
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


UpperCAmelCase__		:     Any     			=      random.Random()






def   lowerCamelCase__							(					a		,		a=1.0		,		a=None		,		a=None      )      ->   Optional[int]:
   if rng is None:
      _A:  Optional[int]					=       global_rng

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

   return values






class UpperCAmelCase   (						unittest.TestCase   ):





   '''simple docstring'''


   def __init__(					self     :							Tuple			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							List[str]=7			,			lowerCAmelCase_     :							Any=4_0_0			,			lowerCAmelCase_     :							str=2_0_0_0			,			lowerCAmelCase_     :							Tuple=1			,			lowerCAmelCase_     :							Optional[int]=0.0			,			lowerCAmelCase_     :							Optional[Any]=1_6_0_0_0			,			lowerCAmelCase_     :							List[str]=True			,			lowerCAmelCase_     :							str=True			,			):

      """simple docstring"""
      _A:  Optional[Any]					=       parent
      _A:  Optional[int]					=       batch_size
      _A:  Tuple					=       min_seq_length
      _A:  Any					=       max_seq_length
      _A:  str					=       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      _A:  Union[str, Any]					=       feature_size
      _A:  Tuple					=       padding_value
      _A:  List[Any]					=       sampling_rate
      _A:  Optional[Any]					=       return_attention_mask
      _A:  Union[str, Any]					=       do_normalize


   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      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  __magic_name__				(					self     :							Any			,			lowerCAmelCase_     :							Optional[Any]=False			,			lowerCAmelCase_     :							Any=False     ):

      """simple docstring"""
      def _flatten(lowerCAmelCase_     :							Optional[Any]     ):
         return list(itertools.chain(*lowerCAmelCase_     )     )

      if equal_length:
         _A:  str					=       floats_list((self.batch_size, self.max_seq_length)     )
      else:
         # make sure that inputs increase in size
         _A:  List[Any]					=       [
             _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:
         _A:  List[Any]					=       [np.asarray(lowerCAmelCase_     ) for x in speech_inputs]

      return speech_inputs






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__							,							unittest.TestCase   ):





   '''simple docstring'''
   __UpperCamelCase		:    Any 				=    WavaVecaFeatureExtractor


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      _A:  Optional[Any]					=       WavaVecaFeatureExtractionTester(self     )


   def  __magic_name__				(					self     :							List[Any]			,			lowerCAmelCase_     :							Any     ):

      """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  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      # Tests that all call wrap to encode_plus and batch_encode_plus
      _A:  str					=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
      # create three inputs of length 800, 1000, and 1200
      _A:  int					=       [floats_list((1, x)     )[0] for x in range(8_0_0			,			1_4_0_0			,			2_0_0     )]
      _A:  str					=       [np.asarray(lowerCAmelCase_     ) for speech_input in speech_inputs]

      # Test not batched input
      _A:  Any					=       feat_extract(speech_inputs[0]			,			return_tensors='''np'''     ).input_values
      _A:  Dict					=       feat_extract(np_speech_inputs[0]			,			return_tensors='''np'''     ).input_values
      self.assertTrue(np.allclose(lowerCAmelCase_			,			lowerCAmelCase_			,			atol=1e-3     )     )

      # Test batched
      _A:  List[Any]					=       feat_extract(lowerCAmelCase_			,			return_tensors='''np'''     ).input_values
      _A:  Optional[int]					=       feat_extract(lowerCAmelCase_			,			return_tensors='''np'''     ).input_values
      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.
      _A:  Any					=       [floats_list((1, x)     )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
      _A:  Union[str, Any]					=       np.asarray(lowerCAmelCase_     )
      _A:  str					=       feat_extract(lowerCAmelCase_			,			return_tensors='''np'''     ).input_values
      _A:  List[Any]					=       feat_extract(lowerCAmelCase_			,			return_tensors='''np'''     ).input_values
      for enc_seq_a, enc_seq_a in zip(lowerCAmelCase_			,			lowerCAmelCase_     ):
         self.assertTrue(np.allclose(lowerCAmelCase_			,			lowerCAmelCase_			,			atol=1e-3     )     )


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      _A:  Optional[Any]					=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
      _A:  List[str]					=       [floats_list((1, x)     )[0] for x in range(8_0_0			,			1_4_0_0			,			2_0_0     )]

      _A:  Union[str, Any]					=       ['''longest''', '''max_length''', '''do_not_pad''']
      _A:  Dict					=       [None, 1_6_0_0, None]
      for max_length, padding in zip(lowerCAmelCase_			,			lowerCAmelCase_     ):
         _A:  Optional[Any]					=       feat_extract(lowerCAmelCase_			,			padding=lowerCAmelCase_			,			max_length=lowerCAmelCase_			,			return_tensors='''np'''     )
         _A:  str					=       processed.input_values

         self._check_zero_mean_unit_variance(input_values[0][:8_0_0]     )
         self.assertTrue(input_values[0][8_0_0:].sum() < 1e-6     )
         self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]     )
         self.assertTrue(input_values[0][1_0_0_0:].sum() < 1e-6     )
         self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]     )


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      _A:  Optional[Any]					=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
      _A:  Dict					=       range(8_0_0			,			1_4_0_0			,			2_0_0     )
      _A:  Optional[Any]					=       [floats_list((1, x)     )[0] for x in lengths]

      _A:  List[Any]					=       ['''longest''', '''max_length''', '''do_not_pad''']
      _A:  Optional[int]					=       [None, 1_6_0_0, None]

      for max_length, padding in zip(lowerCAmelCase_			,			lowerCAmelCase_     ):
         _A:  Dict					=       feat_extract(lowerCAmelCase_			,			max_length=lowerCAmelCase_			,			padding=lowerCAmelCase_     )
         _A:  Dict					=       processed.input_values

         self._check_zero_mean_unit_variance(input_values[0][:8_0_0]     )
         self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]     )
         self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]     )


   def  __magic_name__				(					self     :							Dict     ):

      """simple docstring"""
      _A:  Optional[Any]					=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
      _A:  Any					=       [floats_list((1, x)     )[0] for x in range(8_0_0			,			1_4_0_0			,			2_0_0     )]
      _A:  Optional[int]					=       feat_extract(
          lowerCAmelCase_			,			truncation=lowerCAmelCase_			,			max_length=1_0_0_0			,			padding='''max_length'''			,			return_tensors='''np'''     )
      _A:  List[str]					=       processed.input_values

      self._check_zero_mean_unit_variance(input_values[0, :8_0_0]     )
      self._check_zero_mean_unit_variance(input_values[1]     )
      self._check_zero_mean_unit_variance(input_values[2]     )


   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      _A:  Any					=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
      _A:  Optional[int]					=       [floats_list((1, x)     )[0] for x in range(8_0_0			,			1_4_0_0			,			2_0_0     )]
      _A:  Tuple					=       feat_extract(
          lowerCAmelCase_			,			truncation=lowerCAmelCase_			,			max_length=1_0_0_0			,			padding='''longest'''			,			return_tensors='''np'''     )
      _A:  List[Any]					=       processed.input_values

      self._check_zero_mean_unit_variance(input_values[0, :8_0_0]     )
      self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]     )
      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, 1_0_0_0)     )

      _A:  int					=       [floats_list((1, x)     )[0] for x in range(8_0_0			,			1_4_0_0			,			2_0_0     )]
      _A:  Union[str, Any]					=       feat_extract(
          lowerCAmelCase_			,			truncation=lowerCAmelCase_			,			max_length=2_0_0_0			,			padding='''longest'''			,			return_tensors='''np'''     )
      _A:  Tuple					=       processed.input_values

      self._check_zero_mean_unit_variance(input_values[0, :8_0_0]     )
      self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]     )
      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, 1_2_0_0)     )


   @require_torch
   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      import torch

      _A:  List[str]					=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
      _A:  List[Any]					=       np.random.rand(1_0_0     ).astype(np.floataa     )
      _A:  str					=       np_speech_inputs.tolist()

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


   @slow
   @require_torch
   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      # 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:
         _A:  List[str]					=       WavaVecaConfig.from_pretrained(lowerCAmelCase_     )
         _A:  int					=       WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase_     )

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





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   lowerCamelCase__							(					a		,		a      )      ->   Any:
   _A:  Any					=       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}}'''      )
   _A:  Optional[int]					=       DatasetInfosDict.from_directory(a      )
   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   lowerCamelCase__							(					a		,		a      )      ->   Any:
   _A:  int					=       str(a      )
   dataset_info.write_to_directory(a      )
   _A:  str					=       DatasetInfo.from_directory(a      )
   assert dataset_info == reloaded
   assert os.path.exists(os.path.join(a		,		'''dataset_info.json'''      )      )






def   lowerCamelCase__							(					)      ->   Any:
   _A:  int					=       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=13_37		,		post_processing_size=4_42		,		dataset_size=12_34		,		size_in_bytes=13_37 + 4_42 + 12_34		,		)
   _A:  Optional[Any]					=       dataset_info._to_yaml_dict()
   assert sorted(a      ) == 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)      )
   _A:  str					=       yaml.safe_dump(a      )
   _A:  Optional[int]					=       yaml.safe_load(a      )
   assert dataset_info_yaml_dict == reloaded






def   lowerCamelCase__							(					)      ->   int:
   _A:  Union[str, Any]					=       DatasetInfo()
   _A:  Union[str, Any]					=       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=13_37      ),
            }      ),
    ]		,		)
def   lowerCamelCase__							(					a		,		a      )      ->   Optional[int]:
   _A:  Optional[int]					=       str(a      )
   dataset_infos_dict.write_to_directory(a      )
   _A:  Union[str, Any]					=       DatasetInfosDict.from_directory(a      )

   # the config_name of the dataset_infos_dict take over the attribute
   for config_name, dataset_info in dataset_infos_dict.items():
      _A:  Optional[Any]					=       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
      _A:  Any					=       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(a		,		'''README.md'''      )      )



