
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE__  :     Dict   				=       logging.get_logger(__name__)

class   lowerCAmelCase__	(      UpperCamelCase__     ):
		a__			:		int     =						['pixel_values']






		def __init__(						self						:				Tuple				,     SCREAMING_SNAKE_CASE__						:				Optional[Any] = True				,     SCREAMING_SNAKE_CASE__						:				Optional[Any] = None				,     SCREAMING_SNAKE_CASE__						:				Dict = PILImageResampling.BICUBIC				,     SCREAMING_SNAKE_CASE__						:				Any = True				,     SCREAMING_SNAKE_CASE__						:				str = True				,     SCREAMING_SNAKE_CASE__						:				Tuple = 1 / 2_55				,     SCREAMING_SNAKE_CASE__						:				Any = None				,     SCREAMING_SNAKE_CASE__						:				List[Any] = True				,     SCREAMING_SNAKE_CASE__						:				Any = None				,     SCREAMING_SNAKE_CASE__						:				Tuple = None				,     **SCREAMING_SNAKE_CASE__						:				Optional[Any]				,     )	->				Any:
						super().__init__(**SCREAMING_SNAKE_CASE__    )
						__lowerCamelCase          =      size if size is not None else {'''height''': 2_24, '''width''': 2_24}
						__lowerCamelCase          =      get_size_dict(SCREAMING_SNAKE_CASE__    )
						__lowerCamelCase          =      crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
						__lowerCamelCase          =      get_size_dict(SCREAMING_SNAKE_CASE__				,     default_to_square=SCREAMING_SNAKE_CASE__				,     param_name='''crop_size'''    )

						__lowerCamelCase          =      do_resize
						__lowerCamelCase          =      do_rescale
						__lowerCamelCase          =      do_normalize
						__lowerCamelCase          =      do_center_crop
						__lowerCamelCase          =      crop_size
						__lowerCamelCase          =      size
						__lowerCamelCase          =      resample
						__lowerCamelCase          =      rescale_factor
						__lowerCamelCase          =      image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
						__lowerCamelCase          =      image_std if image_std is not None else IMAGENET_DEFAULT_STD






		def 					__A			(						self						:				Any				,     SCREAMING_SNAKE_CASE__						:				Any				,     SCREAMING_SNAKE_CASE__						:				str				,     SCREAMING_SNAKE_CASE__						:				Any = PILImageResampling.BILINEAR				,     SCREAMING_SNAKE_CASE__						:				List[str] = None				,     **SCREAMING_SNAKE_CASE__						:				Optional[int]				,     )	->				List[str]:
						__lowerCamelCase          =      get_size_dict(SCREAMING_SNAKE_CASE__    )

						if "shortest_edge" in size:
										__lowerCamelCase          =      get_resize_output_image_size(SCREAMING_SNAKE_CASE__				,     size=size['''shortest_edge''']				,     default_to_square=SCREAMING_SNAKE_CASE__    )
										# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
						elif "height" in size and "width" in size:
										__lowerCamelCase          =      (size['''height'''], size['''width'''])
						else:
										raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''    )
						return resize(SCREAMING_SNAKE_CASE__				,     size=SCREAMING_SNAKE_CASE__				,     resample=SCREAMING_SNAKE_CASE__				,     data_format=SCREAMING_SNAKE_CASE__				,     **SCREAMING_SNAKE_CASE__    )






		def 					__A			(						self						:				int				,     SCREAMING_SNAKE_CASE__						:				Tuple				,     SCREAMING_SNAKE_CASE__						:				Optional[Any]				,     SCREAMING_SNAKE_CASE__						:				Dict = None				,     **SCREAMING_SNAKE_CASE__						:				int				,     )	->				List[str]:
						__lowerCamelCase          =      get_size_dict(SCREAMING_SNAKE_CASE__    )
						if "height" not in size or "width" not in size:
										raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''    )
						return center_crop(SCREAMING_SNAKE_CASE__				,     size=(size['''height'''], size['''width'''])				,     data_format=SCREAMING_SNAKE_CASE__				,     **SCREAMING_SNAKE_CASE__    )






		def 					__A			(						self						:				Union[str, Any]				,     SCREAMING_SNAKE_CASE__						:				Any				,     SCREAMING_SNAKE_CASE__						:				Dict				,     SCREAMING_SNAKE_CASE__						:				int = None				,     **SCREAMING_SNAKE_CASE__						:				Optional[Any]    )	->				Dict:
						return rescale(SCREAMING_SNAKE_CASE__				,     scale=SCREAMING_SNAKE_CASE__				,     data_format=SCREAMING_SNAKE_CASE__				,     **SCREAMING_SNAKE_CASE__    )






		def 					__A			(						self						:				Union[str, Any]				,     SCREAMING_SNAKE_CASE__						:				List[Any]				,     SCREAMING_SNAKE_CASE__						:				Dict				,     SCREAMING_SNAKE_CASE__						:				str				,     SCREAMING_SNAKE_CASE__						:				Tuple = None				,     **SCREAMING_SNAKE_CASE__						:				List[Any]				,     )	->				List[str]:
						return normalize(SCREAMING_SNAKE_CASE__				,     mean=SCREAMING_SNAKE_CASE__				,     std=SCREAMING_SNAKE_CASE__				,     data_format=SCREAMING_SNAKE_CASE__				,     **SCREAMING_SNAKE_CASE__    )







		def 					__A			(						self						:				Optional[int]				,     SCREAMING_SNAKE_CASE__						:				Tuple				,     SCREAMING_SNAKE_CASE__						:				int = None				,     SCREAMING_SNAKE_CASE__						:				Any = None				,     SCREAMING_SNAKE_CASE__						:				Dict = None				,     SCREAMING_SNAKE_CASE__						:				Optional[Any] = None				,     SCREAMING_SNAKE_CASE__						:				List[str] = None				,     SCREAMING_SNAKE_CASE__						:				Optional[Any] = None				,     SCREAMING_SNAKE_CASE__						:				List[Any] = None				,     SCREAMING_SNAKE_CASE__						:				List[str] = None				,     SCREAMING_SNAKE_CASE__						:				Any = None				,     SCREAMING_SNAKE_CASE__						:				Union[str, Any] = None				,     SCREAMING_SNAKE_CASE__						:				Optional[int] = None				,     SCREAMING_SNAKE_CASE__						:				Dict = ChannelDimension.FIRST				,     **SCREAMING_SNAKE_CASE__						:				Dict				,     )	->				str:
						__lowerCamelCase          =      do_resize if do_resize is not None else self.do_resize
						__lowerCamelCase          =      do_rescale if do_rescale is not None else self.do_rescale
						__lowerCamelCase          =      do_normalize if do_normalize is not None else self.do_normalize
						__lowerCamelCase          =      do_center_crop if do_center_crop is not None else self.do_center_crop
						__lowerCamelCase          =      crop_size if crop_size is not None else self.crop_size
						__lowerCamelCase          =      get_size_dict(SCREAMING_SNAKE_CASE__				,     param_name='''crop_size'''				,     default_to_square=SCREAMING_SNAKE_CASE__    )
						__lowerCamelCase          =      resample if resample is not None else self.resample
						__lowerCamelCase          =      rescale_factor if rescale_factor is not None else self.rescale_factor
						__lowerCamelCase          =      image_mean if image_mean is not None else self.image_mean
						__lowerCamelCase          =      image_std if image_std is not None else self.image_std

						__lowerCamelCase          =      size if size is not None else self.size
						__lowerCamelCase          =      get_size_dict(SCREAMING_SNAKE_CASE__    )

						if not is_batched(SCREAMING_SNAKE_CASE__    ):
										__lowerCamelCase          =      [images]

						if not valid_images(SCREAMING_SNAKE_CASE__    ):
										raise ValueError(
										    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
										    '''torch.Tensor, tf.Tensor or jax.ndarray.'''    )

						if do_resize and size is None:
										raise ValueError('''Size must be specified if do_resize is True.'''    )

						if do_center_crop and crop_size is None:
										raise ValueError('''Crop size must be specified if do_center_crop is True.'''    )

						if do_rescale and rescale_factor is None:
										raise ValueError('''Rescale factor must be specified if do_rescale is True.'''    )

						# All transformations expect numpy arrays.
						__lowerCamelCase          =      [to_numpy_array(SCREAMING_SNAKE_CASE__    ) for image in images]

						if do_resize:
										__lowerCamelCase          =      [self.resize(image=SCREAMING_SNAKE_CASE__				,     size=SCREAMING_SNAKE_CASE__				,     resample=SCREAMING_SNAKE_CASE__    ) for image in images]

						if do_center_crop:
										__lowerCamelCase          =      [self.center_crop(image=SCREAMING_SNAKE_CASE__				,     size=SCREAMING_SNAKE_CASE__    ) for image in images]

						if do_rescale:
										__lowerCamelCase          =      [self.rescale(image=SCREAMING_SNAKE_CASE__				,     scale=SCREAMING_SNAKE_CASE__    ) for image in images]

						if do_normalize:
										__lowerCamelCase          =      [self.normalize(image=SCREAMING_SNAKE_CASE__				,     mean=SCREAMING_SNAKE_CASE__				,     std=SCREAMING_SNAKE_CASE__    ) for image in images]

						__lowerCamelCase          =      [to_channel_dimension_format(SCREAMING_SNAKE_CASE__				,     SCREAMING_SNAKE_CASE__    ) for image in images]

						__lowerCamelCase          =      {'''pixel_values''': images}
						return BatchFeature(data=SCREAMING_SNAKE_CASE__				,     tensor_type=SCREAMING_SNAKE_CASE__    )





'''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
		)
