




import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()
def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase      ,   __lowerCAmelCase							):
  # Initialise PyTorch model
  lowerCAmelCase__								=			json.load(open(__lowerCAmelCase      ,   '''r'''							)							)
  lowerCAmelCase__								=			GPTNeoConfig(
      hidden_size=config_json['''n_embd''']      ,   num_layers=config_json['''n_layer''']      ,   num_heads=config_json['''n_head''']      ,   attention_types=config_json['''attention_types''']      ,   max_position_embeddings=config_json['''n_positions''']      ,   resid_dropout=config_json['''res_dropout''']      ,   embed_dropout=config_json['''embed_dropout''']      ,   attention_dropout=config_json['''attn_dropout''']      ,   )
  print(F"""Building PyTorch model from configuration: {config}"""							)
  lowerCAmelCase__								=			GPTNeoForCausalLM(__lowerCAmelCase							)

  # Load weights from tf checkpoint
  load_tf_weights_in_gpt_neo(__lowerCAmelCase      ,   __lowerCAmelCase      ,   __lowerCAmelCase							)

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


if __name__ == "__main__":
      __magic_name__     :     Any  							=					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 mesh-tf 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."""
      )
      __magic_name__     :     Optional[Any]  							=					parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)







import os

from ...utils import logging
from .utils import DataProcessor, InputExample


__magic_name__     :     str  							=					logging.get_logger(__name__)





class 						SCREAMING_SNAKE_CASE__		(_a ):
     def __init__(	self  :		Any					,	__lowerCamelCase  :		Any					,	__lowerCamelCase  :		Optional[int]=None	):



       """simple docstring"""
       lowerCAmelCase__								=			language
       lowerCAmelCase__								=			train_language
     def 					A__			(	self  :		str					,	__lowerCamelCase  :		List[str]	):



       """simple docstring"""
       lowerCAmelCase__								=			self.language if self.train_language is None else self.train_language
       lowerCAmelCase__								=			self._read_tsv(os.path.join(__lowerCamelCase					,	F"""XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"""	)	)
       lowerCAmelCase__								=			[]
       for i, line in enumerate(__lowerCamelCase	):
         if i == 0:
           continue
         lowerCAmelCase__								=			F"""train-{i}"""
         lowerCAmelCase__								=			line[0]
         lowerCAmelCase__								=			line[1]
         lowerCAmelCase__								=			'''contradiction''' if line[2] == '''contradictory''' else line[2]
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training input {text_a} is not a string"""	)
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training input {text_b} is not a string"""	)
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training label {label} is not a string"""	)
         examples.append(InputExample(guid=__lowerCamelCase					,	text_a=__lowerCamelCase					,	text_b=__lowerCamelCase					,	label=__lowerCamelCase	)	)
       return examples
     def 					A__			(	self  :		Dict					,	__lowerCamelCase  :		Union[str, Any]	):



       """simple docstring"""
       lowerCAmelCase__								=			self._read_tsv(os.path.join(__lowerCamelCase					,	'''XNLI-1.0/xnli.test.tsv'''	)	)
       lowerCAmelCase__								=			[]
       for i, line in enumerate(__lowerCamelCase	):
         if i == 0:
           continue
         lowerCAmelCase__								=			line[0]
         if language != self.language:
           continue
         lowerCAmelCase__								=			F"""test-{i}"""
         lowerCAmelCase__								=			line[6]
         lowerCAmelCase__								=			line[7]
         lowerCAmelCase__								=			line[1]
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training input {text_a} is not a string"""	)
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training input {text_b} is not a string"""	)
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training label {label} is not a string"""	)
         examples.append(InputExample(guid=__lowerCamelCase					,	text_a=__lowerCamelCase					,	text_b=__lowerCamelCase					,	label=__lowerCamelCase	)	)
       return examples



     def 					A__			(	self  :		List[str]	):



       """simple docstring"""
       return ["contradiction", "entailment", "neutral"]



__magic_name__     :     Tuple  							=					{
    """xnli""": XnliProcessor,
}

__magic_name__     :     List[Any]  							=					{
    """xnli""": """classification""",
}

__magic_name__     :     str  							=					{
    """xnli""": 3,
}

