




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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowercase__							:	Optional[int]											=		logging.get_logger(__name__)
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  ['''pixel_values''']






						def __init__(		self				:	Optional[int]			,      __lowercase				:	bool = True			,      __lowercase				:	Dict[str, int] = None			,      __lowercase				:	PILImageResampling = PILImageResampling.BICUBIC			,      __lowercase				:	bool = True			,      __lowercase				:	Union[int, float] = 1 / 2_55			,      __lowercase				:	bool = True			,      __lowercase				:	Optional[Union[float, List[float]]] = None			,      __lowercase				:	Optional[Union[float, List[float]]] = None			,      __lowercase				:	bool = True			,      **__lowercase				:	Optional[int]			,      ):






							"""simple docstring"""






							super().__init__(**__lowercase		)
							snake_case_						       =	size if size is not None else {"height": 3_84, "width": 3_84}
							snake_case_						       =	get_size_dict(__lowercase			,      default_to_square=__lowercase		)

							snake_case_						       =	do_resize
							snake_case_						       =	size
							snake_case_						       =	resample
							snake_case_						       =	do_rescale
							snake_case_						       =	rescale_factor
							snake_case_						       =	do_normalize
							snake_case_						       =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							snake_case_						       =	image_std if image_std is not None else OPENAI_CLIP_STD
							snake_case_						       =	do_convert_rgb






						def 			snake_case__		(		self				:	Optional[int]			,      __lowercase				:	np.ndarray			,      __lowercase				:	Dict[str, int]			,      __lowercase				:	PILImageResampling = PILImageResampling.BICUBIC			,      __lowercase				:	Optional[Union[str, ChannelDimension]] = None			,      **__lowercase				:	int			,      ):






							"""simple docstring"""






							snake_case_						       =	get_size_dict(__lowercase			,      default_to_square=__lowercase		)
							if "height" not in size or "width" not in size:
								raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"		)
							snake_case_						       =	(size["height"], size["width"])
							return resize(__lowercase			,      size=__lowercase			,      resample=__lowercase			,      data_format=__lowercase			,      **__lowercase		)






						def 			snake_case__		(		self				:	Tuple			,      __lowercase				:	np.ndarray			,      __lowercase				:	Union[int, float]			,      __lowercase				:	Optional[Union[str, ChannelDimension]] = None			,      **__lowercase				:	Dict			,      ):






							"""simple docstring"""






							return rescale(__lowercase			,      scale=__lowercase			,      data_format=__lowercase			,      **__lowercase		)






						def 			snake_case__		(		self				:	Union[str, Any]			,      __lowercase				:	np.ndarray			,      __lowercase				:	Union[float, List[float]]			,      __lowercase				:	Union[float, List[float]]			,      __lowercase				:	Optional[Union[str, ChannelDimension]] = None			,      **__lowercase				:	Union[str, Any]			,      ):






							"""simple docstring"""






							return normalize(__lowercase			,      mean=__lowercase			,      std=__lowercase			,      data_format=__lowercase			,      **__lowercase		)






						def 			snake_case__		(		self				:	Union[str, Any]			,      __lowercase				:	ImageInput			,      __lowercase				:	Optional[bool] = None			,      __lowercase				:	Optional[Dict[str, int]] = None			,      __lowercase				:	PILImageResampling = None			,      __lowercase				:	Optional[bool] = None			,      __lowercase				:	Optional[float] = None			,      __lowercase				:	Optional[bool] = None			,      __lowercase				:	Optional[Union[float, List[float]]] = None			,      __lowercase				:	Optional[Union[float, List[float]]] = None			,      __lowercase				:	Optional[Union[str, TensorType]] = None			,      __lowercase				:	bool = None			,      __lowercase				:	ChannelDimension = ChannelDimension.FIRST			,      **__lowercase				:	Union[str, Any]			,      ):






							"""simple docstring"""






							snake_case_						       =	do_resize if do_resize is not None else self.do_resize
							snake_case_						       =	resample if resample is not None else self.resample
							snake_case_						       =	do_rescale if do_rescale is not None else self.do_rescale
							snake_case_						       =	rescale_factor if rescale_factor is not None else self.rescale_factor
							snake_case_						       =	do_normalize if do_normalize is not None else self.do_normalize
							snake_case_						       =	image_mean if image_mean is not None else self.image_mean
							snake_case_						       =	image_std if image_std is not None else self.image_std
							snake_case_						       =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							snake_case_						       =	size if size is not None else self.size
							snake_case_						       =	get_size_dict(__lowercase			,      default_to_square=__lowercase		)

							snake_case_						       =	make_list_of_images(__lowercase		)

							if not valid_images(__lowercase		):
								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 or resample is None:
								raise ValueError("Size and resample must be specified if do_resize is True."		)

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

							if do_normalize and (image_mean is None or image_std is None):
								raise ValueError("Image mean and std must be specified if do_normalize is True."		)

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
								snake_case_						       =	[convert_to_rgb(__lowercase		) for image in images]

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

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

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

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

							snake_case_						       =	[to_channel_dimension_format(__lowercase			,      __lowercase		) for image in images]

							snake_case_						       =	BatchFeature(data={"pixel_values": images}			,      tensor_type=__lowercase		)

							return encoded_outputs






import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	MobileBertConfig.from_json_file(_A				)
	print(f"Building PyTorch model from configuration: {config}"				)
	snake_case_						       =	MobileBertForPreTraining(_A				)
	# Load weights from tf checkpoint
	snake_case_						       =	load_tf_weights_in_mobilebert(_A	,      _A	,      _A				)
	# Save pytorch-model
	print(f"Save PyTorch model to {pytorch_dump_path}"				)
	torch.save(model.state_dict()	,      _A				)


if __name__ == "__main__":
		lowercase__							:	Dict											=		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(
		    "--mobilebert_config_file",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        "The config json file corresponding to the pre-trained MobileBERT model. \n"
		        "This specifies the model architecture."
		    ),
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		lowercase__							:	Tuple											=		parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
