






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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_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


__lowerCamelCase :      Tuple										=				logging.get_logger(__name__)



class 							SCREAMING_SNAKE_CASE__						(    __lowercase						):





     """simple docstring"""






     a_     =		["pixel_values"]



     def __init__( self					:  str							,		__A					:  Union[str, Any] = True							,		__A					:  Tuple = None							,		__A					:  List[Any] = PIL.Image.BICUBIC							,		__A					:  Optional[Any] = True							,		__A					:  Optional[Any] = None							,		__A					:  Optional[Any] = 1 / 2_5_5							,		__A					:  int = True							,		__A					:  List[str] = True							,		__A					:  Any = None							,		__A					:  Optional[int] = None							,		**__A					:  str							,		):
      super().__init__(**_a	)
      snake_case__			:				Dict       =  size if size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
      snake_case__			:				int       =  get_size_dict(_a	)
      snake_case__			:				Tuple       =  crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
      snake_case__			:				Dict       =  get_size_dict(_a							,		param_name="crop_size"	)

      snake_case__			:				Union[str, Any]       =  do_resize
      snake_case__			:				Any       =  size
      snake_case__			:				List[Any]       =  resample
      snake_case__			:				List[Any]       =  do_center_crop
      snake_case__			:				Union[str, Any]       =  crop_size
      snake_case__			:				str       =  do_rescale
      snake_case__			:				Union[str, Any]       =  rescale_factor
      snake_case__			:				int       =  do_normalize
      snake_case__			:				List[Any]       =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      snake_case__			:				Optional[Any]       =  image_std if image_std is not None else IMAGENET_STANDARD_STD



     def 	_lowercase					( self					:  List[Any]							,		__A					:  str							,		__A					:  Tuple							,		__A					:  str = PIL.Image.BICUBIC							,		__A					:  List[str] = None							,		**__A					:  List[str]							,		):
      snake_case__			:				List[Any]       =  get_size_dict(_a	)
      if "height" not in size or "width" not in size:
       raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
      return resize(
          _a							,		size=(size["height"], size["width"])							,		resample=_a							,		data_format=_a							,		**_a	)



     def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  Tuple							,		__A					:  Tuple							,		__A					:  Union[str, Any] = None							,		**__A					:  Optional[Any]							,		):
      snake_case__			:				Union[str, Any]       =  get_size_dict(_a	)
      if "height" not in size or "width" not in size:
       raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
      return center_crop(_a							,		size=(size["height"], size["width"])							,		data_format=_a							,		**_a	)



     def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  str							,		__A					:  Optional[Any]							,		__A					:  str = None							,		**__A					:  int							,		):
      return rescale(_a							,		scale=_a							,		data_format=_a							,		**_a	)



     def 	_lowercase					( self					:  Dict							,		__A					:  List[str]							,		__A					:  Optional[int]							,		__A					:  Any							,		__A					:  Dict = None							,		**__A					:  Any							,		):
      return normalize(_a							,		mean=_a							,		std=_a							,		data_format=_a							,		**_a	)






     def 	_lowercase					( self					:  Any							,		__A					:  List[Any]							,		__A					:  List[str] = None							,		__A					:  Any = None							,		__A					:  int=None							,		__A					:  List[str] = None							,		__A					:  Union[str, Any] = None							,		__A					:  Any = None							,		__A					:  Union[str, Any] = None							,		__A					:  List[Any] = None							,		__A					:  Dict = None							,		__A					:  Any = None							,		__A					:  List[Any] = None							,		__A					:  Optional[Any] = ChannelDimension.FIRST							,		**__A					:  Tuple							,		):
      snake_case__			:				int       =  do_resize if do_resize is not None else self.do_resize
      snake_case__			:				int       =  resample if resample is not None else self.resample
      snake_case__			:				Optional[Any]       =  do_center_crop if do_center_crop is not None else self.do_center_crop
      snake_case__			:				Any       =  do_rescale if do_rescale is not None else self.do_rescale
      snake_case__			:				Optional[int]       =  rescale_factor if rescale_factor is not None else self.rescale_factor
      snake_case__			:				List[Any]       =  do_normalize if do_normalize is not None else self.do_normalize
      snake_case__			:				Optional[int]       =  image_mean if image_mean is not None else self.image_mean
      snake_case__			:				Tuple       =  image_std if image_std is not None else self.image_std

      snake_case__			:				Any       =  size if size is not None else self.size
      snake_case__			:				str       =  get_size_dict(_a	)
      snake_case__			:				List[Any]       =  crop_size if crop_size is not None else self.crop_size
      snake_case__			:				int       =  get_size_dict(_a							,		param_name="crop_size"	)

      snake_case__			:				Union[str, Any]       =  make_list_of_images(_a	)

      if not valid_images(_a	):
       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_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."	)

      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."	)

      # All transformations expect numpy arrays.
      snake_case__			:				Dict       =  [to_numpy_array(_a	) for image in images]

      if do_resize:
       snake_case__			:				Optional[int]       =  [self.resize(image=_a							,		size=_a							,		resample=_a	) for image in images]

      if do_center_crop:
       snake_case__			:				Optional[Any]       =  [self.center_crop(image=_a							,		size=_a	) for image in images]

      if do_rescale:
       snake_case__			:				Optional[Any]       =  [self.rescale(image=_a							,		scale=_a	) for image in images]

      if do_normalize:
       snake_case__			:				Dict       =  [self.normalize(image=_a							,		mean=_a							,		std=_a	) for image in images]

      snake_case__			:				Tuple       =  [to_channel_dimension_format(_a							,		_a	) for image in images]

      snake_case__			:				Dict       =  {'''pixel_values''': images}
      return BatchFeature(data=_a							,		tensor_type=_a	)











import argparse

import torch

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


logging.set_verbosity_info()







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Tuple					,  snake_case_ :							str					,  snake_case_ :							Union[str, Any]       ):
	# Initialise PyTorch model
	snake_case__			:				List[str]       =  MobileBertConfig.from_json_file(snake_case_       )
	print(F'''Building PyTorch model from configuration: {config}'''       )
	snake_case__			:				Dict       =  MobileBertForPreTraining(snake_case_       )
	# Load weights from tf checkpoint
	snake_case__			:				Any       =  load_tf_weights_in_mobilebert(snake_case_					,  snake_case_					,  snake_case_       )
	# Save pytorch-model
	print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
	torch.save(model.state_dict()					,  snake_case_       )


if __name__ == "__main__":
	__lowerCamelCase :      List[Any]										=				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."""
	)
	__lowerCamelCase :      int										=				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



