

import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
     __lowercase     : str       =						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''')
     __lowercase     : List[str]       =						parser.parse_args()

     if args.model_type == "bert":
          __lowercase     : int       =						BertForMaskedLM.from_pretrained(args.model_name)
          __lowercase     : List[Any]       =						'''bert'''
     else:
          raise ValueError('''args.model_type should be "bert".''')

     __lowercase     : Dict       =						model.state_dict()
     __lowercase     : Optional[Any]       =						{}

     for w in ["word_embeddings", "position_embeddings"]:
          __lowercase     : Union[str, Any]       =						state_dict[f'''{prefix}.embeddings.{w}.weight''']
     for w in ["weight", "bias"]:
          __lowercase     : List[str]       =						state_dict[f'''{prefix}.embeddings.LayerNorm.{w}''']

     __lowercase     : Tuple       =						0
     for teacher_idx in [0, 2, 4, 7, 9, 11]:
          for w in ["weight", "bias"]:
               __lowercase     : List[str]       =						state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
               ]
               __lowercase     : Union[str, Any]       =						state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
               ]
               __lowercase     : int       =						state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
               ]

               __lowercase     : Any       =						state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
               ]
               __lowercase     : Dict       =						state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
               ]

               __lowercase     : Tuple       =						state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
               ]
               __lowercase     : Dict       =						state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
               ]
               __lowercase     : Optional[Any]       =						state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
               ]
          std_idx += 1

     __lowercase     : Any       =						state_dict['''cls.predictions.decoder.weight''']
     __lowercase     : List[str]       =						state_dict['''cls.predictions.bias''']
     if args.vocab_transform:
          for w in ["weight", "bias"]:
               __lowercase     : Any       =						state_dict[f'''cls.predictions.transform.dense.{w}''']
               __lowercase     : str       =						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)







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import PoolFormerImageProcessor




class A    (  unittest.TestCase	):






      def __init__(   self		:	Any     ,  lowercase_		:	List[str]     ,  lowercase_		:	Union[str, Any]=7     ,  lowercase_		:	Dict=3     ,  lowercase_		:	List[Any]=30     ,  lowercase_		:	int=400     ,  lowercase_		:	List[Any]=True     ,  lowercase_		:	str=None     ,  lowercase_		:	str=0.9     ,  lowercase_		:	Union[str, Any]=None     ,  lowercase_		:	List[str]=True     ,  lowercase_		:	List[str]=[0.5, 0.5, 0.5]     ,  lowercase_		:	int=[0.5, 0.5, 0.5]     ,  )   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						int            =size if size is not None else {'shortest_edge': 30}
             _lowerCamelCase			:						Optional[Any]            =crop_size if crop_size is not None else {'height': 30, 'width': 30}
             _lowerCamelCase			:						List[Any]            =parent
             _lowerCamelCase			:						Union[str, Any]            =batch_size
             _lowerCamelCase			:						Dict            =num_channels
             _lowerCamelCase			:						Any            =min_resolution
             _lowerCamelCase			:						Union[str, Any]            =max_resolution
             _lowerCamelCase			:						Any            =do_resize_and_center_crop
             _lowerCamelCase			:						Any            =size
             _lowerCamelCase			:						Optional[Any]            =crop_pct
             _lowerCamelCase			:						Optional[Any]            =crop_size
             _lowerCamelCase			:						List[Any]            =do_normalize
             _lowerCamelCase			:						int            =image_mean
             _lowerCamelCase			:						Optional[int]            =image_std






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Tuple:
             """simple docstring"""




             return {
                 "size": self.size,
                 "do_resize_and_center_crop": self.do_resize_and_center_crop,
                 "crop_pct": self.crop_pct,
                 "crop_size": self.crop_size,
                 "do_normalize": self.do_normalize,
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
             }




@require_torch
@require_vision
class A    (  UpperCamelCase_       ,				unittest.TestCase	):
      UpperCamelCase__				:    Dict =PoolFormerImageProcessor if is_vision_available() else None






      def 			lowerCamelCase			(   self		:	int					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						Any            =PoolFormerImageProcessingTester(self					)






      @property
      def 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




             return self.image_processor_tester.prepare_image_processor_dict()






      def 			lowerCamelCase			(   self		:	Union[str, Any]					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =self.image_processing_class(**self.image_processor_dict					)
             self.assertTrue(hasattr(lowercase_     ,  'do_resize_and_center_crop'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'size'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'crop_pct'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'do_normalize'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'image_mean'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'image_std'					)					)






      def 			lowerCamelCase			(   self		:	int					)   ->		Dict:
             """simple docstring"""




             _lowerCamelCase			:						Any            =self.image_processing_class.from_dict(self.image_processor_dict					)
             self.assertEqual(image_processor.size     ,  {'shortest_edge': 30}					)
             self.assertEqual(image_processor.crop_size     ,  {'height': 30, 'width': 30}					)

             _lowerCamelCase			:						List[Any]            =self.image_processing_class.from_dict(self.image_processor_dict     ,  size=42     ,  crop_size=84					)
             self.assertEqual(image_processor.size     ,  {'shortest_edge': 42}					)
             self.assertEqual(image_processor.crop_size     ,  {'height': 84, 'width': 84}					)






      def 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




             pass






      def 			lowerCamelCase			(   self		:	str					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =self.image_processing_class(**self.image_processor_dict					)
             # create random PIL images
             _lowerCamelCase			:						Dict            =prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  Image.Image					)

             # Test not batched input
             _lowerCamelCase			:						int            =image_processing(image_inputs[0]     ,  return_tensors='pt'					).pixel_values

             self.assertEqual(
                 encoded_images.shape     ,  (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )     ,  )

             # Test batched
             _lowerCamelCase			:						Optional[int]            =image_processing(lowercase_     ,  return_tensors='pt'					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )     ,  )






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						Dict            =self.image_processing_class(**self.image_processor_dict					)
             # create random numpy tensors
             _lowerCamelCase			:						Optional[Any]            =prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  numpify=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  np.ndarray					)

             # Test not batched input
             _lowerCamelCase			:						Dict            =image_processing(image_inputs[0]     ,  return_tensors='pt'					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )     ,  )

             # Test batched
             _lowerCamelCase			:						Optional[int]            =image_processing(lowercase_     ,  return_tensors='pt'					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )     ,  )






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						int            =self.image_processing_class(**self.image_processor_dict					)
             # create random PyTorch tensors
             _lowerCamelCase			:						Dict            =prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  torchify=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  torch.Tensor					)

             # Test not batched input
             _lowerCamelCase			:						str            =image_processing(image_inputs[0]     ,  return_tensors='pt'					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )     ,  )

             # Test batched
             _lowerCamelCase			:						Tuple            =image_processing(lowercase_     ,  return_tensors='pt'					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )     ,  )


