

"""simple docstring"""




import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
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


__A	:       str        =						random.Random()
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__=1.0     ,				lowercase__=None     ,				lowercase__=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      __UpperCamelCase					(  unittest.TestCase		):


		def __init__(self					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]=7	,	__SCREAMING_SNAKE_CASE					:						List[Any]=4_0_0	,	__SCREAMING_SNAKE_CASE					:						str=2_0_0_0	,	__SCREAMING_SNAKE_CASE					:						List[str]=1	,	__SCREAMING_SNAKE_CASE					:						Tuple=0.0	,	__SCREAMING_SNAKE_CASE					:						Any=2_4_0_0_0	,	__SCREAMING_SNAKE_CASE					:						Optional[int]=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     =   return_attention_mask


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							return {
							    "feature_size": self.feature_size,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							}


		def    SCREAMING_SNAKE_CASE__  (self					:						Any	,	__SCREAMING_SNAKE_CASE					:						List[Any]=False	,	__SCREAMING_SNAKE_CASE					:						Any=False):
							def _flatten(__SCREAMING_SNAKE_CASE					:						List[Any]):
												return list(itertools.chain(*__SCREAMING_SNAKE_CASE))

							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(__SCREAMING_SNAKE_CASE) for x in audio_inputs]

							return audio_inputs





@require_torch
class      __UpperCamelCase					(  _A	,						unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       EncodecFeatureExtractor


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							A     =   EnCodecFeatureExtractionTester(self)


		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							# 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(8_0_0	,	1_4_0_0	,	2_0_0)]
							A     =   [np.asarray(__SCREAMING_SNAKE_CASE) for audio_input in audio_inputs]

							# Test not batched input
							A     =   feat_extract(audio_inputs[0]	,	return_tensors="np").input_values
							A     =   feat_extract(np_audio_inputs[0]	,	return_tensors="np").input_values
							self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE	,	atol=1E-3))

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


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
							A     =   np.random.rand(1_0_0).astype(np.floataa)
							A     =   np_audio_inputs.tolist()

							for inputs in [py_audio_inputs, np_audio_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    SCREAMING_SNAKE_CASE__  (self					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						Optional[int]):
							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(__SCREAMING_SNAKE_CASE))[:num_samples]["audio"]

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


		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							# fmt: off
							A     =   torch.tensor(
							    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
							     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
							     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
							     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
							     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
							     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03])
							# fmt: on
							A     =   self._load_datasamples(1)
							A     =   EncodecFeatureExtractor()
							A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(input_values.shape	,	(1, 1, 9_3_6_8_0))
							self.assertTrue(torch.allclose(input_values[0, 0, :3_0]	,	__SCREAMING_SNAKE_CASE	,	atol=1E-6))


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							# fmt: off
							A     =   torch.tensor(
							    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
							     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
							     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
							     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
							     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
							     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03])
							# fmt: on
							A     =   self._load_datasamples(1)
							A     =   [np.tile(input_audio[0][None]	,	reps=(2, 1))]
							input_audio[0][1] *= 0.5
							A     =   EncodecFeatureExtractor(feature_size=2)
							A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(input_values.shape	,	(1, 2, 9_3_6_8_0))
							self.assertTrue(torch.allclose(input_values[0, 0, :3_0]	,	__SCREAMING_SNAKE_CASE	,	atol=1E-6))
							self.assertTrue(torch.allclose(input_values[0, 1, :3_0]	,	EXPECTED_INPUT_VALUES * 0.5	,	atol=1E-6))


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]):
							A     =   self._load_datasamples(2)
							# would be easier if the stride was like
							A     =   EncodecFeatureExtractor(feature_size=1	,	chunk_length_s=1	,	overlap=0.0_1)

							# pad and trunc raise an error ?
							with self.assertRaisesRegex(
							    __SCREAMING_SNAKE_CASE	,	"^Both padding and truncation were set. Make sure you only set one.$"	,	):
												A     =   feature_extractor(
												    __SCREAMING_SNAKE_CASE	,	padding="max_length"	,	truncation=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values

							# truncate to chunk
							A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	truncation=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape	,	(2, 1, 7_1_5_2_0))  # 2 chunks

							# force truncate to max_length
							A     =   feature_extractor(
							    __SCREAMING_SNAKE_CASE	,	truncation=__SCREAMING_SNAKE_CASE	,	max_length=4_8_0_0_0	,	return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape	,	(2, 1, 4_8_0_0_0))

							# pad to chunk
							A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(padded_outputs.shape	,	(2, 1, 9_5_2_8_0))

							# pad to chunk
							A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape	,	(2, 1, 9_5_2_8_0))

							# force pad to max length
							A     =   feature_extractor(
							    __SCREAMING_SNAKE_CASE	,	padding="max_length"	,	max_length=1_0_0_0_0_0	,	return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape	,	(2, 1, 1_0_0_0_0_0))

							# force no pad
							with self.assertRaisesRegex(
							    __SCREAMING_SNAKE_CASE	,	"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"	,	):
												A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values

							A     =   feature_extractor(input_audio[0]	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape	,	(1, 1, 9_3_6_8_0))

							# no pad if no chunk_length_s
							A     =   None
							with self.assertRaisesRegex(
							    __SCREAMING_SNAKE_CASE	,	"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"	,	):
												A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values

							A     =   feature_extractor(input_audio[0]	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape	,	(1, 1, 9_3_6_8_0))

							# no pad if no overlap
							A     =   2
							A     =   None
							with self.assertRaisesRegex(
							    __SCREAMING_SNAKE_CASE	,	"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"	,	):
												A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values

							A     =   feature_extractor(input_audio[0]	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape	,	(1, 1, 9_3_6_8_0))








"""simple docstring"""




__A	:       dict[str, float]        =						{
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.00_09_84_20_73,
    "short-ton": 0.00_11_02_31_22,
    "pound": 2.20_46_24_42_02,
    "stone": 0.15_74_73_17_28,
    "ounce": 35.2_73_99_07_23,
    "carrat": 50_00,
    "atomic-mass-unit": 6.0_2213_6652E26,
}

__A	:       dict[str, float]        =						{
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 10_16.0_46_08,
    "short-ton": 9_07.1_84,
    "pound": 0.45_35_92,
    "stone": 6.3_50_29,
    "ounce": 0.0_28_34_95,
    "carrat": 0.00_02,
    "atomic-mass-unit": 1.6_6054_0199E-27,
}
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
										A     =   (
										    F"""Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
										    F"""Supported values are: {", ".join(lowercase__ )}"""
										)
										raise ValueError(lowercase__ )
					return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
	import doctest

	doctest.testmod()





