



import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
	import torch


UpperCAmelCase							 =					random.Random()

def 			A_      (	__a     :				Tuple ,		__a     :				str=1.0 ,		__a     :				Optional[Any]=None ,		__a     :				str=None		):

				"""simple docstring"""
				if rng is None:
								a__				      =		global_rng

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

				return values

@require_torch
class  __snake_case      (   unittest.TestCase):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_=7  ,  a_=400  ,  a_=2_000  ,  a_=1  ,  a_=0.0  ,  a_=16_000  ,  a_=True  ,  a_=80  ,  a_=16  ,  a_=64  ,  a_="hann_window"  ,  a_=80  ,  a_=7_600  ,  a_=1E-1_0  ,  a_=True  ,  ):
											a__				      =		parent
											a__				      =		batch_size
											a__				      =		min_seq_length
											a__				      =		max_seq_length
											a__				      =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
											a__				      =		feature_size
											a__				      =		padding_value
											a__				      =		sampling_rate
											a__				      =		do_normalize
											a__				      =		num_mel_bins
											a__				      =		hop_length
											a__				      =		win_length
											a__				      =		win_function
											a__				      =		fmin
											a__				      =		fmax
											a__				      =		mel_floor
											a__				      =		return_attention_mask


							def _a      (							self		):
											return {
											    "feature_size": self.feature_size,
											    "padding_value": self.padding_value,
											    "sampling_rate": self.sampling_rate,
											    "do_normalize": self.do_normalize,
											    "num_mel_bins": self.num_mel_bins,
											    "hop_length": self.hop_length,
											    "win_length": self.win_length,
											    "win_function": self.win_function,
											    "fmin": self.fmin,
											    "fmax": self.fmax,
											    "mel_floor": self.mel_floor,
											    "return_attention_mask": self.return_attention_mask,
											}


							def _a      (							self  ,  a_=False  ,  a_=False		):
											def _flatten(a_		):
															return list(itertools.chain(*a_		)		)

											if equal_length:
															a__				      =		floats_list((self.batch_size, self.max_seq_length)		)
											else:
															# make sure that inputs increase in size
															a__				      =		[
															    _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:
															a__				      =		[np.asarray(a_		) for x in speech_inputs]

											return speech_inputs







							def _a      (							self  ,  a_=False  ,  a_=False		):
											if equal_length:
															a__				      =		[floats_list((self.max_seq_length, self.num_mel_bins)		) for _ in range(self.batch_size		)]
											else:
															# make sure that inputs increase in size
															a__				      =		[
															    floats_list((x, self.num_mel_bins)		)
															    for x in range(self.min_seq_length  ,  self.max_seq_length  ,  self.seq_length_diff		)
															]

											if numpify:
															a__				      =		[np.asarray(a_		) for x in speech_inputs]

											return speech_inputs




@require_torch
class  __snake_case      (   SCREAMING_SNAKE_CASE			,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__				:      Union[str, Any]         =					SpeechTaFeatureExtractor


							def _a      (							self		):
											a__				      =		SpeechTaFeatureExtractionTester(self		)


							def _a      (							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 _a      (							self		):
											# Tests that all call wrap to encode_plus and batch_encode_plus
											a__				      =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
											# create three inputs of length 800, 1000, and 1200
											a__				      =		[floats_list((1, x)		)[0] for x in range(800  ,  1_400  ,  200		)]
											a__				      =		[np.asarray(a_		) for speech_input in speech_inputs]

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

											# Test batched
											a__				      =		feat_extract(a_  ,  return_tensors="""np"""		).input_values
											a__				      =		feat_extract(a_  ,  return_tensors="""np"""		).input_values
											for enc_seq_a, enc_seq_a in zip(a_  ,  a_		):
															self.assertTrue(np.allclose(a_  ,  a_  ,  atol=1E-3		)		)


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

											a__				      =		["""longest""", """max_length""", """do_not_pad"""]
											a__				      =		[None, 1_600, None]
											for max_length, padding in zip(a_  ,  a_		):
															a__				      =		feat_extract(a_  ,  padding=a_  ,  max_length=a_  ,  return_tensors="""np"""		)
															a__				      =		processed.input_values

															self._check_zero_mean_unit_variance(input_values[0][:800]		)
															self.assertTrue(input_values[0][800:].sum() < 1E-6		)
															self._check_zero_mean_unit_variance(input_values[1][:1_000]		)
															self.assertTrue(input_values[0][1_000:].sum() < 1E-6		)
															self._check_zero_mean_unit_variance(input_values[2][:1_200]		)


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

											a__				      =		["""longest""", """max_length""", """do_not_pad"""]
											a__				      =		[None, 1_600, None]

											for max_length, padding in zip(a_  ,  a_		):
															a__				      =		feat_extract(a_  ,  max_length=a_  ,  padding=a_		)
															a__				      =		processed.input_values

															self._check_zero_mean_unit_variance(input_values[0][:800]		)
															self._check_zero_mean_unit_variance(input_values[1][:1_000]		)
															self._check_zero_mean_unit_variance(input_values[2][:1_200]		)


							def _a      (							self		):
											a__				      =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
											a__				      =		[floats_list((1, x)		)[0] for x in range(800  ,  1_400  ,  200		)]
											a__				      =		feat_extract(
											    a_  ,  truncation=a_  ,  max_length=1_000  ,  padding="""max_length"""  ,  return_tensors="""np"""		)
											a__				      =		processed.input_values

											self._check_zero_mean_unit_variance(input_values[0, :800]		)
											self._check_zero_mean_unit_variance(input_values[1]		)
											self._check_zero_mean_unit_variance(input_values[2]		)


							def _a      (							self		):
											a__				      =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
											a__				      =		[floats_list((1, x)		)[0] for x in range(800  ,  1_400  ,  200		)]
											a__				      =		feat_extract(
											    a_  ,  truncation=a_  ,  max_length=1_000  ,  padding="""longest"""  ,  return_tensors="""np"""		)
											a__				      =		processed.input_values

											self._check_zero_mean_unit_variance(input_values[0, :800]		)
											self._check_zero_mean_unit_variance(input_values[1, :1_000]		)
											self._check_zero_mean_unit_variance(input_values[2]		)

											# make sure that if max_length < longest -> then pad to max_length
											self.assertTrue(input_values.shape == (3, 1_000)		)

											a__				      =		[floats_list((1, x)		)[0] for x in range(800  ,  1_400  ,  200		)]
											a__				      =		feat_extract(
											    a_  ,  truncation=a_  ,  max_length=2_000  ,  padding="""longest"""  ,  return_tensors="""np"""		)
											a__				      =		processed.input_values

											self._check_zero_mean_unit_variance(input_values[0, :800]		)
											self._check_zero_mean_unit_variance(input_values[1, :1_000]		)
											self._check_zero_mean_unit_variance(input_values[2]		)

											# make sure that if max_length > longest -> then pad to longest
											self.assertTrue(input_values.shape == (3, 1_200)		)


							def _a      (							self		):
											a__				      =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
											a__				      =		np.random.rand(100		).astype(np.floataa		)
											a__				      =		np_speech_inputs.tolist()

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


							def _a      (							self		):
											# Tests that all call wrap to encode_plus and batch_encode_plus
											a__				      =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
											# create three inputs of length 800, 1000, and 1200
											a__				      =		[floats_list((1, x)		)[0] for x in range(800  ,  1_400  ,  200		)]
											a__				      =		[np.asarray(a_		) for speech_input in speech_inputs]

											# Test feature size
											a__				      =		feature_extractor(audio_target=a_  ,  padding=a_  ,  return_tensors="""np"""		).input_values
											self.assertTrue(input_values.ndim == 3		)
											self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins		)

											# Test not batched input
											a__				      =		feature_extractor(speech_inputs[0]  ,  return_tensors="""np"""		).input_values
											a__				      =		feature_extractor(np_speech_inputs[0]  ,  return_tensors="""np"""		).input_values
											self.assertTrue(np.allclose(a_  ,  a_  ,  atol=1E-3		)		)

											# Test batched
											a__				      =		feature_extractor(a_  ,  return_tensors="""np"""		).input_values
											a__				      =		feature_extractor(a_  ,  return_tensors="""np"""		).input_values
											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.
											a__				      =		[floats_list((1, x)		)[0] for x in (800, 800, 800)]
											a__				      =		np.asarray(a_		)
											a__				      =		feature_extractor(a_  ,  return_tensors="""np"""		).input_values
											a__				      =		feature_extractor(a_  ,  return_tensors="""np"""		).input_values
											for enc_seq_a, enc_seq_a in zip(a_  ,  a_		):
															self.assertTrue(np.allclose(a_  ,  a_  ,  atol=1E-3		)		)


							def _a      (							self		):
											a__				      =		self.feat_extract_tester.prepare_inputs_for_target()
											a__				      =		self.feature_extraction_class(**self.feat_extract_dict		)
											a__				      =		feat_extract.model_input_names[0]

											a__				      =		BatchFeature({input_name: speech_inputs}		)

											self.assertTrue(all(len(a_		) == len(a_		) for x, y in zip(a_  ,  processed_features[input_name]		)		)		)

											a__				      =		self.feat_extract_tester.prepare_inputs_for_target(equal_length=a_		)
											a__				      =		BatchFeature({input_name: speech_inputs}  ,  tensor_type="""np"""		)

											a__				      =		processed_features[input_name]

											if len(batch_features_input.shape		) < 3:
															a__				      =		batch_features_input[:, :, None]

											self.assertTrue(
											    batch_features_input.shape
											    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.num_mel_bins)		)


							@require_torch
							def _a      (							self		):
											a__				      =		self.feat_extract_tester.prepare_inputs_for_target(equal_length=a_		)
											a__				      =		self.feature_extraction_class(**self.feat_extract_dict		)
											a__				      =		feat_extract.model_input_names[0]

											a__				      =		BatchFeature({input_name: speech_inputs}  ,  tensor_type="""pt"""		)

											a__				      =		processed_features[input_name]

											if len(batch_features_input.shape		) < 3:
															a__				      =		batch_features_input[:, :, None]

											self.assertTrue(
											    batch_features_input.shape
											    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.num_mel_bins)		)


							@require_torch
							def _a      (							self		):
											a__				      =		self.feature_extraction_class(**self.feat_extract_dict		)
											a__				      =		self.feat_extract_tester.prepare_inputs_for_target()
											a__				      =		feat_extract.model_input_names[0]

											a__				      =		BatchFeature({input_name: speech_inputs}		)

											a__				      =		feat_extract.num_mel_bins  # hack!

											a__				      =		feat_extract.pad(a_  ,  padding="""longest"""  ,  return_tensors="""np"""		)[input_name]
											a__				      =		feat_extract.pad(a_  ,  padding="""longest"""  ,  return_tensors="""pt"""		)[input_name]

											self.assertTrue(abs(input_np.astype(np.floataa		).sum() - input_pt.numpy().astype(np.floataa		).sum()		) < 1E-2		)


							def _a      (							self		):
											a__				      =		self.feat_extract_dict
											a__				      =		True
											a__				      =		self.feature_extraction_class(**a_		)
											a__				      =		self.feat_extract_tester.prepare_inputs_for_target()
											a__				      =		[len(a_		) for x in speech_inputs]
											a__				      =		feat_extract.model_input_names[0]

											a__				      =		BatchFeature({input_name: speech_inputs}		)

											a__				      =		feat_extract.num_mel_bins  # hack!

											a__				      =		feat_extract.pad(a_  ,  padding="""longest"""  ,  return_tensors="""np"""		)
											self.assertIn("""attention_mask"""  ,  a_		)
											self.assertListEqual(list(processed.attention_mask.shape		)  ,  list(processed[input_name].shape[:2]		)		)
											self.assertListEqual(processed.attention_mask.sum(-1		).tolist()  ,  a_		)


							def _a      (							self		):
											a__				      =		self.feat_extract_dict
											a__				      =		True
											a__				      =		self.feature_extraction_class(**a_		)
											a__				      =		self.feat_extract_tester.prepare_inputs_for_target()
											a__				      =		[len(a_		) for x in speech_inputs]
											a__				      =		feat_extract.model_input_names[0]

											a__				      =		BatchFeature({input_name: speech_inputs}		)
											a__				      =		min(a_		)

											a__				      =		feat_extract.num_mel_bins  # hack!

											a__				      =		feat_extract.pad(
											    a_  ,  padding="""max_length"""  ,  max_length=a_  ,  truncation=a_  ,  return_tensors="""np"""		)
											self.assertIn("""attention_mask"""  ,  a_		)
											self.assertListEqual(
											    list(processed_pad.attention_mask.shape		)  ,  [processed_pad[input_name].shape[0], max_length]		)
											self.assertListEqual(
											    processed_pad.attention_mask[:, :max_length].sum(-1		).tolist()  ,  [max_length for x in speech_inputs]		)


							def _a      (							self  ,  a_		):
											from datasets import load_dataset

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

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


							def _a      (							self		):
											# fmt: off
											a__				      =		torch.tensor(
											    [2.3_8_0_4E-0_3, 2.0_7_5_2E-0_3, 1.9_8_3_6E-0_3, 2.1_0_5_7E-0_3, 1.6_1_7_4E-0_3,
											     3.0_5_1_8E-0_4, 9.1_5_5_3E-0_5, 3.3_5_6_9E-0_4, 9.7_6_5_6E-0_4, 1.8_3_1_1E-0_3,
											     2.0_1_4_2E-0_3, 2.1_0_5_7E-0_3, 1.7_3_9_5E-0_3, 4.5_7_7_6E-0_4, -3.9_6_7_3E-0_4,
											     4.5_7_7_6E-0_4, 1.0_0_7_1E-0_3, 9.1_5_5_3E-0_5, 4.8_8_2_8E-0_4, 1.1_5_9_7E-0_3,
											     7.3_2_4_2E-0_4, 9.4_6_0_4E-0_4, 1.8_0_0_5E-0_3, 1.8_3_1_1E-0_3, 8.8_5_0_1E-0_4,
											     4.2_7_2_5E-0_4, 4.8_8_2_8E-0_4, 7.3_2_4_2E-0_4, 1.0_9_8_6E-0_3, 2.1_0_5_7E-0_3]		)
											# fmt: on

											a__				      =		self._load_datasamples(1		)
											a__				      =		SpeechTaFeatureExtractor()
											a__				      =		feature_extractor(a_  ,  return_tensors="""pt"""		).input_values
											self.assertEquals(input_values.shape  ,  (1, 93_680)		)
											self.assertTrue(torch.allclose(input_values[0, :30]  ,  a_  ,  atol=1E-6		)		)







							def _a      (							self		):
											# fmt: off
											a__				      =		torch.tensor(
											    [-2.6_870, -3.0_104, -3.1_356, -3.5_352, -3.0_044, -3.0_353, -3.4_719, -3.6_777,
											     -3.1_520, -2.9_435, -2.6_553, -2.8_795, -2.9_944, -2.5_921, -3.0_279, -3.0_386,
											     -3.0_864, -3.1_291, -3.2_353, -2.7_444, -2.6_831, -2.7_287, -3.1_761, -3.1_571,
											     -3.2_726, -3.0_582, -3.1_007, -3.4_533, -3.4_695, -3.0_998]		)
											# fmt: on

											a__				      =		self._load_datasamples(1		)
											a__				      =		SpeechTaFeatureExtractor()
											a__				      =		feature_extractor(audio_target=a_  ,  return_tensors="""pt"""		).input_values
											self.assertEquals(input_values.shape  ,  (1, 366, 80)		)
											self.assertTrue(torch.allclose(input_values[0, 0, :30]  ,  a_  ,  atol=1E-4		)		)






from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
UpperCAmelCase							 =					299_792_458

# Symbols
UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase							 =					symbols("""ct x y z""")

def 			A_      (	__a     :				float		):

				"""simple docstring"""
				if velocity > c:
								raise ValueError("""Speed must not exceed light speed 299,792,458 [m/s]!"""		)
				elif velocity < 1:
								# Usually the speed should be much higher than 1 (c order of magnitude)
								raise ValueError("""Speed must be greater than or equal to 1!"""		)

				return velocity / c

def 			A_      (	__a     :				float		):

				"""simple docstring"""
				return 1 / sqrt(1 - beta(__a		) ** 2		)

def 			A_      (	__a     :				float		):

				"""simple docstring"""
				return np.array(
				    [
				        [gamma(__a		), -gamma(__a		) * beta(__a		), 0, 0],
				        [-gamma(__a		) * beta(__a		), gamma(__a		), 0, 0],
				        [0, 0, 1, 0],
				        [0, 0, 0, 1],
				    ]		)

def 			A_      (	__a     :				float ,		__a     :				np.ndarray | None = None		):

				"""simple docstring"""
				# Ensure event is not empty
				if event is None:
								a__				      =		np.array([ct, x, y, z]		)  # Symbolic four vector
				else:
								event[0] *= c  # x0 is ct (speed of light * time)

				return transformation_matrix(__a		) @ event


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	# Example of symbolic vector:
	UpperCAmelCase							 =					transform(29_979_245)
	print("""Example of four vector: """)
	print(f"""ct' = {four_vector[0]}""")
	print(f"""x' = {four_vector[1]}""")
	print(f"""y' = {four_vector[2]}""")
	print(f"""z' = {four_vector[3]}""")

	# Substitute symbols with numerical values
	UpperCAmelCase							 =					{ct: c, x: 1, y: 1, z: 1}
	UpperCAmelCase							 =					[four_vector[i].subs(sub_dict) for i in range(4)]

	print(f"""\n{numerical_vector}""")

