






"""simple docstring"""







lowerCamelCase_       :       Dict      						=  [
    (1_0_0_0, """M"""),
    (9_0_0, """CM"""),
    (5_0_0, """D"""),
    (4_0_0, """CD"""),
    (1_0_0, """C"""),
    (9_0, """XC"""),
    (5_0, """L"""),
    (4_0, """XL"""),
    (1_0, """X"""),
    (9, """IX"""),
    (5, """V"""),
    (4, """IV"""),
    (1, """I"""),
]
def _A						(						lowercase		):







    """simple docstring"""
    a					={'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 1_00, '''D''': 5_00, '''M''': 10_00}
    a					=0
    a					=0
    while place < len(_snake_case		):
        if (place + 1 < len(_snake_case		)) 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 _A						(						lowercase		):







    """simple docstring"""
    a					=[]
    for arabic, roman in ROMAN:
        (a)					=divmod(_snake_case	,   _snake_case		)
        result.append(roman * factor		)
        if number == 0:
            break
    return "".join(_snake_case		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()



"""simple docstring"""







import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
       from transformers import WhisperFeatureExtractor

if is_torch_available():
       import torch

lowerCamelCase_       :       Any      						=  random.Random()
def _A						(						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
@require_torchaudio
class 				__A    (	unittest.TestCase      ):






 """simple docstring"""




 def __init__(		self			,   __A			,   __A=7			,   __A=400			,   __A=2000			,   __A=10			,   __A=160			,   __A=8			,   __A=0.0			,   __A=4000			,   __A=False			,   __A=True			,   ) ->					Optional[Any]:
     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					=padding_value
     a					=sampling_rate
     a					=return_attention_mask
     a					=do_normalize
     a					=feature_size
     a					=chunk_length
     a					=hop_length


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     return {
         "feature_size": self.feature_size,
         "hop_length": self.hop_length,
         "chunk_length": self.chunk_length,
         "padding_value": self.padding_value,
         "sampling_rate": self.sampling_rate,
         "return_attention_mask": self.return_attention_mask,
         "do_normalize": self.do_normalize,
     }





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A=False			,   __A=False						) ->					str:
     def _flatten(__A						):
         return list(itertools.chain(*__A						)						)

     if equal_length:
         a					=[floats_list((self.max_seq_length, self.feature_size)						) for _ in range(self.batch_size						)]
     else:
         # make sure that inputs increase in size
         a					=[
             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






@require_torch
@require_torchaudio
class 				__A    (	_SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  WhisperFeatureExtractor if is_speech_available() else None


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     a					=WhisperFeatureExtractionTester(self						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     a					=self.feature_extraction_class(**self.feat_extract_dict						)

     with tempfile.TemporaryDirectory() as tmpdirname:
         a					=feat_extract_first.save_pretrained(__A						)[0]
         check_json_file_has_correct_format(__A						)
         a					=self.feature_extraction_class.from_pretrained(__A						)

     a					=feat_extract_first.to_dict()
     a					=feat_extract_second.to_dict()
     a					=feat_extract_first.mel_filters
     a					=feat_extract_second.mel_filters
     self.assertTrue(np.allclose(__A			,   __A						)						)
     self.assertEqual(__A			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     a					=self.feature_extraction_class(**self.feat_extract_dict						)

     with tempfile.TemporaryDirectory() as tmpdirname:
         a					=os.path.join(__A			,   '''feat_extract.json'''						)
         feat_extract_first.to_json_file(__A						)
         a					=self.feature_extraction_class.from_json_file(__A						)

     a					=feat_extract_first.to_dict()
     a					=feat_extract_second.to_dict()
     a					=feat_extract_first.mel_filters
     a					=feat_extract_second.mel_filters
     self.assertTrue(np.allclose(__A			,   __A						)						)
     self.assertEqual(__A			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     # 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			,   1400			,   200						)]
     a					=[np.asarray(__A						) for speech_input in speech_inputs]

     # Test feature size
     a					=feature_extractor(__A			,   padding='''max_length'''			,   return_tensors='''np'''						).input_features
     self.assertTrue(input_features.ndim == 3						)
     self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames						)
     self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size						)

     # Test not batched input
     a					=feature_extractor(speech_inputs[0]			,   return_tensors='''np'''						).input_features
     a					=feature_extractor(np_speech_inputs[0]			,   return_tensors='''np'''						).input_features
     self.assertTrue(np.allclose(__A			,   __A			,   atol=1E-3						)						)

     # Test batched
     a					=feature_extractor(__A			,   return_tensors='''np'''						).input_features
     a					=feature_extractor(__A			,   return_tensors='''np'''						).input_features
     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_features
     a					=feature_extractor(__A			,   return_tensors='''np'''						).input_features
     for enc_seq_a, enc_seq_a in zip(__A			,   __A						):
         self.assertTrue(np.allclose(__A			,   __A			,   atol=1E-3						)						)

     # Test truncation required
     a					=[floats_list((1, x)						)[0] for x in range(200			,   (feature_extractor.n_samples + 500)			,   200						)]
     a					=[np.asarray(__A						) for speech_input in speech_inputs]

     a					=[x[: feature_extractor.n_samples] for x in speech_inputs]
     a					=[np.asarray(__A						) for speech_input in speech_inputs_truncated]

     a					=feature_extractor(__A			,   return_tensors='''np'''						).input_features
     a					=feature_extractor(__A			,   return_tensors='''np'''						).input_features
     for enc_seq_a, enc_seq_a in zip(__A			,   __A						):
         self.assertTrue(np.allclose(__A			,   __A			,   atol=1E-3						)						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     import torch

     a					=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
     a					=np.random.rand(100			,   32						).astype(np.floataa						)
     a					=np_speech_inputs.tolist()

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


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					Dict:
     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 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     # fmt: off
     a					=torch.tensor(
         [
             0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
             0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
             0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
             -0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
         ]						)
     # fmt: on

     a					=self._load_datasamples(1						)
     a					=WhisperFeatureExtractor()
     a					=feature_extractor(__A			,   return_tensors='''pt'''						).input_features
     self.assertEqual(input_features.shape			,   (1, 80, 3000)						)
     self.assertTrue(torch.allclose(input_features[0, 0, :30]			,   __A			,   atol=1E-4						)						)





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     a					=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
     a					=self._load_datasamples(1						)[0]
     a					=((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535  # Rescale to [0, 65535] to show issue
     a					=feat_extract.zero_mean_unit_var_norm([audio]			,   attention_mask=__A						)[0]

     self.assertTrue(np.all(np.mean(__A						) < 1E-3						)						)
     self.assertTrue(np.all(np.abs(np.var(__A						) - 1						) < 1E-3						)						)