




'''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


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






@dataclasses.dataclass(frozen=_lowerCamelCase     )
class 						a				:
				snake_case_      =		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_      =		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_      =		42  # [num_res, num_atom_type]

				# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
				snake_case_      =		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_      =		42  # [num_res, num_atom_type]

				# Chain indices for multi-chain predictions
				snake_case_      =		None

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

				# Templates used to generate this protein (prediction-only)
				snake_case_      =		None

				# Chain corresponding to each parent
				snake_case_      =		None







def 		__magic_name__    (	__UpperCAmelCase	)		->					Protein:

					'''simple docstring'''

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

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

					'''simple docstring'''

					snake_case_									=				[]

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

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

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

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

					return pdb_headers


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					str:

					'''simple docstring'''

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

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

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

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

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

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

					snake_case_									=				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	):
																				snake_case_									=				parents_per_chain[chain_counter]
															else:
																				snake_case_									=				['''N/A''']

															out_pdb_lines.append(make_parent_line(__UpperCAmelCase	)	)

					return "\n".join(__UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					str:

					'''simple docstring'''

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

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

					snake_case_									=				residue_constants.atom_types

					snake_case_									=				[]

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

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

					snake_case_									=				get_pdb_headers(__UpperCAmelCase	)
					if len(__UpperCAmelCase	) > 0:
										pdb_lines.extend(__UpperCAmelCase	)

					snake_case_									=				aatype.shape[0]
					snake_case_									=				1
					snake_case_									=				0
					snake_case_									=				string.ascii_uppercase
					snake_case_									=				None
					# Add all atom sites.
					for i in range(__UpperCAmelCase	):
										snake_case_									=				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

															snake_case_									=				'''ATOM'''
															snake_case_									=				atom_name if len(__UpperCAmelCase	) == 4 else F" {atom_name}"
															snake_case_									=				''''''
															snake_case_									=				''''''
															snake_case_									=				1.0_0
															snake_case_									=				atom_name[0]  # Protein supports only C, N, O, S, this works.
															snake_case_									=				''''''

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

															# PDB is a columnar format, every space matters here!
															snake_case_									=				(
															    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

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

										if should_terminate:
															# Close the chain.
															snake_case_									=				'''TER'''
															snake_case_									=				(
															    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 		__magic_name__    (	__UpperCAmelCase	)		->					np.ndarray:

					'''simple docstring'''

					return residue_constants.STANDARD_ATOM_MASK[prot.aatype]


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase = None,      __UpperCAmelCase = None,      __UpperCAmelCase = None,      __UpperCAmelCase = None,      __UpperCAmelCase = 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)

