

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 						__magic_name__					:






     def __init__(				self		,						_lowercase		,						_lowercase=13		,						_lowercase=7		,						_lowercase=True		,						_lowercase=True		,						_lowercase=False		,						_lowercase=True		,						_lowercase=99		,						_lowercase=32		,						_lowercase=5		,						_lowercase=4		,						_lowercase=37		,						_lowercase="gelu"		,						_lowercase=0.1		,						_lowercase=0.1		,						_lowercase=512		,						_lowercase=16		,						_lowercase=2		,						_lowercase=0.02		,						_lowercase=3		,						_lowercase=4		,						_lowercase=None		,						)->     List[Any]:
         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 	UpperCAmelCase_				(				self	)->     Any:
         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 	UpperCAmelCase_				(				self	)->     List[Any]:
         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=_lowercase		,						initializer_range=self.initializer_range		,						)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)->     str:
         UpperCamelCase_			  =						BioGptModel(config=_lowercase	)
         model.to(_lowercase	)
         model.eval()
         UpperCamelCase_			  =						model(_lowercase		,						attention_mask=_lowercase	)
         UpperCamelCase_			  =						model(_lowercase	)
         self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, self.seq_length, self.hidden_size)	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						)->     List[str]:
         UpperCamelCase_			  =						BioGptForCausalLM(config=_lowercase	)
         model.to(_lowercase	)
         model.eval()
         UpperCamelCase_			  =						model(_lowercase		,						attention_mask=_lowercase		,						token_type_ids=_lowercase		,						labels=_lowercase	)
         self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.seq_length, self.vocab_size)	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						*_lowercase	)->     Any:
         UpperCamelCase_			  =						BioGptModel(config=_lowercase	)
         model.to(_lowercase	)
         model.eval()

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

         # first forward pass
         UpperCamelCase_  ,       UpperCamelCase_			  =						model(_lowercase		,						attention_mask=_lowercase	).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,)		,						_lowercase	).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=_lowercase	)]		,						dim=1		,						)

         # get two different outputs
         UpperCamelCase_			  =						model(_lowercase		,						attention_mask=_lowercase	)["last_hidden_state"]
         UpperCamelCase_			  =						model(_lowercase		,						past_key_values=_lowercase		,						attention_mask=_lowercase	)["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(_lowercase		,						_lowercase		,						atol=1e-3	)	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						*_lowercase	)->     Tuple:
         UpperCamelCase_			  =						BioGptModel(config=_lowercase	).to(_lowercase	).eval()

         UpperCamelCase_			  =						torch.ones(input_ids.shape		,						dtype=torch.long		,						device=_lowercase	)

         # first forward pass
         UpperCamelCase_			  =						model(_lowercase		,						attention_mask=_lowercase		,						use_cache=_lowercase	)

         UpperCamelCase_  ,       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(_lowercase		,						attention_mask=_lowercase	)["last_hidden_state"]
         UpperCamelCase_			  =						model(_lowercase		,						attention_mask=_lowercase		,						past_key_values=_lowercase	)[
             "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(_lowercase		,						_lowercase		,						atol=1e-3	)	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						*_lowercase		,						_lowercase=False	)->     Any:
         UpperCamelCase_			  =						BioGptForCausalLM(_lowercase	)
         model.to(_lowercase	)
         if gradient_checkpointing:
             model.gradient_checkpointing_enable()

         UpperCamelCase_			  =						model(_lowercase		,						labels=_lowercase	)
         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 	UpperCAmelCase_				(				self		,						_lowercase		,						*_lowercase	)->     Tuple:
         UpperCamelCase_			  =						BioGptModel(_lowercase	)
         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	)		,						0.001	)
                 self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]	) - 0.0	)		,						0.01	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						*_lowercase	)->     int:
         UpperCamelCase_			  =						self.num_labels
         UpperCamelCase_			  =						BioGptForTokenClassification(_lowercase	)
         model.to(_lowercase	)
         model.eval()
         UpperCamelCase_			  =						model(_lowercase		,						attention_mask=_lowercase		,						token_type_ids=_lowercase	)
         self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.seq_length, self.num_labels)	)






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_			  =						self.prepare_config_and_inputs()
         (
             (
             UpperCamelCase_
         )  ,       (
             UpperCamelCase_
         )  ,       (
             UpperCamelCase_
         )  ,       (
             UpperCamelCase_
         )  ,       (
             UpperCamelCase_
         )  ,       (
             UpperCamelCase_
         )  ,       (
             UpperCamelCase_
         )  ,       
         )			  =						config_and_inputs
         UpperCamelCase_			  =						{"input_ids": input_ids, "attention_mask": input_mask}
         return config, inputs_dict






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






     def 	UpperCAmelCase_				(				self	)->     str:
         UpperCamelCase_			  =						BioGptModelTester(self	)
         UpperCamelCase_			  =						ConfigTester(self		,						config_class=_lowercase		,						hidden_size=37	)






     def 	UpperCAmelCase_				(				self	)->     List[Any]:
         self.config_tester.run_common_tests()






     def 	UpperCAmelCase_				(				self	)->     Any:
         UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Tuple:
         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(*_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_biogpt_model_attention_mask_past(*_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_forward_and_backwards(*_lowercase		,						gradient_checkpointing=_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Tuple:
         UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_biogpt_model_past_large_inputs(*_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     str:
         UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_biogpt_weight_initialization(*_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_biogpt_for_token_classification(*_lowercase	)






     @slow
     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_			  =						BioGptForCausalLM.from_pretrained("microsoft/biogpt"	)
         model.to(_lowercase	)
         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(_lowercase		,						return_tensors="pt"		,						padding=_lowercase	)
         UpperCamelCase_			  =						inputs["input_ids"].to(_lowercase	)

         UpperCamelCase_			  =						model.generate(
             input_ids=_lowercase		,						attention_mask=inputs["attention_mask"].to(_lowercase	)		,						)

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

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

         UpperCamelCase_			  =						tokenizer.batch_decode(_lowercase		,						skip_special_tokens=_lowercase	)
         UpperCamelCase_			  =						tokenizer.decode(output_non_padded[0]		,						skip_special_tokens=_lowercase	)
         UpperCamelCase_			  =						tokenizer.decode(output_padded[0]		,						skip_special_tokens=_lowercase	)

         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(_lowercase		,						_lowercase	)
         self.assertListEqual(_lowercase		,						[non_padded_sentence, padded_sentence]	)






     @slow
     def 	UpperCAmelCase_				(				self	)->     Dict:
         for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase_			  =						BioGptModel.from_pretrained(_lowercase	)
             self.assertIsNotNone(_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Dict:
         UpperCamelCase_  ,       UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs_for_common()
         UpperCamelCase_			  =						3
         UpperCamelCase_			  =						input_dict["input_ids"]
         UpperCamelCase_			  =						input_ids.ne(1	).to(_lowercase	)
         UpperCamelCase_			  =						ids_tensor([self.model_tester.batch_size]		,						self.model_tester.type_sequence_label_size	)
         UpperCamelCase_			  =						BioGptForSequenceClassification(_lowercase	)
         model.to(_lowercase	)
         model.eval()
         UpperCamelCase_			  =						model(_lowercase		,						attention_mask=_lowercase		,						labels=_lowercase	)
         self.assertEqual(result.logits.shape		,						(self.model_tester.batch_size, self.model_tester.num_labels)	)






     def 	UpperCAmelCase_				(				self	)->     List[Any]:
         UpperCamelCase_  ,       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(_lowercase	)
         UpperCamelCase_			  =						ids_tensor(
             [self.model_tester.batch_size, config.num_labels]		,						self.model_tester.type_sequence_label_size	).to(torch.float	)
         UpperCamelCase_			  =						BioGptForSequenceClassification(_lowercase	)
         model.to(_lowercase	)
         model.eval()
         UpperCamelCase_			  =						model(_lowercase		,						attention_mask=_lowercase		,						labels=_lowercase	)
         self.assertEqual(result.logits.shape		,						(self.model_tester.batch_size, self.model_tester.num_labels)	)






@require_torch
class 						__magic_name__					(							unittest.TestCase				):






     @slow
     def 	UpperCAmelCase_				(				self	)->     List[Any]:
         UpperCamelCase_			  =						BioGptForCausalLM.from_pretrained("microsoft/biogpt"	)
         UpperCamelCase_			  =						torch.tensor([[2, 4_805, 9, 656, 21]]	)
         UpperCamelCase_			  =						model(_lowercase	)[0]

         UpperCamelCase_			  =						42_384

         UpperCamelCase_			  =						torch.Size((1, 5, vocab_size)	)
         self.assertEqual(output.shape		,						_lowercase	)

         UpperCamelCase_			  =						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]		,						_lowercase		,						atol=1e-4	)	)






     @slow
     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         UpperCamelCase_			  =						BioGptTokenizer.from_pretrained("microsoft/biogpt"	)
         UpperCamelCase_			  =						BioGptForCausalLM.from_pretrained("microsoft/biogpt"	)
         model.to(_lowercase	)

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

         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(_lowercase		,						_lowercase	)



def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_ = 1_0_0_0_0_0_0			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						[i - 1 for i in range(limit + 1			)]

    for i in range(2			,						limit + 1			):
        if phi[i] == i - 1:
            for j in range(2 * i			,						limit + 1			,						SCREAMING_SNAKE_CASE_			):
                phi[j] -= phi[j] // i

    return sum(phi[2 : limit + 1]			)


if __name__ == "__main__":
 print(solution())
