import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowercase     :   List[Any]											=      logging.get_logger(__name__)

__lowercase     :   Optional[Any]											=      {
    '''vocab_file''': '''vocab.txt''',
    '''merges_file''': '''bpe.codes''',
}

__lowercase     :   Optional[int]											=      {
    '''vocab_file''': {
        '''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt''',
        '''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt''',
    },
    '''merges_file''': {
        '''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes''',
        '''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes''',
    },
}

__lowercase     :   Tuple											=      {
    '''vinai/phobert-base''': 256,
    '''vinai/phobert-large''': 256,
}



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :int    						=	set()
    __UpperCamelCase  :Dict    						=	word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)			)
        __UpperCamelCase  :List[str]    						=	char

    __UpperCamelCase  :Optional[int]    						=	set(SCREAMING_SNAKE_CASE			)
    return pairs


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		int      			=   VOCAB_FILES_NAMES
    a__			:		Optional[Any]      			=   PRETRAINED_VOCAB_FILES_MAP
    a__			:		Dict      			=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







    def __init__(			self				,				__lowercase				,				__lowercase				,				__lowercase="<s>"				,				__lowercase="</s>"				,				__lowercase="</s>"				,				__lowercase="<s>"				,				__lowercase="<unk>"				,				__lowercase="<pad>"				,				__lowercase="<mask>"				,				**__lowercase				,				) ->			Any:
        super().__init__(
            bos_token=__lowercase				,				eos_token=__lowercase				,				unk_token=__lowercase				,				sep_token=__lowercase				,				cls_token=__lowercase				,				pad_token=__lowercase				,				mask_token=__lowercase				,				**__lowercase				,				)

        __UpperCamelCase  :Dict    						=	vocab_file
        __UpperCamelCase  :Optional[Any]    						=	merges_file

        __UpperCamelCase  :str    						=	{}
        __UpperCamelCase  :Tuple    						=	0
        __UpperCamelCase  :Optional[Any]    						=	1
        __UpperCamelCase  :Optional[Any]    						=	2
        __UpperCamelCase  :Any    						=	3

        self.add_from_file(__lowercase)

        __UpperCamelCase  :Tuple    						=	{v: k for k, v in self.encoder.items()}

        with open(__lowercase				,				encoding='''utf-8''') as merges_handle:
            __UpperCamelCase  :int    						=	merges_handle.read().split('''\n''')[:-1]
        __UpperCamelCase  :Optional[Any]    						=	[tuple(merge.split()[:-1]) for merge in merges]
        __UpperCamelCase  :str    						=	dict(zip(__lowercase				,				range(len(__lowercase))))
        __UpperCamelCase  :Tuple    						=	{}







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase = None) ->			List[int]:

        if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __UpperCamelCase  :Union[str, Any]    						=	[self.cls_token_id]
        __UpperCamelCase  :List[Any]    						=	[self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase = None				,				__lowercase = False) ->			List[int]:

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

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







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase = None) ->			List[int]:
        __UpperCamelCase  :str    						=	[self.sep_token_id]
        __UpperCamelCase  :Optional[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     UpperCamelCase__     (			self) ->			int:
        return len(self.encoder)







    def     UpperCamelCase__     (			self) ->			Tuple:
        return dict(self.encoder				,				**self.added_tokens_encoder)







    def     UpperCamelCase__     (			self				,				__lowercase) ->			Dict:
        if token in self.cache:
            return self.cache[token]
        __UpperCamelCase  :Optional[Any]    						=	tuple(__lowercase)
        __UpperCamelCase  :List[Any]    						=	tuple(list(word[:-1]) + [word[-1] + '''</w>'''])
        __UpperCamelCase  :List[Any]    						=	get_pairs(__lowercase)

        if not pairs:
            return token

        while True:
            __UpperCamelCase  :List[Any]    						=	min(__lowercase				,				key=lambda __lowercase: self.bpe_ranks.get(__lowercase				,				float('''inf''')))
            if bigram not in self.bpe_ranks:
                break
            __UpperCamelCase  :Any    						=	bigram
            __UpperCamelCase  :Any    						=	[]
            __UpperCamelCase  :Union[str, Any]    						=	0
            while i < len(__lowercase):
                try:
                    __UpperCamelCase  :List[str]    						=	word.index(__lowercase				,				__lowercase)
                except ValueError:
                    new_word.extend(word[i:])
                    break
                else:
                    new_word.extend(word[i:j])
                    __UpperCamelCase  :Union[str, Any]    						=	j

                if word[i] == first and i < len(__lowercase) - 1 and word[i + 1] == second:
                    new_word.append(first + second)
                    i += 2
                else:
                    new_word.append(word[i])
                    i += 1
            __UpperCamelCase  :Any    						=	tuple(__lowercase)
            __UpperCamelCase  :Tuple    						=	new_word
            if len(__lowercase) == 1:
                break
            else:
                __UpperCamelCase  :Tuple    						=	get_pairs(__lowercase)
        __UpperCamelCase  :List[Any]    						=	'''@@ '''.join(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	word[:-4]
        __UpperCamelCase  :Optional[Any]    						=	word
        return word







    def     UpperCamelCase__     (			self				,				__lowercase) ->			List[str]:
        __UpperCamelCase  :int    						=	[]

        __UpperCamelCase  :str    						=	re.findall(r'''\S+\n?'''				,				__lowercase)

        for token in words:
            split_tokens.extend(list(self.bpe(__lowercase).split(''' ''')))
        return split_tokens







    def     UpperCamelCase__     (			self				,				__lowercase) ->			Union[str, Any]:
        return self.encoder.get(__lowercase				,				self.encoder.get(self.unk_token))







    def     UpperCamelCase__     (			self				,				__lowercase) ->			Tuple:
        return self.decoder.get(__lowercase				,				self.unk_token)







    def     UpperCamelCase__     (			self				,				__lowercase) ->			Dict:
        __UpperCamelCase  :str    						=	''' '''.join(__lowercase).replace('''@@ '''				,				'''''').strip()
        return out_string







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase = None) ->			Tuple[str]:
        if not os.path.isdir(__lowercase):
            logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
            return
        __UpperCamelCase  :str    						=	os.path.join(
            __lowercase				,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
        __UpperCamelCase  :List[Any]    						=	os.path.join(
            __lowercase				,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''])

        if os.path.abspath(self.vocab_file) != os.path.abspath(__lowercase):
            copyfile(self.vocab_file				,				__lowercase)

        if os.path.abspath(self.merges_file) != os.path.abspath(__lowercase):
            copyfile(self.merges_file				,				__lowercase)

        return out_vocab_file, out_merge_file







    def     UpperCamelCase__     (			self				,				__lowercase) ->			Union[str, Any]:
        if isinstance(__lowercase				,				__lowercase):
            try:
                with open(__lowercase				,				'''r'''				,				encoding='''utf-8''') as fd:
                    self.add_from_file(__lowercase)
            except FileNotFoundError as fnfe:
                raise fnfe
            except UnicodeError:
                raise Exception(f"""Incorrect encoding detected in {f}, please rebuild the dataset""")
            return

        __UpperCamelCase  :Union[str, Any]    						=	f.readlines()
        for lineTmp in lines:
            __UpperCamelCase  :Any    						=	lineTmp.strip()
            __UpperCamelCase  :Dict    						=	line.rfind(''' ''')
            if idx == -1:
                raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt>\'''')
            __UpperCamelCase  :int    						=	line[:idx]
            __UpperCamelCase  :Any    						=	len(self.encoder)






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 			lowerCamelCase_  :






    '''simple docstring'''






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







    def     UpperCamelCase__     (			self) ->			List[Any]:
        __UpperCamelCase  :int    						=	ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size)

        __UpperCamelCase  :int    						=	None
        if self.use_input_mask:
            __UpperCamelCase  :Optional[Any]    						=	random_attention_mask([self.batch_size, self.seq_length])

        __UpperCamelCase  :Dict    						=	None
        __UpperCamelCase  :List[Any]    						=	None
        __UpperCamelCase  :Tuple    						=	None
        if self.use_labels:
            __UpperCamelCase  :Union[str, Any]    						=	ids_tensor([self.batch_size]				,				self.type_sequence_label_size)
            __UpperCamelCase  :Union[str, Any]    						=	ids_tensor([self.batch_size, self.seq_length]				,				self.num_labels)
            __UpperCamelCase  :str    						=	ids_tensor([self.batch_size]				,				self.num_choices)

        __UpperCamelCase  :Optional[Any]    						=	self.get_config()

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







    def     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :int    						=	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=__lowercase				,				esmfold_config={'''trunk''': {'''num_blocks''': 2}, '''fp16_esm''': False}				,				)
        return config







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase) ->			Union[str, Any]:
        __UpperCamelCase  :int    						=	EsmForProteinFolding(config=__lowercase).float()
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase  :Tuple    						=	model(__lowercase				,				attention_mask=__lowercase)
        __UpperCamelCase  :Any    						=	model(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	model(__lowercase)

        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     UpperCamelCase__     (			self) ->			Optional[Any]:
        __UpperCamelCase  :Dict    						=	self.prepare_config_and_inputs()
        (
            (
            __UpperCamelCase
        )						, (
            __UpperCamelCase
        )						, (
            __UpperCamelCase
        )						, (
            __UpperCamelCase
        )						, (
            __UpperCamelCase
        )						, (
            __UpperCamelCase
        )						, 
        )  :List[str]    						=	config_and_inputs
        __UpperCamelCase  :Tuple    						=	{'''input_ids''': input_ids, '''attention_mask''': input_mask}
        return config, inputs_dict


@require_torch
class 			lowerCamelCase_  (     UpperCAmelCase_		,			UpperCAmelCase_		,			unittest.TestCase				):






    '''simple docstring'''






    a__			:		Optional[int]      			=   False

    a__			:		Optional[int]      			=   (EsmForProteinFolding,) if is_torch_available() else ()
    a__			:		str      			=   ()
    a__			:		Tuple      			=   {} if is_torch_available() else {}
    a__			:		List[Any]      			=   False







    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :Dict    						=	EsmFoldModelTester(self)
        __UpperCamelCase  :Dict    						=	ConfigTester(self				,				config_class=__lowercase				,				hidden_size=37)







    def     UpperCamelCase__     (			self) ->			List[Any]:
        self.config_tester.run_common_tests()







    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :Any    						=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowercase)







    @unittest.skip('''Does not support attention outputs''')
    def     UpperCamelCase__     (			self) ->			Any:
        pass







    @unittest.skip
    def     UpperCamelCase__     (			self) ->			Any:
        pass







    @unittest.skip('''Esm does not support embedding resizing''')
    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        pass







    @unittest.skip('''Esm does not support embedding resizing''')
    def     UpperCamelCase__     (			self) ->			Optional[Any]:
        pass







    @unittest.skip('''ESMFold does not support passing input embeds!''')
    def     UpperCamelCase__     (			self) ->			List[Any]:
        pass







    @unittest.skip('''ESMFold does not support head pruning.''')
    def     UpperCamelCase__     (			self) ->			Optional[int]:
        pass







    @unittest.skip('''ESMFold does not support head pruning.''')
    def     UpperCamelCase__     (			self) ->			Optional[Any]:
        pass







    @unittest.skip('''ESMFold does not support head pruning.''')
    def     UpperCamelCase__     (			self) ->			Dict:
        pass







    @unittest.skip('''ESMFold does not support head pruning.''')
    def     UpperCamelCase__     (			self) ->			Optional[Any]:
        pass







    @unittest.skip('''ESMFold does not support head pruning.''')
    def     UpperCamelCase__     (			self) ->			Optional[int]:
        pass







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







    @unittest.skip('''ESMfold does not output hidden states in the normal way.''')
    def     UpperCamelCase__     (			self) ->			Optional[Any]:
        pass







    @unittest.skip('''ESMFold only has one output format.''')
    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        pass







    @unittest.skip('''This test doesn\'t work for ESMFold and doesn\'t test core functionality''')
    def     UpperCamelCase__     (			self) ->			List[Any]:
        pass







    @unittest.skip('''ESMFold does not support input chunking.''')
    def     UpperCamelCase__     (			self) ->			List[Any]:
        pass







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







    @unittest.skip('''ESMFold doesn\'t support torchscript compilation.''')
    def     UpperCamelCase__     (			self) ->			str:
        pass







    @unittest.skip('''ESMFold doesn\'t support torchscript compilation.''')
    def     UpperCamelCase__     (			self) ->			List[Any]:
        pass







    @unittest.skip('''ESMFold doesn\'t support torchscript compilation.''')
    def     UpperCamelCase__     (			self) ->			List[Any]:
        pass







    @unittest.skip('''ESMFold doesn\'t support data parallel.''')
    def     UpperCamelCase__     (			self) ->			Any:
        pass







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


@require_torch
class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    @slow
    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :Optional[Any]    						=	EsmForProteinFolding.from_pretrained('''facebook/esmfold_v1''').float()
        model.eval()
        __UpperCamelCase  :Tuple    						=	torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]])
        __UpperCamelCase  :List[Any]    						=	model(__lowercase)['''positions''']
        __UpperCamelCase  :Optional[int]    						=	torch.tensor([2.58_28, 0.79_93, -10.93_34]				,				dtype=torch.floataa)
        self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]				,				__lowercase				,				atol=1E-4))





