
'''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


lowercase					:							Optional[int]   =      random.Random()

if is_torch_available():
					import torch







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A=1.0    ,       __A=None    ,       __A=None			) ->			int:
	if rng is None:
		_snake_case     		=	global_rng

	_snake_case     		=	[]
	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		):


		def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=7					,     lowerCAmelCase_=4_00					,     lowerCAmelCase_=20_00					,     lowerCAmelCase_=1					,     lowerCAmelCase_=0.0					,     lowerCAmelCase_=1_60_00					,     lowerCAmelCase_=True					,     lowerCAmelCase_=True					,     ):





			"""simple docstring"""






			_snake_case     		=	parent
			_snake_case     		=	batch_size
			_snake_case     		=	min_seq_length
			_snake_case     		=	max_seq_length
			_snake_case     		=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
			_snake_case     		=	feature_size
			_snake_case     		=	padding_value
			_snake_case     		=	sampling_rate
			_snake_case     		=	return_attention_mask
			_snake_case     		=	do_normalize


		def 				lowerCamelCase       ( self						):





			"""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 				lowerCamelCase       ( self					,     lowerCAmelCase_=False					,     lowerCAmelCase_=False						):





			"""simple docstring"""






			def _flatten(lowerCAmelCase_						):
				return list(itertools.chain(*lowerCAmelCase_						)						)

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

			return speech_inputs


@require_torch
@require_torchaudio
class 		__UpperCAmelCase   (      _lowerCamelCase		,     unittest.TestCase		):
		__lowercase   	= ASTFeatureExtractor


		def 				lowerCamelCase       ( self						):





			"""simple docstring"""






			_snake_case     		=	ASTFeatureExtractionTester(self						)


		def 				lowerCamelCase       ( self						):





			"""simple docstring"""






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

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

			# Test batched
			_snake_case     		=	feat_extract(lowerCAmelCase_					,     padding=lowerCAmelCase_					,     return_tensors='np'						).input_values
			_snake_case     		=	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.
			_snake_case     		=	[floats_list((1, x)						)[0] for x in (8_00, 8_00, 8_00)]
			_snake_case     		=	np.asarray(lowerCAmelCase_						)
			_snake_case     		=	feat_extract(lowerCAmelCase_					,     return_tensors='np'						).input_values
			_snake_case     		=	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 				lowerCamelCase       ( self						):





			"""simple docstring"""






			import torch

			_snake_case     		=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
			_snake_case     		=	np.random.rand(1_00						).astype(np.floataa						)
			_snake_case     		=	np_speech_inputs.tolist()

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


		def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





			"""simple docstring"""






			from datasets import load_dataset

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

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




		@require_torch
		def 				lowerCamelCase       ( self						):





			"""simple docstring"""






			_snake_case     		=	torch.tensor(
			    [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
			     -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
			     -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
			     -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]						)
			# fmt: on

			_snake_case     		=	self._load_datasamples(1						)
			_snake_case     		=	ASTFeatureExtractor()
			_snake_case     		=	feature_extractor(lowerCAmelCase_					,     return_tensors='pt'						).input_values
			self.assertEquals(input_values.shape					,     (1, 10_24, 1_28)						)
			self.assertTrue(torch.allclose(input_values[0, 0, :30]					,     lowerCAmelCase_					,     atol=1E-4						)						)



'''simple docstring'''







lowercase					:							Tuple   =      {
    "a": "AAAAA",
    "b": "AAAAB",
    "c": "AAABA",
    "d": "AAABB",
    "e": "AABAA",
    "f": "AABAB",
    "g": "AABBA",
    "h": "AABBB",
    "i": "ABAAA",
    "j": "BBBAA",
    "k": "ABAAB",
    "l": "ABABA",
    "m": "ABABB",
    "n": "ABBAA",
    "o": "ABBAB",
    "p": "ABBBA",
    "q": "ABBBB",
    "r": "BAAAA",
    "s": "BAAAB",
    "t": "BAABA",
    "u": "BAABB",
    "v": "BBBAB",
    "w": "BABAA",
    "x": "BABAB",
    "y": "BABBA",
    "z": "BABBB",
    " ": " ",
}


lowercase					:							Union[str, Any]   =      {value: key for key, value in encode_dict.items()}







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			str:
	_snake_case     		=	''
	for letter in word.lower():
		if letter.isalpha() or letter == " ":
			encoded += encode_dict[letter]
		else:
			raise Exception('encode() accepts only letters of the alphabet and spaces'			)
	return encoded







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			str:
	if set(__A			) - {"A", "B", " "} != set():
		raise Exception('decode() accepts only \'A\', \'B\' and spaces'			)
	_snake_case     		=	''
	for word in coded.split():
		while len(__A			) != 0:
			decoded += decode_dict[word[:5]]
			_snake_case     		=	word[5:]
		decoded += " "
	return decoded.strip()


if __name__ == "__main__":
					from doctest import testmod

					testmod()

