

'''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 				A__       :





  def __init__(						self :      List[str]				,   _a :      List[Any]				,   _a :      str=sys.maxsize					)      ->       Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE				       ='bilinear'
       _SCREAMING_SNAKE_CASE				       =max_size
       _SCREAMING_SNAKE_CASE				       =short_edge_length





  def __call__(						self :      Optional[Any]				,   _a :      Optional[Any]					)      ->       Optional[int]:





       '''simple docstring'''






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

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

       return img_augs







class 				A__       :





  def __init__(						self :      Any				,   _a :      List[str]					)      ->       List[Any]:





       '''simple docstring'''






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





  def 		A    (						self :      Any				,   _a :      Optional[int]					)      ->       List[str]:





       '''simple docstring'''






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

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





  def __call__(						self :      Optional[int]				,   _a :      Optional[Any]				,   _a :      int=False					)      ->       Optional[Any]:





       '''simple docstring'''






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

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


def 			_lowerCAmelCase   (		_UpperCamelCase		:	Dict				,					_UpperCamelCase		:	str							) ->     Any:
     """simple docstring"""




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







def 			_lowerCAmelCase   (		_UpperCamelCase		:	int				,					_UpperCamelCase		:	Tuple[int, int]							) ->     List[Any]:
     """simple docstring"""




     assert torch.isfinite(lowerCamelCase_							).all(), "Box tensor contains infinite or NaN!"
     _SCREAMING_SNAKE_CASE				       =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_							)






import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()





def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     Any  ,							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     List[Any]  )       ->  int:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      FunnelConfig.from_json_file(lowerCamelCase_  )
			print(F'Building PyTorch model from configuration: {config}'  )
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      FunnelBaseModel(lowerCamelCase_  ) if base_model else FunnelModel(lowerCamelCase_  )

			# Load weights from tf checkpoint
			load_tf_weights_in_funnel(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  )

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}'  )
			torch.save(model.state_dict()  ,							lowerCamelCase_  )


if __name__ == "__main__":
						UpperCamelCase__ :   Optional[Any]						=							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
						)
						parser.add_argument(
						    '''--config_file''',
						    default=None,
						    type=str,
						    required=True,
						    help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
						)
						parser.add_argument(
						    '''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''
						)
						UpperCamelCase__ :   Tuple						=							parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
						)



