

"""simple docstring"""







import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing







class  UpperCamelCase	(    snake_case_						):
   def __init__(    self  ,__UpperCamelCase = "▁"  ,__UpperCamelCase = True  ,__UpperCamelCase = "<unk>"  ,__UpperCamelCase = "</s>"  ,__UpperCamelCase = "<pad>"  ,)			->   Any:


        '''simple docstring'''


        lowercase_			:       str									=			{
            'pad': {'id': 0, 'token': pad_token},
            'eos': {'id': 1, 'token': eos_token},
            'unk': {'id': 2, 'token': unk_token},
        }

        lowercase_			:       List[str]									=			[None] * len(self.special_tokens    )
        for token_dict in self.special_tokens.values():
             lowercase_			:       Dict									=			token_dict['token']

        lowercase_			:       Tuple									=			Tokenizer(Unigram()    )

        lowercase_			:       Tuple									=			normalizers.Sequence(
            [
                normalizers.Nmt(),
                normalizers.NFKC(),
                normalizers.Replace(Regex(' {2,}'    )  ,' '    ),
                normalizers.Lowercase(),
            ]    )
        lowercase_			:       Dict									=			pre_tokenizers.Sequence(
            [
                pre_tokenizers.Metaspace(replacement=__UpperCamelCase  ,add_prefix_space=__UpperCamelCase    ),
                pre_tokenizers.Digits(individual_digits=__UpperCamelCase    ),
                pre_tokenizers.Punctuation(),
            ]    )
        lowercase_			:       Any									=			decoders.Metaspace(replacement=__UpperCamelCase  ,add_prefix_space=__UpperCamelCase    )

        lowercase_			:       List[Any]									=			TemplateProcessing(
            single=f'''$A {self.special_tokens["eos"]["token"]}'''  ,special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])]  ,)

        lowercase_			:       int									=			{
            'model': 'SentencePieceUnigram',
            'replacement': replacement,
            'add_prefix_space': add_prefix_space,
        }

        super().__init__(__UpperCamelCase  ,__UpperCamelCase    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = 8000  ,__UpperCamelCase = True  ,)			->   Tuple:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			trainers.UnigramTrainer(
            vocab_size=__UpperCamelCase  ,special_tokens=self.special_tokens_list  ,show_progress=__UpperCamelCase  ,)

        if isinstance(__UpperCamelCase  ,__UpperCamelCase    ):
             lowercase_			:       str									=			[files]
        self._tokenizer.train(__UpperCamelCase  ,trainer=__UpperCamelCase    )

        self.add_unk_id()
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = 8000  ,__UpperCamelCase = True  ,)			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       str									=			trainers.UnigramTrainer(
            vocab_size=__UpperCamelCase  ,special_tokens=self.special_tokens_list  ,show_progress=__UpperCamelCase  ,)

        self._tokenizer.train_from_iterator(__UpperCamelCase  ,trainer=__UpperCamelCase    )

        self.add_unk_id()







   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_			:       List[str]									=			json.loads(self._tokenizer.to_str()    )

        lowercase_			:       List[Any]									=			self.special_tokens['unk']['id']

        lowercase_			:       Tuple									=			Tokenizer.from_str(json.dumps(__UpperCamelCase    )    )


"""simple docstring"""







import functools
import operator

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


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE								={
    "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						'wav2vec2'
   def __init__(    self  ,__UpperCamelCase=32  ,__UpperCamelCase=768  ,__UpperCamelCase=12  ,__UpperCamelCase=12  ,__UpperCamelCase=3072  ,__UpperCamelCase="gelu"  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.0  ,__UpperCamelCase=0.0  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.02  ,__UpperCamelCase=1e-5  ,__UpperCamelCase="group"  ,__UpperCamelCase="gelu"  ,__UpperCamelCase=(512, 512, 512, 512, 512, 512, 512)  ,__UpperCamelCase=(5, 2, 2, 2, 2, 2, 2)  ,__UpperCamelCase=(10, 3, 3, 3, 3, 2, 2)  ,__UpperCamelCase=False  ,__UpperCamelCase=128  ,__UpperCamelCase=16  ,__UpperCamelCase=False  ,__UpperCamelCase=True  ,__UpperCamelCase=0.05  ,__UpperCamelCase=10  ,__UpperCamelCase=2  ,__UpperCamelCase=0.0  ,__UpperCamelCase=10  ,__UpperCamelCase=0  ,__UpperCamelCase=320  ,__UpperCamelCase=2  ,__UpperCamelCase=0.1  ,__UpperCamelCase=100  ,__UpperCamelCase=256  ,__UpperCamelCase=256  ,__UpperCamelCase=0.1  ,__UpperCamelCase="sum"  ,__UpperCamelCase=False  ,__UpperCamelCase=False  ,__UpperCamelCase=256  ,__UpperCamelCase=(512, 512, 512, 512, 1500)  ,__UpperCamelCase=(5, 3, 3, 1, 1)  ,__UpperCamelCase=(1, 2, 3, 1, 1)  ,__UpperCamelCase=512  ,__UpperCamelCase=0  ,__UpperCamelCase=1  ,__UpperCamelCase=2  ,__UpperCamelCase=False  ,__UpperCamelCase=3  ,__UpperCamelCase=2  ,__UpperCamelCase=3  ,__UpperCamelCase=None  ,__UpperCamelCase=None  ,**__UpperCamelCase  ,)			->   Optional[Any]:


        '''simple docstring'''


        super().__init__(**__UpperCamelCase  ,pad_token_id=__UpperCamelCase  ,bos_token_id=__UpperCamelCase  ,eos_token_id=__UpperCamelCase    )
        lowercase_			:       Optional[Any]									=			hidden_size
        lowercase_			:       Tuple									=			feat_extract_norm
        lowercase_			:       Dict									=			feat_extract_activation
        lowercase_			:       List[str]									=			list(__UpperCamelCase    )
        lowercase_			:       str									=			list(__UpperCamelCase    )
        lowercase_			:       Dict									=			list(__UpperCamelCase    )
        lowercase_			:       Optional[Any]									=			conv_bias
        lowercase_			:       Dict									=			num_conv_pos_embeddings
        lowercase_			:       List[str]									=			num_conv_pos_embedding_groups
        lowercase_			:       Optional[Any]									=			len(self.conv_dim    )
        lowercase_			:       Any									=			num_hidden_layers
        lowercase_			:       List[Any]									=			intermediate_size
        lowercase_			:       List[Any]									=			hidden_act
        lowercase_			:       Optional[int]									=			num_attention_heads
        lowercase_			:       int									=			hidden_dropout
        lowercase_			:       Dict									=			attention_dropout
        lowercase_			:       Union[str, Any]									=			activation_dropout
        lowercase_			:       Tuple									=			feat_proj_dropout
        lowercase_			:       List[str]									=			final_dropout
        lowercase_			:       Union[str, Any]									=			layerdrop
        lowercase_			:       List[str]									=			layer_norm_eps
        lowercase_			:       Optional[int]									=			initializer_range
        lowercase_			:       List[Any]									=			vocab_size
        lowercase_			:       Optional[int]									=			do_stable_layer_norm
        lowercase_			:       Union[str, Any]									=			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
        lowercase_			:       Dict									=			apply_spec_augment
        lowercase_			:       Optional[int]									=			mask_time_prob
        lowercase_			:       Union[str, Any]									=			mask_time_length
        lowercase_			:       List[str]									=			mask_time_min_masks
        lowercase_			:       List[str]									=			mask_feature_prob
        lowercase_			:       Any									=			mask_feature_length
        lowercase_			:       List[Any]									=			mask_feature_min_masks

        # parameters for pretraining with codevector quantized representations
        lowercase_			:       List[Any]									=			num_codevectors_per_group
        lowercase_			:       Optional[int]									=			num_codevector_groups
        lowercase_			:       Dict									=			contrastive_logits_temperature
        lowercase_			:       int									=			feat_quantizer_dropout
        lowercase_			:       Optional[int]									=			num_negatives
        lowercase_			:       str									=			codevector_dim
        lowercase_			:       str									=			proj_codevector_dim
        lowercase_			:       Optional[Any]									=			diversity_loss_weight

        # ctc loss
        lowercase_			:       Tuple									=			ctc_loss_reduction
        lowercase_			:       int									=			ctc_zero_infinity

        # adapter
        lowercase_			:       int									=			add_adapter
        lowercase_			:       Dict									=			adapter_kernel_size
        lowercase_			:       List[str]									=			adapter_stride
        lowercase_			:       Dict									=			num_adapter_layers
        lowercase_			:       Dict									=			output_hidden_size or hidden_size
        lowercase_			:       Optional[Any]									=			adapter_attn_dim

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        lowercase_			:       Dict									=			classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        lowercase_			:       Any									=			list(__UpperCamelCase    )
        lowercase_			:       str									=			list(__UpperCamelCase    )
        lowercase_			:       Any									=			list(__UpperCamelCase    )
        lowercase_			:       Tuple									=			xvector_output_dim







   @property
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        return functools.reduce(operator.mul  ,self.conv_stride  ,1    )

