

"""simple docstring"""





import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


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

if is_torch_available():
							import torch

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=1.0						,     A_=None						,     A_=None ):







	if rng is None:
		lowerCAmelCase__	:						str  				=	global_rng

	lowerCAmelCase__	:						Optional[int]  				=	[]
	for batch_idx in range(shape[0] ):
		values.append([] )
		for _ in range(shape[1] ):
			values[-1].append(rng.random() * scale )

	return values







class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					def __init__(       self			:				Any   ,lowercase_			:				List[str]   ,lowercase_			:				Any=7   ,lowercase_			:				Optional[int]=4_0_0   ,lowercase_			:				Optional[int]=2_0_0_0   ,lowercase_			:				Dict=1   ,lowercase_			:				Optional[Any]=0.0   ,lowercase_			:				int=1_6_0_0_0   ,lowercase_			:				Optional[int]=True   ,lowercase_			:				Dict=True   ,):
						lowerCAmelCase__	:						Any  				=	parent
						lowerCAmelCase__	:						Union[str, Any]  				=	batch_size
						lowerCAmelCase__	:						List[Any]  				=	min_seq_length
						lowerCAmelCase__	:						List[Any]  				=	max_seq_length
						lowerCAmelCase__	:						Any  				=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						lowerCAmelCase__	:						List[Any]  				=	feature_size
						lowerCAmelCase__	:						Union[str, Any]  				=	padding_value
						lowerCAmelCase__	:						Dict  				=	sampling_rate
						lowerCAmelCase__	:						Tuple  				=	return_attention_mask
						lowerCAmelCase__	:						str  				=	do_normalize







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						return {
						    "feature_size": self.feature_size,
						    "padding_value": self.padding_value,
						    "sampling_rate": self.sampling_rate,
						    "return_attention_mask": self.return_attention_mask,
						    "do_normalize": self.do_normalize,
						}







					def        __lowerCAmelCase (       self			:				Any   ,lowercase_			:				Optional[Any]=False   ,lowercase_			:				int=False		):
						def _flatten(lowercase_			:				List[str]		):
							return list(itertools.chain(*_lowerCamelCase		)		)

						if equal_length:
							lowerCAmelCase__	:						Any  				=	floats_list((self.batch_size, self.max_seq_length)		)
						else:
							# make sure that inputs increase in size
							lowerCAmelCase__	:						str  				=	[
							    _flatten(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 				SCREAMING_SNAKE_CASE					(		_lowerCAmelCase    ,							unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       ASTFeatureExtractor







					def        __lowerCAmelCase (       self			:				Tuple		):
						lowerCAmelCase__	:						List[Any]  				=	ASTFeatureExtractionTester(self		)







					def        __lowerCAmelCase (       self			:				Dict		):
						lowerCAmelCase__	:						Union[str, Any]  				=	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_0_0   ,1_4_0_0   ,2_0_0		)]
						lowerCAmelCase__	:						Any  				=	[np.asarray(_lowerCamelCase		) for speech_input in speech_inputs]

						# Test not batched input
						lowerCAmelCase__	:						Optional[int]  				=	feat_extract(speech_inputs[0]   ,return_tensors='''np'''		).input_values
						lowerCAmelCase__	:						Optional[int]  				=	feat_extract(np_speech_inputs[0]   ,return_tensors='''np'''		).input_values
						self.assertTrue(np.allclose(_lowerCamelCase   ,_lowerCamelCase   ,atol=1E-3		)		)

						# Test batched
						lowerCAmelCase__	:						Union[str, Any]  				=	feat_extract(_lowerCamelCase   ,padding=_lowerCamelCase   ,return_tensors='''np'''		).input_values
						lowerCAmelCase__	:						str  				=	feat_extract(_lowerCamelCase   ,padding=_lowerCamelCase   ,return_tensors='''np'''		).input_values
						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__	:						Optional[int]  				=	[floats_list((1, x)		)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
						lowerCAmelCase__	:						Union[str, Any]  				=	np.asarray(_lowerCamelCase		)
						lowerCAmelCase__	:						Dict  				=	feat_extract(_lowerCamelCase   ,return_tensors='''np'''		).input_values
						lowerCAmelCase__	:						Optional[int]  				=	feat_extract(_lowerCamelCase   ,return_tensors='''np'''		).input_values
						for enc_seq_a, enc_seq_a in zip(_lowerCamelCase   ,_lowerCamelCase		):
							self.assertTrue(np.allclose(_lowerCamelCase   ,_lowerCamelCase   ,atol=1E-3		)		)







					@require_torch
					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						import torch

						lowerCAmelCase__	:						str  				=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
						lowerCAmelCase__	:						str  				=	np.random.rand(1_0_0		).astype(np.floataa		)
						lowerCAmelCase__	:						Tuple  				=	np_speech_inputs.tolist()

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







					def        __lowerCAmelCase (       self			:				Optional[int]   ,lowercase_			:				Union[str, Any]		):
						from datasets import load_dataset

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

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







					@require_torch
					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						lowerCAmelCase__	:						List[str]  				=	torch.tensor(
						    [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
						     -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
						     -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
						     -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]		)
						# fmt: on

						lowerCAmelCase__	:						Tuple  				=	self._load_datasamples(1		)
						lowerCAmelCase__	:						Any  				=	ASTFeatureExtractor()
						lowerCAmelCase__	:						str  				=	feature_extractor(_lowerCamelCase   ,return_tensors='''pt'''		).input_values
						self.assertEquals(input_values.shape   ,(1, 1_0_2_4, 1_2_8)		)
						self.assertTrue(torch.allclose(input_values[0, 0, :3_0]   ,_lowerCamelCase   ,atol=1E-4		)		)












"""simple docstring"""


from __future__ import annotations

from typing import Any



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if not postfix_notation:
    return 0

  __A									= {'''+''', '''-''', '''*''', '''/'''}
  __A									= []

  for token in postfix_notation:
    if token in operations:
      __A			,    __A									= stack.pop(), stack.pop()
      if token == "+":
        stack.append(a + b )
      elif token == "-":
        stack.append(a - b )
      elif token == "*":
        stack.append(a * b )
      else:
        if a * b < 0 and a % b != 0:
          stack.append(a // b + 1 )
        else:
          stack.append(a // b )
    else:
      stack.append(int(__UpperCamelCase ) )

  return stack.pop()


if __name__ == "__main__":
       import doctest

       doctest.testmod()




