"""simple docstring"""
import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


SCREAMING_SNAKE_CASE__      						=  ["bart.large", "bart.large.mnli", "bart.large.cnn", "bart_xsum/model.pt"]
SCREAMING_SNAKE_CASE__      						=  {"bart.large": BartModel, "bart.large.mnli": BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse("0.9.0"):
     raise Exception("requires fairseq >= 0.9.0")


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

SCREAMING_SNAKE_CASE__      						=  " Hello world! cécé herlolip"

SCREAMING_SNAKE_CASE__      						=  [
    ("model.classification_heads.mnli.dense.weight", "classification_head.dense.weight"),
    ("model.classification_heads.mnli.dense.bias", "classification_head.dense.bias"),
    ("model.classification_heads.mnli.out_proj.weight", "classification_head.out_proj.weight"),
    ("model.classification_heads.mnli.out_proj.bias", "classification_head.out_proj.bias"),
]





def    lowerCAmelCase__    (			_UpperCamelCase       :				List[Any]					)					->				Optional[Any]:





      """simple docstring"""






      snake_case								=			[
          'encoder.version',
          'decoder.version',
          'model.encoder.version',
          'model.decoder.version',
          '_float_tensor',
      ]
      for k in ignore_keys:
            state_dict.pop(_UpperCamelCase ,			_UpperCamelCase					)





def    lowerCAmelCase__    (			_UpperCamelCase       :				List[Any] ,			_UpperCamelCase       :				Dict ,			_UpperCamelCase       :				Tuple					)					->				Any:





      """simple docstring"""






      snake_case								=			dct.pop(_UpperCamelCase					)
      snake_case								=			val





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[Any]					)					->				Dict:





      """simple docstring"""






      snake_case								=			torch.load(_UpperCamelCase ,			map_location='cpu'					)
      snake_case								=			torch.hub.load('pytorch/fairseq' ,			'bart.large.cnn'					).eval()
      hub_interface.model.load_state_dict(sd['model']					)
      return hub_interface





def    lowerCAmelCase__    (			_UpperCamelCase       :				str					)					->				str:





      """simple docstring"""






      snake_case					,snake_case								=			emb.weight.shape
      snake_case								=			nn.Linear(_UpperCamelCase ,			_UpperCamelCase ,			bias=_UpperCamelCase					)
      snake_case								=			emb.weight.data
      return lin_layer





@torch.no_grad()
def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[Any] ,			_UpperCamelCase       :				Tuple ,			_UpperCamelCase       :				Tuple=None					)					->				Tuple:





      """simple docstring"""






      if not os.path.exists(_UpperCamelCase					):
            snake_case								=			torch.hub.load('pytorch/fairseq' ,			_UpperCamelCase					).eval()
      else:
            snake_case								=			load_xsum_checkpoint(_UpperCamelCase					)

      bart.model.upgrade_state_dict(bart.model.state_dict()					)
      if hf_checkpoint_name is None:
            snake_case								=			checkpoint_path.replace('.' ,			'-'					)
      snake_case								=			BartConfig.from_pretrained(_UpperCamelCase					)
      snake_case								=			bart.encode(_UpperCamelCase					).unsqueeze(0					)
      snake_case								=			BartTokenizer.from_pretrained(_UpperCamelCase					).encode(_UpperCamelCase ,			return_tensors='pt'					).unsqueeze(0					)
      if not torch.eq(_UpperCamelCase ,			_UpperCamelCase					).all():
            raise ValueError(
                f"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"""					)

      if checkpoint_path == "bart.large.mnli":
            snake_case								=			bart.state_dict()
            remove_ignore_keys_(_UpperCamelCase					)
            snake_case								=			state_dict['model.decoder.embed_tokens.weight']
            for src, dest in mnli_rename_keys:
                  rename_key(_UpperCamelCase ,			_UpperCamelCase ,			_UpperCamelCase					)
            snake_case								=			BartForSequenceClassification(_UpperCamelCase					).eval()
            model.load_state_dict(_UpperCamelCase					)
            snake_case								=			bart.predict('mnli' ,			_UpperCamelCase ,			return_logits=_UpperCamelCase					)
            snake_case								=			model(_UpperCamelCase					)[0]  # logits
      else:  # no classification heads to worry about
            snake_case								=			bart.model.state_dict()
            remove_ignore_keys_(_UpperCamelCase					)
            snake_case								=			state_dict['decoder.embed_tokens.weight']
            snake_case								=			bart.extract_features(_UpperCamelCase					)
            if hf_checkpoint_name == "facebook/bart-large":
                  snake_case								=			BartModel(_UpperCamelCase					).eval()
                  model.load_state_dict(_UpperCamelCase					)
                  snake_case								=			model(_UpperCamelCase					).model[0]
            else:
                  snake_case								=			BartForConditionalGeneration(_UpperCamelCase					).eval()  # an existing summarization ckpt
                  model.model.load_state_dict(_UpperCamelCase					)
                  if hasattr(_UpperCamelCase ,			'lm_head'					):
                        snake_case								=			make_linear_from_emb(model.model.shared					)
                  snake_case								=			model.model(_UpperCamelCase					)[0]

    # Check results
      if fairseq_output.shape != new_model_outputs.shape:
            raise ValueError(
                f"""`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"""					)
      if (fairseq_output != new_model_outputs).any().item():
            raise ValueError('Some values in `fairseq_output` are different from `new_model_outputs`'					)
      Path(_UpperCamelCase					).mkdir(exist_ok=_UpperCamelCase					)
      model.save_pretrained(_UpperCamelCase					)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__      						=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
     )
     parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
     parser.add_argument(
         "--hf_config", default=None, type=str, help="Which huggingface architecture to use: bart-large-xsum"
     )
     SCREAMING_SNAKE_CASE__      						=  parser.parse_args()
     convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)



"""simple docstring"""
from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map





def    lowerCAmelCase__    (			_UpperCamelCase       :				Dict[str, torch.Tensor]					)					->				Dict[str, torch.Tensor]:





						"""simple docstring"""






						snake_case								=			[]
						snake_case								=			[]
						snake_case								=			[]

						for rt in rc.restypes:
												snake_case								=			rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
												restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]					)
												snake_case								=			{name: i for i, name in enumerate(_UpperCamelCase					)}
												restype_atomaa_to_atomaa_list.append(
												    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]					)

												restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]					)

						# Add dummy mapping for restype 'UNK'
						restype_atomaa_to_atomaa_list.append([0] * 1_4					)
						restype_atomaa_to_atomaa_list.append([0] * 3_7					)
						restype_atomaa_mask_list.append([0.0] * 1_4					)

						snake_case								=			torch.tensor(
						    _UpperCamelCase ,			dtype=torch.intaa ,			device=protein['aatype'].device ,			)
						snake_case								=			torch.tensor(
						    _UpperCamelCase ,			dtype=torch.intaa ,			device=protein['aatype'].device ,			)
						snake_case								=			torch.tensor(
						    _UpperCamelCase ,			dtype=torch.floataa ,			device=protein['aatype'].device ,			)
						snake_case								=			protein['aatype'].to(torch.long					)

						# create the mapping for (residx, atom14) --> atom37, i.e. an array
						# with shape (num_res, 14) containing the atom37 indices for this protein
						snake_case								=			restype_atomaa_to_atomaa[protein_aatype]
						snake_case								=			restype_atomaa_mask[protein_aatype]

						snake_case								=			residx_atomaa_mask
						snake_case								=			residx_atomaa_to_atomaa.long()

						# create the gather indices for mapping back
						snake_case								=			restype_atomaa_to_atomaa[protein_aatype]
						snake_case								=			residx_atomaa_to_atomaa.long()

						# create the corresponding mask
						snake_case								=			torch.zeros([2_1, 3_7] ,			dtype=torch.floataa ,			device=protein['aatype'].device					)
						for restype, restype_letter in enumerate(rc.restypes					):
												snake_case								=			rc.restype_atoa[restype_letter]
												snake_case								=			rc.residue_atoms[restype_name]
												for atom_name in atom_names:
																		snake_case								=			rc.atom_order[atom_name]
																		snake_case								=			1

						snake_case								=			restype_atomaa_mask[protein_aatype]
						snake_case								=			residx_atomaa_mask

						return protein





def    lowerCAmelCase__    (			_UpperCamelCase       :				Dict[str, torch.Tensor]					)					->				Dict[str, np.ndarray]:





						"""simple docstring"""






						snake_case								=			tree_map(lambda _UpperCamelCase					: torch.tensor(_UpperCamelCase ,			device=batch['aatype'].device					) ,			_UpperCamelCase ,			np.ndarray					)
						snake_case								=			tensor_tree_map(lambda _UpperCamelCase					: np.array(_UpperCamelCase					) ,			make_atomaa_masks(_UpperCamelCase					)					)
						return out


