"""simple docstring"""







from typing import Dict, Tuple, overload

import torch
import torch.types
from torch import nn

from . import residue_constants as rc
from .rigid_utils import Rigid, Rotation
from .tensor_utils import batched_gather



@overload
def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , lowercase_     :	None						)  ->							torch.Tensor:
   ...



@overload
def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor						)  ->							Tuple[torch.Tensor, torch.Tensor]:
   ...



def       A__    (	lowercase_     :	int      , lowercase_     :	Tuple      , lowercase_     :	Optional[int]						)  ->							int:
   __lowerCamelCase    		=					aatype == rc.restype_order['G']
   __lowerCamelCase    		=					rc.atom_order['CA']
   __lowerCamelCase    		=					rc.atom_order['CB']
   __lowerCamelCase    		=					torch.where(
       is_gly[..., None].expand(*((-1,) * len(is_gly.shape						))      , 3						)      , all_atom_positions[..., ca_idx, :]      , all_atom_positions[..., cb_idx, :]      , )

   if all_atom_masks is not None:
      __lowerCamelCase    		=					torch.where(
          lowercase_      , all_atom_masks[..., ca_idx]      , all_atom_masks[..., cb_idx]      , )
      return pseudo_beta, pseudo_beta_mask
   else:
      return pseudo_beta



def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	Dict[str, torch.Tensor]						)  ->							torch.Tensor:
   __lowerCamelCase    		=					batched_gather(
       lowercase_      , batch['residx_atom37_to_atom14']      , dim=-2      , no_batch_dims=len(atomaa.shape[:-2]						)      , )

   __lowerCamelCase    		=					atomaa_data * batch['atom37_atom_exists'][..., None]

   return atomaa_data



def       A__    (	lowercase_     :	Dict[str, torch.Tensor]						)  ->							torch.Tensor:
   __lowerCamelCase    		=					template_feats['template_aatype']
   __lowerCamelCase    		=					template_feats['template_torsion_angles_sin_cos']
   __lowerCamelCase    		=					template_feats['template_alt_torsion_angles_sin_cos']
   __lowerCamelCase    		=					template_feats['template_torsion_angles_mask']
   __lowerCamelCase    		=					torch.cat(
       [
           nn.functional.one_hot(lowercase_      , 22						),
           torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2]      , 14						),
           alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2]      , 14						),
           torsion_angles_mask,
       ]      , dim=-1      , )

   return template_angle_feat



def       A__    (	lowercase_     :	Dict[str, torch.Tensor]      , lowercase_     :	torch.types.Number      , lowercase_     :	torch.types.Number      , lowercase_     :	int      , lowercase_     :	bool = False      , lowercase_     :	float = 1e-20      , lowercase_     :	float = 1e8      , )  ->							torch.Tensor:
   __lowerCamelCase    		=					batch['template_pseudo_beta_mask']
   __lowerCamelCase    		=					template_mask[..., None] * template_mask[..., None, :]

   # Compute distogram (this seems to differ slightly from Alg. 5)
   __lowerCamelCase    		=					batch['template_pseudo_beta']
   __lowerCamelCase    		=					torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2      , dim=-1      , keepdim=lowercase_						)
   __lowerCamelCase    		=					torch.linspace(lowercase_      , lowercase_      , lowercase_      , device=tpb.device						) ** 2
   __lowerCamelCase    		=					torch.cat([lower[1:], lower.new_tensor([inf]						)]      , dim=-1						)
   __lowerCamelCase    		=					((dgram > lower) * (dgram < upper)).type(dgram.dtype						)

   __lowerCamelCase    		=					[dgram, template_mask_ad[..., None]]

   __lowerCamelCase    		=					nn.functional.one_hot(
       batch['template_aatype']      , rc.restype_num + 2      , )

   __lowerCamelCase    		=					batch['template_aatype'].shape[-1]
   to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]      , lowercase_      , -1      , -1						)						)
   to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]      , -1      , lowercase_      , -1						)						)

   __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					[rc.atom_order[a] for a in ['N', 'CA', 'C']]
   __lowerCamelCase    		=					Rigid.make_transform_from_reference(
       n_xyz=batch['template_all_atom_positions'][..., n, :]      , ca_xyz=batch['template_all_atom_positions'][..., ca, :]      , c_xyz=batch['template_all_atom_positions'][..., c, :]      , eps=lowercase_      , )
   __lowerCamelCase    		=					rigids.get_trans()[..., None, :, :]
   __lowerCamelCase    		=					rigids[..., None].invert_apply(lowercase_						)

   __lowerCamelCase    		=					torch.rsqrt(eps + torch.sum(rigid_vec**2      , dim=-1						)						)

   __lowerCamelCase    		=					batch['template_all_atom_mask']
   __lowerCamelCase    		=					t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
   __lowerCamelCase    		=					template_mask[..., None] * template_mask[..., None, :]

   __lowerCamelCase    		=					inv_distance_scalar * template_mask_ad
   __lowerCamelCase    		=					rigid_vec * inv_distance_scalar[..., None]

   if not use_unit_vector:
      __lowerCamelCase    		=					unit_vector * 0.0

   to_concat.extend(torch.unbind(unit_vector[..., None, :]      , dim=-1						)						)
   to_concat.append(template_mask_ad[..., None]						)

   __lowerCamelCase    		=					torch.cat(lowercase_      , dim=-1						)
   __lowerCamelCase    		=					act * template_mask_ad[..., None]

   return act



def       A__    (	lowercase_     :	Dict[str, torch.Tensor]						)  ->							torch.Tensor:
   __lowerCamelCase    		=					nn.functional.one_hot(batch['extra_msa']      , 23						)
   __lowerCamelCase    		=					[
       msa_ahot,
       batch['extra_has_deletion'].unsqueeze(-1						),
       batch['extra_deletion_value'].unsqueeze(-1						),
   ]
   return torch.cat(lowercase_      , dim=-1						)



def       A__    (	lowercase_     :	Rigid      , lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , )  ->							Rigid:
   # [*, N, 8, 4, 4]
   __lowerCamelCase    		=					rrgdf[aatype, ...]

   # [*, N, 8] transformations, i.e.
   #   One [*, N, 8, 3, 3] rotation matrix and
   #   One [*, N, 8, 3]    translation matrix
   __lowerCamelCase    		=					r.from_tensor_axa(lowercase_						)

   __lowerCamelCase    		=					alpha.new_zeros((*((1,) * len(alpha.shape[:-1]						)), 2)						)
   __lowerCamelCase    		=					1

   # [*, N, 8, 2]
   __lowerCamelCase    		=					torch.cat([bb_rot.expand(*alpha.shape[:-2]      , -1      , -1						), alpha]      , dim=-2						)

   # [*, N, 8, 3, 3]
   # Produces rotation matrices of the form:
   # [
   #   [1, 0  , 0  ],
   #   [0, a_2,-a_1],
   #   [0, a_1, a_2]
   # ]
   # This follows the original code rather than the supplement, which uses
   # different indices.

   __lowerCamelCase    		=					alpha.new_zeros(default_r.get_rots().get_rot_mats().shape						)
   __lowerCamelCase    		=					1
   __lowerCamelCase    		=					alpha[..., 1]
   __lowerCamelCase    		=					-alpha[..., 0]
   __lowerCamelCase    		=					alpha

   __lowerCamelCase    		=					default_r.compose(Rigid(Rotation(rot_mats=lowercase_						)      , lowercase_						)						)

   __lowerCamelCase    		=					all_frames[..., 5]
   __lowerCamelCase    		=					all_frames[..., 6]
   __lowerCamelCase    		=					all_frames[..., 7]

   __lowerCamelCase    		=					all_frames[..., 4]
   __lowerCamelCase    		=					chia_frame_to_bb.compose(lowercase_						)
   __lowerCamelCase    		=					chia_frame_to_bb.compose(lowercase_						)
   __lowerCamelCase    		=					chia_frame_to_bb.compose(lowercase_						)

   __lowerCamelCase    		=					Rigid.cat(
       [
           all_frames[..., :5],
           chia_frame_to_bb.unsqueeze(-1						),
           chia_frame_to_bb.unsqueeze(-1						),
           chia_frame_to_bb.unsqueeze(-1						),
       ]      , dim=-1      , )

   __lowerCamelCase    		=					r[..., None].compose(lowercase_						)

   return all_frames_to_global



def       A__    (	lowercase_     :	Rigid      , lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , )  ->							torch.Tensor:
   # [*, N, 14]
   __lowerCamelCase    		=					group_idx[aatype, ...]

   # [*, N, 14, 8]
   __lowerCamelCase    		=					nn.functional.one_hot(
       lowercase_      , num_classes=default_frames.shape[-3]      , )

   # [*, N, 14, 8]
   __lowerCamelCase    		=					r[..., None, :] * group_mask_one_hot

   # [*, N, 14]
   __lowerCamelCase    		=					t_atoms_to_global.map_tensor_fn(lambda lowercase_						: torch.sum(lowercase_      , dim=-1						)						)

   # [*, N, 14, 1]
   __lowerCamelCase    		=					atom_mask[aatype, ...].unsqueeze(-1						)

   # [*, N, 14, 3]
   __lowerCamelCase    		=					lit_positions[aatype, ...]
   __lowerCamelCase    		=					t_atoms_to_global.apply(lowercase_						)
   __lowerCamelCase    		=					pred_positions * atom_mask

   return pred_positions













import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.9.0"):
						raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
A_        =  logging.get_logger(__name__)

A_        =  "Hello world! cécé herlolip"





def  __UpperCamelCase   (     a,  a,  a)			->Any:
					lowerCamelCase__      =							FairseqRobertaModel.from_pretrained(a)
					roberta.eval()  # disable dropout
					lowerCamelCase__      =							roberta.model.encoder.sentence_encoder
					lowerCamelCase__      =							RobertaConfig(
					    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings,  hidden_size=roberta.args.encoder_embed_dim,  num_hidden_layers=roberta.args.encoder_layers,  num_attention_heads=roberta.args.encoder_attention_heads,  intermediate_size=roberta.args.encoder_ffn_embed_dim,  max_position_embeddings=514,  type_vocab_size=1,  layer_norm_eps=1E-5,  )
					if classification_head:
										lowerCamelCase__      =							roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
					print("Our BERT config:",  a)

					lowerCamelCase__      =							RobertaForSequenceClassification(a) if classification_head else RobertaForMaskedLM(a)
					model.eval()

					# Now let's copy all the weights.
					# Embeddings
					lowerCamelCase__      =							roberta_sent_encoder.embed_tokens.weight
					lowerCamelCase__      =							roberta_sent_encoder.embed_positions.weight
					lowerCamelCase__      =							torch.zeros_like(
					    model.roberta.embeddings.token_type_embeddings.weight)  # just zero them out b/c RoBERTa doesn't use them.
					lowerCamelCase__      =							roberta_sent_encoder.emb_layer_norm.weight
					lowerCamelCase__      =							roberta_sent_encoder.emb_layer_norm.bias

					for i in range(config.num_hidden_layers):
										# Encoder: start of layer
										lowerCamelCase__      =							model.roberta.encoder.layer[i]
										lowerCamelCase__      =							roberta_sent_encoder.layers[i]

										# self attention
										lowerCamelCase__      =							layer.attention.self
										assert (
										    roberta_layer.self_attn.k_proj.weight.data.shape
										    == roberta_layer.self_attn.q_proj.weight.data.shape
										    == roberta_layer.self_attn.v_proj.weight.data.shape
										    == torch.Size((config.hidden_size, config.hidden_size))
										)

										lowerCamelCase__      =							roberta_layer.self_attn.q_proj.weight
										lowerCamelCase__      =							roberta_layer.self_attn.q_proj.bias
										lowerCamelCase__      =							roberta_layer.self_attn.k_proj.weight
										lowerCamelCase__      =							roberta_layer.self_attn.k_proj.bias
										lowerCamelCase__      =							roberta_layer.self_attn.v_proj.weight
										lowerCamelCase__      =							roberta_layer.self_attn.v_proj.bias

										# self-attention output
										lowerCamelCase__      =							layer.attention.output
										assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
										lowerCamelCase__      =							roberta_layer.self_attn.out_proj.weight
										lowerCamelCase__      =							roberta_layer.self_attn.out_proj.bias
										lowerCamelCase__      =							roberta_layer.self_attn_layer_norm.weight
										lowerCamelCase__      =							roberta_layer.self_attn_layer_norm.bias

										# intermediate
										lowerCamelCase__      =							layer.intermediate
										assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
										lowerCamelCase__      =							roberta_layer.fca.weight
										lowerCamelCase__      =							roberta_layer.fca.bias

										# output
										lowerCamelCase__      =							layer.output
										assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
										lowerCamelCase__      =							roberta_layer.fca.weight
										lowerCamelCase__      =							roberta_layer.fca.bias
										lowerCamelCase__      =							roberta_layer.final_layer_norm.weight
										lowerCamelCase__      =							roberta_layer.final_layer_norm.bias
										# end of layer

					if classification_head:
										lowerCamelCase__      =							roberta.model.classification_heads["mnli"].dense.weight
										lowerCamelCase__      =							roberta.model.classification_heads["mnli"].dense.bias
										lowerCamelCase__      =							roberta.model.classification_heads["mnli"].out_proj.weight
										lowerCamelCase__      =							roberta.model.classification_heads["mnli"].out_proj.bias
					else:
										# LM Head
										lowerCamelCase__      =							roberta.model.encoder.lm_head.dense.weight
										lowerCamelCase__      =							roberta.model.encoder.lm_head.dense.bias
										lowerCamelCase__      =							roberta.model.encoder.lm_head.layer_norm.weight
										lowerCamelCase__      =							roberta.model.encoder.lm_head.layer_norm.bias
										lowerCamelCase__      =							roberta.model.encoder.lm_head.weight
										lowerCamelCase__      =							roberta.model.encoder.lm_head.bias

					# Let's check that we get the same results.
					lowerCamelCase__      =							roberta.encode(a).unsqueeze(0)  # batch of size 1

					lowerCamelCase__      =							model(a)[0]
					if classification_head:
										lowerCamelCase__      =							roberta.model.classification_heads["mnli"](roberta.extract_features(a))
					else:
										lowerCamelCase__      =							roberta.model(a)[0]
					print(our_output.shape,  their_output.shape)
					lowerCamelCase__      =							torch.max(torch.abs(our_output - their_output)).item()
					print(f"max_absolute_diff = {max_absolute_diff}")  # ~ 1e-7
					lowerCamelCase__      =							torch.allclose(a,  a,  atol=1E-3)
					print("Do both models output the same tensors?",  "🔥" if success else "💩")
					if not success:
										raise Exception("Something went wRoNg")

					pathlib.Path(a).mkdir(parents=a,  exist_ok=a)
					print(f"Saving model to {pytorch_dump_folder_path}")
					model.save_pretrained(a)


if __name__ == "__main__":
						A_        =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--roberta_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						parser.add_argument(
						    "--classification_head", action="store_true", help="Whether to convert a final classification head."
						)
						A_        =  parser.parse_args()
						convert_roberta_checkpoint_to_pytorch(
						    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)

