




from typing import Dict, List, Optional, Tuple, 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_torch_available, is_torch_tensor, logging


if is_torch_available():
  import torch


_lowerCAmelCase						:   List[Any]		=   logging.get_logger(__name__)
class 			_UpperCamelCase      (				lowerCamelCase__		):
      UpperCAmelCase_        =		["""pixel_values"""]
      def __init__(			self   :int ,      lowerCamelCase   :List[Any] = True ,      lowerCamelCase   :Dict = None ,      lowerCamelCase   :Dict = PILImageResampling.BILINEAR ,      lowerCamelCase   :str = True ,      lowerCamelCase   :List[Any] = None ,      lowerCamelCase   :str = True ,      lowerCamelCase   :Union[str, Any] = 1 / 255 ,      lowerCamelCase   :str = True ,      lowerCamelCase   :Any = None ,      lowerCamelCase   :Optional[Any] = None ,      **lowerCamelCase   :Optional[int] ,      )     ->			None:
       super().__init__(**__lowerCamelCase      )
       UpperCAmelCase__							 =						size if size is not None else {'''shortest_edge''': 256}
       UpperCAmelCase__							 =						get_size_dict(__lowerCamelCase ,      default_to_square=__lowerCamelCase      )
       UpperCAmelCase__							 =						crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       UpperCAmelCase__							 =						get_size_dict(__lowerCamelCase ,      param_name="crop_size"      )
       UpperCAmelCase__							 =						do_resize
       UpperCAmelCase__							 =						size
       UpperCAmelCase__							 =						resample
       UpperCAmelCase__							 =						do_center_crop
       UpperCAmelCase__							 =						crop_size
       UpperCAmelCase__							 =						do_rescale
       UpperCAmelCase__							 =						rescale_factor
       UpperCAmelCase__							 =						do_normalize
       UpperCAmelCase__							 =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       UpperCAmelCase__							 =						image_std if image_std is not None else IMAGENET_STANDARD_STD
      def 	UpperCAmelCase_						(			self   :Optional[int] ,      lowerCamelCase   :Optional[Any] ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :List[Any] = PILImageResampling.BICUBIC ,      lowerCamelCase   :Optional[int] = None ,      **lowerCamelCase   :int ,      )     ->			np.ndarray:
       UpperCAmelCase__							 =						get_size_dict(__lowerCamelCase ,      default_to_square=__lowerCamelCase      )
       if "shortest_edge" not in size:
        raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''      )
       UpperCAmelCase__							 =						get_resize_output_image_size(__lowerCamelCase ,      size=size["shortest_edge"] ,      default_to_square=__lowerCamelCase      )
       return resize(__lowerCamelCase ,      size=__lowerCamelCase ,      resample=__lowerCamelCase ,      data_format=__lowerCamelCase ,      **__lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :List[Any] ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :Optional[Any] ,      lowerCamelCase   :List[str] = None ,      **lowerCamelCase   :List[str] ,      )     ->			np.ndarray:
       UpperCAmelCase__							 =						get_size_dict(__lowerCamelCase      )
       if "height" not in size or "width" not in size:
        raise ValueError(f'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'''      )
       return center_crop(__lowerCamelCase ,      size=(size["height"], size["width"]) ,      data_format=__lowerCamelCase ,      **__lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :Dict ,      lowerCamelCase   :Optional[Any] ,      lowerCamelCase   :Tuple = None ,      **lowerCamelCase   :str      )     ->			np.ndarray:
       return rescale(__lowerCamelCase ,      scale=__lowerCamelCase ,      data_format=__lowerCamelCase ,      **__lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :Any ,      lowerCamelCase   :Tuple ,      lowerCamelCase   :str ,      lowerCamelCase   :List[str] = None ,      **lowerCamelCase   :Optional[Any] ,      )     ->			np.ndarray:
       return normalize(__lowerCamelCase ,      mean=__lowerCamelCase ,      std=__lowerCamelCase ,      data_format=__lowerCamelCase ,      **__lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :int ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :Optional[int] = None ,      lowerCamelCase   :Tuple = None ,      lowerCamelCase   :Union[str, Any] = None ,      lowerCamelCase   :List[str] = None ,      lowerCamelCase   :List[Any] = None ,      lowerCamelCase   :Tuple = None ,      lowerCamelCase   :Any = None ,      lowerCamelCase   :Dict = None ,      lowerCamelCase   :Optional[Any] = None ,      lowerCamelCase   :Union[str, Any] = None ,      lowerCamelCase   :Optional[int] = None ,      lowerCamelCase   :int = ChannelDimension.FIRST ,      **lowerCamelCase   :str ,      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						do_resize if do_resize is not None else self.do_resize
       UpperCAmelCase__							 =						size if size is not None else self.size
       UpperCAmelCase__							 =						get_size_dict(__lowerCamelCase ,      default_to_square=__lowerCamelCase      )
       UpperCAmelCase__							 =						resample if resample is not None else self.resample
       UpperCAmelCase__							 =						do_center_crop if do_center_crop is not None else self.do_center_crop
       UpperCAmelCase__							 =						crop_size if crop_size is not None else self.crop_size
       UpperCAmelCase__							 =						get_size_dict(__lowerCamelCase ,      param_name="crop_size"      )
       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__							 =						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:
        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."      )

       # 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_center_crop:
        UpperCAmelCase__							 =						[self.center_crop(image=__lowerCamelCase ,      size=__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__							 =						{'''pixel_values''': images}
       return BatchFeature(data=__lowerCamelCase ,      tensor_type=__lowerCamelCase      )







      def 	UpperCAmelCase_						(			self   :Dict ,      lowerCamelCase   :Any ,      lowerCamelCase   :Optional[int] = None      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						outputs.logits

       # Resize logits and compute semantic segmentation maps
       if target_sizes is not None:
        if len(__lowerCamelCase      ) != len(__lowerCamelCase      ):
         raise ValueError(
             "Make sure that you pass in as many target sizes as the batch dimension of the logits"      )

        if is_torch_tensor(__lowerCamelCase      ):
         UpperCAmelCase__							 =						target_sizes.numpy()

        UpperCAmelCase__							 =						[]

        for idx in range(len(__lowerCamelCase      )      ):
         UpperCAmelCase__							 =						torch.nn.functional.interpolate(
             logits[idx].unsqueeze(dim=0      ) ,      size=target_sizes[idx] ,      mode="bilinear" ,      align_corners=__lowerCamelCase      )
         UpperCAmelCase__							 =						resized_logits[0].argmax(dim=0      )
         semantic_segmentation.append(__lowerCamelCase      )
       else:
        UpperCAmelCase__							 =						logits.argmax(dim=1      )
        UpperCAmelCase__							 =						[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]      )]

       return semantic_segmentation











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,
    )





