

"""simple docstring"""





import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM

def 						__SCREAMING_SNAKE_CASE						(   A_				):
	if "model" in orig_key:
		lowerCAmelCase__			:		List[str]						       =			orig_key.replace('''model.'''		,  ''''''				)
	if "norm1" in orig_key:
		lowerCAmelCase__			:		Any						       =			orig_key.replace('''norm1'''		,  '''attention.output.LayerNorm'''				)
	if "norm2" in orig_key:
		lowerCAmelCase__			:		Union[str, Any]						       =			orig_key.replace('''norm2'''		,  '''output.LayerNorm'''				)
	if "norm" in orig_key:
		lowerCAmelCase__			:		Dict						       =			orig_key.replace('''norm'''		,  '''LayerNorm'''				)
	if "transformer" in orig_key:
		lowerCAmelCase__			:		Union[str, Any]						       =			orig_key.split('''.'''				)[0].split('''_'''				)[-1]
		lowerCAmelCase__			:		List[str]						       =			orig_key.replace(f'transformer_{layer_num}'		,  f'encoder.layer.{layer_num}'				)
	if "mha.attn" in orig_key:
		lowerCAmelCase__			:		str						       =			orig_key.replace('''mha.attn'''		,  '''attention.self'''				)
	if "mha" in orig_key:
		lowerCAmelCase__			:		Optional[int]						       =			orig_key.replace('''mha'''		,  '''attention'''				)
	if "W_q" in orig_key:
		lowerCAmelCase__			:		List[str]						       =			orig_key.replace('''W_q'''		,  '''self.query'''				)
	if "W_k" in orig_key:
		lowerCAmelCase__			:		Tuple						       =			orig_key.replace('''W_k'''		,  '''self.key'''				)
	if "W_v" in orig_key:
		lowerCAmelCase__			:		Any						       =			orig_key.replace('''W_v'''		,  '''self.value'''				)
	if "ff1" in orig_key:
		lowerCAmelCase__			:		Union[str, Any]						       =			orig_key.replace('''ff1'''		,  '''intermediate.dense'''				)
	if "ff2" in orig_key:
		lowerCAmelCase__			:		Optional[Any]						       =			orig_key.replace('''ff2'''		,  '''output.dense'''				)
	if "ff" in orig_key:
		lowerCAmelCase__			:		Dict						       =			orig_key.replace('''ff'''		,  '''output.dense'''				)
	if "mlm_class" in orig_key:
		lowerCAmelCase__			:		Tuple						       =			orig_key.replace('''mlm.mlm_class'''		,  '''cls.predictions.decoder'''				)
	if "mlm" in orig_key:
		lowerCAmelCase__			:		Optional[int]						       =			orig_key.replace('''mlm'''		,  '''cls.predictions.transform'''				)
	if "cls" not in orig_key:
		lowerCAmelCase__			:		Dict						       =			'''nystromformer.''' + orig_key

	return orig_key

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_				):
	for key in orig_state_dict.copy().keys():
		lowerCAmelCase__			:		Optional[Any]						       =			orig_state_dict.pop(A_				)

		if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
			continue
		else:
			lowerCAmelCase__			:		Any						       =			val

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

	return orig_state_dict

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_				):
	lowerCAmelCase__			:		Optional[Any]						       =			torch.load(A_		,  map_location='''cpu'''				)['''model_state_dict''']
	lowerCAmelCase__			:		str						       =			NystromformerConfig.from_json_file(A_				)
	lowerCAmelCase__			:		Any						       =			NystromformerForMaskedLM(A_				)

	lowerCAmelCase__			:		int						       =			convert_checkpoint_helper(A_		,  A_				)

	model.load_state_dict(A_				)
	model.eval()
	model.save_pretrained(A_				)

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


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


"""simple docstring"""





import random
import sys

__UpperCamelCase						:   Tuple							   =	'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''

def 						__SCREAMING_SNAKE_CASE						(   ):
	lowerCAmelCase__			:		List[str]						       =			input('''Enter message: '''				)
	lowerCAmelCase__			:		Optional[int]						       =			'''LFWOAYUISVKMNXPBDCRJTQEGHZ'''
	lowerCAmelCase__			:		List[Any]						       =			input('''Encrypt/Decrypt [e/d]: '''				)

	check_valid_key(A_				)

	if resp.lower().startswith('''e'''				):
		lowerCAmelCase__			:		Tuple						       =			'''encrypt'''
		lowerCAmelCase__			:		Union[str, Any]						       =			encrypt_message(A_		,  A_				)
	elif resp.lower().startswith('''d'''				):
		lowerCAmelCase__			:		Dict						       =			'''decrypt'''
		lowerCAmelCase__			:		str						       =			decrypt_message(A_		,  A_				)

	print(f'\n{mode.title()}ion: \n{translated}'				)

def 						__SCREAMING_SNAKE_CASE						(   A_				):
	lowerCAmelCase__			:		str						       =			list(A_				)
	lowerCAmelCase__			:		int						       =			list(A_				)
	key_list.sort()
	letters_list.sort()

	if key_list != letters_list:
		sys.exit('''Error in the key or symbol set.'''				)

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_				):
	return translate_message(A_		,  A_		,  '''encrypt'''				)

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_				):
	return translate_message(A_		,  A_		,  '''decrypt'''				)

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_				):
	lowerCAmelCase__			:		str						       =			''''''
	lowerCAmelCase__			:		List[str]						       =			LETTERS
	lowerCAmelCase__			:		Optional[int]						       =			key

	if mode == "decrypt":
		lowerCAmelCase__       ,lowerCAmelCase__			:		int						       =			chars_b, chars_a

	for symbol in message:
		if symbol.upper() in chars_a:
			lowerCAmelCase__			:		int						       =			chars_a.find(symbol.upper()				)
			if symbol.isupper():
				translated += chars_b[sym_index].upper()
			else:
				translated += chars_b[sym_index].lower()
		else:
			translated += symbol

	return translated

def 						__SCREAMING_SNAKE_CASE						(   ):
	lowerCAmelCase__			:		Optional[int]						       =			list(A_				)
	random.shuffle(A_				)
	return "".join(A_				)


if __name__ == "__main__":
							main()




