





'''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 				_lowerCAmelCase  (			lowerCamelCase_     :					Tuple						,    lowerCamelCase_     :					str						,    lowerCamelCase_     :					Optional[Any]			):
			# Construct model
			if gpta_config_file == "":
						__lowercase			  =							GPTaConfig()
			else:
						__lowercase			  =							GPTaConfig.from_json_file(lowerCamelCase_			)
			__lowercase			  =							GPTaModel(lowerCamelCase_			)

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

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


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












'''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 							__lowercase    :







	'''simple docstring'''

	def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase=sys.maxsize	)							->							Optional[Any]:







				'''simple docstring'''





				__lowercase			  =							'''bilinear'''
				__lowercase			  =							max_size
				__lowercase			  =							short_edge_length


	def __call__(self       ,_lowerCamelCase	)							->							str:







				'''simple docstring'''





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

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

				return img_augs






class 							__lowercase    :







	'''simple docstring'''

	def __init__(self       ,_lowerCamelCase	)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase			  =							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]       ,cfg.INPUT.MAX_SIZE_TEST	)
				__lowercase			  =							cfg.INPUT.FORMAT
				__lowercase			  =							cfg.SIZE_DIVISIBILITY
				__lowercase			  =							cfg.PAD_VALUE
				__lowercase			  =							cfg.INPUT.MAX_SIZE_TEST
				__lowercase			  =							cfg.MODEL.DEVICE
				__lowercase			  =							torch.tensor(cfg.MODEL.PIXEL_STD	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)       ,1       ,1	)
				__lowercase			  =							torch.tensor(cfg.MODEL.PIXEL_MEAN	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)       ,1       ,1	)
				__lowercase			  =							lambda _lowerCamelCase	: (x - self.pixel_mean) / self.pixel_std


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Dict:







				'''simple docstring'''





				__lowercase			  =							tuple(max(_lowerCamelCase	) for s in zip(*[img.shape for img in images]	)	)
				__lowercase			  =							[im.shape[-2:] for im in images]
				__lowercase			  =							[
				    nn.functional.pad(
				        _lowerCamelCase       ,[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]       ,value=self.pad_value       ,)
				    for size, im in zip(_lowerCamelCase       ,_lowerCamelCase	)
				]

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


	def __call__(self       ,_lowerCamelCase       ,_lowerCamelCase=False	)							->							Tuple:







				'''simple docstring'''





				with torch.no_grad():
							if not isinstance(_lowerCamelCase       ,_lowerCamelCase	):
										__lowercase			  =							[images]
							if single_image:
										assert len(_lowerCamelCase	) == 1
							for i in range(len(_lowerCamelCase	)	):
										if isinstance(images[i]       ,torch.Tensor	):
													images.insert(_lowerCamelCase       ,images.pop(_lowerCamelCase	).to(self.device	).float()	)
										elif not isinstance(images[i]       ,torch.Tensor	):
													images.insert(
													    _lowerCamelCase       ,torch.as_tensor(img_tensorize(images.pop(_lowerCamelCase	)       ,input_format=self.input_format	)	)
													    .to(self.device	)
													    .float()       ,)
            # resize smallest edge
							__lowercase			  =							torch.tensor([im.shape[:2] for im in images]	)
							__lowercase			  =							self.aug(_lowerCamelCase	)
							# 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
							__lowercase			  =							[self.normalizer(_lowerCamelCase	) for x in images]
							# now pad them to do the following operations
							__lowercase  ,						__lowercase			  =							self.pad(_lowerCamelCase	)
							# Normalize

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





def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[Any]						,    lowerCamelCase_     :					Tuple			):
			boxes[:, 0::2] *= scale_yx[:, 1]
			boxes[:, 1::2] *= scale_yx[:, 0]
			return boxes




def 				_lowerCAmelCase  (			lowerCamelCase_     :					str						,    lowerCamelCase_     :					Tuple[int, int]			):
			assert torch.isfinite(lowerCamelCase_			).all(), "Box tensor contains infinite or NaN!"
			__lowercase  ,						__lowercase			  =							box_size
			tensor[:, 0].clamp_(min=0						,    max=lowerCamelCase_			)
			tensor[:, 1].clamp_(min=0						,    max=lowerCamelCase_			)
			tensor[:, 2].clamp_(min=0						,    max=lowerCamelCase_			)
			tensor[:, 3].clamp_(min=0						,    max=lowerCamelCase_			)





