





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,
    get_resize_output_image_size,
    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


lowercase				:	Union[str, Any]      						=  logging.get_logger(__name__)





class 			A__   (							__UpperCAmelCase     ):




   """simple docstring"""



   __A       :    Optional[int]	  =  ['''pixel_values''']

   def __init__( self ,  lowercase = True ,  lowercase = None ,  lowercase = None ,  lowercase = PILImageResampling.BILINEAR ,  lowercase = True ,  lowercase = 1 / 255 ,  lowercase = True ,  lowercase = None ,  lowercase = None ,  **lowercase ,  )      ->						None:







          '''simple docstring'''

          super().__init__(**lowercase)
          a__						:     List[Any]			=    size if size is not None else {'shortest_edge': 384}
          a__						:     Any			=    get_size_dict(lowercase ,  default_to_square=lowercase)

          a__						:     List[Any]			=    do_resize
          a__						:     Dict			=    size
          # Default value set here for backwards compatibility where the value in config is None
          a__						:     List[str]			=    crop_pct if crop_pct is not None else 224 / 256
          a__						:     List[Any]			=    resample
          a__						:     List[Any]			=    do_rescale
          a__						:     str			=    rescale_factor
          a__						:     Union[str, Any]			=    do_normalize
          a__						:     Union[str, Any]			=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          a__						:     Dict			=    image_std if image_std is not None else IMAGENET_STANDARD_STD

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase = PILImageResampling.BICUBIC ,  lowercase = None ,  **lowercase ,  )      ->						np.ndarray:







          '''simple docstring'''

          a__						:     Optional[int]			=    get_size_dict(lowercase ,  default_to_square=lowercase)
          if "shortest_edge" not in size:
                 raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}')
          a__						:     Any			=    size['shortest_edge']

          if shortest_edge < 384:
                 # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
                 a__						:     List[Any]			=    int(shortest_edge / crop_pct)
                 a__						:     Optional[Any]			=    get_resize_output_image_size(lowercase ,  size=lowercase ,  default_to_square=lowercase)
                 a__						:     str			=    resize(image=lowercase ,  size=lowercase ,  resample=lowercase ,  data_format=lowercase ,  **lowercase)
                 # then crop to (shortest_edge, shortest_edge)
                 return center_crop(image=lowercase ,  size=(shortest_edge, shortest_edge) ,  data_format=lowercase ,  **lowercase)
          else:
                 # warping (no cropping) when evaluated at 384 or larger
                 return resize(
                     lowercase ,  size=(shortest_edge, shortest_edge) ,  resample=lowercase ,  data_format=lowercase ,  **lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase = None ,  **lowercase ,  )      ->						Optional[int]:







          '''simple docstring'''

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

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase = None ,  **lowercase ,  )      ->						np.ndarray:







          '''simple docstring'''

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

   def 							__lowercase		( self ,  lowercase ,  lowercase = None ,  lowercase = None ,  lowercase = None ,  lowercase = None ,  lowercase = None ,  lowercase = None ,  lowercase = None ,  lowercase = None ,  lowercase = None ,  lowercase = None ,  lowercase = ChannelDimension.FIRST ,  **lowercase ,  )      ->						PIL.Image.Image:







          '''simple docstring'''

          a__						:     Tuple			=    do_resize if do_resize is not None else self.do_resize
          a__						:     List[str]			=    crop_pct if crop_pct is not None else self.crop_pct
          a__						:     Tuple			=    resample if resample is not None else self.resample
          a__						:     List[Any]			=    do_rescale if do_rescale is not None else self.do_rescale
          a__						:     int			=    rescale_factor if rescale_factor is not None else self.rescale_factor
          a__						:     Dict			=    do_normalize if do_normalize is not None else self.do_normalize
          a__						:     Any			=    image_mean if image_mean is not None else self.image_mean
          a__						:     str			=    image_std if image_std is not None else self.image_std

          a__						:     Tuple			=    size if size is not None else self.size
          a__						:     int			=    get_size_dict(lowercase ,  default_to_square=lowercase)

          a__						:     Tuple			=    make_list_of_images(lowercase)

          if not valid_images(lowercase):
                 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_resize and size["shortest_edge"] < 384 and crop_pct is None:
                 raise ValueError('crop_pct must be specified if size < 384.')

          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.
          a__						:     Optional[Any]			=    [to_numpy_array(lowercase) for image in images]

          if do_resize:
                 a__						:     List[str]			=    [self.resize(image=lowercase ,  size=lowercase ,  crop_pct=lowercase ,  resample=lowercase) for image in images]

          if do_rescale:
                 a__						:     List[Any]			=    [self.rescale(image=lowercase ,  scale=lowercase) for image in images]

          if do_normalize:
                 a__						:     Optional[int]			=    [self.normalize(image=lowercase ,  mean=lowercase ,  std=lowercase) for image in images]

          a__						:     Optional[int]			=    [to_channel_dimension_format(lowercase ,  lowercase) for image in images]

          a__						:     Dict			=    {'pixel_values': images}
          return BatchFeature(data=lowercase ,  tensor_type=lowercase)









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__    )  ->		Any:
       # Construct model
       if gpta_config_file == "":
              a__						:     Optional[int]			=    GPTaConfig()
       else:
              a__						:     List[str]			=    GPTaConfig.from_json_file(A__    )
       a__						:     List[str]			=    GPTaModel(A__    )

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

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


if __name__ == "__main__":
    lowercase				:	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."""
        ),
    )
    lowercase				:	int      						=  parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)


