"""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__   			=      random.Random()







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE=1.0						,	_SCREAMING_SNAKE_CASE=None						,	_SCREAMING_SNAKE_CASE=None  )      ->Any:
  if rng is None:
    a__:	List[str]     				=      global_rng

  a__:	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   __snake_case			(	unittest.TestCase							):



      def __init__(		self       ,     lowercase       ,     lowercase=7       ,     lowercase=4_00       ,     lowercase=20_00       ,     lowercase=1       ,     lowercase=0.0       ,     lowercase=2_40_00       ,     lowercase=True       ,     )      ->     List[str]:





        '''simple docstring'''





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



      def     lowerCamelCase_  (		self)      ->     Dict:





        '''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     lowerCamelCase_  (		self       ,     lowercase=False       ,     lowercase=False)      ->     Union[str, Any]:





        '''simple docstring'''





        def _flatten(lowercase):
          return list(itertools.chain(*lowercase))

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

        return audio_inputs







@require_torch
class   __snake_case			(	__lowerCAmelCase			, unittest.TestCase							):
      a__								=						EncodecFeatureExtractor



      def     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





        a__:	Optional[int]     				=      EnCodecFeatureExtractionTester(self)



      def     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





        a__:	Union[str, Any]     				=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        # create three inputs of length 800, 1000, and 1200
        a__:	Tuple     				=      [floats_list((1, x))[0] for x in range(8_00       ,     14_00       ,     2_00)]
        a__:	Dict     				=      [np.asarray(lowercase) for audio_input in audio_inputs]

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

        # Test batched
        a__:	Optional[int]     				=      feat_extract(lowercase       ,     padding=lowercase       ,     return_tensors='np').input_values
        a__:	Optional[int]     				=      feat_extract(lowercase       ,     padding=lowercase       ,     return_tensors='np').input_values
        for enc_seq_a, enc_seq_a in zip(lowercase       ,     lowercase):
          self.assertTrue(np.allclose(lowercase       ,     lowercase       ,     atol=1e-3))



      def     lowerCamelCase_  (		self)      ->     Optional[int]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:	Optional[Any]     				=      np.random.rand(1_00).astype(np.floataa)
        a__:	int     				=      np_audio_inputs.tolist()

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



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Tuple:





        '''simple docstring'''





        from datasets import load_dataset

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

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



      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        a__:	Union[str, Any]     				=      torch.tensor(
            [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
             3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
             2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
             4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
             7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
             4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03])
        # fmt: on
        a__:	str     				=      self._load_datasamples(1)
        a__:	int     				=      EncodecFeatureExtractor()
        a__:	List[Any]     				=      feature_extractor(lowercase       ,     return_tensors='pt').input_values
        self.assertEquals(input_values.shape       ,     (1, 1, 9_36_80))
        self.assertTrue(torch.allclose(input_values[0, 0, :30]       ,     lowercase       ,     atol=1e-6))



      def     lowerCamelCase_  (		self)      ->     Tuple:





        '''simple docstring'''





        a__:	int     				=      torch.tensor(
            [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
             3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
             2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
             4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
             7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
             4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03])
        # fmt: on
        a__:	List[Any]     				=      self._load_datasamples(1)
        a__:	Optional[int]     				=      [np.tile(input_audio[0][None]       ,     reps=(2, 1))]
        input_audio[0][1] *= 0.5
        a__:	Optional[Any]     				=      EncodecFeatureExtractor(feature_size=2)
        a__:	List[Any]     				=      feature_extractor(lowercase       ,     return_tensors='pt').input_values
        self.assertEquals(input_values.shape       ,     (1, 2, 9_36_80))
        self.assertTrue(torch.allclose(input_values[0, 0, :30]       ,     lowercase       ,     atol=1e-6))
        self.assertTrue(torch.allclose(input_values[0, 1, :30]       ,     EXPECTED_INPUT_VALUES * 0.5       ,     atol=1e-6))



      def     lowerCamelCase_  (		self)      ->     Any:





        '''simple docstring'''





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

        # pad and trunc raise an error ?
        with self.assertRaisesRegex(
            lowercase       ,     '^Both padding and truncation were set. Make sure you only set one.$'       ,     ):
          a__:	Optional[Any]     				=      feature_extractor(
              lowercase       ,     padding='max_length'       ,     truncation=lowercase       ,     return_tensors='pt').input_values

        # truncate to chunk
        a__:	Union[str, Any]     				=      feature_extractor(lowercase       ,     truncation=lowercase       ,     return_tensors='pt').input_values
        self.assertEquals(truncated_outputs.shape       ,     (2, 1, 7_15_20))  # 2 chunks

        # force truncate to max_length
        a__:	List[str]     				=      feature_extractor(
            lowercase       ,     truncation=lowercase       ,     max_length=4_80_00       ,     return_tensors='pt').input_values
        self.assertEquals(truncated_outputs.shape       ,     (2, 1, 4_80_00))

        # pad to chunk
        a__:	List[Any]     				=      feature_extractor(lowercase       ,     padding=lowercase       ,     return_tensors='pt').input_values
        self.assertEquals(padded_outputs.shape       ,     (2, 1, 9_52_80))

        # pad to chunk
        a__:	int     				=      feature_extractor(lowercase       ,     return_tensors='pt').input_values
        self.assertEquals(truncated_outputs.shape       ,     (2, 1, 9_52_80))

        # force pad to max length
        a__:	int     				=      feature_extractor(
            lowercase       ,     padding='max_length'       ,     max_length=10_00_00       ,     return_tensors='pt').input_values
        self.assertEquals(truncated_outputs.shape       ,     (2, 1, 10_00_00))

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

        a__:	str     				=      feature_extractor(input_audio[0]       ,     padding=lowercase       ,     return_tensors='pt').input_values
        self.assertEquals(truncated_outputs.shape       ,     (1, 1, 9_36_80))

        # no pad if no chunk_length_s
        a__:	Any     				=      None
        with self.assertRaisesRegex(
            lowercase       ,     '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'       ,     ):
          a__:	Any     				=      feature_extractor(lowercase       ,     padding=lowercase       ,     return_tensors='pt').input_values

        a__:	Tuple     				=      feature_extractor(input_audio[0]       ,     padding=lowercase       ,     return_tensors='pt').input_values
        self.assertEquals(truncated_outputs.shape       ,     (1, 1, 9_36_80))

        # no pad if no overlap
        a__:	Any     				=      2
        a__:	Tuple     				=      None
        with self.assertRaisesRegex(
            lowercase       ,     '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'       ,     ):
          a__:	Dict     				=      feature_extractor(lowercase       ,     padding=lowercase       ,     return_tensors='pt').input_values

        a__:	Any     				=      feature_extractor(input_audio[0]       ,     padding=lowercase       ,     return_tensors='pt').input_values
        self.assertEquals(truncated_outputs.shape       ,     (1, 1, 9_36_80))





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__							=				{"configuration_pegasus": ["PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusConfig"]}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				["PegasusTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				["PegasusTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				[
						    "PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "PegasusForCausalLM",
						    "PegasusForConditionalGeneration",
						    "PegasusModel",
						    "PegasusPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				[
						    "TFPegasusForConditionalGeneration",
						    "TFPegasusModel",
						    "TFPegasusPreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				[
						    "FlaxPegasusForConditionalGeneration",
						    "FlaxPegasusModel",
						    "FlaxPegasusPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_pegasus import PegasusTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_pegasus_fast import PegasusTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_pegasus import (
												    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PegasusForCausalLM,
												    PegasusForConditionalGeneration,
												    PegasusModel,
												    PegasusPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_pegasus import (
												    FlaxPegasusForConditionalGeneration,
												    FlaxPegasusModel,
												    FlaxPegasusPreTrainedModel,
												)

else:
						import sys

						A__							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




