import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    LiltForQuestionAnswering,
					    LiltForSequenceClassification,
					    LiltForTokenClassification,
					    LiltModel,
					)
					from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST





class       lowercase__		:


						def __init__( self			:       List[str]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Union[str, Any]=13					,	UpperCamelCase__			:       List[Any]=7					,	UpperCamelCase__			:       int=True					,	UpperCamelCase__			:       Optional[int]=True					,	UpperCamelCase__			:       int=True					,	UpperCamelCase__			:       List[Any]=True					,	UpperCamelCase__			:       List[Any]=99					,	UpperCamelCase__			:       Union[str, Any]=24					,	UpperCamelCase__			:       Any=2					,	UpperCamelCase__			:       str=6					,	UpperCamelCase__			:       Optional[Any]=37					,	UpperCamelCase__			:       str="gelu"					,	UpperCamelCase__			:       Dict=0.1					,	UpperCamelCase__			:       int=0.1					,	UpperCamelCase__			:       int=512					,	UpperCamelCase__			:       Dict=16					,	UpperCamelCase__			:       List[Any]=2					,	UpperCamelCase__			:       Dict=0.02					,	UpperCamelCase__			:       Union[str, Any]=3					,	UpperCamelCase__			:       str=None					,	UpperCamelCase__			:       str=1000					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							parent
												SCREAMING_SNAKE_CASE							:					List[str]           =							batch_size
												SCREAMING_SNAKE_CASE							:					Dict           =							seq_length
												SCREAMING_SNAKE_CASE							:					Tuple           =							is_training
												SCREAMING_SNAKE_CASE							:					List[str]           =							use_input_mask
												SCREAMING_SNAKE_CASE							:					Dict           =							use_token_type_ids
												SCREAMING_SNAKE_CASE							:					List[str]           =							use_labels
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							vocab_size
												SCREAMING_SNAKE_CASE							:					str           =							hidden_size
												SCREAMING_SNAKE_CASE							:					Tuple           =							num_hidden_layers
												SCREAMING_SNAKE_CASE							:					int           =							num_attention_heads
												SCREAMING_SNAKE_CASE							:					List[str]           =							intermediate_size
												SCREAMING_SNAKE_CASE							:					Dict           =							hidden_act
												SCREAMING_SNAKE_CASE							:					Dict           =							hidden_dropout_prob
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE							:					int           =							max_position_embeddings
												SCREAMING_SNAKE_CASE							:					List[Any]           =							type_vocab_size
												SCREAMING_SNAKE_CASE							:					List[Any]           =							type_sequence_label_size
												SCREAMING_SNAKE_CASE							:					int           =							initializer_range
												SCREAMING_SNAKE_CASE							:					List[Any]           =							num_labels
												SCREAMING_SNAKE_CASE							:					Dict           =							scope
												SCREAMING_SNAKE_CASE							:					Dict           =							range_bbox


						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size		)

												SCREAMING_SNAKE_CASE							:					List[str]           =							ids_tensor([self.batch_size, self.seq_length, 4]					,	self.range_bbox		)
												# Ensure that bbox is legal
												for i in range(bbox.shape[0]		):
																		for j in range(bbox.shape[1]		):
																								if bbox[i, j, 3] < bbox[i, j, 1]:
																														SCREAMING_SNAKE_CASE							:					Tuple           =							bbox[i, j, 3]
																														SCREAMING_SNAKE_CASE							:					str           =							bbox[i, j, 1]
																														SCREAMING_SNAKE_CASE							:					int           =							t
																								if bbox[i, j, 2] < bbox[i, j, 0]:
																														SCREAMING_SNAKE_CASE							:					Dict           =							bbox[i, j, 2]
																														SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							bbox[i, j, 0]
																														SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							t

												SCREAMING_SNAKE_CASE							:					List[str]           =							None
												if self.use_input_mask:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							ids_tensor([self.batch_size, self.seq_length]					,	vocab_size=2		)

												SCREAMING_SNAKE_CASE							:					Any           =							None
												if self.use_token_type_ids:
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							ids_tensor([self.batch_size, self.seq_length]					,	self.type_vocab_size		)

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

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

												return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												return LiltConfig(
												    vocab_size=self.vocab_size					,	hidden_size=self.hidden_size					,	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					,	)


						def  __A ( self			:       List[Any]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       Tuple					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       Dict					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							LiltModel(config=UpperCamelCase__		)
												model.to(UpperCamelCase__		)
												model.eval()
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							model(UpperCamelCase__					,	bbox=UpperCamelCase__					,	attention_mask=UpperCamelCase__					,	token_type_ids=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Any           =							model(UpperCamelCase__					,	bbox=UpperCamelCase__					,	token_type_ids=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Any           =							model(UpperCamelCase__					,	bbox=UpperCamelCase__		)

												self.parent.assertEqual(result.last_hidden_state.shape					,	(self.batch_size, self.seq_length, self.hidden_size)		)
												self.parent.assertEqual(result.pooler_output.shape					,	(self.batch_size, self.hidden_size)		)


						def  __A ( self			:       Any					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       List[str]					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       Optional[Any]					,	UpperCamelCase__			:       Any					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							self.num_labels
												SCREAMING_SNAKE_CASE							:					Any           =							LiltForTokenClassification(config=UpperCamelCase__		)
												model.to(UpperCamelCase__		)
												model.eval()
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							model(
												    UpperCamelCase__					,	bbox=UpperCamelCase__					,	attention_mask=UpperCamelCase__					,	token_type_ids=UpperCamelCase__					,	labels=UpperCamelCase__		)
												self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.seq_length, self.num_labels)		)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Tuple					,	UpperCamelCase__			:       List[str]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       Dict					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							LiltForQuestionAnswering(config=UpperCamelCase__		)
												model.to(UpperCamelCase__		)
												model.eval()
												SCREAMING_SNAKE_CASE							:					List[Any]           =							model(
												    UpperCamelCase__					,	bbox=UpperCamelCase__					,	attention_mask=UpperCamelCase__					,	token_type_ids=UpperCamelCase__					,	start_positions=UpperCamelCase__					,	end_positions=UpperCamelCase__					,	)
												self.parent.assertEqual(result.start_logits.shape					,	(self.batch_size, self.seq_length)		)
												self.parent.assertEqual(result.end_logits.shape					,	(self.batch_size, self.seq_length)		)



						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.prepare_config_and_inputs()
												(
												    (
												    SCREAMING_SNAKE_CASE
												)       ,						(
												    SCREAMING_SNAKE_CASE
												)       ,						(
												    SCREAMING_SNAKE_CASE
												)       ,						(
												    SCREAMING_SNAKE_CASE
												)       ,						(
												    SCREAMING_SNAKE_CASE
												)       ,						(
												    SCREAMING_SNAKE_CASE
												)       ,						(
												    SCREAMING_SNAKE_CASE
												)       ,						
												)							:					List[Any]           =							config_and_inputs
												SCREAMING_SNAKE_CASE							:					List[str]           =							{
												    '''input_ids''': input_ids,
												    '''bbox''': bbox,
												    '''token_type_ids''': token_type_ids,
												    '''attention_mask''': input_mask,
												}
												return config, inputs_dict






@require_torch
class       lowercase__		(     UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	(
						    (
						        LiltModel,
						        LiltForSequenceClassification,
						        LiltForTokenClassification,
						        LiltForQuestionAnswering,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCamelCase_  =	(
						    {
						        """feature-extraction""": LiltModel,
						        """question-answering""": LiltForQuestionAnswering,
						        """text-classification""": LiltForSequenceClassification,
						        """token-classification""": LiltForTokenClassification,
						        """zero-shot""": LiltForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						UpperCamelCase_  =	False
						UpperCamelCase_  =	False


						def  __A ( self			:       List[Any]					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       List[str]					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





												return True


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							LiltModelTester(self		)
												SCREAMING_SNAKE_CASE							:					Dict           =							ConfigTester(self					,	config_class=UpperCamelCase__					,	hidden_size=37		)


						def  __A ( self			:       Any		):





												'''simple docstring'''





												self.config_tester.run_common_tests()


						def  __A ( self			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*UpperCamelCase__		)


						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.model_tester.prepare_config_and_inputs()
												for type in ["absolute", "relative_key", "relative_key_query"]:
																		SCREAMING_SNAKE_CASE							:					Any           =							type
																		self.model_tester.create_and_check_model(*UpperCamelCase__		)


						def  __A ( self			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__		)


						def  __A ( self			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__		)



						@slow
						def  __A ( self			:       str		):





												'''simple docstring'''





												for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							LiltModel.from_pretrained(UpperCamelCase__		)
																		self.assertIsNotNone(UpperCamelCase__		)






@require_torch
@slow
class       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''		).to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Dict           =							torch.tensor([[1, 2]]					,	device=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					str           =							torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]					,	device=UpperCamelCase__		)

												# forward pass
												with torch.no_grad():
																		SCREAMING_SNAKE_CASE							:					Dict           =							model(input_ids=UpperCamelCase__					,	bbox=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					str           =							torch.Size([1, 2, 768]		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.tensor(
												    [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]					,	device=UpperCamelCase__					,	)

												self.assertTrue(outputs.last_hidden_state.shape					,	UpperCamelCase__		)
												self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]					,	UpperCamelCase__					,	atol=1E-3		)		)






from dataclasses import asdict, dataclass
from typing import Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase	: Tuple    		=  logging.get_logger(__name__)

# TODO Update this
__UpperCamelCase	: List[str]    		=  {
    'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.json',
    # See all ESM models at https://huggingface.co/models?filter=esm
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""esm"""


						def __init__( self			:       Tuple					,	UpperCamelCase__			:       Optional[Any]=None					,	UpperCamelCase__			:       int=None					,	UpperCamelCase__			:       List[str]=None					,	UpperCamelCase__			:       Any=768					,	UpperCamelCase__			:       Optional[Any]=12					,	UpperCamelCase__			:       str=12					,	UpperCamelCase__			:       Optional[int]=3072					,	UpperCamelCase__			:       Optional[Any]=0.1					,	UpperCamelCase__			:       Any=0.1					,	UpperCamelCase__			:       Union[str, Any]=1026					,	UpperCamelCase__			:       Dict=0.02					,	UpperCamelCase__			:       Any=1E-12					,	UpperCamelCase__			:       Dict="absolute"					,	UpperCamelCase__			:       Optional[Any]=True					,	UpperCamelCase__			:       int=None					,	UpperCamelCase__			:       Optional[int]=False					,	UpperCamelCase__			:       Tuple=False					,	UpperCamelCase__			:       List[str]=None					,	UpperCamelCase__			:       Optional[Any]=None					,	**UpperCamelCase__			:       Any					,	):





												'''simple docstring'''





												super().__init__(pad_token_id=UpperCamelCase__					,	mask_token_id=UpperCamelCase__					,	**UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							vocab_size
												SCREAMING_SNAKE_CASE							:					Any           =							hidden_size
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							num_hidden_layers
												SCREAMING_SNAKE_CASE							:					Dict           =							num_attention_heads
												SCREAMING_SNAKE_CASE							:					Any           =							intermediate_size
												SCREAMING_SNAKE_CASE							:					str           =							hidden_dropout_prob
												SCREAMING_SNAKE_CASE							:					List[Any]           =							attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE							:					str           =							max_position_embeddings
												SCREAMING_SNAKE_CASE							:					Tuple           =							initializer_range
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							layer_norm_eps
												SCREAMING_SNAKE_CASE							:					Dict           =							position_embedding_type
												SCREAMING_SNAKE_CASE							:					Any           =							use_cache
												SCREAMING_SNAKE_CASE							:					Dict           =							emb_layer_norm_before
												SCREAMING_SNAKE_CASE							:					List[str]           =							token_dropout
												SCREAMING_SNAKE_CASE							:					List[Any]           =							is_folding_model
												if is_folding_model:
																		if esmfold_config is None:
																								logger.info('''No esmfold_config supplied for folding model, using default values.'''		)
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							EsmFoldConfig()
																		elif isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							EsmFoldConfig(**UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							esmfold_config
																		if vocab_list is None:
																								logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'''		)
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							get_default_vocab_list()
																		else:
																								SCREAMING_SNAKE_CASE							:					Optional[Any]           =							vocab_list
												else:
																		SCREAMING_SNAKE_CASE							:					str           =							None
																		SCREAMING_SNAKE_CASE							:					int           =							None
												if self.esmfold_config is not None and getattr(self.esmfold_config					,	'''use_esm_attn_map'''					,	UpperCamelCase__		):
																		raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'''		)



						def  __A ( self			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							super().to_dict()
												if isinstance(self.esmfold_config					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.esmfold_config.to_dict()
												return output






@dataclass
class       lowercase__		:
						UpperCamelCase_  =	None
						UpperCamelCase_  =	True
						UpperCamelCase_  =	False
						UpperCamelCase_  =	False
						UpperCamelCase_  =	False
						UpperCamelCase_  =	0

						UpperCamelCase_  =	True
						UpperCamelCase_  =	False

						UpperCamelCase_  =	128
						UpperCamelCase_  =	None


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												if self.trunk is None:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							TrunkConfig()
												elif isinstance(self.trunk					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Tuple           =							TrunkConfig(**self.trunk		)



						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							asdict(self		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							self.trunk.to_dict()
												return output






@dataclass
class       lowercase__		:
						UpperCamelCase_  =	48
						UpperCamelCase_  =	1_024
						UpperCamelCase_  =	128
						UpperCamelCase_  =	32
						UpperCamelCase_  =	32
						UpperCamelCase_  =	32
						UpperCamelCase_  =	0
						UpperCamelCase_  =	0
						UpperCamelCase_  =	False
						UpperCamelCase_  =	4
						UpperCamelCase_  =	128
						UpperCamelCase_  =	None


						def  __A ( self			:       Any		):





												'''simple docstring'''





												if self.structure_module is None:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							StructureModuleConfig()
												elif isinstance(self.structure_module					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							StructureModuleConfig(**self.structure_module		)

												if self.max_recycles <= 0:
																		raise ValueError(f"""`max_recycles` should be positive, got {self.max_recycles}."""		)
												if self.sequence_state_dim % self.sequence_state_dim != 0:
																		raise ValueError(
																		    '''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
																		    f""" {self.sequence_state_dim} and {self.sequence_state_dim}."""		)
												if self.pairwise_state_dim % self.pairwise_state_dim != 0:
																		raise ValueError(
																		    '''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
																		    f""" {self.pairwise_state_dim} and {self.pairwise_state_dim}."""		)

												SCREAMING_SNAKE_CASE							:					Dict           =							self.sequence_state_dim // self.sequence_head_width
												SCREAMING_SNAKE_CASE							:					Tuple           =							self.pairwise_state_dim // self.pairwise_head_width

												if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
																		raise ValueError(
																		    '''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
																		    f""" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."""		)
												if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
																		raise ValueError(
																		    '''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
																		    f""" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."""		)
												if self.pairwise_state_dim % 2 != 0:
																		raise ValueError(f"""`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."""		)

												if self.dropout >= 0.4:
																		raise ValueError(f"""`dropout` should not be greater than 0.4, got {self.dropout}."""		)



						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							asdict(self		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.structure_module.to_dict()
												return output






@dataclass
class       lowercase__		:
						UpperCamelCase_  =	384
						UpperCamelCase_  =	128
						UpperCamelCase_  =	16
						UpperCamelCase_  =	128
						UpperCamelCase_  =	12
						UpperCamelCase_  =	4
						UpperCamelCase_  =	8
						UpperCamelCase_  =	0.1
						UpperCamelCase_  =	8
						UpperCamelCase_  =	1
						UpperCamelCase_  =	2
						UpperCamelCase_  =	7
						UpperCamelCase_  =	10
						UpperCamelCase_  =	1E-8
						UpperCamelCase_  =	1E5


						def  __A ( self			:       Dict		):





												'''simple docstring'''





												return asdict(self		)






def    A			(					):
						return (
						    "<cls>",
						    "<pad>",
						    "<eos>",
						    "<unk>",
						    "L",
						    "A",
						    "G",
						    "V",
						    "S",
						    "E",
						    "R",
						    "T",
						    "I",
						    "D",
						    "P",
						    "K",
						    "Q",
						    "N",
						    "F",
						    "Y",
						    "M",
						    "H",
						    "W",
						    "C",
						    "X",
						    "B",
						    "U",
						    "Z",
						    "O",
						    ".",
						    "-",
						    "<null_1>",
						    "<mask>",
						)





