




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 convert_to_rgb, 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


if is_vision_available():
  import PIL


_lowerCAmelCase						:   List[Any]		=   logging.get_logger(__name__)
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		["""pixel_values"""]
      def __init__(			self   :Tuple ,      lowerCamelCase   :bool = True ,      lowerCamelCase   :Dict[str, int] = None ,      lowerCamelCase   :PILImageResampling = PILImageResampling.BICUBIC ,      lowerCamelCase   :bool = True ,      lowerCamelCase   :Union[int, float] = 1 / 255 ,      lowerCamelCase   :bool = True ,      lowerCamelCase   :Optional[Union[float, List[float]]] = None ,      lowerCamelCase   :Optional[Union[float, List[float]]] = None ,      lowerCamelCase   :bool = True ,      **lowerCamelCase   :Dict ,      )     ->			None:
       super().__init__(**lowerCamelCase      )
       UpperCAmelCase__							 =						size if size is not None else {"height": 384, "width": 384}
       UpperCAmelCase__							 =						get_size_dict(lowerCamelCase ,      default_to_square=lowerCamelCase      )

       UpperCAmelCase__							 =						do_resize
       UpperCAmelCase__							 =						size
       UpperCAmelCase__							 =						resample
       UpperCAmelCase__							 =						do_rescale
       UpperCAmelCase__							 =						rescale_factor
       UpperCAmelCase__							 =						do_normalize
       UpperCAmelCase__							 =						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       UpperCAmelCase__							 =						image_std if image_std is not None else OPENAI_CLIP_STD
       UpperCAmelCase__							 =						do_convert_rgb
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :np.ndarray ,      lowerCamelCase   :Dict[str, int] ,      lowerCamelCase   :PILImageResampling = PILImageResampling.BICUBIC ,      lowerCamelCase   :Optional[Union[str, ChannelDimension]] = None ,      **lowerCamelCase   :Dict ,      )     ->			np.ndarray:
       UpperCAmelCase__							 =						get_size_dict(lowerCamelCase ,      default_to_square=lowerCamelCase      )
       if "height" not in size or "width" not in size:
        raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''      )
       UpperCAmelCase__							 =						(size["height"], size["width"])
       return resize(lowerCamelCase ,      size=lowerCamelCase ,      resample=lowerCamelCase ,      data_format=lowerCamelCase ,      **lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Tuple ,      lowerCamelCase   :np.ndarray ,      lowerCamelCase   :Union[int, float] ,      lowerCamelCase   :Optional[Union[str, ChannelDimension]] = None ,      **lowerCamelCase   :int ,      )     ->			Optional[int]:
       return rescale(lowerCamelCase ,      scale=lowerCamelCase ,      data_format=lowerCamelCase ,      **lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Optional[int] ,      lowerCamelCase   :np.ndarray ,      lowerCamelCase   :Union[float, List[float]] ,      lowerCamelCase   :Union[float, List[float]] ,      lowerCamelCase   :Optional[Union[str, ChannelDimension]] = None ,      **lowerCamelCase   :Optional[Any] ,      )     ->			np.ndarray:
       return normalize(lowerCamelCase ,      mean=lowerCamelCase ,      std=lowerCamelCase ,      data_format=lowerCamelCase ,      **lowerCamelCase      )







      def 	UpperCAmelCase_						(			self   :str ,      lowerCamelCase   :ImageInput ,      lowerCamelCase   :Optional[bool] = None ,      lowerCamelCase   :Optional[Dict[str, int]] = None ,      lowerCamelCase   :PILImageResampling = None ,      lowerCamelCase   :Optional[bool] = None ,      lowerCamelCase   :Optional[float] = None ,      lowerCamelCase   :Optional[bool] = None ,      lowerCamelCase   :Optional[Union[float, List[float]]] = None ,      lowerCamelCase   :Optional[Union[float, List[float]]] = None ,      lowerCamelCase   :Optional[Union[str, TensorType]] = None ,      lowerCamelCase   :bool = None ,      lowerCamelCase   :ChannelDimension = ChannelDimension.FIRST ,      **lowerCamelCase   :List[Any] ,      )     ->			PIL.Image.Image:
       UpperCAmelCase__							 =						do_resize if do_resize is not None else self.do_resize
       UpperCAmelCase__							 =						resample if resample is not None else self.resample
       UpperCAmelCase__							 =						do_rescale if do_rescale is not None else self.do_rescale
       UpperCAmelCase__							 =						rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCAmelCase__							 =						do_normalize if do_normalize is not None else self.do_normalize
       UpperCAmelCase__							 =						image_mean if image_mean is not None else self.image_mean
       UpperCAmelCase__							 =						image_std if image_std is not None else self.image_std
       UpperCAmelCase__							 =						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       UpperCAmelCase__							 =						size if size is not None else self.size
       UpperCAmelCase__							 =						get_size_dict(lowerCamelCase ,      default_to_square=lowerCamelCase      )

       UpperCAmelCase__							 =						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 or resample is None:
        raise ValueError("Size and resample must be specified if do_resize 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:
        UpperCAmelCase__							 =						[convert_to_rgb(lowerCamelCase      ) for image in images]

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

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

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

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

       UpperCAmelCase__							 =						[to_channel_dimension_format(lowerCamelCase ,      lowerCamelCase      ) for image in images]

       UpperCAmelCase__							 =						BatchFeature(data={"pixel_values": images} ,      tensor_type=lowerCamelCase      )

       return encoded_outputs











import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_lowerCAmelCase						:   List[Any]		=   ["small", "medium", "large"]

_lowerCAmelCase						:   List[Any]		=   "lm_head.decoder.weight"
_lowerCAmelCase						:   Optional[int]		=   "lm_head.weight"



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   str	):




 """simple docstring"""







 UpperCAmelCase__							 =						torch.load(_lowerCAmelCase	)
 UpperCAmelCase__							 =						d.pop(_lowerCAmelCase	)
 os.makedirs(_lowerCAmelCase						,							exist_ok=_lowerCAmelCase	)
 torch.save(_lowerCAmelCase						,							os.path.join(_lowerCAmelCase						,							_lowerCAmelCase	)	)


if __name__ == "__main__":
  _lowerCAmelCase						:   Dict		=   argparse.ArgumentParser()
  parser.add_argument("--dialogpt_path", default=".", type=str)
  _lowerCAmelCase						:   Union[str, Any]		=   parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    _lowerCAmelCase						:   Union[str, Any]		=   os.path.join(args.dialogpt_path, F'''{MODEL}_ft.pkl''')
    _lowerCAmelCase						:   List[str]		=   F'''./DialoGPT-{MODEL}'''
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )





