
'''simple docstring'''
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


lowercase__											=logging.get_logger(__name__)

lowercase__											={
    'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}







class   a_ (      UpperCamelCase__					):
			lowerCamelCase__	:			Dict       =    'dpt'




			def __init__(       self					,   UpperCAmelCase=7_68					,   UpperCAmelCase=12					,   UpperCAmelCase=12					,   UpperCAmelCase=30_72					,   UpperCAmelCase="gelu"					,   UpperCAmelCase=0.0					,   UpperCAmelCase=0.0					,   UpperCAmelCase=0.02					,   UpperCAmelCase=1e-12					,   UpperCAmelCase=3_84					,   UpperCAmelCase=16					,   UpperCAmelCase=3					,   UpperCAmelCase=False					,   UpperCAmelCase=True					,   UpperCAmelCase=[2, 5, 8, 11]					,   UpperCAmelCase="project"					,   UpperCAmelCase=[4, 2, 1, 0.5]					,   UpperCAmelCase=[96, 1_92, 3_84, 7_68]					,   UpperCAmelCase=2_56					,   UpperCAmelCase=-1					,   UpperCAmelCase=False					,   UpperCAmelCase=True					,   UpperCAmelCase=0.4					,   UpperCAmelCase=2_55					,   UpperCAmelCase=0.1					,   UpperCAmelCase=[1, 10_24, 24, 24]					,   UpperCAmelCase=[0, 1]					,   UpperCAmelCase=None					,   **UpperCAmelCase					,   ):
				super().__init__(**UpperCAmelCase	)

				a_      				=						hidden_size
				a_      				=						is_hybrid

				if self.is_hybrid:
					if backbone_config is None:
						logger.info("""Initializing the config with a `BiT` backbone."""	)
						a_      				=						{
						    """global_padding""": """same""",
						    """layer_type""": """bottleneck""",
						    """depths""": [3, 4, 9],
						    """out_features""": ["""stage1""", """stage2""", """stage3"""],
						    """embedding_dynamic_padding""": True,
						}
						a_      				=						BitConfig(**UpperCAmelCase	)
					elif isinstance(UpperCAmelCase					,   UpperCAmelCase	):
						logger.info("""Initializing the config with a `BiT` backbone."""	)
						a_      				=						BitConfig(**UpperCAmelCase	)
					elif isinstance(UpperCAmelCase					,   UpperCAmelCase	):
						a_      				=						backbone_config
					else:
						raise ValueError(
						    f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'''	)

					a_      				=						backbone_featmap_shape
					a_      				=						neck_ignore_stages

					if readout_type != "project":
						raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode."""	)
				else:
					a_      				=						None
					a_      				=						None
					a_      				=						[]

				a_      				=						num_hidden_layers
				a_      				=						num_attention_heads
				a_      				=						intermediate_size
				a_      				=						hidden_act
				a_      				=						hidden_dropout_prob
				a_      				=						attention_probs_dropout_prob
				a_      				=						initializer_range
				a_      				=						layer_norm_eps
				a_      				=						image_size
				a_      				=						patch_size
				a_      				=						num_channels
				a_      				=						qkv_bias
				a_      				=						backbone_out_indices
				if readout_type not in ["ignore", "add", "project"]:
					raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']"""	)
				a_      				=						readout_type
				a_      				=						reassemble_factors
				a_      				=						neck_hidden_sizes
				a_      				=						fusion_hidden_size
				a_      				=						head_in_index
				a_      				=						use_batch_norm_in_fusion_residual
				# auxiliary head attributes (semantic segmentation)
				a_      				=						use_auxiliary_head
				a_      				=						auxiliary_loss_weight
				a_      				=						semantic_loss_ignore_index
				a_      				=						semantic_classifier_dropout






			def 					lowerCAmelCase__					(       self	):
				a_      				=						copy.deepcopy(self.__dict__	)

				if output["backbone_config"] is not None:
					a_      				=						self.backbone_config.to_dict()

				a_      				=						self.__class__.model_type
				return output


'''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__											={
    '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      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      A__=None						):
	# Initialise PyTorch model
	a_      				=						XLNetConfig.from_json_file(A__						)

	a_      				=						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_      				=						finetuning_task
		a_      				=						GLUE_TASKS_NUM_LABELS[finetuning_task]
		a_      				=						XLNetForSequenceClassification(A__						)
	elif "squad" in finetuning_task:
		a_      				=						finetuning_task
		a_      				=						XLNetForQuestionAnswering(A__						)
	else:
		a_      				=						XLNetLMHeadModel(A__						)

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

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


if __name__ == "__main__":
		lowercase__											=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__											=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
		)
