
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 :			int              =	random.Random()


def 				A	( lowercase   ,	lowercase=1.0   ,	lowercase=None   ,	lowercase=None			)       ->  List[Any]:
		'''simple docstring'''

		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     lowercase (		unittest.TestCase       ):



		def __init__(			self    ,				A_    ,				A_=7    ,				A_=400    ,				A_=2_000    ,				A_=2_048    ,				A_=128    ,				A_=1    ,				A_=512    ,				A_=30    ,				A_=44_100    ,				)  -> str:
				"""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	)  -> int:
				"""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    ,				A_=False    ,				A_=False	)  -> Optional[Any]:
				"""simple docstring"""
				def _flatten(A_	):
						return list(itertools.chain(*A_	)	)

				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(A_	) for x in speech_inputs]
				return speech_inputs






@require_torch
@require_torchaudio
class     lowercase (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	Any     					=    TvltFeatureExtractor



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					TvltFeatureExtractionTester(self	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					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 __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					self.feature_extraction_class(**self.feat_extract_dict	)

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

				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(A_    ,				A_	)	)
				self.assertEqual(A_    ,				A_	)



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.feature_extraction_class(**self.feat_extract_dict	)

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

				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(A_    ,				A_	)	)
				self.assertEqual(A_    ,				A_	)



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				# Initialize feature_extractor
				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    ,				1_400    ,				200	)]
				UpperCamelCase          =					[np.asarray(A_	) for speech_input in speech_inputs]

				# Test not batched input
				UpperCamelCase          =					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          =					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          =					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          =					[floats_list((1, x)	)[0] for x in (800, 800, 800)]
				UpperCamelCase          =					np.asarray(A_	)
				UpperCamelCase          =					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 __UpperCamelCase			(			self    ,				A_	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					load_dataset('hf-internal-testing/librispeech_asr_dummy'    ,				'clean'    ,				split='validation'	)
				# automatic decoding with librispeech
				UpperCamelCase          =					ds.sort('id'	).select(range(A_	)	)[:num_samples]['audio']

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



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self._load_datasamples(1	)
				UpperCamelCase          =					TvltFeatureExtractor()
				UpperCamelCase          =					feature_extractor(A_    ,				return_tensors='pt'	).audio_values

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

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


from __future__ import annotations


def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)       ->  list:
		'''simple docstring'''

		UpperCamelCase          =					[]
		UpperCamelCase     ,  UpperCamelCase          =					input_list[low:mid], input_list[mid : high + 1]
		while left and right:
				result.append((left if left[0] <= right[0] else right).pop(0			)			)
		UpperCamelCase          =					result + left + right
		return input_list


def 				A	( lowercase			)       ->  list:
		'''simple docstring'''

		if len(lowercase			) <= 1:
				return input_list
		UpperCamelCase          =					list(lowercase			)

		# iteration for two-way merging
		UpperCamelCase          =					2
		while p <= len(lowercase			):
				# getting low, high and middle value for merge-sort of single list
				for i in range(0   ,	len(lowercase			)   ,	lowercase			):
						UpperCamelCase          =					i
						UpperCamelCase          =					i + p - 1
						UpperCamelCase          =					(low + high + 1) // 2
						UpperCamelCase          =					merge(lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)
				# final merge of last two parts
				if p * 2 >= len(lowercase			):
						UpperCamelCase          =					i
						UpperCamelCase          =					merge(lowercase   ,	0   ,	lowercase   ,	len(lowercase			) - 1			)
						break
				p *= 2

		return input_list


if __name__ == "__main__":
					_UpperCAmelCase :			Any              =	input("Enter numbers separated by a comma:\n").strip()
					if user_input == "":
										_UpperCAmelCase :			Optional[Any]              =	[]
					else:
										_UpperCAmelCase :			Any              =	[int(item.strip()) for item in user_input.split(",")]
					print(iter_merge_sort(unsorted))
