






"""simple docstring"""






import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize




class 							lowerCAmelCase__				:






      '''simple docstring'''

      def __init__(						self     :  Any						,	lowercase_     :  Dict						,	lowercase_     :  Union[str, Any]=sys.maxsize):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		'''bilinear'''
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		max_size
          SCREAMING_SNAKE_CASE_      :  Any    						=		short_edge_length



      def __call__(						self     :  str						,	lowercase_     :  int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		[]
          for img in imgs:
              SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[str]    						=		img.shape[:2]
              # later: provide list and randomly choose index for resize
              SCREAMING_SNAKE_CASE_      :  Any    						=		np.random.randint(self.short_edge_length[0]						,	self.short_edge_length[1] + 1)
              if size == 0:
                  return img
              SCREAMING_SNAKE_CASE_      :  Dict    						=		size * 1.0 / min(lowercase_						,	lowercase_)
              if h < w:
                  SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  int    						=		size, scale * w
              else:
                  SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Any    						=		scale * h, size
              if max(lowercase_						,	lowercase_) > self.max_size:
                  SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.max_size * 1.0 / max(lowercase_						,	lowercase_)
                  SCREAMING_SNAKE_CASE_      :  Any    						=		newh * scale
                  SCREAMING_SNAKE_CASE_      :  str    						=		neww * scale
              SCREAMING_SNAKE_CASE_      :  int    						=		int(neww + 0.5)
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		int(newh + 0.5)

              if img.dtype == np.uinta:
                  SCREAMING_SNAKE_CASE_      :  Tuple    						=		Image.fromarray(lowercase_)
                  SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		pil_image.resize((neww, newh)						,	PILImageResampling.BILINEAR)
                  SCREAMING_SNAKE_CASE_      :  int    						=		np.asarray(lowercase_)
              else:
                  SCREAMING_SNAKE_CASE_      :  int    						=		img.permute(2						,	0						,	1).unsqueeze(0)  # 3, 0, 1)  # hw(c) -> nchw
                  SCREAMING_SNAKE_CASE_      :  int    						=		nn.functional.interpolate(
                      lowercase_						,	(newh, neww)						,	mode=self.interp_method						,	align_corners=lowercase_).squeeze(0)
              img_augs.append(lowercase_)

          return img_augs




class 							lowerCAmelCase__				:






      '''simple docstring'''

      def __init__(						self     :  Tuple						,	lowercase_     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]						,	cfg.INPUT.MAX_SIZE_TEST)
          SCREAMING_SNAKE_CASE_      :  Any    						=		cfg.INPUT.FORMAT
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		cfg.SIZE_DIVISIBILITY
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		cfg.PAD_VALUE
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		cfg.INPUT.MAX_SIZE_TEST
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		cfg.MODEL.DEVICE
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		torch.tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(len(cfg.MODEL.PIXEL_STD)						,	1						,	1)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		torch.tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(len(cfg.MODEL.PIXEL_STD)						,	1						,	1)
          SCREAMING_SNAKE_CASE_      :  Dict    						=		lambda lowercase_: (x - self.pixel_mean) / self.pixel_std
      def _SCREAMING_SNAKE_CASE (						self     :  Any						,	lowercase_     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		tuple(max(lowercase_) for s in zip(*[img.shape for img in images]))
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		[im.shape[-2:] for im in images]
          SCREAMING_SNAKE_CASE_      :  int    						=		[
              nn.functional.pad(
                  lowercase_						,	[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]						,	value=self.pad_value						,	)
              for size, im in zip(lowercase_						,	lowercase_)
          ]

          return torch.stack(lowercase_), torch.tensor(lowercase_)



      def __call__(						self     :  Tuple						,	lowercase_     :  Optional[int]						,	lowercase_     :  List[Any]=False):




          '''simple docstring'''


          with torch.no_grad():
              if not isinstance(lowercase_						,	lowercase_):
                  SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		[images]
              if single_image:
                  assert len(lowercase_) == 1
              for i in range(len(lowercase_)):
                  if isinstance(images[i]						,	torch.Tensor):
                      images.insert(lowercase_						,	images.pop(lowercase_).to(self.device).float())
                  elif not isinstance(images[i]						,	torch.Tensor):
                      images.insert(
                          lowercase_						,	torch.as_tensor(img_tensorize(images.pop(lowercase_)						,	input_format=self.input_format))
                          .to(self.device)
                          .float()						,	)
            # resize smallest edge
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		torch.tensor([im.shape[:2] for im in images])
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.aug(lowercase_)
              # transpose images and convert to torch tensors
              # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
              # now normalize before pad to avoid useless arithmetic
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		[self.normalizer(lowercase_) for x in images]
              # now pad them to do the following operations
              SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  int    						=		self.pad(lowercase_)
              # Normalize

              if self.size_divisibility > 0:
                  raise NotImplementedError()
              # pad
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		torch.true_divide(lowercase_						,	lowercase_)
              if single_image:
                  return images[0], sizes[0], scales_yx[0]
              else:
                  return images, sizes, scales_yx



def 					_A			(__a    ,				__a							) ->       Dict:
    """simple docstring"""






    boxes[:, 0::2] *= scale_yx[:, 1]
    boxes[:, 1::2] *= scale_yx[:, 0]
    return boxes





def 					_A			(__a    ,				__a							) ->       Any:
    """simple docstring"""






    assert torch.isfinite(__a							).all(), "Box tensor contains infinite or NaN!"
    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		box_size
    tensor[:, 0].clamp_(min=0    ,				max=__a							)
    tensor[:, 1].clamp_(min=0    ,				max=__a							)
    tensor[:, 2].clamp_(min=0    ,				max=__a							)
    tensor[:, 3].clamp_(min=0    ,				max=__a							)










"""simple docstring"""






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							) ->       Dict:
    """simple docstring"""






    if gpta_config_file == "":
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		GPTaConfig()
    else:
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		GPTaConfig.from_json_file(__a							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		GPTaModel(__a							)

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

    # Save pytorch-model
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
    SCREAMING_SNAKE_CASE_      :  List[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__":
  UpperCAmelCase_   : str   			= 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."""
      ),
  )
  UpperCAmelCase_   : Union[str, Any]   			= parser.parse_args()
  convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)


