


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 	__lowerCamelCase			:







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






            '''simple docstring'''




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







       def 		snake_case_	(			self: List[Any]					):






            '''simple docstring'''




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

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

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

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

            __UpperCamelCase =			self.get_config()

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







       def 		snake_case_	(			self: 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=__SCREAMING_SNAKE_CASE,initializer_range=self.initializer_range,)







       def 		snake_case_	(			self: Optional[Any],A_: Union[str, Any],A_: List[Any],A_: Optional[int],A_: int,A_: int,A_: int,A_: Union[str, Any]					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Union[str, Any],A_: Optional[Any],A_: Dict,A_: Any,A_: Optional[int],A_: List[Any],A_: int,A_: List[str],A_: Union[str, Any],A_: Union[str, Any],):






            '''simple docstring'''




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







       def 		snake_case_	(			self: List[Any],A_: Tuple,A_: Optional[int],A_: List[Any],A_: Optional[int],A_: Optional[Any],*A_: List[str]					):






            '''simple docstring'''




            __UpperCamelCase =			BioGptModel(config=__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)
            model.eval()

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

            # first forward pass
            __UpperCamelCase =			model(__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE					).to_tuple()

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

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

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

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

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

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







       def 		snake_case_	(			self: Any,A_: Any,A_: List[Any],A_: List[str],A_: Optional[int],A_: Optional[Any],*A_: str					):






            '''simple docstring'''




            __UpperCamelCase =			BioGptModel(config=__SCREAMING_SNAKE_CASE					).to(__SCREAMING_SNAKE_CASE					).eval()

            __UpperCamelCase =			torch.ones(input_ids.shape,dtype=torch.long,device=__SCREAMING_SNAKE_CASE					)

            # first forward pass
            __UpperCamelCase =			model(__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE,use_cache=__SCREAMING_SNAKE_CASE					)

            __UpperCamelCase =			outputs.to_tuple()

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

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

            __UpperCamelCase =			model(__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE					)['''last_hidden_state''']
            __UpperCamelCase =			model(__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE,past_key_values=__SCREAMING_SNAKE_CASE					)[
                '''last_hidden_state'''
            ]

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







       def 		snake_case_	(			self: Any,A_: Optional[int],A_: int,A_: Any,A_: List[str],A_: Union[str, Any],*A_: str,A_: Union[str, Any]=False					):






            '''simple docstring'''




            __UpperCamelCase =			BioGptForCausalLM(__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)
            if gradient_checkpointing:
                 model.gradient_checkpointing_enable()

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







       def 		snake_case_	(			self: Optional[Any],A_: Optional[Any],*A_: int					):






            '''simple docstring'''




            __UpperCamelCase =			BioGptModel(__SCREAMING_SNAKE_CASE					)
            __UpperCamelCase =			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: Dict,A_: Union[str, Any],A_: str,A_: Dict,A_: Dict,A_: Optional[int],*A_: Union[str, Any]					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




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



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







       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			BioGptModelTester(self					)
            __UpperCamelCase =			ConfigTester(self,config_class=__SCREAMING_SNAKE_CASE,hidden_size=37					)







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            self.config_tester.run_common_tests()







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE					)







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: List[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__SCREAMING_SNAKE_CASE					)







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_forward_and_backwards(*__SCREAMING_SNAKE_CASE,gradient_checkpointing=__SCREAMING_SNAKE_CASE					)







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__SCREAMING_SNAKE_CASE					)







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_weight_initialization(*__SCREAMING_SNAKE_CASE					)







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_for_token_classification(*__SCREAMING_SNAKE_CASE					)







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






            '''simple docstring'''




            __UpperCamelCase =			BioGptForCausalLM.from_pretrained('microsoft/biogpt'					)
            model.to(__SCREAMING_SNAKE_CASE					)
            __UpperCamelCase =			BioGptTokenizer.from_pretrained('microsoft/biogpt'					)

            __UpperCamelCase =			'''left'''

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

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

            __UpperCamelCase =			tokenizer(__SCREAMING_SNAKE_CASE,return_tensors='pt',padding=__SCREAMING_SNAKE_CASE					)
            __UpperCamelCase =			inputs['''input_ids'''].to(__SCREAMING_SNAKE_CASE					)

            __UpperCamelCase =			model.generate(
                input_ids=__SCREAMING_SNAKE_CASE,attention_mask=inputs['attention_mask'].to(__SCREAMING_SNAKE_CASE					),)

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

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

            __UpperCamelCase =			tokenizer.batch_decode(__SCREAMING_SNAKE_CASE,skip_special_tokens=__SCREAMING_SNAKE_CASE					)
            __UpperCamelCase =			tokenizer.decode(output_non_padded[0],skip_special_tokens=__SCREAMING_SNAKE_CASE					)
            __UpperCamelCase =			tokenizer.decode(output_padded[0],skip_special_tokens=__SCREAMING_SNAKE_CASE					)

            __UpperCamelCase =			[
                '''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(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE					)
            self.assertListEqual(__SCREAMING_SNAKE_CASE,[non_padded_sentence, padded_sentence]					)







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






            '''simple docstring'''




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







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




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



@require_torch
class 	__lowerCamelCase			(unittest.TestCase						):







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






            '''simple docstring'''




            __UpperCamelCase =			BioGptForCausalLM.from_pretrained('microsoft/biogpt'					)
            __UpperCamelCase =			torch.tensor([[2, 4805, 9, 656, 21]]					)
            __UpperCamelCase =			model(__SCREAMING_SNAKE_CASE					)[0]

            __UpperCamelCase =			4_2384

            __UpperCamelCase =			torch.Size((1, 5, vocab_size)					)
            self.assertEqual(output.shape,__SCREAMING_SNAKE_CASE					)

            __UpperCamelCase =			torch.tensor(
                [[[-9.5_2_3_6, -9.8_9_1_8, 1_0.4_5_5_7], [-1_1.0_4_6_9, -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],__SCREAMING_SNAKE_CASE,atol=1E-4					)					)







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






            '''simple docstring'''




            __UpperCamelCase =			BioGptTokenizer.from_pretrained('microsoft/biogpt'					)
            __UpperCamelCase =			BioGptForCausalLM.from_pretrained('microsoft/biogpt'					)
            model.to(__SCREAMING_SNAKE_CASE					)

            torch.manual_seed(0					)
            __UpperCamelCase =			tokenizer('COVID-19 is',return_tensors='pt'					).to(__SCREAMING_SNAKE_CASE					)
            __UpperCamelCase =			model.generate(
                **__SCREAMING_SNAKE_CASE,min_length=100,max_length=1024,num_beams=5,early_stopping=__SCREAMING_SNAKE_CASE,)
            __UpperCamelCase =			tokenizer.decode(output_ids[0],skip_special_tokens=__SCREAMING_SNAKE_CASE					)

            __UpperCamelCase =			(
                '''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(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE					)




def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		):
      if index == number_of_items:
            return 0
      UpperCamelCase__     : str											=					0
      UpperCamelCase__     : Optional[Any]											=					0
      UpperCamelCase__     : int											=					knapsack(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		index + 1		)
      if weights[index] <= max_weight:
            UpperCamelCase__     : List[Any]											=					values[index] + knapsack(
                UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		max_weight - weights[index]			,		index + 1		)
      return max(UpperCamelCase__			,		UpperCamelCase__		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


