


'''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

__lowerCamelCase												=						random.Random()


def        a__     (					UpperCamelCase_						:      Tuple,   UpperCamelCase_						:      Optional[int]=1.0,   UpperCamelCase_						:      Dict=None,   UpperCamelCase_						:      List[Any]=None						):
   if rng is None:
      UpperCAmelCase__	:List[str]      =      global_rng

   UpperCAmelCase__	:List[str]      =      []
   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						):


 def __init__(      self							:	Optional[int]			,       __lowerCamelCase							:	int			,       __lowerCamelCase							:	List[str]=7			,       __lowerCamelCase							:	List[Any]=4_0_0			,       __lowerCamelCase							:	List[Any]=2_0_0_0			,       __lowerCamelCase							:	List[str]=2_0_4_8			,       __lowerCamelCase							:	Any=1_2_8			,       __lowerCamelCase							:	Optional[Any]=1			,       __lowerCamelCase							:	Union[str, Any]=5_1_2			,       __lowerCamelCase							:	List[Any]=3_0			,       __lowerCamelCase							:	Any=4_4_1_0_0			,       ):
    UpperCAmelCase__	:int      =      parent
    UpperCAmelCase__	:Optional[Any]      =      batch_size
    UpperCAmelCase__	:Optional[int]      =      min_seq_length
    UpperCAmelCase__	:Any      =      max_seq_length
    UpperCAmelCase__	:Tuple      =      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
    UpperCAmelCase__	:Union[str, Any]      =      spectrogram_length
    UpperCAmelCase__	:Dict      =      feature_size
    UpperCAmelCase__	:Union[str, Any]      =      num_audio_channels
    UpperCAmelCase__	:Optional[int]      =      hop_length
    UpperCAmelCase__	:List[Any]      =      chunk_length
    UpperCAmelCase__	:Any      =      sampling_rate


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict ):
    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        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any]			,       __lowerCamelCase							:	Any=False			,       __lowerCamelCase							:	str=False ):
    def _flatten(__lowerCamelCase							:	str ):
       return list(itertools.chain(*__lowerCamelCase ) )

    if equal_length:
       UpperCAmelCase__	:List[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__	:Optional[int]      =      [
           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__	:List[Any]      =      [np.asarray(__lowerCamelCase ) for x in speech_inputs]
    return speech_inputs




@require_torch
@require_torchaudio
class 				UpperCAmelCase			(			__snake_case						, unittest.TestCase						):
 UpperCAmelCase				=					TvltFeatureExtractor


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict ):
    UpperCAmelCase__	:List[Any]      =      TvltFeatureExtractionTester(self )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any ):
    UpperCAmelCase__	:Tuple      =      self.feature_extraction_class(**self.feat_extract_dict )
    self.assertTrue(hasattr(__lowerCamelCase			,       '''spectrogram_length''' ) )
    self.assertTrue(hasattr(__lowerCamelCase			,       '''feature_size''' ) )
    self.assertTrue(hasattr(__lowerCamelCase			,       '''num_audio_channels''' ) )
    self.assertTrue(hasattr(__lowerCamelCase			,       '''hop_length''' ) )
    self.assertTrue(hasattr(__lowerCamelCase			,       '''chunk_length''' ) )
    self.assertTrue(hasattr(__lowerCamelCase			,       '''sampling_rate''' ) )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    UpperCAmelCase__	:Optional[Any]      =      self.feature_extraction_class(**self.feat_extract_dict )

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

    UpperCAmelCase__	:Tuple      =      feat_extract_first.to_dict()
    UpperCAmelCase__	:Any      =      feat_extract_second.to_dict()
    UpperCAmelCase__	:Optional[int]      =      dict_first.pop('''mel_filters''' )
    UpperCAmelCase__	:Dict      =      dict_second.pop('''mel_filters''' )
    self.assertTrue(np.allclose(__lowerCamelCase			,       __lowerCamelCase ) )
    self.assertEqual(__lowerCamelCase			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:Tuple      =      self.feature_extraction_class(**self.feat_extract_dict )

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

    UpperCAmelCase__	:Any      =      feat_extract_first.to_dict()
    UpperCAmelCase__	:str      =      feat_extract_second.to_dict()
    UpperCAmelCase__	:Tuple      =      dict_first.pop('''mel_filters''' )
    UpperCAmelCase__	:Any      =      dict_second.pop('''mel_filters''' )
    self.assertTrue(np.allclose(__lowerCamelCase			,       __lowerCamelCase ) )
    self.assertEqual(__lowerCamelCase			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    # Initialize feature_extractor
    UpperCAmelCase__	:int      =      self.feature_extraction_class(**self.feat_extract_dict )

    # create three inputs of length 800, 1000, and 1200
    UpperCAmelCase__	:List[Any]      =      [floats_list((1, x) )[0] for x in range(8_0_0			,       1_4_0_0			,       2_0_0 )]
    UpperCAmelCase__	:List[str]      =      [np.asarray(__lowerCamelCase ) for speech_input in speech_inputs]

    # Test not batched input
    UpperCAmelCase__	:Optional[Any]      =      feature_extractor(np_speech_inputs[0]			,       return_tensors='''np'''			,       sampling_rate=4_4_1_0_0 ).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__	:int      =      feature_extractor(__lowerCamelCase			,       return_tensors='''np'''			,       sampling_rate=4_4_1_0_0 ).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__	:Any      =      feature_extractor(
        __lowerCamelCase			,       return_tensors='''np'''			,       sampling_rate=4_4_1_0_0			,       mask_audio=__lowerCamelCase ).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__	:Union[str, Any]      =      [floats_list((1, x) )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
    UpperCAmelCase__	:List[Any]      =      np.asarray(__lowerCamelCase )
    UpperCAmelCase__	:int      =      feature_extractor(__lowerCamelCase			,       return_tensors='''np'''			,       sampling_rate=4_4_1_0_0 ).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        __SCREAMING_SNAKE_CASE					(      self							:	str			,       __lowerCamelCase							:	Optional[Any] ):
    UpperCAmelCase__	:Any      =      load_dataset('''hf-internal-testing/librispeech_asr_dummy'''			,       '''clean'''			,       split='''validation''' )
    # automatic decoding with librispeech
    UpperCAmelCase__	:str      =      ds.sort('''id''' ).select(range(__lowerCamelCase ) )[:num_samples]["""audio"""]

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





 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    UpperCAmelCase__	:List[str]      =      self._load_datasamples(1 )
    UpperCAmelCase__	:Union[str, Any]      =      TvltFeatureExtractor()
    UpperCAmelCase__	:Tuple      =      feature_extractor(__lowerCamelCase			,       return_tensors='''pt''' ).audio_values

    self.assertEquals(audio_values.shape			,       (1, 1, 1_9_2, 1_2_8) )

    UpperCAmelCase__	:List[Any]      =      torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]] )
    self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]			,       __lowerCamelCase			,       atol=1e-4 ) )











"""simple docstring"""







def      UpperCamelCase       ( _A  ,				_A )     ->       None:
				lowercase    :      List[Any]       = len(_A )
				print("""The following activities are selected:""" )

				# The first activity is always selected
				lowercase    :      Optional[int]       = 0
				print(_A  ,				end=""",""" )

				# Consider rest of the activities
				for j in range(_A ):
								# If this activity has start time greater than
								# or equal to the finish time of previously
								# selected activity, then select it
								if start[j] >= finish[i]:
												print(_A  ,				end=""",""" )
												lowercase    :      str       = j


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_lowerCAmelCase            =	[1, 3, 0, 5, 8, 5]
				_lowerCAmelCase            =	[2, 4, 6, 7, 9, 9]
				print_max_activities(start, finish)
