


'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TFRoFormerForCausalLM,
		    TFRoFormerForMaskedLM,
		    TFRoFormerForMultipleChoice,
		    TFRoFormerForQuestionAnswering,
		    TFRoFormerForSequenceClassification,
		    TFRoFormerForTokenClassification,
		    TFRoFormerModel,
		)
		from transformers.models.roformer.modeling_tf_roformer import (
		    TFRoFormerSelfAttention,
		    TFRoFormerSinusoidalPositionalEmbedding,
		)




class 						a__							:





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_=13   ,       lowerCamelCase_=7   ,       lowerCamelCase_=True   ,       lowerCamelCase_=True   ,       lowerCamelCase_=True   ,       lowerCamelCase_=True   ,       lowerCamelCase_=99   ,       lowerCamelCase_=32   ,       lowerCamelCase_=2   ,       lowerCamelCase_=4   ,       lowerCamelCase_=37   ,       lowerCamelCase_="gelu"   ,       lowerCamelCase_=0.1   ,       lowerCamelCase_=0.1   ,       lowerCamelCase_=5_12   ,       lowerCamelCase_=16   ,       lowerCamelCase_=2   ,       lowerCamelCase_=0.02   ,       lowerCamelCase_=3   ,       lowerCamelCase_=4   ,       lowerCamelCase_=None   ,       )			->				Tuple:
														lowerCAmelCase__			  =      parent
														lowerCAmelCase__			  =      13
														lowerCAmelCase__			  =      7
														lowerCAmelCase__			  =      True
														lowerCAmelCase__			  =      True
														lowerCAmelCase__			  =      True
														lowerCAmelCase__			  =      True
														lowerCAmelCase__			  =      99
														lowerCAmelCase__			  =      32
														lowerCAmelCase__			  =      2
														lowerCAmelCase__			  =      4
														lowerCAmelCase__			  =      37
														lowerCAmelCase__			  =      '''gelu'''
														lowerCAmelCase__			  =      0.1
														lowerCAmelCase__			  =      0.1
														lowerCAmelCase__			  =      5_12
														lowerCAmelCase__			  =      16
														lowerCAmelCase__			  =      2
														lowerCAmelCase__			  =      0.02
														lowerCAmelCase__			  =      3
														lowerCAmelCase__			  =      4
														lowerCAmelCase__			  =      None







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														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
														if self.use_token_type_ids:
																					lowerCAmelCase__			  =      ids_tensor([self.batch_size, self.seq_length]   ,       self.type_vocab_size  )

														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__			  =      RoFormerConfig(
														    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   ,       return_dict=lowerCamelCase_   ,       )

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







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Optional[int]:
														lowerCAmelCase__			  =      TFRoFormerModel(config=lowerCamelCase_  )
														lowerCAmelCase__			  =      {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

														lowerCAmelCase__			  =      [input_ids, input_mask]
														lowerCAmelCase__			  =      model(lowerCamelCase_  )

														lowerCAmelCase__			  =      model(lowerCamelCase_  )

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







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				List[str]:
														lowerCAmelCase__			  =      True
														lowerCAmelCase__			  =      TFRoFormerForCausalLM(config=lowerCamelCase_  )
														lowerCAmelCase__			  =      {
														    '''input_ids''': input_ids,
														    '''attention_mask''': input_mask,
														    '''token_type_ids''': token_type_ids,
														}
														lowerCAmelCase__			  =      model(lowerCamelCase_  )['''logits''']
														self.parent.assertListEqual(
														    list(prediction_scores.numpy().shape  )   ,       [self.batch_size, self.seq_length, self.vocab_size]  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Dict:
														lowerCAmelCase__			  =      TFRoFormerForMaskedLM(config=lowerCamelCase_  )
														lowerCAmelCase__			  =      {
														    '''input_ids''': input_ids,
														    '''attention_mask''': input_mask,
														    '''token_type_ids''': token_type_ids,
														}
														lowerCAmelCase__			  =      model(lowerCamelCase_  )
														self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.seq_length, self.vocab_size)  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				List[Any]:
														lowerCAmelCase__			  =      self.num_labels
														lowerCAmelCase__			  =      TFRoFormerForSequenceClassification(config=lowerCamelCase_  )
														lowerCAmelCase__			  =      {
														    '''input_ids''': input_ids,
														    '''attention_mask''': input_mask,
														    '''token_type_ids''': token_type_ids,
														}

														lowerCAmelCase__			  =      model(lowerCamelCase_  )
														self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_labels)  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Optional[Any]:
														lowerCAmelCase__			  =      self.num_choices
														lowerCAmelCase__			  =      TFRoFormerForMultipleChoice(config=lowerCamelCase_  )
														lowerCAmelCase__			  =      tf.tile(tf.expand_dims(lowerCamelCase_   ,       1  )   ,       (1, self.num_choices, 1)  )
														lowerCAmelCase__			  =      tf.tile(tf.expand_dims(lowerCamelCase_   ,       1  )   ,       (1, self.num_choices, 1)  )
														lowerCAmelCase__			  =      tf.tile(tf.expand_dims(lowerCamelCase_   ,       1  )   ,       (1, self.num_choices, 1)  )
														lowerCAmelCase__			  =      {
														    '''input_ids''': multiple_choice_inputs_ids,
														    '''attention_mask''': multiple_choice_input_mask,
														    '''token_type_ids''': multiple_choice_token_type_ids,
														}
														lowerCAmelCase__			  =      model(lowerCamelCase_  )
														self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_choices)  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				int:
														lowerCAmelCase__			  =      self.num_labels
														lowerCAmelCase__			  =      TFRoFormerForTokenClassification(config=lowerCamelCase_  )
														lowerCAmelCase__			  =      {
														    '''input_ids''': input_ids,
														    '''attention_mask''': input_mask,
														    '''token_type_ids''': token_type_ids,
														}
														lowerCAmelCase__			  =      model(lowerCamelCase_  )
														self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.seq_length, self.num_labels)  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Optional[int]:
														lowerCAmelCase__			  =      TFRoFormerForQuestionAnswering(config=lowerCamelCase_  )
														lowerCAmelCase__			  =      {
														    '''input_ids''': input_ids,
														    '''attention_mask''': input_mask,
														    '''token_type_ids''': token_type_ids,
														}

														lowerCAmelCase__			  =      model(lowerCamelCase_  )
														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     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      self.prepare_config_and_inputs()
														(
														    (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   
														)			  =      config_and_inputs
														lowerCAmelCase__			  =      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
														return config, inputs_dict




@require_tf
class 						a__							(				a__							,							a__							,							unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  Optional[int]      		= (
							    (
							        TFRoFormerModel,
							        TFRoFormerForCausalLM,
							        TFRoFormerForMaskedLM,
							        TFRoFormerForQuestionAnswering,
							        TFRoFormerForSequenceClassification,
							        TFRoFormerForTokenClassification,
							        TFRoFormerForMultipleChoice,
							    )
							    if is_tf_available()
							    else ()
							)
							lowercase__     :  Tuple      		= (
							    {
							        "feature-extraction": TFRoFormerModel,
							        "fill-mask": TFRoFormerForMaskedLM,
							        "question-answering": TFRoFormerForQuestionAnswering,
							        "text-classification": TFRoFormerForSequenceClassification,
							        "text-generation": TFRoFormerForCausalLM,
							        "token-classification": TFRoFormerForTokenClassification,
							        "zero-shot": TFRoFormerForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)

							lowercase__     :  Optional[Any]      		= False
							lowercase__     :  List[Any]      		= False







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				List[str]:
														if pipeline_test_casse_name == "TextGenerationPipelineTests":
																					return True

														return False







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      TFRoFormerModelTester(self  )
														lowerCAmelCase__			  =      ConfigTester(self   ,       config_class=lowerCamelCase_   ,       hidden_size=37  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														self.config_tester.run_common_tests()







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_lm_head(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_  )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base'''  )
														self.assertIsNotNone(lowerCamelCase_  )




@require_tf
class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''  )
														lowerCAmelCase__			  =      tf.constant([[0, 1, 2, 3, 4, 5]]  )
														lowerCAmelCase__			  =      model(lowerCamelCase_  )[0]

														# TODO Replace vocab size
														lowerCAmelCase__			  =      5_00_00

														lowerCAmelCase__			  =      [1, 6, vocab_size]
														self.assertEqual(output.shape   ,       lowerCamelCase_  )

														print(output[:, :3, :3]  )

														# TODO Replace values below with what was printed above.
														lowerCAmelCase__			  =      tf.constant(
														    [
														        [
														            [-0.12_053_341, -1.0_264_901, 0.29_221_946],
														            [-1.5_133_783, 0.197_433, 0.15_190_607],
														            [-5.0_135_403, -3.900_256, -0.84_038_764],
														        ]
														    ]  )
														tf.debugging.assert_near(output[:, :3, :3]   ,       lowerCamelCase_   ,       atol=1e-4  )




@require_tf
class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  str      		= 1e-4







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      tf.constant([[4, 10]]  )
														lowerCAmelCase__			  =      TFRoFormerSinusoidalPositionalEmbedding(num_positions=6   ,       embedding_dim=6  )

														lowerCAmelCase__			  =      emba(input_ids.shape  )
														lowerCAmelCase__			  =      tf.constant(
														    [[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]]  )

														tf.debugging.assert_near(lowerCamelCase_   ,       lowerCamelCase_   ,       atol=self.tolerance  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      tf.constant(
														    [
														        [0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
														        [0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
														        [0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
														    ]  )
														lowerCAmelCase__			  =      TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12   ,       embedding_dim=5_12  )
														emba([2, 16, 5_12]  )
														lowerCAmelCase__			  =      emba.weight[:3, :5]

														tf.debugging.assert_near(lowerCamelCase_   ,       lowerCamelCase_   ,       atol=self.tolerance  )




@require_tf
class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  int      		= 1e-4







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														# 2,12,16,64
														lowerCAmelCase__			  =      tf.reshape(tf.range(2 * 12 * 16 * 64   ,       dtype=tf.floataa  )   ,       shape=(2, 12, 16, 64)  ) / 1_00

														lowerCAmelCase__			  =      -tf.reshape(tf.range(2 * 12 * 16 * 64   ,       dtype=tf.floataa  )   ,       shape=(2, 12, 16, 64)  ) / 1_00

														lowerCAmelCase__			  =      TFRoFormerSinusoidalPositionalEmbedding(num_positions=32   ,       embedding_dim=64  )
														lowerCAmelCase__			  =      embed_positions([2, 16, 7_68]  )[None, None, :, :]

														lowerCAmelCase__  ,   lowerCAmelCase__			  =      TFRoFormerSelfAttention.apply_rotary_position_embeddings(
														    lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )

														lowerCAmelCase__			  =      tf.constant(
														    [
														        [0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
														        [-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
														        [-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
														        [-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
														        [0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
														        [3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
														    ]  )
														lowerCAmelCase__			  =      tf.constant(
														    [
														        [0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
														        [0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
														        [1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
														        [2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
														        [-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
														        [-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
														    ]  )

														tf.debugging.assert_near(query_layer[0, 0, :6, :8]   ,       lowerCamelCase_   ,       atol=self.tolerance  )
														tf.debugging.assert_near(key_layer[0, 0, :6, :8]   ,       lowerCamelCase_   ,       atol=self.tolerance  )



'''simple docstring'''






import os
from typing import List, Optional, Union

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


__UpperCAmelCase       							=				logging.get_logger(__name__)

__UpperCAmelCase       							=				{'''vocab_file''': '''vocab.txt'''}

__UpperCAmelCase       							=				{
    '''vocab_file''': {
        '''facebook/esm2_t6_8M_UR50D''': '''https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt''',
        '''facebook/esm2_t12_35M_UR50D''': '''https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt''',
    },
}

__UpperCAmelCase       							=				{
    '''facebook/esm2_t6_8M_UR50D''': 1_024,
    '''facebook/esm2_t12_35M_UR50D''': 1_024,
}


def    _snake_case  (	A			)		->       Optional[Any]:
							with open(A					,       '''r'''			) as f:
														lowerCAmelCase__			  =      f.read().splitlines()
														return [l.strip() for l in lines]






class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  Optional[Any]      		= VOCAB_FILES_NAMES
							lowercase__     :  List[Any]      		= PRETRAINED_VOCAB_FILES_MAP
							lowercase__     :  Union[str, Any]      		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__     :  Union[str, Any]      		= ["input_ids", "attention_mask"]







							def __init__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_="<unk>"   ,       lowerCamelCase_="<cls>"   ,       lowerCamelCase_="<pad>"   ,       lowerCamelCase_="<mask>"   ,       lowerCamelCase_="<eos>"   ,       **lowerCamelCase_   ,       )			->				Tuple:
														super().__init__(**lowerCamelCase_  )
														lowerCAmelCase__			  =      load_vocab_file(lowerCamelCase_  )
														lowerCAmelCase__			  =      dict(enumerate(self.all_tokens  )  )
														lowerCAmelCase__			  =      {tok: ind for ind, tok in enumerate(self.all_tokens  )}
														lowerCAmelCase__			  =      unk_token
														lowerCAmelCase__			  =      cls_token
														lowerCAmelCase__			  =      pad_token
														lowerCAmelCase__			  =      mask_token
														lowerCAmelCase__			  =      eos_token
														lowerCAmelCase__			  =      self.all_tokens
														self._create_trie(self.unique_no_split_tokens  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				str:
														return self._id_to_token.get(lowerCamelCase_   ,       self.unk_token  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				int:
														return self._token_to_id.get(lowerCamelCase_   ,       self._token_to_id.get(self.unk_token  )  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       **lowerCamelCase_  )			->				Union[str, Any]:
														return text.split()







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_=False  )			->				Dict:
														return len(self._id_to_token  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														return {token: i for i, token in enumerate(self.all_tokens  )}







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				int:
														return self._token_to_id.get(lowerCamelCase_   ,       self._token_to_id.get(self.unk_token  )  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				str:
														return self._id_to_token.get(lowerCamelCase_   ,       self.unk_token  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = None  )			->				List[int]:
														lowerCAmelCase__			  =      [self.cls_token_id]
														lowerCAmelCase__			  =      [self.eos_token_id]  # No sep token in ESM vocabulary
														if token_ids_a is None:
																					if self.eos_token_id is None:
																												return cls + token_ids_a
																					else:
																												return cls + token_ids_a + sep
														elif self.eos_token_id is None:
																					raise ValueError('''Cannot tokenize multiple sequences when EOS token is not set!'''  )
														return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = False  )			->				List[int]:
														if already_has_special_tokens:
																					if token_ids_a is not None:
																												raise ValueError(
																												    '''You should not supply a second sequence if the provided sequence of '''
																												    '''ids is already formatted with special tokens for the model.'''  )

																					return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
														lowerCAmelCase__			  =      [1] + ([0] * len(lowerCamelCase_  )) + [1]
														if token_ids_a is not None:
																					mask += [0] * len(lowerCamelCase_  ) + [1]
														return mask







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      os.path.join(lowerCamelCase_   ,       (filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt'''  )
														with open(lowerCamelCase_   ,       '''w'''  ) as f:
																					f.write('''\n'''.join(self.all_tokens  )  )
														return (vocab_file,)







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														return self.get_vocab_size(with_added_tokens=lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = False  )			->				int:
														return super()._add_tokens(lowerCamelCase_   ,       special_tokens=lowerCamelCase_  )