"""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    :       Any					     =Mapping[str, np.ndarray]
__lowerCAmelCase    :       Tuple					     =Mapping[str, Any]  # Is a nested dict.
__lowerCAmelCase    :       Union[str, Any]					     =0.01





@dataclasses.dataclass(frozen=_lowerCamelCase	)
class  _A						:
      snake_case__			:   Union[str, Any]							  =	42  # [num_res, num_atom_type, 3]

      # Amino-acid type for each residue represented as an integer between 0 and
      # 20, where 20 is 'X'.
      snake_case__			:   Any							  =	42  # [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.
      snake_case__			:   List[str]							  =	42  # [num_res, num_atom_type]

      # Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
      snake_case__			:   Optional[Any]							  =	42  # [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.
      snake_case__			:   Tuple							  =	42  # [num_res, num_atom_type]

      # Chain indices for multi-chain predictions
      snake_case__			:   Any							  =	None

      # Optional remark about the protein. Included as a comment in output PDB
      # files
      snake_case__			:   int							  =	None

      # Templates used to generate this protein (prediction-only)
      snake_case__			:   str							  =	None

      # Chain corresponding to each parent
      snake_case__			:   Dict							  =	None






def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any		) ->    Protein:
      '''simple docstring'''


      lowercase														=	R"""(\[[A-Z]+\]\n)"""
      lowercase														=	[tag.strip() for tag in re.split(__UpperCAmelCase					,		__UpperCAmelCase		) if len(__UpperCAmelCase		) > 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(__UpperCAmelCase		)		):
                        if seq[i] not in residue_constants.restypes:
                              lowercase														=	"""X"""  # FIXME: strings are immutable
                  lowercase														=	np.array(
                      [residue_constants.restype_order.get(__UpperCAmelCase					,		residue_constants.restype_num		) for res_symbol in seq]		)
            elif "[TERTIARY]" == g[0]:
                  lowercase														=	[]
                  for axis in range(3		):
                        tertiary.append(list(map(__UpperCAmelCase					,		g[1][axis].split()		)		)		)
                  lowercase														=	np.array(__UpperCAmelCase		)
                  lowercase														=	np.zeros((len(tertiary[0]		) // 3, residue_constants.atom_type_num, 3)		).astype(np.floataa		)
                  for i, atom in enumerate(__UpperCAmelCase		):
                        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(__UpperCAmelCase		),
                          residue_constants.atom_type_num,
                      )		).astype(np.floataa		)
                  for i, atom in enumerate(__UpperCAmelCase		):
                        lowercase														=	1
                  atom_mask *= mask[..., None]

      assert aatype is not None

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


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int					,		lowerCAmelCase__     :Any = 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(__UpperCAmelCase					,		__UpperCAmelCase		) if i == chain_id]

      if parents is None or len(__UpperCAmelCase		) == 0:
            lowercase														=	["""N/A"""]

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

      return pdb_headers


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Dict					,		lowerCAmelCase__     :str		) ->    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														=	4_2
      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(__UpperCAmelCase		)					,		[]		)
                        parent_dict[str(__UpperCAmelCase		)].append(__UpperCAmelCase		)

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

      def make_parent_line(lowerCAmelCase__     :Optional[Any]		) -> str:
            return f'PARENT {" ".join(__UpperCAmelCase		)}'

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

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

                  out_pdb_lines.append(make_parent_line(__UpperCAmelCase		)		)

      return "\n".join(__UpperCAmelCase		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Union[str, Any]		) ->    str:
      '''simple docstring'''


      lowercase														=	residue_constants.restypes + ["""X"""]

      def res_atoa(lowerCAmelCase__     :Tuple		) -> 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(__UpperCAmelCase		)
      if len(__UpperCAmelCase		) > 0:
            pdb_lines.extend(__UpperCAmelCase		)

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

                  lowercase														=	"""ATOM"""
                  lowercase														=	atom_name if len(__UpperCAmelCase		) == 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(__UpperCAmelCase		)
                  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(__UpperCAmelCase		)
                  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(__UpperCAmelCase					,		__UpperCAmelCase		)		)

      pdb_lines.append("""END"""		)
      pdb_lines.append(""""""		)
      return "\n".join(__UpperCAmelCase		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :str		) ->    np.ndarray:
      '''simple docstring'''


      return residue_constants.STANDARD_ATOM_MASK[prot.aatype]






def 			UpperCAmelCase__       (				lowerCAmelCase__     :Optional[int]					,		lowerCAmelCase__     :List[Any]					,		lowerCAmelCase__     :Optional[Any] = None					,		lowerCAmelCase__     :List[Any] = None					,		lowerCAmelCase__     :Tuple = None					,		lowerCAmelCase__     :Optional[int] = None					,		lowerCAmelCase__     :str = 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=__UpperCAmelCase					,		remark=__UpperCAmelCase					,		parents=__UpperCAmelCase					,		parents_chain_index=__UpperCAmelCase					,		)









'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def 		__magic_name__    (	__UpperCAmelCase	)		->					str:

					'''simple docstring'''

					snake_case_									=				[
					    '''encoder.version''',
					    '''decoder.version''',
					    '''model.encoder.version''',
					    '''model.decoder.version''',
					    '''decoder.output_projection.weight''',
					    '''_float_tensor''',
					    '''encoder.embed_positions._float_tensor''',
					    '''decoder.embed_positions._float_tensor''',
					]
					for k in ignore_keys:
										state_dict.pop(__UpperCAmelCase,      __UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

					snake_case_									=				list(s_dict.keys()	)
					for key in keys:
										if "transformer_layers" in key:
															snake_case_									=				s_dict.pop(__UpperCAmelCase	)
										elif "subsample" in key:
															snake_case_									=				s_dict.pop(__UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					int:

					'''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


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Dict:

					'''simple docstring'''

					snake_case_									=				torch.load(__UpperCAmelCase,      map_location='''cpu'''	)
					snake_case_									=				mam_aaa['''args''']
					snake_case_									=				mam_aaa['''model''']
					snake_case_									=				state_dict['''decoder.output_projection.weight''']

					remove_ignore_keys_(__UpperCAmelCase	)
					rename_keys(__UpperCAmelCase	)

					snake_case_									=				state_dict['''decoder.embed_tokens.weight'''].shape[0]

					snake_case_									=				args.share_decoder_input_output_embed

					snake_case_									=				[int(__UpperCAmelCase	) for i in args.conv_kernel_sizes.split(''','''	)]
					snake_case_									=				SpeechaTextConfig(
					    vocab_size=__UpperCAmelCase,      max_source_positions=args.max_source_positions,      max_target_positions=args.max_target_positions,      encoder_layers=args.encoder_layers,      decoder_layers=args.decoder_layers,      encoder_attention_heads=args.encoder_attention_heads,      decoder_attention_heads=args.decoder_attention_heads,      encoder_ffn_dim=args.encoder_ffn_embed_dim,      decoder_ffn_dim=args.decoder_ffn_embed_dim,      d_model=args.encoder_embed_dim,      dropout=args.dropout,      attention_dropout=args.attention_dropout,      activation_dropout=args.activation_dropout,      activation_function='''relu''',      num_conv_layers=len(__UpperCAmelCase	),      conv_channels=args.conv_channels,      conv_kernel_sizes=__UpperCAmelCase,      input_feat_per_channel=args.input_feat_per_channel,      input_channels=args.input_channels,      tie_word_embeddings=__UpperCAmelCase,      num_beams=5,      max_length=200,      use_cache=__UpperCAmelCase,      decoder_start_token_id=2,      early_stopping=__UpperCAmelCase,      )

					snake_case_									=				SpeechaTextForConditionalGeneration(__UpperCAmelCase	)
					snake_case_			,snake_case_									=				model.model.load_state_dict(__UpperCAmelCase,      strict=__UpperCAmelCase	)
					if len(__UpperCAmelCase	) > 0 and not set(__UpperCAmelCase	) <= {
					    "encoder.embed_positions.weights",
					    "decoder.embed_positions.weights",
					}:
										raise ValueError(
										    '''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'''
										    F" but all the following weights are missing {missing}"	)

					if tie_embeds:
										snake_case_									=				make_linear_from_emb(model.model.decoder.embed_tokens	)
					else:
										snake_case_									=				lm_head_weights

					model.save_pretrained(__UpperCAmelCase	)


if __name__ == "__main__":
							a  :				Any									=  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							a  :				List[Any]									=  parser.parse_args()
							convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)

