



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_       :Tuple				  =				random.Random()
def 	lowercase_							(A			:			Optional[int]		, A			:			str=1.0		, A			:			Union[str, Any]=None		, A			:			Dict=None				):
       if rng is None:
              snake_case__							:     Tuple            =     global_rng

       snake_case__							:     Dict            =     []
       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					):






     """simple docstring"""


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






     def        lowercase_       (	self  :  Optional[int] )       ->Union[str, 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        lowercase_       (	self  :  Dict,			_snake_case  :  Any=False,			_snake_case  :  Tuple=False )       ->str:
            def _flatten(_snake_case  :  Union[str, Any] ):
                   return list(itertools.chain(*_snake_case ) )

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

            return audio_inputs




@require_torch
class  snake_case__						(	lowerCAmelCase_    ,					unittest.TestCase					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     EncodecFeatureExtractor






     def        lowercase_       (	self  :  Optional[Any] )       ->Tuple:
            snake_case__							:     Optional[Any]            =     EnCodecFeatureExtractionTester(self )






     def        lowercase_       (	self  :  Union[str, Any] )       ->str:
            # Tests that all call wrap to encode_plus and batch_encode_plus
            snake_case__							:     Tuple            =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
            # create three inputs of length 800, 1000, and 1200
            snake_case__							:     str            =     [floats_list((1, x) )[0] for x in range(8_0_0,			1_4_0_0,			2_0_0 )]
            snake_case__							:     Tuple            =     [np.asarray(_snake_case ) for audio_input in audio_inputs]

            # Test not batched input
            snake_case__							:     List[str]            =     feat_extract(audio_inputs[0],			return_tensors='np' ).input_values
            snake_case__							:     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__							:     Any            =     feat_extract(_snake_case,			padding=_snake_case,			return_tensors='np' ).input_values
            snake_case__							:     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        lowercase_       (	self  :  Optional[Any] )       ->Dict:
            snake_case__							:     Optional[Any]            =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
            snake_case__							:     int            =     np.random.rand(1_0_0 ).astype(np.floataa )
            snake_case__							:     List[str]            =     np_audio_inputs.tolist()

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






     def        lowercase_       (	self  :  int,			_snake_case  :  Optional[Any] )       ->Optional[int]:
            from datasets import load_dataset

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

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






     def        lowercase_       (	self  :  Dict )       ->List[str]:
            # fmt: off
            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__							:     str            =     self._load_datasamples(1 )
            snake_case__							:     str            =     EncodecFeatureExtractor()
            snake_case__							:     List[str]            =     feature_extractor(_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],			_snake_case,			atol=1e-6 ) )






     def        lowercase_       (	self  :  Optional[int] )       ->Any:
            # fmt: off
            snake_case__							:     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
            snake_case__							:     Tuple            =     self._load_datasamples(1 )
            snake_case__							:     List[Any]            =     [np.tile(input_audio[0][None],			reps=(2, 1) )]
            input_audio[0][1] *= 0.5
            snake_case__							:     List[Any]            =     EncodecFeatureExtractor(feature_size=2 )
            snake_case__							:     Union[str, Any]            =     feature_extractor(_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],			_snake_case,			atol=1e-6 ) )
            self.assertTrue(torch.allclose(input_values[0, 1, :3_0],			EXPECTED_INPUT_VALUES * 0.5,			atol=1e-6 ) )






     def        lowercase_       (	self  :  int )       ->Tuple:
            snake_case__							:     str            =     self._load_datasamples(2 )
            # would be easier if the stride was like
            snake_case__							:     str            =     EncodecFeatureExtractor(feature_size=1,			chunk_length_s=1,			overlap=0.0_1 )

            # 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__							:     str            =     feature_extractor(
                       _snake_case,			padding='max_length',			truncation=_snake_case,			return_tensors='pt' ).input_values

            # truncate to chunk
            snake_case__							:     Optional[Any]            =     feature_extractor(_snake_case,			truncation=_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
            snake_case__							:     Dict            =     feature_extractor(
                _snake_case,			truncation=_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
            snake_case__							:     Union[str, Any]            =     feature_extractor(_snake_case,			padding=_snake_case,			return_tensors='pt' ).input_values
            self.assertEquals(padded_outputs.shape,			(2, 1, 9_5_2_8_0) )

            # pad to chunk
            snake_case__							:     List[str]            =     feature_extractor(_snake_case,			return_tensors='pt' ).input_values
            self.assertEquals(truncated_outputs.shape,			(2, 1, 9_5_2_8_0) )

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

            snake_case__							:     List[Any]            =     feature_extractor(input_audio[0],			padding=_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
            snake_case__							:     str            =     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__							:     Any            =     feature_extractor(input_audio[0],			padding=_snake_case,			return_tensors='pt' ).input_values
            self.assertEquals(truncated_outputs.shape,			(1, 1, 9_3_6_8_0) )

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

            snake_case__							:     List[Any]            =     feature_extractor(input_audio[0],			padding=_snake_case,			return_tensors='pt' ).input_values
            self.assertEquals(truncated_outputs.shape,			(1, 1, 9_3_6_8_0) )











import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list




class  snake_case__						(	unittest.TestCase					):






     """simple docstring"""


     def        lowercase_       (	self  :  Optional[int] )       ->Optional[Any]:
            snake_case__							:     Tuple            =     '<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'.split(' ' )
            snake_case__							:     Any            =     dict(zip(_snake_case,			range(len(_snake_case ) ) ) )

            snake_case__							:     List[str]            =     {
                'pad_token': '<pad>',
                'unk_token': '<unk>',
                'bos_token': '<s>',
                'eos_token': '</s>',
            }
            snake_case__							:     Optional[int]            =     {
                'feature_size': 1,
                'padding_value': 0.0,
                'sampling_rate': 1_6_0_0_0,
                'return_attention_mask': False,
                'do_normalize': True,
            }

            snake_case__							:     Union[str, Any]            =     tempfile.mkdtemp()
            snake_case__							:     Optional[int]            =     os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES['vocab_file'] )
            snake_case__							:     int            =     os.path.join(self.tmpdirname,			_snake_case )
            with open(self.vocab_file,			'w',			encoding='utf-8' ) as fp:
                   fp.write(json.dumps(_snake_case ) + '\n' )

            with open(self.feature_extraction_file,			'w',			encoding='utf-8' ) as fp:
                   fp.write(json.dumps(_snake_case ) + '\n' )






     def        lowercase_       (	self  :  Tuple,			**_snake_case  :  Dict )       ->Union[str, Any]:
            snake_case__							:     Tuple            =     self.add_kwargs_tokens_map.copy()
            kwargs.update(_snake_case )
            return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname,			**_snake_case )






     def        lowercase_       (	self  :  Optional[Any],			**_snake_case  :  Dict )       ->Union[str, Any]:
            return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname,			**_snake_case )






     def        lowercase_       (	self  :  List[Any] )       ->str:
            shutil.rmtree(self.tmpdirname )






     def        lowercase_       (	self  :  Optional[int] )       ->Any:
            snake_case__							:     Any            =     self.get_tokenizer()
            snake_case__							:     Any            =     self.get_feature_extractor()

            snake_case__							:     Tuple            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

            processor.save_pretrained(self.tmpdirname )
            snake_case__							:     List[Any]            =     WavaVecaProcessor.from_pretrained(self.tmpdirname )

            self.assertEqual(processor.tokenizer.get_vocab(),			tokenizer.get_vocab() )
            self.assertIsInstance(processor.tokenizer,			_snake_case )

            self.assertEqual(processor.feature_extractor.to_json_string(),			feature_extractor.to_json_string() )
            self.assertIsInstance(processor.feature_extractor,			_snake_case )






     def        lowercase_       (	self  :  Optional[Any] )       ->Any:
            snake_case__							:     str            =     WavaVecaProcessor(tokenizer=self.get_tokenizer(),			feature_extractor=self.get_feature_extractor() )
            processor.save_pretrained(self.tmpdirname )

            snake_case__							:     Optional[int]            =     self.get_tokenizer(bos_token='(BOS)',			eos_token='(EOS)' )
            snake_case__							:     List[str]            =     self.get_feature_extractor(do_normalize=_snake_case,			padding_value=1.0 )

            snake_case__							:     List[str]            =     WavaVecaProcessor.from_pretrained(
                self.tmpdirname,			bos_token='(BOS)',			eos_token='(EOS)',			do_normalize=_snake_case,			padding_value=1.0 )

            self.assertEqual(processor.tokenizer.get_vocab(),			tokenizer_add_kwargs.get_vocab() )
            self.assertIsInstance(processor.tokenizer,			_snake_case )

            self.assertEqual(processor.feature_extractor.to_json_string(),			feature_extractor_add_kwargs.to_json_string() )
            self.assertIsInstance(processor.feature_extractor,			_snake_case )






     def        lowercase_       (	self  :  str )       ->Optional[Any]:
            snake_case__							:     Optional[Any]            =     self.get_feature_extractor()
            snake_case__							:     Tuple            =     self.get_tokenizer()

            snake_case__							:     List[Any]            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

            snake_case__							:     Any            =     floats_list((3, 1_0_0_0) )

            snake_case__							:     int            =     feature_extractor(_snake_case,			return_tensors='np' )
            snake_case__							:     Union[str, Any]            =     processor(_snake_case,			return_tensors='np' )

            for key in input_feat_extract.keys():
                   self.assertAlmostEqual(input_feat_extract[key].sum(),			input_processor[key].sum(),			delta=1e-2 )






     def        lowercase_       (	self  :  Optional[int] )       ->str:
            snake_case__							:     int            =     self.get_feature_extractor()
            snake_case__							:     Any            =     self.get_tokenizer()

            snake_case__							:     List[str]            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

            snake_case__							:     Optional[Any]            =     'This is a test string'

            snake_case__							:     Optional[Any]            =     processor(text=_snake_case )

            snake_case__							:     Any            =     tokenizer(_snake_case )

            for key in encoded_tok.keys():
                   self.assertListEqual(encoded_tok[key],			encoded_processor[key] )






     def        lowercase_       (	self  :  Union[str, Any] )       ->List[str]:
            snake_case__							:     Union[str, Any]            =     self.get_feature_extractor()
            snake_case__							:     List[str]            =     self.get_tokenizer()

            snake_case__							:     Union[str, Any]            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

            snake_case__							:     Optional[Any]            =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            snake_case__							:     Dict            =     processor.batch_decode(_snake_case )
            snake_case__							:     Optional[int]            =     tokenizer.batch_decode(_snake_case )

            self.assertListEqual(_snake_case,			_snake_case )






     def        lowercase_       (	self  :  List[str] )       ->Any:
            snake_case__							:     List[Any]            =     self.get_feature_extractor()
            snake_case__							:     List[str]            =     self.get_tokenizer()

            snake_case__							:     Union[str, Any]            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

            self.assertListEqual(
                processor.model_input_names,			feature_extractor.model_input_names,			msg='`processor` and `feature_extractor` model input names do not match',			)






