

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


snake_case_			  = logging.get_logger(__name__)

snake_case_			  = '''▁'''

snake_case_			  = {'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}

snake_case_			  = {
    '''vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',
    },
    '''monolingual_vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',
    },
}

snake_case_			  = {'''vinai/bartpho-syllable''': 1_024}


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       Any														=     VOCAB_FILES_NAMES
    __lowerCamelCase						:       str														=     PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:       Optional[Any]														=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:       Optional[Any]														=     ["""input_ids""", """attention_mask"""]





    def __init__(      self						,   a						,   a						,   a="<s>"						,   a="</s>"						,   a="</s>"						,   a="<s>"						,   a="<unk>"						,   a="<pad>"						,   a="<mask>"						,   a = None						,   **a						,   ):
         # Mask token behave like a normal word, i.e. include the space before it
         lowercase__    :   Optional[Any]      =       AddedToken(a						,   lstrip=a						,   rstrip=a) if isinstance(a						,   a) else mask_token

         lowercase__    :   int      =       {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=a						,   eos_token=a						,   unk_token=a						,   sep_token=a						,   cls_token=a						,   pad_token=a						,   mask_token=a						,   sp_model_kwargs=self.sp_model_kwargs						,   **a						,   )

         lowercase__    :   Tuple      =       vocab_file
         lowercase__    :   Optional[int]      =       monolingual_vocab_file
         lowercase__    :   Optional[Any]      =       spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(str(a))

         # Load the reduced vocab

         # Keep order of special tokens for backward compatibility
         lowercase__    :   Union[str, Any]      =       {}
         lowercase__    :   Dict      =       0
         for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
              if str(a) not in self.fairseq_tokens_to_ids:
                   lowercase__    :   List[Any]      =       cnt
                   cnt += 1
         with open(a						,   'r'						,   encoding='utf-8') as f:
              for line in f.readlines():
                   lowercase__    :   Tuple      =       line.strip().split()[0]
                   lowercase__    :   List[str]      =       len(self.fairseq_tokens_to_ids)
         if str(a) not in self.fairseq_tokens_to_ids:
              lowercase__    :   Union[str, Any]      =       len(self.fairseq_tokens_to_ids)

         lowercase__    :   Optional[int]      =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}





    def __getstate__(      self):
         lowercase__    :   str      =       self.__dict__.copy()
         lowercase__    :   str      =       None
         lowercase__    :   List[Any]      =       self.sp_model.serialized_model_proto()
         return state





    def __setstate__(      self						,   a):
         lowercase__    :   List[str]      =       d

         # for backward compatibility
         if not hasattr(self						,   'sp_model_kwargs'):
              lowercase__    :   str      =       {}

         lowercase__    :   Union[str, Any]      =       spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto)





    def       snake_case_				(      self						,   a						,   a = None):

         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         lowercase__    :   Tuple      =       [self.cls_token_id]
         lowercase__    :   List[Any]      =       [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep





    def       snake_case_				(      self						,   a						,   a = None						,   a = False):

         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=a						,   token_ids_a=a						,   already_has_special_tokens=a)

         if token_ids_a is None:
              return [1] + ([0] * len(a)) + [1]
         return [1] + ([0] * len(a)) + [1, 1] + ([0] * len(a)) + [1]





    def       snake_case_				(      self						,   a						,   a = None):
         lowercase__    :   List[Any]      =       [self.sep_token_id]
         lowercase__    :   Any      =       [self.cls_token_id]

         if token_ids_a is None:
              return len(cls + token_ids_a + sep) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]





    @property
    def       snake_case_				(      self):
         return len(self.fairseq_ids_to_tokens)





    def       snake_case_				(      self):
         lowercase__    :   Union[str, Any]      =       {self.convert_ids_to_tokens(a): i for i in range(self.vocab_size)}
         vocab.update(self.added_tokens_encoder)
         return vocab





    def       snake_case_				(      self						,   a):
         return self.sp_model.encode(a						,   out_type=a)





    def       snake_case_				(      self						,   a):
         if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
         else:
              return self.unk_token_id





    def       snake_case_				(      self						,   a):
         return self.fairseq_ids_to_tokens[index]





    def       snake_case_				(      self						,   a):
         lowercase__    :   List[Any]      =       ''.join(a).replace(a						,   ' ').strip()
         return out_string





    def       snake_case_				(      self						,   a						,   a = None):
         if not os.path.isdir(a):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
              return
         lowercase__    :   Any      =       os.path.join(
             a						,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
         lowercase__    :   int      =       os.path.join(
             a						,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_vocab_file']						,   )

         if os.path.abspath(self.vocab_file) != os.path.abspath(a) and os.path.isfile(self.vocab_file):
              copyfile(self.vocab_file						,   a)
         elif not os.path.isfile(self.vocab_file):
              with open(a						,   'wb') as fi:
                   lowercase__    :   Any      =       self.sp_model.serialized_model_proto()
                   fi.write(a)

         if os.path.abspath(self.monolingual_vocab_file) != os.path.abspath(
             a) and os.path.isfile(self.monolingual_vocab_file):
              copyfile(self.monolingual_vocab_file						,   a)
         elif not os.path.isfile(self.monolingual_vocab_file):
              with open(a						,   'w'						,   encoding='utf-8') as fp:
                   for token in self.fairseq_tokens_to_ids:
                        if token not in self.all_special_tokens:
                             fp.write(f"""{str(a)} \n""")

         return out_vocab_file, out_monolingual_vocab_file




import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers.models.esm.modeling_esmfold import EsmForProteinFolding


class       SCREAMING_SNAKE_CASE__					:





    def __init__(      self						,   a						,   a=13						,   a=7						,   a=False						,   a=True						,   a=False						,   a=False						,   a=19						,   a=32						,   a=5						,   a=4						,   a=37						,   a="gelu"						,   a=0.1						,   a=0.1						,   a=512						,   a=16						,   a=2						,   a=0.02						,   a=3						,   a=4						,   a=None						,   ):
         lowercase__    :   Optional[Any]      =       parent
         lowercase__    :   Dict      =       batch_size
         lowercase__    :   Union[str, Any]      =       seq_length
         lowercase__    :   Optional[Any]      =       is_training
         lowercase__    :   Tuple      =       use_input_mask
         lowercase__    :   List[str]      =       use_token_type_ids
         lowercase__    :   Optional[Any]      =       use_labels
         lowercase__    :   List[str]      =       vocab_size
         lowercase__    :   Optional[int]      =       hidden_size
         lowercase__    :   List[str]      =       num_hidden_layers
         lowercase__    :   Any      =       num_attention_heads
         lowercase__    :   int      =       intermediate_size
         lowercase__    :   Any      =       hidden_act
         lowercase__    :   Any      =       hidden_dropout_prob
         lowercase__    :   str      =       attention_probs_dropout_prob
         lowercase__    :   List[Any]      =       max_position_embeddings
         lowercase__    :   int      =       type_vocab_size
         lowercase__    :   List[Any]      =       type_sequence_label_size
         lowercase__    :   str      =       initializer_range
         lowercase__    :   List[str]      =       num_labels
         lowercase__    :   Union[str, Any]      =       num_choices
         lowercase__    :   Optional[int]      =       scope





    def       snake_case_				(      self):
         lowercase__    :   List[Any]      =       ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size)

         lowercase__    :   List[Any]      =       None
         if self.use_input_mask:
              lowercase__    :   Union[str, Any]      =       random_attention_mask([self.batch_size, self.seq_length])

         lowercase__    :   int      =       None
         lowercase__    :   Optional[int]      =       None
         lowercase__    :   Optional[int]      =       None
         if self.use_labels:
              lowercase__    :   Optional[int]      =       ids_tensor([self.batch_size]						,   self.type_sequence_label_size)
              lowercase__    :   str      =       ids_tensor([self.batch_size, self.seq_length]						,   self.num_labels)
              lowercase__    :   str      =       ids_tensor([self.batch_size]						,   self.num_choices)

         lowercase__    :   int      =       self.get_config()

         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





    def       snake_case_				(      self):
         lowercase__    :   str      =       EsmConfig(
             vocab_size=33						,   hidden_size=self.hidden_size						,   pad_token_id=1						,   num_hidden_layers=self.num_hidden_layers						,   num_attention_heads=self.num_attention_heads						,   intermediate_size=self.intermediate_size						,   hidden_act=self.hidden_act						,   hidden_dropout_prob=self.hidden_dropout_prob						,   attention_probs_dropout_prob=self.attention_probs_dropout_prob						,   max_position_embeddings=self.max_position_embeddings						,   type_vocab_size=self.type_vocab_size						,   initializer_range=self.initializer_range						,   is_folding_model=a						,   esmfold_config={'trunk': {'num_blocks': 2}, 'fp16_esm': False}						,   )
         return config





    def       snake_case_				(      self						,   a						,   a						,   a						,   a						,   a						,   a):
         lowercase__    :   Dict      =       EsmForProteinFolding(config=a).float()
         model.to(a)
         model.eval()
         lowercase__    :   Union[str, Any]      =       model(a						,   attention_mask=a)
         lowercase__    :   Dict      =       model(a)
         lowercase__    :   int      =       model(a)

         self.parent.assertEqual(result.positions.shape						,   (8, self.batch_size, self.seq_length, 14, 3))
         self.parent.assertEqual(result.angles.shape						,   (8, self.batch_size, self.seq_length, 7, 2))





    def       snake_case_				(      self):
         lowercase__    :   List[str]      =       self.prepare_config_and_inputs()
         (
             (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , 
         )    :   int      =       config_and_inputs
         lowercase__    :   Optional[int]      =       {'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict



@require_torch
class       SCREAMING_SNAKE_CASE__					(__snake_case		,       __snake_case		,       unittest.TestCase     ):
    __lowerCamelCase						:       Dict														=     False

    __lowerCamelCase						:       Dict														=     (EsmForProteinFolding,) if is_torch_available() else ()
    __lowerCamelCase						:       Union[str, Any]														=     ()
    __lowerCamelCase						:       List[Any]														=     {} if is_torch_available() else {}
    __lowerCamelCase						:       Optional[Any]														=     False





    def       snake_case_				(      self):
         lowercase__    :   Tuple      =       EsmFoldModelTester(self)
         lowercase__    :   List[Any]      =       ConfigTester(self						,   config_class=a						,   hidden_size=37)





    def       snake_case_				(      self):
         self.config_tester.run_common_tests()





    def       snake_case_				(      self):
         lowercase__    :   Optional[int]      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a)





    @unittest.skip('Does not support attention outputs')
    def       snake_case_				(      self):
         pass





    @unittest.skip
    def       snake_case_				(      self):
         pass





    @unittest.skip('Esm does not support embedding resizing')
    def       snake_case_				(      self):
         pass





    @unittest.skip('Esm does not support embedding resizing')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support passing input embeds!')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support head pruning.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support head pruning.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support head pruning.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support head pruning.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support head pruning.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not output hidden states in the normal way.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMfold does not output hidden states in the normal way.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold only has one output format.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('This test doesn\'t work for ESMFold and doesn\'t test core functionality')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support input chunking.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold doesn\'t support torchscript compilation.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold doesn\'t support torchscript compilation.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold doesn\'t support torchscript compilation.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold doesn\'t support data parallel.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
    def       snake_case_				(      self):
         pass



@require_torch
class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    @slow
    def       snake_case_				(      self):
         lowercase__    :   Dict      =       EsmForProteinFolding.from_pretrained('facebook/esmfold_v1').float()
         model.eval()
         lowercase__    :   Optional[Any]      =       torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]])
         lowercase__    :   Optional[int]      =       model(a)['positions']
         lowercase__    :   Dict      =       torch.tensor([2.5_828, 0.7_993, -10.9_334]						,   dtype=torch.floataa)
         self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]						,   a						,   atol=1e-4))

