

import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Dict	,     SCREAMING_SNAKE_CASE_		:    Tuple	,     SCREAMING_SNAKE_CASE_		:    Optional[int]	,     SCREAMING_SNAKE_CASE_		:    Tuple			):



     '''simple docstring'''





     lowercase__    :   List[Any]      =       BigBirdConfig.from_json_file(SCREAMING_SNAKE_CASE_			)
     print(f"""Building PyTorch model from configuration: {config}"""			)

     if is_trivia_qa:
          lowercase__    :   str      =       BigBirdForQuestionAnswering(SCREAMING_SNAKE_CASE_			)
     else:
          lowercase__    :   int      =       BigBirdForPreTraining(SCREAMING_SNAKE_CASE_			)

     # Load weights from tf checkpoint
     load_tf_weights_in_big_bird(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     is_trivia_qa=SCREAMING_SNAKE_CASE_			)

     # Save pytorch-model
     print(f"""Save PyTorch model to {pytorch_dump_path}"""			)
     model.save_pretrained(SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
       snake_case_			  = 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(
           '''--big_bird_config_file''',
           default=None,
           type=str,
           required=True,
           help=(
               '''The config json file corresponding to the pre-trained BERT 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.'''
       )
       parser.add_argument(
           '''--is_trivia_qa''', action='''store_true''', help='''Whether to convert a model with a trivia_qa head.'''
       )
       snake_case_			  = parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
       )




from collections import OrderedDict
from typing import Mapping

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


snake_case_			  = logging.get_logger(__name__)

snake_case_			  = {
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       Any														=     """distilbert"""
    __lowerCamelCase						:       List[Any]														=     {
        """hidden_size""": """dim""",
        """num_attention_heads""": """n_heads""",
        """num_hidden_layers""": """n_layers""",
    }





    def __init__(      self						,   a=3_0522						,   a=512						,   a=False						,   a=6						,   a=12						,   a=768						,   a=4 * 768						,   a=0.1						,   a=0.1						,   a="gelu"						,   a=0.02						,   a=0.1						,   a=0.2						,   a=0						,   **a						,   ):
         lowercase__    :   int      =       vocab_size
         lowercase__    :   int      =       max_position_embeddings
         lowercase__    :   List[str]      =       sinusoidal_pos_embds
         lowercase__    :   Tuple      =       n_layers
         lowercase__    :   Optional[int]      =       n_heads
         lowercase__    :   List[Any]      =       dim
         lowercase__    :   Any      =       hidden_dim
         lowercase__    :   Union[str, Any]      =       dropout
         lowercase__    :   Optional[int]      =       attention_dropout
         lowercase__    :   List[Any]      =       activation
         lowercase__    :   Union[str, Any]      =       initializer_range
         lowercase__    :   Dict      =       qa_dropout
         lowercase__    :   Tuple      =       seq_classif_dropout
         super().__init__(**a						,   pad_token_id=a)



class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    @property
    def       snake_case_				(      self):
         if self.task == "multiple-choice":
              lowercase__    :   List[str]      =       {0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
              lowercase__    :   Tuple      =       {0: 'batch', 1: 'sequence'}
         return OrderedDict(
             [
                 ('input_ids', dynamic_axis),
                 ('attention_mask', dynamic_axis),
             ])

