
"""simple docstring"""


import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


A_    							=					{
    """cola""": 2,
    """mnli""": 3,
    """mrpc""": 2,
    """sst-2""": 2,
    """sts-b""": 1,
    """qqp""": 2,
    """qnli""": 2,
    """rte""": 2,
    """wnli""": 2,
}


logging.set_verbosity_info()






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=None						):
			# Initialise PyTorch model
			lowerCamelCase_					=  XLNetConfig.from_json_file(lowerCAmelCase__						)

			lowerCamelCase_					=  finetuning_task.lower() if finetuning_task is not None else ''''''
			if finetuning_task in GLUE_TASKS_NUM_LABELS:
						print(f"Building PyTorch XLNetForSequenceClassification model from configuration: {config}"						)
						lowerCamelCase_					=  finetuning_task
						lowerCamelCase_					=  GLUE_TASKS_NUM_LABELS[finetuning_task]
						lowerCamelCase_					=  XLNetForSequenceClassification(lowerCAmelCase__						)
			elif "squad" in finetuning_task:
						lowerCamelCase_					=  finetuning_task
						lowerCamelCase_					=  XLNetForQuestionAnswering(lowerCAmelCase__						)
			else:
						lowerCamelCase_					=  XLNetLMHeadModel(lowerCAmelCase__						)

			# Load weights from tf checkpoint
			load_tf_weights_in_xlnet(lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						)

			# Save pytorch-model
			lowerCamelCase_					=  os.path.join(lowerCAmelCase__	,lowerCAmelCase__						)
			lowerCamelCase_					=  os.path.join(lowerCAmelCase__	,lowerCAmelCase__						)
			print(f"Save PyTorch model to {os.path.abspath(lowerCAmelCase__						)}"						)
			torch.save(model.state_dict()	,lowerCAmelCase__						)
			print(f"Save configuration file to {os.path.abspath(lowerCAmelCase__						)}"						)
			with open(lowerCAmelCase__	,'''w'''	,encoding='''utf-8'''						) as f:
						f.write(config.to_json_string()						)


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(
						    """--xlnet_config_file""",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        """The config json file corresponding to the pre-trained XLNet model. \n"""
						        """This specifies the model architecture."""
						    ),
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""",
						    default=None,
						    type=str,
						    required=True,
						    help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
						)
						parser.add_argument(
						    """--finetuning_task""",
						    default=None,
						    type=str,
						    help="""Name of a task on which the XLNet TensorFlow model was fine-tuned""",
						)
						A_    							=					parser.parse_args()
						print(args)

						convert_xlnet_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
						)





"""simple docstring"""


from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
						from .features import FeatureType




@dataclass
class  __lowerCamelCase  :
				a__:      List[str]
				a__:      Optional[str]          =    None
				# Automatically constructed
				a__:      ClassVar[str]          =    "dict"
				a__:      ClassVar[Any]          =    None
				a__:      str          =    field(default='Translation'						,	init=lowerCAmelCase						,	repr=lowerCAmelCase			)




				def __call__(				self   ):
							return pa.struct({lang: pa.string() for lang in sorted(self.languages   )}   )





				def        UpperCAmelCase__						(				self   ):
							from .features import Value

							return {k: Value('''string'''   ) for k in sorted(self.languages   )}




@dataclass
class  __lowerCamelCase  :
				a__:      Optional[List]          =    None
				a__:      Optional[int]          =    None
				a__:      Optional[str]          =    None
				# Automatically constructed
				a__:      ClassVar[str]          =    "dict"
				a__:      ClassVar[Any]          =    None
				a__:      str          =    field(default='TranslationVariableLanguages'						,	init=lowerCAmelCase						,	repr=lowerCAmelCase			)




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  sorted(set(self.languages   )   ) if self.languages else None
							lowerCamelCase_					=  len(self.languages   ) if self.languages else None




				def __call__(				self   ):
							return pa.struct({'''language''': pa.list_(pa.string()   ), '''translation''': pa.list_(pa.string()   )}   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  set(self.languages   )
							if self.languages and set(UpperCAmelCase   ) - lang_set:
										raise ValueError(
										    f"Some languages in example ({', '.join(sorted(set(UpperCAmelCase   ) - lang_set   )   )}) are not in valid set ({', '.join(UpperCAmelCase   )})."   )

							# Convert dictionary into tuples, splitting out cases where there are
							# multiple translations for a single language.
							lowerCamelCase_					=  []
							for lang, text in translation_dict.items():
										if isinstance(UpperCAmelCase							,						UpperCAmelCase   ):
													translation_tuples.append((lang, text)   )
										else:
													translation_tuples.extend([(lang, el) for el in text]   )

        # Ensure translations are in ascending order by language code.
							lowerCamelCase_		, lowerCamelCase_					=  zip(*sorted(UpperCAmelCase   )   )

							return {"language": languages, "translation": translations}





				def        UpperCAmelCase__						(				self   ):
							from .features import Sequence, Value

							return {
							    "language": Sequence(Value('''string'''   )   ),
							    "translation": Sequence(Value('''string'''   )   ),
							}



