
"""simple docstring"""
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

lowercase_       :						Union[str, Any]     					=	random.Random()





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[Any],						lowerCamelCase__							:							Optional[Any]=1.0,						lowerCamelCase__							:							List[str]=None,						lowerCamelCase__							:							List[Any]=None				)  ->    Dict:
  if rng is None:
    _SCREAMING_SNAKE_CASE   :				Any							    = global_rng

  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = []
  for batch_idx in range(shape[0]				):
    values.append([]				)
    for _ in range(shape[1]				):
      values[-1].append(rng.random() * scale				)

  return values
class 			UpperCamelCase      (							unittest.TestCase  ):
     def __init__(     self  ,     snake_case__  ,     snake_case__=7  ,     snake_case__=400  ,     snake_case__=2000  ,     snake_case__=2048  ,     snake_case__=128  ,     snake_case__=1  ,     snake_case__=512  ,     snake_case__=30  ,     snake_case__=44100  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = parent
       _SCREAMING_SNAKE_CASE   :				str							    = batch_size
       _SCREAMING_SNAKE_CASE   :				Dict							    = min_seq_length
       _SCREAMING_SNAKE_CASE   :				Any							    = max_seq_length
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = spectrogram_length
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = feature_size
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = num_audio_channels
       _SCREAMING_SNAKE_CASE   :				str							    = hop_length
       _SCREAMING_SNAKE_CASE   :				Tuple							    = chunk_length
       _SCREAMING_SNAKE_CASE   :				int							    = sampling_rate
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """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 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__=False  ,     snake_case__=False				):




       """simple docstring"""


       def _flatten(snake_case__				):
         return list(itertools.chain(*snake_case__				)				)

       if equal_length:
         _SCREAMING_SNAKE_CASE   :				Dict							    = [floats_list((self.max_seq_length, self.feature_size)				) for _ in range(self.batch_size				)]
       else:
         # make sure that inputs increase in size
         _SCREAMING_SNAKE_CASE   :				str							    = [
             floats_list((x, self.feature_size)				)
             for x in range(self.min_seq_length  ,     self.max_seq_length  ,     self.seq_length_diff				)
         ]
       if numpify:
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = [np.asarray(snake_case__				) for x in speech_inputs]
       return speech_inputs
@require_torch
@require_torchaudio
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE      ,       unittest.TestCase  ):
     A__      =			TvltFeatureExtractor
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = TvltFeatureExtractionTester(self				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.feature_extraction_class(**self.feat_extract_dict				)
       self.assertTrue(hasattr(snake_case__  ,     "spectrogram_length"				)				)
       self.assertTrue(hasattr(snake_case__  ,     "feature_size"				)				)
       self.assertTrue(hasattr(snake_case__  ,     "num_audio_channels"				)				)
       self.assertTrue(hasattr(snake_case__  ,     "hop_length"				)				)
       self.assertTrue(hasattr(snake_case__  ,     "chunk_length"				)				)
       self.assertTrue(hasattr(snake_case__  ,     "sampling_rate"				)				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.feature_extraction_class(**self.feat_extract_dict				)

       with tempfile.TemporaryDirectory() as tmpdirname:
         _SCREAMING_SNAKE_CASE   :				Dict							    = feat_extract_first.save_pretrained(snake_case__				)[0]
         check_json_file_has_correct_format(snake_case__				)
         _SCREAMING_SNAKE_CASE   :				Tuple							    = self.feature_extraction_class.from_pretrained(snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Dict							    = feat_extract_first.to_dict()
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = feat_extract_second.to_dict()
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = dict_first.pop("mel_filters"				)
       _SCREAMING_SNAKE_CASE   :				int							    = dict_second.pop("mel_filters"				)
       self.assertTrue(np.allclose(snake_case__  ,     snake_case__				)				)
       self.assertEqual(snake_case__  ,     snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.feature_extraction_class(**self.feat_extract_dict				)

       with tempfile.TemporaryDirectory() as tmpdirname:
         _SCREAMING_SNAKE_CASE   :				str							    = os.path.join(snake_case__  ,     "feat_extract.json"				)
         feat_extract_first.to_json_file(snake_case__				)
         _SCREAMING_SNAKE_CASE   :				List[str]							    = self.feature_extraction_class.from_json_file(snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = feat_extract_first.to_dict()
       _SCREAMING_SNAKE_CASE   :				Dict							    = feat_extract_second.to_dict()
       _SCREAMING_SNAKE_CASE   :				int							    = dict_first.pop("mel_filters"				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = dict_second.pop("mel_filters"				)
       self.assertTrue(np.allclose(snake_case__  ,     snake_case__				)				)
       self.assertEqual(snake_case__  ,     snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.feature_extraction_class(**self.feat_extract_dict				)

       # create three inputs of length 800, 1000, and 1200
       _SCREAMING_SNAKE_CASE   :				Dict							    = [floats_list((1, x)				)[0] for x in range(800  ,     1400  ,     200				)]
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = [np.asarray(snake_case__				) for speech_input in speech_inputs]

       # Test not batched input
       _SCREAMING_SNAKE_CASE   :				int							    = feature_extractor(np_speech_inputs[0]  ,     return_tensors="np"  ,     sampling_rate=44100				).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
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = feature_extractor(snake_case__  ,     return_tensors="np"  ,     sampling_rate=44100				).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
       _SCREAMING_SNAKE_CASE   :				Any							    = feature_extractor(
           snake_case__  ,     return_tensors="np"  ,     sampling_rate=44100  ,     mask_audio=snake_case__				).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.
       _SCREAMING_SNAKE_CASE   :				Dict							    = [floats_list((1, x)				)[0] for x in (800, 800, 800)]
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = np.asarray(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = feature_extractor(snake_case__  ,     return_tensors="np"  ,     sampling_rate=44100				).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 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


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

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




     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self._load_datasamples(1				)
       _SCREAMING_SNAKE_CASE   :				int							    = TvltFeatureExtractor()
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = feature_extractor(snake_case__  ,     return_tensors="pt"				).audio_values

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

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]				)
       self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]  ,     snake_case__  ,     atol=1E-4				)				)





"""simple docstring"""
import operator as op





def    _lowerCAmelCase    (     lowerCamelCase__							:							Tuple				)  ->    List[str]:
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = []
  _SCREAMING_SNAKE_CASE   :				str							    = lambda lowerCamelCase__,						lowerCamelCase__				: int(x / y				)  # noqa: E731 integer division operation
  _SCREAMING_SNAKE_CASE   :				Any							    = {
      "^": op.pow,
      "*": op.mul,
      "/": div,
      "+": op.add,
      "-": op.sub,
  }  # operators & their respective operation

  # print table header
  print("Symbol".center(8				),						"Action".center(1_2				),						"Stack",						sep=" | "				)
  print("-" * (3_0 + len(lowerCamelCase__				))				)

  for x in post_fix:
    if x.isdigit():  # if x in digit
      stack.append(lowerCamelCase__				)  # append x to stack
      # output in tabular format
      print(x.rjust(8				),						("push(" + x + ")").ljust(1_2				),						",".join(lowerCamelCase__				),						sep=" | "				)
    else:
      _SCREAMING_SNAKE_CASE   :				Dict							    = stack.pop()  # pop stack
      # output in tabular format
      print("".rjust(8				),						("pop(" + b + ")").ljust(1_2				),						",".join(lowerCamelCase__				),						sep=" | "				)

      _SCREAMING_SNAKE_CASE   :				Any							    = stack.pop()  # pop stack
      # output in tabular format
      print("".rjust(8				),						("pop(" + a + ")").ljust(1_2				),						",".join(lowerCamelCase__				),						sep=" | "				)

      stack.append(
          str(opr[x](int(lowerCamelCase__				),						int(lowerCamelCase__				)				)				)				)  # evaluate the 2 values popped from stack & push result to stack
      # output in tabular format
      print(
          x.rjust(8				),						("push(" + a + x + b + ")").ljust(1_2				),						",".join(lowerCamelCase__				),						sep=" | ",						)

  return int(stack[0]				)


if __name__ == "__main__":
      lowercase_       :						int     					=	input('''\n\nEnter a Postfix Equation (space separated) = ''').split(''' ''')
      print('''\n\tResult = ''', solve(Postfix))



