






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

__a      :       Any				  =   random.Random()



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[Any]			,				__lowercase   :			str=1.0			,				__lowercase   :			Optional[Any]=None			,				__lowercase   :			Union[str, Any]=None    )						->      Tuple:





    """simple docstring"""

    if rng is None:
        __A         =	global_rng

    __A         =	[]
    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  ):







     '''simple docstring'''




     def __init__(				self       :			List[Any] ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			Dict=7 ,	UpperCamelCase_       :			Union[str, Any]=400 ,	UpperCamelCase_       :			str=2_000 ,	UpperCamelCase_       :			Any=2_048 ,	UpperCamelCase_       :			List[Any]=128 ,	UpperCamelCase_       :			Tuple=1 ,	UpperCamelCase_       :			List[str]=512 ,	UpperCamelCase_       :			Union[str, Any]=30 ,	UpperCamelCase_       :			Optional[Any]=44_100 ,	):


         """simple docstring"""



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




     def 		lowerCAmelCase_     (				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 		lowerCAmelCase_     (				self       :			int ,	UpperCamelCase_       :			List[str]=False ,	UpperCamelCase_       :			str=False				):


         """simple docstring"""



         def _flatten(UpperCamelCase_       :			int				):
             return list(itertools.chain(*UpperCamelCase_				)				)

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


@require_torch
@require_torchaudio
class       __lowercase      (	lowercase_			,      unittest.TestCase  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				TvltFeatureExtractor




     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         __A         =	TvltFeatureExtractionTester(self				)




     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         __A         =	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 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



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

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

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




     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



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

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

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




     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



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

         # create three inputs of length 800, 1000, and 1200
         __A         =	[floats_list((1, x)				)[0] for x in range(800 ,	1_400 ,	200				)]
         __A         =	[np.asarray(UpperCamelCase_				) for speech_input in speech_inputs]

         # Test not batched input
         __A         =	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
         __A         =	feature_extractor(UpperCamelCase_ ,	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
         __A         =	feature_extractor(
             UpperCamelCase_ ,	return_tensors="""np""" ,	sampling_rate=44_100 ,	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.
         __A         =	[floats_list((1, x)				)[0] for x in (800, 800, 800)]
         __A         =	np.asarray(UpperCamelCase_				)
         __A         =	feature_extractor(UpperCamelCase_ ,	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 		lowerCAmelCase_     (				self       :			Any ,	UpperCamelCase_       :			str				):


         """simple docstring"""



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

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





     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



         __A         =	self._load_datasamples(1				)
         __A         =	TvltFeatureExtractor()
         __A         =	feature_extractor(UpperCamelCase_ ,	return_tensors="""pt"""				).audio_values

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

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












from __future__ import annotations



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[int]			,				__lowercase   :			int    )						->      list[list[int]]:





    """simple docstring"""

    __A         =	[]
    __A         =	[]
    __A         =	0
    __A         =	sum(__lowercase    )
    create_state_space_tree(__lowercase			,				__lowercase			,				__lowercase			,				__lowercase			,				__lowercase			,				__lowercase    )
    return result



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[int]			,				__lowercase   :			int			,				__lowercase   :			int			,				__lowercase   :			list[int]			,				__lowercase   :			list[list[int]]			,				__lowercase   :			int			,				)						->      None:





    """simple docstring"""

    if sum(__lowercase    ) > max_sum or (remaining_nums_sum + sum(__lowercase    )) < max_sum:
        return
    if sum(__lowercase    ) == max_sum:
        result.append(__lowercase    )
        return
    for index in range(__lowercase			,				len(__lowercase    )    ):
        create_state_space_tree(
            __lowercase			,				__lowercase			,				index + 1			,				[*path, nums[index]]			,				__lowercase			,				remaining_nums_sum - nums[index]			,				)
__a      :       str				  =   [3, 34, 4, 12, 5, 2]
__a      :       Optional[Any]				  =   9
__a      :       List[str]				  =   generate_sum_of_subsets_soln(nums, max_sum)






print(*result)




