





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

_lowercase		:  List[Any]			       =random.Random()


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__=1.0							,lowerCAmelCase__=None							,lowerCAmelCase__=None						):
		if rng is None:
				lowerCamelCase_	:     Tuple 						=      global_rng

		lowerCamelCase_	:     List[Any] 						=      []
		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     :     str       ,      lowerCamelCase     :     Any       ,      lowerCamelCase     :     int=7       ,      lowerCamelCase     :     Union[str, Any]=4_00       ,      lowerCamelCase     :     str=20_00       ,      lowerCamelCase     :     Any=24       ,      lowerCamelCase     :     int=24       ,      lowerCamelCase     :     Tuple=0.0       ,      lowerCamelCase     :     List[Any]=1_60_00       ,      lowerCamelCase     :     int=True       ,      lowerCamelCase     :     Any=True       ,      ):
							lowerCamelCase_	:     str 						=      parent
							lowerCamelCase_	:     List[Any] 						=      batch_size
							lowerCamelCase_	:     List[str] 						=      min_seq_length
							lowerCamelCase_	:     str 						=      max_seq_length
							lowerCamelCase_	:     Union[str, Any] 						=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							lowerCamelCase_	:     Optional[Any] 						=      feature_size
							lowerCamelCase_	:     Optional[int] 						=      num_mel_bins
							lowerCamelCase_	:     Dict 						=      padding_value
							lowerCamelCase_	:     List[str] 						=      sampling_rate
							lowerCamelCase_	:     Any 						=      return_attention_mask
							lowerCamelCase_	:     Optional[int] 						=      do_normalize



					def   __a     (		self     :     int						):
							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     :     Any       ,      lowerCamelCase     :     str=False       ,      lowerCamelCase     :     Dict=False						):
							def _flatten(lowerCamelCase     :     Any						):
									return list(itertools.chain(*lowerCamelCase						)						)

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






@require_torch
@require_torchaudio
class 						UpperCamelCase_  (  snake_case__  ,     unittest.TestCase							):
					_a      :						Dict						    =					SpeechaTextFeatureExtractor if is_speech_available() else None



					def   __a     (		self     :     Any						):
							lowerCamelCase_	:     Tuple 						=      SpeechaTextFeatureExtractionTester(self						)



					def   __a     (		self     :     Any       ,      lowerCamelCase     :     List[str]						):
							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     :     str						):
							# Tests that all call wrap to encode_plus and batch_encode_plus
							lowerCamelCase_	:     Dict 						=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
							# create three inputs of length 800, 1000, and 1200
							lowerCamelCase_	:     List[Any] 						=      [floats_list((1, x)						)[0] for x in range(8_00       ,      14_00       ,      2_00						)]
							lowerCamelCase_	:     str 						=      [np.asarray(lowerCamelCase						) for speech_input in speech_inputs]

							# Test feature size
							lowerCamelCase_	:     Union[str, Any] 						=      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
							lowerCamelCase_	:     Optional[Any] 						=      feature_extractor(speech_inputs[0]       ,      return_tensors='np'						).input_features
							lowerCamelCase_	:     Optional[Any] 						=      feature_extractor(np_speech_inputs[0]       ,      return_tensors='np'						).input_features
							self.assertTrue(np.allclose(lowerCamelCase       ,      lowerCamelCase       ,      atol=1E-3						)						)

							# Test batched
							lowerCamelCase_	:     Tuple 						=      feature_extractor(lowerCamelCase       ,      return_tensors='np'						).input_features
							lowerCamelCase_	:     str 						=      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.
							lowerCamelCase_	:     Union[str, Any] 						=      [floats_list((1, x)						)[0] for x in (8_00, 8_00, 8_00)]
							lowerCamelCase_	:     int 						=      np.asarray(lowerCamelCase						)
							lowerCamelCase_	:     Tuple 						=      feature_extractor(lowerCamelCase       ,      return_tensors='np'						).input_features
							lowerCamelCase_	:     Any 						=      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     :     Any						):
							lowerCamelCase_	:     List[Any] 						=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
							lowerCamelCase_	:     str 						=      [floats_list((1, x)						)[0] for x in range(8_00       ,      14_00       ,      2_00						)]

							lowerCamelCase_	:     List[Any] 						=      ['longest', 'max_length', 'do_not_pad']
							lowerCamelCase_	:     Optional[Any] 						=      [None, 16, None]
							for max_length, padding in zip(lowerCamelCase       ,      lowerCamelCase						):
									lowerCamelCase_	:     str 						=      feature_extractor(
									    lowerCamelCase       ,      padding=lowerCamelCase       ,      max_length=lowerCamelCase       ,      return_attention_mask=lowerCamelCase						)
									lowerCamelCase_	:     Tuple 						=      inputs.input_features
									lowerCamelCase_	:     Tuple 						=      inputs.attention_mask
									lowerCamelCase_	:     Optional[Any] 						=      [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     :     int						):
							lowerCamelCase_	:     Optional[int] 						=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
							lowerCamelCase_	:     str 						=      [floats_list((1, x)						)[0] for x in range(8_00       ,      14_00       ,      2_00						)]

							lowerCamelCase_	:     Optional[int] 						=      ['longest', 'max_length', 'do_not_pad']
							lowerCamelCase_	:     List[Any] 						=      [None, 16, None]
							for max_length, padding in zip(lowerCamelCase       ,      lowerCamelCase						):
									lowerCamelCase_	:     List[str] 						=      feature_extractor(
									    lowerCamelCase       ,      max_length=lowerCamelCase       ,      padding=lowerCamelCase       ,      return_tensors='np'       ,      return_attention_mask=lowerCamelCase						)
									lowerCamelCase_	:     List[str] 						=      inputs.input_features
									lowerCamelCase_	:     int 						=      inputs.attention_mask
									lowerCamelCase_	:     Optional[Any] 						=      [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     :     Tuple						):
							lowerCamelCase_	:     List[str] 						=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
							lowerCamelCase_	:     Tuple 						=      [floats_list((1, x)						)[0] for x in range(8_00       ,      14_00       ,      2_00						)]
							lowerCamelCase_	:     List[str] 						=      feature_extractor(
							    lowerCamelCase       ,      padding='max_length'       ,      max_length=4       ,      truncation=lowerCamelCase       ,      return_tensors='np'       ,      return_attention_mask=lowerCamelCase       ,      )
							lowerCamelCase_	:     str 						=      inputs.input_features
							lowerCamelCase_	:     List[str] 						=      inputs.attention_mask
							lowerCamelCase_	:     Union[str, Any] 						=      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     :     Any						):
							lowerCamelCase_	:     Union[str, Any] 						=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
							lowerCamelCase_	:     Union[str, Any] 						=      [floats_list((1, x)						)[0] for x in range(8_00       ,      14_00       ,      2_00						)]
							lowerCamelCase_	:     Optional[int] 						=      feature_extractor(
							    lowerCamelCase       ,      padding='longest'       ,      max_length=4       ,      truncation=lowerCamelCase       ,      return_tensors='np'       ,      return_attention_mask=lowerCamelCase       ,      )
							lowerCamelCase_	:     int 						=      inputs.input_features
							lowerCamelCase_	:     Optional[Any] 						=      inputs.attention_mask
							lowerCamelCase_	:     Optional[int] 						=      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)						)

							lowerCamelCase_	:     Optional[int] 						=      [floats_list((1, x)						)[0] for x in range(8_00       ,      14_00       ,      2_00						)]
							lowerCamelCase_	:     List[Any] 						=      feature_extractor(
							    lowerCamelCase       ,      padding='longest'       ,      max_length=16       ,      truncation=lowerCamelCase       ,      return_tensors='np'       ,      return_attention_mask=lowerCamelCase       ,      )
							lowerCamelCase_	:     Dict 						=      inputs.input_features
							lowerCamelCase_	:     Any 						=      inputs.attention_mask
							lowerCamelCase_	:     Any 						=      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     :     str						):
							import torch

							lowerCamelCase_	:     Optional[int] 						=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
							lowerCamelCase_	:     Union[str, Any] 						=      np.random.rand(1_00       ,      32						).astype(np.floataa						)
							lowerCamelCase_	:     Optional[Any] 						=      np_speech_inputs.tolist()

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



					def   __a     (		self     :     Tuple       ,      lowerCamelCase     :     str						):
							from datasets import load_dataset

							lowerCamelCase_	:     List[str] 						=      load_dataset('hf-internal-testing/librispeech_asr_dummy'       ,      'clean'       ,      split='validation'						)
							# automatic decoding with librispeech
							lowerCamelCase_	:     List[str] 						=      ds.sort('id'						).select(range(lowerCamelCase						)						)[:num_samples]['audio']

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





					def   __a     (		self     :     Optional[Any]						):
							# fmt: off
							lowerCamelCase_	:     Tuple 						=      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

							lowerCamelCase_	:     Optional[Any] 						=      self._load_datasamples(1						)
							lowerCamelCase_	:     List[str] 						=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
							lowerCamelCase_	:     str 						=      feature_extractor(lowerCamelCase       ,      return_tensors='pt'						).input_features
							self.assertEquals(input_features.shape       ,      (1, 5_84, 24)						)
							self.assertTrue(np.allclose(input_features[0, 0, :30]       ,      lowerCamelCase       ,      atol=1E-4						)						)







def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     Union[str, Any] 						=      []
		lowerCamelCase_	:     Tuple 						=      []
		lowerCamelCase_	:     Dict 						=      {
		    '^': 3,
		    '*': 2,
		    '/': 2,
		    '%': 2,
		    '+': 1,
		    '-': 1,
		}  # Priority of each operator
		lowerCamelCase_	:     Optional[int] 						=      len(lowerCAmelCase__						) if (len(lowerCAmelCase__						) > 7) else 7

		# Print table header for output
		print(
		    'Symbol'.center(8						)							,'Stack'.center(lowerCAmelCase__						)							,'Postfix'.center(lowerCAmelCase__						)							,sep=' | '							,)
		print('-' * (print_width * 3 + 7)						)

		for x in infix:
				if x.isalpha() or x.isdigit():
						post_fix.append(lowerCAmelCase__						)  # if x is Alphabet / Digit, add it to Postfix
				elif x == "(":
						stack.append(lowerCAmelCase__						)  # if x is "(" push to Stack
				elif x == ")":  # if x is ")" pop stack until "(" is encountered
						while stack[-1] != "(":
								post_fix.append(stack.pop()						)  # Pop stack & add the content to Postfix
						stack.pop()
				else:
						if len(lowerCAmelCase__						) == 0:
								stack.append(lowerCAmelCase__						)  # If stack is empty, push x to stack
						else:  # while priority of x is not > priority of element in the stack
								while len(lowerCAmelCase__						) > 0 and priority[x] <= priority[stack[-1]]:
										post_fix.append(stack.pop()						)  # pop stack & add to Postfix
								stack.append(lowerCAmelCase__						)  # push x to stack

				print(
				    x.center(8						)							,(''.join(lowerCAmelCase__						)).ljust(lowerCAmelCase__						)							,(''.join(lowerCAmelCase__						)).ljust(lowerCAmelCase__						)							,sep=' | '							,)  # Output in tabular format

		while len(lowerCAmelCase__						) > 0:  # while stack is not empty
				post_fix.append(stack.pop()						)  # pop stack & add to Postfix
				print(
				    ' '.center(8						)							,(''.join(lowerCAmelCase__						)).ljust(lowerCAmelCase__						)							,(''.join(lowerCAmelCase__						)).ljust(lowerCAmelCase__						)							,sep=' | '							,)  # Output in tabular format

		return "".join(lowerCAmelCase__						)  # return Postfix as str


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     Dict 						=      list(infix[::-1]						)  # reverse the infix equation

		for i in range(len(lowerCAmelCase__						)						):
				if infix[i] == "(":
						lowerCamelCase_	:     str 						=      ')'  # change "(" to ")"
				elif infix[i] == ")":
						lowerCamelCase_	:     Optional[Any] 						=      '('  # change ")" to "("

		return (infix_2_postfix(''.join(lowerCAmelCase__						)						))[
		    ::-1
		]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
		_lowercase		:  int			       =input("""\nEnter an Infix Equation = """)  # Input an Infix equation
		_lowercase		:  Optional[Any]			       ="""""".join(Infix.split())  # Remove spaces from the input
		print("""\n\t""", Infix, """(Infix) -> """, infix_2_prefix(Infix), """(Prefix)""")
