



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


UpperCamelCase_									=   random.Random()







def       lowerCamelCase_  (    _a       :						Any   ,		_a       :						List[Any]=1.0   ,		_a       :						Tuple=None   ,		_a       :						Optional[int]=None    ):



    '''simple docstring'''




    if rng is None:
        UpperCAmelCase_     :       int  		=					global_rng

    UpperCAmelCase_     :       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 ):






   '''simple docstring'''






   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     str  ,lowerCamelCase_:     List[str]=7  ,lowerCamelCase_:     int=400  ,lowerCamelCase_:     Tuple=2000  ,lowerCamelCase_:     Optional[Any]=1  ,lowerCamelCase_:     Optional[int]=0.0  ,lowerCamelCase_:     Dict=24000  ,lowerCamelCase_:     Dict=True  ,)	->      Any:
       UpperCAmelCase_     :       List[str]  		=					parent
       UpperCAmelCase_     :       List[str]  		=					batch_size
       UpperCAmelCase_     :       List[str]  		=					min_seq_length
       UpperCAmelCase_     :       Optional[int]  		=					max_seq_length
       UpperCAmelCase_     :       int  		=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       UpperCAmelCase_     :       int  		=					feature_size
       UpperCAmelCase_     :       List[str]  		=					padding_value
       UpperCAmelCase_     :       Optional[Any]  		=					sampling_rate
       UpperCAmelCase_     :       Optional[int]  		=					return_attention_mask


   def       A__    (    self:     Optional[int]							)	->      Optional[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       A__    (    self:     List[Any]  ,lowerCamelCase_:     List[Any]=False  ,lowerCamelCase_:     Dict=False							)	->      List[str]:
       def _flatten(lowerCamelCase_:     Optional[int]							):
           return list(itertools.chain(*lowerCamelCase_							)							)

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

       return audio_inputs







@require_torch
class       _snake_case				(     __snake_case ,							unittest.TestCase ):






   '''simple docstring'''






   A__      : List[Any]       =						EncodecFeatureExtractor


   def       A__    (    self:     Union[str, Any]							)	->      Union[str, Any]:
       UpperCAmelCase_     :       List[str]  		=					EnCodecFeatureExtractionTester(self							)


   def       A__    (    self:     List[Any]							)	->      Dict:
       # Tests that all call wrap to encode_plus and batch_encode_plus
       UpperCAmelCase_     :       List[Any]  		=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
       # create three inputs of length 800, 1000, and 1200
       UpperCAmelCase_     :       str  		=					[floats_list((1, x)							)[0] for x in range(800  ,1400  ,200							)]
       UpperCAmelCase_     :       int  		=					[np.asarray(lowerCamelCase_							) for audio_input in audio_inputs]

       # Test not batched input
       UpperCAmelCase_     :       Union[str, Any]  		=					feat_extract(audio_inputs[0]  ,return_tensors="""np"""							).input_values
       UpperCAmelCase_     :       Dict  		=					feat_extract(np_audio_inputs[0]  ,return_tensors="""np"""							).input_values
       self.assertTrue(np.allclose(lowerCamelCase_  ,lowerCamelCase_  ,atol=1e-3							)							)

       # Test batched
       UpperCAmelCase_     :       Tuple  		=					feat_extract(lowerCamelCase_  ,padding=lowerCamelCase_  ,return_tensors="""np"""							).input_values
       UpperCAmelCase_     :       Any  		=					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:     str							)	->      List[str]:
       UpperCAmelCase_     :       Union[str, Any]  		=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
       UpperCAmelCase_     :       Union[str, Any]  		=					np.random.rand(100							).astype(np.floataa							)
       UpperCAmelCase_     :       Union[str, Any]  		=					np_audio_inputs.tolist()

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


   def       A__    (    self:     List[str]  ,lowerCamelCase_:     Union[str, Any]							)	->      Optional[Any]:
       from datasets import load_dataset

       UpperCAmelCase_     :       int  		=					load_dataset("""hf-internal-testing/librispeech_asr_dummy"""  ,"""clean"""  ,split="""validation"""							)
       # automatic decoding with librispeech
       UpperCAmelCase_     :       int  		=					ds.sort("""id"""							).select(range(lowerCamelCase_							)							)[:num_samples]["""audio"""]

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


   def       A__    (    self:     Optional[int]							)	->      Union[str, Any]:
       # fmt: off
       UpperCAmelCase_     :       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
       UpperCAmelCase_     :       Optional[Any]  		=					self._load_datasamples(1							)
       UpperCAmelCase_     :       Union[str, Any]  		=					EncodecFeatureExtractor()
       UpperCAmelCase_     :       List[Any]  		=					feature_extractor(lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(input_values.shape  ,(1, 1, 93680)							)
       self.assertTrue(torch.allclose(input_values[0, 0, :30]  ,lowerCamelCase_  ,atol=1e-6							)							)


   def       A__    (    self:     List[str]							)	->      Dict:
       # fmt: off
       UpperCAmelCase_     :       Optional[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
       UpperCAmelCase_     :       Tuple  		=					self._load_datasamples(1							)
       UpperCAmelCase_     :       List[Any]  		=					[np.tile(input_audio[0][None]  ,reps=(2, 1)							)]
       input_audio[0][1] *= 0.5
       UpperCAmelCase_     :       int  		=					EncodecFeatureExtractor(feature_size=2							)
       UpperCAmelCase_     :       int  		=					feature_extractor(lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(input_values.shape  ,(1, 2, 93680)							)
       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:     Optional[int]							)	->      List[str]:
       UpperCAmelCase_     :       Dict  		=					self._load_datasamples(2							)
       # would be easier if the stride was like
       UpperCAmelCase_     :       Any  		=					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.$"""  ,):
           UpperCAmelCase_     :       Optional[int]  		=					feature_extractor(
               lowerCamelCase_  ,padding="""max_length"""  ,truncation=lowerCamelCase_  ,return_tensors="""pt"""							).input_values

       # truncate to chunk
       UpperCAmelCase_     :       str  		=					feature_extractor(lowerCamelCase_  ,truncation=lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(truncated_outputs.shape  ,(2, 1, 71520)							)  # 2 chunks

       # force truncate to max_length
       UpperCAmelCase_     :       Optional[Any]  		=					feature_extractor(
           lowerCamelCase_  ,truncation=lowerCamelCase_  ,max_length=48000  ,return_tensors="""pt"""							).input_values
       self.assertEquals(truncated_outputs.shape  ,(2, 1, 48000)							)

       # pad to chunk
       UpperCAmelCase_     :       Dict  		=					feature_extractor(lowerCamelCase_  ,padding=lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(padded_outputs.shape  ,(2, 1, 95280)							)

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

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

       UpperCAmelCase_     :       Any  		=					feature_extractor(input_audio[0]  ,padding=lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(truncated_outputs.shape  ,(1, 1, 93680)							)

       # no pad if no chunk_length_s
       UpperCAmelCase_     :       str  		=					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.$"""  ,):
           UpperCAmelCase_     :       Dict  		=					feature_extractor(lowerCamelCase_  ,padding=lowerCamelCase_  ,return_tensors="""pt"""							).input_values

       UpperCAmelCase_     :       Any  		=					feature_extractor(input_audio[0]  ,padding=lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(truncated_outputs.shape  ,(1, 1, 93680)							)

       # no pad if no overlap
       UpperCAmelCase_     :       int  		=					2
       UpperCAmelCase_     :       Optional[Any]  		=					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.$"""  ,):
           UpperCAmelCase_     :       int  		=					feature_extractor(lowerCamelCase_  ,padding=lowerCamelCase_  ,return_tensors="""pt"""							).input_values

       UpperCAmelCase_     :       Optional[Any]  		=					feature_extractor(input_audio[0]  ,padding=lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(truncated_outputs.shape  ,(1, 1, 93680)							)



"""simple docstring"""



from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers



class        a    :



    """simple docstring"""



    def __init__(  self:			List[Any]     ,    UpperCamelCase:			str							):






         """simple docstring"""


         A__				      =	vocab
         A__				      =	normalizers.BertNormalizer(
             clean_text=UpperCamelCase     ,    handle_chinese_chars=UpperCamelCase     ,    strip_accents=UpperCamelCase     ,    lowercase=UpperCamelCase     ,    )
         try:
              import rjieba
         except ImportError:
              raise ImportError(
                  """You need to install rjieba to use RoFormerTokenizer. """
                  """See https://pypi.org/project/rjieba/ for installation."""							)
         A__				      =	rjieba

    def 					UpperCamelCase						(  self:			Optional[Any]     ,    UpperCamelCase:			int     ,    UpperCamelCase:			NormalizedString							):






         """simple docstring"""


         A__				      =	[]

         # this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
         for token, start, end in self.jieba.tokenize(str(UpperCamelCase							)     ,    hmm=UpperCamelCase							):
              if token in self.vocab:
                   splits.append(normalized_string[start:end]							)
              else:
                   A__				      =	self.normalizers.normalize_str(UpperCamelCase							).split()
                   for token in token_list:
                        if token:
                             A__				      =	start + len(UpperCamelCase							)
                             splits.append(normalized_string[start:end]							)
                             A__				      =	end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

         return splits





    def 					UpperCamelCase						(  self:			Tuple     ,    UpperCamelCase:			PreTokenizedString							):






         """simple docstring"""


         pretok.split(self.jieba_split							)






