import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()







def 						A__  (					__A   , __A   , __A	):







				'''simple docstring'''

				if gpta_config_file == "":
								_lowerCamelCase     :		Optional[Any] 						=						GPTaConfig()
				else:
								_lowerCamelCase     :		str 						=						GPTaConfig.from_json_file(UpperCAmelCase__	)
				_lowerCamelCase     :		int 						=						GPTaModel(UpperCAmelCase__	)

				# Load weights from numpy
				load_tf_weights_in_gpta(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__	)

				# Save pytorch-model
				_lowerCamelCase     :		Dict 						=						pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
				_lowerCamelCase     :		Optional[Any] 						=						pytorch_dump_folder_path + """/""" + CONFIG_NAME
				print(F"""Save PyTorch model to {pytorch_weights_dump_path}"""	)
				torch.save(model.state_dict()   , UpperCAmelCase__	)
				print(F"""Save configuration file to {pytorch_config_dump_path}"""	)
				with open(UpperCAmelCase__   , """w"""   , encoding="""utf-8"""	) as f:
								f.write(config.to_json_string()	)


if __name__ == "__main__":
				lowerCAmelCase			:							Dict     =argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--gpt2_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				parser.add_argument(
				    "--gpt2_config_file",
				    default="",
				    type=str,
				    help=(
				        "An optional config json file corresponding to the pre-trained OpenAI model. \n"
				        "This specifies the model architecture."
				    ),
				)
				lowerCAmelCase			:							Optional[int]     =parser.parse_args()
				convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)





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 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       ['pixel_values']



      def __init__(					self     :				Optional[Any]    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				Dict[str, int] = None    ,  _UpperCamelCase     :				PILImageResampling = PIL.Image.BICUBIC    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				Dict[str, int] = None    ,  _UpperCamelCase     :				Union[int, float] = 1 / 255    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				Optional[Union[float, List[float]]] = None    ,  _UpperCamelCase     :				Optional[Union[float, List[float]]] = None    ,  **_UpperCamelCase     :				str    ,  )				->None:


          """simple docstring"""


          super().__init__(**_UpperCamelCase)
          _lowerCamelCase     :		Tuple 						=						size if size is not None else {"""height""": 256, """width""": 256}
          _lowerCamelCase     :		Optional[Any] 						=						get_size_dict(_UpperCamelCase)
          _lowerCamelCase     :		Any 						=						crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
          _lowerCamelCase     :		Any 						=						get_size_dict(_UpperCamelCase    ,  param_name="""crop_size""")

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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any    ,  _UpperCamelCase     :				np.ndarray    ,  _UpperCamelCase     :				Dict[str, int]    ,  _UpperCamelCase     :				PILImageResampling = PIL.Image.BICUBIC    ,  _UpperCamelCase     :				Optional[Union[str, ChannelDimension]] = None    ,  **_UpperCamelCase     :				Union[str, Any]    ,  )				->np.ndarray:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						get_size_dict(_UpperCamelCase)
          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(
              _UpperCamelCase    ,  size=(size["""height"""], size["""width"""])    ,  resample=_UpperCamelCase    ,  data_format=_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				np.ndarray    ,  _UpperCamelCase     :				Dict[str, int]    ,  _UpperCamelCase     :				Optional[Union[str, ChannelDimension]] = None    ,  **_UpperCamelCase     :				List[str]    ,  )				->np.ndarray:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						get_size_dict(_UpperCamelCase)
          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(_UpperCamelCase    ,  size=(size["""height"""], size["""width"""])    ,  data_format=_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				np.ndarray    ,  _UpperCamelCase     :				Union[int, float]    ,  _UpperCamelCase     :				Optional[Union[str, ChannelDimension]] = None    ,  **_UpperCamelCase     :				Union[str, Any]    ,  )				->str:


          """simple docstring"""


          return rescale(_UpperCamelCase    ,  scale=_UpperCamelCase    ,  data_format=_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				np.ndarray    ,  _UpperCamelCase     :				Union[float, List[float]]    ,  _UpperCamelCase     :				Union[float, List[float]]    ,  _UpperCamelCase     :				Optional[Union[str, ChannelDimension]] = None    ,  **_UpperCamelCase     :				Union[str, Any]    ,  )				->np.ndarray:


          """simple docstring"""


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




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				ImageInput    ,  _UpperCamelCase     :				bool = None    ,  _UpperCamelCase     :				Dict[str, int] = None    ,  _UpperCamelCase     :				Tuple=None    ,  _UpperCamelCase     :				bool = None    ,  _UpperCamelCase     :				Dict[str, int] = None    ,  _UpperCamelCase     :				bool = None    ,  _UpperCamelCase     :				float = None    ,  _UpperCamelCase     :				bool = None    ,  _UpperCamelCase     :				Optional[Union[float, List[float]]] = None    ,  _UpperCamelCase     :				Optional[Union[float, List[float]]] = None    ,  _UpperCamelCase     :				Optional[Union[str, TensorType]] = None    ,  _UpperCamelCase     :				ChannelDimension = ChannelDimension.FIRST    ,  **_UpperCamelCase     :				List[Any]    ,  )				->PIL.Image.Image:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						do_resize if do_resize is not None else self.do_resize
          _lowerCamelCase     :		List[str] 						=						resample if resample is not None else self.resample
          _lowerCamelCase     :		Dict 						=						do_center_crop if do_center_crop is not None else self.do_center_crop
          _lowerCamelCase     :		Optional[Any] 						=						do_rescale if do_rescale is not None else self.do_rescale
          _lowerCamelCase     :		Dict 						=						rescale_factor if rescale_factor is not None else self.rescale_factor
          _lowerCamelCase     :		Dict 						=						do_normalize if do_normalize is not None else self.do_normalize
          _lowerCamelCase     :		int 						=						image_mean if image_mean is not None else self.image_mean
          _lowerCamelCase     :		Dict 						=						image_std if image_std is not None else self.image_std

          _lowerCamelCase     :		Optional[Any] 						=						size if size is not None else self.size
          _lowerCamelCase     :		Optional[int] 						=						get_size_dict(_UpperCamelCase)
          _lowerCamelCase     :		List[str] 						=						crop_size if crop_size is not None else self.crop_size
          _lowerCamelCase     :		Dict 						=						get_size_dict(_UpperCamelCase    ,  param_name="""crop_size""")

          _lowerCamelCase     :		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 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.
          _lowerCamelCase     :		Union[str, Any] 						=						[to_numpy_array(_UpperCamelCase) for image in images]

          if do_resize:
              _lowerCamelCase     :		Any 						=						[self.resize(image=_UpperCamelCase    ,  size=_UpperCamelCase    ,  resample=_UpperCamelCase) for image in images]

          if do_center_crop:
              _lowerCamelCase     :		str 						=						[self.center_crop(image=_UpperCamelCase    ,  size=_UpperCamelCase) for image in images]

          if do_rescale:
              _lowerCamelCase     :		Optional[int] 						=						[self.rescale(image=_UpperCamelCase    ,  scale=_UpperCamelCase) for image in images]

          if do_normalize:
              _lowerCamelCase     :		List[str] 						=						[self.normalize(image=_UpperCamelCase    ,  mean=_UpperCamelCase    ,  std=_UpperCamelCase) for image in images]

          _lowerCamelCase     :		List[str] 						=						[to_channel_dimension_format(_UpperCamelCase    ,  _UpperCamelCase) for image in images]

          _lowerCamelCase     :		int 						=						{"""pixel_values""": images}
          return BatchFeature(data=_UpperCamelCase    ,  tensor_type=_UpperCamelCase)




