





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

SCREAMING_SNAKE_CASE__							:      Tuple         =    random.Random()






def    _A	(   lowerCamelCase				,			lowerCamelCase=1.0				,			lowerCamelCase=None				,			lowerCamelCase=None ):
   if rng is None:
      a__						:    Any   		=	global_rng

   a__						:    Dict   		=	[]
   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 	__lowerCAmelCase      (       unittest.TestCase	):





   def __init__(			self       ,      snake_case       ,      snake_case=7       ,      snake_case=400       ,      snake_case=2_000       ,      snake_case=24       ,      snake_case=24       ,      snake_case=0.0       ,      snake_case=16_000       ,      snake_case=True       ,      snake_case=True       ,      )					->	Union[str, Any]:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	parent
      a__						:    Any   		=	batch_size
      a__						:    Optional[int]   		=	min_seq_length
      a__						:    Optional[Any]   		=	max_seq_length
      a__						:    Tuple   		=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      a__						:    Optional[int]   		=	feature_size
      a__						:    str   		=	num_mel_bins
      a__						:    Dict   		=	padding_value
      a__						:    Optional[int]   		=	sampling_rate
      a__						:    Optional[int]   		=	return_attention_mask
      a__						:    Any   		=	do_normalize





   def 						_snake_case			(			self   )					->	Tuple:
      """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 						_snake_case			(			self       ,      snake_case=False       ,      snake_case=False   )					->	Tuple:
      """simple docstring"""





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

      if equal_length:
         a__						:    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
         a__						:    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:
         a__						:    Union[str, Any]   		=	[np.asarray(snake_case   ) for x in speech_inputs]
      return speech_inputs







@require_torch
@require_torchaudio
class 	__lowerCAmelCase      (       _UpperCamelCase		,unittest.TestCase	):
   _UpperCamelCase			:    Any  		=					SpeechaTextFeatureExtractor if is_speech_available() else None





   def 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      a__						:    Any   		=	SpeechaTextFeatureExtractionTester(self   )





   def 						_snake_case			(			self       ,      snake_case   )					->	Optional[Any]:
      """simple docstring"""





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





   def 						_snake_case			(			self   )					->	Any:
      """simple docstring"""





      a__						:    int   		=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      # create three inputs of length 800, 1000, and 1200
      a__						:    Optional[int]   		=	[floats_list((1, x)   )[0] for x in range(800       ,      1_400       ,      200   )]
      a__						:    Union[str, Any]   		=	[np.asarray(snake_case   ) for speech_input in speech_inputs]

      # Test feature size
      a__						:    Optional[Any]   		=	feature_extractor(snake_case       ,      padding=snake_case       ,      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
      a__						:    Optional[Any]   		=	feature_extractor(speech_inputs[0]       ,      return_tensors="np"   ).input_features
      a__						:    List[Any]   		=	feature_extractor(np_speech_inputs[0]       ,      return_tensors="np"   ).input_features
      self.assertTrue(np.allclose(snake_case       ,      snake_case       ,      atol=1E-3   )   )

      # Test batched
      a__						:    int   		=	feature_extractor(snake_case       ,      return_tensors="np"   ).input_features
      a__						:    Tuple   		=	feature_extractor(snake_case       ,      return_tensors="np"   ).input_features
      for enc_seq_a, enc_seq_a in zip(snake_case       ,      snake_case   ):
         self.assertTrue(np.allclose(snake_case       ,      snake_case       ,      atol=1E-3   )   )

      # Test 2-D numpy arrays are batched.
      a__						:    List[Any]   		=	[floats_list((1, x)   )[0] for x in (800, 800, 800)]
      a__						:    List[str]   		=	np.asarray(snake_case   )
      a__						:    Dict   		=	feature_extractor(snake_case       ,      return_tensors="np"   ).input_features
      a__						:    Dict   		=	feature_extractor(snake_case       ,      return_tensors="np"   ).input_features
      for enc_seq_a, enc_seq_a in zip(snake_case       ,      snake_case   ):
         self.assertTrue(np.allclose(snake_case       ,      snake_case       ,      atol=1E-3   )   )





   def 						_snake_case			(			self   )					->	Optional[Any]:
      """simple docstring"""





      a__						:    str   		=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      a__						:    Optional[int]   		=	[floats_list((1, x)   )[0] for x in range(800       ,      1_400       ,      200   )]

      a__						:    Tuple   		=	["longest", "max_length", "do_not_pad"]
      a__						:    Union[str, Any]   		=	[None, 16, None]
      for max_length, padding in zip(snake_case       ,      snake_case   ):
         a__						:    Union[str, Any]   		=	feature_extractor(
             snake_case       ,      padding=snake_case       ,      max_length=snake_case       ,      return_attention_mask=snake_case   )
         a__						:    str   		=	inputs.input_features
         a__						:    Any   		=	inputs.attention_mask
         a__						:    Tuple   		=	[np.sum(snake_case   ) 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 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      a__						:    List[Any]   		=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      a__						:    Optional[Any]   		=	[floats_list((1, x)   )[0] for x in range(800       ,      1_400       ,      200   )]

      a__						:    Tuple   		=	["longest", "max_length", "do_not_pad"]
      a__						:    int   		=	[None, 16, None]
      for max_length, padding in zip(snake_case       ,      snake_case   ):
         a__						:    int   		=	feature_extractor(
             snake_case       ,      max_length=snake_case       ,      padding=snake_case       ,      return_tensors="np"       ,      return_attention_mask=snake_case   )
         a__						:    Dict   		=	inputs.input_features
         a__						:    List[Any]   		=	inputs.attention_mask
         a__						:    int   		=	[np.sum(snake_case   ) 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 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      a__						:    int   		=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      a__						:    Dict   		=	[floats_list((1, x)   )[0] for x in range(800       ,      1_400       ,      200   )]
      a__						:    Tuple   		=	feature_extractor(
          snake_case       ,      padding="max_length"       ,      max_length=4       ,      truncation=snake_case       ,      return_tensors="np"       ,      return_attention_mask=snake_case       ,      )
      a__						:    List[str]   		=	inputs.input_features
      a__						:    Any   		=	inputs.attention_mask
      a__						:    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]   )
      self._check_zero_mean_unit_variance(input_features[2]   )





   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      a__						:    List[Any]   		=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      a__						:    str   		=	[floats_list((1, x)   )[0] for x in range(800       ,      1_400       ,      200   )]
      a__						:    Optional[Any]   		=	feature_extractor(
          snake_case       ,      padding="longest"       ,      max_length=4       ,      truncation=snake_case       ,      return_tensors="np"       ,      return_attention_mask=snake_case       ,      )
      a__						:    int   		=	inputs.input_features
      a__						:    int   		=	inputs.attention_mask
      a__						:    List[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, 4, 24)   )

      a__						:    int   		=	[floats_list((1, x)   )[0] for x in range(800       ,      1_400       ,      200   )]
      a__						:    Tuple   		=	feature_extractor(
          snake_case       ,      padding="longest"       ,      max_length=16       ,      truncation=snake_case       ,      return_tensors="np"       ,      return_attention_mask=snake_case       ,      )
      a__						:    Optional[int]   		=	inputs.input_features
      a__						:    List[str]   		=	inputs.attention_mask
      a__						:    List[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 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      import torch

      a__						:    List[str]   		=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      a__						:    Optional[int]   		=	np.random.rand(100       ,      32   ).astype(np.floataa   )
      a__						:    Union[str, Any]   		=	np_speech_inputs.tolist()

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





   def 						_snake_case			(			self       ,      snake_case   )					->	str:
      """simple docstring"""





      from datasets import load_dataset

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

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





   def 						_snake_case			(			self   )					->	Tuple:
      """simple docstring"""





      a__						:    int   		=	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

      a__						:    List[Any]   		=	self._load_datasamples(1   )
      a__						:    Union[str, Any]   		=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      a__						:    Union[str, Any]   		=	feature_extractor(snake_case       ,      return_tensors="pt"   ).input_features
      self.assertEquals(input_features.shape       ,      (1, 584, 24)   )
      self.assertTrue(np.allclose(input_features[0, 0, :30]       ,      snake_case       ,      atol=1E-4   )   )








def    _A	(   lowerCamelCase ):
   a__						:    Tuple   		=	[]
   a__						:    str   		=	set({"(", "[", "{"} )
   a__						:    List[str]   		=	set({")", "]", "}"} )
   a__						:    int   		=	{"{": "}", "[": "]", "(": ")"}

   for i in range(len(lowerCamelCase ) ):
      if s[i] in open_brackets:
         stack.append(s[i] )

      elif s[i] in closed_brackets and (
          len(lowerCamelCase ) == 0 or (len(lowerCamelCase ) > 0 and open_to_closed[stack.pop()] != s[i])
      ):
         return False

   return len(lowerCamelCase ) == 0






def    _A	(   ):
   a__						:    Optional[int]   		=	input("Enter sequence of brackets: " )
   if is_balanced(lowerCamelCase ):
      print(lowerCamelCase				,			"is balanced" )
   else:
      print(lowerCamelCase				,			"is not balanced" )


if __name__ == "__main__":
  main()

