





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,
    convert_to_rgb,
    get_resize_output_image_size,
    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


a__:      Any									=		logging.get_logger(__name__)


if is_vision_available():
       import PIL




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	['''pixel_values''']



 def __init__(							self,__lowerCamelCase = True,__lowerCamelCase = None,__lowerCamelCase = PILImageResampling.BICUBIC,__lowerCamelCase = True,__lowerCamelCase = None,__lowerCamelCase = True,__lowerCamelCase = 1 / 255,__lowerCamelCase = True,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = True,**__lowerCamelCase,):
        super().__init__(**__lowerCamelCase )
        A__											=						size if size is not None else {'''shortest_edge''': 224}
        A__											=						get_size_dict(__lowerCamelCase,default_to_square=__lowerCamelCase )
        A__											=						crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        A__											=						get_size_dict(__lowerCamelCase,default_to_square=__lowerCamelCase,param_name='''crop_size''' )

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



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = PILImageResampling.BICUBIC,__lowerCamelCase = None,**__lowerCamelCase,):
        A__											=						get_size_dict(__lowerCamelCase,default_to_square=__lowerCamelCase )
        if "shortest_edge" not in size:
               raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
        A__											=						get_resize_output_image_size(__lowerCamelCase,size=size['''shortest_edge'''],default_to_square=__lowerCamelCase )
        return resize(__lowerCamelCase,size=__lowerCamelCase,resample=__lowerCamelCase,data_format=__lowerCamelCase,**__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = None,**__lowerCamelCase,):
        A__											=						get_size_dict(__lowerCamelCase )
        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(__lowerCamelCase,size=(size['''height'''], size['''width''']),data_format=__lowerCamelCase,**__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = None,**__lowerCamelCase,):
        return rescale(__lowerCamelCase,scale=__lowerCamelCase,data_format=__lowerCamelCase,**__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = None,**__lowerCamelCase,):
        return normalize(__lowerCamelCase,mean=__lowerCamelCase,std=__lowerCamelCase,data_format=__lowerCamelCase,**__lowerCamelCase )




 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = ChannelDimension.FIRST,**__lowerCamelCase,):
        A__											=						do_resize if do_resize is not None else self.do_resize
        A__											=						size if size is not None else self.size
        A__											=						get_size_dict(__lowerCamelCase,param_name='''size''',default_to_square=__lowerCamelCase )
        A__											=						resample if resample is not None else self.resample
        A__											=						do_center_crop if do_center_crop is not None else self.do_center_crop
        A__											=						crop_size if crop_size is not None else self.crop_size
        A__											=						get_size_dict(__lowerCamelCase,param_name='''crop_size''',default_to_square=__lowerCamelCase )
        A__											=						do_rescale if do_rescale is not None else self.do_rescale
        A__											=						rescale_factor if rescale_factor is not None else self.rescale_factor
        A__											=						do_normalize if do_normalize is not None else self.do_normalize
        A__											=						image_mean if image_mean is not None else self.image_mean
        A__											=						image_std if image_std is not None else self.image_std
        A__											=						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        A__											=						make_list_of_images(__lowerCamelCase )

        if not valid_images(__lowerCamelCase ):
               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.''' )

        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:
               A__											=						[convert_to_rgb(__lowerCamelCase ) for image in images]

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

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

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

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

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

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

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












import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()


def 	UpperCamelCase__(       UpperCamelCase__			:       Any ,     UpperCamelCase__			:       int ,     UpperCamelCase__			:       int  )->int:
       # Initialise PyTorch model
       A__											=						BertConfig.from_json_file(UpperCamelCase__  )
       print(f"Building PyTorch model from configuration: {config}"  )
       A__											=						BertForPreTraining(UpperCamelCase__  )

       # Load weights from tf checkpoint
       load_tf_weights_in_bert(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       # Save pytorch-model
       print(f"Save PyTorch model to {pytorch_dump_path}"  )
       torch.save(model.state_dict() ,     UpperCamelCase__  )


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(
           '--bert_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained BERT 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__:      Any									=		parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





