
'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from packaging import version

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


UpperCAmelCase__ :			str											=		logging.get_logger(__name__)

UpperCAmelCase__ :			Optional[Any]											=		{
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :Optional[int]   =  'data2vec-vision'





	def __init__(       self      :							Optional[Any]   ,				__magic_name__      :							Optional[int]=768   ,				__magic_name__      :							Any=12   ,				__magic_name__      :							int=12   ,				__magic_name__      :							str=3072   ,				__magic_name__      :							Optional[Any]="gelu"   ,				__magic_name__      :							Optional[int]=0.0   ,				__magic_name__      :							Optional[int]=0.0   ,				__magic_name__      :							List[Any]=0.02   ,				__magic_name__      :							Any=1E-12   ,				__magic_name__      :							Dict=224   ,				__magic_name__      :							Dict=16   ,				__magic_name__      :							List[Any]=3   ,				__magic_name__      :							Tuple=False   ,				__magic_name__      :							Optional[int]=False   ,				__magic_name__      :							Any=False   ,				__magic_name__      :							Optional[Any]=False   ,				__magic_name__      :							List[Any]=0.1   ,				__magic_name__      :							int=0.1   ,				__magic_name__      :							Tuple=True   ,				__magic_name__      :							Dict=[3, 5, 7, 11]   ,				__magic_name__      :							int=[1, 2, 3, 6]   ,				__magic_name__      :							Union[str, Any]=True   ,				__magic_name__      :							int=0.4   ,				__magic_name__      :							int=256   ,				__magic_name__      :							Union[str, Any]=1   ,				__magic_name__      :							Union[str, Any]=False   ,				__magic_name__      :							Optional[int]=255   ,				**__magic_name__      :							int   ,				):


				"""simple docstring"""

				super().__init__(**__magic_name__				)

				lowerCAmelCase__										=				hidden_size
				lowerCAmelCase__										=				num_hidden_layers
				lowerCAmelCase__										=				num_attention_heads
				lowerCAmelCase__										=				intermediate_size
				lowerCAmelCase__										=				hidden_act
				lowerCAmelCase__										=				hidden_dropout_prob
				lowerCAmelCase__										=				attention_probs_dropout_prob
				lowerCAmelCase__										=				initializer_range
				lowerCAmelCase__										=				layer_norm_eps

				lowerCAmelCase__										=				image_size
				lowerCAmelCase__										=				patch_size
				lowerCAmelCase__										=				num_channels
				lowerCAmelCase__										=				use_mask_token
				lowerCAmelCase__										=				use_absolute_position_embeddings
				lowerCAmelCase__										=				use_relative_position_bias
				lowerCAmelCase__										=				use_shared_relative_position_bias
				lowerCAmelCase__										=				layer_scale_init_value
				lowerCAmelCase__										=				drop_path_rate
				lowerCAmelCase__										=				use_mean_pooling
				# decode head attributes (semantic segmentation)
				lowerCAmelCase__										=				out_indices
				lowerCAmelCase__										=				pool_scales
				# auxiliary head attributes (semantic segmentation)
				lowerCAmelCase__										=				use_auxiliary_head
				lowerCAmelCase__										=				auxiliary_loss_weight
				lowerCAmelCase__										=				auxiliary_channels
				lowerCAmelCase__										=				auxiliary_num_convs
				lowerCAmelCase__										=				auxiliary_concat_input
				lowerCAmelCase__										=				semantic_loss_ignore_index






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :Any   =  version.parse('1.11'     )





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				    ]				)







	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]				):


				"""simple docstring"""

				return 1E-4







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      lowerCamelCase_					(							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
			)


