



'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

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


a__						:			str										=			logging.get_logger(__name__)

a__						:			Dict										=			{
    'nghuyong/ernie-1.0-base-zh': 'https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-2.0-base-en': 'https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json',
    'nghuyong/ernie-2.0-large-en': 'https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json',
    'nghuyong/ernie-3.0-base-zh': 'https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-medium-zh': 'https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-mini-zh': 'https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-micro-zh': 'https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-nano-zh': 'https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json',
    'nghuyong/ernie-gram-zh': 'https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json',
    'nghuyong/ernie-health-zh': 'https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json',
}
class        UpperCAmelCase__  (    UpperCAmelCase_):
 __SCREAMING_SNAKE_CASE					      =				'''ernie'''



 def __init__(  self		,	lowercase=3_0_5_2_2		,	lowercase=7_6_8		,	lowercase=1_2		,	lowercase=1_2		,	lowercase=3_0_7_2		,	lowercase="gelu"		,	lowercase=0.1		,	lowercase=0.1		,	lowercase=5_1_2		,	lowercase=2		,	lowercase=3		,	lowercase=False		,	lowercase=0.02		,	lowercase=1E-12		,	lowercase=0		,	lowercase="absolute"		,	lowercase=True		,	lowercase=None		,	**lowercase		,	)  ->							Optional[Any]:
  super().__init__(pad_token_id=lowercase		,	**lowercase			)

  __UpperCamelCase				      =  vocab_size
  __UpperCamelCase				      =  hidden_size
  __UpperCamelCase				      =  num_hidden_layers
  __UpperCamelCase				      =  num_attention_heads
  __UpperCamelCase				      =  hidden_act
  __UpperCamelCase				      =  intermediate_size
  __UpperCamelCase				      =  hidden_dropout_prob
  __UpperCamelCase				      =  attention_probs_dropout_prob
  __UpperCamelCase				      =  max_position_embeddings
  __UpperCamelCase				      =  type_vocab_size
  __UpperCamelCase				      =  task_type_vocab_size
  __UpperCamelCase				      =  use_task_id
  __UpperCamelCase				      =  initializer_range
  __UpperCamelCase				      =  layer_norm_eps
  __UpperCamelCase				      =  position_embedding_type
  __UpperCamelCase				      =  use_cache
  __UpperCamelCase				      =  classifier_dropout


class        UpperCAmelCase__  (    UpperCAmelCase_):



 @property
 def    __lowerCamelCase				(  self			)  ->							Mapping[str, Mapping[int, str]]:
  if self.task == "multiple-choice":
   __UpperCamelCase				      =  {0: """batch""", 1: """choice""", 2: """sequence"""}
  else:
   __UpperCamelCase				      =  {0: """batch""", 1: """sequence"""}
  return OrderedDict(
      [
          ("""input_ids""", dynamic_axis),
          ("""attention_mask""", dynamic_axis),
          ("""token_type_ids""", dynamic_axis),
          ("""task_type_ids""", 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 _lowercase   (  __A							,__A							,__A       ):



 '''simple docstring'''



 __UpperCamelCase				      =  TaConfig.from_json_file(__A       )
 print(f"Building PyTorch model from configuration: {config}"       )
 __UpperCamelCase				      =  TaForConditionalGeneration(__A       )

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

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


if __name__ == "__main__":
 a__						:			int										=			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.'
 )
 a__						:			Optional[Any]										=			parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





