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

import sentencepiece as spm

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


_SCREAMING_SNAKE_CASE     				=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     				=		'▁'

_SCREAMING_SNAKE_CASE     				=		{'vocab_file': 'sentencepiece.bpe.model'}

_SCREAMING_SNAKE_CASE     				=		{
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'
        ),
    }
}

_SCREAMING_SNAKE_CASE     				=		{
    'facebook/nllb-200-distilled-600M': 1_024,
}

# fmt: off
_SCREAMING_SNAKE_CASE     				=		['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']



class 							a				(     __lowerCAmelCase			):







 """simple docstring"""






 lowerCamelCase      :Optional[int]            =						VOCAB_FILES_NAMES
 lowerCamelCase      :Tuple            =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCamelCase      :Tuple            =						PRETRAINED_VOCAB_FILES_MAP
 lowerCamelCase      :Dict            =						['''input_ids''', '''attention_mask''']

 lowerCamelCase      :Optional[Any]            =						[]
 lowerCamelCase      :Optional[int]            =						[]



 def __init__(   self						,							lowerCAmelCase_						,							lowerCAmelCase_="<s>"						,							lowerCAmelCase_="</s>"						,							lowerCAmelCase_="</s>"						,							lowerCAmelCase_="<s>"						,							lowerCAmelCase_="<unk>"						,							lowerCAmelCase_="<pad>"						,							lowerCAmelCase_="<mask>"						,							lowerCAmelCase_=None						,							lowerCAmelCase_=None						,							lowerCAmelCase_=None						,							lowerCAmelCase_ = None						,							lowerCAmelCase_=None						,							lowerCAmelCase_=False						,							**lowerCAmelCase_						,							)    ->   List[Any]:
        _A	  =    AddedToken(lowerCamelCase__						,							lstrip=lowerCamelCase__						,							rstrip=lowerCamelCase__				) if isinstance(lowerCamelCase__						,							lowerCamelCase__				) else mask_token

        _A	  =    {} if sp_model_kwargs is None else sp_model_kwargs
        _A	  =    legacy_behaviour

        super().__init__(
            bos_token=lowerCamelCase__						,							eos_token=lowerCamelCase__						,							unk_token=lowerCamelCase__						,							sep_token=lowerCamelCase__						,							cls_token=lowerCamelCase__						,							pad_token=lowerCamelCase__						,							mask_token=lowerCamelCase__						,							tokenizer_file=lowerCamelCase__						,							src_lang=lowerCamelCase__						,							tgt_lang=lowerCamelCase__						,							additional_special_tokens=lowerCamelCase__						,							sp_model_kwargs=self.sp_model_kwargs						,							legacy_behaviour=lowerCamelCase__						,							**lowerCamelCase__						,							)

        _A	  =    spm.SentencePieceProcessor(**self.sp_model_kwargs				)
        self.sp_model.Load(str(lowerCamelCase__				)				)
        _A	  =    vocab_file
        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
        # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
        # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

        # Mimic fairseq token-to-id alignment for the first 4 token
        _A	  =    {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

        # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
        _A	  =    1

        _A	  =    len(self.sp_model				)
        _A	  =    {
            code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowerCamelCase__				)
        }
        _A	  =    {v: k for k, v in self.lang_code_to_id.items()}
        _A	  =    len(self.sp_model				) + len(self.lang_code_to_id				) + self.fairseq_offset

        self.fairseq_tokens_to_ids.update(self.lang_code_to_id				)
        _A	  =    {v: k for k, v in self.fairseq_tokens_to_ids.items()}
        _A	  =    list(self.lang_code_to_id.keys()				)

        if additional_special_tokens is not None:
               # Only add those special tokens if they are not already there.
               self._additional_special_tokens.extend(
                   [t for t in additional_special_tokens if t not in self._additional_special_tokens]				)

        _A	  =    src_lang if src_lang is not None else """eng_Latn"""
        _A	  =    self.lang_code_to_id[self._src_lang]
        _A	  =    tgt_lang
        self.set_src_lang_special_tokens(self._src_lang				)



 def __getstate__(   self				)    ->   str:
        _A	  =    self.__dict__.copy()
        _A	  =    None
        _A	  =    self.sp_model.serialized_model_proto()
        return state



 def __setstate__(   self						,							lowerCAmelCase_				)    ->   Dict:
        _A	  =    d

        # for backward compatibility
        if not hasattr(self						,							"""sp_model_kwargs"""				):
               _A	  =    {}

        _A	  =    spm.SentencePieceProcessor(**self.sp_model_kwargs				)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)



 @property
 def 	UpperCAmelCase				(   self				)    ->   List[Any]:
        return len(self.sp_model				) + len(self.lang_code_to_id				) + self.fairseq_offset + 1  # Plus 1 for the mask token



 @property
 def 	UpperCAmelCase				(   self				)    ->   str:
        return self._src_lang



 @src_lang.setter
 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   None:
        _A	  =    new_src_lang
        self.set_src_lang_special_tokens(self._src_lang				)



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_ = None						,							lowerCAmelCase_ = False				)    ->   List[int]:

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

        _A	  =    [1] * len(self.prefix_tokens				)
        _A	  =    [1] * len(self.suffix_tokens				)
        if token_ids_a is None:
               return prefix_ones + ([0] * len(lowerCamelCase__				)) + suffix_ones
        return prefix_ones + ([0] * len(lowerCamelCase__				)) + ([0] * len(lowerCamelCase__				)) + suffix_ones



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_ = None				)    ->   List[int]:
        if token_ids_a is None:
               return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_ = None				)    ->   List[int]:
        _A	  =    [self.sep_token_id]
        _A	  =    [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]



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_						,							lowerCAmelCase_						,							lowerCAmelCase_						,							**lowerCAmelCase_				)    ->   Optional[int]:
        if src_lang is None or tgt_lang is None:
               raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""				)
        _A	  =    src_lang
        _A	  =    self(lowerCamelCase__						,							add_special_tokens=lowerCamelCase__						,							return_tensors=lowerCamelCase__						,							**lowerCamelCase__				)
        _A	  =    self.convert_tokens_to_ids(lowerCamelCase__				)
        _A	  =    tgt_lang_id
        return inputs



 def 	UpperCAmelCase				(   self				)    ->   List[str]:
        _A	  =    {self.convert_ids_to_tokens(lowerCamelCase__				): i for i in range(self.vocab_size				)}
        vocab.update(self.added_tokens_encoder				)
        return vocab



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   List[str]:
        return self.sp_model.encode(lowerCamelCase__						,							out_type=lowerCamelCase__				)



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   str:
        if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
        _A	  =    self.sp_model.PieceToId(lowerCamelCase__				)

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   Dict:
        if index in self.fairseq_ids_to_tokens:
               return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset				)



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   int:
        _A	  =    """""".join(lowerCamelCase__				).replace(lowerCamelCase__						,							""" """				).strip()
        return out_string



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_ = None				)    ->   Tuple[str]:
        if not os.path.isdir(lowerCamelCase__				):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
               return
        _A	  =    os.path.join(
            lowerCamelCase__						,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

        if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCamelCase__				) and os.path.isfile(self.vocab_file				):
               copyfile(self.vocab_file						,							lowerCamelCase__				)
        elif not os.path.isfile(self.vocab_file				):
               with open(lowerCamelCase__						,							"""wb"""				) as fi:
                      _A	  =    self.sp_model.serialized_model_proto()
                      fi.write(lowerCamelCase__				)

        return (out_vocab_file,)



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_ = "eng_Latn"						,							lowerCAmelCase_ = None						,							lowerCAmelCase_ = "fra_Latn"						,							**lowerCAmelCase_						,							)    ->   BatchEncoding:
        _A	  =    src_lang
        _A	  =    tgt_lang
        return super().prepare_seqaseq_batch(lowerCamelCase__						,							lowerCamelCase__						,							**lowerCamelCase__				)



 def 	UpperCAmelCase				(   self				)    ->   int:
        return self.set_src_lang_special_tokens(self.src_lang				)



 def 	UpperCAmelCase				(   self				)    ->   Tuple:
        return self.set_tgt_lang_special_tokens(self.tgt_lang				)



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   None:
        _A	  =    self.lang_code_to_id[src_lang]
        if self.legacy_behaviour:
               _A	  =    []
               _A	  =    [self.eos_token_id, self.cur_lang_code]
        else:
               _A	  =    [self.cur_lang_code]
               _A	  =    [self.eos_token_id]



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   None:
        _A	  =    self.lang_code_to_id[lang]
        if self.legacy_behaviour:
               _A	  =    []
               _A	  =    [self.eos_token_id, self.cur_lang_code]
        else:
               _A	  =    [self.cur_lang_code]
               _A	  =    [self.eos_token_id]










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     ,		lowerCamelCase__     ,		lowerCamelCase__=13     ,		lowerCamelCase__=7     ,		lowerCamelCase__=False     ,		lowerCamelCase__=True     ,		lowerCamelCase__=False     ,		lowerCamelCase__=False     ,		lowerCamelCase__=19     ,		lowerCamelCase__=32     ,		lowerCamelCase__=5     ,		lowerCamelCase__=4     ,		lowerCamelCase__=37     ,		lowerCamelCase__="gelu"     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=512     ,		lowerCamelCase__=16     ,		lowerCamelCase__=2     ,		lowerCamelCase__=0.02     ,		lowerCamelCase__=3     ,		lowerCamelCase__=4     ,		lowerCamelCase__=None     ,		)       -> Any:



							'''simple docstring'''
							__lowerCamelCase									=       parent
							__lowerCamelCase									=       batch_size
							__lowerCamelCase									=       seq_length
							__lowerCamelCase									=       is_training
							__lowerCamelCase									=       use_input_mask
							__lowerCamelCase									=       use_token_type_ids
							__lowerCamelCase									=       use_labels
							__lowerCamelCase									=       vocab_size
							__lowerCamelCase									=       hidden_size
							__lowerCamelCase									=       num_hidden_layers
							__lowerCamelCase									=       num_attention_heads
							__lowerCamelCase									=       intermediate_size
							__lowerCamelCase									=       hidden_act
							__lowerCamelCase									=       hidden_dropout_prob
							__lowerCamelCase									=       attention_probs_dropout_prob
							__lowerCamelCase									=       max_position_embeddings
							__lowerCamelCase									=       type_vocab_size
							__lowerCamelCase									=       type_sequence_label_size
							__lowerCamelCase									=       initializer_range
							__lowerCamelCase									=       num_labels
							__lowerCamelCase									=       num_choices
							__lowerCamelCase									=       scope


			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size				)

							__lowerCamelCase									=       None
							if self.use_input_mask:
											__lowerCamelCase									=       random_attention_mask([self.batch_size, self.seq_length]				)

							__lowerCamelCase									=       None
							__lowerCamelCase									=       None
							__lowerCamelCase									=       None
							if self.use_labels:
											__lowerCamelCase									=       ids_tensor([self.batch_size]     ,		self.type_sequence_label_size				)
											__lowerCamelCase									=       ids_tensor([self.batch_size, self.seq_length]     ,		self.num_labels				)
											__lowerCamelCase									=       ids_tensor([self.batch_size]     ,		self.num_choices				)

							__lowerCamelCase									=       self.get_config()

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


			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							__lowerCamelCase									=       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=lowerCamelCase__     ,		esmfold_config={'trunk': {'num_blocks': 2}, 'fp16_esm': False}     ,		)
							return config


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       EsmForProteinFolding(config=lowerCamelCase__				).float()
							model.to(lowerCamelCase__				)
							model.eval()
							__lowerCamelCase									=       model(lowerCamelCase__     ,		attention_mask=lowerCamelCase__				)
							__lowerCamelCase									=       model(lowerCamelCase__				)
							__lowerCamelCase									=       model(lowerCamelCase__				)

							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  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       self.prepare_config_and_inputs()
							(
							    (
							    __lowerCamelCase
							)			,		(
							    __lowerCamelCase
							)			,		(
							    __lowerCamelCase
							)			,		(
							    __lowerCamelCase
							)			,		(
							    __lowerCamelCase
							)			,		(
							    __lowerCamelCase
							)			,		
							)									=       config_and_inputs
							__lowerCamelCase									=       {'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict







@require_torch
class        __lowerCAmelCase					(			__magic_name__     ,							__magic_name__     ,							unittest.TestCase					):

			"""simple docstring"""







			snake_case_  						=			False

			snake_case_  						=			(EsmForProteinFolding,) if is_torch_available() else ()
			snake_case_  						=			()
			snake_case_  						=			{} if is_torch_available() else {}
			snake_case_  						=			False


			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							__lowerCamelCase									=       EsmFoldModelTester(self				)
							__lowerCamelCase									=       ConfigTester(self     ,		config_class=lowerCamelCase__     ,		hidden_size=37				)


			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							self.config_tester.run_common_tests()


			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase__				)


			@unittest.skip('Does not support attention outputs'				)
			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							pass


			@unittest.skip
			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							pass


			@unittest.skip('Esm does not support embedding resizing'				)
			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							pass


			@unittest.skip('Esm does not support embedding resizing'				)
			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							pass


			@unittest.skip('ESMFold does not support passing input embeds!'				)
			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							pass


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



							'''simple docstring'''
							pass


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



							'''simple docstring'''
							pass


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



							'''simple docstring'''
							pass


			@unittest.skip('ESMFold does not support head pruning.'				)
			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							pass


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



							'''simple docstring'''
							pass


			@unittest.skip('ESMFold does not output hidden states in the normal way.'				)
			def  lowercase_       ( self				)       -> List[str]:



							'''simple docstring'''
							pass


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



							'''simple docstring'''
							pass


			@unittest.skip('ESMFold only has one output format.'				)
			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							pass


			@unittest.skip('This test doesn\'t work for ESMFold and doesn\'t test core functionality'				)
			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							pass


			@unittest.skip('ESMFold does not support input chunking.'				)
			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							pass


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



							'''simple docstring'''
							pass


			@unittest.skip('ESMFold doesn\'t support torchscript compilation.'				)
			def  lowercase_       ( self				)       -> Union[str, Any]:



							'''simple docstring'''
							pass


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



							'''simple docstring'''
							pass


			@unittest.skip('ESMFold doesn\'t support torchscript compilation.'				)
			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							pass


			@unittest.skip('ESMFold doesn\'t support data parallel.'				)
			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							pass






			@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'				)
			def  lowercase_       ( self				)       -> List[Any]:



							'''simple docstring'''
							pass







@require_torch
class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			@slow
			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       EsmForProteinFolding.from_pretrained('facebook/esmfold_v1'				).float()
							model.eval()
							__lowerCamelCase									=       torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]				)
							__lowerCamelCase									=       model(lowerCamelCase__				)['positions']
							__lowerCamelCase									=       torch.tensor([2.58_28, 0.79_93, -10.93_34]     ,		dtype=torch.floataa				)
							self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]     ,		lowerCamelCase__     ,		atol=1e-4				)				)


