
'''simple docstring'''
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


a_											=						logging.get_logger(__name__)


if is_vision_available():
  import PIL






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						["""pixel_values"""]





 def __init__(    self ,       lowercase_ = True ,       lowercase_ = None ,       lowercase_ = PILImageResampling.BICUBIC ,       lowercase_ = True ,       lowercase_ = None ,       lowercase_ = True ,       lowercase_ = 1 / 2_55 ,       lowercase_ = True ,       lowercase_ = None ,       lowercase_ = None ,       lowercase_ = True ,       **lowercase_ ,       ):
     super().__init__(**lowercase_)
     snake_case_ :    List[str]     			=						size if size is not None else {"shortest_edge": 2_24}
     snake_case_ :    Optional[int]     			=						get_size_dict(lowercase_ ,       default_to_square=lowercase_)
     snake_case_ :    List[Any]     			=						crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
     snake_case_ :    Any     			=						get_size_dict(lowercase_ ,       default_to_square=lowercase_ ,       param_name="crop_size")

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





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





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ = None ,       **lowercase_ ,       ):
     snake_case_ :    Union[str, Any]     			=						get_size_dict(lowercase_)
     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(lowercase_ ,       size=(size["height"], size["width"]) ,       data_format=lowercase_ ,       **lowercase_)





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ = None ,       **lowercase_ ,       ):
     return rescale(lowercase_ ,       scale=lowercase_ ,       data_format=lowercase_ ,       **lowercase_)





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ = None ,       **lowercase_ ,       ):
     return normalize(lowercase_ ,       mean=lowercase_ ,       std=lowercase_ ,       data_format=lowercase_ ,       **lowercase_)





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

     snake_case_ :    Union[str, Any]     			=						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:
         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:
         snake_case_ :    Any     			=						[convert_to_rgb(lowercase_) for image in images]

     # All transformations expect numpy arrays.
     snake_case_ :    List[str]     			=						[to_numpy_array(lowercase_) for image in images]

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

     if do_center_crop:
         snake_case_ :    Tuple     			=						[self.center_crop(image=lowercase_ ,       size=lowercase_) for image in images]

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

     if do_normalize:
         snake_case_ :    Optional[Any]     			=						[self.normalize(image=lowercase_ ,       mean=lowercase_ ,       std=lowercase_) for image in images]

     snake_case_ :    Tuple     			=						[to_channel_dimension_format(lowercase_ ,       lowercase_) for image in images]

     snake_case_ :    int     			=						{"pixel_values": images}
     return BatchFeature(data=lowercase_ ,       tensor_type=lowercase_)






'''simple docstring'''
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


a_											=						["small", "medium", "large"]

a_											=						"lm_head.decoder.weight"
a_											=						"lm_head.weight"

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						torch.load(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[Any]     			=						d.pop(__SCREAMING_SNAKE_CASE       )
    os.makedirs(__SCREAMING_SNAKE_CASE,		exist_ok=__SCREAMING_SNAKE_CASE       )
    torch.save(__SCREAMING_SNAKE_CASE,		os.path.join(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )       )


if __name__ == "__main__":
  a_											=						argparse.ArgumentParser()
  parser.add_argument("--dialogpt_path", default=".", type=str)
  a_											=						parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    a_											=						os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
    a_											=						f"""./DialoGPT-{MODEL}"""
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )




