


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     snake_case__  (      _A  ):
				__lowerCAmelCase		=      ['''pixel_values''']
				def __init__(  self     ,      _UpperCAmelCase = True     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = PILImageResampling.BICUBIC     ,      _UpperCAmelCase = True     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = True     ,      _UpperCAmelCase = 1 / 255     ,      _UpperCAmelCase = True     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = True     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										super().__init__(**_UpperCAmelCase      )
										__magic_name__				:							List[Any]				=      size if size is not None else {'shortest_edge': 224}
										__magic_name__				:							Tuple				=      get_size_dict(_UpperCAmelCase     ,      default_to_square=_UpperCAmelCase      )
										__magic_name__				:							str				=      crop_size if crop_size is not None else {'height': 224, 'width': 224}
										__magic_name__				:							List[Any]				=      get_size_dict(_UpperCAmelCase      )

										__magic_name__				:							List[str]				=      do_resize
										__magic_name__				:							Any				=      size
										__magic_name__				:							int				=      resample
										__magic_name__				:							str				=      do_center_crop
										__magic_name__				:							List[str]				=      crop_size
										__magic_name__				:							str				=      do_rescale
										__magic_name__				:							List[Any]				=      rescale_factor
										__magic_name__				:							str				=      do_normalize
										__magic_name__				:							List[Any]				=      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										__magic_name__				:							Union[str, Any]				=      image_std if image_std is not None else OPENAI_CLIP_STD
										__magic_name__				:							str				=      do_convert_rgb
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase = PILImageResampling.BICUBIC     ,      _UpperCAmelCase = None     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										__magic_name__				:							Optional[Any]				=      get_size_dict(_UpperCAmelCase     ,      default_to_square=_UpperCAmelCase      )
										__magic_name__				:							int				=      get_resize_output_image_size(
										    _UpperCAmelCase     ,      size=(size['height'], size['width'])     ,      default_to_square=_UpperCAmelCase      )
										return resize(_UpperCAmelCase     ,      size=_UpperCAmelCase     ,      resample=_UpperCAmelCase     ,      data_format=_UpperCAmelCase     ,      **_UpperCAmelCase      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase = None     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										__magic_name__				:							Optional[int]				=      get_size_dict(_UpperCAmelCase      )
										return center_crop(_UpperCAmelCase     ,      size=(size['height'], size['width'])     ,      data_format=_UpperCAmelCase     ,      **_UpperCAmelCase      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase = None     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										return rescale(_UpperCAmelCase     ,      scale=_UpperCAmelCase     ,      data_format=_UpperCAmelCase     ,      **_UpperCAmelCase      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase = None     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




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





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = ChannelDimension.FIRST     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										__magic_name__				:							Optional[Any]				=      do_resize if do_resize is not None else self.do_resize
										__magic_name__				:							List[Any]				=      size if size is not None else self.size
										__magic_name__				:							Tuple				=      get_size_dict(_UpperCAmelCase     ,      default_to_square=_UpperCAmelCase      )
										__magic_name__				:							List[Any]				=      resample if resample is not None else self.resample
										__magic_name__				:							str				=      do_center_crop if do_center_crop is not None else self.do_center_crop
										__magic_name__				:							Optional[Any]				=      crop_size if crop_size is not None else self.crop_size
										__magic_name__				:							List[str]				=      get_size_dict(_UpperCAmelCase      )
										__magic_name__				:							List[str]				=      do_rescale if do_rescale is not None else self.do_rescale
										__magic_name__				:							List[str]				=      rescale_factor if rescale_factor is not None else self.rescale_factor
										__magic_name__				:							Union[str, Any]				=      do_normalize if do_normalize is not None else self.do_normalize
										__magic_name__				:							List[Any]				=      image_mean if image_mean is not None else self.image_mean
										__magic_name__				:							Optional[int]				=      image_std if image_std is not None else self.image_std
										__magic_name__				:							Optional[int]				=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										__magic_name__				:							int				=      make_list_of_images(_UpperCAmelCase      )

										if not valid_images(_UpperCAmelCase      ):
																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:
																__magic_name__				:							int				=      [convert_to_rgb(_UpperCAmelCase      ) for image in images]

										# All transformations expect numpy arrays.
										__magic_name__				:							Union[str, Any]				=      [to_numpy_array(_UpperCAmelCase      ) for image in images]

										if do_resize:
																__magic_name__				:							str				=      [self.resize(image=_UpperCAmelCase     ,      size=_UpperCAmelCase     ,      resample=_UpperCAmelCase      ) for image in images]

										if do_center_crop:
																__magic_name__				:							Union[str, Any]				=      [self.center_crop(image=_UpperCAmelCase     ,      size=_UpperCAmelCase      ) for image in images]

										if do_rescale:
																__magic_name__				:							Union[str, Any]				=      [self.rescale(image=_UpperCAmelCase     ,      scale=_UpperCAmelCase      ) for image in images]

										if do_normalize:
																__magic_name__				:							Union[str, Any]				=      [self.normalize(image=_UpperCAmelCase     ,      mean=_UpperCAmelCase     ,      std=_UpperCAmelCase      ) for image in images]

										__magic_name__				:							Any				=      [to_channel_dimension_format(_UpperCAmelCase     ,      _UpperCAmelCase      ) for image in images]

										__magic_name__				:							Dict				=      {'pixel_values': images}
										return BatchFeature(data=_UpperCAmelCase     ,      tensor_type=_UpperCAmelCase      )




import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]      ,						lowercase__  :List[Any]      ,						lowercase__  :str      ,						lowercase__  :List[str]      ,						lowercase__  :List[Any]				)       ->  List[Any]:
						# Initialise PyTorch model
						__magic_name__				:							Any				=      BertConfig.from_pretrained(
						    'bert-large-cased'      ,						vocab_size=lowercase__      ,						max_position_embeddings=5_1_2      ,						is_decoder=lowercase__      ,						add_cross_attention=lowercase__      ,						)
						__magic_name__				:							Optional[int]				=      bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						__magic_name__				:							Optional[int]				=      BertGenerationConfig(**lowercase__				)
						if is_encoder:
												__magic_name__				:							Dict				=      BertGenerationEncoder(lowercase__				)
						else:
												__magic_name__				:							str				=      BertGenerationDecoder(lowercase__				)
						print(f"Building PyTorch model from configuration: {config}"				)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert_generation(
						    lowercase__      ,						lowercase__      ,						model_class='bert'      ,						is_encoder_named_decoder=lowercase__      ,						is_encoder=lowercase__      ,						)

						# Save pytorch-model
						print(f"Save PyTorch model and config to {pytorch_dump_path}"				)
						model.save_pretrained(lowercase__				)


if __name__ == "__main__":
			snake_case__     							= argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
			)
			parser.add_argument(
			    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			parser.add_argument(
			    '--is_encoder_named_decoder',
			    action='store_true',
			    help='If decoder has to be renamed to encoder in PyTorch model.',
			)
			parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
			parser.add_argument('--vocab_size', default=5_0358, type=int, help='Vocab size of model')
			snake_case__     							= parser.parse_args()
			convert_tf_checkpoint_to_pytorch(
			    args.tf_hub_path,
			    args.pytorch_dump_path,
			    args.is_encoder_named_decoder,
			    args.vocab_size,
			    is_encoder=args.is_encoder,
			)


