

import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
 from transformers import SpeechaTextFeatureExtractor

SCREAMING_SNAKE_CASE		:Optional[Any]						=     random.Random()




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=1.0			,						SCREAMING_SNAKE_CASE_=None			,						SCREAMING_SNAKE_CASE_=None			)->						Optional[Any]:



    """simple docstring"""






    if rng is None:
        UpperCamelCase_			  =						global_rng

    UpperCamelCase_			  =						[]
    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 						__magic_name__					(							unittest.TestCase				):






     def __init__(				self		,						_lowercase		,						_lowercase=7		,						_lowercase=400		,						_lowercase=2_000		,						_lowercase=24		,						_lowercase=24		,						_lowercase=0.0		,						_lowercase=16_000		,						_lowercase=True		,						_lowercase=True		,						)->     List[str]:
         UpperCamelCase_			  =						parent
         UpperCamelCase_			  =						batch_size
         UpperCamelCase_			  =						min_seq_length
         UpperCamelCase_			  =						max_seq_length
         UpperCamelCase_			  =						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         UpperCamelCase_			  =						feature_size
         UpperCamelCase_			  =						num_mel_bins
         UpperCamelCase_			  =						padding_value
         UpperCamelCase_			  =						sampling_rate
         UpperCamelCase_			  =						return_attention_mask
         UpperCamelCase_			  =						do_normalize






     def 	UpperCAmelCase_				(				self	)->     Any:
         return {
             "feature_size": self.feature_size,
             "num_mel_bins": self.num_mel_bins,
             "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		,						_lowercase=False		,						_lowercase=False	)->     int:
         def _flatten(_lowercase	):
             return list(itertools.chain(*_lowercase	)	)

         if equal_length:
             UpperCamelCase_			  =						[floats_list((self.max_seq_length, self.feature_size)	) for _ in range(self.batch_size	)]
         else:
             # make sure that inputs increase in size
             UpperCamelCase_			  =						[
                 floats_list((x, self.feature_size)	)
                 for x in range(self.min_seq_length		,						self.max_seq_length		,						self.seq_length_diff	)
             ]
         if numpify:
             UpperCamelCase_			  =						[np.asarray(_lowercase	) for x in speech_inputs]
         return speech_inputs






@require_torch
@require_torchaudio
class 						__magic_name__					(							snake_case			,      unittest.TestCase				):
     UpperCamelCase_	:Dict										=      SpeechaTextFeatureExtractor if is_speech_available() else None






     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         UpperCamelCase_			  =						SpeechaTextFeatureExtractionTester(self	)






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     Tuple:
         self.assertTrue(np.all(np.mean(_lowercase		,						axis=0	) < 1e-3	)	)
         self.assertTrue(np.all(np.abs(np.var(_lowercase		,						axis=0	) - 1	) < 1e-3	)	)






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

         # Test feature size
         UpperCamelCase_			  =						feature_extractor(_lowercase		,						padding=_lowercase		,						return_tensors="np"	).input_features
         self.assertTrue(input_features.ndim == 3	)
         self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size	)

         # Test not batched input
         UpperCamelCase_			  =						feature_extractor(speech_inputs[0]		,						return_tensors="np"	).input_features
         UpperCamelCase_			  =						feature_extractor(np_speech_inputs[0]		,						return_tensors="np"	).input_features
         self.assertTrue(np.allclose(_lowercase		,						_lowercase		,						atol=1e-3	)	)

         # Test batched
         UpperCamelCase_			  =						feature_extractor(_lowercase		,						return_tensors="np"	).input_features
         UpperCamelCase_			  =						feature_extractor(_lowercase		,						return_tensors="np"	).input_features
         for enc_seq_a, enc_seq_a in zip(_lowercase		,						_lowercase	):
             self.assertTrue(np.allclose(_lowercase		,						_lowercase		,						atol=1e-3	)	)

         # Test 2-D numpy arrays are batched.
         UpperCamelCase_			  =						[floats_list((1, x)	)[0] for x in (800, 800, 800)]
         UpperCamelCase_			  =						np.asarray(_lowercase	)
         UpperCamelCase_			  =						feature_extractor(_lowercase		,						return_tensors="np"	).input_features
         UpperCamelCase_			  =						feature_extractor(_lowercase		,						return_tensors="np"	).input_features
         for enc_seq_a, enc_seq_a in zip(_lowercase		,						_lowercase	):
             self.assertTrue(np.allclose(_lowercase		,						_lowercase		,						atol=1e-3	)	)






     def 	UpperCAmelCase_				(				self	)->     Tuple:
         UpperCamelCase_			  =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
         UpperCamelCase_			  =						[floats_list((1, x)	)[0] for x in range(800		,						1_400		,						200	)]

         UpperCamelCase_			  =						["longest", "max_length", "do_not_pad"]
         UpperCamelCase_			  =						[None, 16, None]
         for max_length, padding in zip(_lowercase		,						_lowercase	):
             UpperCamelCase_			  =						feature_extractor(
                 _lowercase		,						padding=_lowercase		,						max_length=_lowercase		,						return_attention_mask=_lowercase	)
             UpperCamelCase_			  =						inputs.input_features
             UpperCamelCase_			  =						inputs.attention_mask
             UpperCamelCase_			  =						[np.sum(_lowercase	) for x in attention_mask]

             self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]	)
             self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]	)
             self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]	)






     def 	UpperCAmelCase_				(				self	)->     str:
         UpperCamelCase_			  =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
         UpperCamelCase_			  =						[floats_list((1, x)	)[0] for x in range(800		,						1_400		,						200	)]

         UpperCamelCase_			  =						["longest", "max_length", "do_not_pad"]
         UpperCamelCase_			  =						[None, 16, None]
         for max_length, padding in zip(_lowercase		,						_lowercase	):
             UpperCamelCase_			  =						feature_extractor(
                 _lowercase		,						max_length=_lowercase		,						padding=_lowercase		,						return_tensors="np"		,						return_attention_mask=_lowercase	)
             UpperCamelCase_			  =						inputs.input_features
             UpperCamelCase_			  =						inputs.attention_mask
             UpperCamelCase_			  =						[np.sum(_lowercase	) for x in attention_mask]

             self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]	)
             self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6	)
             self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]	)
             self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6	)
             self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]	)






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_			  =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
         UpperCamelCase_			  =						[floats_list((1, x)	)[0] for x in range(800		,						1_400		,						200	)]
         UpperCamelCase_			  =						feature_extractor(
             _lowercase		,						padding="max_length"		,						max_length=4		,						truncation=_lowercase		,						return_tensors="np"		,						return_attention_mask=_lowercase		,						)
         UpperCamelCase_			  =						inputs.input_features
         UpperCamelCase_			  =						inputs.attention_mask
         UpperCamelCase_			  =						np.sum(attention_mask == 1		,						axis=1	)

         self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
         self._check_zero_mean_unit_variance(input_features[1]	)
         self._check_zero_mean_unit_variance(input_features[2]	)






     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         UpperCamelCase_			  =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
         UpperCamelCase_			  =						[floats_list((1, x)	)[0] for x in range(800		,						1_400		,						200	)]
         UpperCamelCase_			  =						feature_extractor(
             _lowercase		,						padding="longest"		,						max_length=4		,						truncation=_lowercase		,						return_tensors="np"		,						return_attention_mask=_lowercase		,						)
         UpperCamelCase_			  =						inputs.input_features
         UpperCamelCase_			  =						inputs.attention_mask
         UpperCamelCase_			  =						np.sum(attention_mask == 1		,						axis=1	)

         self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
         self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]	)
         self._check_zero_mean_unit_variance(input_features[2]	)

         # make sure that if max_length < longest -> then pad to max_length
         self.assertEqual(input_features.shape		,						(3, 4, 24)	)

         UpperCamelCase_			  =						[floats_list((1, x)	)[0] for x in range(800		,						1_400		,						200	)]
         UpperCamelCase_			  =						feature_extractor(
             _lowercase		,						padding="longest"		,						max_length=16		,						truncation=_lowercase		,						return_tensors="np"		,						return_attention_mask=_lowercase		,						)
         UpperCamelCase_			  =						inputs.input_features
         UpperCamelCase_			  =						inputs.attention_mask
         UpperCamelCase_			  =						np.sum(attention_mask == 1		,						axis=1	)

         self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
         self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]	)
         self._check_zero_mean_unit_variance(input_features[2]	)

         # make sure that if max_length < longest -> then pad to max_length
         self.assertEqual(input_features.shape		,						(3, 6, 24)	)






     def 	UpperCAmelCase_				(				self	)->     int:
         import torch

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

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






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     str:
         from datasets import load_dataset

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

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






     def 	UpperCAmelCase_				(				self	)->     List[Any]:
         # fmt: off
         UpperCamelCase_			  =						np.array([
             -1.5_745, -1.7_713, -1.7_020, -1.6_069, -1.2_250, -1.1_105, -0.9_072, -0.8_241,
             -1.2_310, -0.8_098, -0.3_320, -0.4_101, -0.7_985, -0.4_996, -0.8_213, -0.9_128,
             -1.0_420, -1.1_286, -1.0_440, -0.7_999, -0.8_405, -1.2_275, -1.5_443, -1.4_625,
         ]	)
         # fmt: on

         UpperCamelCase_			  =						self._load_datasamples(1	)
         UpperCamelCase_			  =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
         UpperCamelCase_			  =						feature_extractor(_lowercase		,						return_tensors="pt"	).input_features
         self.assertEquals(input_features.shape		,						(1, 584, 24)	)
         self.assertTrue(np.allclose(input_features[0, 0, :30]		,						_lowercase		,						atol=1e-4	)	)



def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						str:



    """simple docstring"""






    if isinstance(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			):
        raise TypeError("'float' object cannot be interpreted as an integer"			)
    if isinstance(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			):
        raise TypeError("'str' object cannot be interpreted as an integer"			)

    if num == 0:
        return "0b0"

    UpperCamelCase_			  =						False

    if num < 0:
        UpperCamelCase_			  =						True
        UpperCamelCase_			  =						-num

    UpperCamelCase_			  =						[]
    while num > 0:
        binary.insert(0			,						num % 2			)
        num >>= 1

    if negative:
        return "-0b" + "".join(str(SCREAMING_SNAKE_CASE_			) for e in binary			)

    return "0b" + "".join(str(SCREAMING_SNAKE_CASE_			) for e in binary			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
