
import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM
def       UpperCAmelCase__							(    __magic_name__  :       int					):


					'''simple docstring'''

					if "model" in orig_key:
										lowerCAmelCase						:   Any							=       orig_key.replace('''model.'''							,					''''''					)
					if "norm1" in orig_key:
										lowerCAmelCase						:   Optional[int]							=       orig_key.replace('''norm1'''							,					'''attention.output.LayerNorm'''					)
					if "norm2" in orig_key:
										lowerCAmelCase						:   int							=       orig_key.replace('''norm2'''							,					'''output.LayerNorm'''					)
					if "norm" in orig_key:
										lowerCAmelCase						:   Dict							=       orig_key.replace('''norm'''							,					'''LayerNorm'''					)
					if "transformer" in orig_key:
										lowerCAmelCase						:   int							=       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						:   Tuple							=       orig_key.replace('''mha.attn'''							,					'''attention.self'''					)
					if "mha" in orig_key:
										lowerCAmelCase						:   List[str]							=       orig_key.replace('''mha'''							,					'''attention'''					)
					if "W_q" in orig_key:
										lowerCAmelCase						:   Tuple							=       orig_key.replace('''W_q'''							,					'''self.query'''					)
					if "W_k" in orig_key:
										lowerCAmelCase						:   Union[str, Any]							=       orig_key.replace('''W_k'''							,					'''self.key'''					)
					if "W_v" in orig_key:
										lowerCAmelCase						:   Optional[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						:   List[str]							=       orig_key.replace('''ff2'''							,					'''output.dense'''					)
					if "ff" in orig_key:
										lowerCAmelCase						:   Optional[int]							=       orig_key.replace('''ff'''							,					'''output.dense'''					)
					if "mlm_class" in orig_key:
										lowerCAmelCase						:   Dict							=       orig_key.replace('''mlm.mlm_class'''							,					'''cls.predictions.decoder'''					)
					if "mlm" in orig_key:
										lowerCAmelCase						:   Optional[Any]							=       orig_key.replace('''mlm'''							,					'''cls.predictions.transform'''					)
					if "cls" not in orig_key:
										lowerCAmelCase						:   List[str]							=       '''nystromformer.''' + orig_key

					return orig_key
def       UpperCAmelCase__							(    __magic_name__  :       Optional[Any]							,					__magic_name__  :       str					):


					'''simple docstring'''

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

										if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
															continue
										else:
															lowerCAmelCase						:   Optional[Any]							=       val

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

					return orig_state_dict
def       UpperCAmelCase__							(    __magic_name__  :       List[str]							,					__magic_name__  :       str							,					__magic_name__  :       List[str]					):


					'''simple docstring'''

					lowerCAmelCase						:   Any							=       torch.load(__magic_name__							,					map_location='''cpu'''					)['''model_state_dict''']
					lowerCAmelCase						:   Dict							=       NystromformerConfig.from_json_file(__magic_name__					)
					lowerCAmelCase						:   Dict							=       NystromformerForMaskedLM(__magic_name__					)

					lowerCAmelCase						:   Tuple							=       convert_checkpoint_helper(__magic_name__							,					__magic_name__					)

					model.load_state_dict(__magic_name__					)
					model.eval()
					model.save_pretrained(__magic_name__					)

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


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE     :				List[str]             =       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.'
		)
		__SCREAMING_SNAKE_CASE     :				Any             =       parser.parse_args()
		convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


def       UpperCAmelCase__							(    __magic_name__  :       Union[str, Any]							,					__magic_name__  :       int					):


					'''simple docstring'''

					sequence.append(__magic_name__					)

					lowerCAmelCase						:   Tuple							=       0
					while sequence[index] != target:
										index += 1

					sequence.pop()

					if index == len(__magic_name__					):
										return None

					return index


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE     :				Dict             =       input('Enter numbers separated by comma:\n').strip()
		__SCREAMING_SNAKE_CASE     :				int             =       [int(item) for item in user_input.split(',')]

		__SCREAMING_SNAKE_CASE     :				str             =       input('Enter a single number to be found in the list:\n')
		__SCREAMING_SNAKE_CASE     :				Tuple             =       int(target_input)
		__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       sentinel_linear_search(sequence, target)
		if result is not None:
				print(f"""{target} found at positions: {result}""")
		else:
				print('Not found')
