



'''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__       :	Optional[Any]    = random.Random()

if is_torch_available():
						import torch


def 				lowerCAmelCase_   (	_lowerCamelCase:   Optional[Any]       ,      _lowerCamelCase:   Union[str, Any]=1.0       ,      _lowerCamelCase:   Any=None       ,      _lowerCamelCase:   Any=None					):





		if rng is None:
				__SCREAMING_SNAKE_CASE       :		Tuple           =		global_rng

		__SCREAMING_SNAKE_CASE       :		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    ):


			'''simple docstring'''

			def __init__(							self     : Union[str, Any]							,     lowerCAmelCase__     : Dict							,     lowerCAmelCase__     : int=7							,     lowerCAmelCase__     : Tuple=4_0_0							,     lowerCAmelCase__     : Optional[int]=2_0_0_0							,     lowerCAmelCase__     : Optional[Any]=1							,     lowerCAmelCase__     : Optional[Any]=0.0							,     lowerCAmelCase__     : Dict=1_6_0_0_0							,     lowerCAmelCase__     : List[Any]=True							,     lowerCAmelCase__     : List[str]=True							,     ):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : Dict						):





					"""simple docstring"""






					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 UpperCamelCase__					(							self     : int							,     lowerCAmelCase__     : Optional[Any]=False							,     lowerCAmelCase__     : str=False						):





					"""simple docstring"""






					def _flatten(lowerCAmelCase__     : Dict						):
							return list(itertools.chain(*lowerCAmelCase__						)						)

					if equal_length:
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		floats_list((self.batch_size, self.max_seq_length)						)
					else:
							# make sure that inputs increase in size
							__SCREAMING_SNAKE_CASE       :		List[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:
							__SCREAMING_SNAKE_CASE       :		Any           =		[np.asarray(lowerCAmelCase__						) for x in speech_inputs]

					return speech_inputs






@require_torch
@require_torchaudio
class 						_UpperCamelCase  (							__lowercase    ,					unittest.TestCase    ):


			'''simple docstring'''

			_A      :		Optional[int]			    =		ASTFeatureExtractor

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		ASTFeatureExtractionTester(self						)

			def UpperCamelCase__					(							self     : Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		int           =		self.feature_extraction_class(**self.feat_extract_tester.prepare_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(8_0_0							,     1_4_0_0							,     2_0_0						)]
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		[np.asarray(lowerCAmelCase__						) for speech_input in speech_inputs]

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

					# Test batched
					__SCREAMING_SNAKE_CASE       :		List[str]           =		feat_extract(lowerCAmelCase__							,     padding=lowerCAmelCase__							,     return_tensors="""np"""						).input_values
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		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.
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		[floats_list((1, x)						)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
					__SCREAMING_SNAKE_CASE       :		int           =		np.asarray(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		feat_extract(lowerCAmelCase__							,     return_tensors="""np"""						).input_values
					__SCREAMING_SNAKE_CASE       :		Any           =		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 UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					import torch

					__SCREAMING_SNAKE_CASE       :		Tuple           =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
					__SCREAMING_SNAKE_CASE       :		Any           =		np.random.rand(1_0_0						).astype(np.floataa						)
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		np_speech_inputs.tolist()

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

			def UpperCamelCase__					(							self     : int							,     lowerCAmelCase__     : Optional[int]						):





					"""simple docstring"""






					from datasets import load_dataset

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

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



			@require_torch
			def UpperCamelCase__					(							self     : Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Dict           =		torch.tensor(
					    [-0.98_94, -1.27_76, -0.90_66, -1.27_76, -0.93_49, -1.26_09, -1.03_86, -1.27_76,
					     -1.15_61, -1.27_76, -1.20_52, -1.27_23, -1.21_90, -1.21_32, -1.27_76, -1.11_33,
					     -1.19_53, -1.13_43, -1.15_84, -1.22_03, -1.17_70, -1.24_74, -1.23_81, -1.19_36,
					     -0.92_70, -0.83_17, -0.80_49, -0.77_06, -0.75_65, -0.78_69]						)
					# fmt: on

					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self._load_datasamples(1						)
					__SCREAMING_SNAKE_CASE       :		int           =		ASTFeatureExtractor()
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		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						)						)

from __future__ import annotations

import math

lowercase  :					Any													=       '2020.9.26'
lowercase  :					Union[str, Any]													=       'xcodz-dot, cclaus, dhruvmanila'




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			float						,  _lowerCamelCase :			float						,  _lowerCamelCase :			float						,  _lowerCamelCase :			float						,  _lowerCamelCase :			float)					->    tuple[float, float]:






							'''simple docstring'''





							if not all(isinstance(_lowerCamelCase						,  (float, int)) for val in locals().values()):
														__UpperCamelCase	:   str			   =					F'Input values must either be float or int: {list(locals().values())}'
														raise TypeError(_lowerCamelCase)
							__UpperCamelCase	:   List[str]			   =					((x * distance) / (z + distance)) * scale
							__UpperCamelCase	:   List[Any]			   =					((y * distance) / (z + distance)) * scale
							return projected_x, projected_y




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			float						,  _lowerCamelCase :			float						,  _lowerCamelCase :			float						,  _lowerCamelCase :			str						,  _lowerCamelCase :			float)					->    tuple[float, float, float]:






							'''simple docstring'''





							if not isinstance(_lowerCamelCase						,  _lowerCamelCase):
														raise TypeError("Axis must be a str")
							__UpperCamelCase	:   str			   =					locals()
							del input_variables["axis"]
							if not all(isinstance(_lowerCamelCase						,  (float, int)) for val in input_variables.values()):
														__UpperCamelCase	:   Dict			   =					(
														    "Input values except axis must either be float or int: "
														    F'{list(input_variables.values())}'
														)
														raise TypeError(_lowerCamelCase)
							__UpperCamelCase	:   Optional[Any]			   =					(angle % 360) / 450 * 180 / math.pi
							if axis == "z":
														__UpperCamelCase	:   Tuple			   =					x * math.cos(_lowerCamelCase) - y * math.sin(_lowerCamelCase)
														__UpperCamelCase	:   Union[str, Any]			   =					y * math.cos(_lowerCamelCase) + x * math.sin(_lowerCamelCase)
														__UpperCamelCase	:   Any			   =					z
							elif axis == "x":
														__UpperCamelCase	:   Dict			   =					y * math.cos(_lowerCamelCase) - z * math.sin(_lowerCamelCase)
														__UpperCamelCase	:   Any			   =					z * math.cos(_lowerCamelCase) + y * math.sin(_lowerCamelCase)
														__UpperCamelCase	:   List[str]			   =					x
							elif axis == "y":
														__UpperCamelCase	:   Any			   =					x * math.cos(_lowerCamelCase) - z * math.sin(_lowerCamelCase)
														__UpperCamelCase	:   Any			   =					z * math.cos(_lowerCamelCase) + x * math.sin(_lowerCamelCase)
														__UpperCamelCase	:   Dict			   =					y
							else:
														raise ValueError("not a valid axis, choose one of 'x', 'y', 'z'")

							return new_x, new_y, new_z


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(f"{convert_to_ad(1.0, 2.0, 3.0, 1_0.0, 1_0.0) = }")
		print(f"{rotate(1.0, 2.0, 3.0, 'y', 9_0.0) = }")