



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_		:  List[Any]										=  random.Random()


def 						UpperCAmelCase__   (   UpperCAmelCase__			:List[Any]			,       UpperCAmelCase__			:Optional[Any]=1.0			,       UpperCAmelCase__			:Any=None			,       UpperCAmelCase__			:Dict=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 		_lowercase		(							unittest.TestCase						):

   def __init__(   self      :							Any				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							str=7				, __lowerCAmelCase      :							Optional[Any]=400				, __lowerCAmelCase      :							Dict=2000				, __lowerCAmelCase      :							Dict=1				, __lowerCAmelCase      :							List[str]=0.0				, __lowerCAmelCase      :							Tuple=2_4000				, __lowerCAmelCase      :							Dict=True				, )							->					Optional[Any]:


    """simple docstring"""






    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 		A				(   self      :							str				)							->					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 		A				(   self      :							Tuple				, __lowerCAmelCase      :							Dict=False				, __lowerCAmelCase      :							Union[str, Any]=False				)							->					List[Any]:


    """simple docstring"""






    def _flatten(__lowerCAmelCase      :							Dict				):
     return list(itertools.chain(*__lowerCAmelCase				)				)

    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(__lowerCAmelCase				) for x in audio_inputs]

    return audio_inputs



@require_torch
class 		_lowercase		(							UpperCAmelCase__, unittest.TestCase						):
   _UpperCAmelCase  						=      EncodecFeatureExtractor

   def 		A				(   self      :							List[Any]				)							->					int:


    """simple docstring"""






    a							=		EnCodecFeatureExtractionTester(self				)

   def 		A				(   self      :							Optional[Any]				)							->					List[Any]:


    """simple docstring"""






    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(__lowerCAmelCase				) 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(__lowerCAmelCase				, __lowerCAmelCase				, atol=1E-3				)				)

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

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


    """simple docstring"""






    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 		A				(   self      :							Tuple				, __lowerCAmelCase      :							str				)							->					int:


    """simple docstring"""






    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(__lowerCAmelCase				)				)[:num_samples]["audio"]

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

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


    """simple docstring"""






    a							=		torch.tensor(
        [2.38_04E-03, 2.07_52E-03, 1.98_36E-03, 2.10_57E-03, 1.61_74E-03,
         3.05_18E-04, 9.15_53E-05, 3.35_69E-04, 9.76_56E-04, 1.83_11E-03,
         2.01_42E-03, 2.10_57E-03, 1.73_95E-03, 4.57_76E-04, -3.96_73E-04,
         4.57_76E-04, 1.00_71E-03, 9.15_53E-05, 4.88_28E-04, 1.15_97E-03,
         7.32_42E-04, 9.46_04E-04, 1.80_05E-03, 1.83_11E-03, 8.85_01E-04,
         4.27_25E-04, 4.88_28E-04, 7.32_42E-04, 1.09_86E-03, 2.10_57E-03]				)
    # fmt: on
    a							=		self._load_datasamples(1				)
    a							=		EncodecFeatureExtractor()
    a							=		feature_extractor(__lowerCAmelCase				, return_tensors="pt"				).input_values
    self.assertEquals(input_values.shape				, (1, 1, 9_3680)				)
    self.assertTrue(torch.allclose(input_values[0, 0, :30]				, __lowerCAmelCase				, atol=1E-6				)				)

   def 		A				(   self      :							Dict				)							->					Optional[Any]:


    """simple docstring"""






    a							=		torch.tensor(
        [2.38_04E-03, 2.07_52E-03, 1.98_36E-03, 2.10_57E-03, 1.61_74E-03,
         3.05_18E-04, 9.15_53E-05, 3.35_69E-04, 9.76_56E-04, 1.83_11E-03,
         2.01_42E-03, 2.10_57E-03, 1.73_95E-03, 4.57_76E-04, -3.96_73E-04,
         4.57_76E-04, 1.00_71E-03, 9.15_53E-05, 4.88_28E-04, 1.15_97E-03,
         7.32_42E-04, 9.46_04E-04, 1.80_05E-03, 1.83_11E-03, 8.85_01E-04,
         4.27_25E-04, 4.88_28E-04, 7.32_42E-04, 1.09_86E-03, 2.10_57E-03]				)
    # 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(__lowerCAmelCase				, return_tensors="pt"				).input_values
    self.assertEquals(input_values.shape				, (1, 2, 9_3680)				)
    self.assertTrue(torch.allclose(input_values[0, 0, :30]				, __lowerCAmelCase				, atol=1E-6				)				)
    self.assertTrue(torch.allclose(input_values[0, 1, :30]				, EXPECTED_INPUT_VALUES * 0.5				, atol=1E-6				)				)




   def 		A				(   self      :							int				)							->					List[str]:


    """simple docstring"""






    a							=		self._load_datasamples(2				)
    # would be easier if the stride was like
    a							=		EncodecFeatureExtractor(feature_size=1				, chunk_length_s=1				, overlap=0.0_1				)

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

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

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

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

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

    # force pad to max length
    a							=		feature_extractor(
        __lowerCAmelCase				, 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(
        __lowerCAmelCase				, "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"				, ):
     a							=		feature_extractor(__lowerCAmelCase				, padding=__lowerCAmelCase				, return_tensors="pt"				).input_values

    a							=		feature_extractor(input_audio[0]				, padding=__lowerCAmelCase				, 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(
        __lowerCAmelCase				, "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"				, ):
     a							=		feature_extractor(__lowerCAmelCase				, padding=__lowerCAmelCase				, return_tensors="pt"				).input_values

    a							=		feature_extractor(input_audio[0]				, padding=__lowerCAmelCase				, 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(
        __lowerCAmelCase				, "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"				, ):
     a							=		feature_extractor(__lowerCAmelCase				, padding=__lowerCAmelCase				, return_tensors="pt"				).input_values

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






from __future__ import annotations


def 						UpperCAmelCase__   (   UpperCAmelCase__			:float | int			,       UpperCAmelCase__			:float | int			,       UpperCAmelCase__			:float | int			,       ):






 '''simple docstring'''

 if not all((nth_term, start_term_a, common_ratio_r)       ):
  return []
 a							=		[]
 a							=		1
 a							=		common_ratio_r
 for _ in range(int(UpperCAmelCase__       )       ):
  if not series:
   series.append(UpperCAmelCase__       )
  else:
   power += 1
   series.append(float(start_term_a * multiple       )       )
   a							=		pow(float(UpperCAmelCase__       )			,       UpperCAmelCase__       )
 return series


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     A_		:  Optional[Any]										=  float(input('''Enter the last number (n term) of the Geometric Series'''))
     A_		:  Optional[Any]										=  float(input('''Enter the starting term (a) of the Geometric Series'''))
     A_		:  Optional[int]										=  float(
         input('''Enter the common ratio between two terms (r) of the Geometric Series''')
     )
     print('''Formula of Geometric Series => a + ar + ar^2 ... +ar^n''')
     print(geometric_series(nth_term, start_term_a, common_ratio_r))

