"""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_     =	Mapping[str, np.ndarray]
lowerCAmelCase_     =	Mapping[str, Any]  # Is a nested dict.
lowerCAmelCase_     =	0.01




@dataclasses.dataclass(frozen=__snake_case )
class  _snake_case		:







				"""simple docstring"""






				a			=						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'.
				a			=						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.
				a			=						42  # [num_res, num_atom_type]

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

				# Chain indices for multi-chain predictions
				a			=						None

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

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

				# Chain corresponding to each parent
				a			=						None



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Protein:
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    R"""(\[[A-Z]+\]\n)"""
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    [tag.strip() for tag in re.split(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ) if len(__SCREAMING_SNAKE_CASE  ) > 0]
							_SCREAMING_SNAKE_CASE		:						Iterator[Tuple[str, List[str]]]			  =    zip(tags[0::2]     ,	[l.split("""\n"""  ) for l in tags[1::2]]  )

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

							assert aatype is not None

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



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

							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    prot.remark
							if remark is not None:
														pdb_headers.append(F"""REMARK {remark}"""  )

							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    prot.parents
							_SCREAMING_SNAKE_CASE		:						int			  =    prot.parents_chain_index
							if parents is not None and parents_chain_index is not None:
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    [p for i, p in zip(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ) if i == chain_id]

							if parents is None or len(__SCREAMING_SNAKE_CASE  ) == 0:
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    ["""N/A"""]

							pdb_headers.append(F"""PARENT {" ".join(__SCREAMING_SNAKE_CASE  )}"""  )

							return pdb_headers



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    str:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    []
							_SCREAMING_SNAKE_CASE		:						Dict			  =    pdb_str.split("""\n"""  )

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

							_SCREAMING_SNAKE_CASE		:						List[List[str]]
							if prot.parents is not None and len(prot.parents  ) > 0:
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    []
														if prot.parents_chain_index is not None:
																					_SCREAMING_SNAKE_CASE		:						Dict[str, List[str]]			  =    {}
																					for p, i in zip(prot.parents     ,	prot.parents_chain_index  ):
																												parent_dict.setdefault(str(__SCREAMING_SNAKE_CASE  )     ,	[]  )
																												parent_dict[str(__SCREAMING_SNAKE_CASE  )].append(__SCREAMING_SNAKE_CASE  )

																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    max([int(__SCREAMING_SNAKE_CASE  ) for chain_idx in parent_dict]  )
																					for i in range(max_idx + 1  ):
																												_SCREAMING_SNAKE_CASE		:						Tuple			  =    parent_dict.get(str(__SCREAMING_SNAKE_CASE  )     ,	["""N/A"""]  )
																												parents_per_chain.append(__SCREAMING_SNAKE_CASE  )
														else:
																					parents_per_chain.append(list(prot.parents  )  )
							else:
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    [["""N/A"""]]

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

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

							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    0
							for i, l in enumerate(__SCREAMING_SNAKE_CASE  ):
														if "PARENT" not in l and "REMARK" not in l:
																					out_pdb_lines.append(__SCREAMING_SNAKE_CASE  )
														if "TER" in l and "END" not in lines[i + 1]:
																					chain_counter += 1
																					if not chain_counter >= len(__SCREAMING_SNAKE_CASE  ):
																												_SCREAMING_SNAKE_CASE		:						List[str]			  =    parents_per_chain[chain_counter]
																					else:
																												_SCREAMING_SNAKE_CASE		:						Tuple			  =    ["""N/A"""]

																					out_pdb_lines.append(make_parent_line(__SCREAMING_SNAKE_CASE  )  )

							return "\n".join(__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    str:
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    residue_constants.restypes + ["""X"""]

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

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    residue_constants.atom_types

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    []

							_SCREAMING_SNAKE_CASE		:						int			  =    prot.atom_mask
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    prot.aatype
							_SCREAMING_SNAKE_CASE		:						int			  =    prot.atom_positions
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    prot.residue_index.astype(np.intaa  )
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    prot.b_factors
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    prot.chain_index

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

							_SCREAMING_SNAKE_CASE		:						int			  =    get_pdb_headers(__SCREAMING_SNAKE_CASE  )
							if len(__SCREAMING_SNAKE_CASE  ) > 0:
														pdb_lines.extend(__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						Tuple			  =    aatype.shape[0]
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    1
							_SCREAMING_SNAKE_CASE		:						Dict			  =    0
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    string.ascii_uppercase
							_SCREAMING_SNAKE_CASE		:						int			  =    None
							# Add all atom sites.
							for i in range(__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Any			  =    res_atoa(aatype[i]  )
														for atom_name, pos, mask, b_factor in zip(__SCREAMING_SNAKE_CASE     ,	atom_positions[i]     ,	atom_mask[i]     ,	b_factors[i]  ):
																					if mask < 0.5:
																												continue

																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    """ATOM"""
																					_SCREAMING_SNAKE_CASE		:						Any			  =    atom_name if len(__SCREAMING_SNAKE_CASE  ) == 4 else F""" {atom_name}"""
																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    """"""
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    """"""
																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    1.00
																					_SCREAMING_SNAKE_CASE		:						Tuple			  =    atom_name[0]  # Protein supports only C, N, O, S, this works.
																					_SCREAMING_SNAKE_CASE		:						Tuple			  =    """"""

																					_SCREAMING_SNAKE_CASE		:						Dict			  =    """A"""
																					if chain_index is not None:
																												_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    chain_tags[chain_index[i]]

																					# PDB is a columnar format, every space matters here!
																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    (
																					    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(__SCREAMING_SNAKE_CASE  )
																					atom_index += 1

														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    i == n - 1
														if chain_index is not None:
																					if i != n - 1 and chain_index[i + 1] != prev_chain_index:
																												_SCREAMING_SNAKE_CASE		:						Any			  =    True
																												_SCREAMING_SNAKE_CASE		:						int			  =    chain_index[i + 1]

														if should_terminate:
																					# Close the chain.
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    """TER"""
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    (
																					    F"""{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]  ):>3} {chain_tag:>1}{residue_index[i]:>4}"""
																					)
																					pdb_lines.append(__SCREAMING_SNAKE_CASE  )
																					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(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )  )

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



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    np.ndarray:
							return residue_constants.STANDARD_ATOM_MASK[prot.aatype]



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	)->    Protein:
							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=__SCREAMING_SNAKE_CASE     ,	remark=__SCREAMING_SNAKE_CASE     ,	parents=__SCREAMING_SNAKE_CASE     ,	parents_chain_index=__SCREAMING_SNAKE_CASE     ,	)



"""simple docstring"""





import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
			import torch

			from transformers import AutoModelForCausalLM




@require_torch
class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""")
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""").to(_A)
											_SCREAMING_SNAKE_CASE		:						Any			  =    -1

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    ids_tensor((1, 5)      ,    vocab_size=model.config.vocab_size).to(_A)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    model.generate(_A      ,    max_new_tokens=1_0      ,    do_sample=_A)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    tokenizer.decode(greedy_ids[0])

											with CaptureStdout() as cs:
																		_SCREAMING_SNAKE_CASE		:						Any			  =    TextStreamer(_A)
																		model.generate(_A      ,    max_new_tokens=1_0      ,    do_sample=_A      ,    streamer=_A)
											# The greedy text should be printed to stdout, except for the final "\n" in the streamer
											_SCREAMING_SNAKE_CASE		:						str			  =    cs.out[:-1]

											self.assertEqual(_A      ,    _A)





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""")
											_SCREAMING_SNAKE_CASE		:						Any			  =    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""").to(_A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    -1

											_SCREAMING_SNAKE_CASE		:						str			  =    ids_tensor((1, 5)      ,    vocab_size=model.config.vocab_size).to(_A)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model.generate(_A      ,    max_new_tokens=1_0      ,    do_sample=_A)
											_SCREAMING_SNAKE_CASE		:						Any			  =    tokenizer.decode(greedy_ids[0])

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    TextIteratorStreamer(_A)
											_SCREAMING_SNAKE_CASE		:						Any			  =    {"""input_ids""": input_ids, """max_new_tokens""": 1_0, """do_sample""": False, """streamer""": streamer}
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    Thread(target=model.generate      ,    kwargs=_A)
											thread.start()
											_SCREAMING_SNAKE_CASE		:						Any			  =    """"""
											for new_text in streamer:
																		streamer_text += new_text

											self.assertEqual(_A      ,    _A)





				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""")
											_SCREAMING_SNAKE_CASE		:						Dict			  =    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""").to(_A)
											_SCREAMING_SNAKE_CASE		:						Any			  =    -1

											_SCREAMING_SNAKE_CASE		:						Any			  =    ids_tensor((1, 5)      ,    vocab_size=model.config.vocab_size).to(_A)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model.generate(_A      ,    max_new_tokens=1_0      ,    do_sample=_A)
											_SCREAMING_SNAKE_CASE		:						str			  =    greedy_ids[:, input_ids.shape[1] :]
											_SCREAMING_SNAKE_CASE		:						Dict			  =    tokenizer.decode(new_greedy_ids[0])

											with CaptureStdout() as cs:
																		_SCREAMING_SNAKE_CASE		:						Any			  =    TextStreamer(_A      ,    skip_prompt=_A)
																		model.generate(_A      ,    max_new_tokens=1_0      ,    do_sample=_A      ,    streamer=_A)
											# The greedy text should be printed to stdout, except for the final "\n" in the streamer
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    cs.out[:-1]

											self.assertEqual(_A      ,    _A)





				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    AutoTokenizer.from_pretrained("""distilgpt2""")
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    AutoModelForCausalLM.from_pretrained("""distilgpt2""").to(_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    -1

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.ones((1, 5)      ,    device=_A).long() * model.config.bos_token_id
											with CaptureStdout() as cs:
																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    TextStreamer(_A      ,    skip_special_tokens=_A)
																		model.generate(_A      ,    max_new_tokens=1      ,    do_sample=_A      ,    streamer=_A)

											# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
											# re-tokenized, must only contain one token
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    cs.out[:-1]  # Remove the final "\n"
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    tokenizer(_A      ,    return_tensors="""pt""")
											self.assertEqual(streamer_text_tokenized.input_ids.shape      ,    (1, 1))





				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""")
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""").to(_A)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    -1

											_SCREAMING_SNAKE_CASE		:						Dict			  =    ids_tensor((1, 5)      ,    vocab_size=model.config.vocab_size).to(_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    TextIteratorStreamer(_A      ,    timeout=0.001)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    {"""input_ids""": input_ids, """max_new_tokens""": 1_0, """do_sample""": False, """streamer""": streamer}
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    Thread(target=model.generate      ,    kwargs=_A)
											thread.start()

											# The streamer will timeout after 0.001 seconds, so an exception will be raised
											with self.assertRaises(_A):
																		_SCREAMING_SNAKE_CASE		:						str			  =    """"""
																		for new_text in streamer:
																									streamer_text += new_text


