





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


_SCREAMING_SNAKE_CASE					=		Mapping[str, np.ndarray]
_SCREAMING_SNAKE_CASE					=		Mapping[str, Any]  # Is a nested dict.
_SCREAMING_SNAKE_CASE					=		0.0_1
@dataclasses.dataclass(frozen=snake_case_					)
class SCREAMING_SNAKE_CASE_						:
						__magic_name__:			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'.
						__magic_name__:			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.
						__magic_name__:			np.ndarray  # [num_res, num_atom_type]

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

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

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

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

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


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	snake_case_		:			Optional[Any]											=	r'(\[[A-Z]+\]\n)'
	snake_case_		:			List[str]											=	[tag.strip() for tag in re.split(__a   , __a   ) if len(__a   ) > 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_		:			Tuple											=	None
	snake_case_		:			Tuple											=	None
	snake_case_		:			str											=	None
	for g in groups:
		if "[PRIMARY]" == g[0]:
			snake_case_		:			int											=	g[1][0].strip()
			for i in range(len(__a   )   ):
				if seq[i] not in residue_constants.restypes:
					snake_case_		:			List[Any]											=	'X'  # FIXME: strings are immutable
			snake_case_		:			Tuple											=	np.array(
			    [residue_constants.restype_order.get(__a   , 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(__a   , g[1][axis].split()   )   )   )
			snake_case_		:			int											=	np.array(__a   )
			snake_case_		:			Dict											=	np.zeros((len(tertiary[0]   ) // 3, residue_constants.atom_type_num, 3)   ).astype(np.floataa   )
			for i, atom in enumerate(__a   ):
				snake_case_		:			Dict											=	np.transpose(tertiary_np[:, i::3]   )
			atom_positions *= PICO_TO_ANGSTROM
		elif "[MASK]" == g[0]:
			snake_case_		:			Union[str, Any]											=	np.array(list(map({'-': 0, '+': 1}.get   , g[1][0].strip()   )   )   )
			snake_case_		:			List[str]											=	np.zeros(
			    (
			        len(__a   ),
			        residue_constants.atom_type_num,
			    )   ).astype(np.floataa   )
			for i, atom in enumerate(__a   ):
				snake_case_		:			str											=	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   ):
	snake_case_		:			List[str]											=	[]

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

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

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

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

	return pdb_headers


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	snake_case_		:			List[str]											=	[]
	snake_case_		:			Tuple											=	pdb_str.split('\n'   )

	snake_case_		:			Dict											=	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_		:			Optional[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(__a   )   , []   )
				parent_dict[str(__a   )].append(__a   )

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

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

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

	snake_case_		:			Optional[int]											=	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   ):
				snake_case_		:			List[Any]											=	parents_per_chain[chain_counter]
			else:
				snake_case_		:			str											=	['N/A']

			out_pdb_lines.append(make_parent_line(__a   )   )

	return "\n".join(__a   )


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	snake_case_		:			Dict											=	residue_constants.restypes + ['X']

	def res_atoa(__a   ) -> 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[Any]											=	prot.aatype
	snake_case_		:			Optional[Any]											=	prot.atom_positions
	snake_case_		:			Optional[Any]											=	prot.residue_index.astype(np.intaa   )
	snake_case_		:			Dict											=	prot.b_factors
	snake_case_		:			int											=	prot.chain_index

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

	snake_case_		:			Union[str, Any]											=	get_pdb_headers(__a   )
	if len(__a   ) > 0:
		pdb_lines.extend(__a   )

	snake_case_		:			List[str]											=	aatype.shape[0]
	snake_case_		:			Union[str, Any]											=	1
	snake_case_		:			Optional[Any]											=	0
	snake_case_		:			int											=	string.ascii_uppercase
	snake_case_		:			str											=	None
	# Add all atom sites.
	for i in range(__a   ):
		snake_case_		:			int											=	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

			snake_case_		:			Tuple											=	'ATOM'
			snake_case_		:			Union[str, Any]											=	atom_name if len(__a   ) == 4 else f""" {atom_name}"""
			snake_case_		:			Optional[int]											=	''
			snake_case_		:			Tuple											=	''
			snake_case_		:			Union[str, Any]											=	1.00
			snake_case_		:			Optional[int]											=	atom_name[0]  # Protein supports only C, N, O, S, this works.
			snake_case_		:			int											=	''

			snake_case_		:			Union[str, Any]											=	'A'
			if chain_index is not None:
				snake_case_		:			int											=	chain_tags[chain_index[i]]

			# PDB is a columnar format, every space matters here!
			snake_case_		:			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(__a   )
			atom_index += 1

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

		if should_terminate:
			# Close the chain.
			snake_case_		:			str											=	'TER'
			snake_case_		:			int											=	(
			    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   ):
	return residue_constants.STANDARD_ATOM_MASK[prot.aatype]


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a = None   , __a = None   , __a = None   , __a = None   , __a = None   , ):
	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   , )








import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE      	=		get_tests_dir("""fixtures/test_sentencepiece.model""")
_SCREAMING_SNAKE_CASE      	=		get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

_SCREAMING_SNAKE_CASE      	=		"""pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			str       =       CamembertTokenizer
						__magic_name__:			Optional[Any]       =       CamembertTokenizerFast
						__magic_name__:			Optional[int]       =       True
						__magic_name__:			str       =       True






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							super().setUp()

							# We have a SentencePiece fixture for testing
							snake_case_		:			Union[str, Any]											=	CamembertTokenizer(_A	)
							tokenizer.save_pretrained(self.tmpdirname	)






						def UpperCAmelCase_				(      self      :      int	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			int											=	'<pad>'
							snake_case_		:			int											=	1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A	)		,   _A	)
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A	)		,   _A	)






						def UpperCAmelCase_				(      self      :      str	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	list(self.get_tokenizer().get_vocab().keys()	)

							self.assertEqual(vocab_keys[0]		,   '<s>NOTUSED'	)
							self.assertEqual(vocab_keys[1]		,   '<pad>'	)
							self.assertEqual(vocab_keys[-1]		,   '<mask>'	)
							self.assertEqual(len(_A	)		,   1004	)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Dict:







							"""simple docstring"""


							self.assertEqual(self.get_tokenizer().vocab_size		,   1005	)






						def UpperCAmelCase_				(      self      :      Dict	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			Any											=	CamembertTokenizer(_A	)
							tokenizer.save_pretrained(self.tmpdirname	)
							snake_case_		:			List[Any]											=	CamembertTokenizerFast.from_pretrained(self.tmpdirname	)

							snake_case_		:			str											=	'I was born in 92000, and this is falsé.'

							snake_case_		:			Dict											=	tokenizer.encode(_A	)
							snake_case_		:			Tuple											=	rust_tokenizer.encode(_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			List[Any]											=	tokenizer.encode(_A		,   add_special_tokens=_A	)
							snake_case_		:			Any											=	rust_tokenizer.encode(_A		,   add_special_tokens=_A	)
							self.assertListEqual(_A		,   _A	)

							# <unk> tokens are not the same for `rust` than for `slow`.
							# Because spm gives back raw token instead of `unk` in EncodeAsPieces
							# tokens = tokenizer.tokenize(sequence)
							snake_case_		:			Union[str, Any]											=	tokenizer.convert_ids_to_tokens(_A	)
							snake_case_		:			Optional[Any]											=	rust_tokenizer.tokenize(_A	)
							self.assertListEqual(_A		,   _A	)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    List[Any]:







							"""simple docstring"""


							if not self.test_rust_tokenizer:
								return

							snake_case_		:			Union[str, Any]											=	self.get_tokenizer()
							snake_case_		:			Any											=	self.get_rust_tokenizer()

							snake_case_		:			Union[str, Any]											=	'I was born in 92000, and this is falsé.'

							snake_case_		:			Optional[int]											=	tokenizer.tokenize(_A	)
							snake_case_		:			Dict											=	rust_tokenizer.tokenize(_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			Union[str, Any]											=	tokenizer.encode(_A		,   add_special_tokens=_A	)
							snake_case_		:			str											=	rust_tokenizer.encode(_A		,   add_special_tokens=_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			Optional[int]											=	self.get_rust_tokenizer()
							snake_case_		:			int											=	tokenizer.encode(_A	)
							snake_case_		:			int											=	rust_tokenizer.encode(_A	)
							self.assertListEqual(_A		,   _A	)






						@slow
						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	{'input_ids': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
							# fmt: on

							# camembert is a french model. So we also use french texts.
							snake_case_		:			Dict											=	[
							    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
							    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
							    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
							    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
							    'telles que la traduction et la synthèse de texte.',
							]

							self.tokenizer_integration_test_util(
							    expected_encoding=_A		,   model_name='camembert-base'		,   revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'		,   sequences=_A		,   )

