

import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   BertModel     ,					SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   str      ):



     '''simple docstring'''





     lowercase__   :       Tuple				=	('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

     lowercase__   :       Dict				=	(
         ('layer.', 'layer_'),
         ('word_embeddings.weight', 'word_embeddings'),
         ('position_embeddings.weight', 'position_embeddings'),
         ('token_type_embeddings.weight', 'token_type_embeddings'),
         ('.', '/'),
         ('LayerNorm/weight', 'LayerNorm/gamma'),
         ('LayerNorm/bias', 'LayerNorm/beta'),
         ('weight', 'kernel'),
     )

     if not os.path.isdir(SCREAMING_SNAKE_CASE_      ):
          os.makedirs(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       str				=	model.state_dict()

     def to_tf_var_name(SCREAMING_SNAKE_CASE_		:   str      ):
          for patt, repl in iter(SCREAMING_SNAKE_CASE_      ):
               lowercase__   :       Optional[int]				=	name.replace(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
          return f"""bert/{name}"""

     def create_tf_var(SCREAMING_SNAKE_CASE_		:   np.ndarray     ,					SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   tf.Session      ):
          lowercase__   :       Optional[Any]				=	tf.dtypes.as_dtype(tensor.dtype      )
          lowercase__   :       List[str]				=	tf.get_variable(dtype=SCREAMING_SNAKE_CASE_     ,					shape=tensor.shape     ,					name=SCREAMING_SNAKE_CASE_     ,					initializer=tf.zeros_initializer()      )
          session.run(tf.variables_initializer([tf_var]      )      )
          session.run(SCREAMING_SNAKE_CASE_      )
          return tf_var

     tf.reset_default_graph()
     with tf.Session() as session:
          for var_name in state_dict:
               lowercase__   :       Tuple				=	to_tf_var_name(SCREAMING_SNAKE_CASE_      )
               lowercase__   :       str				=	state_dict[var_name].numpy()
               if any(x in var_name for x in tensors_to_transpose      ):
                    lowercase__   :       str				=	torch_tensor.T
               lowercase__   :       Any				=	create_tf_var(tensor=SCREAMING_SNAKE_CASE_     ,					name=SCREAMING_SNAKE_CASE_     ,					session=SCREAMING_SNAKE_CASE_      )
               tf.keras.backend.set_value(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
               lowercase__   :       Optional[int]				=	session.run(SCREAMING_SNAKE_CASE_      )
               print(f"""Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )}"""      )

          lowercase__   :       Tuple				=	tf.train.Saver(tf.trainable_variables()      )
          saver.save(SCREAMING_SNAKE_CASE_     ,					os.path.join(SCREAMING_SNAKE_CASE_     ,					model_name.replace('-'     ,					'_'      ) + '.ckpt'      )      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[Any]=None      ):



     '''simple docstring'''





     lowercase__   :       List[str]				=	argparse.ArgumentParser()
     parser.add_argument('--model_name'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='model name e.g. bert-base-uncased'      )
     parser.add_argument(
         '--cache_dir'     ,					type=SCREAMING_SNAKE_CASE_     ,					default=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='Directory containing pytorch model'      )
     parser.add_argument('--pytorch_model_path'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='/path/to/<pytorch-model-name>.bin'      )
     parser.add_argument('--tf_cache_dir'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='Directory in which to save tensorflow model'      )
     lowercase__   :       List[str]				=	parser.parse_args(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       int				=	BertModel.from_pretrained(
         pretrained_model_name_or_path=args.model_name     ,					state_dict=torch.load(args.pytorch_model_path      )     ,					cache_dir=args.cache_dir     ,					)

     convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE_     ,					ckpt_dir=args.tf_cache_dir     ,					model_name=args.model_name      )


if __name__ == "__main__":
       main()




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


snake_case_        =			logging.get_logger(__name__)


if is_vision_available():
       import PIL


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Optional[Any]														=					["""pixel_values"""]





    def __init__(				self						,       a = True						,       a = None						,       a = PILImageResampling.BICUBIC						,       a = True						,       a = None						,       a = True						,       a = 1 / 255						,       a = True						,       a = None						,       a = None						,       a = True						,       **a						,       ):
         super().__init__(**a)
         lowercase__   :       List[str]				=	size if size is not None else {'shortest_edge': 224}
         lowercase__   :       str				=	get_size_dict(a						,       default_to_square=a)
         lowercase__   :       Optional[int]				=	crop_size if crop_size is not None else {'height': 224, 'width': 224}
         lowercase__   :       Union[str, Any]				=	get_size_dict(a						,       default_to_square=a						,       param_name='crop_size')

         lowercase__   :       List[str]				=	do_resize
         lowercase__   :       List[Any]				=	size
         lowercase__   :       Tuple				=	resample
         lowercase__   :       int				=	do_center_crop
         lowercase__   :       Union[str, Any]				=	crop_size
         lowercase__   :       int				=	do_rescale
         lowercase__   :       List[str]				=	rescale_factor
         lowercase__   :       Tuple				=	do_normalize
         lowercase__   :       str				=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         lowercase__   :       Optional[Any]				=	image_std if image_std is not None else OPENAI_CLIP_STD
         lowercase__   :       List[Any]				=	do_convert_rgb





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





    def 				snake_case_    (				self						,       a						,       a						,       a = None						,       **a						,       ):
         lowercase__   :       List[str]				=	get_size_dict(a)
         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(a						,       size=(size['height'], size['width'])						,       data_format=a						,       **a)





    def 				snake_case_    (				self						,       a						,       a						,       a = None						,       **a						,       ):
         return rescale(a						,       scale=a						,       data_format=a						,       **a)





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a = None						,       **a						,       ):
         return normalize(a						,       mean=a						,       std=a						,       data_format=a						,       **a)





    def 				snake_case_    (				self						,       a						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = ChannelDimension.FIRST						,       **a						,       ):
         lowercase__   :       int				=	do_resize if do_resize is not None else self.do_resize
         lowercase__   :       Tuple				=	size if size is not None else self.size
         lowercase__   :       Union[str, Any]				=	get_size_dict(a						,       param_name='size'						,       default_to_square=a)
         lowercase__   :       Optional[Any]				=	resample if resample is not None else self.resample
         lowercase__   :       int				=	do_center_crop if do_center_crop is not None else self.do_center_crop
         lowercase__   :       List[Any]				=	crop_size if crop_size is not None else self.crop_size
         lowercase__   :       Union[str, Any]				=	get_size_dict(a						,       param_name='crop_size'						,       default_to_square=a)
         lowercase__   :       Dict				=	do_rescale if do_rescale is not None else self.do_rescale
         lowercase__   :       int				=	rescale_factor if rescale_factor is not None else self.rescale_factor
         lowercase__   :       List[str]				=	do_normalize if do_normalize is not None else self.do_normalize
         lowercase__   :       Optional[Any]				=	image_mean if image_mean is not None else self.image_mean
         lowercase__   :       List[str]				=	image_std if image_std is not None else self.image_std
         lowercase__   :       Any				=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         lowercase__   :       str				=	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:
              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:
              lowercase__   :       str				=	[convert_to_rgb(a) for image in images]

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

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

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

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

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

         lowercase__   :       Optional[int]				=	[to_channel_dimension_format(a						,       a) for image in images]

         lowercase__   :       Dict				=	{'pixel_values': images}
         return BatchFeature(data=a						,       tensor_type=a)

