
"""simple docstring"""


import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM



def 					_lowerCamelCase(__UpperCamelCase )       ->				Dict:







	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			="""nystromformer.""" + orig_key

	return orig_key



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase )       ->				Union[str, Any]:







	for key in orig_state_dict.copy().keys():
		_lowerCAmelCase			=orig_state_dict.pop(lowercase_ )

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

	_lowerCAmelCase			=orig_state_dict["""cls.predictions.decoder.bias"""]
	_lowerCAmelCase			=(
	    torch.arange(config.max_position_embeddings ).expand((1, -1) ) + 2
	)

	return orig_state_dict



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase )       ->				Dict:
	_lowerCAmelCase			=torch.load(lowercase_					,				map_location="""cpu""" )["""model_state_dict"""]
	_lowerCAmelCase			=NystromformerConfig.from_json_file(lowercase_ )
	_lowerCAmelCase			=NystromformerForMaskedLM(lowercase_ )

	_lowerCAmelCase			=convert_checkpoint_helper(lowercase_					,				lowercase_ )

	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__":
			__A 		= 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.'
			)
			__A 		= parser.parse_args()
			convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)

"""simple docstring"""


from typing import Dict, Optional, Tuple

import torch



def 					_lowerCamelCase(__UpperCamelCase )       ->				torch.Tensor:
	_lowerCAmelCase			=boundaries[1] - boundaries[0]
	_lowerCAmelCase			=boundaries + step / 2
	_lowerCAmelCase			=torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1 )]					,				dim=0 )
	return bin_centers



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				)       ->				Tuple[torch.Tensor, torch.Tensor]:
	_lowerCAmelCase			=_calculate_bin_centers(__UpperCamelCase )
	return (
	    torch.sum(aligned_distance_error_probs * bin_centers					,				dim=-1 ),
	    bin_centers[-1],
	)



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase = 31					,				__UpperCamelCase = 64					,				**__UpperCamelCase					,				)       ->				Dict[str, torch.Tensor]:
	_lowerCAmelCase			=torch.linspace(0					,				__UpperCamelCase					,				steps=(no_bins - 1)					,				device=logits.device )

	_lowerCAmelCase			=torch.nn.functional.softmax(__UpperCamelCase					,				dim=-1 )
	_lowerCAmelCase		,							_lowerCAmelCase			=_calculate_expected_aligned_error(
	    alignment_confidence_breaks=__UpperCamelCase					,				aligned_distance_error_probs=__UpperCamelCase					,				)

	return {
	    "aligned_confidence_probs": aligned_confidence_probs,
	    "predicted_aligned_error": predicted_aligned_error,
	    "max_predicted_aligned_error": max_predicted_aligned_error,
	}



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase = None					,				__UpperCamelCase = 31					,				__UpperCamelCase = 64					,				__UpperCamelCase = 1E-8					,				**__UpperCamelCase					,				)       ->				torch.Tensor:
	if residue_weights is None:
		_lowerCAmelCase			=logits.new_ones(logits.shape[-2] )

	_lowerCAmelCase			=torch.linspace(0					,				__UpperCamelCase					,				steps=(no_bins - 1)					,				device=logits.device )

	_lowerCAmelCase			=_calculate_bin_centers(__UpperCamelCase )
	torch.sum(__UpperCamelCase )
	_lowerCAmelCase			=logits.shape[-2]
	_lowerCAmelCase			=max(__UpperCamelCase					,				19 )

	_lowerCAmelCase			=1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

	_lowerCAmelCase			=torch.nn.functional.softmax(__UpperCamelCase					,				dim=-1 )

	_lowerCAmelCase			=1.0 / (1 + (bin_centers**2) / (da**2))
	_lowerCAmelCase			=torch.sum(probs * tm_per_bin					,				dim=-1 )

	_lowerCAmelCase			=residue_weights / (eps + residue_weights.sum())
	_lowerCAmelCase			=torch.sum(predicted_tm_term * normed_residue_mask					,				dim=-1 )

	_lowerCAmelCase			=per_alignment * residue_weights

	_lowerCAmelCase			=(weighted == torch.max(__UpperCamelCase )).nonzero()[0]
	return per_alignment[tuple(__UpperCamelCase )]



