
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							:Optional[Any]							 =				Mapping[str, np.ndarray]
SCREAMING_SNAKE_CASE							:List[str]							 =				Mapping[str, Any]  # Is a nested dict.
SCREAMING_SNAKE_CASE							:int							 =				0.01





@dataclasses.dataclass(frozen=__SCREAMING_SNAKE_CASE  )
class 							UpperCAmelCase      :




	'''simple docstring'''





	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 						UpperCAmelCase			(	a_						)		->	Protein:







				"""simple docstring"""



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

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







				"""simple docstring"""



				__A   			= []

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

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

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

				pdb_headers.append(F'''PARENT {' '.join(a_						)}'''						)

				return pdb_headers


def 						UpperCAmelCase			(	a_		,   a_						)		->	str:







				"""simple docstring"""



				__A   			= []
				__A   			= pdb_str.split("\n"						)

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

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

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

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

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

				__A   			= 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_						):
																__A   			= parents_per_chain[chain_counter]
												else:
																__A   			= ["N/A"]

												out_pdb_lines.append(make_parent_line(a_						)						)

				return "\n".join(a_						)


def 						UpperCAmelCase			(	a_						)		->	str:







				"""simple docstring"""



				__A   			= residue_constants.restypes + ["X"]

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

				__A   			= residue_constants.atom_types

				__A   			= []

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

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

				__A   			= get_pdb_headers(a_						)
				if len(a_						) > 0:
								pdb_lines.extend(a_						)

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

												__A   			= "ATOM"
												__A   			= atom_name if len(a_						) == 4 else F''' {atom_name}'''
												__A   			= ""
												__A   			= ""
												__A   			= 1.00
												__A   			= atom_name[0]  # Protein supports only C, N, O, S, this works.
												__A   			= ""

												__A   			= "A"
												if chain_index is not None:
																__A   			= chain_tags[chain_index[i]]

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

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

								if should_terminate:
												# Close the chain.
												__A   			= "TER"
												__A   			= (
												    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 						UpperCAmelCase			(	a_						)		->	np.ndarray:







				"""simple docstring"""



				return residue_constants.STANDARD_ATOM_MASK[prot.aatype]


def 						UpperCAmelCase			(	a_		,   a_		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = 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=a_		,   remark=a_		,   parents=a_		,   parents_chain_index=a_		,   )

'''simple docstring'''




import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors






@require_tokenizers
class 				a__(					lowerCAmelCase__ ,						unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Dict      =       MvpTokenizer
			UpperCAmelCase_			:     Optional[Any]      =       MvpTokenizerFast
			UpperCAmelCase_			:     str      =       True
			UpperCAmelCase_			:     List[Any]      =       filter_roberta_detectors



			def 							a_       (   self):




					"""simple docstring"""


					super().setUp()
					lowerCAmelCase   					=  [
					    """l""",
					    """o""",
					    """w""",
					    """e""",
					    """r""",
					    """s""",
					    """t""",
					    """i""",
					    """d""",
					    """n""",
					    """\u0120""",
					    """\u0120l""",
					    """\u0120n""",
					    """\u0120lo""",
					    """\u0120low""",
					    """er""",
					    """\u0120lowest""",
					    """\u0120newer""",
					    """\u0120wider""",
					    """<unk>""",
					]
					lowerCAmelCase   					=  dict(zip(__lowerCAmelCase       ,    range(len(__lowerCAmelCase))))
					lowerCAmelCase   					=  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
					lowerCAmelCase   					=  {"""unk_token""": """<unk>"""}

					lowerCAmelCase   					=  os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES["""vocab_file"""])
					lowerCAmelCase   					=  os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES["""merges_file"""])
					with open(self.vocab_file       ,    """w"""       ,    encoding="""utf-8""") as fp:
							fp.write(json.dumps(__lowerCAmelCase) + """\n""")
					with open(self.merges_file       ,    """w"""       ,    encoding="""utf-8""") as fp:
							fp.write("""\n""".join(__lowerCAmelCase))



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					kwargs.update(self.special_tokens_map)
					return self.tokenizer_class.from_pretrained(self.tmpdirname       ,    **__lowerCAmelCase)



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					kwargs.update(self.special_tokens_map)
					return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,    **__lowerCAmelCase)



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					return "lower newer", "lower newer"



			@cached_property
			def 							a_       (   self):




					"""simple docstring"""


					return MvpTokenizer.from_pretrained("""RUCAIBox/mvp""")



			@cached_property
			def 							a_       (   self):




					"""simple docstring"""


					return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp""")



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					lowerCAmelCase   					=  [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(__lowerCAmelCase       ,    max_length=len(__lowerCAmelCase)       ,    padding=__lowerCAmelCase       ,    return_tensors="""pt""")
							self.assertIsInstance(__lowerCAmelCase       ,    __lowerCAmelCase)

							self.assertEqual((2, 9)       ,    batch.input_ids.shape)
							self.assertEqual((2, 9)       ,    batch.attention_mask.shape)
							lowerCAmelCase   					=  batch.input_ids.tolist()[0]
							self.assertListEqual(__lowerCAmelCase       ,    __lowerCAmelCase)
							# Test that special tokens are reset



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(__lowerCAmelCase       ,    padding=__lowerCAmelCase       ,    return_tensors="""pt""")
							# check if input_ids are returned and no labels
							self.assertIn("""input_ids"""       ,    __lowerCAmelCase)
							self.assertIn("""attention_mask"""       ,    __lowerCAmelCase)
							self.assertNotIn("""labels"""       ,    __lowerCAmelCase)
							self.assertNotIn("""decoder_attention_mask"""       ,    __lowerCAmelCase)



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  [
					    """Summary of the text.""",
					    """Another summary.""",
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(text_target=__lowerCAmelCase       ,    max_length=32       ,    padding="""max_length"""       ,    return_tensors="""pt""")
							self.assertEqual(32       ,    targets["""input_ids"""].shape[1])



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(
							    ["""I am a small frog""" * 1024, """I am a small frog"""]       ,    padding=__lowerCAmelCase       ,    truncation=__lowerCAmelCase       ,    return_tensors="""pt""")
							self.assertIsInstance(__lowerCAmelCase       ,    __lowerCAmelCase)
							self.assertEqual(batch.input_ids.shape       ,    (2, 1024))



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  ["""A long paragraph for summarization."""]
					lowerCAmelCase   					=  [
					    """Summary of the text.""",
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(__lowerCAmelCase       ,    text_target=__lowerCAmelCase       ,    return_tensors="""pt""")
							lowerCAmelCase   					=  inputs["""input_ids"""]
							lowerCAmelCase   					=  inputs["""labels"""]
							self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
							self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
							self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
							self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())



			def 							a_       (   self):




					"""simple docstring"""


					pass







			def 							a_       (   self):




					"""simple docstring"""


					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
									lowerCAmelCase   					=  self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase       ,    **__lowerCAmelCase)
									lowerCAmelCase   					=  self.tokenizer_class.from_pretrained(__lowerCAmelCase       ,    **__lowerCAmelCase)
									lowerCAmelCase   					=  """A, <mask> AllenNLP sentence."""
									lowerCAmelCase   					=  tokenizer_r.encode_plus(__lowerCAmelCase       ,    add_special_tokens=__lowerCAmelCase       ,    return_token_type_ids=__lowerCAmelCase)
									lowerCAmelCase   					=  tokenizer_p.encode_plus(__lowerCAmelCase       ,    add_special_tokens=__lowerCAmelCase       ,    return_token_type_ids=__lowerCAmelCase)

									# token_type_ids should put 0 everywhere
									self.assertEqual(sum(tokens_r["""token_type_ids"""])       ,    sum(tokens_p["""token_type_ids"""]))

									# attention_mask should put 1 everywhere, so sum over length should be 1
									self.assertEqual(
									    sum(tokens_r["""attention_mask"""]) / len(tokens_r["""attention_mask"""])       ,    sum(tokens_p["""attention_mask"""]) / len(tokens_p["""attention_mask"""])       ,    )

									lowerCAmelCase   					=  tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""])
									lowerCAmelCase   					=  tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""])

									# Rust correctly handles the space before the mask while python doesnt
									self.assertSequenceEqual(tokens_p["""input_ids"""]       ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
									self.assertSequenceEqual(tokens_r["""input_ids"""]       ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])

									self.assertSequenceEqual(
									    __lowerCAmelCase       ,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""])
									self.assertSequenceEqual(
									    __lowerCAmelCase       ,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""])




