

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 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=1.0		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None					)							->   Any:

     """simple docstring"""







     if rng is None:
          _lowerCAmelCase :       List[str]	   =     global_rng

     _lowerCAmelCase :       Tuple	   =     []
     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			:						int  ,    A_			:						Any  ,    A_			:						str=7  ,    A_			:						Optional[Any]=4_0_0  ,    A_			:						Any=2_0_0_0  ,    A_			:						List[Any]=1  ,    A_			:						Optional[int]=0.0  ,    A_			:						Optional[int]=2_4_0_0_0  ,    A_			:						Optional[Any]=True  ,    ):






      '''simple docstring'''




      _lowerCAmelCase :       Any	   =     parent
      _lowerCAmelCase :       Any	   =     batch_size
      _lowerCAmelCase :       List[str]	   =     min_seq_length
      _lowerCAmelCase :       Dict	   =     max_seq_length
      _lowerCAmelCase :       List[str]	   =     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      _lowerCAmelCase :       List[str]	   =     feature_size
      _lowerCAmelCase :       List[Any]	   =     padding_value
      _lowerCAmelCase :       Any	   =     sampling_rate
      _lowerCAmelCase :       str	   =     return_attention_mask

 def  __magic_name__							(		self			:						Union[str, Any]							):






      '''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  __magic_name__							(		self			:						List[Any]  ,    A_			:						Dict=False  ,    A_			:						Union[str, Any]=False							):






      '''simple docstring'''




      def _flatten(A_			:						Optional[Any]							):
           return list(itertools.chain(*A_							)							)

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

      return audio_inputs





@require_torch
class A__     (			__lowerCAmelCase	,		unittest.TestCase			):



 """simple docstring"""







 _lowercase  :       Tuple  			=    EncodecFeatureExtractor

 def  __magic_name__							(		self			:						Any							):






      '''simple docstring'''




      _lowerCAmelCase :       Optional[int]	   =     EnCodecFeatureExtractionTester(self							)

 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




      _lowerCAmelCase :       Union[str, Any]	   =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
      # create three inputs of length 800, 1000, and 1200
      _lowerCAmelCase :       Tuple	   =     [floats_list((1, x)							)[0] for x in range(8_0_0  ,    1_4_0_0  ,    2_0_0							)]
      _lowerCAmelCase :       Dict	   =     [np.asarray(A_							) for audio_input in audio_inputs]

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

      # Test batched
      _lowerCAmelCase :       Optional[int]	   =     feat_extract(A_  ,    padding=A_  ,    return_tensors="np"							).input_values
      _lowerCAmelCase :       Optional[int]	   =     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  __magic_name__							(		self			:						Dict							):






      '''simple docstring'''




      _lowerCAmelCase :       Optional[Any]	   =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
      _lowerCAmelCase :       Optional[Any]	   =     np.random.rand(1_0_0							).astype(np.floataa							)
      _lowerCAmelCase :       int	   =     np_audio_inputs.tolist()

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

 def  __magic_name__							(		self			:						List[str]  ,    A_			:						int							):






      '''simple docstring'''




      from datasets import load_dataset

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

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

 def  __magic_name__							(		self			:						Any							):






      '''simple docstring'''




      _lowerCAmelCase :       Union[str, Any]	   =     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
      _lowerCAmelCase :       str	   =     self._load_datasamples(1							)
      _lowerCAmelCase :       int	   =     EncodecFeatureExtractor()
      _lowerCAmelCase :       List[Any]	   =     feature_extractor(A_  ,    return_tensors="pt"							).input_values
      self.assertEquals(input_values.shape  ,    (1, 1, 9_3_6_8_0)							)
      self.assertTrue(torch.allclose(input_values[0, 0, :3_0]  ,    A_  ,    atol=1E-6							)							)

 def  __magic_name__							(		self			:						Tuple							):






      '''simple docstring'''




      _lowerCAmelCase :       int	   =     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
      _lowerCAmelCase :       List[Any]	   =     self._load_datasamples(1							)
      _lowerCAmelCase :       Optional[int]	   =     [np.tile(input_audio[0][None]  ,    reps=(2, 1)							)]
      input_audio[0][1] *= 0.5
      _lowerCAmelCase :       Optional[Any]	   =     EncodecFeatureExtractor(feature_size=2							)
      _lowerCAmelCase :       List[Any]	   =     feature_extractor(A_  ,    return_tensors="pt"							).input_values
      self.assertEquals(input_values.shape  ,    (1, 2, 9_3_6_8_0)							)
      self.assertTrue(torch.allclose(input_values[0, 0, :3_0]  ,    A_  ,    atol=1E-6							)							)
      self.assertTrue(torch.allclose(input_values[0, 1, :3_0]  ,    EXPECTED_INPUT_VALUES * 0.5  ,    atol=1E-6							)							)


 def  __magic_name__							(		self			:						str							):






      '''simple docstring'''




      _lowerCAmelCase :       Any	   =     self._load_datasamples(2							)
      # would be easier if the stride was like
      _lowerCAmelCase :       Any	   =     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.$"  ,    ):
           _lowerCAmelCase :       Optional[Any]	   =     feature_extractor(
               A_  ,    padding="max_length"  ,    truncation=A_  ,    return_tensors="pt"							).input_values

      # truncate to chunk
      _lowerCAmelCase :       Union[str, Any]	   =     feature_extractor(A_  ,    truncation=A_  ,    return_tensors="pt"							).input_values
      self.assertEquals(truncated_outputs.shape  ,    (2, 1, 7_1_5_2_0)							)  # 2 chunks

      # force truncate to max_length
      _lowerCAmelCase :       List[str]	   =     feature_extractor(
          A_  ,    truncation=A_  ,    max_length=4_8_0_0_0  ,    return_tensors="pt"							).input_values
      self.assertEquals(truncated_outputs.shape  ,    (2, 1, 4_8_0_0_0)							)

      # pad to chunk
      _lowerCAmelCase :       List[Any]	   =     feature_extractor(A_  ,    padding=A_  ,    return_tensors="pt"							).input_values
      self.assertEquals(padded_outputs.shape  ,    (2, 1, 9_5_2_8_0)							)

      # pad to chunk
      _lowerCAmelCase :       int	   =     feature_extractor(A_  ,    return_tensors="pt"							).input_values
      self.assertEquals(truncated_outputs.shape  ,    (2, 1, 9_5_2_8_0)							)

      # force pad to max length
      _lowerCAmelCase :       int	   =     feature_extractor(
          A_  ,    padding="max_length"  ,    max_length=1_0_0_0_0_0  ,    return_tensors="pt"							).input_values
      self.assertEquals(truncated_outputs.shape  ,    (2, 1, 1_0_0_0_0_0)							)

      # 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.$"  ,    ):
           _lowerCAmelCase :       Union[str, Any]	   =     feature_extractor(A_  ,    padding=A_  ,    return_tensors="pt"							).input_values

      _lowerCAmelCase :       str	   =     feature_extractor(input_audio[0]  ,    padding=A_  ,    return_tensors="pt"							).input_values
      self.assertEquals(truncated_outputs.shape  ,    (1, 1, 9_3_6_8_0)							)

      # no pad if no chunk_length_s
      _lowerCAmelCase :       Any	   =     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.$"  ,    ):
           _lowerCAmelCase :       Any	   =     feature_extractor(A_  ,    padding=A_  ,    return_tensors="pt"							).input_values

      _lowerCAmelCase :       Tuple	   =     feature_extractor(input_audio[0]  ,    padding=A_  ,    return_tensors="pt"							).input_values
      self.assertEquals(truncated_outputs.shape  ,    (1, 1, 9_3_6_8_0)							)

      # no pad if no overlap
      _lowerCAmelCase :       Any	   =     2
      _lowerCAmelCase :       Tuple	   =     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.$"  ,    ):
           _lowerCAmelCase :       Dict	   =     feature_extractor(A_  ,    padding=A_  ,    return_tensors="pt"							).input_values

      _lowerCAmelCase :       Any	   =     feature_extractor(input_audio[0]  ,    padding=A_  ,    return_tensors="pt"							).input_values
      self.assertEquals(truncated_outputs.shape  ,    (1, 1, 9_3_6_8_0)							)







"""simple docstring"""
from __future__ import annotations

import sys







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->int:
  if n < 0:
    raise ValueError('Negative arguments are not supported'  )
  return _fib(_SCREAMING_SNAKE_CASE  )[0]







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->tuple[int, int]:
  if n == 0:  # (F(0), F(1))
    return (0, 1)

  # F(2n) = F(n)[2F(n+1) − F(n)]
  # F(2n+1) = F(n+1)^2+F(n)^2
  a__				,       a__:	Optional[int]     				=      _fib(n // 2  )
  a__:	List[str]     				=      a * (b * 2 - a)
  a__:	Dict     				=      a * a + b * b
  return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
 lowercase__   			=      int(sys.argv[1])
 print(f"fibonacci({n}) is {fibonacci(n)}")


