





import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
      from transformers import WhisperFeatureExtractor

if is_torch_available():
      import torch

lowerCAmelCase_						   =    random.Random()


def     lowerCamelCase_	(      lowerCAmelCase:	Any    ,							lowerCAmelCase:	str=1.0    ,							lowerCAmelCase:	int=None    ,							lowerCAmelCase:	Union[str, Any]=None				)->		Optional[int]:
  if rng is None:
    _snake_case   :				List[str]														=	global_rng

  _snake_case   :				List[str]														=	[]
  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      :					str      ,				UpperCamelCase      :					Optional[Any]      ,				UpperCamelCase      :					Optional[Any]=7      ,				UpperCamelCase      :					Tuple=4_00      ,				UpperCamelCase      :					int=20_00      ,				UpperCamelCase      :					List[str]=10      ,				UpperCamelCase      :					int=1_60      ,				UpperCamelCase      :					Optional[Any]=8      ,				UpperCamelCase      :					Union[str, Any]=0.0      ,				UpperCamelCase      :					List[Any]=40_00      ,				UpperCamelCase      :					Any=False      ,				UpperCamelCase      :					Dict=True      ,				):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	parent
    _snake_case   :				Tuple														=	batch_size
    _snake_case   :				List[Any]														=	min_seq_length
    _snake_case   :				Dict														=	max_seq_length
    _snake_case   :				List[str]														=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
    _snake_case   :				Optional[int]														=	padding_value
    _snake_case   :				int														=	sampling_rate
    _snake_case   :				Optional[int]														=	return_attention_mask
    _snake_case   :				Union[str, Any]														=	do_normalize
    _snake_case   :				int														=	feature_size
    _snake_case   :				List[str]														=	chunk_length
    _snake_case   :				List[Any]														=	hop_length







  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





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







  def UpperCamelCase_ (      self      :					Tuple      ,				UpperCamelCase      :					Optional[Any]=False      ,				UpperCamelCase      :					List[str]=False							):


    '''simple docstring'''





    def _flatten(UpperCamelCase      :					Tuple							):
      return list(itertools.chain(*UpperCamelCase							)							)

    if equal_length:
      _snake_case   :				Any														=	[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   :				Any														=	[
          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   :				List[str]														=	[np.asarray(UpperCamelCase							) for x in speech_inputs]
    return speech_inputs




@require_torch
@require_torchaudio
class        _lowerCAmelCase    (			UpperCAmelCase_							,    unittest.TestCase							):


  '''simple docstring'''
  a_							:       List[Any]      					=WhisperFeatureExtractor if is_speech_available() else None







  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    _snake_case   :				int														=	WhisperFeatureExtractionTester(self							)







  def UpperCamelCase_ (      self      :					str							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	self.feature_extraction_class(**self.feat_extract_dict							)

    with tempfile.TemporaryDirectory() as tmpdirname:
      _snake_case   :				Optional[int]														=	feat_extract_first.save_pretrained(UpperCamelCase							)[0]
      check_json_file_has_correct_format(UpperCamelCase							)
      _snake_case   :				Optional[Any]														=	self.feature_extraction_class.from_pretrained(UpperCamelCase							)

    _snake_case   :				Dict														=	feat_extract_first.to_dict()
    _snake_case   :				Union[str, Any]														=	feat_extract_second.to_dict()
    _snake_case   :				Optional[Any]														=	feat_extract_first.mel_filters
    _snake_case   :				Union[str, Any]														=	feat_extract_second.mel_filters
    self.assertTrue(np.allclose(UpperCamelCase      ,				UpperCamelCase							)							)
    self.assertEqual(UpperCamelCase      ,				UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	self.feature_extraction_class(**self.feat_extract_dict							)

    with tempfile.TemporaryDirectory() as tmpdirname:
      _snake_case   :				List[Any]														=	os.path.join(UpperCamelCase      ,				'feat_extract.json'							)
      feat_extract_first.to_json_file(UpperCamelCase							)
      _snake_case   :				str														=	self.feature_extraction_class.from_json_file(UpperCamelCase							)

    _snake_case   :				int														=	feat_extract_first.to_dict()
    _snake_case   :				Union[str, Any]														=	feat_extract_second.to_dict()
    _snake_case   :				Optional[int]														=	feat_extract_first.mel_filters
    _snake_case   :				Any														=	feat_extract_second.mel_filters
    self.assertTrue(np.allclose(UpperCamelCase      ,				UpperCamelCase							)							)
    self.assertEqual(UpperCamelCase      ,				UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





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

    # Test feature size
    _snake_case   :				Optional[Any]														=	feature_extractor(UpperCamelCase      ,				padding='max_length'      ,				return_tensors='np'							).input_features
    self.assertTrue(input_features.ndim == 3							)
    self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames							)
    self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size							)

    # Test not batched input
    _snake_case   :				Dict														=	feature_extractor(speech_inputs[0]      ,				return_tensors='np'							).input_features
    _snake_case   :				Optional[int]														=	feature_extractor(np_speech_inputs[0]      ,				return_tensors='np'							).input_features
    self.assertTrue(np.allclose(UpperCamelCase      ,				UpperCamelCase      ,				atol=1e-3							)							)

    # Test batched
    _snake_case   :				Tuple														=	feature_extractor(UpperCamelCase      ,				return_tensors='np'							).input_features
    _snake_case   :				List[str]														=	feature_extractor(UpperCamelCase      ,				return_tensors='np'							).input_features
    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.
    _snake_case   :				Optional[Any]														=	[floats_list((1, x)							)[0] for x in (8_00, 8_00, 8_00)]
    _snake_case   :				Tuple														=	np.asarray(UpperCamelCase							)
    _snake_case   :				Tuple														=	feature_extractor(UpperCamelCase      ,				return_tensors='np'							).input_features
    _snake_case   :				Optional[int]														=	feature_extractor(UpperCamelCase      ,				return_tensors='np'							).input_features
    for enc_seq_a, enc_seq_a in zip(UpperCamelCase      ,				UpperCamelCase							):
      self.assertTrue(np.allclose(UpperCamelCase      ,				UpperCamelCase      ,				atol=1e-3							)							)

    # Test truncation required
    _snake_case   :				Optional[int]														=	[floats_list((1, x)							)[0] for x in range(2_00      ,				(feature_extractor.n_samples + 5_00)      ,				2_00							)]
    _snake_case   :				List[Any]														=	[np.asarray(UpperCamelCase							) for speech_input in speech_inputs]

    _snake_case   :				Tuple														=	[x[: feature_extractor.n_samples] for x in speech_inputs]
    _snake_case   :				Optional[Any]														=	[np.asarray(UpperCamelCase							) for speech_input in speech_inputs_truncated]

    _snake_case   :				Tuple														=	feature_extractor(UpperCamelCase      ,				return_tensors='np'							).input_features
    _snake_case   :				Any														=	feature_extractor(UpperCamelCase      ,				return_tensors='np'							).input_features
    for enc_seq_a, enc_seq_a in zip(UpperCamelCase      ,				UpperCamelCase							):
      self.assertTrue(np.allclose(UpperCamelCase      ,				UpperCamelCase      ,				atol=1e-3							)							)







  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    import torch

    _snake_case   :				Optional[int]														=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
    _snake_case   :				Any														=	np.random.rand(1_00      ,				32							).astype(np.floataa							)
    _snake_case   :				List[str]														=	np_speech_inputs.tolist()

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







  def UpperCamelCase_ (      self      :					Dict      ,				UpperCamelCase      :					Union[str, Any]							):


    '''simple docstring'''





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

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







  def UpperCamelCase_ (      self      :					Union[str, Any]							):


    '''simple docstring'''





    _snake_case   :				Optional[Any]														=	torch.tensor(
        [
            0.11_93, -0.09_46, -0.10_98, -0.01_96, 0.02_25, -0.06_90, -0.17_36, 0.09_51,
            0.09_71, -0.08_17, -0.07_02, 0.01_62, 0.02_60, 0.00_17, -0.01_92, -0.16_78,
            0.07_09, -0.18_67, -0.06_55, -0.02_74, -0.02_34, -0.18_84, -0.05_16, -0.05_54,
            -0.02_74, -0.14_25, -0.14_23, 0.08_37, 0.03_77, -0.08_54
        ]							)
    # fmt: on

    _snake_case   :				int														=	self._load_datasamples(1							)
    _snake_case   :				int														=	WhisperFeatureExtractor()
    _snake_case   :				Tuple														=	feature_extractor(UpperCamelCase      ,				return_tensors='pt'							).input_features
    self.assertEqual(input_features.shape      ,				(1, 80, 30_00)							)
    self.assertTrue(torch.allclose(input_features[0, 0, :30]      ,				UpperCamelCase      ,				atol=1e-4							)							)







  def UpperCamelCase_ (      self      :					List[Any]							):


    '''simple docstring'''





    _snake_case   :				Tuple														=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
    _snake_case   :				str														=	self._load_datasamples(1							)[0]
    _snake_case   :				Optional[int]														=	((audio - audio.min()) / (audio.max() - audio.min())) * 6_55_35  # Rescale to [0, 65535] to show issue
    _snake_case   :				Tuple														=	feat_extract.zero_mean_unit_var_norm([audio]      ,				attention_mask=UpperCamelCase							)[0]

    self.assertTrue(np.all(np.mean(UpperCamelCase							) < 1e-3							)							)
    self.assertTrue(np.all(np.abs(np.var(UpperCamelCase							) - 1							) < 1e-3							)							)











from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters


def     lowerCamelCase_	(      )->		Any:
  _snake_case   :				List[str]														=	{
      'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
      'path': ['test_1.py', 'test_2.py', 'unit_test.py'],
      'content': ['a ' * 20, 'a ' * 30, 'b ' * 7],
  }
  _snake_case   :				Optional[Any]														=	Dataset.from_dict(lowerCAmelCase				)
  return dataset







class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				Union[str, Any]														=	get_dataset()
    _snake_case   :				Tuple														=	make_duplicate_clusters(UpperCamelCase      ,				0.85							)
    self.assertEqual(len(duplicate_clusters[0]							)      ,				2							)







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	get_dataset()
    _snake_case							,					_snake_case   :				str														=	deduplicate_dataset(UpperCamelCase							)
    self.assertEqual(len(UpperCamelCase							)      ,				2							)
    print(UpperCamelCase							)
    self.assertEqual(duplicate_clusters[0][0]['copies']      ,				2							)
    self.assertEqual(duplicate_clusters[0][0]['is_extreme']      ,				UpperCamelCase							)




