

"""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			=					random.Random()
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase=1.0				,     __UpperCamelCase=None				,     __UpperCamelCase=None   ):
 '''simple docstring'''





 if rng is None:
  UpperCAmelCase__       :	int        =       global_rng

 UpperCAmelCase__       :	Dict        =       []
 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		:      List[Any]       ,A		:      List[Any]       ,A		:      Optional[int]=7       ,A		:      int=400       ,A		:      Optional[int]=2_000       ,A		:      Dict=2_048       ,A		:      Optional[Any]=128       ,A		:      str=1       ,A		:      str=512       ,A		:      Dict=30       ,A		:      Any=44_100       ,):






   '''simple docstring'''
   UpperCAmelCase__       :	Any        =       parent
   UpperCAmelCase__       :	Optional[int]        =       batch_size
   UpperCAmelCase__       :	str        =       min_seq_length
   UpperCAmelCase__       :	int        =       max_seq_length
   UpperCAmelCase__       :	int        =       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
   UpperCAmelCase__       :	Tuple        =       spectrogram_length
   UpperCAmelCase__       :	str        =       feature_size
   UpperCAmelCase__       :	str        =       num_audio_channels
   UpperCAmelCase__       :	Any        =       hop_length
   UpperCAmelCase__       :	int        =       chunk_length
   UpperCAmelCase__       :	int        =       sampling_rate

  def  __lowercase							(	self		:      List[Any]     ):






   '''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  __lowercase							(	self		:      List[Any]       ,A		:      Dict=False       ,A		:      Any=False     ):






   '''simple docstring'''
   def _flatten(A		:      Dict     ):
    return list(itertools.chain(*A     )     )

   if equal_length:
    UpperCAmelCase__       :	Any        =       [floats_list((self.max_seq_length, self.feature_size)     ) for _ in range(self.batch_size     )]
   else:
    # make sure that inputs increase in size
    UpperCAmelCase__       :	Union[str, 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:
    UpperCAmelCase__       :	int        =       [np.asarray(A     ) for x in speech_inputs]
   return speech_inputs







@require_torch
@require_torchaudio
class   __lowercase      (     __lowerCamelCase						,      unittest.TestCase       ):
  snake_case_												=							TvltFeatureExtractor

  def  __lowercase							(	self		:      Tuple     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[int]        =       TvltFeatureExtractionTester(self     )

  def  __lowercase							(	self		:      Any     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Tuple        =       self.feature_extraction_class(**self.feat_extract_dict     )
   self.assertTrue(hasattr(A       ,"""spectrogram_length"""     )     )
   self.assertTrue(hasattr(A       ,"""feature_size"""     )     )
   self.assertTrue(hasattr(A       ,"""num_audio_channels"""     )     )
   self.assertTrue(hasattr(A       ,"""hop_length"""     )     )
   self.assertTrue(hasattr(A       ,"""chunk_length"""     )     )
   self.assertTrue(hasattr(A       ,"""sampling_rate"""     )     )

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






   '''simple docstring'''
   UpperCAmelCase__       :	Union[str, Any]        =       self.feature_extraction_class(**self.feat_extract_dict     )

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

   UpperCAmelCase__       :	List[Any]        =       feat_extract_first.to_dict()
   UpperCAmelCase__       :	Union[str, Any]        =       feat_extract_second.to_dict()
   UpperCAmelCase__       :	List[Any]        =       dict_first.pop("""mel_filters"""     )
   UpperCAmelCase__       :	Union[str, Any]        =       dict_second.pop("""mel_filters"""     )
   self.assertTrue(np.allclose(A       ,A     )     )
   self.assertEqual(A       ,A     )

  def  __lowercase							(	self		:      Dict     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[int]        =       self.feature_extraction_class(**self.feat_extract_dict     )

   with tempfile.TemporaryDirectory() as tmpdirname:
    UpperCAmelCase__       :	Optional[int]        =       os.path.join(A       ,"""feat_extract.json"""     )
    feat_extract_first.to_json_file(A     )
    UpperCAmelCase__       :	List[str]        =       self.feature_extraction_class.from_json_file(A     )

   UpperCAmelCase__       :	Dict        =       feat_extract_first.to_dict()
   UpperCAmelCase__       :	Optional[Any]        =       feat_extract_second.to_dict()
   UpperCAmelCase__       :	int        =       dict_first.pop("""mel_filters"""     )
   UpperCAmelCase__       :	List[str]        =       dict_second.pop("""mel_filters"""     )
   self.assertTrue(np.allclose(A       ,A     )     )
   self.assertEqual(A       ,A     )

  def  __lowercase							(	self		:      Tuple     ):






   '''simple docstring'''
   # Initialize feature_extractor
   UpperCAmelCase__       :	Optional[Any]        =       self.feature_extraction_class(**self.feat_extract_dict     )

   # create three inputs of length 800, 1000, and 1200
   UpperCAmelCase__       :	Tuple        =       [floats_list((1, x)     )[0] for x in range(800       ,1_400       ,200     )]
   UpperCAmelCase__       :	List[str]        =       [np.asarray(A     ) for speech_input in speech_inputs]

   # Test not batched input
   UpperCAmelCase__       :	Union[str, Any]        =       feature_extractor(np_speech_inputs[0]       ,return_tensors="""np"""       ,sampling_rate=44_100     ).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__       :	Optional[Any]        =       feature_extractor(A       ,return_tensors="""np"""       ,sampling_rate=44_100     ).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__       :	Optional[Any]        =       feature_extractor(
       A       ,return_tensors="""np"""       ,sampling_rate=44_100       ,mask_audio=A     ).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__       :	Tuple        =       [floats_list((1, x)     )[0] for x in (800, 800, 800)]
   UpperCAmelCase__       :	str        =       np.asarray(A     )
   UpperCAmelCase__       :	List[Any]        =       feature_extractor(A       ,return_tensors="""np"""       ,sampling_rate=44_100     ).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  __lowercase							(	self		:      str       ,A		:      Dict     ):






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

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




  def  __lowercase							(	self		:      str     ):






   '''simple docstring'''
   UpperCAmelCase__       :	str        =       self._load_datasamples(1     )
   UpperCAmelCase__       :	str        =       TvltFeatureExtractor()
   UpperCAmelCase__       :	Dict        =       feature_extractor(A       ,return_tensors="""pt"""     ).audio_values

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

   UpperCAmelCase__       :	Dict        =       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]       ,A       ,atol=1e-4     )     )












from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate



@dataclass(frozen=__SCREAMING_SNAKE_CASE    )
class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		str				=	field(default="""summarization"""			,     metadata={"""include_in_asdict_even_if_is_default""": True}    )
    lowerCamelCase__    :		ClassVar[Features]				=	Features({"""text""": Value("""string"""    )}    )
    lowerCamelCase__    :		ClassVar[Features]				=	Features({"""summary""": Value("""string"""    )}    )
    lowerCamelCase__    :		str				=	"text"
    lowerCamelCase__    :		str				=	"summary"



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return {self.text_column: "text", self.summary_column: "summary"}
