


import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					str			,     SCREAMING_SNAKE_CASE__			:					Optional[Any]			,     SCREAMING_SNAKE_CASE__			:					Optional[int]				):
     # Initialise PyTorch model
     __UpperCamelCase    							=AlbertConfig.from_json_file(SCREAMING_SNAKE_CASE__				)
     print(F'Building PyTorch model from configuration: {config}'				)
     __UpperCamelCase    							=AlbertForPreTraining(SCREAMING_SNAKE_CASE__				)

     # Load weights from tf checkpoint
     load_tf_weights_in_albert(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)

     # Save pytorch-model
     print(F'Save PyTorch model to {pytorch_dump_path}'				)
     torch.save(model.state_dict()			,     SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
   _A		       =						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
   )
   parser.add_argument(
       '--albert_config_file',
       default=None,
       type=str,
       required=True,
       help=(
           'The config json file corresponding to the pre-trained ALBERT model. \n'
           'This specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _A		       =						parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)









'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__:    List[str]					    =       logging.get_logger(__name__)

A__:    Union[str, Any]					    =       {
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}







class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  int	       =							"data2vec-text"






      def __init__( self			:str     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=3_0_5_2_2     ,						SCREAMING_SNAKE_CASE			:Any=7_6_8     ,						SCREAMING_SNAKE_CASE			:List[Any]=1_2     ,						SCREAMING_SNAKE_CASE			:List[str]=1_2     ,						SCREAMING_SNAKE_CASE			:Dict=3_0_7_2     ,						SCREAMING_SNAKE_CASE			:List[str]="gelu"     ,						SCREAMING_SNAKE_CASE			:Any=0.1     ,						SCREAMING_SNAKE_CASE			:List[str]=0.1     ,						SCREAMING_SNAKE_CASE			:int=5_1_2     ,						SCREAMING_SNAKE_CASE			:int=2     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=0.02     ,						SCREAMING_SNAKE_CASE			:Dict=1e-12     ,						SCREAMING_SNAKE_CASE			:int=1     ,						SCREAMING_SNAKE_CASE			:Dict=0     ,						SCREAMING_SNAKE_CASE			:List[Any]=2     ,						SCREAMING_SNAKE_CASE			:str="absolute"     ,						SCREAMING_SNAKE_CASE			:Tuple=True     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=None     ,						**SCREAMING_SNAKE_CASE			:Union[str, Any]     ,						)       ->					List[str]:




             '''simple docstring'''
             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE     ,						bos_token_id=SCREAMING_SNAKE_CASE     ,						eos_token_id=SCREAMING_SNAKE_CASE     ,						**SCREAMING_SNAKE_CASE	)

             _a    : Optional[Any]     					=vocab_size
             _a    : Optional[Any]     					=hidden_size
             _a    : Any     					=num_hidden_layers
             _a    : List[str]     					=num_attention_heads
             _a    : Union[str, Any]     					=hidden_act
             _a    : Any     					=intermediate_size
             _a    : str     					=hidden_dropout_prob
             _a    : Optional[Any]     					=attention_probs_dropout_prob
             _a    : Optional[Any]     					=max_position_embeddings
             _a    : Union[str, Any]     					=type_vocab_size
             _a    : Tuple     					=initializer_range
             _a    : Optional[int]     					=layer_norm_eps
             _a    : Tuple     					=position_embedding_type
             _a    : int     					=use_cache
             _a    : List[str]     					=classifier_dropout







class A__       (						UpperCAmelCase__      ):






      @property
      def __UpperCAmelCase       ( self			:int	)       ->					Mapping[str, Mapping[int, str]]:




             '''simple docstring'''
             if self.task == "multiple-choice":
                    _a    : Tuple     					={0: """batch""", 1: """choice""", 2: """sequence"""}
             else:
                    _a    : List[Any]     					={0: """batch""", 1: """sequence"""}
             return OrderedDict(
                 [
                     ("""input_ids""", dynamic_axis),
                     ("""attention_mask""", dynamic_axis),
                 ]	)


