
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 			lowercase_		:


       def __init__(    self						,       __A						,       __A=13						,       __A=7						,       __A=True						,       __A=True						,       __A=False						,       __A=True						,       __A=99						,       __A=32						,       __A=5						,       __A=4						,       __A=37						,       __A="gelu"						,       __A=0.1						,       __A=0.1						,       __A=512						,       __A=16						,       __A=2						,       __A=0.02						,       __A=3						,       __A=4						,       __A=None						,       )						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=parent
              SCREAMING_SNAKE_CASE_							:				str		=batch_size
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=seq_length
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=is_training
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=use_input_mask
              SCREAMING_SNAKE_CASE_							:				str		=use_token_type_ids
              SCREAMING_SNAKE_CASE_							:				Any		=use_labels
              SCREAMING_SNAKE_CASE_							:				Tuple		=vocab_size
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=hidden_size
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=num_hidden_layers
              SCREAMING_SNAKE_CASE_							:				Dict		=num_attention_heads
              SCREAMING_SNAKE_CASE_							:				Any		=intermediate_size
              SCREAMING_SNAKE_CASE_							:				List[str]		=hidden_act
              SCREAMING_SNAKE_CASE_							:				int		=hidden_dropout_prob
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=attention_probs_dropout_prob
              SCREAMING_SNAKE_CASE_							:				Tuple		=max_position_embeddings
              SCREAMING_SNAKE_CASE_							:				int		=type_vocab_size
              SCREAMING_SNAKE_CASE_							:				str		=type_sequence_label_size
              SCREAMING_SNAKE_CASE_							:				int		=initializer_range
              SCREAMING_SNAKE_CASE_							:				str		=num_labels
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=num_choices
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=scope


       def 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Dict		=ids_tensor([self.batch_size, self.seq_length]						,       self.vocab_size				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=None
              if self.use_input_mask:
                     SCREAMING_SNAKE_CASE_							:				Tuple		=random_attention_mask([self.batch_size, self.seq_length]				)

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

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

              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_config()

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


       def 	_snake_case				(    self				)						->    Tuple:
              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=__A						,       initializer_range=self.initializer_range						,       )


       def 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=BioGptModel(config=__A				)
              model.to(__A				)
              model.eval()
              SCREAMING_SNAKE_CASE_							:				Tuple		=model(__A						,       attention_mask=__A				)
              SCREAMING_SNAKE_CASE_							:				str		=model(__A				)
              self.parent.assertEqual(result.last_hidden_state.shape						,       (self.batch_size, self.seq_length, self.hidden_size)				)


       def 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A						,       )						->    Any:
              SCREAMING_SNAKE_CASE_							:				int		=BioGptForCausalLM(config=__A				)
              model.to(__A				)
              model.eval()
              SCREAMING_SNAKE_CASE_							:				List[str]		=model(__A						,       attention_mask=__A						,       token_type_ids=__A						,       labels=__A				)
              self.parent.assertEqual(result.logits.shape						,       (self.batch_size, self.seq_length, self.vocab_size)				)


       def 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A						,       __A						,       *__A				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=BioGptModel(config=__A				)
              model.to(__A				)
              model.eval()

              # create attention mask
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.ones(input_ids.shape						,       dtype=torch.long						,       device=__A				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.seq_length // 2
              SCREAMING_SNAKE_CASE_							:				List[Any]		=0

              # first forward pass
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=model(__A						,       attention_mask=__A				).to_tuple()

              # create hypothetical next token and extent to next_input_ids
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=ids_tensor((self.batch_size, 1)						,       config.vocab_size				)

              # change a random masked slice from input_ids
              SCREAMING_SNAKE_CASE_							:				Dict		=ids_tensor((1,)						,       __A				).item() + 1
              SCREAMING_SNAKE_CASE_							:				List[str]		=ids_tensor((self.batch_size, 1)						,       config.vocab_size				).squeeze(-1				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=random_other_next_tokens

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

              # get two different outputs
              SCREAMING_SNAKE_CASE_							:				int		=model(__A						,       attention_mask=__A				)['''last_hidden_state''']
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=model(__A						,       past_key_values=__A						,       attention_mask=__A				)['''last_hidden_state''']

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

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


       def 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A						,       __A						,       *__A				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=BioGptModel(config=__A				).to(__A				).eval()

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.ones(input_ids.shape						,       dtype=torch.long						,       device=__A				)

              # first forward pass
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=model(__A						,       attention_mask=__A						,       use_cache=__A				)

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				str		=outputs.to_tuple()

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

              # append to next input_ids and
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.cat([input_ids, next_tokens]						,       dim=-1				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.cat([attention_mask, next_attn_mask]						,       dim=-1				)

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=model(__A						,       attention_mask=__A				)['''last_hidden_state''']
              SCREAMING_SNAKE_CASE_							:				List[str]		=model(__A						,       attention_mask=__A						,       past_key_values=__A				)[
                  '''last_hidden_state'''
              ]

              # select random slice
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=ids_tensor((1,)						,       output_from_past.shape[-1]				).item()
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=output_from_no_past[:, -3:, random_slice_idx].detach()
              SCREAMING_SNAKE_CASE_							:				Tuple		=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(__A						,       __A						,       atol=1e-3				)				)


       def 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A						,       __A						,       *__A						,       __A=False				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=BioGptForCausalLM(__A				)
              model.to(__A				)
              if gradient_checkpointing:
                     model.gradient_checkpointing_enable()

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=model(__A						,       labels=__A				)
              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 	_snake_case				(    self						,       __A						,       *__A				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=BioGptModel(__A				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=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				)						,       0.001				)
                            self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]				) - 0.0				)						,       0.01				)


       def 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A						,       __A						,       *__A				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.num_labels
              SCREAMING_SNAKE_CASE_							:				str		=BioGptForTokenClassification(__A				)
              model.to(__A				)
              model.eval()
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=model(__A						,       attention_mask=__A						,       token_type_ids=__A				)
              self.parent.assertEqual(result.logits.shape						,       (self.batch_size, self.seq_length, self.num_labels)				)





       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				Dict		=self.prepare_config_and_inputs()
              (
                  (
                  SCREAMING_SNAKE_CASE_
              )	,     (
                  SCREAMING_SNAKE_CASE_
              )	,     (
                  SCREAMING_SNAKE_CASE_
              )	,     (
                  SCREAMING_SNAKE_CASE_
              )	,     (
                  SCREAMING_SNAKE_CASE_
              )	,     (
                  SCREAMING_SNAKE_CASE_
              )	,     (
                  SCREAMING_SNAKE_CASE_
              )	,     
              )							:				Any		=config_and_inputs
              SCREAMING_SNAKE_CASE_							:				Dict		={'''input_ids''': input_ids, '''attention_mask''': input_mask}
              return config, inputs_dict






@require_torch
class 			lowercase_		(      A  ,      A  ,      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						(
           (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
           if is_torch_available()
           else ()
       )
       __lowerCamelCase				       =						(BioGptForCausalLM,) if is_torch_available() else ()
       __lowerCamelCase				       =						(
           {
               "feature-extraction": BioGptModel,
               "text-classification": BioGptForSequenceClassification,
               "text-generation": BioGptForCausalLM,
               "token-classification": BioGptForTokenClassification,
               "zero-shot": BioGptForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       __lowerCamelCase				       =						False


       def 	_snake_case				(    self				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=BioGptModelTester(self				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=ConfigTester(self						,       config_class=__A						,       hidden_size=37				)


       def 	_snake_case				(    self				)						->    Any:
              self.config_tester.run_common_tests()


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*__A				)


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.model_tester.prepare_config_and_inputs()
              for type in ["absolute", "relative_key", "relative_key_query"]:
                     SCREAMING_SNAKE_CASE_							:				Tuple		=type
                     self.model_tester.create_and_check_model(*__A				)


       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				Any		=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__A				)


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_							:				str		=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_forward_and_backwards(*__A						,       gradient_checkpointing=__A				)


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__A				)


       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_biogpt_weight_initialization(*__A				)


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_biogpt_for_token_classification(*__A				)


       @slow
       def 	_snake_case				(    self				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				Tuple		=BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''				)
              model.to(__A				)
              SCREAMING_SNAKE_CASE_							:				Any		=BioGptTokenizer.from_pretrained('''microsoft/biogpt'''				)

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		='''left'''

              # Define PAD Token = EOS Token = 50256
              SCREAMING_SNAKE_CASE_							:				Tuple		=tokenizer.eos_token
              SCREAMING_SNAKE_CASE_							:				int		=model.config.eos_token_id

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

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=tokenizer(__A						,       return_tensors='''pt'''						,       padding=__A				)
              SCREAMING_SNAKE_CASE_							:				str		=inputs['''input_ids'''].to(__A				)

              SCREAMING_SNAKE_CASE_							:				Tuple		=model.generate(
                  input_ids=__A						,       attention_mask=inputs['''attention_mask'''].to(__A				)						,       )

              SCREAMING_SNAKE_CASE_							:				str		=tokenizer(sentences[0]						,       return_tensors='''pt'''				).input_ids.to(__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=model.generate(input_ids=__A				)

              SCREAMING_SNAKE_CASE_							:				Tuple		=inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].long().sum().cpu().item()
              SCREAMING_SNAKE_CASE_							:				Any		=tokenizer(sentences[1]						,       return_tensors='''pt'''				).input_ids.to(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=model.generate(input_ids=__A						,       max_length=model.config.max_length - num_paddings				)

              SCREAMING_SNAKE_CASE_							:				List[str]		=tokenizer.batch_decode(__A						,       skip_special_tokens=__A				)
              SCREAMING_SNAKE_CASE_							:				int		=tokenizer.decode(output_non_padded[0]						,       skip_special_tokens=__A				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=tokenizer.decode(output_padded[0]						,       skip_special_tokens=__A				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=[
                  '''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(__A						,       __A				)
              self.assertListEqual(__A						,       [non_padded_sentence, padded_sentence]				)


       @slow
       def 	_snake_case				(    self				)						->    Dict:
              for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     SCREAMING_SNAKE_CASE_							:				str		=BioGptModel.from_pretrained(__A				)
                     self.assertIsNotNone(__A				)


       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.model_tester.prepare_config_and_inputs_for_common()
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=3
              SCREAMING_SNAKE_CASE_							:				str		=input_dict['''input_ids''']
              SCREAMING_SNAKE_CASE_							:				int		=input_ids.ne(1				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=ids_tensor([self.model_tester.batch_size]						,       self.model_tester.type_sequence_label_size				)
              SCREAMING_SNAKE_CASE_							:				str		=BioGptForSequenceClassification(__A				)
              model.to(__A				)
              model.eval()
              SCREAMING_SNAKE_CASE_							:				List[str]		=model(__A						,       attention_mask=__A						,       labels=__A				)
              self.assertEqual(result.logits.shape						,       (self.model_tester.batch_size, self.model_tester.num_labels)				)





       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.model_tester.prepare_config_and_inputs_for_common()
              SCREAMING_SNAKE_CASE_							:				Dict		=3
              SCREAMING_SNAKE_CASE_							:				List[Any]		='''multi_label_classification'''
              SCREAMING_SNAKE_CASE_							:				str		=input_dict['''input_ids''']
              SCREAMING_SNAKE_CASE_							:				List[Any]		=input_ids.ne(1				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=ids_tensor(
                  [self.model_tester.batch_size, config.num_labels]						,       self.model_tester.type_sequence_label_size				).to(torch.float				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=BioGptForSequenceClassification(__A				)
              model.to(__A				)
              model.eval()
              SCREAMING_SNAKE_CASE_							:				List[Any]		=model(__A						,       attention_mask=__A						,       labels=__A				)
              self.assertEqual(result.logits.shape						,       (self.model_tester.batch_size, self.model_tester.num_labels)				)






@require_torch
class 			lowercase_		(      unittest.TestCase       ):


       @slow
       def 	_snake_case				(    self				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				Dict		=BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''				)
              SCREAMING_SNAKE_CASE_							:				Any		=torch.tensor([[2, 4_805, 9, 656, 21]]				)
              SCREAMING_SNAKE_CASE_							:				Any		=model(__A				)[0]

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=42_384

              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.Size((1, 5, vocab_size)				)
              self.assertEqual(output.shape						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.tensor(
                  [[[-9.5_236, -9.8_918, 10.4_557], [-11.0_469, -9.6_423, 8.1_022], [-8.8_664, -7.8_826, 5.5_325]]]				)

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





       @slow
       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				str		=BioGptTokenizer.from_pretrained('''microsoft/biogpt'''				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''				)
              model.to(__A				)

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

              SCREAMING_SNAKE_CASE_							:				List[str]		=(
                  '''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(__A						,       __A				)


import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase											=    """▁"""

_lowercase											=    get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
@require_tokenizers
class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						BigBirdTokenizer
       __lowerCamelCase				       =						BigBirdTokenizerFast
       __lowerCamelCase				       =						True
       __lowerCamelCase				       =						True


       def 	_snake_case				(    self				)						->    Tuple:
              super().setUp()

              SCREAMING_SNAKE_CASE_							:				str		=self.tokenizer_class(__A						,       keep_accents=__A				)
              tokenizer.save_pretrained(self.tmpdirname				)


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				str		='''<s>'''
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=1

              self.assertEqual(self.get_tokenizer()._convert_token_to_id(__A				)						,       __A				)
              self.assertEqual(self.get_tokenizer()._convert_id_to_token(__A				)						,       __A				)


       def 	_snake_case				(    self				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				str		=list(self.get_tokenizer().get_vocab().keys()				)

              self.assertEqual(vocab_keys[0]						,       '''<unk>'''				)
              self.assertEqual(vocab_keys[1]						,       '''<s>'''				)
              self.assertEqual(vocab_keys[-1]						,       '''[MASK]'''				)
              self.assertEqual(len(__A				)						,       1_004				)


       def 	_snake_case				(    self				)						->    Optional[Any]:
              self.assertEqual(self.get_tokenizer().vocab_size						,       1_000				)


       def 	_snake_case				(    self				)						->    str:
              if not self.test_rust_tokenizer:
                     return

              SCREAMING_SNAKE_CASE_							:				Dict		=self.get_tokenizer()
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.get_rust_tokenizer()

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		='''I was born in 92000, and this is falsé.'''

              SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer.tokenize(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=rust_tokenizer.tokenize(__A				)
              self.assertListEqual(__A						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=tokenizer.encode(__A						,       add_special_tokens=__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=rust_tokenizer.encode(__A						,       add_special_tokens=__A				)
              self.assertListEqual(__A						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=self.get_rust_tokenizer()
              SCREAMING_SNAKE_CASE_							:				int		=tokenizer.encode(__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=rust_tokenizer.encode(__A				)
              self.assertListEqual(__A						,       __A				)


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=BigBirdTokenizer(__A						,       keep_accents=__A				)

              SCREAMING_SNAKE_CASE_							:				str		=tokenizer.tokenize('''This is a test'''				)
              self.assertListEqual(__A						,       ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

              self.assertListEqual(
                  tokenizer.convert_tokens_to_ids(__A				)						,       [285, 46, 10, 170, 382]						,       )

              SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
              self.assertListEqual(
                  __A						,       [
                      SPIECE_UNDERLINE + '''I''',
                      SPIECE_UNDERLINE + '''was''',
                      SPIECE_UNDERLINE + '''b''',
                      '''or''',
                      '''n''',
                      SPIECE_UNDERLINE + '''in''',
                      SPIECE_UNDERLINE + '''''',
                      '''9''',
                      '''2''',
                      '''0''',
                      '''0''',
                      '''0''',
                      ''',''',
                      SPIECE_UNDERLINE + '''and''',
                      SPIECE_UNDERLINE + '''this''',
                      SPIECE_UNDERLINE + '''is''',
                      SPIECE_UNDERLINE + '''f''',
                      '''al''',
                      '''s''',
                      '''é''',
                      '''.''',
                  ]						,       )
              SCREAMING_SNAKE_CASE_							:				Dict		=tokenizer.convert_tokens_to_ids(__A				)
              self.assertListEqual(
                  __A						,       [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]						,       )

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=tokenizer.convert_ids_to_tokens(__A				)
              self.assertListEqual(
                  __A						,       [
                      SPIECE_UNDERLINE + '''I''',
                      SPIECE_UNDERLINE + '''was''',
                      SPIECE_UNDERLINE + '''b''',
                      '''or''',
                      '''n''',
                      SPIECE_UNDERLINE + '''in''',
                      SPIECE_UNDERLINE + '''''',
                      '''<unk>''',
                      '''2''',
                      '''0''',
                      '''0''',
                      '''0''',
                      ''',''',
                      SPIECE_UNDERLINE + '''and''',
                      SPIECE_UNDERLINE + '''this''',
                      SPIECE_UNDERLINE + '''is''',
                      SPIECE_UNDERLINE + '''f''',
                      '''al''',
                      '''s''',
                      '''<unk>''',
                      '''.''',
                  ]						,       )


       @cached_property
       def 	_snake_case				(    self				)						->    Union[str, Any]:
              return BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''				)


       @slow
       def 	_snake_case				(    self				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				str		='''Hello World!'''
              SCREAMING_SNAKE_CASE_							:				Dict		=[65, 18_536, 2_260, 101, 66]

              self.assertListEqual(__A						,       self.big_tokenizer.encode(__A				)				)


       @slow
       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=(
                  '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
                  ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
              )
              # fmt: off
              SCREAMING_SNAKE_CASE_							:				List[Any]		=[65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66]  # noqa: E231
              # fmt: on
              self.assertListEqual(__A						,       self.big_tokenizer.encode(__A				)				)


       @require_torch
       @slow
       def 	_snake_case				(    self				)						->    int:
              import torch

              from transformers import BigBirdConfig, BigBirdModel

              # Build sequence
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=list(self.big_tokenizer.get_vocab().keys()				)[:10]
              SCREAMING_SNAKE_CASE_							:				Any		=''' '''.join(__A				)
              SCREAMING_SNAKE_CASE_							:				int		=self.big_tokenizer.encode_plus(__A						,       return_tensors='''pt'''						,       return_token_type_ids=__A				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.big_tokenizer.batch_encode_plus(
                  [sequence + ''' ''' + sequence]						,       return_tensors='''pt'''						,       return_token_type_ids=__A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=BigBirdConfig(attention_type='''original_full'''				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=BigBirdModel(__A				)

              assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

              with torch.no_grad():
                     model(**__A				)
                     model(**__A				)


       @slow
       def 	_snake_case				(    self				)						->    Tuple:
              SCREAMING_SNAKE_CASE_							:				Any		=BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer.decode(tokenizer('''Paris is the [MASK].'''				).input_ids				)

              self.assertTrue(decoded_text == '''[CLS] Paris is the[MASK].[SEP]'''				)





       @slow
       def 	_snake_case				(    self				)						->    Any:
              # fmt: off
              SCREAMING_SNAKE_CASE_							:				Optional[int]		={'''input_ids''': [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
              # fmt: on

              self.tokenizer_integration_test_util(
                  expected_encoding=__A						,       model_name='''google/bigbird-roberta-base'''						,       revision='''215c99f1600e06f83acce68422f2035b2b5c3510'''						,       )
