






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__	:			Union[str, 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   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_=None				):






				'''simple docstring'''
				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__":
							A__	:			List[str]			   = 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__	:			List[str]			   = 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
							)








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


_snake_case             =     logging.get_logger(__name__)

_snake_case             =     {
    '''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
    '''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
    '''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Dict     		=		"mobilenet_v2"






   def __init__(		self						,				__A=3						,				__A=224						,				__A=1.0						,				__A=8						,				__A=8						,				__A=6						,				__A=32						,				__A=True						,				__A=True						,				__A="relu6"						,				__A=True						,				__A=0.8						,				__A=0.02						,				__A=0.001						,				__A=255						,				**__A						,				):






     """simple docstring"""





     super().__init__(**__A )

     if depth_multiplier <= 0:
       raise ValueError("depth_multiplier must be greater than zero." )

     lowerCamelCase :					str    =				num_channels
     lowerCamelCase :					Any    =				image_size
     lowerCamelCase :					Union[str, Any]    =				depth_multiplier
     lowerCamelCase :					Tuple    =				depth_divisible_by
     lowerCamelCase :					Dict    =				min_depth
     lowerCamelCase :					Dict    =				expand_ratio
     lowerCamelCase :					Optional[Any]    =				output_stride
     lowerCamelCase :					int    =				first_layer_is_expansion
     lowerCamelCase :					Union[str, Any]    =				finegrained_output
     lowerCamelCase :					Optional[Any]    =				hidden_act
     lowerCamelCase :					Optional[Any]    =				tf_padding
     lowerCamelCase :					Optional[Any]    =				classifier_dropout_prob
     lowerCamelCase :					Dict    =				initializer_range
     lowerCamelCase :					str    =				layer_norm_eps
     lowerCamelCase :					Optional[Any]    =				semantic_loss_ignore_index



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Union[str, Any]     		=		version.parse("1.11"    )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return OrderedDict([("pixel_values", {0: "batch"})] )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     if self.task == "image-classification":
       return OrderedDict([("logits", {0: "batch"})] )
     else:
       return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})] )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return 1e-4



