


"""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


lowerCamelCase_  :  List[str]     		=    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
class  __A							(			unittest.TestCase     ):






 """simple docstring"""




 def __init__(						self					,       __A					,       __A=7					,       __A=400					,       __A=2000					,       __A=1					,       __A=0.0					,       __A=2_4000					,       __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											=feature_size
     a											=padding_value
     a											=sampling_rate
     a											=return_attention_mask


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

     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(__A		) for x in audio_inputs]

     return audio_inputs






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






 """simple docstring"""




 __lowerCAmelCase					   =    EncodecFeatureExtractor


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Union[str, Any]:
     a											=EnCodecFeatureExtractionTester(self		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Union[str, 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(800					,       1400					,       200		)]
     a											=[np.asarray(__A		) 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(__A					,       __A					,       atol=1E-3		)		)

     # Test batched
     a											=feat_extract(__A					,       padding=__A					,       return_tensors='''np'''		).input_values
     a											=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		)		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Union[str, Any]:
     a											=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
     a											=np.random.rand(100		).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					,       __A		)   ->	Optional[Any]:
     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(__A		)		)[:num_samples]['''audio''']

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


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Dict:
     # fmt: off
     a											=torch.tensor(
         [2.3_8_0_4E-0_3, 2.0_7_5_2E-0_3, 1.9_8_3_6E-0_3, 2.1_0_5_7E-0_3, 1.6_1_7_4E-0_3,
          3.0_5_1_8E-0_4, 9.1_5_5_3E-0_5, 3.3_5_6_9E-0_4, 9.7_6_5_6E-0_4, 1.8_3_1_1E-0_3,
          2.0_1_4_2E-0_3, 2.1_0_5_7E-0_3, 1.7_3_9_5E-0_3, 4.5_7_7_6E-0_4, -3.9_6_7_3E-0_4,
          4.5_7_7_6E-0_4, 1.0_0_7_1E-0_3, 9.1_5_5_3E-0_5, 4.8_8_2_8E-0_4, 1.1_5_9_7E-0_3,
          7.3_2_4_2E-0_4, 9.4_6_0_4E-0_4, 1.8_0_0_5E-0_3, 1.8_3_1_1E-0_3, 8.8_5_0_1E-0_4,
          4.2_7_2_5E-0_4, 4.8_8_2_8E-0_4, 7.3_2_4_2E-0_4, 1.0_9_8_6E-0_3, 2.1_0_5_7E-0_3]		)
     # fmt: on
     a											=self._load_datasamples(1		)
     a											=EncodecFeatureExtractor()
     a											=feature_extractor(__A					,       return_tensors='''pt'''		).input_values
     self.assertEquals(input_values.shape					,       (1, 1, 9_3680)		)
     self.assertTrue(torch.allclose(input_values[0, 0, :30]					,       __A					,       atol=1E-6		)		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Dict:
     # fmt: off
     a											=torch.tensor(
         [2.3_8_0_4E-0_3, 2.0_7_5_2E-0_3, 1.9_8_3_6E-0_3, 2.1_0_5_7E-0_3, 1.6_1_7_4E-0_3,
          3.0_5_1_8E-0_4, 9.1_5_5_3E-0_5, 3.3_5_6_9E-0_4, 9.7_6_5_6E-0_4, 1.8_3_1_1E-0_3,
          2.0_1_4_2E-0_3, 2.1_0_5_7E-0_3, 1.7_3_9_5E-0_3, 4.5_7_7_6E-0_4, -3.9_6_7_3E-0_4,
          4.5_7_7_6E-0_4, 1.0_0_7_1E-0_3, 9.1_5_5_3E-0_5, 4.8_8_2_8E-0_4, 1.1_5_9_7E-0_3,
          7.3_2_4_2E-0_4, 9.4_6_0_4E-0_4, 1.8_0_0_5E-0_3, 1.8_3_1_1E-0_3, 8.8_5_0_1E-0_4,
          4.2_7_2_5E-0_4, 4.8_8_2_8E-0_4, 7.3_2_4_2E-0_4, 1.0_9_8_6E-0_3, 2.1_0_5_7E-0_3]		)
     # 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(__A					,       return_tensors='''pt'''		).input_values
     self.assertEquals(input_values.shape					,       (1, 2, 9_3680)		)
     self.assertTrue(torch.allclose(input_values[0, 0, :30]					,       __A					,       atol=1E-6		)		)
     self.assertTrue(torch.allclose(input_values[0, 1, :30]					,       EXPECTED_INPUT_VALUES * 0.5					,       atol=1E-6		)		)





 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Union[str, Any]:
     a											=self._load_datasamples(2		)
     # would be easier if the stride was like
     a											=EncodecFeatureExtractor(feature_size=1					,       chunk_length_s=1					,       overlap=0.01		)

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

     # truncate to chunk
     a											=feature_extractor(__A					,       truncation=__A					,       return_tensors='''pt'''		).input_values
     self.assertEquals(truncated_outputs.shape					,       (2, 1, 7_1520)		)  # 2 chunks

     # force truncate to max_length
     a											=feature_extractor(
         __A					,       truncation=__A					,       max_length=4_8000					,       return_tensors='''pt'''		).input_values
     self.assertEquals(truncated_outputs.shape					,       (2, 1, 4_8000)		)

     # pad to chunk
     a											=feature_extractor(__A					,       padding=__A					,       return_tensors='''pt'''		).input_values
     self.assertEquals(padded_outputs.shape					,       (2, 1, 9_5280)		)

     # pad to chunk
     a											=feature_extractor(__A					,       return_tensors='''pt'''		).input_values
     self.assertEquals(truncated_outputs.shape					,       (2, 1, 9_5280)		)

     # force pad to max length
     a											=feature_extractor(
         __A					,       padding='''max_length'''					,       max_length=10_0000					,       return_tensors='''pt'''		).input_values
     self.assertEquals(truncated_outputs.shape					,       (2, 1, 10_0000)		)

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

     a											=feature_extractor(input_audio[0]					,       padding=__A					,       return_tensors='''pt'''		).input_values
     self.assertEquals(truncated_outputs.shape					,       (1, 1, 9_3680)		)

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

     a											=feature_extractor(input_audio[0]					,       padding=__A					,       return_tensors='''pt'''		).input_values
     self.assertEquals(truncated_outputs.shape					,       (1, 1, 9_3680)		)

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

     a											=feature_extractor(input_audio[0]					,       padding=__A					,       return_tensors='''pt'''		).input_values
     self.assertEquals(truncated_outputs.shape					,       (1, 1, 9_3680)		)



"""simple docstring"""







import pytest

from datasets.utils.version import Version
@pytest.mark.parametrize(
    '''other, expected_equality'''			,       [
        (Version('''1.0.0'''						), True),
        ('''1.0.0''', True),
        (Version('''2.0.0'''						), False),
        ('''2.0.0''', False),
        ('''1''', False),
        ('''a''', False),
        (1, False),
        (None, False),
    ]			,       )
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    a											=Version('''1.0.0'''						)
    assert (version == other) is expected_equality
    assert (version != other) is not expected_equality
    assert (hash(lowercase						) == hash(lowercase						)) is expected_equality