

"""simple docstring"""
import argparse

import torch

from transformers import MraConfig, MraForMaskedLM







def       UpperCamelCase_					(     lowerCamelCase							:					Dict   )						->     int:



       """simple docstring"""







       if "model" in orig_key:
              __magic_name__			:		str        =     orig_key.replace('''model.'''     , ''''''   )
       if "norm1" in orig_key:
              __magic_name__			:		Tuple        =     orig_key.replace('''norm1'''     , '''attention.output.LayerNorm'''   )
       if "norm2" in orig_key:
              __magic_name__			:		int        =     orig_key.replace('''norm2'''     , '''output.LayerNorm'''   )
       if "norm" in orig_key:
              __magic_name__			:		List[Any]        =     orig_key.replace('''norm'''     , '''LayerNorm'''   )
       if "transformer" in orig_key:
              __magic_name__			:		int        =     orig_key.split('''.'''   )[0].split('''_'''   )[-1]
              __magic_name__			:		Union[str, Any]        =     orig_key.replace(f"""transformer_{layer_num}"""     , f"""encoder.layer.{layer_num}"""   )
       if "mha.attn" in orig_key:
              __magic_name__			:		Any        =     orig_key.replace('''mha.attn'''     , '''attention.self'''   )
       if "mha" in orig_key:
              __magic_name__			:		Optional[Any]        =     orig_key.replace('''mha'''     , '''attention'''   )
       if "W_q" in orig_key:
              __magic_name__			:		Any        =     orig_key.replace('''W_q'''     , '''self.query'''   )
       if "W_k" in orig_key:
              __magic_name__			:		Any        =     orig_key.replace('''W_k'''     , '''self.key'''   )
       if "W_v" in orig_key:
              __magic_name__			:		List[Any]        =     orig_key.replace('''W_v'''     , '''self.value'''   )
       if "ff.0" in orig_key:
              __magic_name__			:		Optional[Any]        =     orig_key.replace('''ff.0'''     , '''intermediate.dense'''   )
       if "ff.2" in orig_key:
              __magic_name__			:		Tuple        =     orig_key.replace('''ff.2'''     , '''output.dense'''   )
       if "ff" in orig_key:
              __magic_name__			:		List[Any]        =     orig_key.replace('''ff'''     , '''output.dense'''   )
       if "mlm_class" in orig_key:
              __magic_name__			:		Dict        =     orig_key.replace('''mlm.mlm_class'''     , '''cls.predictions.decoder'''   )
       if "mlm" in orig_key:
              __magic_name__			:		Tuple        =     orig_key.replace('''mlm'''     , '''cls.predictions.transform'''   )
       if "backbone.backbone.encoders" in orig_key:
              __magic_name__			:		int        =     orig_key.replace('''backbone.backbone.encoders'''     , '''encoder.layer'''   )
       if "cls" not in orig_key:
              __magic_name__			:		Union[str, Any]        =     '''mra.''' + orig_key

       return orig_key







def       UpperCamelCase_					(     lowerCamelCase							:					Any     , lowerCamelCase							:					Tuple   )						->     Any:



       """simple docstring"""







       for key in orig_state_dict.copy().keys():
              __magic_name__			:		Optional[int]        =     orig_state_dict.pop(lowerCamelCase   )

              if ("pooler" in key) or ("sen_class" in key):
                     continue
              else:
                     __magic_name__			:		Tuple        =     val

       __magic_name__			:		Dict        =     orig_state_dict['''cls.predictions.decoder.bias''']
       __magic_name__			:		List[Any]        =     torch.arange(lowerCamelCase   ).expand((1, -1)   ) + 2

       return orig_state_dict







def       UpperCamelCase_					(     lowerCamelCase							:					Union[str, Any]     , lowerCamelCase							:					List[Any]     , lowerCamelCase							:					Any   )						->     Optional[int]:



       """simple docstring"""







       __magic_name__			:		Any        =     torch.load(lowerCamelCase     , map_location='''cpu'''   )['''model_state_dict''']
       __magic_name__			:		Tuple        =     MraConfig.from_json_file(lowerCamelCase   )
       __magic_name__			:		List[Any]        =     MraForMaskedLM(lowerCamelCase   )

       __magic_name__			:		str        =     convert_checkpoint_helper(config.max_position_embeddings     , lowerCamelCase   )

       print(model.load_state_dict(lowerCamelCase   )   )
       model.eval()
       model.save_pretrained(lowerCamelCase   )

       print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""   )


if __name__ == "__main__":
 A									=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Mra pytorch checkpoint."""
 )
 parser.add_argument(
     """--config_file""",
     default=None,
     type=str,
     required=True,
     help="""The json file for Mra model config.""",
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 A									=						parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



"""simple docstring"""
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros







def       UpperCamelCase_					(     lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					Tuple   )						->     str:



       """simple docstring"""







       __magic_name__       ,							__magic_name__			:		Optional[Any]        =     image.shape
       __magic_name__			:		str        =     cols - block_size[1] + 1
       __magic_name__			:		int        =     rows - block_size[0] + 1
       __magic_name__			:		Optional[Any]        =     zeros((dst_height * dst_width, block_size[1] * block_size[0])   )
       __magic_name__			:		List[Any]        =     0
       for i in range(0     , lowerCamelCase   ):
              for j in range(0     , lowerCamelCase   ):
                     __magic_name__			:		List[Any]        =     ravel(image[i : i + block_size[0], j : j + block_size[1]]   )
                     __magic_name__			:		str        =     window
                     row += 1

       return image_array







def       UpperCamelCase_					(     lowerCamelCase							:					str     , lowerCamelCase							:					List[str]   )						->     Tuple:



       """simple docstring"""







       __magic_name__       ,							__magic_name__			:		List[str]        =     image.shape[0], image.shape[1]
       __magic_name__			:		Any        =     filter_kernel.shape[0]
       __magic_name__			:		Optional[Any]        =     k_size // 2
       # Pads image with the edge values of array.
       __magic_name__			:		Union[str, Any]        =     pad(lowerCamelCase     , lowerCamelCase     , mode='''edge'''   )

       # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
       __magic_name__			:		List[Any]        =     imacol(lowerCamelCase     , (k_size, k_size)   )

       #  turn the kernel into shape(k*k, 1)
       __magic_name__			:		Tuple        =     ravel(lowerCamelCase   )
       # reshape and get the dst image
       __magic_name__			:		int        =     dot(lowerCamelCase     , lowerCamelCase   ).reshape(lowerCamelCase     , lowerCamelCase   )
       return dst


if __name__ == "__main__":
 # read original image
 A									=						imread(R"""../image_data/lena.jpg""")
 # turn image in gray scale value
 A									=						cvtColor(img, COLOR_BGR2GRAY)
 # Laplace operator
 A									=						array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
 A									=						img_convolve(gray, Laplace_kernel).astype(uinta)
 imshow("""Laplacian""", out)
 waitKey(0)
