





import cva
import numpy as np

class 		__a				:



       def __init__(  self						: List[str]       ,      UpperCAmelCase						: Optional[Any]       ,      UpperCAmelCase						: Any      ):





           if k in (0.04, 0.06):
               lowerCAmelCase_       :						Optional[Any]							= k
               lowerCAmelCase_       :						int							= window_size
           else:
               raise ValueError("""invalid k value"""      )



       def __str__(  self						: Any      ):
           return str(self.k      )



       def 			A				(  self						: List[Any]       ,      UpperCAmelCase						: Any      ):
           lowerCAmelCase_       :						Optional[Any]							= cva.imread(a__       ,      0      )
           lowerCAmelCase_       :						List[str]							= img.shape
           lowerCAmelCase_       :						list[list[int]]							= []
           lowerCAmelCase_       :						str							= img.copy()
           lowerCAmelCase_       :						str							= cva.cvtColor(a__       ,      cva.COLOR_GRAY2RGB      )
           lowerCAmelCase_       :						Tuple							= np.gradient(a__      )
           lowerCAmelCase_       :						Optional[int]							= dx**2
           lowerCAmelCase_       :						Any							= dy**2
           lowerCAmelCase_       :						Optional[Any]							= dx * dy
           lowerCAmelCase_       :						Any							= 0.04
           lowerCAmelCase_       :						Any							= self.window_size // 2
           for y in range(a__       ,      h - offset      ):
               for x in range(a__       ,      w - offset      ):
                   lowerCAmelCase_       :						List[str]							= ixx[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   lowerCAmelCase_       :						str							= iyy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   lowerCAmelCase_       :						Optional[Any]							= ixy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()

                   lowerCAmelCase_       :						List[str]							= (wxx * wyy) - (wxy**2)
                   lowerCAmelCase_       :						int							= wxx + wyy
                   lowerCAmelCase_       :						List[str]							= det - k * (trace**2)
                   # Can change the value
                   if r > 0.5:
                       corner_list.append([x, y, r]      )
                       color_img.itemset((y, x, 0)       ,      0      )
                       color_img.itemset((y, x, 1)       ,      0      )
                       color_img.itemset((y, x, 2)       ,      2_55      )
           return color_img, corner_list


if __name__ == "__main__":
  __UpperCAmelCase							=   HarrisCorner(0.04, 3)
  __UpperCAmelCase		,       __UpperCAmelCase							=   edge_detect.detect('path_to_image')
  cva.imwrite('detect.png', color_img)









import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
       _lowerCamelCase      							=   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')
       _lowerCamelCase      							=   parser.parse_args()

       if args.model_type == "roberta":
              _lowerCamelCase      							=   RobertaForMaskedLM.from_pretrained(args.model_name)
              _lowerCamelCase      							=   'roberta'
       elif args.model_type == "gpt2":
              _lowerCamelCase      							=   GPTaLMHeadModel.from_pretrained(args.model_name)
              _lowerCamelCase      							=   'transformer'

       _lowerCamelCase      							=   model.state_dict()
       _lowerCamelCase      							=   {}

       # Embeddings #
       if args.model_type == "gpt2":
              for param_name in ["wte.weight", "wpe.weight"]:
                     _lowerCamelCase      							=   state_dict[F'''{prefix}.{param_name}''']
       else:
              for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
                     _lowerCamelCase      							=   F'''{prefix}.embeddings.{w}.weight'''
                     _lowerCamelCase      							=   state_dict[param_name]
              for w in ["weight", "bias"]:
                     _lowerCamelCase      							=   F'''{prefix}.embeddings.LayerNorm.{w}'''
                     _lowerCamelCase      							=   state_dict[param_name]

    # Transformer Blocks #
       _lowerCamelCase      							=   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"]:
                                   _lowerCamelCase      							=   state_dict[
                                       F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
                                   ]
                     _lowerCamelCase      							=   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"]:
                                   _lowerCamelCase      							=   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"]:
                     _lowerCamelCase      							=   state_dict[F'''{layer}''']
              if args.vocab_transform:
                     for w in ["weight", "bias"]:
                            _lowerCamelCase      							=   state_dict[F'''lm_head.dense.{w}''']
                            _lowerCamelCase      							=   state_dict[F'''lm_head.layer_norm.{w}''']
       elif args.model_type == "gpt2":
              for w in ["weight", "bias"]:
                     _lowerCamelCase      							=   state_dict[F'''{prefix}.ln_f.{w}''']
              _lowerCamelCase      							=   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)

