

'''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


lowercase_      :				Any   					=    {
    "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      SCREAMING_SNAKE_CASE				(				lowercase_    :      Union[str, Any] ,  lowercase_    :      Union[str, Any] ,  lowercase_    :      Dict ,  lowercase_    :      Dict=None						):
						lowercase        =							XLNetConfig.from_json_file(_SCREAMING_SNAKE_CASE						)

						lowercase        =							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}"""						)
												lowercase        =							finetuning_task
												lowercase        =							GLUE_TASKS_NUM_LABELS[finetuning_task]
												lowercase        =							XLNetForSequenceClassification(_SCREAMING_SNAKE_CASE						)
						elif "squad" in finetuning_task:
												lowercase        =							finetuning_task
												lowercase        =							XLNetForQuestionAnswering(_SCREAMING_SNAKE_CASE						)
						else:
												lowercase        =							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
						lowercase        =							os.path.join(_SCREAMING_SNAKE_CASE ,  _SCREAMING_SNAKE_CASE						)
						lowercase        =							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__":
						lowercase_      :				Union[str, 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(
						    '''--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''',
						)
						lowercase_      :				List[Any]   					=    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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_      :				Any   					=    logging.get_logger(__name__)

lowercase_      :				str   					=    {
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					'''vit_msn'''







	def __init__(    self ,				_lowerCAmelCase=768 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=3072 ,				_lowerCAmelCase="gelu" ,				_lowerCAmelCase=0.0 ,				_lowerCAmelCase=0.0 ,				_lowerCAmelCase=0.02 ,				_lowerCAmelCase=1E-06 ,				_lowerCAmelCase=224 ,				_lowerCAmelCase=16 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=True ,				**_lowerCAmelCase ,				)    ->    List[Any]:
							'''simple docstring'''






							super().__init__(**_lowerCAmelCase						)

							lowercase        =							hidden_size
							lowercase        =							num_hidden_layers
							lowercase        =							num_attention_heads
							lowercase        =							intermediate_size
							lowercase        =							hidden_act
							lowercase        =							hidden_dropout_prob
							lowercase        =							attention_probs_dropout_prob
							lowercase        =							initializer_range
							lowercase        =							layer_norm_eps
							lowercase        =							image_size
							lowercase        =							patch_size
							lowercase        =							num_channels
							lowercase        =							qkv_bias


