3, 1000)       )

      __magic_name__     =      [floats_list((1, x)       )[0] for x in range(800 ,						1400 ,						200       )]
      __magic_name__     =      feat_extract(
          UpperCamelCase_ ,						truncation=UpperCamelCase_ ,						max_length=2000 ,						padding='''longest''' ,						return_tensors='''np'''       )
      __magic_name__     =      processed.input_values

      self._check_zero_mean_unit_variance(input_values[0, :800]       )
      self._check_zero_mean_unit_variance(input_values[1, :1000]       )
      self._check_zero_mean_unit_variance(input_values[2]       )

      # make sure that if max_length > longest -> then pad to longest
      self.assertTrue(input_values.shape == (3, 1200)       )







     @require_torch
     def 	lowerCAmelCase__    (      self       ):
      import torch

      __magic_name__     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
      __magic_name__     =      np.random.rand(100       ).astype(np.floataa       )
      __magic_name__     =      np_speech_inputs.tolist()

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







     @slow
     @require_torch
     def 	lowerCAmelCase__    (      self       ):
      # this test makes sure that models that are using
      # group norm don't have their feature extractor return the
      # attention_mask
      for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
       __magic_name__     =      WavaVecaConfig.from_pretrained(UpperCamelCase_       )
       __magic_name__     =      WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase_       )

       # only "layer" feature extraction norm should make use of
       # attention_mask
       self.assertEqual(feat_extract.return_attention_mask ,						config.feat_extract_norm == '''layer'''       )




"""simple docstring"""






import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict





@pytest.mark.parametrize(
    '''files'''      ,   [
        ['''full:README.md''', '''dataset_infos.json'''],
        ['''empty:README.md''', '''dataset_infos.json'''],
        ['''dataset_infos.json'''],
        ['''full:README.md'''],
    ]      ,   )
def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      List[str]:
 __magic_name__     =      tmp_path_factory.mktemp('''dset_infos_dir'''						)
 if "full:README.md" in files:
  with open(dataset_infos_dir / '''README.md'''      ,   '''w'''						) as f:
   f.write('''---\ndataset_info:\n  dataset_size: 42\n---'''						)
 if "empty:README.md" in files:
  with open(dataset_infos_dir / '''README.md'''      ,   '''w'''						) as f:
   f.write(''''''						)
    # we want to support dataset_infos.json for backward compatibility
 if "dataset_infos.json" in files:
  with open(dataset_infos_dir / '''dataset_infos.json'''      ,   '''w'''						) as f:
   f.write('''{"default": {"dataset_size": 42}}'''						)
 __magic_name__     =      DatasetInfosDict.from_directory(__UpperCamelCase						)
 assert dataset_infos
 assert dataset_infos["default"].dataset_size == 42





@pytest.mark.parametrize(
    '''dataset_info'''      ,   [
        DatasetInfo(),
        DatasetInfo(
            description='''foo'''      ,   features=Features({'''a''': Value('''int32'''						)}						)      ,   builder_name='''builder'''      ,   config_name='''config'''      ,   version='''1.0.0'''      ,   splits=[{'''name''': '''train'''}]      ,   download_size=42      ,   ),
    ]      ,   )
def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      Tuple:
 __magic_name__     =      str(__UpperCamelCase						)
 dataset_info.write_to_directory(__UpperCamelCase						)
 __magic_name__     =      DatasetInfo.from_directory(__UpperCamelCase						)
 assert dataset_info == reloaded
 assert os.path.exists(os.path.join(__UpperCamelCase      ,   '''dataset_info.json'''						)						)





def 	lowercase      (   ) ->      Any:
 __magic_name__     =      DatasetInfo(
     description='''foo'''      ,   citation='''bar'''      ,   homepage='''https://foo.bar'''      ,   license='''CC0'''      ,   features=Features({'''a''': Value('''int32'''						)}						)      ,   post_processed={}      ,   supervised_keys=()      ,   task_templates=[]      ,   builder_name='''builder'''      ,   config_name='''config'''      ,   version='''1.0.0'''      ,   splits=[{'''name''': '''train''', '''num_examples''': 42}]      ,   download_checksums={}      ,   download_size=1337      ,   post_processing_size=442      ,   dataset_size=1234      ,   size_in_bytes=1337 + 442 + 1234      ,   )
 __magic_name__     =      dataset_info._to_yaml_dict()
 assert sorted(__UpperCamelCase						) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML						)
 for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
  assert key in dataset_info_yaml_dict
  assert isinstance(dataset_info_yaml_dict[key]      ,   (list, dict, int, str)						)
 __magic_name__     =      yaml.safe_dump(__UpperCamelCase						)
 __magic_name__     =      yaml.safe_load(__UpperCamelCase						)
 assert dataset_info_yaml_dict == reloaded





def 	lowercase      (   ) ->      str:
 __magic_name__     =      DatasetInfo()
 __magic_name__     =      dataset_info._to_yaml_dict()
 assert dataset_info_yaml_dict == {}





@pytest.mark.parametrize(
    '''dataset_infos_dict'''      ,   [
        DatasetInfosDict(),
        DatasetInfosDict({'''default''': DatasetInfo()}						),
        DatasetInfosDict({'''my_config_name''': DatasetInfo()}						),
        DatasetInfosDict(
            {
                '''default''': DatasetInfo(
                    description='''foo'''      ,   features=Features({'''a''': Value('''int32'''						)}						)      ,   builder_name='''builder'''      ,   config_name='''config'''      ,   version='''1.0.0'''      ,   splits=[{'''name''': '''train'''}]      ,   download_size=42      ,   )
            }						),
        DatasetInfosDict(
            {
                '''v1''': DatasetInfo(dataset_size=42						),
                '''v2''': DatasetInfo(dataset_size=1337						),
            }						),
    ]      ,   )
def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      str:
 __magic_name__     =      str(__UpperCamelCase						)
 dataset_infos_dict.write_to_directory(__UpperCamelCase						)
 __magic_name__     =      DatasetInfosDict.from_directory(__UpperCamelCase						)

 # the config_name of the dataset_infos_dict take over the attribute
 for config_name, dataset_info in dataset_infos_dict.items():
  __magic_name__     =      config_name
  # the yaml representation doesn't include fields like description or citation
  # so we just test that we can recover what we can from the yaml
  __magic_name__     =      DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()						)
 assert dataset_infos_dict == reloaded

 if dataset_infos_dict:
  assert os.path.exists(os.path.join(__UpperCamelCase      ,   '''README.md'''						)						)

