


'''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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   Any=1.0						,      _SCREAMING_SNAKE_CASE      :   Optional[int]=None						,      _SCREAMING_SNAKE_CASE      :   str=None   ):
					if rng is None:
										__a			:       Optional[int]					    =  global_rng

					__a			:       Optional[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					):




	def __init__(   self						,				__a						,				__a=7						,				__a=400						,				__a=2000						,				__a=1						,				__a=0.0						,				__a=1_6000						,				__a=True						,				__a=True						,				):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   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       __UpperCAmelCase							(   self						,				__a=False						,				__a=False							):

						'''simple docstring'''





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

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

						return speech_inputs





@require_torch
@require_torchaudio
class 							__UpperCamelCase				(		A__		,      unittest.TestCase					):
	A_				=    ASTFeatureExtractor




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       int					    =  ASTFeatureExtractionTester(self							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
						# create three inputs of length 800, 1000, and 1200
						__a			:       str					    =  [floats_list((1, x)							)[0] for x in range(800						,				1400						,				200							)]
						__a			:       str					    =  [np.asarray(__a							) for speech_input in speech_inputs]

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

						# Test batched
						__a			:       int					    =  feat_extract(__a						,				padding=__a						,				return_tensors='np'							).input_values
						__a			:       Optional[int]					    =  feat_extract(__a						,				padding=__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			:       Dict					    =  [floats_list((1, x)							)[0] for x in (800, 800, 800)]
						__a			:       Optional[int]					    =  np.asarray(__a							)
						__a			:       List[Any]					    =  feat_extract(__a						,				return_tensors='np'							).input_values
						__a			:       int					    =  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							)							)




	@require_torch
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						import torch

						__a			:       Optional[int]					    =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
						__a			:       List[str]					    =  np.random.rand(100							).astype(np.floataa							)
						__a			:       Any					    =  np_speech_inputs.tolist()

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




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						from datasets import load_dataset

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

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





	@require_torch
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  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

						__a			:       str					    =  self._load_datasamples(1							)
						__a			:       List[str]					    =  ASTFeatureExtractor()
						__a			:       str					    =  feature_extractor(__a						,				return_tensors='pt'							).input_values
						self.assertEquals(input_values.shape						,				(1, 1024, 128)							)
						self.assertTrue(torch.allclose(input_values[0, 0, :30]						,				__a						,				atol=1E-4							)							)











lowerCamelCase	:							List[Any]				=[
    (1000, '''M'''),
    (900, '''CM'''),
    (500, '''D'''),
    (400, '''CD'''),
    (100, '''C'''),
    (90, '''XC'''),
    (50, '''L'''),
    (40, '''XL'''),
    (10, '''X'''),
    (9, '''IX'''),
    (5, '''V'''),
    (4, '''IV'''),
    (1, '''I'''),
]







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	int:
     UpperCamelCase__			:   Optional[int]       				=  {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
     UpperCamelCase__			:   Any       				=  0
     UpperCamelCase__			:   Any       				=  0
     while place < len(__lowerCAmelCase			):
          if (place + 1 < len(__lowerCAmelCase			)) and (vals[roman[place]] < vals[roman[place + 1]]):
               total += vals[roman[place + 1]] - vals[roman[place]]
               place += 2
          else:
               total += vals[roman[place]]
               place += 1
     return total







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	str:
     UpperCamelCase__			:   Optional[int]       				=  []
     for arabic, roman in ROMAN:
          ((UpperCamelCase__)				,     (UpperCamelCase__))			:   Optional[int]       				=  divmod(__lowerCAmelCase					,    __lowerCAmelCase			)
          result.append(roman * factor			)
          if number == 0:
               break
     return "".join(__lowerCAmelCase			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()