



import math
import unittest

from transformers import BioGptConfig, 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 (
      BioGptForCausalLM,
      BioGptForSequenceClassification,
      BioGptForTokenClassification,
      BioGptModel,
      BioGptTokenizer,
  )
  from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST

class 					_A :



       def __init__(	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  Tuple=13  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=7  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=True  ,	__SCREAMING_SNAKE_CASE							:  Tuple=True  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=False  ,	__SCREAMING_SNAKE_CASE							:  Tuple=True  ,	__SCREAMING_SNAKE_CASE							:  List[str]=99  ,	__SCREAMING_SNAKE_CASE							:  int=32  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=5  ,	__SCREAMING_SNAKE_CASE							:  Dict=4  ,	__SCREAMING_SNAKE_CASE							:  Dict=37  ,	__SCREAMING_SNAKE_CASE							:  List[Any]="gelu"  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=0.1  ,	__SCREAMING_SNAKE_CASE							:  Dict=0.1  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=512  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=16  ,	__SCREAMING_SNAKE_CASE							:  Any=2  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=0.02  ,	__SCREAMING_SNAKE_CASE							:  str=3  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=4  ,	__SCREAMING_SNAKE_CASE							:  Dict=None  ,	):


           '''simple docstring'''

           __a		 =       parent
           __a		 =       batch_size
           __a		 =       seq_length
           __a		 =       is_training
           __a		 =       use_input_mask
           __a		 =       use_token_type_ids
           __a		 =       use_labels
           __a		 =       vocab_size
           __a		 =       hidden_size
           __a		 =       num_hidden_layers
           __a		 =       num_attention_heads
           __a		 =       intermediate_size
           __a		 =       hidden_act
           __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		 =       scope



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

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

           __a		 =       None
           if self.use_input_mask:
               __a		 =       random_attention_mask([self.batch_size, self.seq_length])

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

           __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]  ,	self.num_choices)

           __a		 =       self.get_config()

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



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


           '''simple docstring'''

           return BioGptConfig(
               vocab_size=self.vocab_size  ,	hidden_size=self.hidden_size  ,	num_hidden_layers=self.num_hidden_layers  ,	num_attention_heads=self.num_attention_heads  ,	intermediate_size=self.intermediate_size  ,	hidden_act=self.hidden_act  ,	hidden_dropout_prob=self.hidden_dropout_prob  ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,	max_position_embeddings=self.max_position_embeddings  ,	type_vocab_size=self.type_vocab_size  ,	is_decoder=__lowerCamelCase  ,	initializer_range=self.initializer_range  ,	)



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


           '''simple docstring'''

           __a		 =       BioGptModel(config=__lowerCamelCase)
           model.to(__lowerCamelCase)
           model.eval()
           __a		 =       model(__lowerCamelCase  ,	attention_mask=__lowerCamelCase)
           __a		 =       model(__lowerCamelCase)
           self.parent.assertEqual(result.last_hidden_state.shape  ,	(self.batch_size, self.seq_length, self.hidden_size))



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


           '''simple docstring'''

           __a		 =       BioGptForCausalLM(config=__lowerCamelCase)
           model.to(__lowerCamelCase)
           model.eval()
           __a		 =       model(__lowerCamelCase  ,	attention_mask=__lowerCamelCase  ,	token_type_ids=__lowerCamelCase  ,	labels=__lowerCamelCase)
           self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.seq_length, self.vocab_size))



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


           '''simple docstring'''

           __a		 =       BioGptModel(config=__lowerCamelCase)
           model.to(__lowerCamelCase)
           model.eval()

           # create attention mask
           __a		 =       torch.ones(input_ids.shape  ,	dtype=torch.long  ,	device=__lowerCamelCase)
           __a		 =       self.seq_length // 2
           __a		 =       0

           # first forward pass
           __a		 =       model(__lowerCamelCase  ,	attention_mask=__lowerCamelCase).to_tuple()

           # create hypothetical next token and extent to next_input_ids
           __a		 =       ids_tensor((self.batch_size, 1)  ,	config.vocab_size)

           # change a random masked slice from input_ids
           __a		 =       ids_tensor((1,)  ,	__lowerCamelCase).item() + 1
           __a		 =       ids_tensor((self.batch_size, 1)  ,	config.vocab_size).squeeze(-1)
           __a		 =       random_other_next_tokens

           # append to next input_ids and attn_mask
           __a		 =       torch.cat([input_ids, next_tokens]  ,	dim=-1)
           __a		 =       torch.cat(
               [attn_mask, torch.ones((attn_mask.shape[0], 1)  ,	dtype=torch.long  ,	device=__lowerCamelCase)]  ,	dim=1  ,	)

           # get two different outputs
           __a		 =       model(__lowerCamelCase  ,	attention_mask=__lowerCamelCase)['''last_hidden_state''']
           __a		 =       model(__lowerCamelCase  ,	past_key_values=__lowerCamelCase  ,	attention_mask=__lowerCamelCase)['''last_hidden_state''']

           # select random slice
           __a		 =       ids_tensor((1,)  ,	output_from_past.shape[-1]).item()
           __a		 =       output_from_no_past[:, -1, random_slice_idx].detach()
           __a		 =       output_from_past[:, 0, random_slice_idx].detach()

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



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


           '''simple docstring'''

           __a		 =       BioGptModel(config=__lowerCamelCase).to(__lowerCamelCase).eval()

           __a		 =       torch.ones(input_ids.shape  ,	dtype=torch.long  ,	device=__lowerCamelCase)

           # first forward pass
           __a		 =       model(__lowerCamelCase  ,	attention_mask=__lowerCamelCase  ,	use_cache=__lowerCamelCase)

           __a		 =       outputs.to_tuple()

           # create hypothetical multiple next token and extent to next_input_ids
           __a		 =       ids_tensor((self.batch_size, 3)  ,	config.vocab_size)
           __a		 =       ids_tensor((self.batch_size, 3)  ,	2)

           # append to next input_ids and
           __a		 =       torch.cat([input_ids, next_tokens]  ,	dim=-1)
           __a		 =       torch.cat([attention_mask, next_attn_mask]  ,	dim=-1)

           __a		 =       model(__lowerCamelCase  ,	attention_mask=__lowerCamelCase)['''last_hidden_state''']
           __a		 =       model(__lowerCamelCase  ,	attention_mask=__lowerCamelCase  ,	past_key_values=__lowerCamelCase)[
               '''last_hidden_state'''
           ]

           # select random slice
           __a		 =       ids_tensor((1,)  ,	output_from_past.shape[-1]).item()
           __a		 =       output_from_no_past[:, -3:, random_slice_idx].detach()
           __a		 =       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(__lowerCamelCase  ,	__lowerCamelCase  ,	atol=1E-3))



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


           '''simple docstring'''

           __a		 =       BioGptForCausalLM(__lowerCamelCase)
           model.to(__lowerCamelCase)
           if gradient_checkpointing:
               model.gradient_checkpointing_enable()

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



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


           '''simple docstring'''

           __a		 =       BioGptModel(__lowerCamelCase)
           __a		 =       model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers)
           for key in model.state_dict().keys():
               if "c_proj" in key and "weight" in key:
                   self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std)  ,	0.0_01)
                   self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0)  ,	0.01)



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


           '''simple docstring'''

           __a		 =       self.num_labels
           __a		 =       BioGptForTokenClassification(__lowerCamelCase)
           model.to(__lowerCamelCase)
           model.eval()
           __a		 =       model(__lowerCamelCase  ,	attention_mask=__lowerCamelCase  ,	token_type_ids=__lowerCamelCase)
           self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.seq_length, self.num_labels))





       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

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


@require_torch
class 					_A (			lowerCamelCase__   ,lowerCamelCase__   ,lowerCamelCase__   ,unittest.TestCase					):
       UpperCamelCase__						:					List[Any]		      =		(
           (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
           if is_torch_available()
           else ()
       )
       UpperCamelCase__						:					List[Any]		      =		(BioGptForCausalLM,) if is_torch_available() else ()
       UpperCamelCase__						:					Union[str, Any]		      =		(
           {
               '''feature-extraction''': BioGptModel,
               '''text-classification''': BioGptForSequenceClassification,
               '''text-generation''': BioGptForCausalLM,
               '''token-classification''': BioGptForTokenClassification,
               '''zero-shot''': BioGptForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       UpperCamelCase__						:					List[Any]		      =		False



       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           __a		 =       BioGptModelTester(self)
           __a		 =       ConfigTester(self  ,	config_class=__lowerCamelCase  ,	hidden_size=37)



       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           self.config_tester.run_common_tests()



       def 	_lowerCamelCase    (	self							:  List[str]):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__lowerCamelCase)



       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
               __a		 =       type
               self.model_tester.create_and_check_model(*__lowerCamelCase)



       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__lowerCamelCase)



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_forward_and_backwards(*__lowerCamelCase  ,	gradient_checkpointing=__lowerCamelCase)



       def 	_lowerCamelCase    (	self							:  List[str]):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__lowerCamelCase)



       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_weight_initialization(*__lowerCamelCase)



       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_for_token_classification(*__lowerCamelCase)



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


           '''simple docstring'''

           __a		 =       BioGptForCausalLM.from_pretrained('''microsoft/biogpt''')
           model.to(__lowerCamelCase)
           __a		 =       BioGptTokenizer.from_pretrained('''microsoft/biogpt''')

           __a		 =       '''left'''

           # Define PAD Token = EOS Token = 50256
           __a		 =       tokenizer.eos_token
           __a		 =       model.config.eos_token_id

           # use different length sentences to test batching
           __a		 =       [
               '''Hello, my dog is a little''',
               '''Today, I''',
           ]

           __a		 =       tokenizer(__lowerCamelCase  ,	return_tensors='''pt'''  ,	padding=__lowerCamelCase)
           __a		 =       inputs['''input_ids'''].to(__lowerCamelCase)

           __a		 =       model.generate(
               input_ids=__lowerCamelCase  ,	attention_mask=inputs['''attention_mask'''].to(__lowerCamelCase)  ,	)

           __a		 =       tokenizer(sentences[0]  ,	return_tensors='''pt''').input_ids.to(__lowerCamelCase)
           __a		 =       model.generate(input_ids=__lowerCamelCase)

           __a		 =       inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].long().sum().cpu().item()
           __a		 =       tokenizer(sentences[1]  ,	return_tensors='''pt''').input_ids.to(__lowerCamelCase)
           __a		 =       model.generate(input_ids=__lowerCamelCase  ,	max_length=model.config.max_length - num_paddings)

           __a		 =       tokenizer.batch_decode(__lowerCamelCase  ,	skip_special_tokens=__lowerCamelCase)
           __a		 =       tokenizer.decode(output_non_padded[0]  ,	skip_special_tokens=__lowerCamelCase)
           __a		 =       tokenizer.decode(output_padded[0]  ,	skip_special_tokens=__lowerCamelCase)

           __a		 =       [
               '''Hello, my dog is a little bit bigger than a little bit.''',
               '''Today, I have a good idea of how to use the information''',
           ]
           self.assertListEqual(__lowerCamelCase  ,	__lowerCamelCase)
           self.assertListEqual(__lowerCamelCase  ,	[non_padded_sentence, padded_sentence])



       @slow
       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a		 =       BioGptModel.from_pretrained(__lowerCamelCase)
               self.assertIsNotNone(__lowerCamelCase)



       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs_for_common()
           __a		 =       3
           __a		 =       input_dict['''input_ids''']
           __a		 =       input_ids.ne(1).to(__lowerCamelCase)
           __a		 =       ids_tensor([self.model_tester.batch_size]  ,	self.model_tester.type_sequence_label_size)
           __a		 =       BioGptForSequenceClassification(__lowerCamelCase)
           model.to(__lowerCamelCase)
           model.eval()
           __a		 =       model(__lowerCamelCase  ,	attention_mask=__lowerCamelCase  ,	labels=__lowerCamelCase)
           self.assertEqual(result.logits.shape  ,	(self.model_tester.batch_size, self.model_tester.num_labels))





       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs_for_common()
           __a		 =       3
           __a		 =       '''multi_label_classification'''
           __a		 =       input_dict['''input_ids''']
           __a		 =       input_ids.ne(1).to(__lowerCamelCase)
           __a		 =       ids_tensor(
               [self.model_tester.batch_size, config.num_labels]  ,	self.model_tester.type_sequence_label_size).to(torch.float)
           __a		 =       BioGptForSequenceClassification(__lowerCamelCase)
           model.to(__lowerCamelCase)
           model.eval()
           __a		 =       model(__lowerCamelCase  ,	attention_mask=__lowerCamelCase  ,	labels=__lowerCamelCase)
           self.assertEqual(result.logits.shape  ,	(self.model_tester.batch_size, self.model_tester.num_labels))


@require_torch
class 					_A (			unittest.TestCase					):



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


           '''simple docstring'''

           __a		 =       BioGptForCausalLM.from_pretrained('''microsoft/biogpt''')
           __a		 =       torch.tensor([[2, 4_805, 9, 656, 21]])
           __a		 =       model(__lowerCamelCase)[0]

           __a		 =       42_384

           __a		 =       torch.Size((1, 5, vocab_size))
           self.assertEqual(output.shape  ,	__lowerCamelCase)

           __a		 =       torch.tensor(
               [[[-9.52_36, -9.89_18, 10.45_57], [-11.04_69, -9.64_23, 8.10_22], [-8.86_64, -7.88_26, 5.53_25]]])

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





       @slow
       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           __a		 =       BioGptTokenizer.from_pretrained('''microsoft/biogpt''')
           __a		 =       BioGptForCausalLM.from_pretrained('''microsoft/biogpt''')
           model.to(__lowerCamelCase)

           torch.manual_seed(0)
           __a		 =       tokenizer('''COVID-19 is'''  ,	return_tensors='''pt''').to(__lowerCamelCase)
           __a		 =       model.generate(
               **__lowerCamelCase  ,	min_length=100  ,	max_length=1_024  ,	num_beams=5  ,	early_stopping=__lowerCamelCase  ,	)
           __a		 =       tokenizer.decode(output_ids[0]  ,	skip_special_tokens=__lowerCamelCase)

           __a		 =       (
               '''COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'''
               ''' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'''
               ''' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'''
               ''' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'''
               ''' more than 800,000 deaths.'''
           )
           self.assertEqual(__lowerCamelCase  ,	__lowerCamelCase)










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,)





