
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM


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

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

		return orig_key


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

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

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

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

		return orig_state_dict


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

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

		UpperCamelCase		=     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__":
					_UpperCAmelCase					: 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."
					)
					_UpperCAmelCase					: 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)
