




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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


__A    :				Optional[int]    						=	logging.get_logger(__name__)




class       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							a__    	=  ['''pixel_values''']





							def __init__(       self							:						str							,  a							:						bool = True							,  a							:						Dict[str, int] = None							,  a							:						bool = True							,  a							:						Dict[str, int] = None							,  a							:						PILImageResampling = PILImageResampling.BICUBIC							,  a							:						bool = True							,  a							:						Union[int, float] = 1 / 255							,  a							:						bool = True							,  a							:						Optional[Union[float, List[float]]] = None							,  a							:						Optional[Union[float, List[float]]] = None							,  **a							:						Optional[Any]							,  )      ->       None:
										super().__init__(**a      )
										SCREAMING_SNAKE_CASE     =       crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
										SCREAMING_SNAKE_CASE     =       get_size_dict(a							,  param_name="""crop_size"""      )
										SCREAMING_SNAKE_CASE     =       size if size is not None else {"""height""": 224, """width""": 224}
										SCREAMING_SNAKE_CASE     =       get_size_dict(a      )

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





							def  _UpperCAmelCase  (       self							:						int							,  a							:						np.ndarray							,  a							:						Dict[str, int]							,  a							:						Optional[int] = None							,  a							:						Optional[Union[str, ChannelDimension]] = None							,  **a							:						Union[str, Any]							,  )      ->       np.ndarray:
										SCREAMING_SNAKE_CASE     =       self.size if size is None else size
										SCREAMING_SNAKE_CASE     =       get_size_dict(a      )
										SCREAMING_SNAKE_CASE     =       get_size_dict(a							,  param_name="""crop_size"""      )

										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       get_image_size(a      )
										SCREAMING_SNAKE_CASE     =       min(a							,  a      )
										SCREAMING_SNAKE_CASE     =       (size["""height"""] / crop_size["""height"""]) * min_dim
										SCREAMING_SNAKE_CASE     =       (size["""width"""] / crop_size["""width"""]) * min_dim
										return center_crop(a							,  size=(cropped_height, cropped_width)							,  data_format=a							,  **a      )





							def  _UpperCAmelCase  (       self							:						Optional[Any]							,  a							:						np.ndarray							,  a							:						Dict[str, int]							,  a							:						PILImageResampling = PIL.Image.BICUBIC							,  a							:						Optional[Union[str, ChannelDimension]] = None							,  **a							:						Dict							,  )      ->       np.ndarray:
										SCREAMING_SNAKE_CASE     =       get_size_dict(a      )
										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()}"""      )
										return resize(
										    a							,  size=(size["""height"""], size["""width"""])							,  resample=a							,  data_format=a							,  **a      )





							def  _UpperCAmelCase  (       self							:						Tuple							,  a							:						np.ndarray							,  a							:						Union[int, float]							,  a							:						Optional[Union[str, ChannelDimension]] = None							,  **a							:						List[Any]							,  )      ->       str:
										return rescale(a							,  scale=a							,  data_format=a							,  **a      )





							def  _UpperCAmelCase  (       self							:						str							,  a							:						np.ndarray							,  a							:						Union[float, List[float]]							,  a							:						Union[float, List[float]]							,  a							:						Optional[Union[str, ChannelDimension]] = None							,  **a							:						Optional[int]							,  )      ->       np.ndarray:
										return normalize(a							,  mean=a							,  std=a							,  data_format=a							,  **a      )







							def  _UpperCAmelCase  (       self							:						List[str]							,  a							:						ImageInput							,  a							:						Optional[bool] = None							,  a							:						Optional[Dict[str, int]] = None							,  a							:						Optional[bool] = None							,  a							:						Optional[Dict[str, int]] = None							,  a							:						PILImageResampling = None							,  a							:						Optional[bool] = None							,  a							:						Optional[float] = None							,  a							:						Optional[bool] = None							,  a							:						Optional[Union[float, List[float]]] = None							,  a							:						Optional[Union[float, List[float]]] = None							,  a							:						Optional[Union[str, TensorType]] = None							,  a							:						ChannelDimension = ChannelDimension.FIRST							,  **a							:						Dict							,  )      ->       PIL.Image.Image:
										SCREAMING_SNAKE_CASE     =       do_center_crop if do_center_crop is not None else self.do_center_crop
										SCREAMING_SNAKE_CASE     =       crop_size if crop_size is not None else self.crop_size
										SCREAMING_SNAKE_CASE     =       get_size_dict(a							,  param_name="""crop_size"""      )
										SCREAMING_SNAKE_CASE     =       do_resize if do_resize is not None else self.do_resize
										SCREAMING_SNAKE_CASE     =       size if size is not None else self.size
										SCREAMING_SNAKE_CASE     =       get_size_dict(a      )
										SCREAMING_SNAKE_CASE     =       resample if resample is not None else self.resample
										SCREAMING_SNAKE_CASE     =       do_rescale if do_rescale is not None else self.do_rescale
										SCREAMING_SNAKE_CASE     =       rescale_factor if rescale_factor is not None else self.rescale_factor
										SCREAMING_SNAKE_CASE     =       do_normalize if do_normalize is not None else self.do_normalize
										SCREAMING_SNAKE_CASE     =       image_mean if image_mean is not None else self.image_mean
										SCREAMING_SNAKE_CASE     =       image_std if image_std is not None else self.image_std

										SCREAMING_SNAKE_CASE     =       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_center_crop and crop_size is None:
													raise ValueError("""If `do_center_crop` is set to `True`, `crop_size` must be provided."""      )

										if do_resize and size is None:
													raise ValueError("""Size 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 image standard deviation must be specified if do_normalize is True."""      )

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

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

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

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

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

										SCREAMING_SNAKE_CASE     =       [to_channel_dimension_format(a							,  a      ) for image in images]

										SCREAMING_SNAKE_CASE     =       {"""pixel_values""": images}
										return BatchFeature(data=a							,  tensor_type=a      )












import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       ConvBertConfig.from_json_file(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE     =       ConvBertModel(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       load_tf_weights_in_convbert(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )
			model.save_pretrained(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       TFConvBertModel.from_pretrained(SCREAMING_SNAKE_CASE       ,						from_pt=SCREAMING_SNAKE_CASE )
			tf_model.save_pretrained(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
		__A    :				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(
		    """--convbert_config_file""",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        """The config json file corresponding to the pre-trained ConvBERT 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."""
		)
		__A    :				List[Any]    						=	parser.parse_args()
		convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)






