





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
       snake_case__					:							List[Any]													=     argparse.ArgumentParser(
           description=(
               'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
               ' Distillation'
           )
       )
       parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
       parser.add_argument('--model_name', default='roberta-large', type=str)
       parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
       parser.add_argument('--vocab_transform', action='store_true')
       snake_case__					:							str													=     parser.parse_args()

       if args.model_type == "roberta":
              snake_case__					:							List[Any]													=     RobertaForMaskedLM.from_pretrained(args.model_name)
              snake_case__					:							Optional[int]													=     'roberta'
       elif args.model_type == "gpt2":
              snake_case__					:							List[Any]													=     GPTaLMHeadModel.from_pretrained(args.model_name)
              snake_case__					:							int													=     'transformer'

       snake_case__					:							Optional[Any]													=     model.state_dict()
       snake_case__					:							Tuple													=     {}

       # Embeddings #
       if args.model_type == "gpt2":
              for param_name in ["wte.weight", "wpe.weight"]:
                     snake_case__					:							int													=     state_dict[f'{prefix}.{param_name}']
       else:
              for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
                     snake_case__					:							Dict													=     f'{prefix}.embeddings.{w}.weight'
                     snake_case__					:							Optional[int]													=     state_dict[param_name]
              for w in ["weight", "bias"]:
                     snake_case__					:							Tuple													=     f'{prefix}.embeddings.LayerNorm.{w}'
                     snake_case__					:							Optional[Any]													=     state_dict[param_name]

    # Transformer Blocks #
       snake_case__					:							Optional[Any]													=     0
       for teacher_idx in [0, 2, 4, 7, 9, 11]:
              if args.model_type == "gpt2":
                     for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
                            for w in ["weight", "bias"]:
                                   snake_case__					:							str													=     state_dict[
                                       f'{prefix}.h.{teacher_idx}.{layer}.{w}'
                                   ]
                     snake_case__					:							Union[str, Any]													=     state_dict[f'{prefix}.h.{teacher_idx}.attn.bias']
              else:
                     for layer in [
                         "attention.self.query",
                         "attention.self.key",
                         "attention.self.value",
                         "attention.output.dense",
                         "attention.output.LayerNorm",
                         "intermediate.dense",
                         "output.dense",
                         "output.LayerNorm",
                     ]:
                            for w in ["weight", "bias"]:
                                   snake_case__					:							List[Any]													=     state_dict[
                                       f'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'
                                   ]
              std_idx += 1

       # Language Modeling Head ###s
       if args.model_type == "roberta":
              for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
                     snake_case__					:							str													=     state_dict[f'{layer}']
              if args.vocab_transform:
                     for w in ["weight", "bias"]:
                            snake_case__					:							Dict													=     state_dict[f'lm_head.dense.{w}']
                            snake_case__					:							int													=     state_dict[f'lm_head.layer_norm.{w}']
       elif args.model_type == "gpt2":
              for w in ["weight", "bias"]:
                     snake_case__					:							Optional[Any]													=     state_dict[f'{prefix}.ln_f.{w}']
              snake_case__					:							Optional[Any]													=     state_dict['lm_head.weight']

       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)












from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt



def _a				(	lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   int					,				lowerCamelCase:   int )	->						np.ndarray:







      '''simple docstring'''






      __A          =				cva.getAffineTransform(lowerCamelCase					,				lowerCamelCase )
      return cva.warpAffine(lowerCamelCase					,				lowerCamelCase					,				(rows, cols) )


if __name__ == "__main__":
       # read original image
       snake_case__					:							List[Any]													=     cva.imread(
           str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
       )
       # turn image in gray scale value
       snake_case__					:							List[str]													=     cva.cvtColor(image, cva.COLOR_BGR2GRAY)
       # get image shape
       snake_case__						,    snake_case__					:							str													=     gray_img.shape

       # set different points to rotate image
       snake_case__					:							Any													=     np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
       snake_case__					:							str													=     np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
       snake_case__					:							int													=     np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
       snake_case__					:							List[str]													=     np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

       # add all rotated images in a list
       snake_case__					:							Optional[Any]													=     [
           gray_img,
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
       ]

       # plot different image rotations
       snake_case__					:							Optional[Any]													=     plt.figure(1)
       snake_case__					:							Dict													=     ['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
       for i, image in enumerate(images):
              plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')
              plt.title(titles[i])
              plt.axis('off')
              plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
       plt.show()





