"""simple docstring"""
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__      						=  get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
     from transformers.models.plbart.modeling_plbart import shift_tokens_right

SCREAMING_SNAKE_CASE__      						=  50_003
SCREAMING_SNAKE_CASE__      						=  50_002



@require_sentencepiece
@require_tokenizers
class   lowerCAmelCase_    (			UpperCamelCase_     ,    unittest.TestCase					):



    """simple docstring"""
    _lowerCAmelCase     :    Dict			  =						PLBartTokenizer
    _lowerCAmelCase     :    Optional[int]			  =						None
    _lowerCAmelCase     :    List[str]			  =						False

    def 		snake_case (				self    ):






          """simple docstring"""

          super().setUp()

          # We have a SentencePiece fixture for testing
          snake_case								=			PLBartTokenizer(__A		,    language_codes='base'		,    keep_accents=__A    )
          tokenizer.save_pretrained(self.tmpdirname    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			PLBartTokenizer(__A		,    language_codes='base'		,    keep_accents=__A    )

          snake_case								=			tokenizer.tokenize('This is a test'    )
          self.assertListEqual(__A		,    ['▁This', '▁is', '▁a', '▁t', 'est']    )

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(__A    )		,    [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]		,    )

          snake_case								=			tokenizer.tokenize('I was born in 92000, and this is falsé.'    )
          self.assertListEqual(
              __A		,    [
                  SPIECE_UNDERLINE + 'I',
                  SPIECE_UNDERLINE + 'was',
                  SPIECE_UNDERLINE + 'b',
                  'or',
                  'n',
                  SPIECE_UNDERLINE + 'in',
                  SPIECE_UNDERLINE + '',
                  '9',
                  '2',
                  '0',
                  '0',
                  '0',
                  ',',
                  SPIECE_UNDERLINE + 'and',
                  SPIECE_UNDERLINE + 'this',
                  SPIECE_UNDERLINE + 'is',
                  SPIECE_UNDERLINE + 'f',
                  'al',
                  's',
                  'é',
                  '.',
              ]		,    )
          snake_case								=			tokenizer.convert_tokens_to_ids(__A    )
          self.assertListEqual(
              __A		,    [
                  value + tokenizer.fairseq_offset
                  for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
              ]		,    )

          snake_case								=			tokenizer.convert_ids_to_tokens(__A    )
          self.assertListEqual(
              __A		,    [
                  SPIECE_UNDERLINE + 'I',
                  SPIECE_UNDERLINE + 'was',
                  SPIECE_UNDERLINE + 'b',
                  'or',
                  'n',
                  SPIECE_UNDERLINE + 'in',
                  SPIECE_UNDERLINE + '',
                  '<unk>',
                  '2',
                  '0',
                  '0',
                  '0',
                  ',',
                  SPIECE_UNDERLINE + 'and',
                  SPIECE_UNDERLINE + 'this',
                  SPIECE_UNDERLINE + 'is',
                  SPIECE_UNDERLINE + 'f',
                  'al',
                  's',
                  '<unk>',
                  '.',
              ]		,    )

          snake_case								=			tokenizer.vocab_size
          snake_case								=			[tokenizer.convert_ids_to_tokens(__A    ) for x in range(end - 4		,    __A    )]

          self.assertListEqual(__A		,    ['__java__', '__python__', '__en_XX__', '<mask>']    )

          snake_case								=			"java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
          snake_case								=			tokenizer(__A    ).input_ids
          self.assertEqual(
              tokenizer.decode(__A		,    skip_special_tokens=__A		,    clean_up_tokenization_spaces=__A    )		,    __A		,    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			PLBartTokenizer(__A		,    language_codes='multi'		,    keep_accents=__A    )

          snake_case								=			tokenizer.tokenize('This is a test'    )
          self.assertListEqual(__A		,    ['▁This', '▁is', '▁a', '▁t', 'est']    )

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(__A    )		,    [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]		,    )

          snake_case								=			tokenizer.tokenize('I was born in 92000, and this is falsé.'    )
          self.assertListEqual(
              __A		,    [
                  SPIECE_UNDERLINE + 'I',
                  SPIECE_UNDERLINE + 'was',
                  SPIECE_UNDERLINE + 'b',
                  'or',
                  'n',
                  SPIECE_UNDERLINE + 'in',
                  SPIECE_UNDERLINE + '',
                  '9',
                  '2',
                  '0',
                  '0',
                  '0',
                  ',',
                  SPIECE_UNDERLINE + 'and',
                  SPIECE_UNDERLINE + 'this',
                  SPIECE_UNDERLINE + 'is',
                  SPIECE_UNDERLINE + 'f',
                  'al',
                  's',
                  'é',
                  '.',
              ]		,    )
          snake_case								=			tokenizer.convert_tokens_to_ids(__A    )
          self.assertListEqual(
              __A		,    [
                  value + tokenizer.fairseq_offset
                  for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
              ]		,    )

          snake_case								=			tokenizer.convert_ids_to_tokens(__A    )
          self.assertListEqual(
              __A		,    [
                  SPIECE_UNDERLINE + 'I',
                  SPIECE_UNDERLINE + 'was',
                  SPIECE_UNDERLINE + 'b',
                  'or',
                  'n',
                  SPIECE_UNDERLINE + 'in',
                  SPIECE_UNDERLINE + '',
                  '<unk>',
                  '2',
                  '0',
                  '0',
                  '0',
                  ',',
                  SPIECE_UNDERLINE + 'and',
                  SPIECE_UNDERLINE + 'this',
                  SPIECE_UNDERLINE + 'is',
                  SPIECE_UNDERLINE + 'f',
                  'al',
                  's',
                  '<unk>',
                  '.',
              ]		,    )
          snake_case								=			tokenizer.vocab_size
          snake_case								=			[tokenizer.convert_ids_to_tokens(__A    ) for x in range(end - 7		,    __A    )]

          self.assertListEqual(
              __A		,    ['__java__', '__python__', '__en_XX__', '__javascript__', '__php__', '__ruby__', '__go__']    )
          snake_case								=			"java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
          snake_case								=			tokenizer(__A    ).input_ids
          self.assertEqual(
              tokenizer.decode(__A		,    skip_special_tokens=__A		,    clean_up_tokenization_spaces=__A    )		,    __A		,    )





@require_torch
@require_sentencepiece
@require_tokenizers
class   lowerCAmelCase_    (			unittest.TestCase					):



    """simple docstring"""
    _lowerCAmelCase     :    List[Any]			  =						"""uclanlp/plbart-python-en_XX"""
    _lowerCAmelCase     :    str			  =						[
        """def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])""",
        """def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])""",
    ]
    _lowerCAmelCase     :    List[Any]			  =						[
        """Returns the maximum value of a b c.""",
        """Sums the values of a b c.""",
    ]
    _lowerCAmelCase     :    Union[str, Any]			  =						[
        134,
        5452,
        33460,
        33441,
        33463,
        33465,
        33463,
        33449,
        988,
        20,
        33456,
        19,
        33456,
        771,
        39,
        4258,
        889,
        3318,
        33441,
        33463,
        33465,
        33463,
        33449,
        2471,
        2,
        PYTHON_CODE,
    ]

    @classmethod
    def 		snake_case (				cls    ):






          """simple docstring"""

          snake_case								=			PLBartTokenizer.from_pretrained(
              cls.checkpoint_name		,    language_codes='base'		,    src_lang='python'		,    tgt_lang='en_XX'    )
          snake_case								=			1
          return cls

    def 		snake_case (				self    ):






          """simple docstring"""

          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__java__']		,    5_00_01    )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__python__']		,    5_00_02    )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__en_XX__']		,    5_00_03    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.tokenizer.batch_encode_plus(self.src_text    ).input_ids[0]
          self.assertListEqual(self.expected_src_tokens		,    __A    )

    def 		snake_case (				self    ):






          """simple docstring"""

          self.assertIn(__A		,    self.tokenizer.all_special_ids    )
          snake_case								=			[EN_CODE, 90_37, 3_34_42, 57, 7_52, 1_53, 14, 56, 18, 9, 2]
          snake_case								=			self.tokenizer.decode(__A		,    skip_special_tokens=__A    )
          snake_case								=			self.tokenizer.decode(generated_ids[1:]		,    skip_special_tokens=__A    )
          self.assertEqual(__A		,    __A    )
          self.assertNotIn(self.tokenizer.eos_token		,    __A    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			["def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])" * 20]
          self.assertIsInstance(src_text[0]		,    __A    )
          snake_case								=			10
          snake_case								=			self.tokenizer(__A		,    max_length=__A		,    truncation=__A    ).input_ids[0]
          self.assertEqual(ids[-2]		,    2    )
          self.assertEqual(ids[-1]		,    __A    )
          self.assertEqual(len(__A    )		,    __A    )

    def 		snake_case (				self    ):






          """simple docstring"""

          self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', '__java__']    )		,    [5_00_04, 5_00_01]    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			tempfile.mkdtemp()
          snake_case								=			self.tokenizer.fairseq_tokens_to_ids
          self.tokenizer.save_pretrained(__A    )
          snake_case								=			PLBartTokenizer.from_pretrained(__A    )
          self.assertDictEqual(new_tok.fairseq_tokens_to_ids		,    __A    )

    @require_torch
    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.tokenizer(self.src_text		,    text_target=self.tgt_text		,    padding=__A		,    return_tensors='pt'    )
          snake_case								=			shift_tokens_right(batch['labels']		,    self.tokenizer.pad_token_id    )

          # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
          self.assertEqual(batch.input_ids[1][-2:].tolist()		,    [2, PYTHON_CODE]    )
          self.assertEqual(batch.decoder_input_ids[1][0]		,    __A    )
          self.assertEqual(batch.decoder_input_ids[1][-1]		,    2    )
          self.assertEqual(batch.labels[1][-2:].tolist()		,    [2, EN_CODE]    )

    @require_torch
    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.tokenizer(
              self.src_text		,    text_target=self.tgt_text		,    padding=__A		,    truncation=__A		,    max_length=len(self.expected_src_tokens    )		,    return_tensors='pt'		,    )
          snake_case								=			shift_tokens_right(batch['labels']		,    self.tokenizer.pad_token_id    )

          self.assertIsInstance(__A		,    __A    )

          self.assertEqual((2, 26)		,    batch.input_ids.shape    )
          self.assertEqual((2, 26)		,    batch.attention_mask.shape    )
          snake_case								=			batch.input_ids.tolist()[0]
          self.assertListEqual(self.expected_src_tokens		,    __A    )
          self.assertEqual(2		,    batch.decoder_input_ids[0, -1]    )  # EOS
          # Test that special tokens are reset
          self.assertEqual(self.tokenizer.prefix_tokens		,    []    )
          self.assertEqual(self.tokenizer.suffix_tokens		,    [self.tokenizer.eos_token_id, PYTHON_CODE]    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.tokenizer(self.src_text		,    padding=__A		,    truncation=__A		,    max_length=3		,    return_tensors='pt'    )
          snake_case								=			self.tokenizer(
              text_target=self.tgt_text		,    padding=__A		,    truncation=__A		,    max_length=10		,    return_tensors='pt'    )
          snake_case								=			targets["input_ids"]
          snake_case								=			shift_tokens_right(__A		,    self.tokenizer.pad_token_id    )

          self.assertEqual(batch.input_ids.shape[1]		,    3    )
          self.assertEqual(batch.decoder_input_ids.shape[1]		,    10    )

    @require_torch
    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.tokenizer._build_translation_inputs(
              'A test'		,    return_tensors='pt'		,    src_lang='en_XX'		,    tgt_lang='java'    )

          self.assertEqual(
              nested_simplify(__A    )		,    {
                  # A, test, EOS, en_XX
                  'input_ids': [[1_50, 2_42, 2, 5_00_03]],
                  'attention_mask': [[1, 1, 1, 1]],
                  # java
                  'forced_bos_token_id': 5_00_01,
              }		,    )



"""simple docstring"""
from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map





def    lowerCAmelCase__    (			_UpperCamelCase       :				Dict[str, torch.Tensor]					)					->				Dict[str, torch.Tensor]:





						"""simple docstring"""






						snake_case								=			[]
						snake_case								=			[]
						snake_case								=			[]

						for rt in rc.restypes:
												snake_case								=			rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
												restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]					)
												snake_case								=			{name: i for i, name in enumerate(_UpperCamelCase					)}
												restype_atomaa_to_atomaa_list.append(
												    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]					)

												restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]					)

						# Add dummy mapping for restype 'UNK'
						restype_atomaa_to_atomaa_list.append([0] * 1_4					)
						restype_atomaa_to_atomaa_list.append([0] * 3_7					)
						restype_atomaa_mask_list.append([0.0] * 1_4					)

						snake_case								=			torch.tensor(
						    _UpperCamelCase ,			dtype=torch.intaa ,			device=protein['aatype'].device ,			)
						snake_case								=			torch.tensor(
						    _UpperCamelCase ,			dtype=torch.intaa ,			device=protein['aatype'].device ,			)
						snake_case								=			torch.tensor(
						    _UpperCamelCase ,			dtype=torch.floataa ,			device=protein['aatype'].device ,			)
						snake_case								=			protein['aatype'].to(torch.long					)

						# create the mapping for (residx, atom14) --> atom37, i.e. an array
						# with shape (num_res, 14) containing the atom37 indices for this protein
						snake_case								=			restype_atomaa_to_atomaa[protein_aatype]
						snake_case								=			restype_atomaa_mask[protein_aatype]

						snake_case								=			residx_atomaa_mask
						snake_case								=			residx_atomaa_to_atomaa.long()

						# create the gather indices for mapping back
						snake_case								=			restype_atomaa_to_atomaa[protein_aatype]
						snake_case								=			residx_atomaa_to_atomaa.long()

						# create the corresponding mask
						snake_case								=			torch.zeros([2_1, 3_7] ,			dtype=torch.floataa ,			device=protein['aatype'].device					)
						for restype, restype_letter in enumerate(rc.restypes					):
												snake_case								=			rc.restype_atoa[restype_letter]
												snake_case								=			rc.residue_atoms[restype_name]
												for atom_name in atom_names:
																		snake_case								=			rc.atom_order[atom_name]
																		snake_case								=			1

						snake_case								=			restype_atomaa_mask[protein_aatype]
						snake_case								=			residx_atomaa_mask

						return protein





def    lowerCAmelCase__    (			_UpperCamelCase       :				Dict[str, torch.Tensor]					)					->				Dict[str, np.ndarray]:





						"""simple docstring"""






						snake_case								=			tree_map(lambda _UpperCamelCase					: torch.tensor(_UpperCamelCase ,			device=batch['aatype'].device					) ,			_UpperCamelCase ,			np.ndarray					)
						snake_case								=			tensor_tree_map(lambda _UpperCamelCase					: np.array(_UpperCamelCase					) ,			make_atomaa_masks(_UpperCamelCase					)					)
						return out


