



"""simple docstring"""




import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     RemBertConfig,
     RemBertForCausalLM,
     RemBertForMaskedLM,
     RemBertForMultipleChoice,
     RemBertForQuestionAnswering,
     RemBertForSequenceClassification,
     RemBertForTokenClassification,
     RemBertModel,
 )
 from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST






class 			UpperCAmelCase_						:



      def __init__(  self  :			int  ,			A  :			List[Any]  ,			A  :			Tuple=1_3  ,			A  :			Tuple=7  ,			A  :			int=True  ,			A  :			Dict=True  ,			A  :			List[Any]=True  ,			A  :			str=True  ,			A  :			Dict=9_9  ,			A  :			str=3_2  ,			A  :			Union[str, Any]=1_8  ,			A  :			Any=4_3  ,			A  :			Optional[int]=5  ,			A  :			Tuple=4  ,			A  :			List[str]=3_7  ,			A  :			Tuple="gelu"  ,			A  :			Optional[Any]=0.1  ,			A  :			int=0.1  ,			A  :			str=5_1_2  ,			A  :			Optional[int]=1_6  ,			A  :			List[Any]=2  ,			A  :			int=0.02  ,			A  :			str=3  ,			A  :			str=4  ,			A  :			Optional[int]=None  ,			):
           _UpperCAmelCase    :			Union[str, Any]              =					parent
           _UpperCAmelCase    :			Any              =					batch_size
           _UpperCAmelCase    :			Optional[Any]              =					seq_length
           _UpperCAmelCase    :			str              =					is_training
           _UpperCAmelCase    :			List[str]              =					use_input_mask
           _UpperCAmelCase    :			Any              =					use_token_type_ids
           _UpperCAmelCase    :			Union[str, Any]              =					use_labels
           _UpperCAmelCase    :			Union[str, Any]              =					vocab_size
           _UpperCAmelCase    :			Tuple              =					hidden_size
           _UpperCAmelCase    :			Optional[Any]              =					input_embedding_size
           _UpperCAmelCase    :			Optional[Any]              =					output_embedding_size
           _UpperCAmelCase    :			Tuple              =					num_hidden_layers
           _UpperCAmelCase    :			List[str]              =					num_attention_heads
           _UpperCAmelCase    :			Union[str, Any]              =					intermediate_size
           _UpperCAmelCase    :			Union[str, Any]              =					hidden_act
           _UpperCAmelCase    :			Union[str, Any]              =					hidden_dropout_prob
           _UpperCAmelCase    :			Dict              =					attention_probs_dropout_prob
           _UpperCAmelCase    :			Optional[int]              =					max_position_embeddings
           _UpperCAmelCase    :			Dict              =					type_vocab_size
           _UpperCAmelCase    :			List[Any]              =					type_sequence_label_size
           _UpperCAmelCase    :			Optional[int]              =					initializer_range
           _UpperCAmelCase    :			Optional[Any]              =					num_labels
           _UpperCAmelCase    :			int              =					num_choices
           _UpperCAmelCase    :			Dict              =					scope



      def 		snake_case_      (  self  :			List[Any]		):
           _UpperCAmelCase    :			int              =					ids_tensor([self.batch_size, self.seq_length]  ,			self.vocab_size		)

           _UpperCAmelCase    :			List[str]              =					None
           if self.use_input_mask:
                _UpperCAmelCase    :			List[Any]              =					random_attention_mask([self.batch_size, self.seq_length]		)

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

           _UpperCAmelCase    :			List[Any]              =					None
           _UpperCAmelCase    :			List[str]              =					None
           _UpperCAmelCase    :			Dict              =					None
           if self.use_labels:
                _UpperCAmelCase    :			List[str]              =					ids_tensor([self.batch_size]  ,			self.type_sequence_label_size		)
                _UpperCAmelCase    :			Union[str, Any]              =					ids_tensor([self.batch_size, self.seq_length]  ,			self.num_labels		)
                _UpperCAmelCase    :			Dict              =					ids_tensor([self.batch_size]  ,			self.num_choices		)

           _UpperCAmelCase    :			Optional[Any]              =					RemBertConfig(
               vocab_size=self.vocab_size  ,			hidden_size=self.hidden_size  ,			input_embedding_size=self.input_embedding_size  ,			output_embedding_size=self.output_embedding_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  ,			is_decoder=UpperCAmelCase_  ,			initializer_range=self.initializer_range  ,			)

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



      def 		snake_case_      (  self  :			str		):
           (
               _UpperCAmelCase  
           )    :			Optional[Any]              =					self.prepare_config_and_inputs()

           _UpperCAmelCase    :			Tuple              =					True
           _UpperCAmelCase    :			Optional[Any]              =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
           _UpperCAmelCase    :			str              =					ids_tensor([self.batch_size, self.seq_length]  ,			vocab_size=2		)

           return (
               config,
               input_ids,
               token_type_ids,
               input_mask,
               sequence_labels,
               token_labels,
               choice_labels,
               encoder_hidden_states,
               encoder_attention_mask,
           )



      def 		snake_case_      (  self  :			Tuple  ,			A  :			Any  ,			A  :			Tuple  ,			A  :			Tuple  ,			A  :			str  ,			A  :			Dict  ,			A  :			List[Any]  ,			A  :			int		):
           _UpperCAmelCase    :			str              =					RemBertModel(config=UpperCAmelCase_		)
           model.to(UpperCAmelCase_		)
           model.eval()
           _UpperCAmelCase    :			Optional[int]              =					model(UpperCAmelCase_  ,			attention_mask=UpperCAmelCase_  ,			token_type_ids=UpperCAmelCase_		)
           _UpperCAmelCase    :			str              =					model(UpperCAmelCase_  ,			token_type_ids=UpperCAmelCase_		)
           _UpperCAmelCase    :			Optional[Any]              =					model(UpperCAmelCase_		)
           self.parent.assertEqual(result.last_hidden_state.shape  ,			(self.batch_size, self.seq_length, self.hidden_size)		)



      def 		snake_case_      (  self  :			str  ,			A  :			Any  ,			A  :			List[Any]  ,			A  :			Tuple  ,			A  :			int  ,			A  :			Union[str, Any]  ,			A  :			Any  ,			A  :			Any  ,			A  :			List[Any]  ,			A  :			Union[str, Any]  ,			):
           _UpperCAmelCase    :			List[Any]              =					True
           _UpperCAmelCase    :			Optional[int]              =					RemBertModel(UpperCAmelCase_		)
           model.to(UpperCAmelCase_		)
           model.eval()
           _UpperCAmelCase    :			int              =					model(
               UpperCAmelCase_  ,			attention_mask=UpperCAmelCase_  ,			token_type_ids=UpperCAmelCase_  ,			encoder_hidden_states=UpperCAmelCase_  ,			encoder_attention_mask=UpperCAmelCase_  ,			)
           _UpperCAmelCase    :			Optional[int]              =					model(
               UpperCAmelCase_  ,			attention_mask=UpperCAmelCase_  ,			token_type_ids=UpperCAmelCase_  ,			encoder_hidden_states=UpperCAmelCase_  ,			)
           _UpperCAmelCase    :			Optional[Any]              =					model(UpperCAmelCase_  ,			attention_mask=UpperCAmelCase_  ,			token_type_ids=UpperCAmelCase_		)
           self.parent.assertEqual(result.last_hidden_state.shape  ,			(self.batch_size, self.seq_length, self.hidden_size)		)



      def 		snake_case_      (  self  :			Tuple  ,			A  :			Dict  ,			A  :			int  ,			A  :			str  ,			A  :			Union[str, Any]  ,			A  :			Optional[int]  ,			A  :			Dict  ,			A  :			List[Any]  ,			A  :			Any  ,			A  :			Any  ,			):
           _UpperCAmelCase    :			Any              =					RemBertForCausalLM(config=UpperCAmelCase_		)
           model.to(UpperCAmelCase_		)
           model.eval()
           _UpperCAmelCase    :			List[str]              =					model(UpperCAmelCase_  ,			attention_mask=UpperCAmelCase_  ,			token_type_ids=UpperCAmelCase_  ,			labels=UpperCAmelCase_		)
           self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.seq_length, self.vocab_size)		)



      def 		snake_case_      (  self  :			Union[str, Any]  ,			A  :			List[str]  ,			A  :			Any  ,			A  :			Any  ,			A  :			Dict  ,			A  :			Optional[Any]  ,			A  :			str  ,			A  :			Union[str, Any]		):
           _UpperCAmelCase    :			Any              =					RemBertForMaskedLM(config=UpperCAmelCase_		)
           model.to(UpperCAmelCase_		)
           model.eval()
           _UpperCAmelCase    :			List[str]              =					model(UpperCAmelCase_  ,			attention_mask=UpperCAmelCase_  ,			token_type_ids=UpperCAmelCase_  ,			labels=UpperCAmelCase_		)
           self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.seq_length, self.vocab_size)		)



      def 		snake_case_      (  self  :			Tuple  ,			A  :			Optional[int]  ,			A  :			Optional[int]  ,			A  :			List[str]  ,			A  :			List[Any]  ,			A  :			Tuple  ,			A  :			Any  ,			A  :			List[Any]  ,			A  :			Union[str, Any]  ,			A  :			Tuple  ,			):
           _UpperCAmelCase    :			Optional[Any]              =					True
           _UpperCAmelCase    :			Dict              =					True
           _UpperCAmelCase    :			List[Any]              =					RemBertForCausalLM(config=UpperCAmelCase_		)
           model.to(UpperCAmelCase_		)
           model.eval()

           # first forward pass
           _UpperCAmelCase    :			int              =					model(
               UpperCAmelCase_  ,			attention_mask=UpperCAmelCase_  ,			encoder_hidden_states=UpperCAmelCase_  ,			encoder_attention_mask=UpperCAmelCase_  ,			use_cache=UpperCAmelCase_  ,			)
           _UpperCAmelCase    :			Optional[Any]              =					outputs.past_key_values

           # create hypothetical multiple next token and extent to next_input_ids
           _UpperCAmelCase    :			List[Any]              =					ids_tensor((self.batch_size, 3)  ,			config.vocab_size		)
           _UpperCAmelCase    :			Tuple              =					ids_tensor((self.batch_size, 3)  ,			vocab_size=2		)

           # append to next input_ids and
           _UpperCAmelCase    :			int              =					torch.cat([input_ids, next_tokens]  ,			dim=-1		)
           _UpperCAmelCase    :			Any              =					torch.cat([input_mask, next_mask]  ,			dim=-1		)

           _UpperCAmelCase    :			List[Any]              =					model(
               UpperCAmelCase_  ,			attention_mask=UpperCAmelCase_  ,			encoder_hidden_states=UpperCAmelCase_  ,			encoder_attention_mask=UpperCAmelCase_  ,			output_hidden_states=UpperCAmelCase_  ,			)['hidden_states'][0]
           _UpperCAmelCase    :			Any              =					model(
               UpperCAmelCase_  ,			attention_mask=UpperCAmelCase_  ,			encoder_hidden_states=UpperCAmelCase_  ,			encoder_attention_mask=UpperCAmelCase_  ,			past_key_values=UpperCAmelCase_  ,			output_hidden_states=UpperCAmelCase_  ,			)['hidden_states'][0]

           # select random slice
           _UpperCAmelCase    :			List[Any]              =					ids_tensor((1,)  ,			output_from_past.shape[-1]		).item()
           _UpperCAmelCase    :			Tuple              =					output_from_no_past[:, -3:, random_slice_idx].detach()
           _UpperCAmelCase    :			Optional[Any]              =					output_from_past[:, :, random_slice_idx].detach()

           self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]		)

           # test that outputs are equal for slice
           self.parent.assertTrue(torch.allclose(UpperCAmelCase_  ,			UpperCAmelCase_  ,			atol=1e-3		)		)



      def 		snake_case_      (  self  :			Optional[int]  ,			A  :			List[Any]  ,			A  :			Tuple  ,			A  :			Any  ,			A  :			Dict  ,			A  :			Dict  ,			A  :			Dict  ,			A  :			Optional[Any]		):
           _UpperCAmelCase    :			List[str]              =					RemBertForQuestionAnswering(config=UpperCAmelCase_		)
           model.to(UpperCAmelCase_		)
           model.eval()
           _UpperCAmelCase    :			Tuple              =					model(
               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 		snake_case_      (  self  :			int  ,			A  :			Dict  ,			A  :			List[str]  ,			A  :			Union[str, Any]  ,			A  :			Union[str, Any]  ,			A  :			Optional[Any]  ,			A  :			List[str]  ,			A  :			Any		):
           _UpperCAmelCase    :			Any              =					self.num_labels
           _UpperCAmelCase    :			Tuple              =					RemBertForSequenceClassification(UpperCAmelCase_		)
           model.to(UpperCAmelCase_		)
           model.eval()
           _UpperCAmelCase    :			Optional[int]              =					model(UpperCAmelCase_  ,			attention_mask=UpperCAmelCase_  ,			token_type_ids=UpperCAmelCase_  ,			labels=UpperCAmelCase_		)
           self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.num_labels)		)



      def 		snake_case_      (  self  :			List[str]  ,			A  :			List[str]  ,			A  :			Optional[Any]  ,			A  :			int  ,			A  :			Any  ,			A  :			List[str]  ,			A  :			Tuple  ,			A  :			List[Any]		):
           _UpperCAmelCase    :			List[str]              =					self.num_labels
           _UpperCAmelCase    :			List[Any]              =					RemBertForTokenClassification(config=UpperCAmelCase_		)
           model.to(UpperCAmelCase_		)
           model.eval()
           _UpperCAmelCase    :			Union[str, Any]              =					model(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 		snake_case_      (  self  :			List[str]  ,			A  :			List[str]  ,			A  :			Any  ,			A  :			Union[str, Any]  ,			A  :			List[str]  ,			A  :			int  ,			A  :			List[Any]  ,			A  :			Union[str, Any]		):
           _UpperCAmelCase    :			Optional[Any]              =					self.num_choices
           _UpperCAmelCase    :			int              =					RemBertForMultipleChoice(config=UpperCAmelCase_		)
           model.to(UpperCAmelCase_		)
           model.eval()
           _UpperCAmelCase    :			Tuple              =					input_ids.unsqueeze(1		).expand(-1  ,			self.num_choices  ,			-1		).contiguous()
           _UpperCAmelCase    :			Tuple              =					token_type_ids.unsqueeze(1		).expand(-1  ,			self.num_choices  ,			-1		).contiguous()
           _UpperCAmelCase    :			List[Any]              =					input_mask.unsqueeze(1		).expand(-1  ,			self.num_choices  ,			-1		).contiguous()
           _UpperCAmelCase    :			List[str]              =					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  :			Tuple		):
           _UpperCAmelCase    :			Optional[int]              =					self.prepare_config_and_inputs()
           (
               _UpperCAmelCase  
           )    :			List[Any]              =					config_and_inputs
           _UpperCAmelCase    :			str              =					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
           return config, inputs_dict







@require_torch
class 			UpperCAmelCase_						(       _UpperCamelCase ,    _UpperCamelCase ,    unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:     str				  =				(
          (
              RemBertModel,
              RemBertForMaskedLM,
              RemBertForCausalLM,
              RemBertForMultipleChoice,
              RemBertForQuestionAnswering,
              RemBertForSequenceClassification,
              RemBertForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      __SCREAMING_SNAKE_CASE		:     Any				  =				(RemBertForCausalLM,) if is_torch_available() else ()
      __SCREAMING_SNAKE_CASE		:     List[Any]				  =				(
          {
              "feature-extraction": RemBertModel,
              "fill-mask": RemBertForMaskedLM,
              "question-answering": RemBertForQuestionAnswering,
              "text-classification": RemBertForSequenceClassification,
              "text-generation": RemBertForCausalLM,
              "token-classification": RemBertForTokenClassification,
              "zero-shot": RemBertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )



      def 		snake_case_      (  self  :			Optional[int]		):
           _UpperCAmelCase    :			str              =					RemBertModelTester(self		)
           _UpperCAmelCase    :			List[str]              =					ConfigTester(self  ,			config_class=UpperCAmelCase_  ,			hidden_size=3_7		)



      def 		snake_case_      (  self  :			Tuple		):
           self.config_tester.run_common_tests()



      def 		snake_case_      (  self  :			Any		):
           _UpperCAmelCase    :			Union[str, Any]              =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase_		)



      def 		snake_case_      (  self  :			List[Any]		):
           _UpperCAmelCase    :			List[Any]              =					self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                _UpperCAmelCase    :			str              =					type
                self.model_tester.create_and_check_model(*UpperCAmelCase_		)



      def 		snake_case_      (  self  :			str		):
           _UpperCAmelCase    :			Tuple              =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_		)



      def 		snake_case_      (  self  :			Tuple		):
           _UpperCAmelCase    :			Tuple              =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase_		)



      def 		snake_case_      (  self  :			Tuple		):
           _UpperCAmelCase    :			Dict              =					self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_decoder_model_past_large_inputs(*UpperCAmelCase_		)



      def 		snake_case_      (  self  :			List[str]		):
           _UpperCAmelCase    :			Dict              =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_		)



      def 		snake_case_      (  self  :			Any		):
           _UpperCAmelCase    :			List[Any]              =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_		)



      def 		snake_case_      (  self  :			Dict		):
           _UpperCAmelCase    :			Any              =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_		)



      def 		snake_case_      (  self  :			Optional[Any]		):
           _UpperCAmelCase    :			Optional[int]              =					self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_model_as_decoder(*UpperCAmelCase_		)



      def 		snake_case_      (  self  :			Optional[int]		):
           # This regression test was failing with PyTorch < 1.3
           (
               _UpperCAmelCase  
           )    :			Union[str, Any]              =					self.model_tester.prepare_config_and_inputs_for_decoder()

           _UpperCAmelCase    :			List[str]              =					None

           self.model_tester.create_and_check_model_as_decoder(
               UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			)



      @slow
      def 		snake_case_      (  self  :			Tuple		):
           for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase    :			int              =					RemBertModel.from_pretrained(UpperCAmelCase_		)
                self.assertIsNotNone(UpperCAmelCase_		)







@require_torch
class 			UpperCAmelCase_						(       unittest.TestCase		):



      @slow
      def 		snake_case_      (  self  :			List[Any]		):
           # Test exact values at the last hidden layer
           _UpperCAmelCase    :			Union[str, Any]              =					RemBertModel.from_pretrained("google/rembert"		)
           _UpperCAmelCase    :			str              =					torch.tensor([[3_1_2, 5_6_4_9_8, 3_1_3, 2_1_2_5, 3_1_3]]		)
           _UpperCAmelCase    :			List[str]              =					torch.tensor([[0, 0, 0, 1, 1]]		)
           with torch.no_grad():
                _UpperCAmelCase    :			str              =					model(UpperCAmelCase_  ,			token_type_ids=UpperCAmelCase_  ,			output_hidden_states=UpperCAmelCase_		)

           _UpperCAmelCase    :			int              =					1_1_5_2

           _UpperCAmelCase    :			Union[str, Any]              =					torch.Size((1, 5, hidden_size)		)
           self.assertEqual(output["last_hidden_state"].shape  ,			UpperCAmelCase_		)

           _UpperCAmelCase    :			Dict              =					torch.tensor(
               [
                   [
                       [0.0_754, -0.2_022, 0.1_904],
                       [-0.3_354, -0.3_692, -0.4_791],
                       [-0.2_314, -0.6_729, -0.0_749],
                       [-0.0_396, -0.3_105, -0.4_234],
                       [-0.1_571, -0.0_525, 0.5_353],
                   ]
               ]		)

           # Running on the original tf implementation gives slightly different results here.
           # Not clear why this variations is present
           # TODO: Find reason for discrepancy
           # expected_original_implementation = [[
           #     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
           #     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
           #     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
           #     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
           #     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
           # ]]

           self.assertTrue(torch.allclose(output["last_hidden_state"][:, :, :3]  ,			UpperCAmelCase_  ,			atol=1e-4		)		)





"""simple docstring"""




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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_xglm import XGLMTokenizer
else:
 _lowerCAmelCase       :      Tuple							=							None


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

_lowerCAmelCase       :      str							=							{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase       :      Optional[Any]							=							{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json",
    },
}

_lowerCAmelCase       :      Union[str, Any]							=							{
    "facebook/xglm-564M": 20_48,
}






class 			UpperCAmelCase_						(       _UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:     str				  =				VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE		:     List[str]				  =				PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE		:     str				  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE		:     int				  =				['input_ids', 'attention_mask']
      __SCREAMING_SNAKE_CASE		:     str				  =				XGLMTokenizer



      def __init__(  self  :			List[Any]  ,			A  :			Union[str, Any]=None  ,			A  :			Optional[Any]=None  ,			A  :			Optional[Any]="<s>"  ,			A  :			Optional[int]="</s>"  ,			A  :			str="</s>"  ,			A  :			int="<s>"  ,			A  :			int="<unk>"  ,			A  :			List[str]="<pad>"  ,			**A  :			Tuple  ,			):
           # Compatibility with the original tokenizer
           _UpperCAmelCase    :			Union[str, Any]              =					7
           _UpperCAmelCase    :			Optional[int]              =					[f'<madeupword{i}>' for i in range(self.num_madeup_words		)]

           _UpperCAmelCase    :			Any              =					kwargs.get("additional_special_tokens"  ,			[]		)
           kwargs["additional_special_tokens"] += [
               word for word in madeup_words if word not in kwargs["additional_special_tokens"]
           ]

           super().__init__(
               A  ,			tokenizer_file=A  ,			bos_token=A  ,			eos_token=A  ,			sep_token=A  ,			cls_token=A  ,			unk_token=A  ,			pad_token=A  ,			**A  ,			)

           _UpperCAmelCase    :			Any              =					vocab_file
           _UpperCAmelCase    :			Union[str, Any]              =					False if not self.vocab_file else True



      def 		snake_case_      (  self  :			List[str]  ,			A  :			List[int]  ,			A  :			Optional[List[int]] = None		):

           if token_ids_a is None:
                return [self.sep_token_id] + token_ids_a
           _UpperCAmelCase    :			Tuple              =					[self.sep_token_id]
           return sep + token_ids_a + sep + sep + token_ids_a



      def 		snake_case_      (  self  :			str  ,			A  :			List[int]  ,			A  :			Optional[List[int]] = None		):
           _UpperCAmelCase    :			Optional[int]              =					[self.sep_token_id]

           if token_ids_a is None:
                return len(sep + token_ids_a		) * [0]
           return len(sep + token_ids_a + sep + sep + token_ids_a		) * [0]



      def 		snake_case_      (  self  :			Dict  ,			A  :			str  ,			A  :			Optional[str] = None		):
           if not self.can_save_slow_tokenizer:
                raise ValueError(
                    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                    "tokenizer."		)

           if not os.path.isdir(A		):
                logger.error(f'Vocabulary path ({save_directory}) should be a directory.'		)
                return
           _UpperCAmelCase    :			str              =					os.path.join(
               A  ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

           if os.path.abspath(self.vocab_file		) != os.path.abspath(A		):
                copyfile(self.vocab_file  ,			A		)

           return (out_vocab_file,)
