





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

_snake_case           =					random.Random()


def _A  (       __magic_name__    ,   __magic_name__=1.0    ,   __magic_name__=None    ,   __magic_name__=None			):


   if rng is None:
      lowercase__					  =    global_rng

   lowercase__					  =    []
   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    :Optional[Any]       ,				_lowercase    :Tuple       ,				_lowercase    :int=7       ,				_lowercase    :Any=4_00       ,				_lowercase    :Any=20_00       ,				_lowercase    :List[Any]=20_48       ,				_lowercase    :List[str]=1_28       ,				_lowercase    :Optional[Any]=1       ,				_lowercase    :Any=5_12       ,				_lowercase    :str=30       ,				_lowercase    :Tuple=4_41_00       ,				):






     '''simple docstring'''





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







  def     UpperCAmelCase	(     self    :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     UpperCAmelCase	(     self    :List[Any]       ,				_lowercase    :Dict=False       ,				_lowercase    :int=False   ):






     '''simple docstring'''





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

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





@require_torch
@require_torchaudio
class    lowerCAmelCase				(  A__				,				unittest.TestCase   ):
  __lowerCamelCase						=     TvltFeatureExtractor







  def     UpperCAmelCase	(     self    :int   ):






     '''simple docstring'''





     lowercase__					  =    TvltFeatureExtractionTester(self   )







  def     UpperCAmelCase	(     self    :List[Any]   ):






     '''simple docstring'''





     lowercase__					  =    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    :str   ):






     '''simple docstring'''





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

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

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







  def     UpperCAmelCase	(     self    :Any   ):






     '''simple docstring'''





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

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

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







  def     UpperCAmelCase	(     self    :int   ):






     '''simple docstring'''





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

     # create three inputs of length 800, 1000, and 1200
     lowercase__					  =    [floats_list((1, x)   )[0] for x in range(8_00       ,				14_00       ,				2_00   )]
     lowercase__					  =    [np.asarray(__UpperCAmelCase   ) for speech_input in speech_inputs]

     # Test not batched input
     lowercase__					  =    feature_extractor(np_speech_inputs[0]       ,				return_tensors="np"       ,				sampling_rate=4_41_00   ).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
     lowercase__					  =    feature_extractor(__UpperCAmelCase       ,				return_tensors="np"       ,				sampling_rate=4_41_00   ).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
     lowercase__					  =    feature_extractor(
         __UpperCAmelCase       ,				return_tensors="np"       ,				sampling_rate=4_41_00       ,				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.
     lowercase__					  =    [floats_list((1, x)   )[0] for x in (8_00, 8_00, 8_00)]
     lowercase__					  =    np.asarray(__UpperCAmelCase   )
     lowercase__					  =    feature_extractor(__UpperCAmelCase       ,				return_tensors="np"       ,				sampling_rate=4_41_00   ).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    :Union[str, Any]       ,				_lowercase    :Union[str, Any]   ):






     '''simple docstring'''





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

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







  def     UpperCAmelCase	(     self    :str   ):






     '''simple docstring'''





     lowercase__					  =    self._load_datasamples(1   )
     lowercase__					  =    TvltFeatureExtractor()
     lowercase__					  =    feature_extractor(__UpperCAmelCase       ,				return_tensors="pt"   ).audio_values

     self.assertEquals(audio_values.shape       ,				(1, 1, 1_92, 1_28)   )

     lowercase__					  =    torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]   )
     self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]       ,				__UpperCAmelCase       ,				atol=1e-4   )   )









"""simple docstring"""




def 						A							(  snake_case__		,					snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   1  # To kept the Calculated Value
			# Since C(n, k) = C(n, n-k)
			if k > (n - k):
						SCREAMING_SNAKE_CASE__										=   n - k
			# Calculate C(n,k)
			for i in range(snake_case__     ):
						result *= n - i
						result //= i + 1
			return result

def 						A							(  snake_case__     ):




			'''simple docstring'''


			return binomial_coefficient(2 * node_count		,					snake_case__     ) // (node_count + 1)

def 						A							(  snake_case__     ):




			'''simple docstring'''


			if n < 0:
						raise ValueError("""factorial() not defined for negative values"""     )
			SCREAMING_SNAKE_CASE__										=   1
			for i in range(1		,					n + 1     ):
						result *= i
			return result

def 						A							(  snake_case__     ):




			'''simple docstring'''


			return catalan_number(snake_case__     ) * factorial(snake_case__     )


if __name__ == "__main__":
						A_				:      Optional[int]           =			int(input("Enter the number of nodes: ").strip() or 0)
						if node_count <= 0:
												raise ValueError("We need some nodes to work with.")
						print(
						    F'Given {node_count} nodes, there are {binary_tree_count(node_count)} '
						    F'binary trees and {catalan_number(node_count)} binary search trees.'
						)


