





import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A					:				int				=			logging.get_logger(__name__)

_A					:				Union[str, Any]				=			{
    """facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		Optional[int]				=	"""data2vec-audio"""



    def __init__(       self   ,				A_=32   ,				A_=7_68   ,				A_=12   ,				A_=12   ,				A_=30_72   ,				A_="gelu"   ,				A_=0.1   ,				A_=0.1   ,				A_=0.1   ,				A_=0.0   ,				A_=0.1   ,				A_=0.1   ,				A_=0.02   ,				A_=1E-5   ,				A_="gelu"   ,				A_=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)   ,				A_=(5, 2, 2, 2, 2, 2, 2)   ,				A_=(10, 3, 3, 3, 3, 2, 2)   ,				A_=False   ,				A_=16   ,				A_=19   ,				A_=5   ,				A_=0.05   ,				A_=10   ,				A_=2   ,				A_=0.0   ,				A_=10   ,				A_=0   ,				A_="sum"   ,				A_=False   ,				A_=False   ,				A_=2_56   ,				A_=(5_12, 5_12, 5_12, 5_12, 15_00)   ,				A_=(5, 3, 3, 1, 1)   ,				A_=(1, 2, 3, 1, 1)   ,				A_=5_12   ,				A_=0   ,				A_=1   ,				A_=2   ,				A_=False   ,				A_=3   ,				A_=2   ,				A_=3   ,				A_=None   ,				**A_   ,				):





        '''simple docstring'''

        super().__init__(**A_   ,				pad_token_id=A_   ,				bos_token_id=A_   ,				eos_token_id=A_     )
        SCREAMING_SNAKE_CASE__											=			hidden_size
        SCREAMING_SNAKE_CASE__											=			feat_extract_activation
        SCREAMING_SNAKE_CASE__											=			list(A_     )
        SCREAMING_SNAKE_CASE__											=			list(A_     )
        SCREAMING_SNAKE_CASE__											=			list(A_     )
        SCREAMING_SNAKE_CASE__											=			conv_bias
        SCREAMING_SNAKE_CASE__											=			num_conv_pos_embeddings
        SCREAMING_SNAKE_CASE__											=			num_conv_pos_embedding_groups
        SCREAMING_SNAKE_CASE__											=			conv_pos_kernel_size
        SCREAMING_SNAKE_CASE__											=			len(self.conv_dim     )
        SCREAMING_SNAKE_CASE__											=			num_hidden_layers
        SCREAMING_SNAKE_CASE__											=			intermediate_size
        SCREAMING_SNAKE_CASE__											=			hidden_act
        SCREAMING_SNAKE_CASE__											=			num_attention_heads
        SCREAMING_SNAKE_CASE__											=			hidden_dropout
        SCREAMING_SNAKE_CASE__											=			attention_dropout
        SCREAMING_SNAKE_CASE__											=			activation_dropout
        SCREAMING_SNAKE_CASE__											=			feat_proj_dropout
        SCREAMING_SNAKE_CASE__											=			final_dropout
        SCREAMING_SNAKE_CASE__											=			layerdrop
        SCREAMING_SNAKE_CASE__											=			layer_norm_eps
        SCREAMING_SNAKE_CASE__											=			initializer_range
        SCREAMING_SNAKE_CASE__											=			vocab_size
        SCREAMING_SNAKE_CASE__											=			use_weighted_layer_sum

        if (
            (len(self.conv_stride     ) != self.num_feat_extract_layers)
            or (len(self.conv_kernel     ) != self.num_feat_extract_layers)
            or (len(self.conv_dim     ) != self.num_feat_extract_layers)
        ):
            raise ValueError(
                '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                f''' {len(self.conv_dim     )}`, `len(config.conv_stride) = {len(self.conv_stride     )}`,'''
                f''' `len(config.conv_kernel) = {len(self.conv_kernel     )}`.'''     )

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        SCREAMING_SNAKE_CASE__											=			mask_time_prob
        SCREAMING_SNAKE_CASE__											=			mask_time_length
        SCREAMING_SNAKE_CASE__											=			mask_time_min_masks
        SCREAMING_SNAKE_CASE__											=			mask_feature_prob
        SCREAMING_SNAKE_CASE__											=			mask_feature_length
        SCREAMING_SNAKE_CASE__											=			mask_feature_min_masks

        # ctc loss
        SCREAMING_SNAKE_CASE__											=			ctc_loss_reduction
        SCREAMING_SNAKE_CASE__											=			ctc_zero_infinity

        # adapter
        SCREAMING_SNAKE_CASE__											=			add_adapter
        SCREAMING_SNAKE_CASE__											=			adapter_kernel_size
        SCREAMING_SNAKE_CASE__											=			adapter_stride
        SCREAMING_SNAKE_CASE__											=			num_adapter_layers
        SCREAMING_SNAKE_CASE__											=			output_hidden_size or hidden_size

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        SCREAMING_SNAKE_CASE__											=			classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        SCREAMING_SNAKE_CASE__											=			list(A_     )
        SCREAMING_SNAKE_CASE__											=			list(A_     )
        SCREAMING_SNAKE_CASE__											=			list(A_     )
        SCREAMING_SNAKE_CASE__											=			xvector_output_dim




    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return math.prod(self.conv_stride     )







import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


_A					:				Dict				=			get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")



class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Any				=	BartphoTokenizer
    lowerCamelCase__    :		Tuple				=	False
    lowerCamelCase__    :		Dict				=	True



    def  lowercase_					(       self     ):





        '''simple docstring'''

        super().setUp()

        SCREAMING_SNAKE_CASE__											=			['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
        SCREAMING_SNAKE_CASE__											=			dict(zip(A_   ,				range(len(A_     )     )     )     )
        SCREAMING_SNAKE_CASE__											=			{'''unk_token''': '''<unk>'''}

        SCREAMING_SNAKE_CASE__											=			os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''monolingual_vocab_file''']     )
        with open(self.monolingual_vocab_file   ,				'''w'''   ,				encoding='''utf-8'''     ) as fp:
            for token in vocab_tokens:
                fp.write(f'''{token} {vocab_tokens[token]}\n'''     )

        SCREAMING_SNAKE_CASE__											=			BartphoTokenizer(A_   ,				self.monolingual_vocab_file   ,				**self.special_tokens_map     )
        tokenizer.save_pretrained(self.tmpdirname     )



    def  lowercase_					(       self   ,				**A_     ):





        '''simple docstring'''

        kwargs.update(self.special_tokens_map     )
        return BartphoTokenizer.from_pretrained(self.tmpdirname   ,				**A_     )



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			'''This is a là test'''
        SCREAMING_SNAKE_CASE__											=			'''This is a<unk><unk> test'''
        return input_text, output_text




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			BartphoTokenizer(A_   ,				self.monolingual_vocab_file   ,				**self.special_tokens_map     )
        SCREAMING_SNAKE_CASE__											=			'''This is a là test'''
        SCREAMING_SNAKE_CASE__											=			'''▁This ▁is ▁a ▁l à ▁t est'''.split()
        SCREAMING_SNAKE_CASE__											=			tokenizer.tokenize(A_     )
        self.assertListEqual(A_   ,				A_     )

        SCREAMING_SNAKE_CASE__											=			tokens + [tokenizer.unk_token]
        SCREAMING_SNAKE_CASE__											=			[4, 5, 6, 3, 3, 7, 8, 3]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(A_     )   ,				A_     )
