






import unittest

from transformers import XLMConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         XLMForMultipleChoice,
         XLMForQuestionAnswering,
         XLMForQuestionAnsweringSimple,
         XLMForSequenceClassification,
         XLMForTokenClassification,
         XLMModel,
         XLMWithLMHeadModel,
     )
     from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST




class       snake_case__ :

       '''simple docstring'''



       def __init__(				self      ,    a__      ,    a__=13      ,    a__=7      ,    a__=True      ,    a__=True      ,    a__=True      ,    a__=True      ,    a__=True      ,    a__=False      ,    a__=False      ,    a__=False      ,    a__=2      ,    a__=99      ,    a__=0      ,    a__=32      ,    a__=5      ,    a__=4      ,    a__=0.1      ,    a__=0.1      ,    a__=5_12      ,    a__=2      ,    a__=0.02      ,    a__=2      ,    a__=4      ,    a__="last"      ,    a__=True      ,    a__=None      ,    a__=0      ,    )			->   List[str]:






            '''simple docstring'''





            __snake_case      :Any 							=    parent
            __snake_case      :int 							=    batch_size
            __snake_case      :Any 							=    seq_length
            __snake_case      :Optional[Any] 							=    is_training
            __snake_case      :Union[str, Any] 							=    use_input_lengths
            __snake_case      :Optional[Any] 							=    use_token_type_ids
            __snake_case      :Tuple 							=    use_labels
            __snake_case      :str 							=    gelu_activation
            __snake_case      :Optional[int] 							=    sinusoidal_embeddings
            __snake_case      :Optional[int] 							=    causal
            __snake_case      :List[str] 							=    asm
            __snake_case      :List[Any] 							=    n_langs
            __snake_case      :int 							=    vocab_size
            __snake_case      :Union[str, Any] 							=    n_special
            __snake_case      :Tuple 							=    hidden_size
            __snake_case      :Tuple 							=    num_hidden_layers
            __snake_case      :Tuple 							=    num_attention_heads
            __snake_case      :str 							=    hidden_dropout_prob
            __snake_case      :Union[str, Any] 							=    attention_probs_dropout_prob
            __snake_case      :str 							=    max_position_embeddings
            __snake_case      :Any 							=    type_sequence_label_size
            __snake_case      :List[Any] 							=    initializer_range
            __snake_case      :List[Any] 							=    num_labels
            __snake_case      :int 							=    num_choices
            __snake_case      :Optional[Any] 							=    summary_type
            __snake_case      :List[str] 							=    use_proj
            __snake_case      :List[str] 							=    scope
            __snake_case      :List[str] 							=    bos_token_id



       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    ids_tensor([self.batch_size, self.seq_length]      ,    self.vocab_size     )
            __snake_case      :str 							=    random_attention_mask([self.batch_size, self.seq_length]     )

            __snake_case      :List[str] 							=    None
            if self.use_input_lengths:
                 __snake_case      :int 							=    (
                     ids_tensor([self.batch_size]      ,    vocab_size=2     ) + self.seq_length - 2
                 )  # small variation of seq_length

            __snake_case      :int 							=    None
            if self.use_token_type_ids:
                 __snake_case      :Optional[int] 							=    ids_tensor([self.batch_size, self.seq_length]      ,    self.n_langs     )

            __snake_case      :Dict 							=    None
            __snake_case      :Union[str, Any] 							=    None
            __snake_case      :Tuple 							=    None
            if self.use_labels:
                 __snake_case      :Any 							=    ids_tensor([self.batch_size]      ,    self.type_sequence_label_size     )
                 __snake_case      :List[Any] 							=    ids_tensor([self.batch_size, self.seq_length]      ,    self.num_labels     )
                 __snake_case      :Optional[int] 							=    ids_tensor([self.batch_size]      ,    2     ).float()
                 __snake_case      :Optional[int] 							=    ids_tensor([self.batch_size]      ,    self.num_choices     )

            __snake_case      :Tuple 							=    self.get_config()

            return (
                config,
                input_ids,
                token_type_ids,
                input_lengths,
                sequence_labels,
                token_labels,
                is_impossible_labels,
                choice_labels,
                input_mask,
            )



       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            return XLMConfig(
                vocab_size=self.vocab_size      ,    n_special=self.n_special      ,    emb_dim=self.hidden_size      ,    n_layers=self.num_hidden_layers      ,    n_heads=self.num_attention_heads      ,    dropout=self.hidden_dropout_prob      ,    attention_dropout=self.attention_probs_dropout_prob      ,    gelu_activation=self.gelu_activation      ,    sinusoidal_embeddings=self.sinusoidal_embeddings      ,    asm=self.asm      ,    causal=self.causal      ,    n_langs=self.n_langs      ,    max_position_embeddings=self.max_position_embeddings      ,    initializer_range=self.initializer_range      ,    summary_type=self.summary_type      ,    use_proj=self.use_proj      ,    num_labels=self.num_labels      ,    bos_token_id=self.bos_token_id      ,    )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    )			->   Optional[Any]:






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    XLMModel(config=lowerCamelCase_     )
            model.to(lowerCamelCase_     )
            model.eval()
            __snake_case      :int 							=    model(lowerCamelCase_      ,    lengths=lowerCamelCase_      ,    langs=lowerCamelCase_     )
            __snake_case      :Optional[int] 							=    model(lowerCamelCase_      ,    langs=lowerCamelCase_     )
            __snake_case      :Optional[int] 							=    model(lowerCamelCase_     )
            self.parent.assertEqual(result.last_hidden_state.shape      ,    (self.batch_size, self.seq_length, self.hidden_size)     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    )			->   Optional[Any]:






            '''simple docstring'''





            __snake_case      :int 							=    XLMWithLMHeadModel(lowerCamelCase_     )
            model.to(lowerCamelCase_     )
            model.eval()

            __snake_case      :Tuple 							=    model(lowerCamelCase_      ,    token_type_ids=lowerCamelCase_      ,    labels=lowerCamelCase_     )
            self.parent.assertEqual(result.loss.shape      ,    ()     )
            self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.seq_length, self.vocab_size)     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    )			->   Dict:






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    XLMForQuestionAnsweringSimple(lowerCamelCase_     )
            model.to(lowerCamelCase_     )
            model.eval()

            __snake_case      :Optional[int] 							=    model(lowerCamelCase_     )

            __snake_case      :Tuple 							=    model(lowerCamelCase_      ,    start_positions=lowerCamelCase_      ,    end_positions=lowerCamelCase_     )
            __snake_case      :Optional[Any] 							=    outputs
            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        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    )			->   str:






            '''simple docstring'''





            __snake_case      :List[str] 							=    XLMForQuestionAnswering(lowerCamelCase_     )
            model.to(lowerCamelCase_     )
            model.eval()

            __snake_case      :Union[str, Any] 							=    model(lowerCamelCase_     )

            __snake_case      :Dict 							=    model(
                lowerCamelCase_      ,    start_positions=lowerCamelCase_      ,    end_positions=lowerCamelCase_      ,    cls_index=lowerCamelCase_      ,    is_impossible=lowerCamelCase_      ,    p_mask=lowerCamelCase_      ,    )

            __snake_case      :Tuple 							=    model(
                lowerCamelCase_      ,    start_positions=lowerCamelCase_      ,    end_positions=lowerCamelCase_      ,    cls_index=lowerCamelCase_      ,    is_impossible=lowerCamelCase_      ,    )

            ((__snake_case   )							,			   )      :int 							=    result_with_labels.to_tuple()

            __snake_case      :List[Any] 							=    model(lowerCamelCase_      ,    start_positions=lowerCamelCase_      ,    end_positions=lowerCamelCase_     )

            ((__snake_case   )							,			   )      :List[Any] 							=    result_with_labels.to_tuple()

            self.parent.assertEqual(result_with_labels.loss.shape      ,    ()     )
            self.parent.assertEqual(result.start_top_log_probs.shape      ,    (self.batch_size, model.config.start_n_top)     )
            self.parent.assertEqual(result.start_top_index.shape      ,    (self.batch_size, model.config.start_n_top)     )
            self.parent.assertEqual(
                result.end_top_log_probs.shape      ,    (self.batch_size, model.config.start_n_top * model.config.end_n_top)     )
            self.parent.assertEqual(
                result.end_top_index.shape      ,    (self.batch_size, model.config.start_n_top * model.config.end_n_top)     )
            self.parent.assertEqual(result.cls_logits.shape      ,    (self.batch_size,)     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :int 							=    XLMForSequenceClassification(lowerCamelCase_     )
            model.to(lowerCamelCase_     )
            model.eval()

            __snake_case      :Union[str, Any] 							=    model(lowerCamelCase_     )
            __snake_case      :str 							=    model(lowerCamelCase_      ,    labels=lowerCamelCase_     )
            self.parent.assertEqual(result.loss.shape      ,    ()     )
            self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.type_sequence_label_size)     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    )			->   List[str]:






            '''simple docstring'''





            __snake_case      :Dict 							=    self.num_labels
            __snake_case      :str 							=    XLMForTokenClassification(lowerCamelCase_     )
            model.to(lowerCamelCase_     )
            model.eval()

            __snake_case      :Tuple 							=    model(lowerCamelCase_      ,    attention_mask=lowerCamelCase_      ,    labels=lowerCamelCase_     )
            self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.seq_length, self.num_labels)     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    )			->   List[str]:






            '''simple docstring'''





            __snake_case      :Any 							=    self.num_choices
            __snake_case      :Optional[Any] 							=    XLMForMultipleChoice(config=lowerCamelCase_     )
            model.to(lowerCamelCase_     )
            model.eval()
            __snake_case      :Dict 							=    input_ids.unsqueeze(1     ).expand(-1      ,    self.num_choices      ,    -1     ).contiguous()
            __snake_case      :str 							=    token_type_ids.unsqueeze(1     ).expand(-1      ,    self.num_choices      ,    -1     ).contiguous()
            __snake_case      :Union[str, Any] 							=    input_mask.unsqueeze(1     ).expand(-1      ,    self.num_choices      ,    -1     ).contiguous()
            __snake_case      :Optional[int] 							=    model(
                lowerCamelCase_      ,    attention_mask=lowerCamelCase_      ,    token_type_ids=lowerCamelCase_      ,    labels=lowerCamelCase_      ,    )
            self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.num_choices)     )



       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            __snake_case      :str 							=    self.prepare_config_and_inputs()
            (
                (
                __snake_case   
            )							,			(
                __snake_case   
            )							,			(
                __snake_case   
            )							,			(
                __snake_case   
            )							,			(
                __snake_case   
            )							,			(
                __snake_case   
            )							,			(
                __snake_case   
            )							,			(
                __snake_case   
            )							,			(
                __snake_case   
            )							,			   
            )      :Union[str, Any] 							=    config_and_inputs
            __snake_case      :Any 							=    {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
            return config, inputs_dict




@require_torch
class       snake_case__ (				a__						,     a__						,     a__						,     unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase				:					List[Any]								=			(
           (
               XLMModel,
               XLMWithLMHeadModel,
               XLMForQuestionAnswering,
               XLMForSequenceClassification,
               XLMForQuestionAnsweringSimple,
               XLMForTokenClassification,
               XLMForMultipleChoice,
           )
           if is_torch_available()
           else ()
       )
       lowerCamelCase				:					int								=			(
           (XLMWithLMHeadModel,) if is_torch_available() else ()
       )  # TODO (PVP): Check other models whether language generation is also applicable
       lowerCamelCase				:					Tuple								=			(
           {
               "feature-extraction": XLMModel,
               "fill-mask": XLMWithLMHeadModel,
               "question-answering": XLMForQuestionAnsweringSimple,
               "text-classification": XLMForSequenceClassification,
               "text-generation": XLMWithLMHeadModel,
               "token-classification": XLMForTokenClassification,
               "zero-shot": XLMForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__     )			->   List[Any]:






            '''simple docstring'''





            if (
                pipeline_test_casse_name == "QAPipelineTests"
                and tokenizer_name is not None
                and not tokenizer_name.endswith("""Fast"""     )
            ):
                 # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
                 # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
                 # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
                 return True

            return False



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__=False     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    super()._prepare_for_class(lowerCamelCase_      ,    lowerCamelCase_      ,    return_labels=lowerCamelCase_     )

            if return_labels:
                 if model_class.__name__ == "XLMForQuestionAnswering":
                      __snake_case      :str 							=    torch.zeros(
                          self.model_tester.batch_size      ,    dtype=torch.long      ,    device=lowerCamelCase_     )
                      __snake_case      :Union[str, Any] 							=    torch.zeros(
                          self.model_tester.batch_size      ,    dtype=torch.long      ,    device=lowerCamelCase_     )

            return inputs_dict



       def        __lowercase    (				self     )			->   Optional[Any]:






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    XLMModelTester(self     )
            __snake_case      :str 							=    ConfigTester(self      ,    config_class=lowerCamelCase_      ,    emb_dim=37     )



       def        __lowercase    (				self     )			->   Optional[Any]:






            '''simple docstring'''





            self.config_tester.run_common_tests()



       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            __snake_case      :Tuple 							=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_model(*lowerCamelCase_     )



       def        __lowercase    (				self     )			->   Optional[Any]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_lm_head(*lowerCamelCase_     )



       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            __snake_case      :List[str] 							=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_simple_qa(*lowerCamelCase_     )



       def        __lowercase    (				self     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :Dict 							=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_qa(*lowerCamelCase_     )



       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_sequence_classif(*lowerCamelCase_     )



       def        __lowercase    (				self     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_token_classif(*lowerCamelCase_     )



       def        __lowercase    (				self     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_for_multiple_choice(*lowerCamelCase_     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__=False      ,    a__=1     )			->   Optional[int]:






            '''simple docstring'''





            self.assertIsInstance(lowerCamelCase_      ,    lowerCamelCase_     )
            self.assertListEqual(
                [isinstance(lowerCamelCase_      ,    lowerCamelCase_     ) for iter_attentions in attentions]      ,    [True] * len(lowerCamelCase_     )     )
            self.assertEqual(len(lowerCamelCase_     )      ,    (max_length - min_length) * num_beam_groups     )

            for idx, iter_attentions in enumerate(lowerCamelCase_     ):
                 # adds PAD dummy token
                 __snake_case      :Tuple 							=    min_length + idx + 1
                 __snake_case      :int 							=    min_length + idx + 1

                 __snake_case      :Optional[Any] 							=    (
                     batch_size * num_beam_groups,
                     config.num_attention_heads,
                     tgt_len,
                     src_len,
                 )
                 # check attn size
                 self.assertListEqual(
                     [layer_attention.shape for layer_attention in iter_attentions]      ,    [expected_shape] * len(lowerCamelCase_     )     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__=False      ,    a__=1     )			->   int:






            '''simple docstring'''





            self.assertIsInstance(lowerCamelCase_      ,    lowerCamelCase_     )
            self.assertListEqual(
                [isinstance(lowerCamelCase_      ,    lowerCamelCase_     ) for iter_hidden_states in hidden_states]      ,    [True] * len(lowerCamelCase_     )      ,    )
            self.assertEqual(len(lowerCamelCase_     )      ,    (max_length - min_length) * num_beam_groups     )

            for idx, iter_hidden_states in enumerate(lowerCamelCase_     ):
                 # adds PAD dummy token
                 __snake_case      :Optional[Any] 							=    min_length + idx + 1
                 __snake_case      :Union[str, Any] 							=    (batch_size * num_beam_groups, seq_len, config.hidden_size)
                 # check hidden size
                 self.assertListEqual(
                     [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]      ,    [expected_shape] * len(lowerCamelCase_     )      ,    )
            pass



       @slow
       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __snake_case      :Optional[Any] 							=    XLMModel.from_pretrained(lowerCamelCase_     )
                 self.assertIsNotNone(lowerCamelCase_     )




@require_torch
class       snake_case__ (				unittest.TestCase):

       '''simple docstring'''



       @slow
       def        __lowercase    (				self     )			->   int:






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048"""     )
            model.to(lowerCamelCase_     )
            __snake_case      :List[Any] 							=    torch.tensor([[14, 4_47]]      ,    dtype=torch.long      ,    device=lowerCamelCase_     )  # the president
            __snake_case      :Union[str, Any] 							=    [
                14,
                4_47,
                14,
                4_47,
                14,
                4_47,
                14,
                4_47,
                14,
                4_47,
                14,
                4_47,
                14,
                4_47,
                14,
                4_47,
                14,
                4_47,
                14,
                4_47,
            ]  # the president the president the president the president the president the president the president the president the president the president
            # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
            __snake_case      :Tuple 							=    model.generate(lowerCamelCase_      ,    do_sample=lowerCamelCase_     )
            self.assertListEqual(output_ids[0].cpu().numpy().tolist()      ,    lowerCamelCase_     )






'''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_  )