
"""simple docstring"""

import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_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_torch_available():
						import torch

if is_datasets_available():
						from datasets import load_dataset

UpperCamelCase			:       str									=				random.Random()






def 							A		(       snake_case   :Optional[Any]       ,      snake_case   :str=1.0       ,      snake_case   :List[Any]=None       ,      snake_case   :Optional[Any]=None	)   ->			Optional[int]:
	if rng is None:
		__UpperCamelCase       						= global_rng

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

	return values

class __lowerCAmelCase	(   unittest.TestCase    ):





					def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase=7			,  __UpperCAmelCase=400			,  __UpperCAmelCase=2000			,  __UpperCAmelCase=2048			,  __UpperCAmelCase=128			,  __UpperCAmelCase=1			,  __UpperCAmelCase=512			,  __UpperCAmelCase=30			,  __UpperCAmelCase=4_4100			,  ):

						'''simple docstring'''




						__UpperCamelCase       						= parent
						__UpperCamelCase       						= batch_size
						__UpperCamelCase       						= min_seq_length
						__UpperCamelCase       						= max_seq_length
						__UpperCamelCase       						= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						__UpperCamelCase       						= spectrogram_length
						__UpperCamelCase       						= feature_size
						__UpperCamelCase       						= num_audio_channels
						__UpperCamelCase       						= hop_length
						__UpperCamelCase       						= chunk_length
						__UpperCamelCase       						= sampling_rate





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						return {
						    "spectrogram_length": self.spectrogram_length,
						    "feature_size": self.feature_size,
						    "num_audio_channels": self.num_audio_channels,
						    "hop_length": self.hop_length,
						    "chunk_length": self.chunk_length,
						    "sampling_rate": self.sampling_rate,
						}







					def 		UpperCAmelCase      (			self			,  __UpperCAmelCase=False			,  __UpperCAmelCase=False   ):

						'''simple docstring'''




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

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


@require_torch
@require_torchaudio
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
					lowercase		     =		TvltFeatureExtractor





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						__UpperCamelCase       						= TvltFeatureExtractionTester(self   )





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						__UpperCamelCase       						= self.feature_extraction_class(**self.feat_extract_dict   )
						self.assertTrue(hasattr(__UpperCAmelCase			,  'spectrogram_length'   )   )
						self.assertTrue(hasattr(__UpperCAmelCase			,  'feature_size'   )   )
						self.assertTrue(hasattr(__UpperCAmelCase			,  'num_audio_channels'   )   )
						self.assertTrue(hasattr(__UpperCAmelCase			,  'hop_length'   )   )
						self.assertTrue(hasattr(__UpperCAmelCase			,  'chunk_length'   )   )
						self.assertTrue(hasattr(__UpperCAmelCase			,  'sampling_rate'   )   )





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						__UpperCamelCase       						= self.feature_extraction_class(**self.feat_extract_dict   )

						with tempfile.TemporaryDirectory() as tmpdirname:
							__UpperCamelCase       						= feat_extract_first.save_pretrained(__UpperCAmelCase   )[0]
							check_json_file_has_correct_format(__UpperCAmelCase   )
							__UpperCamelCase       						= self.feature_extraction_class.from_pretrained(__UpperCAmelCase   )

						__UpperCamelCase       						= feat_extract_first.to_dict()
						__UpperCamelCase       						= feat_extract_second.to_dict()
						__UpperCamelCase       						= dict_first.pop('mel_filters'   )
						__UpperCamelCase       						= dict_second.pop('mel_filters'   )
						self.assertTrue(np.allclose(__UpperCAmelCase			,  __UpperCAmelCase   )   )
						self.assertEqual(__UpperCAmelCase			,  __UpperCAmelCase   )





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						__UpperCamelCase       						= self.feature_extraction_class(**self.feat_extract_dict   )

						with tempfile.TemporaryDirectory() as tmpdirname:
							__UpperCamelCase       						= os.path.join(__UpperCAmelCase			,  'feat_extract.json'   )
							feat_extract_first.to_json_file(__UpperCAmelCase   )
							__UpperCamelCase       						= self.feature_extraction_class.from_json_file(__UpperCAmelCase   )

						__UpperCamelCase       						= feat_extract_first.to_dict()
						__UpperCamelCase       						= feat_extract_second.to_dict()
						__UpperCamelCase       						= dict_first.pop('mel_filters'   )
						__UpperCamelCase       						= dict_second.pop('mel_filters'   )
						self.assertTrue(np.allclose(__UpperCAmelCase			,  __UpperCAmelCase   )   )
						self.assertEqual(__UpperCAmelCase			,  __UpperCAmelCase   )





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						__UpperCamelCase       						= self.feature_extraction_class(**self.feat_extract_dict   )

						# create three inputs of length 800, 1000, and 1200
						__UpperCamelCase       						= [floats_list((1, x)   )[0] for x in range(800			,  1400			,  200   )]
						__UpperCamelCase       						= [np.asarray(__UpperCAmelCase   ) for speech_input in speech_inputs]

						# Test not batched input
						__UpperCamelCase       						= feature_extractor(np_speech_inputs[0]			,  return_tensors='np'			,  sampling_rate=4_4100   ).audio_values

						self.assertTrue(encoded_audios.ndim == 4   )
						self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size   )
						self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length   )
						self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels   )

						# Test batched
						__UpperCamelCase       						= feature_extractor(__UpperCAmelCase			,  return_tensors='np'			,  sampling_rate=4_4100   ).audio_values

						self.assertTrue(encoded_audios.ndim == 4   )
						self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size   )
						self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length   )
						self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels   )

						# Test audio masking
						__UpperCamelCase       						= feature_extractor(
						    __UpperCAmelCase			,  return_tensors='np'			,  sampling_rate=4_4100			,  mask_audio=__UpperCAmelCase   ).audio_values

						self.assertTrue(encoded_audios.ndim == 4   )
						self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size   )
						self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length   )
						self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels   )

						# Test 2-D numpy arrays are batched.
						__UpperCamelCase       						= [floats_list((1, x)   )[0] for x in (800, 800, 800)]
						__UpperCamelCase       						= np.asarray(__UpperCAmelCase   )
						__UpperCamelCase       						= feature_extractor(__UpperCAmelCase			,  return_tensors='np'			,  sampling_rate=4_4100   ).audio_values
						self.assertTrue(encoded_audios.ndim == 4   )
						self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size   )
						self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length   )
						self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels   )





					def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

						'''simple docstring'''




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

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







					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						__UpperCamelCase       						= self._load_datasamples(1   )
						__UpperCamelCase       						= TvltFeatureExtractor()
						__UpperCamelCase       						= feature_extractor(__UpperCAmelCase			,  return_tensors='pt'   ).audio_values

						self.assertEquals(audio_values.shape			,  (1, 1, 192, 128)   )

						__UpperCamelCase       						= torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]   )
						self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]			,  __UpperCAmelCase			,  atol=1E-4   )   )





"""simple docstring"""

import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

UpperCamelCase			:       Tuple									=				collections.namedtuple("_Datasets", ["train", "validation", "test"])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
UpperCamelCase			:       Optional[int]									=				"https://storage.googleapis.com/cvdf-datasets/mnist/"






def 							A		(       snake_case   :Union[str, Any]	)   ->			int:
 __UpperCamelCase       						= numpy.dtype(numpy.uintaa	).newbyteorder('>'	)
 return numpy.frombuffer(bytestream.read(4	)       ,      dtype=snake_case	)[0]






@deprecated(snake_case       ,      'Please use tf.data to implement this functionality.'	)
def 							A		(       snake_case   :Union[str, Any]	)   ->			Dict:
 print('Extracting'       ,      f.name	)
 with gzip.GzipFile(fileobj=snake_case	) as bytestream:
  __UpperCamelCase       						= _readaa(snake_case	)
  if magic != 2_0_5_1:
   raise ValueError(
       'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)	)
  __UpperCamelCase       						= _readaa(snake_case	)
  __UpperCamelCase       						= _readaa(snake_case	)
  __UpperCamelCase       						= _readaa(snake_case	)
  __UpperCamelCase       						= bytestream.read(rows * cols * num_images	)
  __UpperCamelCase       						= numpy.frombuffer(snake_case       ,      dtype=numpy.uinta	)
  __UpperCamelCase       						= data.reshape(snake_case       ,      snake_case       ,      snake_case       ,      1	)
  return data






@deprecated(snake_case       ,      'Please use tf.one_hot on tensors.'	)
def 							A		(       snake_case   :int       ,      snake_case   :Optional[Any]	)   ->			List[Any]:
 __UpperCamelCase       						= labels_dense.shape[0]
 __UpperCamelCase       						= numpy.arange(snake_case	) * num_classes
 __UpperCamelCase       						= numpy.zeros((num_labels, num_classes)	)
 __UpperCamelCase       						= 1
 return labels_one_hot






@deprecated(snake_case       ,      'Please use tf.data to implement this functionality.'	)
def 							A		(       snake_case   :int       ,      snake_case   :Optional[int]=False       ,      snake_case   :str=1_0	)   ->			Any:
 print('Extracting'       ,      f.name	)
 with gzip.GzipFile(fileobj=snake_case	) as bytestream:
  __UpperCamelCase       						= _readaa(snake_case	)
  if magic != 2_0_4_9:
   raise ValueError(
       'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)	)
  __UpperCamelCase       						= _readaa(snake_case	)
  __UpperCamelCase       						= bytestream.read(snake_case	)
  __UpperCamelCase       						= numpy.frombuffer(snake_case       ,      dtype=numpy.uinta	)
  if one_hot:
   return _dense_to_one_hot(snake_case       ,      snake_case	)
  return labels

class __lowerCAmelCase	:





     @deprecated(
         __UpperCAmelCase			,  'Please use alternatives such as official/mnist/_DataSet.py'
         ' from tensorflow/models.'			,  )
     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase=False			,  __UpperCAmelCase=False			,  __UpperCAmelCase=dtypes.floataa			,  __UpperCAmelCase=True			,  __UpperCAmelCase=None			,  ):

      '''simple docstring'''




      __UpperCamelCase						,			__UpperCamelCase       						= random_seed.get_seed(__UpperCAmelCase   )
      # If op level seed is not set, use whatever graph level seed is returned
      numpy.random.seed(seeda if seed is None else seeda   )
      __UpperCamelCase       						= dtypes.as_dtype(__UpperCAmelCase   ).base_dtype
      if dtype not in (dtypes.uinta, dtypes.floataa):
       raise TypeError('Invalid image dtype %r, expected uint8 or float32' % dtype   )
      if fake_data:
       __UpperCamelCase       						= 1_0000
       __UpperCamelCase       						= one_hot
      else:
       assert (
           images.shape[0] == labels.shape[0]
       ), F'images.shape: {images.shape} labels.shape: {labels.shape}'
       __UpperCamelCase       						= images.shape[0]

       # Convert shape from [num examples, rows, columns, depth]
       # to [num examples, rows*columns] (assuming depth == 1)
       if reshape:
        assert images.shape[3] == 1
        __UpperCamelCase       						= images.reshape(
            images.shape[0]			,  images.shape[1] * images.shape[2]   )
       if dtype == dtypes.floataa:
        # Convert from [0, 255] -> [0.0, 1.0].
        __UpperCamelCase       						= images.astype(numpy.floataa   )
        __UpperCamelCase       						= numpy.multiply(__UpperCAmelCase			,  1.0 / 2_5_5.0   )
      __UpperCamelCase       						= images
      __UpperCamelCase       						= labels
      __UpperCamelCase       						= 0
      __UpperCamelCase       						= 0





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._images





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._labels





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._num_examples





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._epochs_completed







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase=False			,  __UpperCAmelCase=True   ):

      '''simple docstring'''




      if fake_data:
       __UpperCamelCase       						= [1] * 784
       __UpperCamelCase       						= [1] + [0] * 9 if self.one_hot else 0
       return (
           [fake_image for _ in range(__UpperCAmelCase   )],
           [fake_label for _ in range(__UpperCAmelCase   )],
       )
      __UpperCamelCase       						= self._index_in_epoch
      # Shuffle for the first epoch
      if self._epochs_completed == 0 and start == 0 and shuffle:
       __UpperCamelCase       						= numpy.arange(self._num_examples   )
       numpy.random.shuffle(__UpperCAmelCase   )
       __UpperCamelCase       						= self.images[perma]
       __UpperCamelCase       						= self.labels[perma]
      # Go to the next epoch
      if start + batch_size > self._num_examples:
       # Finished epoch
       self._epochs_completed += 1
       # Get the rest examples in this epoch
       __UpperCamelCase       						= self._num_examples - start
       __UpperCamelCase       						= self._images[start : self._num_examples]
       __UpperCamelCase       						= self._labels[start : self._num_examples]
       # Shuffle the data
       if shuffle:
        __UpperCamelCase       						= numpy.arange(self._num_examples   )
        numpy.random.shuffle(__UpperCAmelCase   )
        __UpperCamelCase       						= self.images[perm]
        __UpperCamelCase       						= self.labels[perm]
       # Start next epoch
       __UpperCamelCase       						= 0
       __UpperCamelCase       						= batch_size - rest_num_examples
       __UpperCamelCase       						= self._index_in_epoch
       __UpperCamelCase       						= self._images[start:end]
       __UpperCamelCase       						= self._labels[start:end]
       return (
           numpy.concatenate((images_rest_part, images_new_part)			,  axis=0   ),
           numpy.concatenate((labels_rest_part, labels_new_part)			,  axis=0   ),
       )
      else:
       self._index_in_epoch += batch_size
       __UpperCamelCase       						= self._index_in_epoch
       return self._images[start:end], self._labels[start:end]






@deprecated(snake_case       ,      'Please write your own downloading logic.'	)
def 							A		(       snake_case   :List[str]       ,      snake_case   :List[Any]       ,      snake_case   :Dict	)   ->			Union[str, Any]:
 if not gfile.Exists(snake_case	):
  gfile.MakeDirs(snake_case	)
 __UpperCamelCase       						= os.path.join(snake_case       ,      snake_case	)
 if not gfile.Exists(snake_case	):
  urllib.request.urlretrieve(snake_case       ,      snake_case	)  # noqa: S310
  with gfile.GFile(snake_case	) as f:
   __UpperCamelCase       						= f.size()
  print('Successfully downloaded'       ,      snake_case       ,      snake_case       ,      'bytes.'	)
 return filepath






@deprecated(
    snake_case       ,      'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'	)
def 							A		(       snake_case   :str       ,      snake_case   :int=False       ,      snake_case   :Optional[int]=False       ,      snake_case   :int=dtypes.floataa       ,      snake_case   :List[Any]=True       ,      snake_case   :Any=5_0_0_0       ,      snake_case   :Any=None       ,      snake_case   :Tuple=DEFAULT_SOURCE_URL       ,      )   ->			Union[str, Any]:
 if fake_data:

  def fake():
   return _DataSet(
       []       ,      []       ,      fake_data=snake_case       ,      one_hot=snake_case       ,      dtype=snake_case       ,      seed=snake_case	)

  __UpperCamelCase       						= fake()
  __UpperCamelCase       						= fake()
  __UpperCamelCase       						= fake()
  return _Datasets(train=snake_case       ,      validation=snake_case       ,      test=snake_case	)

 if not source_url:  # empty string check
  __UpperCamelCase       						= DEFAULT_SOURCE_URL

 __UpperCamelCase       						= 'train-images-idx3-ubyte.gz'
 __UpperCamelCase       						= 'train-labels-idx1-ubyte.gz'
 __UpperCamelCase       						= 't10k-images-idx3-ubyte.gz'
 __UpperCamelCase       						= 't10k-labels-idx1-ubyte.gz'

 __UpperCamelCase       						= _maybe_download(
     snake_case       ,      snake_case       ,      source_url + train_images_file	)
 with gfile.Open(snake_case       ,      'rb'	) as f:
  __UpperCamelCase       						= _extract_images(snake_case	)

 __UpperCamelCase       						= _maybe_download(
     snake_case       ,      snake_case       ,      source_url + train_labels_file	)
 with gfile.Open(snake_case       ,      'rb'	) as f:
  __UpperCamelCase       						= _extract_labels(snake_case       ,      one_hot=snake_case	)

 __UpperCamelCase       						= _maybe_download(
     snake_case       ,      snake_case       ,      source_url + test_images_file	)
 with gfile.Open(snake_case       ,      'rb'	) as f:
  __UpperCamelCase       						= _extract_images(snake_case	)

 __UpperCamelCase       						= _maybe_download(
     snake_case       ,      snake_case       ,      source_url + test_labels_file	)
 with gfile.Open(snake_case       ,      'rb'	) as f:
  __UpperCamelCase       						= _extract_labels(snake_case       ,      one_hot=snake_case	)

 if not 0 <= validation_size <= len(snake_case	):
  __UpperCamelCase       						= (
      'Validation size should be between 0 and '
      f'{len(snake_case	)}. Received: {validation_size}.'
  )
  raise ValueError(snake_case	)

 __UpperCamelCase       						= train_images[:validation_size]
 __UpperCamelCase       						= train_labels[:validation_size]
 __UpperCamelCase       						= train_images[validation_size:]
 __UpperCamelCase       						= train_labels[validation_size:]

 __UpperCamelCase       						= {'dtype': dtype, 'reshape': reshape, 'seed': seed}

 __UpperCamelCase       						= _DataSet(snake_case       ,      snake_case       ,      **snake_case	)
 __UpperCamelCase       						= _DataSet(snake_case       ,      snake_case       ,      **snake_case	)
 __UpperCamelCase       						= _DataSet(snake_case       ,      snake_case       ,      **snake_case	)

 return _Datasets(train=snake_case       ,      validation=snake_case       ,      test=snake_case	)



