





"""simple docstring"""





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    _lowerCAmelCase  :
       """simple docstring"""




       def __init__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=1_3       ,					__UpperCAmelCase=7       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=2       ,					__UpperCAmelCase=9_9       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase=3_2       ,					__UpperCAmelCase=5       ,					__UpperCAmelCase=4       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=5_1_2       ,					__UpperCAmelCase=2       ,					__UpperCAmelCase=0.02       ,					__UpperCAmelCase=2       ,					__UpperCAmelCase=4       ,					__UpperCAmelCase="last"       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=0       ,					):



         '''simple docstring'''

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size )
         lowerCAmelCase__     :Tuple											=				random_attention_mask([self.batch_size, self.seq_length] )

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

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

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

         lowerCAmelCase__     :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 	snake_case		(       self ):



         '''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 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				XLMModel(config=__UpperCAmelCase )
         model.to(__UpperCAmelCase )
         model.eval()
         lowerCAmelCase__     :int											=				model(__UpperCAmelCase       ,					lengths=__UpperCAmelCase       ,					langs=__UpperCAmelCase )
         lowerCAmelCase__     :Optional[int]											=				model(__UpperCAmelCase       ,					langs=__UpperCAmelCase )
         lowerCAmelCase__     :List[str]											=				model(__UpperCAmelCase )
         self.parent.assertEqual(result.last_hidden_state.shape       ,					(self.batch_size, self.seq_length, self.hidden_size) )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				XLMWithLMHeadModel(__UpperCAmelCase )
         model.to(__UpperCAmelCase )
         model.eval()

         lowerCAmelCase__     :Any											=				model(__UpperCAmelCase       ,					token_type_ids=__UpperCAmelCase       ,					labels=__UpperCAmelCase )
         self.parent.assertEqual(result.loss.shape       ,					() )
         self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.seq_length, self.vocab_size) )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				XLMForQuestionAnsweringSimple(__UpperCAmelCase )
         model.to(__UpperCAmelCase )
         model.eval()

         lowerCAmelCase__     :str											=				model(__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				model(__UpperCAmelCase       ,					start_positions=__UpperCAmelCase       ,					end_positions=__UpperCAmelCase )
         lowerCAmelCase__     :Tuple											=				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 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				XLMForQuestionAnswering(__UpperCAmelCase )
         model.to(__UpperCAmelCase )
         model.eval()

         lowerCAmelCase__     :Tuple											=				model(__UpperCAmelCase )

         lowerCAmelCase__     :str											=				model(
             __UpperCAmelCase       ,					start_positions=__UpperCAmelCase       ,					end_positions=__UpperCAmelCase       ,					cls_index=__UpperCAmelCase       ,					is_impossible=__UpperCAmelCase       ,					p_mask=__UpperCAmelCase       ,					)

         lowerCAmelCase__     :int											=				model(
             __UpperCAmelCase       ,					start_positions=__UpperCAmelCase       ,					end_positions=__UpperCAmelCase       ,					cls_index=__UpperCAmelCase       ,					is_impossible=__UpperCAmelCase       ,					)

         ((lowerCAmelCase__)				,							)     :Tuple											=				result_with_labels.to_tuple()

         lowerCAmelCase__     :Tuple											=				model(__UpperCAmelCase       ,					start_positions=__UpperCAmelCase       ,					end_positions=__UpperCAmelCase )

         ((lowerCAmelCase__)				,							)     :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 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				XLMForSequenceClassification(__UpperCAmelCase )
         model.to(__UpperCAmelCase )
         model.eval()

         lowerCAmelCase__     :Optional[int]											=				model(__UpperCAmelCase )
         lowerCAmelCase__     :str											=				model(__UpperCAmelCase       ,					labels=__UpperCAmelCase )
         self.parent.assertEqual(result.loss.shape       ,					() )
         self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.type_sequence_label_size) )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				self.num_labels
         lowerCAmelCase__     :Any											=				XLMForTokenClassification(__UpperCAmelCase )
         model.to(__UpperCAmelCase )
         model.eval()

         lowerCAmelCase__     :Union[str, Any]											=				model(__UpperCAmelCase       ,					attention_mask=__UpperCAmelCase       ,					labels=__UpperCAmelCase )
         self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.seq_length, self.num_labels) )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				self.num_choices
         lowerCAmelCase__     :str											=				XLMForMultipleChoice(config=__UpperCAmelCase )
         model.to(__UpperCAmelCase )
         model.eval()
         lowerCAmelCase__     :str											=				input_ids.unsqueeze(1 ).expand(-1       ,					self.num_choices       ,					-1 ).contiguous()
         lowerCAmelCase__     :Tuple											=				token_type_ids.unsqueeze(1 ).expand(-1       ,					self.num_choices       ,					-1 ).contiguous()
         lowerCAmelCase__     :Dict											=				input_mask.unsqueeze(1 ).expand(-1       ,					self.num_choices       ,					-1 ).contiguous()
         lowerCAmelCase__     :Dict											=				model(
             __UpperCAmelCase       ,					attention_mask=__UpperCAmelCase       ,					token_type_ids=__UpperCAmelCase       ,					labels=__UpperCAmelCase       ,					)
         self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_choices) )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				self.prepare_config_and_inputs()
         (
             (
             lowerCAmelCase__
         )				,							(
             lowerCAmelCase__
         )				,							(
             lowerCAmelCase__
         )				,							(
             lowerCAmelCase__
         )				,							(
             lowerCAmelCase__
         )				,							(
             lowerCAmelCase__
         )				,							(
             lowerCAmelCase__
         )				,							(
             lowerCAmelCase__
         )				,							(
             lowerCAmelCase__
         )				,							
         )     :Any											=				config_and_inputs
         lowerCAmelCase__     :Tuple											=				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
         return config, inputs_dict






@require_torch
class    _lowerCAmelCase  (	a    ,  a    ,  a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:str						    =   (
           (
               XLMModel,
               XLMWithLMHeadModel,
               XLMForQuestionAnswering,
               XLMForSequenceClassification,
               XLMForQuestionAnsweringSimple,
               XLMForTokenClassification,
               XLMForMultipleChoice,
           )
           if is_torch_available()
           else ()
       )
       __magic_name__	:List[Any]						    =   (
           (XLMWithLMHeadModel,) if is_torch_available() else ()
       )  # TODO (PVP): Check other models whether language generation is also applicable
       __magic_name__	:Any						    =   (
           {
               """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 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''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 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase=False ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				super()._prepare_for_class(__UpperCAmelCase       ,					__UpperCAmelCase       ,					return_labels=__UpperCAmelCase )

         if return_labels:
           if model_class.__name__ == "XLMForQuestionAnswering":
             lowerCAmelCase__     :Optional[int]											=				torch.zeros(
                 self.model_tester.batch_size       ,					dtype=torch.long       ,					device=__UpperCAmelCase )
             lowerCAmelCase__     :List[Any]											=				torch.zeros(
                 self.model_tester.batch_size       ,					dtype=torch.long       ,					device=__UpperCAmelCase )

         return inputs_dict






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				XLMModelTester(self )
         lowerCAmelCase__     :Tuple											=				ConfigTester(self       ,					config_class=__UpperCAmelCase       ,					emb_dim=3_7 )






       def 	snake_case		(       self ):



         '''simple docstring'''

         self.config_tester.run_common_tests()






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_model(*__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_lm_head(*__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_simple_qa(*__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_qa(*__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_sequence_classif(*__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_token_classif(*__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_for_multiple_choice(*__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=1 ):



         '''simple docstring'''

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

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

           lowerCAmelCase__     :Dict											=				(
               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(__UpperCAmelCase ) )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=1 ):



         '''simple docstring'''

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

         for idx, iter_hidden_states in enumerate(__UpperCAmelCase ):
           # adds PAD dummy token
           lowerCAmelCase__     :Tuple											=				min_length + idx + 1
           lowerCAmelCase__     :Dict											=				(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(__UpperCAmelCase )       ,					)
         pass






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowerCAmelCase__     :List[str]											=				XLMModel.from_pretrained(__UpperCAmelCase )
           self.assertIsNotNone(__UpperCAmelCase )






@require_torch
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048' )
         model.to(__UpperCAmelCase )
         lowerCAmelCase__     :Tuple											=				torch.tensor([[1_4, 4_4_7]]       ,					dtype=torch.long       ,					device=__UpperCAmelCase )  # the president
         lowerCAmelCase__     :Dict											=				[
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
         ]  # 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
         lowerCAmelCase__     :Tuple											=				model.generate(__UpperCAmelCase       ,					do_sample=__UpperCAmelCase )
         self.assertListEqual(output_ids[0].cpu().numpy().tolist()       ,					__UpperCAmelCase )




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


SCREAMING_SNAKE_CASE__					:     List[str]       =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					:     Optional[int]       =     {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE__					:     List[Any]       =     {
    'vocab_file': {
        'yjernite/retribert-base-uncased': (
            'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'yjernite/retribert-base-uncased': (
            'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'
        ),
    },
}

SCREAMING_SNAKE_CASE__					:     Tuple       =     {
    'yjernite/retribert-base-uncased': 512,
}


SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     {
    'yjernite/retribert-base-uncased': {'do_lower_case': True},
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE     					=    PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE     					=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE     					=    PRETRAINED_INIT_CONFIGURATION
     __SCREAMING_SNAKE_CASE     					=    RetriBertTokenizer
     __SCREAMING_SNAKE_CASE     					=    ['''input_ids''', '''attention_mask''']


     def __init__(  self							,					A_=None							,					A_=None							,					A_=True							,					A_="[UNK]"							,					A_="[SEP]"							,					A_="[PAD]"							,					A_="[CLS]"							,					A_="[MASK]"							,					A_=True							,					A_=None							,					**A_							,					):
       super().__init__(
           A_							,					tokenizer_file=A_							,					do_lower_case=A_							,					unk_token=A_							,					sep_token=A_							,					pad_token=A_							,					cls_token=A_							,					mask_token=A_							,					tokenize_chinese_chars=A_							,					strip_accents=A_							,					**A_							,					)

       _UpperCAmelCase		:							List[str]							=     json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
       if (
           normalizer_state.get("""lowercase"""							,					A_    ) != do_lower_case
           or normalizer_state.get("""strip_accents"""							,					A_    ) != strip_accents
           or normalizer_state.get("""handle_chinese_chars"""							,					A_    ) != tokenize_chinese_chars
       ):
         _UpperCAmelCase		:							str							=     getattr(A_							,					normalizer_state.pop("""type"""    )    )
         _UpperCAmelCase		:							Optional[int]							=     do_lower_case
         _UpperCAmelCase		:							List[Any]							=     strip_accents
         _UpperCAmelCase		:							List[str]							=     tokenize_chinese_chars
         _UpperCAmelCase		:							Dict							=     normalizer_class(**A_    )

       _UpperCAmelCase		:							Dict							=     do_lower_case


     def        __snake_case(  self							,					A_							,					A_=None    ):
       _UpperCAmelCase		:							Tuple							=     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
         output += token_ids_a + [self.sep_token_id]

       return output


     def        __snake_case(  self							,					A_							,					A_ = None    ):
       _UpperCAmelCase		:							List[Any]							=     [self.sep_token_id]
       _UpperCAmelCase		:							Tuple							=     [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    ) * [0] + len(token_ids_a + sep    ) * [1]




     def        __snake_case(  self							,					A_							,					A_ = None    ):
       _UpperCAmelCase		:							Dict							=     self._tokenizer.model.save(A_							,					name=A_    )
       return tuple(A_    )





