



"""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


lowercase__						:							Any        =      random.Random()

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Dict       ,  lowerCAmelCase__ :							Any=1.0       ,  lowerCAmelCase__ :							List[str]=None       ,  lowerCAmelCase__ :							Optional[Any]=None		)		->  str:

  """simple docstring"""




  if rng is None:
    lowerCAmelCase_  :							Any								=					global_rng

  lowerCAmelCase_  :							List[Any]								=					[]
  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  UpperCamelCase__  (      unittest.TestCase    ):




      """simple docstring"""




      def __init__( self							: str       ,     SCREAMING_SNAKE_CASE_							: Union[str, Any]       ,     SCREAMING_SNAKE_CASE_							: Any=7       ,     SCREAMING_SNAKE_CASE_							: Tuple=4_0_0       ,     SCREAMING_SNAKE_CASE_							: Optional[Any]=2_0_0_0       ,     SCREAMING_SNAKE_CASE_							: int=1       ,     SCREAMING_SNAKE_CASE_							: List[str]=0.0       ,     SCREAMING_SNAKE_CASE_							: Any=2_4_0_0_0       ,     SCREAMING_SNAKE_CASE_							: Dict=True       ,     ):
        lowerCAmelCase_  :							Dict								=					parent
        lowerCAmelCase_  :							Tuple								=					batch_size
        lowerCAmelCase_  :							int								=					min_seq_length
        lowerCAmelCase_  :							Any								=					max_seq_length
        lowerCAmelCase_  :							Any								=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        lowerCAmelCase_  :							Any								=					feature_size
        lowerCAmelCase_  :							Tuple								=					padding_value
        lowerCAmelCase_  :							str								=					sampling_rate
        lowerCAmelCase_  :							str								=					return_attention_mask



      def 	SCREAMING_SNAKE_CASE__					( self							: Any			):
        return {
            "feature_size": self.feature_size,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
        }





      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[int]       ,     SCREAMING_SNAKE_CASE_							: Tuple=False       ,     SCREAMING_SNAKE_CASE_							: Any=False			):
        def _flatten(SCREAMING_SNAKE_CASE_							: Optional[Any]			):
          return list(itertools.chain(*SCREAMING_SNAKE_CASE_			)			)

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

        return audio_inputs







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




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	EncodecFeatureExtractor



      def 	SCREAMING_SNAKE_CASE__					( self							: Dict			):
        lowerCAmelCase_  :							int								=					EnCodecFeatureExtractionTester(self			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Union[str, Any]			):
        # Tests that all call wrap to encode_plus and batch_encode_plus
        lowerCAmelCase_  :							Optional[int]								=					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_  :							List[str]								=					[np.asarray(SCREAMING_SNAKE_CASE_			) 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[Any]								=					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
        lowerCAmelCase_  :							int								=					feat_extract(SCREAMING_SNAKE_CASE_       ,     padding=SCREAMING_SNAKE_CASE_       ,     return_tensors='np'			).input_values
        lowerCAmelCase_  :							str								=					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 	SCREAMING_SNAKE_CASE__					( self							: List[Any]			):
        lowerCAmelCase_  :							int								=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
        lowerCAmelCase_  :							Optional[int]								=					np.random.rand(1_0_0			).astype(np.floataa			)
        lowerCAmelCase_  :							str								=					np_audio_inputs.tolist()

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



      def 	SCREAMING_SNAKE_CASE__					( self							: Tuple       ,     SCREAMING_SNAKE_CASE_							: str			):
        from datasets import load_dataset

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

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



      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[Any]			):
        # fmt: off
        lowerCAmelCase_  :							Dict								=					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
        lowerCAmelCase_  :							int								=					self._load_datasamples(1			)
        lowerCAmelCase_  :							List[Any]								=					EncodecFeatureExtractor()
        lowerCAmelCase_  :							Optional[Any]								=					feature_extractor(SCREAMING_SNAKE_CASE_       ,     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]       ,     SCREAMING_SNAKE_CASE_       ,     atol=1E-6			)			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Tuple			):
        # fmt: off
        lowerCAmelCase_  :							Any								=					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
        lowerCAmelCase_  :							int								=					self._load_datasamples(1			)
        lowerCAmelCase_  :							Dict								=					[np.tile(input_audio[0][None]       ,     reps=(2, 1)			)]
        input_audio[0][1] *= 0.5
        lowerCAmelCase_  :							Optional[int]								=					EncodecFeatureExtractor(feature_size=2			)
        lowerCAmelCase_  :							List[Any]								=					feature_extractor(SCREAMING_SNAKE_CASE_       ,     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]       ,     SCREAMING_SNAKE_CASE_       ,     atol=1E-6			)			)
        self.assertTrue(torch.allclose(input_values[0, 1, :3_0]       ,     EXPECTED_INPUT_VALUES * 0.5       ,     atol=1E-6			)			)





      def 	SCREAMING_SNAKE_CASE__					( self							: List[Any]			):
        lowerCAmelCase_  :							int								=					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(
            SCREAMING_SNAKE_CASE_       ,     '^Both padding and truncation were set. Make sure you only set one.$'       ,     ):
          lowerCAmelCase_  :							List[str]								=					feature_extractor(
              SCREAMING_SNAKE_CASE_       ,     padding='max_length'       ,     truncation=SCREAMING_SNAKE_CASE_       ,     return_tensors='pt'			).input_values

        # truncate to chunk
        lowerCAmelCase_  :							List[str]								=					feature_extractor(SCREAMING_SNAKE_CASE_       ,     truncation=SCREAMING_SNAKE_CASE_       ,     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_  :							Tuple								=					feature_extractor(
            SCREAMING_SNAKE_CASE_       ,     truncation=SCREAMING_SNAKE_CASE_       ,     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_  :							Union[str, Any]								=					feature_extractor(SCREAMING_SNAKE_CASE_       ,     padding=SCREAMING_SNAKE_CASE_       ,     return_tensors='pt'			).input_values
        self.assertEquals(padded_outputs.shape       ,     (2, 1, 9_5_2_8_0)			)

        # pad to chunk
        lowerCAmelCase_  :							Union[str, Any]								=					feature_extractor(SCREAMING_SNAKE_CASE_       ,     return_tensors='pt'			).input_values
        self.assertEquals(truncated_outputs.shape       ,     (2, 1, 9_5_2_8_0)			)

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

        lowerCAmelCase_  :							str								=					feature_extractor(input_audio[0]       ,     padding=SCREAMING_SNAKE_CASE_       ,     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_  :							List[str]								=					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.$'       ,     ):
          lowerCAmelCase_  :							Optional[int]								=					feature_extractor(SCREAMING_SNAKE_CASE_       ,     padding=SCREAMING_SNAKE_CASE_       ,     return_tensors='pt'			).input_values

        lowerCAmelCase_  :							Dict								=					feature_extractor(input_audio[0]       ,     padding=SCREAMING_SNAKE_CASE_       ,     return_tensors='pt'			).input_values
        self.assertEquals(truncated_outputs.shape       ,     (1, 1, 9_3_6_8_0)			)

        # no pad if no overlap
        lowerCAmelCase_  :							int								=					2
        lowerCAmelCase_  :							int								=					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.$'       ,     ):
          lowerCAmelCase_  :							Tuple								=					feature_extractor(SCREAMING_SNAKE_CASE_       ,     padding=SCREAMING_SNAKE_CASE_       ,     return_tensors='pt'			).input_values

        lowerCAmelCase_  :							Tuple								=					feature_extractor(input_audio[0]       ,     padding=SCREAMING_SNAKE_CASE_       ,     return_tensors='pt'			).input_values
        self.assertEquals(truncated_outputs.shape       ,     (1, 1, 9_3_6_8_0)			)










"""simple docstring"""







import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
     import jax







@require_flax
class  UpperCamelCase__  :




      """simple docstring"""




      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[int]			):
        lowerCAmelCase_		,lowerCAmelCase_  :							Any								=					self.prepare_init_args_and_inputs_for_common()

        lowerCAmelCase_  :							List[str]								=					self.model_class(**SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Any								=					model.init(inputs_dict['prng_key']       ,     inputs_dict['sample']			)
        jax.lax.stop_gradient(SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Tuple								=					model.apply(SCREAMING_SNAKE_CASE_       ,     inputs_dict['sample']			)

        if isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			):
          lowerCAmelCase_  :							str								=					output.sample

        self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Any								=					inputs_dict['sample'].shape
        self.assertEqual(output.shape       ,     SCREAMING_SNAKE_CASE_       ,     'Input and output shapes do not match'			)



      def 	SCREAMING_SNAKE_CASE__					( self							: List[str]			):
        lowerCAmelCase_		,lowerCAmelCase_  :							Union[str, Any]								=					self.prepare_init_args_and_inputs_for_common()

        lowerCAmelCase_  :							Tuple								=					1_6
        lowerCAmelCase_  :							Dict								=					(1_6, 3_2)

        lowerCAmelCase_  :							Any								=					self.model_class(**SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Optional[Any]								=					model.init(inputs_dict['prng_key']       ,     inputs_dict['sample']			)
        jax.lax.stop_gradient(SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Optional[Any]								=					model.apply(SCREAMING_SNAKE_CASE_       ,     inputs_dict['sample']			)

        if isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			):
          lowerCAmelCase_  :							Dict								=					output.sample

        self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Optional[Any]								=					inputs_dict['sample'].shape
        self.assertEqual(output.shape       ,     SCREAMING_SNAKE_CASE_       ,     'Input and output shapes do not match'			)





      def 	SCREAMING_SNAKE_CASE__					( self							: List[Any]			):
        lowerCAmelCase_  :							List[str]								=					'kwargs' in inspect.signature(self.model_class.__init__			).parameters
        lowerCAmelCase_  :							Optional[Any]								=					len(self.model_class._deprecated_kwargs			) > 0

        if has_kwarg_in_model_class and not has_deprecated_kwarg:
          raise ValueError(
              F"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
              ' under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are'
              ' no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ='
              ' [<deprecated_argument>]`'			)

        if not has_kwarg_in_model_class and has_deprecated_kwarg:
          raise ValueError(
              F"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
              ' under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to'
              F" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
              ' from `_deprecated_kwargs = [<deprecated_argument>]`'			)





