






"""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


UpperCamelCase			=      random.Random()






def     A(  snake_case_						,       snake_case_=1.0						,       snake_case_=None						,       snake_case_=None		):
       """simple docstring"""





       if rng is None:
              lowercase__:    Union[str, Any]        =      global_rng

       lowercase__:    Dict        =      []
       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			,				UpperCAmelCase_			,				UpperCAmelCase_=7			,				UpperCAmelCase_=400			,				UpperCAmelCase_=2_000			,				UpperCAmelCase_=1			,				UpperCAmelCase_=0.0			,				UpperCAmelCase_=24_000			,				UpperCAmelCase_=True			,				)	-> Any:




          '''simple docstring'''




          lowercase__:    Optional[Any]        =      parent
          lowercase__:    Optional[int]        =      batch_size
          lowercase__:    Dict        =      min_seq_length
          lowercase__:    Any        =      max_seq_length
          lowercase__:    List[Any]        =      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
          lowercase__:    int        =      feature_size
          lowercase__:    Tuple        =      padding_value
          lowercase__:    int        =      sampling_rate
          lowercase__:    Optional[int]        =      return_attention_mask





   def __lowercase    (   self)	-> int:




          '''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,
          }





   def __lowercase    (   self			,				UpperCAmelCase_=False			,				UpperCAmelCase_=False)	-> Optional[int]:




          '''simple docstring'''




          def _flatten(UpperCAmelCase_):
                 return list(itertools.chain(*snake_case_))

          if equal_length:
                 lowercase__:    List[Any]        =      floats_list((self.batch_size, self.max_seq_length))
          else:
                 # make sure that inputs increase in size
                 lowercase__:    List[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:
                 lowercase__:    str        =      [np.asarray(snake_case_) for x in audio_inputs]

          return audio_inputs







@require_torch
class 		_a       (		_a    ,					unittest.TestCase   ):





   '''simple docstring'''






   UpperCamelCase__      					=    EncodecFeatureExtractor





   def __lowercase    (   self)	-> Union[str, Any]:




          '''simple docstring'''




          lowercase__:    Union[str, Any]        =      EnCodecFeatureExtractionTester(self)





   def __lowercase    (   self)	-> str:




          '''simple docstring'''




          lowercase__:    List[Any]        =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
          # create three inputs of length 800, 1000, and 1200
          lowercase__:    List[str]        =      [floats_list((1, x))[0] for x in range(800			,				1_400			,				200)]
          lowercase__:    Any        =      [np.asarray(snake_case_) for audio_input in audio_inputs]

          # Test not batched input
          lowercase__:    List[str]        =      feat_extract(audio_inputs[0]			,				return_tensors="np").input_values
          lowercase__:    Optional[int]        =      feat_extract(np_audio_inputs[0]			,				return_tensors="np").input_values
          self.assertTrue(np.allclose(snake_case_			,				snake_case_			,				atol=1E-3))

          # Test batched
          lowercase__:    List[str]        =      feat_extract(snake_case_			,				padding=snake_case_			,				return_tensors="np").input_values
          lowercase__:    Dict        =      feat_extract(snake_case_			,				padding=snake_case_			,				return_tensors="np").input_values
          for enc_seq_a, enc_seq_a in zip(snake_case_			,				snake_case_):
                 self.assertTrue(np.allclose(snake_case_			,				snake_case_			,				atol=1E-3))





   def __lowercase    (   self)	-> List[str]:




          '''simple docstring'''




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

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





   def __lowercase    (   self			,				UpperCAmelCase_)	-> Union[str, Any]:




          '''simple docstring'''




          from datasets import load_dataset

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

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





   def __lowercase    (   self)	-> Tuple:




          '''simple docstring'''




          lowercase__:    Optional[int]        =      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
          lowercase__:    str        =      self._load_datasamples(1)
          lowercase__:    Tuple        =      EncodecFeatureExtractor()
          lowercase__:    List[str]        =      feature_extractor(snake_case_			,				return_tensors="pt").input_values
          self.assertEquals(input_values.shape			,				(1, 1, 93_680))
          self.assertTrue(torch.allclose(input_values[0, 0, :30]			,				snake_case_			,				atol=1E-6))





   def __lowercase    (   self)	-> Tuple:




          '''simple docstring'''




          lowercase__:    Any        =      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
          lowercase__:    Optional[Any]        =      self._load_datasamples(1)
          lowercase__:    Any        =      [np.tile(input_audio[0][None]			,				reps=(2, 1))]
          input_audio[0][1] *= 0.5
          lowercase__:    Dict        =      EncodecFeatureExtractor(feature_size=2)
          lowercase__:    List[Any]        =      feature_extractor(snake_case_			,				return_tensors="pt").input_values
          self.assertEquals(input_values.shape			,				(1, 2, 93_680))
          self.assertTrue(torch.allclose(input_values[0, 0, :30]			,				snake_case_			,				atol=1E-6))
          self.assertTrue(torch.allclose(input_values[0, 1, :30]			,				EXPECTED_INPUT_VALUES * 0.5			,				atol=1E-6))





   def __lowercase    (   self)	-> List[str]:




          '''simple docstring'''




          lowercase__:    str        =      self._load_datasamples(2)
          # would be easier if the stride was like
          lowercase__:    int        =      EncodecFeatureExtractor(feature_size=1			,				chunk_length_s=1			,				overlap=0.01)

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

          # truncate to chunk
          lowercase__:    Any        =      feature_extractor(snake_case_			,				truncation=snake_case_			,				return_tensors="pt").input_values
          self.assertEquals(truncated_outputs.shape			,				(2, 1, 71_520))  # 2 chunks

          # force truncate to max_length
          lowercase__:    Tuple        =      feature_extractor(
              snake_case_			,				truncation=snake_case_			,				max_length=48_000			,				return_tensors="pt").input_values
          self.assertEquals(truncated_outputs.shape			,				(2, 1, 48_000))

          # pad to chunk
          lowercase__:    Dict        =      feature_extractor(snake_case_			,				padding=snake_case_			,				return_tensors="pt").input_values
          self.assertEquals(padded_outputs.shape			,				(2, 1, 95_280))

          # pad to chunk
          lowercase__:    Any        =      feature_extractor(snake_case_			,				return_tensors="pt").input_values
          self.assertEquals(truncated_outputs.shape			,				(2, 1, 95_280))

          # force pad to max length
          lowercase__:    Any        =      feature_extractor(
              snake_case_			,				padding="max_length"			,				max_length=100_000			,				return_tensors="pt").input_values
          self.assertEquals(truncated_outputs.shape			,				(2, 1, 100_000))

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

          lowercase__:    Any        =      feature_extractor(input_audio[0]			,				padding=snake_case_			,				return_tensors="pt").input_values
          self.assertEquals(truncated_outputs.shape			,				(1, 1, 93_680))

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

          lowercase__:    Tuple        =      feature_extractor(input_audio[0]			,				padding=snake_case_			,				return_tensors="pt").input_values
          self.assertEquals(truncated_outputs.shape			,				(1, 1, 93_680))

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

          lowercase__:    List[Any]        =      feature_extractor(input_audio[0]			,				padding=snake_case_			,				return_tensors="pt").input_values
          self.assertEquals(truncated_outputs.shape			,				(1, 1, 93_680))














"""simple docstring"""





def     A(  snake_case_		):
       """simple docstring"""





       if not isinstance(snake_case_						,       snake_case_		):
              raise ValueError("Input series is not valid, valid series - [1, 2/3, 2]"		)
       if len(snake_case_		) == 0:
              raise ValueError("Input list must be a non empty list"		)
       if len(snake_case_		) == 1 and series[0] != 0:
              return True
       lowercase__:    str        =      []
       lowercase__:    Optional[int]        =      len(snake_case_		)
       for i in range(0						,       snake_case_		):
              if series[i] == 0:
                     raise ValueError("Input series cannot have 0 as an element"		)
              rec_series.append(1 / series[i]		)
       lowercase__:    Optional[int]        =      rec_series[1] - rec_series[0]
       for index in range(2						,       snake_case_		):
              if rec_series[index] - rec_series[index - 1] != common_diff:
                     return False
       return True






def     A(  snake_case_		):
       """simple docstring"""





       if not isinstance(snake_case_						,       snake_case_		):
              raise ValueError("Input series is not valid, valid series - [2, 4, 6]"		)
       if len(snake_case_		) == 0:
              raise ValueError("Input list must be a non empty list"		)
       lowercase__:    Optional[int]        =      0
       for val in series:
              answer += 1 / val
       return len(snake_case_		) / answer


if __name__ == "__main__":
  import doctest

  doctest.testmod()






