

'''simple docstring'''






import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowerCAmelCase__   		=      random.Random()


def 						_A	(       A__	,							A__=1.0	,							A__=None	,							A__=None ):






 """simple docstring"""



 if rng is None:
  __lowercase				    =					global_rng

 __lowercase				    =					[]
 for batch_idx in range(shape[0] ):
  values.append([] )
  for _ in range(shape[1] ):
   values[-1].append(rng.random() * scale )

 return values






class      lowercase_      (unittest.TestCase ):




  """simple docstring"""




  def __init__( self  :	Any   ,lowercase__  :	Optional[int]   ,lowercase__  :	Union[str, Any]=7   ,lowercase__  :	Tuple=4_0_0   ,lowercase__  :	Optional[int]=2_0_0_0   ,lowercase__  :	Dict=1   ,lowercase__  :	List[str]=0.0   ,lowercase__  :	str=1_6_0_0_0   ,lowercase__  :	Optional[Any]=True   ,lowercase__  :	List[str]=True   ,):
   __lowercase				    =					parent
   __lowercase				    =					batch_size
   __lowercase				    =					min_seq_length
   __lowercase				    =					max_seq_length
   __lowercase				    =					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
   __lowercase				    =					feature_size
   __lowercase				    =					padding_value
   __lowercase				    =					sampling_rate
   __lowercase				    =					return_attention_mask
   __lowercase				    =					do_normalize






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   return {
       "feature_size": self.feature_size,
       "padding_value": self.padding_value,
       "sampling_rate": self.sampling_rate,
       "return_attention_mask": self.return_attention_mask,
       "do_normalize": self.do_normalize,
   }






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]   ,lowercase__  :	Optional[Any]=False   ,lowercase__  :	Dict=False     ):
   def _flatten(lowercase__  :	List[Any]     ):
    return list(itertools.chain(*__lowercase     )     )

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

   return speech_inputs






class      lowercase_      (snake_case__							,			unittest.TestCase ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Optional[Any]							     =     WavaVecaFeatureExtractor






  def    SCREAMING_SNAKE_CASE ( self  :	Any     ):
   __lowercase				    =					WavaVecaFeatureExtractionTester(self     )






  def    SCREAMING_SNAKE_CASE ( self  :	Any   ,lowercase__  :	str     ):
   self.assertTrue(np.all(np.mean(__lowercase   ,axis=0     ) < 1e-3     )     )
   self.assertTrue(np.all(np.abs(np.var(__lowercase   ,axis=0     ) - 1     ) < 1e-3     )     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   # Tests that all call wrap to encode_plus and batch_encode_plus
   __lowercase				    =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
   # create three inputs of length 800, 1000, and 1200
   __lowercase				    =					[floats_list((1, x)     )[0] for x in range(8_0_0   ,1_4_0_0   ,2_0_0     )]
   __lowercase				    =					[np.asarray(__lowercase     ) for speech_input in speech_inputs]

   # Test not batched input
   __lowercase				    =					feat_extract(speech_inputs[0]   ,return_tensors='''np'''     ).input_values
   __lowercase				    =					feat_extract(np_speech_inputs[0]   ,return_tensors='''np'''     ).input_values
   self.assertTrue(np.allclose(__lowercase   ,__lowercase   ,atol=1e-3     )     )

   # Test batched
   __lowercase				    =					feat_extract(__lowercase   ,return_tensors='''np'''     ).input_values
   __lowercase				    =					feat_extract(__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     )     )

   # Test 2-D numpy arrays are batched.
   __lowercase				    =					[floats_list((1, x)     )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
   __lowercase				    =					np.asarray(__lowercase     )
   __lowercase				    =					feat_extract(__lowercase   ,return_tensors='''np'''     ).input_values
   __lowercase				    =					feat_extract(__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    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   __lowercase				    =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
   __lowercase				    =					[floats_list((1, x)     )[0] for x in range(8_0_0   ,1_4_0_0   ,2_0_0     )]

   __lowercase				    =					['''longest''', '''max_length''', '''do_not_pad''']
   __lowercase				    =					[None, 1_6_0_0, None]
   for max_length, padding in zip(__lowercase   ,__lowercase     ):
    __lowercase				    =					feat_extract(__lowercase   ,padding=__lowercase   ,max_length=__lowercase   ,return_tensors='''np'''     )
    __lowercase				    =					processed.input_values

    self._check_zero_mean_unit_variance(input_values[0][:8_0_0]     )
    self.assertTrue(input_values[0][8_0_0:].sum() < 1e-6     )
    self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]     )
    self.assertTrue(input_values[0][1_0_0_0:].sum() < 1e-6     )
    self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   __lowercase				    =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
   __lowercase				    =					range(8_0_0   ,1_4_0_0   ,2_0_0     )
   __lowercase				    =					[floats_list((1, x)     )[0] for x in lengths]

   __lowercase				    =					['''longest''', '''max_length''', '''do_not_pad''']
   __lowercase				    =					[None, 1_6_0_0, None]

   for max_length, padding in zip(__lowercase   ,__lowercase     ):
    __lowercase				    =					feat_extract(__lowercase   ,max_length=__lowercase   ,padding=__lowercase     )
    __lowercase				    =					processed.input_values

    self._check_zero_mean_unit_variance(input_values[0][:8_0_0]     )
    self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]     )
    self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]     ):
   __lowercase				    =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
   __lowercase				    =					[floats_list((1, x)     )[0] for x in range(8_0_0   ,1_4_0_0   ,2_0_0     )]
   __lowercase				    =					feat_extract(
       __lowercase   ,truncation=__lowercase   ,max_length=1_0_0_0   ,padding='''max_length'''   ,return_tensors='''np'''     )
   __lowercase				    =					processed.input_values

   self._check_zero_mean_unit_variance(input_values[0, :8_0_0]     )
   self._check_zero_mean_unit_variance(input_values[1]     )
   self._check_zero_mean_unit_variance(input_values[2]     )






  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   __lowercase				    =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
   __lowercase				    =					[floats_list((1, x)     )[0] for x in range(8_0_0   ,1_4_0_0   ,2_0_0     )]
   __lowercase				    =					feat_extract(
       __lowercase   ,truncation=__lowercase   ,max_length=1_0_0_0   ,padding='''longest'''   ,return_tensors='''np'''     )
   __lowercase				    =					processed.input_values

   self._check_zero_mean_unit_variance(input_values[0, :8_0_0]     )
   self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]     )
   self._check_zero_mean_unit_variance(input_values[2]     )

   # make sure that if max_length < longest -> then pad to max_length
   self.assertTrue(input_values.shape == (3, 1_0_0_0)     )

   __lowercase				    =					[floats_list((1, x)     )[0] for x in range(8_0_0   ,1_4_0_0   ,2_0_0     )]
   __lowercase				    =					feat_extract(
       __lowercase   ,truncation=__lowercase   ,max_length=2_0_0_0   ,padding='''longest'''   ,return_tensors='''np'''     )
   __lowercase				    =					processed.input_values

   self._check_zero_mean_unit_variance(input_values[0, :8_0_0]     )
   self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]     )
   self._check_zero_mean_unit_variance(input_values[2]     )

   # make sure that if max_length > longest -> then pad to longest
   self.assertTrue(input_values.shape == (3, 1_2_0_0)     )






  @require_torch
  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   import torch

   __lowercase				    =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
   __lowercase				    =					np.random.rand(1_0_0     ).astype(np.floataa     )
   __lowercase				    =					np_speech_inputs.tolist()

   for inputs in [py_speech_inputs, np_speech_inputs]:
    __lowercase				    =					feature_extractor.pad([{'''input_values''': inputs}]   ,return_tensors='''np'''     )
    self.assertTrue(np_processed.input_values.dtype == np.floataa     )
    __lowercase				    =					feature_extractor.pad([{'''input_values''': inputs}]   ,return_tensors='''pt'''     )
    self.assertTrue(pt_processed.input_values.dtype == torch.floataa     )






  @slow
  @require_torch
  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   # this test makes sure that models that are using
   # group norm don't have their feature extractor return the
   # attention_mask
   for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
    __lowercase				    =					WavaVecaConfig.from_pretrained(__lowercase     )
    __lowercase				    =					WavaVecaFeatureExtractor.from_pretrained(__lowercase     )

    # only "layer" feature extraction norm should make use of
    # attention_mask
    self.assertEqual(feat_extract.return_attention_mask   ,config.feat_extract_norm == '''layer'''     )








"""simple docstring"""





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_camembert import CamembertTokenizer
else:
							_UpperCamelCase					      =   None


_UpperCamelCase					      =   logging.get_logger(__name__)

_UpperCamelCase					      =   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase					      =   {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/tokenizer.json',
    },
}

_UpperCamelCase					      =   {
    'camembert-base': 512,
}

_UpperCamelCase					      =   '▁'
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							List[str]									=		VOCAB_FILES_NAMES
				__snake_case :							Optional[int]									=		PRETRAINED_VOCAB_FILES_MAP
				__snake_case :							Dict									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__snake_case :							List[str]									=		["""input_ids""", """attention_mask"""]
				__snake_case :							List[Any]									=		CamembertTokenizer




				def __init__(  self	:List[Any]    ,     __lowercase	:Optional[int]=None    ,     __lowercase	:str=None    ,     __lowercase	:Optional[Any]="<s>"    ,     __lowercase	:List[str]="</s>"    ,     __lowercase	:Tuple="</s>"    ,     __lowercase	:int="<s>"    ,     __lowercase	:Union[str, Any]="<unk>"    ,     __lowercase	:Optional[int]="<pad>"    ,     __lowercase	:Union[str, Any]="<mask>"    ,     __lowercase	:Tuple=["<s>NOTUSED", "</s>NOTUSED"]    ,     **__lowercase	:List[str]    ,     ):
											# Mask token behave like a normal word, i.e. include the space before it
											__lowerCamelCase   :   Optional[int]       =AddedToken(__lowercase    ,     lstrip=__lowercase    ,     rstrip=__lowercase      ) if isinstance(__lowercase    ,     __lowercase      ) else mask_token

											super().__init__(
											    __lowercase    ,     tokenizer_file=__lowercase    ,     bos_token=__lowercase    ,     eos_token=__lowercase    ,     sep_token=__lowercase    ,     cls_token=__lowercase    ,     unk_token=__lowercase    ,     pad_token=__lowercase    ,     mask_token=__lowercase    ,     additional_special_tokens=__lowercase    ,     **__lowercase    ,     )

											__lowerCamelCase   :   Any       =vocab_file
											__lowerCamelCase   :   Union[str, Any]       =False if not self.vocab_file else True




				def        __lowercase	(  self	:List[str]    ,     __lowercase	:List[int]    ,     __lowercase	:Optional[List[int]] = None      ):

											if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											__lowerCamelCase   :   Tuple       =[self.cls_token_id]
											__lowerCamelCase   :   str       =[self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep




				def        __lowercase	(  self	:Tuple    ,     __lowercase	:List[int]    ,     __lowercase	:Optional[List[int]] = None      ):
											__lowerCamelCase   :   Any       =[self.sep_token_id]
											__lowerCamelCase   :   str       =[self.cls_token_id]

											if token_ids_a is None:
																		return len(cls + token_ids_a + sep      ) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]




				def        __lowercase	(  self	:List[Any]    ,     __lowercase	:str    ,     __lowercase	:Optional[str] = None      ):
											if not self.can_save_slow_tokenizer:
																		raise ValueError(
																		    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																		    '''tokenizer.'''      )

											if not os.path.isdir(__lowercase      ):
																		logger.error(f'Vocabulary path ({save_directory}) should be a directory'      )
																		return
											__lowerCamelCase   :   List[str]       =os.path.join(
											    __lowercase    ,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

											if os.path.abspath(self.vocab_file      ) != os.path.abspath(__lowercase      ):
																		copyfile(self.vocab_file    ,     __lowercase      )

											return (out_vocab_file,)




