

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

snake_case_        =			random.Random()

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[Any]     ,					SCREAMING_SNAKE_CASE_		:   str=1.0     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]=None     ,					SCREAMING_SNAKE_CASE_		:   Tuple=None      ):



					'''simple docstring'''





					if rng is None:
										lowercase__   :       Dict				=	global_rng

					lowercase__   :       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


@require_torch
@require_torchaudio
class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):





				def __init__(				self						,       a						,       a=7						,       a=400						,       a=2000						,       a=24						,       a=24						,       a=0.0						,       a=1_6000						,       a=True						,       a=True						,       ):
									lowercase__   :       List[Any]				=	parent
									lowercase__   :       int				=	batch_size
									lowercase__   :       Optional[Any]				=	min_seq_length
									lowercase__   :       Tuple				=	max_seq_length
									lowercase__   :       Dict				=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									lowercase__   :       Dict				=	feature_size
									lowercase__   :       str				=	num_mel_bins
									lowercase__   :       Union[str, Any]				=	padding_value
									lowercase__   :       Optional[int]				=	sampling_rate
									lowercase__   :       Optional[Any]				=	return_attention_mask
									lowercase__   :       List[str]				=	do_normalize





				def 				snake_case_    (				self):
									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						,       a=False						,       a=False):
									def _flatten(a):
														return list(itertools.chain(*_A))

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



@require_torch
@require_torchaudio
class 				SCREAMING_SNAKE_CASE__ (__snake_case		,	unittest.TestCase					):
				__lowerCamelCase						:	Dict														=					SpeechaTextFeatureExtractor if is_speech_available() else None





				def 				snake_case_    (				self):
									lowercase__   :       Dict				=	SpeechaTextFeatureExtractionTester(self)





				def 				snake_case_    (				self						,       a):
									self.assertTrue(np.all(np.mean(_A						,       axis=0) < 1e-3))
									self.assertTrue(np.all(np.abs(np.var(_A						,       axis=0) - 1) < 1e-3))





				def 				snake_case_    (				self):
									# Tests that all call wrap to encode_plus and batch_encode_plus
									lowercase__   :       Union[str, Any]				=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
									# create three inputs of length 800, 1000, and 1200
									lowercase__   :       List[Any]				=	[floats_list((1, x))[0] for x in range(800						,       1400						,       200)]
									lowercase__   :       Union[str, Any]				=	[np.asarray(_A) for speech_input in speech_inputs]

									# Test feature size
									lowercase__   :       Optional[int]				=	feature_extractor(_A						,       padding=_A						,       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
									lowercase__   :       int				=	feature_extractor(speech_inputs[0]						,       return_tensors='np').input_features
									lowercase__   :       Any				=	feature_extractor(np_speech_inputs[0]						,       return_tensors='np').input_features
									self.assertTrue(np.allclose(_A						,       _A						,       atol=1e-3))

									# Test batched
									lowercase__   :       Optional[int]				=	feature_extractor(_A						,       return_tensors='np').input_features
									lowercase__   :       int				=	feature_extractor(_A						,       return_tensors='np').input_features
									for enc_seq_a, enc_seq_a in zip(_A						,       _A):
														self.assertTrue(np.allclose(_A						,       _A						,       atol=1e-3))

									# Test 2-D numpy arrays are batched.
									lowercase__   :       Dict				=	[floats_list((1, x))[0] for x in (800, 800, 800)]
									lowercase__   :       Union[str, Any]				=	np.asarray(_A)
									lowercase__   :       Optional[Any]				=	feature_extractor(_A						,       return_tensors='np').input_features
									lowercase__   :       Tuple				=	feature_extractor(_A						,       return_tensors='np').input_features
									for enc_seq_a, enc_seq_a in zip(_A						,       _A):
														self.assertTrue(np.allclose(_A						,       _A						,       atol=1e-3))





				def 				snake_case_    (				self):
									lowercase__   :       Optional[Any]				=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
									lowercase__   :       List[Any]				=	[floats_list((1, x))[0] for x in range(800						,       1400						,       200)]

									lowercase__   :       Union[str, Any]				=	['longest', 'max_length', 'do_not_pad']
									lowercase__   :       Union[str, Any]				=	[None, 16, None]
									for max_length, padding in zip(_A						,       _A):
														lowercase__   :       str				=	feature_extractor(
														    _A						,       padding=_A						,       max_length=_A						,       return_attention_mask=_A)
														lowercase__   :       Dict				=	inputs.input_features
														lowercase__   :       Dict				=	inputs.attention_mask
														lowercase__   :       List[Any]				=	[np.sum(_A) 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):
									lowercase__   :       Union[str, Any]				=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
									lowercase__   :       Optional[Any]				=	[floats_list((1, x))[0] for x in range(800						,       1400						,       200)]

									lowercase__   :       List[Any]				=	['longest', 'max_length', 'do_not_pad']
									lowercase__   :       List[str]				=	[None, 16, None]
									for max_length, padding in zip(_A						,       _A):
														lowercase__   :       Dict				=	feature_extractor(
														    _A						,       max_length=_A						,       padding=_A						,       return_tensors='np'						,       return_attention_mask=_A)
														lowercase__   :       Dict				=	inputs.input_features
														lowercase__   :       Optional[Any]				=	inputs.attention_mask
														lowercase__   :       List[str]				=	[np.sum(_A) 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):
									lowercase__   :       Tuple				=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
									lowercase__   :       Any				=	[floats_list((1, x))[0] for x in range(800						,       1400						,       200)]
									lowercase__   :       str				=	feature_extractor(
									    _A						,       padding='max_length'						,       max_length=4						,       truncation=_A						,       return_tensors='np'						,       return_attention_mask=_A						,       )
									lowercase__   :       Union[str, Any]				=	inputs.input_features
									lowercase__   :       str				=	inputs.attention_mask
									lowercase__   :       str				=	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):
									lowercase__   :       Union[str, Any]				=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
									lowercase__   :       List[Any]				=	[floats_list((1, x))[0] for x in range(800						,       1400						,       200)]
									lowercase__   :       Optional[Any]				=	feature_extractor(
									    _A						,       padding='longest'						,       max_length=4						,       truncation=_A						,       return_tensors='np'						,       return_attention_mask=_A						,       )
									lowercase__   :       Union[str, Any]				=	inputs.input_features
									lowercase__   :       Optional[Any]				=	inputs.attention_mask
									lowercase__   :       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, : 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))

									lowercase__   :       Optional[Any]				=	[floats_list((1, x))[0] for x in range(800						,       1400						,       200)]
									lowercase__   :       Tuple				=	feature_extractor(
									    _A						,       padding='longest'						,       max_length=16						,       truncation=_A						,       return_tensors='np'						,       return_attention_mask=_A						,       )
									lowercase__   :       Tuple				=	inputs.input_features
									lowercase__   :       Union[str, Any]				=	inputs.attention_mask
									lowercase__   :       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, 6, 24))





				def 				snake_case_    (				self):
									import torch

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

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





				def 				snake_case_    (				self						,       a):
									from datasets import load_dataset

									lowercase__   :       Tuple				=	load_dataset('hf-internal-testing/librispeech_asr_dummy'						,       'clean'						,       split='validation')
									# automatic decoding with librispeech
									lowercase__   :       int				=	ds.sort('id').select(range(_A))[:num_samples]['audio']

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





				def 				snake_case_    (				self):
									# fmt: off
									lowercase__   :       Any				=	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

									lowercase__   :       Optional[int]				=	self._load_datasamples(1)
									lowercase__   :       List[str]				=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
									lowercase__   :       str				=	feature_extractor(_A						,       return_tensors='pt').input_features
									self.assertEquals(input_features.shape						,       (1, 584, 24))
									self.assertTrue(np.allclose(input_features[0, 0, :30]						,       _A						,       atol=1e-4))




from __future__ import annotations

from collections.abc import Callable

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Callable[[int | float], int | float]     ,					SCREAMING_SNAKE_CASE_		:   int | float     ,					SCREAMING_SNAKE_CASE_		:   int | float     ,					SCREAMING_SNAKE_CASE_		:   int = 100     ,					):



     '''simple docstring'''





     lowercase__   :       Tuple				=	x_start
     lowercase__   :       Tuple				=	fnc(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       List[Any]				=	0.0
     for _ in range(SCREAMING_SNAKE_CASE_      ):
          # Approximates small segments of curve as linear and solve
          # for trapezoidal area
          lowercase__   :       Any				=	(x_end - x_start) / steps + xa
          lowercase__   :       Optional[Any]				=	fnc(SCREAMING_SNAKE_CASE_      )
          area += abs(fxa + fxa      ) * (xa - xa) / 2
          # Increment step
          lowercase__   :       Any				=	xa
          lowercase__   :       str				=	fxa
     return area


if __name__ == "__main__":

       def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Tuple      ):



            '''simple docstring'''





            return x**3 + x**2

       print('''f(x) = x^3 + x^2''')
       print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
       snake_case_        =			10
       while i <= 100_000:
              print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
              i *= 10

