

import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
				from transformers import SpeechaTextFeatureExtractor

_a:    Tuple 							=			random.Random()



def       __lowerCAmelCase      (						A       ,     A=1.0       ,     A=None       ,     A=None    ):
	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

@require_torch
@require_torchaudio
class 						__UpperCamelCase    (					unittest.TestCase						):




	def __init__(		self	:						List[str]		,	lowerCAmelCase	:						Dict		,	lowerCAmelCase	:						Any=7		,	lowerCAmelCase	:						Tuple=400		,	lowerCAmelCase	:						Tuple=2_000		,	lowerCAmelCase	:						Any=24		,	lowerCAmelCase	:						Tuple=24		,	lowerCAmelCase	:						Any=0.0		,	lowerCAmelCase	:						Optional[Any]=16_000		,	lowerCAmelCase	:						Any=True		,	lowerCAmelCase	:						Dict=True		,	):


		'''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_       				=			feature_size
		UpperCAmelCase_       				=			num_mel_bins
		UpperCAmelCase_       				=			padding_value
		UpperCAmelCase_       				=			sampling_rate
		UpperCAmelCase_       				=			return_attention_mask
		UpperCAmelCase_       				=			do_normalize




	def       __A					(		self	:						str							):


		'''simple docstring'''







		return {
		    "feature_size": self.feature_size,
		    "num_mel_bins": self.num_mel_bins,
		    "padding_value": self.padding_value,
		    "sampling_rate": self.sampling_rate,
		    "return_attention_mask": self.return_attention_mask,
		    "do_normalize": self.do_normalize,
		}




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						Tuple=False		,	lowerCAmelCase	:						Optional[Any]=False							):


		'''simple docstring'''







		def _flatten(lowerCAmelCase	:						List[Any]							):
			return list(itertools.chain(*lowerCAmelCase							)							)

		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(lowerCAmelCase							) for x in speech_inputs]
		return speech_inputs


@require_torch
@require_torchaudio
class 						__UpperCamelCase    (					lowercase     ,     unittest.TestCase						):
	SCREAMING_SNAKE_CASE__    				=     SpeechaTextFeatureExtractor if is_speech_available() else None




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			SpeechaTextFeatureExtractionTester(self							)




	def       __A					(		self	:						Any		,	lowerCAmelCase	:						Optional[Any]							):


		'''simple docstring'''







		self.assertTrue(np.all(np.mean(lowerCAmelCase		,	axis=0							) < 1e-3							)							)
		self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase		,	axis=0							) - 1							) < 1e-3							)							)




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.feature_extraction_class(**self.feat_extract_tester.prepare_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(lowerCAmelCase							) for speech_input in speech_inputs]

		# Test feature size
		UpperCAmelCase_       				=			feature_extractor(lowerCAmelCase		,	padding=lowerCAmelCase		,	return_tensors="np"							).input_features
		self.assertTrue(input_features.ndim == 3							)
		self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size							)

		# Test not batched input
		UpperCAmelCase_       				=			feature_extractor(speech_inputs[0]		,	return_tensors="np"							).input_features
		UpperCAmelCase_       				=			feature_extractor(np_speech_inputs[0]		,	return_tensors="np"							).input_features
		self.assertTrue(np.allclose(lowerCAmelCase		,	lowerCAmelCase		,	atol=1e-3							)							)

		# Test batched
		UpperCAmelCase_       				=			feature_extractor(lowerCAmelCase		,	return_tensors="np"							).input_features
		UpperCAmelCase_       				=			feature_extractor(lowerCAmelCase		,	return_tensors="np"							).input_features
		for enc_seq_a, enc_seq_a in zip(lowerCAmelCase		,	lowerCAmelCase							):
			self.assertTrue(np.allclose(lowerCAmelCase		,	lowerCAmelCase		,	atol=1e-3							)							)

		# Test 2-D numpy arrays are batched.
		UpperCAmelCase_       				=			[floats_list((1, x)							)[0] for x in (800, 800, 800)]
		UpperCAmelCase_       				=			np.asarray(lowerCAmelCase							)
		UpperCAmelCase_       				=			feature_extractor(lowerCAmelCase		,	return_tensors="np"							).input_features
		UpperCAmelCase_       				=			feature_extractor(lowerCAmelCase		,	return_tensors="np"							).input_features
		for enc_seq_a, enc_seq_a in zip(lowerCAmelCase		,	lowerCAmelCase							):
			self.assertTrue(np.allclose(lowerCAmelCase		,	lowerCAmelCase		,	atol=1e-3							)							)




	def       __A					(		self	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
		UpperCAmelCase_       				=			[floats_list((1, x)							)[0] for x in range(800		,	1_400		,	200							)]

		UpperCAmelCase_       				=			["longest", "max_length", "do_not_pad"]
		UpperCAmelCase_       				=			[None, 16, None]
		for max_length, padding in zip(lowerCAmelCase		,	lowerCAmelCase							):
			UpperCAmelCase_       				=			feature_extractor(
			    lowerCAmelCase		,	padding=lowerCAmelCase		,	max_length=lowerCAmelCase		,	return_attention_mask=lowerCAmelCase							)
			UpperCAmelCase_       				=			inputs.input_features
			UpperCAmelCase_       				=			inputs.attention_mask
			UpperCAmelCase_       				=			[np.sum(lowerCAmelCase							) for x in attention_mask]

			self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]							)
			self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]							)
			self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]							)




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
		UpperCAmelCase_       				=			[floats_list((1, x)							)[0] for x in range(800		,	1_400		,	200							)]

		UpperCAmelCase_       				=			["longest", "max_length", "do_not_pad"]
		UpperCAmelCase_       				=			[None, 16, None]
		for max_length, padding in zip(lowerCAmelCase		,	lowerCAmelCase							):
			UpperCAmelCase_       				=			feature_extractor(
			    lowerCAmelCase		,	max_length=lowerCAmelCase		,	padding=lowerCAmelCase		,	return_tensors="np"		,	return_attention_mask=lowerCAmelCase							)
			UpperCAmelCase_       				=			inputs.input_features
			UpperCAmelCase_       				=			inputs.attention_mask
			UpperCAmelCase_       				=			[np.sum(lowerCAmelCase							) for x in attention_mask]

			self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]							)
			self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6							)
			self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]							)
			self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6							)
			self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]							)




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


		'''simple docstring'''







		UpperCAmelCase_       				=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
		UpperCAmelCase_       				=			[floats_list((1, x)							)[0] for x in range(800		,	1_400		,	200							)]
		UpperCAmelCase_       				=			feature_extractor(
		    lowerCAmelCase		,	padding="max_length"		,	max_length=4		,	truncation=lowerCAmelCase		,	return_tensors="np"		,	return_attention_mask=lowerCAmelCase		,	)
		UpperCAmelCase_       				=			inputs.input_features
		UpperCAmelCase_       				=			inputs.attention_mask
		UpperCAmelCase_       				=			np.sum(attention_mask == 1		,	axis=1							)

		self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]							)
		self._check_zero_mean_unit_variance(input_features[1]							)
		self._check_zero_mean_unit_variance(input_features[2]							)




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
		UpperCAmelCase_       				=			[floats_list((1, x)							)[0] for x in range(800		,	1_400		,	200							)]
		UpperCAmelCase_       				=			feature_extractor(
		    lowerCAmelCase		,	padding="longest"		,	max_length=4		,	truncation=lowerCAmelCase		,	return_tensors="np"		,	return_attention_mask=lowerCAmelCase		,	)
		UpperCAmelCase_       				=			inputs.input_features
		UpperCAmelCase_       				=			inputs.attention_mask
		UpperCAmelCase_       				=			np.sum(attention_mask == 1		,	axis=1							)

		self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]							)
		self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]							)
		self._check_zero_mean_unit_variance(input_features[2]							)

		# make sure that if max_length < longest -> then pad to max_length
		self.assertEqual(input_features.shape		,	(3, 4, 24)							)

		UpperCAmelCase_       				=			[floats_list((1, x)							)[0] for x in range(800		,	1_400		,	200							)]
		UpperCAmelCase_       				=			feature_extractor(
		    lowerCAmelCase		,	padding="longest"		,	max_length=16		,	truncation=lowerCAmelCase		,	return_tensors="np"		,	return_attention_mask=lowerCAmelCase		,	)
		UpperCAmelCase_       				=			inputs.input_features
		UpperCAmelCase_       				=			inputs.attention_mask
		UpperCAmelCase_       				=			np.sum(attention_mask == 1		,	axis=1							)

		self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]							)
		self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]							)
		self._check_zero_mean_unit_variance(input_features[2]							)

		# make sure that if max_length < longest -> then pad to max_length
		self.assertEqual(input_features.shape		,	(3, 6, 24)							)




	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







		import torch

		UpperCAmelCase_       				=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
		UpperCAmelCase_       				=			np.random.rand(100		,	32							).astype(np.floataa							)
		UpperCAmelCase_       				=			np_speech_inputs.tolist()

		for inputs in [py_speech_inputs, np_speech_inputs]:
			UpperCAmelCase_       				=			feature_extractor.pad([{"input_features": inputs}]		,	return_tensors="np"							)
			self.assertTrue(np_processed.input_features.dtype == np.floataa							)
			UpperCAmelCase_       				=			feature_extractor.pad([{"input_features": inputs}]		,	return_tensors="pt"							)
			self.assertTrue(pt_processed.input_features.dtype == torch.floataa							)




	def       __A					(		self	:						Dict		,	lowerCAmelCase	:						Union[str, Any]							):


		'''simple docstring'''







		from datasets import load_dataset

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

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




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			np.array([
		    -1.5_745, -1.7_713, -1.7_020, -1.6_069, -1.2_250, -1.1_105, -0.9_072, -0.8_241,
		    -1.2_310, -0.8_098, -0.3_320, -0.4_101, -0.7_985, -0.4_996, -0.8_213, -0.9_128,
		    -1.0_420, -1.1_286, -1.0_440, -0.7_999, -0.8_405, -1.2_275, -1.5_443, -1.4_625,
		]							)
		# fmt: on

		UpperCAmelCase_       				=			self._load_datasamples(1							)
		UpperCAmelCase_       				=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
		UpperCAmelCase_       				=			feature_extractor(lowerCAmelCase		,	return_tensors="pt"							).input_features
		self.assertEquals(input_features.shape		,	(1, 584, 24)							)
		self.assertTrue(np.allclose(input_features[0, 0, :30]		,	lowerCAmelCase		,	atol=1e-4							)							)


def       __lowerCAmelCase      (						A    ):
	if len(A    ) <= 1:
		return lst

	UpperCAmelCase_       				=			1

	while i < len(A    ):
		if lst[i - 1] <= lst[i]:
			i += 1
		else:
			UpperCAmelCase_				,   UpperCAmelCase_       				=			lst[i], lst[i - 1]
			i -= 1
			if i == 0:
				UpperCAmelCase_       				=			1

	return lst


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