




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			,						lowerCamelCase__			,						lowerCamelCase__=sys.maxsize							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    'bilinear'
							__lowerCamelCase		       =    max_size
							__lowerCamelCase		       =    short_edge_length






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



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

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

							return img_augs







class     __lowerCAmelCase :

			"""simple docstring"""







			def __init__(      self			,						lowerCamelCase__							)    ->      Optional[int]:



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


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    tuple(max(lowerCamelCase__							) for s in zip(*[img.shape for img in images]							)							)
							__lowerCamelCase		       =    [im.shape[-2:] for im in images]
							__lowerCamelCase		       =    [
							    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							)    ->      List[Any]:



							'''simple docstring'''
							with torch.no_grad():
											if not isinstance(lowerCamelCase__			,						lowerCamelCase__							):
															__lowerCamelCase		       =    [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
											__lowerCamelCase		       =    torch.tensor([im.shape[:2] for im in images]							)
											__lowerCamelCase		       =    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
											__lowerCamelCase		       =    [self.normalizer(lowerCamelCase__							) for x in images]
											# now pad them to do the following operations
											__lowerCamelCase       ,						__lowerCamelCase		       =    self.pad(lowerCamelCase__							)
											# Normalize

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







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      str      )     ->       List[Any]:





				"""simple docstring"""


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







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      Tuple[int, int]      )     ->       Dict:





				"""simple docstring"""


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









'''simple docstring'''




import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      Dict			,     __lowercase							:      str			,     __lowercase							:      Optional[Any]			,     __lowercase							:      str       )		->       List[str]:


     '''simple docstring'''



     _UpperCAmelCase     							=   TapasConfig.from_json_file(__lowercase       )
     # set absolute/relative position embeddings parameter
     _UpperCAmelCase     							=   reset_position_index_per_cell

     # set remaining parameters of TapasConfig as well as the model based on the task
     if task == "SQA":
          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "WTQ":
          # run_task_main.py hparams
          _UpperCAmelCase     							=   4
          _UpperCAmelCase     							=   True
          # hparam_utils.py hparams
          _UpperCAmelCase     							=   0.66_4694
          _UpperCAmelCase     							=   0.20_7951
          _UpperCAmelCase     							=   0.12_1194
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   False
          _UpperCAmelCase     							=   0.035_2513

          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          _UpperCAmelCase     							=   4
          _UpperCAmelCase     							=   False
          # hparam_utils.py hparams
          _UpperCAmelCase     							=   36.4519
          _UpperCAmelCase     							=   0.90_3421
          _UpperCAmelCase     							=   222.088
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   0.76_3141

          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "TABFACT":
          _UpperCAmelCase     							=   TapasForSequenceClassification(config=__lowercase       )
     elif task == "MLM":
          _UpperCAmelCase     							=   TapasForMaskedLM(config=__lowercase       )
     elif task == "INTERMEDIATE_PRETRAINING":
          _UpperCAmelCase     							=   TapasModel(config=__lowercase       )
     else:
          raise ValueError(f'Task {task} not supported.'       )

     print(f'Building PyTorch model from configuration: {config}'       )
     # Load weights from tf checkpoint
     load_tf_weights_in_tapas(__lowercase			,     __lowercase			,     __lowercase       )

     # Save pytorch-model (weights and configuration)
     print(f'Save PyTorch model to {pytorch_dump_path}'       )
     model.save_pretrained(__lowercase       )

     # Save tokenizer files
     print(f'Save tokenizer files to {pytorch_dump_path}'       )
     _UpperCAmelCase     							=   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"			,     model_max_length=512       )
     tokenizer.save_pretrained(__lowercase       )

     print("Used relative position embeddings:"			,     model.config.reset_position_index_per_cell       )


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE     :List[str]       =				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
  )
  parser.add_argument(
      '''--reset_position_index_per_cell''',
      default=False,
      action='''store_true''',
      help='''Whether to use relative position embeddings or not. Defaults to True.''',
  )
  parser.add_argument(
      '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
  )
  parser.add_argument(
      '''--tapas_config_file''',
      default=None,
      type=str,
      required=True,
      help=(
          '''The config json file corresponding to the pre-trained TAPAS model. \n'''
          '''This specifies the model architecture.'''
      ),
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  __SCREAMING_SNAKE_CASE     :List[str]       =				parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.task,
      args.reset_position_index_per_cell,
      args.tf_checkpoint_path,
      args.tapas_config_file,
      args.pytorch_dump_path,
  )




