


"""simple docstring"""







import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


a__				:							int						=     [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['''memory_attention''', '''encoder_attn'''],
    ['''attention''', '''attn'''],
    ['''/''', '''.'''],
    ['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
    ['''.LayerNorm.beta''', '''_layer_norm.bias'''],
    ['''r.layer_''', '''r.layers.'''],
    ['''output_proj''', '''out_proj'''],
    ['''ffn.dense_1.''', '''fc2.'''],
    ['''ffn.dense.''', '''fc1.'''],
    ['''ffn_layer_norm''', '''final_layer_norm'''],
    ['''kernel''', '''weight'''],
    ['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
    ['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
    ['''embeddings.weights''', '''shared.weight'''],
]




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					for pegasus_name, hf_name in PATTERNS:
										__SCREAMING_SNAKE_CASE						   =				k.replace(lowerCAmelCase_  ,						lowerCAmelCase_   )
					return k




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				DEFAULTS.copy()
					cfg_kwargs.update(lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				PegasusConfig(**lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				PegasusForConditionalGeneration(lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				torch_model.model.state_dict()
					__SCREAMING_SNAKE_CASE						   =				{}
					for k, v in tf_weights.items():
										__SCREAMING_SNAKE_CASE						   =				rename_state_dict_key(lowerCAmelCase_   )
										if new_k not in sd:
															raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})"""   )

										if "dense" in k or "proj" in new_k:
															__SCREAMING_SNAKE_CASE						   =				v.T
										__SCREAMING_SNAKE_CASE						   =				torch.tensor(lowerCAmelCase_  ,						dtype=sd[new_k].dtype   )
										assert v.shape == sd[new_k].shape, f"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
					# make sure embedding.padding_idx is respected
					__SCREAMING_SNAKE_CASE						   =				torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]   )
					__SCREAMING_SNAKE_CASE						   =				mapping["shared.weight"]
					__SCREAMING_SNAKE_CASE						   =				mapping["shared.weight"]
					__SCREAMING_SNAKE_CASE						   =				{k: torch.zeros_like(lowerCAmelCase_   ) for k, v in sd.items() if k.endswith("bias"   ) and k not in mapping}
					mapping.update(**lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				torch_model.model.load_state_dict(lowerCAmelCase_  ,						strict=lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				[
					    k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
					]
					assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
					assert extra == [], f"""no matches found for the following tf keys {extra}"""
					return torch_model




def    UpperCAmelCase__       (lowerCAmelCase_="./ckpt/aeslc/model.ckpt-32000"   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				tf.train.list_variables(lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				{}
					__SCREAMING_SNAKE_CASE						   =				["Adafactor", "global_step"]
					for name, shape in tqdm(lowerCAmelCase_  ,						desc="converting tf checkpoint to dict"   ):
										__SCREAMING_SNAKE_CASE						   =				any(pat in name for pat in ignore_name   )
										if skip_key:
															continue
										__SCREAMING_SNAKE_CASE						   =				tf.train.load_variable(lowerCAmelCase_  ,						lowerCAmelCase_   )
										__SCREAMING_SNAKE_CASE						   =				array
					return tf_weights




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				Path(lowerCAmelCase_   ).parent.name
					__SCREAMING_SNAKE_CASE						   =				task_specific_params[f"""summarization_{dataset}"""]["max_position_embeddings"]
					__SCREAMING_SNAKE_CASE						   =				PegasusTokenizer.from_pretrained("sshleifer/pegasus"  ,						model_max_length=lowerCAmelCase_   )
					assert tok.model_max_length == desired_max_model_length
					tok.save_pretrained(lowerCAmelCase_   )

					# convert model
					__SCREAMING_SNAKE_CASE						   =				get_tf_weights_as_numpy(lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				task_specific_params[f"""summarization_{dataset}"""]
					if dataset == "large":
										__SCREAMING_SNAKE_CASE						   =				task_specific_params
					__SCREAMING_SNAKE_CASE						   =				convert_pegasus(lowerCAmelCase_  ,						lowerCAmelCase_   )
					torch_model.save_pretrained(lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				torch_model.state_dict()
					sd.pop("model.decoder.embed_positions.weight"   )
					sd.pop("model.encoder.embed_positions.weight"   )
					torch.save(lowerCAmelCase_  ,						Path(lowerCAmelCase_   ) / "pytorch_model.bin"   )


if __name__ == "__main__":
		a__				:							Any						=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
		parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
		a__				:							List[str]						=     parser.parse_args()
		if args.save_dir is None:
				a__				:							int						=     Path(args.tf_ckpt_path).parent.name
				a__				:							Tuple						=     os.path.join('''pegasus''', dataset)
		convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)




'''simple docstring'''

import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple

import numpy as np

from . import residue_constants


lowerCamelCase							:   str  						=	Mapping[str, np.ndarray]
lowerCamelCase							:   List[Any]  						=	Mapping[str, Any]  # Is a nested dict.
lowerCamelCase							:   Any  						=	0.0_1
@dataclasses.dataclass(frozen=lowercase_     )
class   __lowerCAmelCase						:





      '''simple docstring'''






      lowerCAmelCase__					:     np.ndarray  # [num_res, num_atom_type, 3]

      # Amino-acid type for each residue represented as an integer between 0 and
      # 20, where 20 is 'X'.
      lowerCAmelCase__					:     np.ndarray  # [num_res]

      # Binary float mask to indicate presence of a particular atom. 1.0 if an atom
      # is present and 0.0 if not. This should be used for loss masking.
      lowerCAmelCase__					:     np.ndarray  # [num_res, num_atom_type]

      # Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
      lowerCAmelCase__					:     np.ndarray  # [num_res]

      # B-factors, or temperature factors, of each residue (in sq. angstroms units),
      # representing the displacement of the residue from its ground truth mean
      # value.
      lowerCAmelCase__					:     np.ndarray  # [num_res, num_atom_type]

      # Chain indices for multi-chain predictions
      lowerCAmelCase__					:     Optional[np.ndarray]  					=							None

      # Optional remark about the protein. Included as a comment in output PDB
      # files
      lowerCAmelCase__					:     Optional[str]  					=							None

      # Templates used to generate this protein (prediction-only)
      lowerCAmelCase__					:     Optional[Sequence[str]]  					=							None

      # Chain corresponding to each parent
      lowerCAmelCase__					:     Optional[Sequence[int]]  					=							None






def _SCREAMING_SNAKE_CASE      (A       )       ->       Protein:






       """simple docstring"""







       lowercase__      			= R'''(\[[A-Z]+\]\n)'''
       lowercase__      			= [tag.strip() for tag in re.split(A  , A       ) if len(A       ) > 0]
       lowercase__      			= zip(tags[0::2]  , [l.split('''\n'''       ) for l in tags[1::2]]       )

       lowercase__      			= ["N", "CA", "C"]
       lowercase__      			= None
       lowercase__      			= None
       lowercase__      			= None
       for g in groups:
              if "[PRIMARY]" == g[0]:
                     lowercase__      			= g[1][0].strip()
                     for i in range(len(A       )       ):
                            if seq[i] not in residue_constants.restypes:
                                   lowercase__      			= '''X'''  # FIXME: strings are immutable
                     lowercase__      			= np.array(
                         [residue_constants.restype_order.get(A  , residue_constants.restype_num       ) for res_symbol in seq]       )
              elif "[TERTIARY]" == g[0]:
                     lowercase__      			= []
                     for axis in range(3       ):
                            tertiary.append(list(map(A  , g[1][axis].split()       )       )       )
                     lowercase__      			= np.array(A       )
                     lowercase__      			= np.zeros((len(tertiary[0]       ) // 3, residue_constants.atom_type_num, 3)       ).astype(np.floataa       )
                     for i, atom in enumerate(A       ):
                            lowercase__      			= np.transpose(tertiary_np[:, i::3]       )
                     atom_positions *= PICO_TO_ANGSTROM
              elif "[MASK]" == g[0]:
                     lowercase__      			= np.array(list(map({'''-''': 0, '''+''': 1}.get  , g[1][0].strip()       )       )       )
                     lowercase__      			= np.zeros(
                         (
                             len(A       ),
                             residue_constants.atom_type_num,
                         )       ).astype(np.floataa       )
                     for i, atom in enumerate(A       ):
                            lowercase__      			= 1
                     atom_mask *= mask[..., None]

       assert aatype is not None

       return Protein(
           atom_positions=A  , atom_mask=A  , aatype=A  , residue_index=np.arange(len(A       )       )  , b_factors=A  , )

def _SCREAMING_SNAKE_CASE      (A  , A = 0       )       ->       List[str]:






       """simple docstring"""







       lowercase__      			= []

       lowercase__      			= prot.remark
       if remark is not None:
              pdb_headers.append(f"REMARK {remark}"       )

       lowercase__      			= prot.parents
       lowercase__      			= prot.parents_chain_index
       if parents is not None and parents_chain_index is not None:
              lowercase__      			= [p for i, p in zip(A  , A       ) if i == chain_id]

       if parents is None or len(A       ) == 0:
              lowercase__      			= ['''N/A''']

       pdb_headers.append(f"PARENT {' '.join(A       )}"       )

       return pdb_headers

def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       str:






       """simple docstring"""







       lowercase__      			= []
       lowercase__      			= pdb_str.split('''\n'''       )

       lowercase__      			= prot.remark
       if remark is not None:
              out_pdb_lines.append(f"REMARK {remark}"       )

       lowercase__      			= 42
       if prot.parents is not None and len(prot.parents       ) > 0:
              lowercase__      			= []
              if prot.parents_chain_index is not None:
                     lowercase__      			= {}
                     for p, i in zip(prot.parents  , prot.parents_chain_index       ):
                            parent_dict.setdefault(str(A       )  , []       )
                            parent_dict[str(A       )].append(A       )

                     lowercase__      			= max([int(A       ) for chain_idx in parent_dict]       )
                     for i in range(max_idx + 1       ):
                            lowercase__      			= parent_dict.get(str(A       )  , ['''N/A''']       )
                            parents_per_chain.append(A       )
              else:
                     parents_per_chain.append(list(prot.parents       )       )
       else:
              lowercase__      			= [['''N/A''']]

       def make_parent_line(A       ) -> str:
              return f"PARENT {' '.join(A       )}"

       out_pdb_lines.append(make_parent_line(parents_per_chain[0]       )       )

       lowercase__      			= 0
       for i, l in enumerate(A       ):
              if "PARENT" not in l and "REMARK" not in l:
                     out_pdb_lines.append(A       )
              if "TER" in l and "END" not in lines[i + 1]:
                     chain_counter += 1
                     if not chain_counter >= len(A       ):
                            lowercase__      			= parents_per_chain[chain_counter]
                     else:
                            lowercase__      			= ['''N/A''']

                     out_pdb_lines.append(make_parent_line(A       )       )

       return "\n".join(A       )

def _SCREAMING_SNAKE_CASE      (A       )       ->       str:






       """simple docstring"""







       lowercase__      			= residue_constants.restypes + ['''X''']

       def res_atoa(A       ) -> str:
              return residue_constants.restype_atoa.get(restypes[r]  , '''UNK'''       )

       lowercase__      			= residue_constants.atom_types

       lowercase__      			= []

       lowercase__      			= prot.atom_mask
       lowercase__      			= prot.aatype
       lowercase__      			= prot.atom_positions
       lowercase__      			= prot.residue_index.astype(np.intaa       )
       lowercase__      			= prot.b_factors
       lowercase__      			= prot.chain_index

       if np.any(aatype > residue_constants.restype_num       ):
              raise ValueError('''Invalid aatypes.'''       )

       lowercase__      			= get_pdb_headers(A       )
       if len(A       ) > 0:
              pdb_lines.extend(A       )

       lowercase__      			= aatype.shape[0]
       lowercase__      			= 1
       lowercase__      			= 0
       lowercase__      			= string.ascii_uppercase
       lowercase__      			= None
       # Add all atom sites.
       for i in range(A       ):
              lowercase__      			= res_atoa(aatype[i]       )
              for atom_name, pos, mask, b_factor in zip(A  , atom_positions[i]  , atom_mask[i]  , b_factors[i]       ):
                     if mask < 0.5:
                            continue

                     lowercase__      			= '''ATOM'''
                     lowercase__      			= atom_name if len(A       ) == 4 else f" {atom_name}"
                     lowercase__      			= ''''''
                     lowercase__      			= ''''''
                     lowercase__      			= 1.00
                     lowercase__      			= atom_name[0]  # Protein supports only C, N, O, S, this works.
                     lowercase__      			= ''''''

                     lowercase__      			= '''A'''
                     if chain_index is not None:
                            lowercase__      			= chain_tags[chain_index[i]]

                     # PDB is a columnar format, every space matters here!
                     lowercase__      			= (
                         f"{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}"
                         f"{res_name_a:>3} {chain_tag:>1}"
                         f"{residue_index[i]:>4}{insertion_code:>1}   "
                         f"{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}"
                         f"{occupancy:>6.2f}{b_factor:>6.2f}          "
                         f"{element:>2}{charge:>2}"
                     )
                     pdb_lines.append(A       )
                     atom_index += 1

              lowercase__      			= i == n - 1
              if chain_index is not None:
                     if i != n - 1 and chain_index[i + 1] != prev_chain_index:
                            lowercase__      			= True
                            lowercase__      			= chain_index[i + 1]

              if should_terminate:
                     # Close the chain.
                     lowercase__      			= '''TER'''
                     lowercase__      			= (
                         f"{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]       ):>3} {chain_tag:>1}{residue_index[i]:>4}"
                     )
                     pdb_lines.append(A       )
                     atom_index += 1

                     if i != n - 1:
                            # "prev" is a misnomer here. This happens at the beginning of
                            # each new chain.
                            pdb_lines.extend(get_pdb_headers(A  , A       )       )

       pdb_lines.append('''END'''       )
       pdb_lines.append(''''''       )
       return "\n".join(A       )

def _SCREAMING_SNAKE_CASE      (A       )       ->       np.ndarray:






       """simple docstring"""







       return residue_constants.STANDARD_ATOM_MASK[prot.aatype]

def _SCREAMING_SNAKE_CASE      (A  , A  , A = None  , A = None  , A = None  , A = None  , A = None  , )       ->       Protein:






       """simple docstring"""







       return Protein(
           aatype=features['''aatype''']  , atom_positions=result['''final_atom_positions''']  , atom_mask=result['''final_atom_mask''']  , residue_index=features['''residue_index'''] + 1  , b_factors=b_factors if b_factors is not None else np.zeros_like(result['''final_atom_mask''']       )  , chain_index=A  , remark=A  , parents=A  , parents_chain_index=A  , )




