




import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Union[str, Any]     ):


      '''simple docstring'''



      if "model" in orig_key:
            lowerCamelCase_		     =    orig_key.replace('model.'	,							''     )
      if "norm1" in orig_key:
            lowerCamelCase_		     =    orig_key.replace('norm1'	,							'attention.output.LayerNorm'     )
      if "norm2" in orig_key:
            lowerCamelCase_		     =    orig_key.replace('norm2'	,							'output.LayerNorm'     )
      if "norm" in orig_key:
            lowerCamelCase_		     =    orig_key.replace('norm'	,							'LayerNorm'     )
      if "transformer" in orig_key:
            lowerCamelCase_		     =    orig_key.split('.'     )[0].split('_'     )[-1]
            lowerCamelCase_		     =    orig_key.replace(f"""transformer_{layer_num}"""	,							f"""encoder.layer.{layer_num}"""     )
      if "mha.attn" in orig_key:
            lowerCamelCase_		     =    orig_key.replace('mha.attn'	,							'attention.self'     )
      if "mha" in orig_key:
            lowerCamelCase_		     =    orig_key.replace('mha'	,							'attention'     )
      if "W_q" in orig_key:
            lowerCamelCase_		     =    orig_key.replace('W_q'	,							'self.query'     )
      if "W_k" in orig_key:
            lowerCamelCase_		     =    orig_key.replace('W_k'	,							'self.key'     )
      if "W_v" in orig_key:
            lowerCamelCase_		     =    orig_key.replace('W_v'	,							'self.value'     )
      if "ff1" in orig_key:
            lowerCamelCase_		     =    orig_key.replace('ff1'	,							'intermediate.dense'     )
      if "ff2" in orig_key:
            lowerCamelCase_		     =    orig_key.replace('ff2'	,							'output.dense'     )
      if "ff" in orig_key:
            lowerCamelCase_		     =    orig_key.replace('ff'	,							'output.dense'     )
      if "mlm_class" in orig_key:
            lowerCamelCase_		     =    orig_key.replace('mlm.mlm_class'	,							'cls.predictions.decoder'     )
      if "mlm" in orig_key:
            lowerCamelCase_		     =    orig_key.replace('mlm'	,							'cls.predictions.transform'     )
      if "cls" not in orig_key:
            lowerCamelCase_		     =    'yoso.' + orig_key

      return orig_key

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Any	,							lowercase	:       Optional[int]     ):


      '''simple docstring'''



      for key in orig_state_dict.copy().keys():
            lowerCamelCase_		     =    orig_state_dict.pop(lowercase     )

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

      lowerCamelCase_		     =    orig_state_dict['cls.predictions.decoder.bias']
      lowerCamelCase_		     =    torch.arange(lowercase     ).expand((1, -1)     ) + 2

      return orig_state_dict

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Tuple	,							lowercase	:       Tuple	,							lowercase	:       int     ):


      '''simple docstring'''



      lowerCamelCase_		     =    torch.load(lowercase	,							map_location='cpu'     )['model_state_dict']
      lowerCamelCase_		     =    YosoConfig.from_json_file(lowercase     )
      lowerCamelCase_		     =    YosoForMaskedLM(lowercase     )

      lowerCamelCase_		     =    convert_checkpoint_helper(config.max_position_embeddings	,							lowercase     )

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

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


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





from random import randint
from tempfile import TemporaryFile

import numpy as np


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  str:
		'''simple docstring'''

		UpperCamelCase		=     0
		if start < end:
				UpperCamelCase		=     randint(lowercase    ,       lowercase )
				UpperCamelCase		=     a[end]
				UpperCamelCase		=     a[pivot]
				UpperCamelCase		=     temp

				UpperCamelCase	,  UpperCamelCase		=     _in_place_partition(lowercase    ,       lowercase    ,       lowercase )
				count += _in_place_quick_sort(lowercase    ,       lowercase    ,       p - 1 )
				count += _in_place_quick_sort(lowercase    ,       p + 1    ,       lowercase )
		return count


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  int:
		'''simple docstring'''

		UpperCamelCase		=     0
		UpperCamelCase		=     randint(lowercase    ,       lowercase )
		UpperCamelCase		=     a[end]
		UpperCamelCase		=     a[pivot]
		UpperCamelCase		=     temp
		UpperCamelCase		=     start - 1
		for index in range(lowercase    ,       lowercase ):
				count += 1
				if a[index] < a[end]:  # check if current val is less than pivot value
						UpperCamelCase		=     new_pivot_index + 1
						UpperCamelCase		=     a[new_pivot_index]
						UpperCamelCase		=     a[index]
						UpperCamelCase		=     temp

		UpperCamelCase		=     a[new_pivot_index + 1]
		UpperCamelCase		=     a[end]
		UpperCamelCase		=     temp
		return new_pivot_index + 1, count


_UpperCAmelCase					: Union[str, Any]      =       TemporaryFile()
_UpperCAmelCase					: List[Any]      =       100  # 1000 elements are to be sorted


_UpperCAmelCase   ,_UpperCAmelCase					: Any      =       0, 1  # mean and standard deviation
_UpperCAmelCase					: Any      =       np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)


outfile.seek(0)  # using the same array
_UpperCAmelCase					: Any      =       np.load(outfile)
_UpperCAmelCase					: str      =       len(M) - 1
_UpperCAmelCase					: List[str]      =       _in_place_quick_sort(M, 0, r)

print(
    "No of Comparisons for 100 elements selected from a standard normal distribution"
    "is :"
)


print(z)
