





import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_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_speech_available():
						from transformers import WhisperFeatureExtractor

if is_torch_available():
						import torch

lowerCAmelCase_						   =    random.Random()


def     lowerCamelCase_	(      lowerCAmelCase:	Any    ,							lowerCAmelCase:	str=1.0    ,							lowerCAmelCase:	int=None    ,							lowerCAmelCase:	Union[str, Any]=None				)->		Optional[int]:
		if rng is None:
				_snake_case   :				List[str]														=	global_rng

		_snake_case   :				List[str]														=	[]
		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							):


		'''simple docstring'''







		def __init__(      self      :					str      ,				UpperCamelCase      :					Optional[Any]      ,				UpperCamelCase      :					Optional[Any]=7      ,				UpperCamelCase      :					Tuple=4_00      ,				UpperCamelCase      :					int=20_00      ,				UpperCamelCase      :					List[str]=10      ,				UpperCamelCase      :					int=1_60      ,				UpperCamelCase      :					Optional[Any]=8      ,				UpperCamelCase      :					Union[str, Any]=0.0      ,				UpperCamelCase      :					List[Any]=40_00      ,				UpperCamelCase      :					Any=False      ,				UpperCamelCase      :					Dict=True      ,				):


				'''simple docstring'''





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







		def UpperCamelCase_ (      self      :					Tuple							):


				'''simple docstring'''





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







		def UpperCamelCase_ (      self      :					Tuple      ,				UpperCamelCase      :					Optional[Any]=False      ,				UpperCamelCase      :					List[str]=False							):


				'''simple docstring'''





				def _flatten(UpperCamelCase      :					Tuple							):
						return list(itertools.chain(*UpperCamelCase							)							)

				if equal_length:
						_snake_case   :				Any														=	[floats_list((self.max_seq_length, self.feature_size)							) for _ in range(self.batch_size							)]
				else:
						# make sure that inputs increase in size
						_snake_case   :				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:
						_snake_case   :				List[str]														=	[np.asarray(UpperCamelCase							) for x in speech_inputs]
				return speech_inputs




@require_torch
@require_torchaudio
class        _lowerCAmelCase    (			UpperCAmelCase_							,    unittest.TestCase							):


		'''simple docstring'''
		a_							:       List[Any]      					=WhisperFeatureExtractor if is_speech_available() else None







		def UpperCamelCase_ (      self      :					Optional[Any]							):


				'''simple docstring'''





				_snake_case   :				int														=	WhisperFeatureExtractionTester(self							)







		def UpperCamelCase_ (      self      :					str							):


				'''simple docstring'''





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

				with tempfile.TemporaryDirectory() as tmpdirname:
						_snake_case   :				Optional[int]														=	feat_extract_first.save_pretrained(UpperCamelCase							)[0]
						check_json_file_has_correct_format(UpperCamelCase							)
						_snake_case   :				Optional[Any]														=	self.feature_extraction_class.from_pretrained(UpperCamelCase							)

				_snake_case   :				Dict														=	feat_extract_first.to_dict()
				_snake_case   :				Union[str, Any]														=	feat_extract_second.to_dict()
				_snake_case   :				Optional[Any]														=	feat_extract_first.mel_filters
				_snake_case   :				Union[str, Any]														=	feat_extract_second.mel_filters
				self.assertTrue(np.allclose(UpperCamelCase      ,				UpperCamelCase							)							)
				self.assertEqual(UpperCamelCase      ,				UpperCamelCase							)







		def UpperCamelCase_ (      self      :					Tuple							):


				'''simple docstring'''





				_snake_case   :				List[Any]														=	self.feature_extraction_class(**self.feat_extract_dict							)

				with tempfile.TemporaryDirectory() as tmpdirname:
						_snake_case   :				List[Any]														=	os.path.join(UpperCamelCase      ,				'feat_extract.json'							)
						feat_extract_first.to_json_file(UpperCamelCase							)
						_snake_case   :				str														=	self.feature_extraction_class.from_json_file(UpperCamelCase							)

				_snake_case   :				int														=	feat_extract_first.to_dict()
				_snake_case   :				Union[str, Any]														=	feat_extract_second.to_dict()
				_snake_case   :				Optional[int]														=	feat_extract_first.mel_filters
				_snake_case   :				Any														=	feat_extract_second.mel_filters
				self.assertTrue(np.allclose(UpperCamelCase      ,				UpperCamelCase							)							)
				self.assertEqual(UpperCamelCase      ,				UpperCamelCase							)







		def UpperCamelCase_ (      self      :					Any							):


				'''simple docstring'''





				_snake_case   :				Any														=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
				# create three inputs of length 800, 1000, and 1200
				_snake_case   :				Optional[Any]														=	[floats_list((1, x)							)[0] for x in range(8_00      ,				14_00      ,				2_00							)]
				_snake_case   :				Dict														=	[np.asarray(UpperCamelCase							) for speech_input in speech_inputs]

				# Test feature size
				_snake_case   :				Optional[Any]														=	feature_extractor(UpperCamelCase      ,				padding='max_length'      ,				return_tensors='np'							).input_features
				self.assertTrue(input_features.ndim == 3							)
				self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames							)
				self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size							)

				# Test not batched input
				_snake_case   :				Dict														=	feature_extractor(speech_inputs[0]      ,				return_tensors='np'							).input_features
				_snake_case   :				Optional[int]														=	feature_extractor(np_speech_inputs[0]      ,				return_tensors='np'							).input_features
				self.assertTrue(np.allclose(UpperCamelCase      ,				UpperCamelCase      ,				atol=1e-3							)							)

				# Test batched
				_snake_case   :				Tuple														=	feature_extractor(UpperCamelCase      ,				return_tensors='np'							).input_features
				_snake_case   :				List[str]														=	feature_extractor(UpperCamelCase      ,				return_tensors='np'							).input_features
				for enc_seq_a, enc_seq_a in zip(UpperCamelCase      ,				UpperCamelCase							):
						self.assertTrue(np.allclose(UpperCamelCase      ,				UpperCamelCase      ,				atol=1e-3							)							)

				# Test 2-D numpy arrays are batched.
				_snake_case   :				Optional[Any]														=	[floats_list((1, x)							)[0] for x in (8_00, 8_00, 8_00)]
				_snake_case   :				Tuple														=	np.asarray(UpperCamelCase							)
				_snake_case   :				Tuple														=	feature_extractor(UpperCamelCase      ,				return_tensors='np'							).input_features
				_snake_case   :				Optional[int]														=	feature_extractor(UpperCamelCase      ,				return_tensors='np'							).input_features
				for enc_seq_a, enc_seq_a in zip(UpperCamelCase      ,				UpperCamelCase							):
						self.assertTrue(np.allclose(UpperCamelCase      ,				UpperCamelCase      ,				atol=1e-3							)							)

				# Test truncation required
				_snake_case   :				Optional[int]														=	[floats_list((1, x)							)[0] for x in range(2_00      ,				(feature_extractor.n_samples + 5_00)      ,				2_00							)]
				_snake_case   :				List[Any]														=	[np.asarray(UpperCamelCase							) for speech_input in speech_inputs]

				_snake_case   :				Tuple														=	[x[: feature_extractor.n_samples] for x in speech_inputs]
				_snake_case   :				Optional[Any]														=	[np.asarray(UpperCamelCase							) for speech_input in speech_inputs_truncated]

				_snake_case   :				Tuple														=	feature_extractor(UpperCamelCase      ,				return_tensors='np'							).input_features
				_snake_case   :				Any														=	feature_extractor(UpperCamelCase      ,				return_tensors='np'							).input_features
				for enc_seq_a, enc_seq_a in zip(UpperCamelCase      ,				UpperCamelCase							):
						self.assertTrue(np.allclose(UpperCamelCase      ,				UpperCamelCase      ,				atol=1e-3							)							)







		def UpperCamelCase_ (      self      :					Tuple							):


				'''simple docstring'''





				import torch

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

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







		def UpperCamelCase_ (      self      :					Dict      ,				UpperCamelCase      :					Union[str, Any]							):


				'''simple docstring'''





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

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







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


				'''simple docstring'''





				_snake_case   :				Optional[Any]														=	torch.tensor(
				    [
				        0.11_93, -0.09_46, -0.10_98, -0.01_96, 0.02_25, -0.06_90, -0.17_36, 0.09_51,
				        0.09_71, -0.08_17, -0.07_02, 0.01_62, 0.02_60, 0.00_17, -0.01_92, -0.16_78,
				        0.07_09, -0.18_67, -0.06_55, -0.02_74, -0.02_34, -0.18_84, -0.05_16, -0.05_54,
				        -0.02_74, -0.14_25, -0.14_23, 0.08_37, 0.03_77, -0.08_54
				    ]							)
				# fmt: on

				_snake_case   :				int														=	self._load_datasamples(1							)
				_snake_case   :				int														=	WhisperFeatureExtractor()
				_snake_case   :				Tuple														=	feature_extractor(UpperCamelCase      ,				return_tensors='pt'							).input_features
				self.assertEqual(input_features.shape      ,				(1, 80, 30_00)							)
				self.assertTrue(torch.allclose(input_features[0, 0, :30]      ,				UpperCamelCase      ,				atol=1e-4							)							)







		def UpperCamelCase_ (      self      :					List[Any]							):


				'''simple docstring'''





				_snake_case   :				Tuple														=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
				_snake_case   :				str														=	self._load_datasamples(1							)[0]
				_snake_case   :				Optional[int]														=	((audio - audio.min()) / (audio.max() - audio.min())) * 6_55_35  # Rescale to [0, 65535] to show issue
				_snake_case   :				Tuple														=	feat_extract.zero_mean_unit_var_norm([audio]      ,				attention_mask=UpperCamelCase							)[0]

				self.assertTrue(np.all(np.mean(UpperCamelCase							) < 1e-3							)							)
				self.assertTrue(np.all(np.abs(np.var(UpperCamelCase							) - 1							) < 1e-3							)							)











from __future__ import annotations

from random import random



class        _lowerCAmelCase    :


  '''simple docstring'''







  def __init__(      self      :					Dict      ,				UpperCamelCase      :					int | None = None							):


    '''simple docstring'''





    _snake_case   :				str														=	value
    _snake_case   :				List[Any]														=	random()
    _snake_case   :				Node | None														=	None
    _snake_case   :				Node | None														=	None







  def __repr__(      self      :					Optional[Any]							):


    '''simple docstring'''





    from pprint import pformat

    if self.left is None and self.right is None:
      return f"""'{self.value}: {self.prior:.5}'"""
    else:
      return pformat(
          {f"""{self.value}: {self.prior:.5}""": (self.left, self.right)}      ,				indent=1							)







  def __str__(      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	str(self.value							) + ' '
    _snake_case   :				List[Any]														=	str(self.left or ''							)
    _snake_case   :				int														=	str(self.right or ''							)
    return value + left + right




def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	int				)->		tuple[Node | None, Node | None]:
  if root is None:  # None tree is split into 2 Nones
    return None, None
  elif root.value is None:
    return None, None
  else:
    if value < root.value:
      _snake_case							,					_snake_case   :				Optional[Any]														=	split(root.left    ,							lowerCAmelCase				)
      return left, root
    else:
      _snake_case							,					_snake_case   :				List[str]														=	split(root.right    ,							lowerCAmelCase				)
      return root, right


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	Node | None				)->		Node | None:
  if (not left) or (not right):  # If one node is None, return the other
    return left or right
  elif left.prior < right.prior:
    _snake_case   :				str														=	merge(left.right    ,							lowerCAmelCase				)
    return left
  else:
    _snake_case   :				Union[str, Any]														=	merge(lowerCAmelCase    ,							right.left				)
    return right


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	int				)->		Node | None:
  _snake_case   :				Tuple														=	Node(lowerCAmelCase				)
  _snake_case							,					_snake_case   :				Optional[int]														=	split(lowerCAmelCase    ,							lowerCAmelCase				)
  return merge(merge(lowerCAmelCase    ,							lowerCAmelCase				)    ,							lowerCAmelCase				)


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	int				)->		Node | None:
  _snake_case							,					_snake_case   :				Optional[int]														=	split(lowerCAmelCase    ,							value - 1				)
  _snake_case							,					_snake_case   :				List[str]														=	split(lowerCAmelCase    ,							lowerCAmelCase				)
  return merge(lowerCAmelCase    ,							lowerCAmelCase				)


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None				)->		None:
  if not root:  # None
    return
  else:
    inorder(root.left				)
    print(root.value    ,							end=','				)
    inorder(root.right				)


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	str				)->		Node | None:
  for arg in args.split():
    if arg[0] == "+":
      _snake_case   :				List[str]														=	insert(lowerCAmelCase    ,							int(arg[1:]				)				)

    elif arg[0] == "-":
      _snake_case   :				Any														=	erase(lowerCAmelCase    ,							int(arg[1:]				)				)

    else:
      print('Unknown command'				)

  return root


def     lowerCamelCase_	(      )->		None:
  _snake_case   :				Tuple														=	None
  print(
      'enter numbers to create a tree, + value to add value into treap, '
      '- value to erase all nodes with value. \'q\' to quit. '				)

  _snake_case   :				List[Any]														=	input()
  while args != "q":
    _snake_case   :				int														=	interact_treap(lowerCAmelCase    ,							lowerCAmelCase				)
    print(lowerCAmelCase				)
    _snake_case   :				Tuple														=	input()

  print('good by!'				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()




