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						:



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__=sys.maxsize       ):
            _A=	'bilinear'
            _A=	max_size
            _A=	short_edge_length





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

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

            return img_augs





class      lowerCAmelCase						:



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



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

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





     def __call__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__=False       ):






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

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


def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Union[str, Any]:




       '''simple docstring'''






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





def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Optional[int]:




       '''simple docstring'''






       assert torch.isfinite(lowerCAmelCase_   ).all(), "Box tensor contains infinite or NaN!"
       _A=	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 BertForMaskedLM


if __name__ == "__main__":
      UpperCAmelCase_								=							argparse.ArgumentParser(
          description=(
              '''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
              ''' Distillation'''
          )
      )
      parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
      parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
      parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
      parser.add_argument('''--vocab_transform''', action='''store_true''')
      UpperCAmelCase_								=							parser.parse_args()

      if args.model_type == "bert":
            UpperCAmelCase_								=							BertForMaskedLM.from_pretrained(args.model_name)
            UpperCAmelCase_								=							'''bert'''
      else:
            raise ValueError('''args.model_type should be "bert".''')

      UpperCAmelCase_								=							model.state_dict()
      UpperCAmelCase_								=							{}

      for w in ["word_embeddings", "position_embeddings"]:
            UpperCAmelCase_								=							state_dict[F"{prefix}.embeddings.{w}.weight"]
      for w in ["weight", "bias"]:
            UpperCAmelCase_								=							state_dict[F"{prefix}.embeddings.LayerNorm.{w}"]

      UpperCAmelCase_								=							0
      for teacher_idx in [0, 2, 4, 7, 9, 11]:
            for w in ["weight", "bias"]:
                  UpperCAmelCase_								=							state_dict[
                      F"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"
                  ]
                  UpperCAmelCase_								=							state_dict[
                      F"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"
                  ]
                  UpperCAmelCase_								=							state_dict[
                      F"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"
                  ]

                  UpperCAmelCase_								=							state_dict[
                      F"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"
                  ]
                  UpperCAmelCase_								=							state_dict[
                      F"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"
                  ]

                  UpperCAmelCase_								=							state_dict[
                      F"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"
                  ]
                  UpperCAmelCase_								=							state_dict[
                      F"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"
                  ]
                  UpperCAmelCase_								=							state_dict[
                      F"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"
                  ]
            std_idx += 1

      UpperCAmelCase_								=							state_dict['''cls.predictions.decoder.weight''']
      UpperCAmelCase_								=							state_dict['''cls.predictions.bias''']
      if args.vocab_transform:
            for w in ["weight", "bias"]:
                  UpperCAmelCase_								=							state_dict[F"cls.predictions.transform.dense.{w}"]
                  UpperCAmelCase_								=							state_dict[F"cls.predictions.transform.LayerNorm.{w}"]

      print(F"N layers selected for distillation: {std_idx}")
      print(F"Number of params transferred for distillation: {len(compressed_sd.keys())}")

      print(F"Save transferred checkpoint to {args.dump_checkpoint}.")
      torch.save(compressed_sd, args.dump_checkpoint)