





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=UpperCAmelCase_							)
class        _lowerCAmelCase    :


  '''simple docstring'''
  a_							:       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'.
  a_							:       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.
  a_							:       np.ndarray  # [num_res, num_atom_type]

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

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

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

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

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




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

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

  assert aatype is not None

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


def     lowerCamelCase_	(      lowerCAmelCase:	Protein    ,							lowerCAmelCase:	int = 0				)->		List[str]:
  _snake_case   :				List[str]														=	[]

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

  _snake_case   :				str														=	prot.parents
  _snake_case   :				Optional[Any]														=	prot.parents_chain_index
  if parents is not None and parents_chain_index is not None:
    _snake_case   :				Union[str, Any]														=	[p for i, p in zip(lowerCAmelCase    ,							lowerCAmelCase				) if i == chain_id]

  if parents is None or len(lowerCAmelCase				) == 0:
    _snake_case   :				Optional[int]														=	['N/A']

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

  return pdb_headers


def     lowerCamelCase_	(      lowerCAmelCase:	Protein    ,							lowerCAmelCase:	str				)->		str:
  _snake_case   :				List[str]														=	[]
  _snake_case   :				Union[str, Any]														=	pdb_str.split('\n'				)

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

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

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

  def make_parent_line(lowerCAmelCase:	Sequence[str]				) -> str:
    return F"""PARENT {' '.join(lowerCAmelCase				)}"""

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

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

      out_pdb_lines.append(make_parent_line(lowerCAmelCase				)				)

  return "\n".join(lowerCAmelCase				)


def     lowerCamelCase_	(      lowerCAmelCase:	Protein				)->		str:
  _snake_case   :				int														=	residue_constants.restypes + ['X']

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

  _snake_case   :				str														=	residue_constants.atom_types

  _snake_case   :				List[str]														=	[]

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

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

  _snake_case   :				Any														=	get_pdb_headers(lowerCAmelCase				)
  if len(lowerCAmelCase				) > 0:
    pdb_lines.extend(lowerCAmelCase				)

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

      _snake_case   :				Optional[Any]														=	'ATOM'
      _snake_case   :				Tuple														=	atom_name if len(lowerCAmelCase				) == 4 else F""" {atom_name}"""
      _snake_case   :				Dict														=	''
      _snake_case   :				Any														=	''
      _snake_case   :				int														=	1.0_0
      _snake_case   :				str														=	atom_name[0]  # Protein supports only C, N, O, S, this works.
      _snake_case   :				List[str]														=	''

      _snake_case   :				List[Any]														=	'A'
      if chain_index is not None:
        _snake_case   :				List[Any]														=	chain_tags[chain_index[i]]

      # PDB is a columnar format, every space matters here!
      _snake_case   :				str														=	(
          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(lowerCAmelCase				)
      atom_index += 1

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

    if should_terminate:
      # Close the chain.
      _snake_case   :				int														=	'TER'
      _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(lowerCAmelCase				)
      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(lowerCAmelCase    ,							lowerCAmelCase				)				)

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


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







def     lowerCamelCase_	(      lowerCAmelCase:	FeatureDict    ,							lowerCAmelCase:	ModelOutput    ,							lowerCAmelCase:	Optional[np.ndarray] = None    ,							lowerCAmelCase:	Optional[np.ndarray] = None    ,							lowerCAmelCase:	Optional[str] = None    ,							lowerCAmelCase:	Optional[Sequence[str]] = None    ,							lowerCAmelCase:	Optional[Sequence[int]] = 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=lowerCAmelCase    ,							remark=lowerCAmelCase    ,							parents=lowerCAmelCase    ,							parents_chain_index=lowerCAmelCase    ,							)











import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
      import tensorflow as tf

if is_keras_nlp_available():
      from transformers.models.gpta import TFGPTaTokenizer


lowerCAmelCase_						   =    ["""gpt2"""]
lowerCAmelCase_						   =    """gpt2"""

if is_tf_available():



      class        _lowerCAmelCase    (			tf.Module							):


        '''simple docstring'''







        def __init__(      self      :					List[str]      ,				UpperCamelCase      :					Dict							):


          '''simple docstring'''





          super().__init__()
          _snake_case   :				Optional[int]														=	tokenizer
          _snake_case   :				Union[str, Any]														=	AutoConfig.from_pretrained(UpperCamelCase							)
          _snake_case   :				int														=	TFGPTaLMHeadModel.from_config(UpperCamelCase							)







        @tf.function(input_signature=(tf.TensorSpec((None,)      ,				tf.string      ,				name='text'							),)							)
        def UpperCamelCase_ (      self      :					Optional[int]      ,				UpperCamelCase      :					Optional[int]							):


          '''simple docstring'''





          _snake_case   :				Dict														=	self.tokenizer(UpperCamelCase							)
          _snake_case   :				Union[str, Any]														=	tokenized['input_ids'].to_tensor()

          _snake_case   :				Any														=	tf.cast(input_ids_dense > 0      ,				tf.intaa							)
          # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

          _snake_case   :				Tuple														=	self.model(input_ids=UpperCamelCase      ,				attention_mask=UpperCamelCase							)['logits']

          return outputs



@require_tf
@require_keras_nlp
class        _lowerCAmelCase    (			unittest.TestCase							):


  '''simple docstring'''







  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    super().setUp()

    _snake_case   :				Optional[int]														=	[GPTaTokenizer.from_pretrained(UpperCamelCase							) for checkpoint in (TOKENIZER_CHECKPOINTS)]
    _snake_case   :				Tuple														=	[TFGPTaTokenizer.from_pretrained(UpperCamelCase							) for checkpoint in TOKENIZER_CHECKPOINTS]
    assert len(self.tokenizers							) == len(self.tf_tokenizers							)

    _snake_case   :				Any														=	[
        'This is a straightforward English test sentence.',
        'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
        'Now we\'re going to add some Chinese: 一 二 三 一二三',
        'And some much more rare Chinese: 齉 堃 齉堃',
        'Je vais aussi écrire en français pour tester les accents',
        'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
    ]
    _snake_case   :				Tuple														=	list(zip(self.test_sentences      ,				self.test_sentences[::-1]							)							)







  def UpperCamelCase_ (      self      :					Union[str, Any]							):


    '''simple docstring'''





    for tokenizer, tf_tokenizer in zip(self.tokenizers      ,				self.tf_tokenizers							):
      for test_inputs in self.test_sentences:
        _snake_case   :				Optional[int]														=	tokenizer([test_inputs]      ,				return_tensors='tf'							)
        _snake_case   :				Tuple														=	tf_tokenizer([test_inputs]							)

        for key in python_outputs.keys():
          # convert them to numpy to avoid messing with ragged tensors
          _snake_case   :				Dict														=	python_outputs[key].numpy()
          _snake_case   :				Optional[Any]														=	tf_outputs[key].numpy()

          self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape							)							)
          self.assertTrue(tf.reduce_all(tf.cast(UpperCamelCase      ,				tf.intaa							) == tf_outputs_values							)							)







  @slow
  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    for tf_tokenizer in self.tf_tokenizers:
      _snake_case   :				str														=	tf.function(UpperCamelCase							)
      for test_inputs in self.test_sentences:
        _snake_case   :				int														=	tf.constant(UpperCamelCase							)
        _snake_case   :				Tuple														=	compiled_tokenizer(UpperCamelCase							)
        _snake_case   :				int														=	tf_tokenizer(UpperCamelCase							)

        for key in eager_outputs.keys():
          self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]							)							)







  @slow
  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    for tf_tokenizer in self.tf_tokenizers:
      _snake_case   :				Union[str, Any]														=	ModelToSave(tokenizer=UpperCamelCase							)
      _snake_case   :				Optional[Any]														=	tf.convert_to_tensor([self.test_sentences[0]]							)
      _snake_case   :				Tuple														=	model.serving(UpperCamelCase							)  # Build model with some sample inputs
      with TemporaryDirectory() as tempdir:
        _snake_case   :				str														=	Path(UpperCamelCase							) / 'saved.model'
        tf.saved_model.save(UpperCamelCase      ,				UpperCamelCase      ,				signatures={'serving_default': model.serving}							)
        _snake_case   :				Optional[int]														=	tf.saved_model.load(UpperCamelCase							)
      _snake_case   :				List[str]														=	loaded_model.signatures['serving_default'](UpperCamelCase							)['output_0']
      # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
      self.assertTrue(tf.reduce_all(out == loaded_output							)							)







  @slow
  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    for tf_tokenizer in self.tf_tokenizers:
      _snake_case   :				Optional[Any]														=	tf.convert_to_tensor([self.test_sentences[0]]							)
      _snake_case   :				Any														=	tf_tokenizer(UpperCamelCase							)  # Build model with some sample inputs

      _snake_case   :				Optional[Any]														=	tf_tokenizer.get_config()
      _snake_case   :				Tuple														=	TFGPTaTokenizer.from_config(UpperCamelCase							)
      _snake_case   :				Optional[Any]														=	model_from_config(UpperCamelCase							)

      for key in from_config_output.keys():
        self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]							)							)







  @slow
  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    for tf_tokenizer in self.tf_tokenizers:
      # for the test to run
      _snake_case   :				Union[str, Any]														=	12_31_23

      for max_length in [3, 5, 10_24]:
        _snake_case   :				Union[str, Any]														=	tf.convert_to_tensor([self.test_sentences[0]]							)
        _snake_case   :				List[str]														=	tf_tokenizer(UpperCamelCase      ,				max_length=UpperCamelCase							)

        _snake_case   :				int														=	out['input_ids'].numpy().shape[1]

        assert out_length == max_length




