





"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase       =	logging.get_logger(__name__)

_lowerCamelCase       =	{
    '''flaubert/flaubert_small_cased''': '''https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json''',
    '''flaubert/flaubert_base_uncased''': '''https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json''',
    '''flaubert/flaubert_base_cased''': '''https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json''',
    '''flaubert/flaubert_large_cased''': '''https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json''',
}





class 			snake_case		(   __UpperCAmelCase   ):
      lowerCamelCase__						= '''flaubert'''
      lowerCamelCase__						= {
          '''hidden_size''': '''emb_dim''',
          '''num_attention_heads''': '''n_heads''',
          '''num_hidden_layers''': '''n_layers''',
          '''n_words''': '''vocab_size''',  # For backward compatibility
      }

      def __init__(			self   :Dict						,							_lowerCamelCase   :Union[str, Any]=False						,							_lowerCamelCase   :Optional[int]=0.0						,							_lowerCamelCase   :Dict=3_0_1_4_5						,							_lowerCamelCase   :List[str]=2_0_4_8						,							_lowerCamelCase   :Optional[int]=1_2						,							_lowerCamelCase   :Union[str, Any]=1_6						,							_lowerCamelCase   :Any=0.1						,							_lowerCamelCase   :List[str]=0.1						,							_lowerCamelCase   :Union[str, Any]=True						,							_lowerCamelCase   :str=False						,							_lowerCamelCase   :List[str]=False						,							_lowerCamelCase   :Union[str, Any]=False						,							_lowerCamelCase   :Optional[int]=1						,							_lowerCamelCase   :List[Any]=True						,							_lowerCamelCase   :Any=5_1_2						,							_lowerCamelCase   :Tuple=2_0_4_8**-0.5						,							_lowerCamelCase   :str=1e-12						,							_lowerCamelCase   :Optional[Any]=0.0_2						,							_lowerCamelCase   :int=0						,							_lowerCamelCase   :List[Any]=1						,							_lowerCamelCase   :Tuple=2						,							_lowerCamelCase   :int=3						,							_lowerCamelCase   :Optional[Any]=5						,							_lowerCamelCase   :Optional[int]=True						,							_lowerCamelCase   :List[str]="first"						,							_lowerCamelCase   :List[Any]=True						,							_lowerCamelCase   :List[str]=None						,							_lowerCamelCase   :List[Any]=True						,							_lowerCamelCase   :str=0.1						,							_lowerCamelCase   :Union[str, Any]=5						,							_lowerCamelCase   :Tuple=5						,							_lowerCamelCase   :Union[str, Any]=0						,							_lowerCamelCase   :List[str]=0						,							_lowerCamelCase   :Tuple=2						,							_lowerCamelCase   :Tuple=0						,							**_lowerCamelCase   :Tuple						,							):
       __SCREAMING_SNAKE_CASE :			Any												=       pre_norm
       __SCREAMING_SNAKE_CASE :			int												=       layerdrop
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       vocab_size
       __SCREAMING_SNAKE_CASE :			List[str]												=       emb_dim
       __SCREAMING_SNAKE_CASE :			Dict												=       n_layers
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       n_heads
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       dropout
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       attention_dropout
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       gelu_activation
       __SCREAMING_SNAKE_CASE :			Tuple												=       sinusoidal_embeddings
       __SCREAMING_SNAKE_CASE :			List[str]												=       causal
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       asm
       __SCREAMING_SNAKE_CASE :			int												=       n_langs
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       use_lang_emb
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       layer_norm_eps
       __SCREAMING_SNAKE_CASE :			List[Any]												=       bos_index
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       eos_index
       __SCREAMING_SNAKE_CASE :			int												=       pad_index
       __SCREAMING_SNAKE_CASE :			List[Any]												=       unk_index
       __SCREAMING_SNAKE_CASE :			int												=       mask_index
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       is_encoder
       __SCREAMING_SNAKE_CASE :			List[str]												=       max_position_embeddings
       __SCREAMING_SNAKE_CASE :			List[Any]												=       embed_init_std
       __SCREAMING_SNAKE_CASE :			Tuple												=       init_std
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       summary_type
       __SCREAMING_SNAKE_CASE :			List[Any]												=       summary_use_proj
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       summary_activation
       __SCREAMING_SNAKE_CASE :			List[Any]												=       summary_proj_to_labels
       __SCREAMING_SNAKE_CASE :			List[str]												=       summary_first_dropout
       __SCREAMING_SNAKE_CASE :			Tuple												=       start_n_top
       __SCREAMING_SNAKE_CASE :			Any												=       end_n_top
       __SCREAMING_SNAKE_CASE :			int												=       mask_token_id
       __SCREAMING_SNAKE_CASE :			Dict												=       lang_id

       if "n_words" in kwargs:
        __SCREAMING_SNAKE_CASE :			Any												=       kwargs['''n_words''']

       super().__init__(pad_token_id=_lowerCamelCase						,							bos_token_id=_lowerCamelCase						,							**_lowerCamelCase						)







class 			snake_case		(   __UpperCAmelCase   ):

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








"""simple docstring"""





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()




def 		lowerCAmelCase_	(  lowercase_   :							List[str] ,      lowercase_   :							List[str] ,      lowercase_   :							Dict		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Tuple												=       TaConfig.from_json_file(lowercase_		)
 print(F'''Building PyTorch model from configuration: {config}'''		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       TaForConditionalGeneration(lowercase_		)

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(lowercase_ ,      lowercase_ ,      lowercase_		)

 # Save pytorch-model
 print(F'''Save PyTorch model to {pytorch_dump_path}'''		)
 model.save_pretrained(lowercase_		)


if __name__ == "__main__":
    _lowerCamelCase       =	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(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    _lowerCamelCase       =	parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

