



from typing import Dict, Optional, Tuple

import torch

def       A_   (						__a  :					torch.Tensor		):

				"""simple docstring"""
				a__					   =		boundaries[1] - boundaries[0]
				a__					   =		boundaries + step / 2
				a__					   =		torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1		)]			,		dim=0		)
				return bin_centers

def       A_   (						__a  :					torch.Tensor			,		__a  :					torch.Tensor			,		):

				"""simple docstring"""
				a__					   =		_calculate_bin_centers(__a		)
				return (
				    torch.sum(aligned_distance_error_probs * bin_centers			,		dim=-1		),
				    bin_centers[-1],
				)

def       A_   (						__a  :					torch.Tensor			,		__a  :					int = 31			,		__a  :					int = 64			,		**__a  :					Any			,		):

				"""simple docstring"""
				a__					   =		torch.linspace(0			,		__a			,		steps=(no_bins - 1)			,		device=logits.device		)

				a__					   =		torch.nn.functional.softmax(__a			,		dim=-1		)
				a__   ,      a__					   =		_calculate_expected_aligned_error(
				    alignment_confidence_breaks=__a			,		aligned_distance_error_probs=__a			,		)

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




def       A_   (						__a  :					torch.Tensor			,		__a  :					Optional[torch.Tensor] = None			,		__a  :					int = 31			,		__a  :					int = 64			,		__a  :					float = 1e-8			,		**__a  :					str			,		):

				"""simple docstring"""
				if residue_weights is None:
								a__					   =		logits.new_ones(logits.shape[-2]		)

				a__					   =		torch.linspace(0			,		__a			,		steps=(no_bins - 1)			,		device=logits.device		)

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

				a__					   =		1.2_4 * (clipped_n - 15) ** (1.0 / 3) - 1.8

				a__					   =		torch.nn.functional.softmax(__a			,		dim=-1		)

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

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

				a__					   =		per_alignment * residue_weights

				a__					   =		(weighted == torch.max(__a		)).nonzero()[0]
				return per_alignment[tuple(__a		)]






import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()




def    UpperCAmelCase					(  lowercase__						:					int				,					lowercase__						:					str				,					lowercase__						:					Union[str, Any]				,					lowercase__						:					Optional[int]				,					lowercase__						:					Optional[Any]	):



			'''simple docstring'''





			a__					=  BertConfig.from_pretrained(
			    """bert-large-cased"""				,					vocab_size=lowercase__				,					max_position_embeddings=512				,					is_decoder=lowercase__				,					add_cross_attention=lowercase__				,					)
			a__					=  bert_config.to_dict()
			del bert_config_dict["type_vocab_size"]
			a__					=  BertGenerationConfig(**lowercase__	)
			if is_encoder:
						a__					=  BertGenerationEncoder(lowercase__	)
			else:
						a__					=  BertGenerationDecoder(lowercase__	)
			print(f'Building PyTorch model from configuration: {config}'	)

			# Load weights from tf checkpoint
			load_tf_weights_in_bert_generation(
			    lowercase__				,					lowercase__				,					model_class="""bert"""				,					is_encoder_named_decoder=lowercase__				,					is_encoder=lowercase__				,					)

			# Save pytorch-model
			print(f'Save PyTorch model and config to {pytorch_dump_path}'	)
			model.save_pretrained(lowercase__	)


if __name__ == "__main__":
	_lowercase				:					str     					=argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--tf_hub_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
	)
	parser.add_argument(
	    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	parser.add_argument(
	    """--is_encoder_named_decoder""",
	    action="""store_true""",
	    help="""If decoder has to be renamed to encoder in PyTorch model.""",
	)
	parser.add_argument("""--is_encoder""", action="""store_true""", help="""If model is an encoder.""")
	parser.add_argument("""--vocab_size""", default=5_03_58, type=int, help="""Vocab size of model""")
	_lowercase				:					List[str]     					=parser.parse_args()
	convert_tf_checkpoint_to_pytorch(
	    args.tf_hub_path,
	    args.pytorch_dump_path,
	    args.is_encoder_named_decoder,
	    args.vocab_size,
	    is_encoder=args.is_encoder,
	)

