



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= {
    """google/bit-50""": """https://huggingface.co/google/bit-50/resolve/main/config.json""",
}




class 			snake_case_						(						a_     ,a_				):
							__lowerCAmelCase    					=					"bit"
							__lowerCAmelCase    					=					["preactivation", "bottleneck"]
							__lowerCAmelCase    					=					["SAME", "VALID"]






							def __init__(   self			,     a_=3			,     a_=6_4			,     a_=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]			,     a_=[3, 4, 6, 3]			,     a_="preactivation"			,     a_="relu"			,     a_=None			,     a_=3_2			,     a_=0.0			,     a_=False			,     a_=3_2			,     a_=1			,     a_=None			,     a_=None			,     **a_			,     ):
									super().__init__(**a_      )
									if layer_type not in self.layer_types:
											raise ValueError(F"""layer_type={layer_type} is not one of {','.join(self.layer_types      )}"""      )
									if global_padding is not None:
											if global_padding.upper() in self.supported_padding:
													a_     :      int 						=     global_padding.upper()
											else:
													raise ValueError(F"""Padding strategy {global_padding} not supported"""      )
									a_     :      Dict 						=     num_channels
									a_     :      Dict 						=     embedding_size
									a_     :      Optional[Any] 						=     hidden_sizes
									a_     :      Optional[int] 						=     depths
									a_     :      Tuple 						=     layer_type
									a_     :      List[Any] 						=     hidden_act
									a_     :      Union[str, Any] 						=     global_padding
									a_     :      List[Any] 						=     num_groups
									a_     :      List[str] 						=     drop_path_rate
									a_     :      List[Any] 						=     embedding_dynamic_padding
									a_     :      List[str] 						=     output_stride
									a_     :      Any 						=     width_factor

									a_     :      List[str] 						=     ["stem"] + [F"""stage{idx}""" for idx in range(1			,     len(a_      ) + 1      )]
									a_						,				a_     :      List[Any] 						=     get_aligned_output_features_output_indices(
									    out_features=a_			,     out_indices=a_			,     stage_names=self.stage_names      )




"""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


SCREAMING_SNAKE_CASE_				= {
    """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        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=None	)			->      int:
		# Initialise PyTorch model
		a_     :      Optional[int] 						=     XLNetConfig.from_json_file(SCREAMING_SNAKE_CASE__	)

		a_     :      List[Any] 						=     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}"""	)
				a_     :      Tuple 						=     finetuning_task
				a_     :      Dict 						=     GLUE_TASKS_NUM_LABELS[finetuning_task]
				a_     :      Tuple 						=     XLNetForSequenceClassification(SCREAMING_SNAKE_CASE__	)
		elif "squad" in finetuning_task:
				a_     :      Tuple 						=     finetuning_task
				a_     :      List[Any] 						=     XLNetForQuestionAnswering(SCREAMING_SNAKE_CASE__	)
		else:
				a_     :      Any 						=     XLNetLMHeadModel(SCREAMING_SNAKE_CASE__	)

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

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


if __name__ == "__main__":
					SCREAMING_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(
					    """--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""",
					)
					SCREAMING_SNAKE_CASE_				= 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
					)