






import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


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

    return orig_key


def   _A       (    SCREAMING_SNAKE_CASE__  :     List[Any]					,  SCREAMING_SNAKE_CASE__  :     Union[str, Any]	):
    for key in orig_state_dict.copy().keys():
        UpperCamelCase	:Optional[Any]     							=       orig_state_dict.pop(SCREAMING_SNAKE_CASE__	)

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            UpperCamelCase	:Any     							=       val

    UpperCamelCase	:Optional[int]     							=       orig_state_dict['''cls.predictions.decoder.bias''']
    UpperCamelCase	:List[Any]     							=       torch.arange(SCREAMING_SNAKE_CASE__	).expand((1, -1)	) + 2

    return orig_state_dict


def   _A       (    SCREAMING_SNAKE_CASE__  :     Union[str, Any]					,  SCREAMING_SNAKE_CASE__  :     int					,  SCREAMING_SNAKE_CASE__  :     Optional[int]	):
    UpperCamelCase	:Union[str, Any]     							=       torch.load(SCREAMING_SNAKE_CASE__					,  map_location='''cpu'''	)['''model_state_dict''']
    UpperCamelCase	:Tuple     							=       MraConfig.from_json_file(SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:Optional[Any]     							=       MraForMaskedLM(SCREAMING_SNAKE_CASE__	)

    UpperCamelCase	:Any     							=       convert_checkpoint_helper(config.max_position_embeddings					,  SCREAMING_SNAKE_CASE__	)

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

    print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''	)


if __name__ == "__main__":
 __snake_case							 =  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."""
 )
 __snake_case							 =  parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)













import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

__snake_case							 =  0.2_5 + 0.0j
__snake_case							 =  -0.4 + 0.6j
__snake_case							 =  -0.1 + 0.651j
__snake_case							 =  -2.0
__snake_case							 =  56
__snake_case							 =  2.0
__snake_case							 =  6_66


def   _A       (    SCREAMING_SNAKE_CASE__  :     complex					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray	):
    return numpy.exp(SCREAMING_SNAKE_CASE__	) + c_parameter


def   _A       (    SCREAMING_SNAKE_CASE__  :     complex					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray	):
    return z_values * z_values + c_parameter


def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     int	):
    UpperCamelCase	:List[Any]     							=       numpy.linspace(-window_size					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:Any     							=       x.reshape((nb_pixels, 1)	)
    UpperCamelCase	:Tuple     							=       numpy.linspace(-window_size					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:int     							=       y.reshape((1, nb_pixels)	)
    return x + 1.0J * y


def   _A       (    SCREAMING_SNAKE_CASE__  :     Callable[[Any, numpy.ndarray], numpy.ndarray]					,  SCREAMING_SNAKE_CASE__  :     Any					,  SCREAMING_SNAKE_CASE__  :     int					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray					,  SCREAMING_SNAKE_CASE__  :     float | None = None					,  ):
    UpperCamelCase	:Dict     							=       z_a.astype('''complex64'''	)
    for _ in range(SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:int     							=       eval_function(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
        if infinity is not None:
            numpy.nan_to_num(SCREAMING_SNAKE_CASE__					,  copy=SCREAMING_SNAKE_CASE__					,  nan=SCREAMING_SNAKE_CASE__	)
            UpperCamelCase	:Tuple     							=       infinity
    return z_n


def   _A       (    SCREAMING_SNAKE_CASE__  :     str					,  SCREAMING_SNAKE_CASE__  :     Any					,  SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray					,  ):
    UpperCamelCase	:Any     							=       (abs(SCREAMING_SNAKE_CASE__	)).transpose()
    UpperCamelCase	:Any     							=       abs_z_final[::-1, :]
    pyplot.matshow(abs_z_final < escape_radius	)
    pyplot.title(F'''Julia set of ${function_label}$, $c={function_params}$'''	)
    pyplot.show()


def   _A       (    ):
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in multiply'''	)
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''invalid value encountered in multiply'''					,  )
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in absolute'''	)
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in exp'''	)


if __name__ == "__main__":
 __snake_case							 =  prepare_grid(window_size, nb_pixels)

 ignore_overflow_warnings()  # See file header for explanations

 __snake_case							 =  24
 __snake_case							 =  2 * abs(c_cauliflower) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_cauliflower,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_cauliflower, escape_radius, z_final)

 __snake_case							 =  64
 __snake_case							 =  2 * abs(c_polynomial_a) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_polynomial_a,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

 __snake_case							 =  1_61
 __snake_case							 =  2 * abs(c_polynomial_a) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_polynomial_a,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

 __snake_case							 =  12
 __snake_case							 =  1_0_0_0_0.0
 __snake_case							 =  iterate_function(
     eval_exponential,
     c_exponential,
     nb_iterations,
     z_a + 2,
     infinity=1.0E1_0,
 )
 show_results("""e^z+c""", c_exponential, escape_radius, z_final)





