



import os
import tempfile
import unittest

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

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 (
     FlaubertForMultipleChoice,
     FlaubertForQuestionAnswering,
     FlaubertForQuestionAnsweringSimple,
     FlaubertForSequenceClassification,
     FlaubertForTokenClassification,
     FlaubertModel,
     FlaubertWithLMHeadModel,
 )
 from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST

class 					_A (			__UpperCAmelCase					):



       def __init__(	self							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=13  ,	__SCREAMING_SNAKE_CASE							:  Any=7  ,	__SCREAMING_SNAKE_CASE							:  Any=True  ,	__SCREAMING_SNAKE_CASE							:  Any=True  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=True  ,	__SCREAMING_SNAKE_CASE							:  int=True  ,	__SCREAMING_SNAKE_CASE							:  int=True  ,	__SCREAMING_SNAKE_CASE							:  List[str]=False  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=False  ,	__SCREAMING_SNAKE_CASE							:  int=False  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=2  ,	__SCREAMING_SNAKE_CASE							:  Tuple=99  ,	__SCREAMING_SNAKE_CASE							:  Dict=0  ,	__SCREAMING_SNAKE_CASE							:  str=32  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=5  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=4  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=0.1  ,	__SCREAMING_SNAKE_CASE							:  Any=0.1  ,	__SCREAMING_SNAKE_CASE							:  Dict=512  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=12  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=2  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=0.02  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=3  ,	__SCREAMING_SNAKE_CASE							:  Tuple=4  ,	__SCREAMING_SNAKE_CASE							:  List[Any]="last"  ,	__SCREAMING_SNAKE_CASE							:  Tuple=None  ,	__SCREAMING_SNAKE_CASE							:  Any=None  ,	):


           '''simple docstring'''

           __a		 =       parent
           __a		 =       batch_size
           __a		 =       seq_length
           __a		 =       is_training
           __a		 =       use_input_lengths
           __a		 =       use_token_type_ids
           __a		 =       use_labels
           __a		 =       gelu_activation
           __a		 =       sinusoidal_embeddings
           __a		 =       causal
           __a		 =       asm
           __a		 =       n_langs
           __a		 =       vocab_size
           __a		 =       n_special
           __a		 =       hidden_size
           __a		 =       num_hidden_layers
           __a		 =       num_attention_heads
           __a		 =       hidden_dropout_prob
           __a		 =       attention_probs_dropout_prob
           __a		 =       max_position_embeddings
           __a		 =       type_vocab_size
           __a		 =       type_sequence_label_size
           __a		 =       initializer_range
           __a		 =       num_labels
           __a		 =       num_choices
           __a		 =       summary_type
           __a		 =       use_proj
           __a		 =       scope



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


           '''simple docstring'''

           __a		 =       ids_tensor([self.batch_size, self.seq_length]  ,	self.vocab_size)
           __a		 =       random_attention_mask([self.batch_size, self.seq_length])

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

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

           __a		 =       None
           __a		 =       None
           __a		 =       None
           if self.use_labels:
               __a		 =       ids_tensor([self.batch_size]  ,	self.type_sequence_label_size)
               __a		 =       ids_tensor([self.batch_size, self.seq_length]  ,	self.num_labels)
               __a		 =       ids_tensor([self.batch_size]  ,	2).float()
               __a		 =       ids_tensor([self.batch_size]  ,	self.num_choices)

           __a		 =       self.get_config()

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



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           return FlaubertConfig(
               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  ,	)



       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  Tuple  ,	):


           '''simple docstring'''

           __a		 =       FlaubertModel(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a		 =       model(__SCREAMING_SNAKE_CASE  ,	lengths=__SCREAMING_SNAKE_CASE  ,	langs=__SCREAMING_SNAKE_CASE)
           __a		 =       model(__SCREAMING_SNAKE_CASE  ,	langs=__SCREAMING_SNAKE_CASE)
           __a		 =       model(__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.last_hidden_state.shape  ,	(self.batch_size, self.seq_length, self.hidden_size))



       def 	_lowerCamelCase    (	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	):


           '''simple docstring'''

           __a		 =       FlaubertWithLMHeadModel(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()

           __a		 =       model(__SCREAMING_SNAKE_CASE  ,	token_type_ids=__SCREAMING_SNAKE_CASE  ,	labels=__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.loss.shape  ,	())
           self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.seq_length, self.vocab_size))



       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	):


           '''simple docstring'''

           __a		 =       FlaubertForQuestionAnsweringSimple(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()

           __a		 =       model(__SCREAMING_SNAKE_CASE)

           __a		 =       model(__SCREAMING_SNAKE_CASE  ,	start_positions=__SCREAMING_SNAKE_CASE  ,	end_positions=__SCREAMING_SNAKE_CASE)
           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 	_lowerCamelCase    (	self							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Tuple  ,	):


           '''simple docstring'''

           __a		 =       FlaubertForQuestionAnswering(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()

           __a		 =       model(__SCREAMING_SNAKE_CASE)

           __a		 =       model(
               __SCREAMING_SNAKE_CASE  ,	start_positions=__SCREAMING_SNAKE_CASE  ,	end_positions=__SCREAMING_SNAKE_CASE  ,	cls_index=__SCREAMING_SNAKE_CASE  ,	is_impossible=__SCREAMING_SNAKE_CASE  ,	p_mask=__SCREAMING_SNAKE_CASE  ,	)

           __a		 =       model(
               __SCREAMING_SNAKE_CASE  ,	start_positions=__SCREAMING_SNAKE_CASE  ,	end_positions=__SCREAMING_SNAKE_CASE  ,	cls_index=__SCREAMING_SNAKE_CASE  ,	is_impossible=__SCREAMING_SNAKE_CASE  ,	)

           ((__a) ,     )		 =       result_with_labels.to_tuple()

           __a		 =       model(__SCREAMING_SNAKE_CASE  ,	start_positions=__SCREAMING_SNAKE_CASE  ,	end_positions=__SCREAMING_SNAKE_CASE)

           ((__a) ,     )		 =       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 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]  ,	):


           '''simple docstring'''

           __a		 =       FlaubertForSequenceClassification(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()

           __a		 =       model(__SCREAMING_SNAKE_CASE)
           __a		 =       model(__SCREAMING_SNAKE_CASE  ,	labels=__SCREAMING_SNAKE_CASE)

           self.parent.assertEqual(result.loss.shape  ,	())
           self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.type_sequence_label_size))



       def 	_lowerCamelCase    (	self							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  int  ,	__SCREAMING_SNAKE_CASE							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	):


           '''simple docstring'''

           __a		 =       self.num_labels
           __a		 =       FlaubertForTokenClassification(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()

           __a		 =       model(__SCREAMING_SNAKE_CASE  ,	attention_mask=__SCREAMING_SNAKE_CASE  ,	labels=__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.seq_length, self.num_labels))



       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  int  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	):


           '''simple docstring'''

           __a		 =       self.num_choices
           __a		 =       FlaubertForMultipleChoice(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a		 =       input_ids.unsqueeze(1).expand(-1  ,	self.num_choices  ,	-1).contiguous()
           __a		 =       token_type_ids.unsqueeze(1).expand(-1  ,	self.num_choices  ,	-1).contiguous()
           __a		 =       input_mask.unsqueeze(1).expand(-1  ,	self.num_choices  ,	-1).contiguous()
           __a		 =       model(
               __SCREAMING_SNAKE_CASE  ,	attention_mask=__SCREAMING_SNAKE_CASE  ,	token_type_ids=__SCREAMING_SNAKE_CASE  ,	labels=__SCREAMING_SNAKE_CASE  ,	)
           self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.num_choices))





       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           __a		 =       self.prepare_config_and_inputs()
           (
               (
               __a
           ) ,     (
               __a
           ) ,     (
               __a
           ) ,     (
               __a
           ) ,     (
               __a
           ) ,     (
               __a
           ) ,     (
               __a
           ) ,     (
               __a
           ) ,     (
               __a
           ) ,     
           )		 =       config_and_inputs
           __a		 =       {
               '''input_ids''': input_ids,
               '''token_type_ids''': token_type_ids,
               '''lengths''': input_lengths,
               '''attention_mask''': input_mask,
           }
           return config, inputs_dict


@require_torch
class 					_A (			__UpperCAmelCase   ,__UpperCAmelCase   ,unittest.TestCase					):
       UpperCamelCase__						:					Any		      =		(
           (
               FlaubertModel,
               FlaubertWithLMHeadModel,
               FlaubertForQuestionAnswering,
               FlaubertForQuestionAnsweringSimple,
               FlaubertForSequenceClassification,
               FlaubertForTokenClassification,
               FlaubertForMultipleChoice,
           )
           if is_torch_available()
           else ()
       )
       UpperCamelCase__						:					List[Any]		      =		(
           {
               '''feature-extraction''': FlaubertModel,
               '''fill-mask''': FlaubertWithLMHeadModel,
               '''question-answering''': FlaubertForQuestionAnsweringSimple,
               '''text-classification''': FlaubertForSequenceClassification,
               '''token-classification''': FlaubertForTokenClassification,
               '''zero-shot''': FlaubertForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )



       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Union[str, 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 	_lowerCamelCase    (	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  int=False):


           '''simple docstring'''

           __a		 =       super()._prepare_for_class(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	return_labels=__SCREAMING_SNAKE_CASE)

           if return_labels:
               if model_class.__name__ == "FlaubertForQuestionAnswering":
                   __a		 =       torch.zeros(
                       self.model_tester.batch_size  ,	dtype=torch.long  ,	device=__SCREAMING_SNAKE_CASE)
                   __a		 =       torch.zeros(
                       self.model_tester.batch_size  ,	dtype=torch.long  ,	device=__SCREAMING_SNAKE_CASE)

           return inputs_dict



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           __a		 =       FlaubertModelTester(self)
           __a		 =       ConfigTester(self  ,	config_class=__SCREAMING_SNAKE_CASE  ,	emb_dim=37)



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           self.config_tester.run_common_tests()



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_model(*__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_lm_head(*__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_simple_qa(*__SCREAMING_SNAKE_CASE)



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


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_qa(*__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_sequence_classif(*__SCREAMING_SNAKE_CASE)



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


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_token_classif(*__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_multiple_choice(*__SCREAMING_SNAKE_CASE)



       @slow
       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a		 =       FlaubertModel.from_pretrained(__SCREAMING_SNAKE_CASE)
               self.assertIsNotNone(__SCREAMING_SNAKE_CASE)





       @slow
       @require_torch_gpu
       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           __a ,     __a		 =       self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
               # FlauBertForMultipleChoice behaves incorrectly in JIT environments.
               if model_class == FlaubertForMultipleChoice:
                   return

               __a		 =       True
               __a		 =       model_class(config=__SCREAMING_SNAKE_CASE)

               __a		 =       self._prepare_for_class(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)
               __a		 =       torch.jit.trace(
                   __SCREAMING_SNAKE_CASE  ,	(inputs_dict['''input_ids'''].to('''cpu'''), inputs_dict['''attention_mask'''].to('''cpu''')))

               with tempfile.TemporaryDirectory() as tmp:
                   torch.jit.save(__SCREAMING_SNAKE_CASE  ,	os.path.join(__SCREAMING_SNAKE_CASE  ,	'''traced_model.pt'''))
                   __a		 =       torch.jit.load(os.path.join(__SCREAMING_SNAKE_CASE  ,	'''traced_model.pt''')  ,	map_location=__SCREAMING_SNAKE_CASE)
                   loaded(inputs_dict['''input_ids'''].to(__SCREAMING_SNAKE_CASE)  ,	inputs_dict['''attention_mask'''].to(__SCREAMING_SNAKE_CASE))


@require_torch
class 					_A (			unittest.TestCase					):



       @slow
       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       FlaubertModel.from_pretrained('''flaubert/flaubert_base_cased''')
           __a		 =       torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]])
           with torch.no_grad():
               __a		 =       model(__SCREAMING_SNAKE_CASE)[0]
           __a		 =       torch.Size((1, 11, 768))
           self.assertEqual(output.shape  ,	__SCREAMING_SNAKE_CASE)
           __a		 =       torch.tensor(
               [[[-2.62_51, -1.42_98, -0.02_27], [-2.85_10, -1.63_87, 0.22_58], [-2.81_14, -1.18_32, -0.30_66]]])

           self.assertTrue(torch.allclose(output[:, :3, :3]  ,	__SCREAMING_SNAKE_CASE  ,	atol=1E-4))










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

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


if is_sentencepiece_available():
 from .tokenization_barthez import BarthezTokenizer
else:
 __snake_case		:List[Any]   	=  None

__snake_case		:Dict   	=  logging.get_logger(__name__)

__snake_case		:Optional[int]   	=  {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__snake_case		:Union[str, Any]   	=  {
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

__snake_case		:Optional[Any]   	=  {
    '''moussaKam/mbarthez''': 1024,
    '''moussaKam/barthez''': 1024,
    '''moussaKam/barthez-orangesum-title''': 1024,
}

__snake_case		:Optional[int]   	=  '''▁'''

class 					_A (			__UpperCAmelCase					):
       UpperCamelCase__						:					Tuple		      =		VOCAB_FILES_NAMES
       UpperCamelCase__						:					Tuple		      =		PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase__						:					str		      =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase__						:					str		      =		['''input_ids''', '''attention_mask''']
       UpperCamelCase__						:					Dict		      =		BarthezTokenizer



       def __init__(	self							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=None  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=None  ,	__SCREAMING_SNAKE_CASE							:  Tuple="<s>"  ,	__SCREAMING_SNAKE_CASE							:  List[str]="</s>"  ,	__SCREAMING_SNAKE_CASE							:  Tuple="</s>"  ,	__SCREAMING_SNAKE_CASE							:  int="<s>"  ,	__SCREAMING_SNAKE_CASE							:  Any="<unk>"  ,	__SCREAMING_SNAKE_CASE							:  int="<pad>"  ,	__SCREAMING_SNAKE_CASE							:  Any="<mask>"  ,	**__SCREAMING_SNAKE_CASE							:  Any  ,	):


           '''simple docstring'''

           __a		 =       AddedToken(__SCREAMING_SNAKE_CASE  ,	lstrip=__SCREAMING_SNAKE_CASE  ,	rstrip=__SCREAMING_SNAKE_CASE) if isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE) else mask_token

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

           __a		 =       vocab_file
           __a		 =       False if not self.vocab_file else True



       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  List[int]  ,	__SCREAMING_SNAKE_CASE							:  Optional[List[int]] = None):


           '''simple docstring'''

           if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           __a		 =       [self.cls_token_id]
           __a		 =       [self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep



       def 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  List[int]  ,	__SCREAMING_SNAKE_CASE							:  Optional[List[int]] = None):


           '''simple docstring'''

           __a		 =       [self.sep_token_id]
           __a		 =       [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 + sep + token_ids_a + sep) * [0]





       def 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Optional[str] = None):


           '''simple docstring'''

           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(__SCREAMING_SNAKE_CASE):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory')
               return
           __a		 =       os.path.join(
               __SCREAMING_SNAKE_CASE  ,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

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

           return (out_vocab_file,)





