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				:   Any		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   Tuple=13		,							_UpperCamelCase				:   Any=7		,							_UpperCamelCase				:   Dict=True		,							_UpperCamelCase				:   int=True		,							_UpperCamelCase				:   Optional[Any]=False		,							_UpperCamelCase				:   Dict=True		,							_UpperCamelCase				:   Optional[Any]=99		,							_UpperCamelCase				:   List[Any]=32		,							_UpperCamelCase				:   str=5		,							_UpperCamelCase				:   str=4		,							_UpperCamelCase				:   Tuple=37		,							_UpperCamelCase				:   Optional[int]="gelu"		,							_UpperCamelCase				:   str=0.1		,							_UpperCamelCase				:   Optional[int]=0.1		,							_UpperCamelCase				:   Any=512		,							_UpperCamelCase				:   Union[str, Any]=16		,							_UpperCamelCase				:   str=2		,							_UpperCamelCase				:   str=0.0_2		,							_UpperCamelCase				:   Any=3		,							_UpperCamelCase				:   Optional[int]=4		,							_UpperCamelCase				:   int=None		,							)   ->			List[Any]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    parent
            SCREAMING_SNAKE_CASE           =    batch_size
            SCREAMING_SNAKE_CASE           =    seq_length
            SCREAMING_SNAKE_CASE           =    is_training
            SCREAMING_SNAKE_CASE           =    use_input_mask
            SCREAMING_SNAKE_CASE           =    use_token_type_ids
            SCREAMING_SNAKE_CASE           =    use_labels
            SCREAMING_SNAKE_CASE           =    vocab_size
            SCREAMING_SNAKE_CASE           =    hidden_size
            SCREAMING_SNAKE_CASE           =    num_hidden_layers
            SCREAMING_SNAKE_CASE           =    num_attention_heads
            SCREAMING_SNAKE_CASE           =    intermediate_size
            SCREAMING_SNAKE_CASE           =    hidden_act
            SCREAMING_SNAKE_CASE           =    hidden_dropout_prob
            SCREAMING_SNAKE_CASE           =    attention_probs_dropout_prob
            SCREAMING_SNAKE_CASE           =    max_position_embeddings
            SCREAMING_SNAKE_CASE           =    type_vocab_size
            SCREAMING_SNAKE_CASE           =    type_sequence_label_size
            SCREAMING_SNAKE_CASE           =    initializer_range
            SCREAMING_SNAKE_CASE           =    num_labels
            SCREAMING_SNAKE_CASE           =    num_choices
            SCREAMING_SNAKE_CASE           =    scope




       def 		__snake_case(  self				:   Any )   ->			List[str]:



            '''simple docstring'''

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

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

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

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

            SCREAMING_SNAKE_CASE           =    self.get_config()

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




       def 		__snake_case(  self				:   Tuple )   ->			Dict:



            '''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=_UpperCamelCase		,							initializer_range=self.initializer_range		,							)




       def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   Union[str, Any] )   ->			int:



            '''simple docstring'''

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




       def 		__snake_case(  self				:   Dict		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   Union[str, Any]		,							)   ->			Union[str, Any]:



            '''simple docstring'''

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




       def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   int		,							*_UpperCamelCase				:   Dict )   ->			int:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    BioGptModel(config=_UpperCamelCase )
            model.to(_UpperCamelCase )
            model.eval()

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

            # first forward pass
            SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							attention_mask=_UpperCamelCase ).to_tuple()

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

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

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

            # get two different outputs
            SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							attention_mask=_UpperCamelCase )["last_hidden_state"]
            SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							past_key_values=_UpperCamelCase		,							attention_mask=_UpperCamelCase )["last_hidden_state"]

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

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




       def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   Tuple		,							*_UpperCamelCase				:   List[Any] )   ->			int:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    BioGptModel(config=_UpperCamelCase ).to(_UpperCamelCase ).eval()

            SCREAMING_SNAKE_CASE           =    torch.ones(input_ids.shape		,							dtype=torch.long		,							device=_UpperCamelCase )

            # first forward pass
            SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							attention_mask=_UpperCamelCase		,							use_cache=_UpperCamelCase )

            SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    outputs.to_tuple()

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

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

            SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							attention_mask=_UpperCamelCase )["last_hidden_state"]
            SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							attention_mask=_UpperCamelCase		,							past_key_values=_UpperCamelCase )[
                "last_hidden_state"
            ]

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

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

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




       def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   Union[str, Any]		,							*_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   Optional[Any]=False )   ->			List[str]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    BioGptForCausalLM(_UpperCamelCase )
            model.to(_UpperCamelCase )
            if gradient_checkpointing:
                 model.gradient_checkpointing_enable()

            SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							labels=_UpperCamelCase )
            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				:   Tuple		,							_UpperCamelCase				:   Dict		,							*_UpperCamelCase				:   List[Any] )   ->			Tuple:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    BioGptModel(_UpperCamelCase )
            SCREAMING_SNAKE_CASE           =    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_0_1 )
                      self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 )		,							0.0_1 )




       def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   int		,							*_UpperCamelCase				:   Dict )   ->			Union[str, Any]:



            '''simple docstring'''

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





       def 		__snake_case(  self				:   Any )   ->			Dict:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    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				
            )     ,								
            )           =    config_and_inputs
            SCREAMING_SNAKE_CASE           =    {"input_ids": input_ids, "attention_mask": input_mask}
            return config, inputs_dict



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




       def 		__snake_case(  self				:   Dict )   ->			Optional[Any]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    BioGptModelTester(self )
            SCREAMING_SNAKE_CASE           =    ConfigTester(self		,							config_class=_UpperCamelCase		,							hidden_size=37 )




       def 		__snake_case(  self				:   Tuple )   ->			str:



            '''simple docstring'''

            self.config_tester.run_common_tests()




       def 		__snake_case(  self				:   str )   ->			List[Any]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*_UpperCamelCase )




       def 		__snake_case(  self				:   int )   ->			str:



            '''simple docstring'''

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




       def 		__snake_case(  self				:   List[str] )   ->			Union[str, Any]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_model_attention_mask_past(*_UpperCamelCase )




       def 		__snake_case(  self				:   List[Any] )   ->			str:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_forward_and_backwards(*_UpperCamelCase		,							gradient_checkpointing=_UpperCamelCase )




       def 		__snake_case(  self				:   Tuple )   ->			List[Any]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_model_past_large_inputs(*_UpperCamelCase )




       def 		__snake_case(  self				:   Dict )   ->			str:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_weight_initialization(*_UpperCamelCase )




       def 		__snake_case(  self				:   Dict )   ->			Dict:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_for_token_classification(*_UpperCamelCase )




       @slow
       def 		__snake_case(  self				:   Any )   ->			Union[str, Any]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
            model.to(_UpperCamelCase )
            SCREAMING_SNAKE_CASE           =    BioGptTokenizer.from_pretrained("microsoft/biogpt" )

            SCREAMING_SNAKE_CASE           =    "left"

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

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

            SCREAMING_SNAKE_CASE           =    tokenizer(_UpperCamelCase		,							return_tensors="pt"		,							padding=_UpperCamelCase )
            SCREAMING_SNAKE_CASE           =    inputs["input_ids"].to(_UpperCamelCase )

            SCREAMING_SNAKE_CASE           =    model.generate(
                input_ids=_UpperCamelCase		,							attention_mask=inputs["attention_mask"].to(_UpperCamelCase )		,							)

            SCREAMING_SNAKE_CASE           =    tokenizer(sentences[0]		,							return_tensors="pt" ).input_ids.to(_UpperCamelCase )
            SCREAMING_SNAKE_CASE           =    model.generate(input_ids=_UpperCamelCase )

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

            SCREAMING_SNAKE_CASE           =    tokenizer.batch_decode(_UpperCamelCase		,							skip_special_tokens=_UpperCamelCase )
            SCREAMING_SNAKE_CASE           =    tokenizer.decode(output_non_padded[0]		,							skip_special_tokens=_UpperCamelCase )
            SCREAMING_SNAKE_CASE           =    tokenizer.decode(output_padded[0]		,							skip_special_tokens=_UpperCamelCase )

            SCREAMING_SNAKE_CASE           =    [
                "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(_UpperCamelCase		,							_UpperCamelCase )
            self.assertListEqual(_UpperCamelCase		,							[non_padded_sentence, padded_sentence] )




       @slow
       def 		__snake_case(  self				:   Optional[int] )   ->			Optional[int]:



            '''simple docstring'''

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




       def 		__snake_case(  self				:   Tuple )   ->			Dict:



            '''simple docstring'''

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





       def 		__snake_case(  self				:   Union[str, Any] )   ->			str:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()
            SCREAMING_SNAKE_CASE           =    3
            SCREAMING_SNAKE_CASE           =    "multi_label_classification"
            SCREAMING_SNAKE_CASE           =    input_dict["input_ids"]
            SCREAMING_SNAKE_CASE           =    input_ids.ne(1 ).to(_UpperCamelCase )
            SCREAMING_SNAKE_CASE           =    ids_tensor(
                [self.model_tester.batch_size, config.num_labels]		,							self.model_tester.type_sequence_label_size ).to(torch.float )
            SCREAMING_SNAKE_CASE           =    BioGptForSequenceClassification(_UpperCamelCase )
            model.to(_UpperCamelCase )
            model.eval()
            SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							attention_mask=_UpperCamelCase		,							labels=_UpperCamelCase )
            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				:   Union[str, Any] )   ->			List[str]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
            SCREAMING_SNAKE_CASE           =    torch.tensor([[2, 4_805, 9, 656, 21]] )
            SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase )[0]

            SCREAMING_SNAKE_CASE           =    42_384

            SCREAMING_SNAKE_CASE           =    torch.Size((1, 5, vocab_size) )
            self.assertEqual(output.shape		,							_UpperCamelCase )

            SCREAMING_SNAKE_CASE           =    torch.tensor(
                [[[-9.5_2_3_6, -9.8_9_1_8, 10.4_557], [-11.0_469, -9.6_4_2_3, 8.1_0_2_2], [-8.8_6_6_4, -7.8_8_2_6, 5.5_3_2_5]]] )

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





       @slow
       def 		__snake_case(  self				:   str )   ->			List[Any]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    BioGptTokenizer.from_pretrained("microsoft/biogpt" )
            SCREAMING_SNAKE_CASE           =    BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
            model.to(_UpperCamelCase )

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

            SCREAMING_SNAKE_CASE           =    (
                "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(_UpperCamelCase		,							_UpperCamelCase )





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int					):
					while b:
										SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    b, a % b
					return a





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int					):
					return a if b == 0 else euclidean_gcd_recursive(UpperCAmelCase__ ,	a % b					)





def 		__lowerCamelCase							():
					print(F"euclidean_gcd(3, 5) = {euclidean_gcd(3 ,	5					)}"					)
					print(F"euclidean_gcd(5, 3) = {euclidean_gcd(5 ,	3					)}"					)
					print(F"euclidean_gcd(1, 3) = {euclidean_gcd(1 ,	3					)}"					)
					print(F"euclidean_gcd(3, 6) = {euclidean_gcd(3 ,	6					)}"					)
					print(F"euclidean_gcd(6, 3) = {euclidean_gcd(6 ,	3					)}"					)

					print(F"euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 ,	5					)}"					)
					print(F"euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 ,	3					)}"					)
					print(F"euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 ,	3					)}"					)
					print(F"euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 ,	6					)}"					)
					print(F"euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 ,	3					)}"					)


if __name__ == "__main__":
							main()




