



"""simple docstring"""







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 				UpperCamelCase__       :




      """simple docstring"""




      def __init__(				self    :				str   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				List[str]=1_3   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=7   ,      SCREAMING_SNAKE_CASE_    :				Dict=True   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=True   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=False   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=True   ,      SCREAMING_SNAKE_CASE_    :				List[str]=9_9   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=3_2   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=5   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=4   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=3_7   ,      SCREAMING_SNAKE_CASE_    :				List[Any]="gelu"   ,      SCREAMING_SNAKE_CASE_    :				str=0.1   ,      SCREAMING_SNAKE_CASE_    :				Any=0.1   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=5_1_2   ,      SCREAMING_SNAKE_CASE_    :				Any=1_6   ,      SCREAMING_SNAKE_CASE_    :				List[str]=2   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=0.02   ,      SCREAMING_SNAKE_CASE_    :				List[str]=3   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=4   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=None   ,      ):
        lowerCAmelCase_       :    str								=			parent
        lowerCAmelCase_       :    int								=			batch_size
        lowerCAmelCase_       :    Union[str, Any]								=			seq_length
        lowerCAmelCase_       :    Optional[Any]								=			is_training
        lowerCAmelCase_       :    Union[str, Any]								=			use_input_mask
        lowerCAmelCase_       :    List[str]								=			use_token_type_ids
        lowerCAmelCase_       :    str								=			use_labels
        lowerCAmelCase_       :    List[str]								=			vocab_size
        lowerCAmelCase_       :    List[Any]								=			hidden_size
        lowerCAmelCase_       :    str								=			num_hidden_layers
        lowerCAmelCase_       :    Tuple								=			num_attention_heads
        lowerCAmelCase_       :    Optional[Any]								=			intermediate_size
        lowerCAmelCase_       :    Any								=			hidden_act
        lowerCAmelCase_       :    List[Any]								=			hidden_dropout_prob
        lowerCAmelCase_       :    List[Any]								=			attention_probs_dropout_prob
        lowerCAmelCase_       :    Union[str, Any]								=			max_position_embeddings
        lowerCAmelCase_       :    int								=			type_vocab_size
        lowerCAmelCase_       :    Optional[int]								=			type_sequence_label_size
        lowerCAmelCase_       :    Optional[Any]								=			initializer_range
        lowerCAmelCase_       :    int								=			num_labels
        lowerCAmelCase_       :    Tuple								=			num_choices
        lowerCAmelCase_       :    Tuple								=			scope



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    List[Any]								=			ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size	)

        lowerCAmelCase_       :    Dict								=			None
        if self.use_input_mask:
          lowerCAmelCase_       :    Any								=			random_attention_mask([self.batch_size, self.seq_length]	)

        lowerCAmelCase_       :    List[str]								=			None
        if self.use_token_type_ids:
          lowerCAmelCase_       :    List[Any]								=			ids_tensor([self.batch_size, self.seq_length]   ,      self.type_vocab_size	)

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

        lowerCAmelCase_       :    List[Any]								=			self.get_config()

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



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        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 					SCREAMING_SNAKE_CASE__			(				self    :				int   ,      SCREAMING_SNAKE_CASE_    :				List[str]   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				List[str]   ,      SCREAMING_SNAKE_CASE_    :				List[str]   ,      SCREAMING_SNAKE_CASE_    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Dict	):
        lowerCAmelCase_       :    Dict								=			BioGptModel(config=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    Any								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    List[str]								=			model(SCREAMING_SNAKE_CASE_	)
        self.parent.assertEqual(result.last_hidden_state.shape   ,      (self.batch_size, self.seq_length, self.hidden_size)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				int   ,      ):
        lowerCAmelCase_       :    Optional[int]								=			BioGptForCausalLM(config=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    Optional[Any]								=			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 					SCREAMING_SNAKE_CASE__			(				self    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      *SCREAMING_SNAKE_CASE_    :				Optional[Any]	):
        lowerCAmelCase_       :    Union[str, Any]								=			BioGptModel(config=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()

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

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

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

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

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

        # get two different outputs
        lowerCAmelCase_       :    int								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_	)['last_hidden_state']
        lowerCAmelCase_       :    int								=			model(SCREAMING_SNAKE_CASE_   ,      past_key_values=SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_	)['last_hidden_state']

        # select random slice
        lowerCAmelCase_       :    Optional[Any]								=			ids_tensor((1,)   ,      output_from_past.shape[-1]	).item()
        lowerCAmelCase_       :    Dict								=			output_from_no_past[:, -1, random_slice_idx].detach()
        lowerCAmelCase_       :    List[Any]								=			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 					SCREAMING_SNAKE_CASE__			(				self    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				List[str]   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      *SCREAMING_SNAKE_CASE_    :				str	):
        lowerCAmelCase_       :    Dict								=			BioGptModel(config=SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	).eval()

        lowerCAmelCase_       :    str								=			torch.ones(input_ids.shape   ,      dtype=torch.long   ,      device=SCREAMING_SNAKE_CASE_	)

        # first forward pass
        lowerCAmelCase_       :    int								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      use_cache=SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    int								=			outputs.to_tuple()

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

        # append to next input_ids and
        lowerCAmelCase_       :    Optional[Any]								=			torch.cat([input_ids, next_tokens]   ,      dim=-1	)
        lowerCAmelCase_       :    Dict								=			torch.cat([attention_mask, next_attn_mask]   ,      dim=-1	)

        lowerCAmelCase_       :    Dict								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_	)['last_hidden_state']
        lowerCAmelCase_       :    Optional[int]								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      past_key_values=SCREAMING_SNAKE_CASE_	)[
            'last_hidden_state'
        ]

        # select random slice
        lowerCAmelCase_       :    Union[str, Any]								=			ids_tensor((1,)   ,      output_from_past.shape[-1]	).item()
        lowerCAmelCase_       :    Any								=			output_from_no_past[:, -3:, random_slice_idx].detach()
        lowerCAmelCase_       :    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(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      atol=1E-3	)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      *SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				List[str]=False	):
        lowerCAmelCase_       :    Optional[Any]								=			BioGptForCausalLM(SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        if gradient_checkpointing:
          model.gradient_checkpointing_enable()

        lowerCAmelCase_       :    Optional[Any]								=			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 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      *SCREAMING_SNAKE_CASE_    :				int	):
        lowerCAmelCase_       :    str								=			BioGptModel(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    int								=			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 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      *SCREAMING_SNAKE_CASE_    :				List[Any]	):
        lowerCAmelCase_       :    Tuple								=			self.num_labels
        lowerCAmelCase_       :    List[str]								=			BioGptForTokenClassification(SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    List[Any]								=			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 					SCREAMING_SNAKE_CASE__			(				self    :				int	):
        lowerCAmelCase_       :    Optional[int]								=			self.prepare_config_and_inputs()
        (
            lowerCAmelCase_
        )       :    List[str]								=			config_and_inputs
        lowerCAmelCase_       :    Optional[int]								=			{'input_ids': input_ids, 'attention_mask': input_mask}
        return config, inputs_dict







@require_torch
class 				UpperCamelCase__       (     lowercase_, lowercase_, lowercase_, unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					(
          (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
          if is_torch_available()
          else ()
      )
      _SCREAMING_SNAKE_CASE      =					(BioGptForCausalLM,) if is_torch_available() else ()
      _SCREAMING_SNAKE_CASE      =					(
          {
              """feature-extraction""": BioGptModel,
              """text-classification""": BioGptForSequenceClassification,
              """text-generation""": BioGptForCausalLM,
              """token-classification""": BioGptForTokenClassification,
              """zero-shot""": BioGptForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      _SCREAMING_SNAKE_CASE      =					False



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        lowerCAmelCase_       :    str								=			BioGptModelTester(self	)
        lowerCAmelCase_       :    Dict								=			ConfigTester(self   ,      config_class=SCREAMING_SNAKE_CASE_   ,      hidden_size=3_7	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        self.config_tester.run_common_tests()



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        lowerCAmelCase_       :    List[Any]								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        lowerCAmelCase_       :    Tuple								=			self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
          lowerCAmelCase_       :    int								=			type
          self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        lowerCAmelCase_       :    Tuple								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_biogpt_model_attention_mask_past(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        lowerCAmelCase_       :    Dict								=			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 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        lowerCAmelCase_       :    Optional[Any]								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_biogpt_model_past_large_inputs(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        lowerCAmelCase_       :    List[Any]								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_biogpt_weight_initialization(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int	):
        lowerCAmelCase_       :    int								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_biogpt_for_token_classification(*SCREAMING_SNAKE_CASE_	)



      @slow
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        lowerCAmelCase_       :    Dict								=			BioGptForCausalLM.from_pretrained('microsoft/biogpt'	)
        model.to(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Dict								=			BioGptTokenizer.from_pretrained('microsoft/biogpt'	)

        lowerCAmelCase_       :    str								=			'left'

        # Define PAD Token = EOS Token = 50256
        lowerCAmelCase_       :    Optional[Any]								=			tokenizer.eos_token
        lowerCAmelCase_       :    Dict								=			model.config.eos_token_id

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

        lowerCAmelCase_       :    int								=			tokenizer(SCREAMING_SNAKE_CASE_   ,      return_tensors='pt'   ,      padding=SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Optional[Any]								=			inputs['input_ids'].to(SCREAMING_SNAKE_CASE_	)

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

        lowerCAmelCase_       :    List[str]								=			tokenizer(sentences[0]   ,      return_tensors='pt'	).input_ids.to(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Optional[int]								=			model.generate(input_ids=SCREAMING_SNAKE_CASE_	)

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

        lowerCAmelCase_       :    Optional[int]								=			tokenizer.batch_decode(SCREAMING_SNAKE_CASE_   ,      skip_special_tokens=SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    int								=			tokenizer.decode(output_non_padded[0]   ,      skip_special_tokens=SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    List[Any]								=			tokenizer.decode(output_padded[0]   ,      skip_special_tokens=SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Union[str, 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(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)
        self.assertListEqual(SCREAMING_SNAKE_CASE_   ,      [non_padded_sentence, padded_sentence]	)



      @slow
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_       :    List[str]								=			BioGptModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    Tuple								=			self.model_tester.prepare_config_and_inputs_for_common()
        lowerCAmelCase_       :    str								=			3
        lowerCAmelCase_       :    Dict								=			input_dict['input_ids']
        lowerCAmelCase_       :    List[Any]								=			input_ids.ne(1	).to(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Tuple								=			ids_tensor([self.model_tester.batch_size]   ,      self.model_tester.type_sequence_label_size	)
        lowerCAmelCase_       :    List[str]								=			BioGptForSequenceClassification(SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    Any								=			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 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    str								=			self.model_tester.prepare_config_and_inputs_for_common()
        lowerCAmelCase_       :    str								=			3
        lowerCAmelCase_       :    Tuple								=			'multi_label_classification'
        lowerCAmelCase_       :    int								=			input_dict['input_ids']
        lowerCAmelCase_       :    List[Any]								=			input_ids.ne(1	).to(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Optional[int]								=			ids_tensor(
            [self.model_tester.batch_size, config.num_labels]   ,      self.model_tester.type_sequence_label_size	).to(torch.float	)
        lowerCAmelCase_       :    Union[str, Any]								=			BioGptForSequenceClassification(SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    List[Any]								=			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 				UpperCamelCase__       (     unittest.TestCase  ):




      """simple docstring"""




      @slow
      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    Any								=			BioGptForCausalLM.from_pretrained('microsoft/biogpt'	)
        lowerCAmelCase_       :    Dict								=			torch.tensor([[2, 4_8_0_5, 9, 6_5_6, 2_1]]	)
        lowerCAmelCase_       :    Dict								=			model(SCREAMING_SNAKE_CASE_	)[0]

        lowerCAmelCase_       :    Union[str, Any]								=			4_2_3_8_4

        lowerCAmelCase_       :    Optional[Any]								=			torch.Size((1, 5, vocab_size)	)
        self.assertEqual(output.shape   ,      SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    int								=			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]   ,      SCREAMING_SNAKE_CASE_   ,      atol=1E-4	)	)





      @slow
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any	):
        lowerCAmelCase_       :    List[Any]								=			BioGptTokenizer.from_pretrained('microsoft/biogpt'	)
        lowerCAmelCase_       :    Optional[int]								=			BioGptForCausalLM.from_pretrained('microsoft/biogpt'	)
        model.to(SCREAMING_SNAKE_CASE_	)

        torch.manual_seed(0	)
        lowerCAmelCase_       :    Dict								=			tokenizer('COVID-19 is'   ,      return_tensors='pt'	).to(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    int								=			model.generate(
            **SCREAMING_SNAKE_CASE_   ,      min_length=1_0_0   ,      max_length=1_0_2_4   ,      num_beams=5   ,      early_stopping=SCREAMING_SNAKE_CASE_   ,      )
        lowerCAmelCase_       :    Optional[int]								=			tokenizer.decode(output_ids[0]   ,      skip_special_tokens=SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Dict								=			(
            '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_	)










"""simple docstring"""







from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt

def     UpperCamelCase_   (   lowerCAmelCase__				:    int						)						->       bool:

  """simple docstring"""




  lowerCAmelCase_       :    int								=			int(number**0.5						)
  return number == sq * sq

def     UpperCamelCase_   (   lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    int						)						->       tuple[int, int]:

  """simple docstring"""




  lowerCAmelCase_       :    int								=			x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
  lowerCAmelCase_       :    int								=			x_den * y_den * z_den
  lowerCAmelCase_       :    int								=			gcd(lowerCAmelCase__   ,       lowerCAmelCase__						)
  top //= hcf
  bottom //= hcf
  return top, bottom

def     UpperCamelCase_   (   lowerCAmelCase__				:    int = 35						)						->       int:

  """simple docstring"""




  lowerCAmelCase_       :    set								=			set()
  lowerCAmelCase_       :    int
  lowerCAmelCase_       :    Fraction								=			Fraction(0						)
  lowerCAmelCase_       :    tuple[int, int]

  for x_num in range(1   ,       order + 1						):
    for x_den in range(x_num + 1   ,       order + 1						):
      for y_num in range(1   ,       order + 1						):
        for y_den in range(y_num + 1   ,       order + 1						):
          # n=1
          lowerCAmelCase_       :    str								=			x_num * y_den + x_den * y_num
          lowerCAmelCase_       :    int								=			x_den * y_den
          lowerCAmelCase_       :    int								=			gcd(lowerCAmelCase__   ,       lowerCAmelCase__						)
          z_num //= hcf
          z_den //= hcf
          if 0 < z_num < z_den <= order:
            lowerCAmelCase_       :    List[str]								=			add_three(
                lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)
            unique_s.add(lowerCAmelCase__						)

          # n=2
          lowerCAmelCase_       :    Optional[int]								=			(
              x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
          )
          lowerCAmelCase_       :    Dict								=			x_den * x_den * y_den * y_den
          if is_sq(lowerCAmelCase__						) and is_sq(lowerCAmelCase__						):
            lowerCAmelCase_       :    Optional[int]								=			int(sqrt(lowerCAmelCase__						)						)
            lowerCAmelCase_       :    List[str]								=			int(sqrt(lowerCAmelCase__						)						)
            lowerCAmelCase_       :    Union[str, Any]								=			gcd(lowerCAmelCase__   ,       lowerCAmelCase__						)
            z_num //= hcf
            z_den //= hcf
            if 0 < z_num < z_den <= order:
              lowerCAmelCase_       :    Union[str, Any]								=			add_three(
                  lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)
              unique_s.add(lowerCAmelCase__						)

                    # n=-1
          lowerCAmelCase_       :    Dict								=			x_num * y_num
          lowerCAmelCase_       :    Optional[int]								=			x_den * y_num + x_num * y_den
          lowerCAmelCase_       :    Any								=			gcd(lowerCAmelCase__   ,       lowerCAmelCase__						)
          z_num //= hcf
          z_den //= hcf
          if 0 < z_num < z_den <= order:
            lowerCAmelCase_       :    Tuple								=			add_three(
                lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)
            unique_s.add(lowerCAmelCase__						)

          # n=2
          lowerCAmelCase_       :    List[str]								=			x_num * x_num * y_num * y_num
          lowerCAmelCase_       :    Optional[Any]								=			(
              x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
          )
          if is_sq(lowerCAmelCase__						) and is_sq(lowerCAmelCase__						):
            lowerCAmelCase_       :    Tuple								=			int(sqrt(lowerCAmelCase__						)						)
            lowerCAmelCase_       :    Optional[Any]								=			int(sqrt(lowerCAmelCase__						)						)
            lowerCAmelCase_       :    Optional[int]								=			gcd(lowerCAmelCase__   ,       lowerCAmelCase__						)
            z_num //= hcf
            z_den //= hcf
            if 0 < z_num < z_den <= order:
              lowerCAmelCase_       :    Any								=			add_three(
                  lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)
              unique_s.add(lowerCAmelCase__						)

  for num, den in unique_s:
    total += Fraction(lowerCAmelCase__   ,       lowerCAmelCase__						)

  return total.denominator + total.numerator


if __name__ == "__main__":
     print(f'{solution() = }')





