






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


__A  							=  random.Random()







def 			lowercase__  (				A_:	Union[str, Any]     ,			A_:	Union[str, Any]=1.0     ,			A_:	List[str]=None     ,			A_:	Dict=None		)  ->							Dict:







       """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
class 							_A (    unittest.TestCase							):
    """simple docstring"""

    def __init__(							self			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    int   ,    __SCREAMING_SNAKE_CASE			:    List[str]=7   ,    __SCREAMING_SNAKE_CASE			:    List[str]=400   ,    __SCREAMING_SNAKE_CASE			:    Tuple=2000   ,    __SCREAMING_SNAKE_CASE			:    int=1   ,    __SCREAMING_SNAKE_CASE			:    Any=0.0   ,    __SCREAMING_SNAKE_CASE			:    str=24000   ,    __SCREAMING_SNAKE_CASE			:    Tuple=True   ,    )			->       List[Any]:
           __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    							=padding_value
           __UpperCAmelCase    							=sampling_rate
           __UpperCAmelCase    							=return_attention_mask



    def _a       (							self			:    str			)			->       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 _a       (							self			:    Any   ,    __SCREAMING_SNAKE_CASE			:    List[str]=False   ,    __SCREAMING_SNAKE_CASE			:    str=False			)			->       List[Any]:
           def _flatten(__SCREAMING_SNAKE_CASE			:    Dict			):
                  return list(itertools.chain(*__SCREAMING_SNAKE_CASE			)			)

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

           return audio_inputs




@require_torch
class 							_A (    UpperCamelCase				,				unittest.TestCase							):
    """simple docstring"""

    lowerCamelCase  :       Dict   =   EncodecFeatureExtractor



    def _a       (							self			:    int			)			->       Any:
           __UpperCAmelCase    							=EnCodecFeatureExtractionTester(self			)



    def _a       (							self			:    Optional[int]			)			->       str:
           # 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   ,    1400   ,    200			)]
           __UpperCAmelCase    							=[np.asarray(__SCREAMING_SNAKE_CASE			) for audio_input in audio_inputs]

           # Test not batched input
           __UpperCAmelCase    							=feat_extract(audio_inputs[0]   ,    return_tensors="""np"""			).input_values
           __UpperCAmelCase    							=feat_extract(np_audio_inputs[0]   ,    return_tensors="""np"""			).input_values
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    atol=1e-3			)			)

           # Test batched
           __UpperCAmelCase    							=feat_extract(__SCREAMING_SNAKE_CASE   ,    padding=__SCREAMING_SNAKE_CASE   ,    return_tensors="""np"""			).input_values
           __UpperCAmelCase    							=feat_extract(__SCREAMING_SNAKE_CASE   ,    padding=__SCREAMING_SNAKE_CASE   ,    return_tensors="""np"""			).input_values
           for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			):
                  self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    atol=1e-3			)			)



    def _a       (							self			:    Optional[Any]			)			->       Optional[Any]:
           __UpperCAmelCase    							=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
           __UpperCAmelCase    							=np.random.rand(100			).astype(np.floataa			)
           __UpperCAmelCase    							=np_audio_inputs.tolist()

           for inputs in [py_audio_inputs, np_audio_inputs]:
                  __UpperCAmelCase    							=feature_extractor.pad([{"""input_values""": inputs}]   ,    return_tensors="""np"""			)
                  self.assertTrue(np_processed.input_values.dtype == np.floataa			)
                  __UpperCAmelCase    							=feature_extractor.pad([{"""input_values""": inputs}]   ,    return_tensors="""pt"""			)
                  self.assertTrue(pt_processed.input_values.dtype == torch.floataa			)



    def _a       (							self			:    str   ,    __SCREAMING_SNAKE_CASE			:    str			)			->       Optional[Any]:
           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(__SCREAMING_SNAKE_CASE			)			)[:num_samples]["""audio"""]

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



    def _a       (							self			:    Optional[int]			)			->       int:
           # fmt: off
           __UpperCAmelCase    							=torch.tensor(
               [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
                3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
                2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
                4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
                7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
                4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]			)
           # fmt: on
           __UpperCAmelCase    							=self._load_datasamples(1			)
           __UpperCAmelCase    							=EncodecFeatureExtractor()
           __UpperCAmelCase    							=feature_extractor(__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""			).input_values
           self.assertEquals(input_values.shape   ,    (1, 1, 93680)			)
           self.assertTrue(torch.allclose(input_values[0, 0, :30]   ,    __SCREAMING_SNAKE_CASE   ,    atol=1e-6			)			)



    def _a       (							self			:    Dict			)			->       List[str]:
           # fmt: off
           __UpperCAmelCase    							=torch.tensor(
               [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
                3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
                2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
                4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
                7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
                4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]			)
           # fmt: on
           __UpperCAmelCase    							=self._load_datasamples(1			)
           __UpperCAmelCase    							=[np.tile(input_audio[0][None]   ,    reps=(2, 1)			)]
           input_audio[0][1] *= 0.5
           __UpperCAmelCase    							=EncodecFeatureExtractor(feature_size=2			)
           __UpperCAmelCase    							=feature_extractor(__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""			).input_values
           self.assertEquals(input_values.shape   ,    (1, 2, 93680)			)
           self.assertTrue(torch.allclose(input_values[0, 0, :30]   ,    __SCREAMING_SNAKE_CASE   ,    atol=1e-6			)			)
           self.assertTrue(torch.allclose(input_values[0, 1, :30]   ,    EXPECTED_INPUT_VALUES * 0.5   ,    atol=1e-6			)			)



    def _a       (							self			:    Optional[int]			)			->       Optional[int]:
           __UpperCAmelCase    							=self._load_datasamples(2			)
           # would be easier if the stride was like
           __UpperCAmelCase    							=EncodecFeatureExtractor(feature_size=1   ,    chunk_length_s=1   ,    overlap=0.01			)

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

           # truncate to chunk
           __UpperCAmelCase    							=feature_extractor(__SCREAMING_SNAKE_CASE   ,    truncation=__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape   ,    (2, 1, 71520)			)  # 2 chunks

           # force truncate to max_length
           __UpperCAmelCase    							=feature_extractor(
               __SCREAMING_SNAKE_CASE   ,    truncation=__SCREAMING_SNAKE_CASE   ,    max_length=48000   ,    return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape   ,    (2, 1, 48000)			)

           # pad to chunk
           __UpperCAmelCase    							=feature_extractor(__SCREAMING_SNAKE_CASE   ,    padding=__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""			).input_values
           self.assertEquals(padded_outputs.shape   ,    (2, 1, 95280)			)

           # pad to chunk
           __UpperCAmelCase    							=feature_extractor(__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape   ,    (2, 1, 95280)			)

           # force pad to max length
           __UpperCAmelCase    							=feature_extractor(
               __SCREAMING_SNAKE_CASE   ,    padding="""max_length"""   ,    max_length=100000   ,    return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape   ,    (2, 1, 100000)			)

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

           __UpperCAmelCase    							=feature_extractor(input_audio[0]   ,    padding=__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape   ,    (1, 1, 93680)			)

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

           __UpperCAmelCase    							=feature_extractor(input_audio[0]   ,    padding=__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape   ,    (1, 1, 93680)			)

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

           __UpperCAmelCase    							=feature_extractor(input_audio[0]   ,    padding=__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape   ,    (1, 1, 93680)			)












from collections import namedtuple

__A  							=  namedtuple("from_to", "from_ to")

__A  							=  {
    "millimeter": "mm",
    "centimeter": "cm",
    "meter": "m",
    "kilometer": "km",
    "inch": "in",
    "inche": "in",  # Trailing 's' has been stripped off
    "feet": "ft",
    "foot": "ft",
    "yard": "yd",
    "mile": "mi",
}

__A  							=  {
    "mm": from_to(0.001, 10_00),
    "cm": from_to(0.01, 1_00),
    "m": from_to(1, 1),
    "km": from_to(10_00, 0.001),
    "in": from_to(0.0254, 39.3701),
    "ft": from_to(0.3048, 3.2_8084),
    "yd": from_to(0.9144, 1.0_9361),
    "mi": from_to(1609.34, 0.0_0062_1371),
}







def 			lowercase__  (				A_:	float     ,			A_:	str     ,			A_:	str		)  ->							float:







       """simple docstring"""






       __UpperCAmelCase    							=from_type.lower().rstrip("""s"""		)
       __UpperCAmelCase    							=TYPE_CONVERSION.get(A_     ,			A_		)
       __UpperCAmelCase    							=to_type.lower().rstrip("""s"""		)
       __UpperCAmelCase    							=TYPE_CONVERSION.get(A_     ,			A_		)
       if new_from not in METRIC_CONVERSION:
              __UpperCAmelCase    							=(
                  F'''Invalid \'from_type\' value: {from_type!r}.\n'''
                  F'''Conversion abbreviations are: {", ".join(A_		)}'''
              )
              raise ValueError(A_		)
       if new_to not in METRIC_CONVERSION:
              __UpperCAmelCase    							=(
                  F'''Invalid \'to_type\' value: {to_type!r}.\n'''
                  F'''Conversion abbreviations are: {", ".join(A_		)}'''
              )
              raise ValueError(A_		)
       return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
   import doctest

   doctest.testmod()




