


import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

__lowerCAmelCase		       =							logging.get_logger(__name__)


__lowerCAmelCase		       =							{name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}




def    snake_case_     (  snake_case			,							snake_case			,							snake_case			,							snake_case     )  ->				Any:
       if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
              raise ValueError(f'Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()     )}.'     )

       if tokenizer_name is None:
              lowercase__:   Tuple  			=   TOKENIZER_CLASSES
       else:
              lowercase__:   Union[str, Any]  			=   {tokenizer_name: getattr(snake_case			,							tokenizer_name + 'Fast'     )}

       logger.info(f'Loading tokenizer classes: {tokenizer_names}'     )

       for tokenizer_name in tokenizer_names:
              lowercase__:   List[str]  			=   TOKENIZER_CLASSES[tokenizer_name]

              lowercase__:   Tuple  			=   True
              if checkpoint_name is None:
                     lowercase__:   Dict  			=   list(tokenizer_class.max_model_input_sizes.keys()     )
              else:
                     lowercase__:   List[Any]  			=   [checkpoint_name]

              logger.info(f'For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}'     )

              for checkpoint in checkpoint_names:
                     logger.info(f'Loading {tokenizer_class.__class__.__name__} {checkpoint}'     )

                     # Load tokenizer
                     lowercase__:   List[str]  			=   tokenizer_class.from_pretrained(snake_case			,							force_download=snake_case     )

                     # Save fast tokenizer
                     logger.info(f'Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}'     )

                     # For organization names we create sub-directories
                     if "/" in checkpoint:
                            lowercase__			,						lowercase__:   List[str]  			=   checkpoint.split('/'     )
                            lowercase__:   Any  			=   os.path.join(snake_case			,							snake_case     )
                     elif add_prefix:
                            lowercase__:   Optional[Any]  			=   checkpoint
                            lowercase__:   Tuple  			=   dump_path
                     else:
                            lowercase__:   str  			=   None
                            lowercase__:   Optional[int]  			=   dump_path

                     logger.info(f'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'     )

                     if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()     )[0]:
                            lowercase__:   Dict  			=   list(tokenizer.pretrained_vocab_files_map.values()     )[0][checkpoint]
                            lowercase__:   Tuple  			=   file_path.split(snake_case     )[-1][0]
                            if next_char == "/":
                                   lowercase__:   str  			=   os.path.join(snake_case			,							snake_case     )
                                   lowercase__:   str  			=   None

                            logger.info(f'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'     )

                     lowercase__:   List[str]  			=   tokenizer.save_pretrained(
                         snake_case			,							legacy_format=snake_case			,							filename_prefix=snake_case     )
                     logger.info(f'=> File names {file_names}'     )

                     for file_name in file_names:
                            if not file_name.endswith('tokenizer.json'     ):
                                   os.remove(snake_case     )
                                   logger.info(f'=> removing {file_name}'     )


if __name__ == "__main__":
 __lowerCAmelCase		       =							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--dump_path''', default=None, type=str, required=True, help='''Path to output generated fast tokenizer files.'''
 )
 parser.add_argument(
     '''--tokenizer_name''',
     default=None,
     type=str,
     help=(
         F'''Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '''
         '''download and convert all the checkpoints from AWS.'''
     ),
 )
 parser.add_argument(
     '''--checkpoint_name''',
     default=None,
     type=str,
     help='''Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.''',
 )
 parser.add_argument(
     '''--force_download''',
     action='''store_true''',
     help='''Re-download checkpoints.''',
 )
 __lowerCAmelCase		       =							parser.parse_args()

 convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)






import math

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


__lowerCAmelCase		       =							logging.get_logger(__name__)

__lowerCAmelCase		       =							{
    '''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   __a			(    __UpperCamelCase      ):
 __lowercase :							Optional[int]        = 'data2vec-audio'
 def __init__(   self				, lowerCAmelCase__=32				, lowerCAmelCase__=768				, lowerCAmelCase__=12				, lowerCAmelCase__=12				, lowerCAmelCase__=3_072				, lowerCAmelCase__="gelu"				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.0				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.0_2				, lowerCAmelCase__=1E-5				, lowerCAmelCase__="gelu"				, lowerCAmelCase__=(512, 512, 512, 512, 512, 512, 512)				, lowerCAmelCase__=(5, 2, 2, 2, 2, 2, 2)				, lowerCAmelCase__=(10, 3, 3, 3, 3, 2, 2)				, lowerCAmelCase__=False				, lowerCAmelCase__=16				, lowerCAmelCase__=19				, lowerCAmelCase__=5				, lowerCAmelCase__=0.0_5				, lowerCAmelCase__=10				, lowerCAmelCase__=2				, lowerCAmelCase__=0.0				, lowerCAmelCase__=10				, lowerCAmelCase__=0				, lowerCAmelCase__="sum"				, lowerCAmelCase__=False				, lowerCAmelCase__=False				, lowerCAmelCase__=256				, lowerCAmelCase__=(512, 512, 512, 512, 1_500)				, lowerCAmelCase__=(5, 3, 3, 1, 1)				, lowerCAmelCase__=(1, 2, 3, 1, 1)				, lowerCAmelCase__=512				, lowerCAmelCase__=0				, lowerCAmelCase__=1				, lowerCAmelCase__=2				, lowerCAmelCase__=False				, lowerCAmelCase__=3				, lowerCAmelCase__=2				, lowerCAmelCase__=3				, lowerCAmelCase__=None				, **lowerCAmelCase__				, )					->				Optional[int]:




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

        # ctc loss
        lowercase__:   Union[str, Any]  			=   ctc_loss_reduction
        lowercase__:   str  			=   ctc_zero_infinity

        # adapter
        lowercase__:   str  			=   add_adapter
        lowercase__:   List[Any]  			=   adapter_kernel_size
        lowercase__:   Tuple  			=   adapter_stride
        lowercase__:   Dict  			=   num_adapter_layers
        lowercase__:   Optional[Any]  			=   output_hidden_size or hidden_size

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        lowercase__:   List[Any]  			=   classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        lowercase__:   int  			=   list(lowerCAmelCase__ )
        lowercase__:   Dict  			=   list(lowerCAmelCase__ )
        lowercase__:   int  			=   list(lowerCAmelCase__ )
        lowercase__:   str  			=   xvector_output_dim
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[int]:




        '''simple docstring'''
        return math.prod(self.conv_stride )


