

'''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


SCREAMING_SNAKE_CASE_    =   random.Random()

def   lowerCamelCase__       (      a__			,      a__=1.0			,      a__=None			,      a__=None)		->    List[Any]:



    """simple docstring"""







    if rng is None:
        _snake_case : Dict												=		global_rng

    _snake_case : List[str]												=		[]
    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 			SCREAMING_SNAKE_CASE					(							unittest.TestCase					):






      '''simple docstring'''






      def __init__(					self :		Tuple     ,					snake_case :		Optional[Any]     ,					snake_case :		Dict=7     ,					snake_case :		List[Any]=400     ,					snake_case :		Dict=2000     ,					snake_case :		List[Any]=1     ,					snake_case :		Any=0.0     ,					snake_case :		Any=24000     ,					snake_case :		Any=True     ,					):



          """simple docstring"""





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




      def 	__UpperCAmelCase			(					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 	__UpperCAmelCase			(					self :		List[str]     ,					snake_case :		List[Any]=False     ,					snake_case :		List[str]=False   ):



          """simple docstring"""





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

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

          return audio_inputs



@require_torch
class 			SCREAMING_SNAKE_CASE					(							lowercase_      ,unittest.TestCase					):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				Dict    =				EncodecFeatureExtractor




      def 	__UpperCAmelCase			(					self :		Any   ):



          """simple docstring"""





          _snake_case : Optional[Any]												=		EnCodecFeatureExtractionTester(self   )




      def 	__UpperCAmelCase			(					self :		List[Any]   ):



          """simple docstring"""





          _snake_case : Union[str, Any]												=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
          # create three inputs of length 800, 1000, and 1200
          _snake_case : int												=		[floats_list((1, x)   )[0] for x in range(800     ,					1400     ,					200   )]
          _snake_case : List[str]												=		[np.asarray(snake_case   ) for audio_input in audio_inputs]

          # Test not batched input
          _snake_case : Dict												=		feat_extract(audio_inputs[0]     ,					return_tensors='np'   ).input_values
          _snake_case : List[Any]												=		feat_extract(np_audio_inputs[0]     ,					return_tensors='np'   ).input_values
          self.assertTrue(np.allclose(snake_case     ,					snake_case     ,					atol=1e-3   )   )

          # Test batched
          _snake_case : List[Any]												=		feat_extract(snake_case     ,					padding=snake_case     ,					return_tensors='np'   ).input_values
          _snake_case : Optional[int]												=		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 	__UpperCAmelCase			(					self :		Dict   ):



          """simple docstring"""





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

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




      def 	__UpperCAmelCase			(					self :		Dict     ,					snake_case :		Dict   ):



          """simple docstring"""





          from datasets import load_dataset

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

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




      def 	__UpperCAmelCase			(					self :		Dict   ):



          """simple docstring"""





          _snake_case : 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
          _snake_case : List[str]												=		self._load_datasamples(1   )
          _snake_case : Union[str, Any]												=		EncodecFeatureExtractor()
          _snake_case : List[str]												=		feature_extractor(snake_case     ,					return_tensors='pt'   ).input_values
          self.assertEquals(input_values.shape     ,					(1, 1, 93680)   )
          self.assertTrue(torch.allclose(input_values[0, 0, :30]     ,					snake_case     ,					atol=1e-6   )   )




      def 	__UpperCAmelCase			(					self :		Tuple   ):



          """simple docstring"""





          _snake_case : Dict												=		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
          _snake_case : Optional[Any]												=		self._load_datasamples(1   )
          _snake_case : Optional[int]												=		[np.tile(input_audio[0][None]     ,					reps=(2, 1)   )]
          input_audio[0][1] *= 0.5
          _snake_case : int												=		EncodecFeatureExtractor(feature_size=2   )
          _snake_case : Optional[Any]												=		feature_extractor(snake_case     ,					return_tensors='pt'   ).input_values
          self.assertEquals(input_values.shape     ,					(1, 2, 93680)   )
          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 	__UpperCAmelCase			(					self :		int   ):



          """simple docstring"""





          _snake_case : Union[str, Any]												=		self._load_datasamples(2   )
          # would be easier if the stride was like
          _snake_case : Optional[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.$'     ,					):
              _snake_case : int												=		feature_extractor(
                  snake_case     ,					padding='max_length'     ,					truncation=snake_case     ,					return_tensors='pt'   ).input_values

          # truncate to chunk
          _snake_case : List[Any]												=		feature_extractor(snake_case     ,					truncation=snake_case     ,					return_tensors='pt'   ).input_values
          self.assertEquals(truncated_outputs.shape     ,					(2, 1, 71520)   )  # 2 chunks

          # force truncate to max_length
          _snake_case : Dict												=		feature_extractor(
              snake_case     ,					truncation=snake_case     ,					max_length=48000     ,					return_tensors='pt'   ).input_values
          self.assertEquals(truncated_outputs.shape     ,					(2, 1, 48000)   )

          # pad to chunk
          _snake_case : Optional[int]												=		feature_extractor(snake_case     ,					padding=snake_case     ,					return_tensors='pt'   ).input_values
          self.assertEquals(padded_outputs.shape     ,					(2, 1, 95280)   )

          # pad to chunk
          _snake_case : Optional[Any]												=		feature_extractor(snake_case     ,					return_tensors='pt'   ).input_values
          self.assertEquals(truncated_outputs.shape     ,					(2, 1, 95280)   )

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

          _snake_case : int												=		feature_extractor(input_audio[0]     ,					padding=snake_case     ,					return_tensors='pt'   ).input_values
          self.assertEquals(truncated_outputs.shape     ,					(1, 1, 93680)   )

          # no pad if no chunk_length_s
          _snake_case : 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.$'     ,					):
              _snake_case : int												=		feature_extractor(snake_case     ,					padding=snake_case     ,					return_tensors='pt'   ).input_values

          _snake_case : str												=		feature_extractor(input_audio[0]     ,					padding=snake_case     ,					return_tensors='pt'   ).input_values
          self.assertEquals(truncated_outputs.shape     ,					(1, 1, 93680)   )

          # no pad if no overlap
          _snake_case : List[str]												=		2
          _snake_case : Union[str, Any]												=		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.$'     ,					):
              _snake_case : Optional[Any]												=		feature_extractor(snake_case     ,					padding=snake_case     ,					return_tensors='pt'   ).input_values

          _snake_case : Tuple												=		feature_extractor(input_audio[0]     ,					padding=snake_case     ,					return_tensors='pt'   ).input_values
          self.assertEquals(truncated_outputs.shape     ,					(1, 1, 93680)   )






'''simple docstring'''



def   lowerCamelCase__       (      a__			,      a__)		->    int:



    """simple docstring"""







    _snake_case : Tuple												=		0
    for ele in arr:
        total_sum += product_sum(a__			,      depth + 1) if isinstance(a__			,      a__) else ele
    return total_sum * depth

def   lowerCamelCase__       (      a__)		->    int:



    """simple docstring"""







    return product_sum(a__			,      1)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



