





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	,   _a	,   _a=13	,   _a=7	,   _a=True	,   _a=True	,   _a=False	,   _a=True	,   _a=99	,   _a=32	,   _a=5	,   _a=4	,   _a=37	,   _a="gelu"	,   _a=0.1	,   _a=0.1	,   _a=512	,   _a=16	,   _a=2	,   _a=0.0_2	,   _a=3	,   _a=4	,   _a=None	,   )			->					Union[str, Any]:
							lowerCAmelCase_												=				parent
							lowerCAmelCase_												=				batch_size
							lowerCAmelCase_												=				seq_length
							lowerCAmelCase_												=				is_training
							lowerCAmelCase_												=				use_input_mask
							lowerCAmelCase_												=				use_token_type_ids
							lowerCAmelCase_												=				use_labels
							lowerCAmelCase_												=				vocab_size
							lowerCAmelCase_												=				hidden_size
							lowerCAmelCase_												=				num_hidden_layers
							lowerCAmelCase_												=				num_attention_heads
							lowerCAmelCase_												=				intermediate_size
							lowerCAmelCase_												=				hidden_act
							lowerCAmelCase_												=				hidden_dropout_prob
							lowerCAmelCase_												=				attention_probs_dropout_prob
							lowerCAmelCase_												=				max_position_embeddings
							lowerCAmelCase_												=				type_vocab_size
							lowerCAmelCase_												=				type_sequence_label_size
							lowerCAmelCase_												=				initializer_range
							lowerCAmelCase_												=				num_labels
							lowerCAmelCase_												=				num_choices
							lowerCAmelCase_												=				scope




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

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

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

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

							lowerCAmelCase_												=				self.get_config()

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




					def 					__a     (       self						)			->					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=snake_case__	,   initializer_range=self.initializer_range	,   )




					def 					__a     (       self	,   _a	,   _a	,   _a	,   _a	,   _a	,   _a	,   _a						)			->					List[Any]:
							lowerCAmelCase_												=				BioGptModel(config=snake_case__						)
							model.to(snake_case__						)
							model.eval()
							lowerCAmelCase_												=				model(snake_case__	,   attention_mask=snake_case__						)
							lowerCAmelCase_												=				model(snake_case__						)
							self.parent.assertEqual(result.last_hidden_state.shape	,   (self.batch_size, self.seq_length, self.hidden_size)						)




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




					def 					__a     (       self	,   _a	,   _a	,   _a	,   _a	,   _a	,   *_a						)			->					Optional[int]:
							lowerCAmelCase_												=				BioGptModel(config=snake_case__						)
							model.to(snake_case__						)
							model.eval()

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

							# first forward pass
							lowerCAmelCase_												=				model(snake_case__	,   attention_mask=snake_case__						).to_tuple()

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

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

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

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

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

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




					def 					__a     (       self	,   _a	,   _a	,   _a	,   _a	,   _a	,   *_a						)			->					Tuple:
							lowerCAmelCase_												=				BioGptModel(config=snake_case__						).to(snake_case__						).eval()

							lowerCAmelCase_												=				torch.ones(input_ids.shape	,   dtype=torch.long	,   device=snake_case__						)

							# first forward pass
							lowerCAmelCase_												=				model(snake_case__	,   attention_mask=snake_case__	,   use_cache=snake_case__						)

							lowerCAmelCase_												=				outputs.to_tuple()

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

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

							lowerCAmelCase_												=				model(snake_case__	,   attention_mask=snake_case__						)['last_hidden_state']
							lowerCAmelCase_												=				model(snake_case__	,   attention_mask=snake_case__	,   past_key_values=snake_case__						)[
							    'last_hidden_state'
							]

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




					def 					__a     (       self	,   _a	,   _a	,   _a	,   _a	,   _a	,   *_a	,   _a=False						)			->					str:
							lowerCAmelCase_												=				BioGptForCausalLM(snake_case__						)
							model.to(snake_case__						)
							if gradient_checkpointing:
									model.gradient_checkpointing_enable()

							lowerCAmelCase_												=				model(snake_case__	,   labels=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 					__a     (       self	,   _a	,   *_a						)			->					Dict:
							lowerCAmelCase_												=				BioGptModel(snake_case__						)
							lowerCAmelCase_												=				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 					__a     (       self	,   _a	,   _a	,   _a	,   _a	,   _a	,   *_a						)			->					Dict:
							lowerCAmelCase_												=				self.num_labels
							lowerCAmelCase_												=				BioGptForTokenClassification(snake_case__						)
							model.to(snake_case__						)
							model.eval()
							lowerCAmelCase_												=				model(snake_case__	,   attention_mask=snake_case__	,   token_type_ids=snake_case__						)
							self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.seq_length, self.num_labels)						)




					def 					__a     (       self						)			->					int:
							lowerCAmelCase_												=				self.prepare_config_and_inputs()
							(
							    lowerCAmelCase_    
							)												=				config_and_inputs
							lowerCAmelCase_												=				{'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict





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




					def 					__a     (       self						)			->					int:
							lowerCAmelCase_												=				BioGptModelTester(self						)
							lowerCAmelCase_												=				ConfigTester(self	,   config_class=snake_case__	,   hidden_size=37						)




					def 					__a     (       self						)			->					Union[str, Any]:
							self.config_tester.run_common_tests()




					def 					__a     (       self						)			->					Optional[int]:
							lowerCAmelCase_												=				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*snake_case__						)




					def 					__a     (       self						)			->					Optional[Any]:
							lowerCAmelCase_												=				self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
									lowerCAmelCase_												=				type
									self.model_tester.create_and_check_model(*snake_case__						)




					def 					__a     (       self						)			->					List[Any]:
							lowerCAmelCase_												=				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_model_attention_mask_past(*snake_case__						)




					def 					__a     (       self						)			->					Union[str, Any]:
							lowerCAmelCase_												=				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_forward_and_backwards(*snake_case__	,   gradient_checkpointing=snake_case__						)




					def 					__a     (       self						)			->					str:
							lowerCAmelCase_												=				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_model_past_large_inputs(*snake_case__						)




					def 					__a     (       self						)			->					str:
							lowerCAmelCase_												=				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_weight_initialization(*snake_case__						)




					def 					__a     (       self						)			->					str:
							lowerCAmelCase_												=				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_for_token_classification(*snake_case__						)




					@slow
					def 					__a     (       self						)			->					Optional[int]:
							lowerCAmelCase_												=				BioGptForCausalLM.from_pretrained("microsoft/biogpt"						)
							model.to(snake_case__						)
							lowerCAmelCase_												=				BioGptTokenizer.from_pretrained("microsoft/biogpt"						)

							lowerCAmelCase_												=				'left'

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

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

							lowerCAmelCase_												=				tokenizer(snake_case__	,   return_tensors="pt"	,   padding=snake_case__						)
							lowerCAmelCase_												=				inputs['input_ids'].to(snake_case__						)

							lowerCAmelCase_												=				model.generate(
							    input_ids=snake_case__	,   attention_mask=inputs["attention_mask"].to(snake_case__						)	,   )

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

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

							lowerCAmelCase_												=				tokenizer.batch_decode(snake_case__	,   skip_special_tokens=snake_case__						)
							lowerCAmelCase_												=				tokenizer.decode(output_non_padded[0]	,   skip_special_tokens=snake_case__						)
							lowerCAmelCase_												=				tokenizer.decode(output_padded[0]	,   skip_special_tokens=snake_case__						)

							lowerCAmelCase_												=				[
							    '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(snake_case__	,   snake_case__						)
							self.assertListEqual(snake_case__	,   [non_padded_sentence, padded_sentence]						)




					@slow
					def 					__a     (       self						)			->					Dict:
							for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCAmelCase_												=				BioGptModel.from_pretrained(snake_case__						)
									self.assertIsNotNone(snake_case__						)




					def 					__a     (       self						)			->					int:
							lowerCAmelCase_												=				self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase_												=				3
							lowerCAmelCase_												=				input_dict['input_ids']
							lowerCAmelCase_												=				input_ids.ne(1						).to(snake_case__						)
							lowerCAmelCase_												=				ids_tensor([self.model_tester.batch_size]	,   self.model_tester.type_sequence_label_size						)
							lowerCAmelCase_												=				BioGptForSequenceClassification(snake_case__						)
							model.to(snake_case__						)
							model.eval()
							lowerCAmelCase_												=				model(snake_case__	,   attention_mask=snake_case__	,   labels=snake_case__						)
							self.assertEqual(result.logits.shape	,   (self.model_tester.batch_size, self.model_tester.num_labels)						)




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





@require_torch
class 	__magic_name__			(unittest.TestCase ):




					@slow
					def 					__a     (       self						)			->					List[Any]:
							lowerCAmelCase_												=				BioGptForCausalLM.from_pretrained("microsoft/biogpt"						)
							lowerCAmelCase_												=				torch.tensor([[2, 4805, 9, 656, 21]]						)
							lowerCAmelCase_												=				model(snake_case__						)[0]

							lowerCAmelCase_												=				42384

							lowerCAmelCase_												=				torch.Size((1, 5, vocab_size)						)
							self.assertEqual(output.shape	,   snake_case__						)

							lowerCAmelCase_												=				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]	,   snake_case__	,   atol=1E-4						)						)




					@slow
					def 					__a     (       self						)			->					Dict:
							lowerCAmelCase_												=				BioGptTokenizer.from_pretrained("microsoft/biogpt"						)
							lowerCAmelCase_												=				BioGptForCausalLM.from_pretrained("microsoft/biogpt"						)
							model.to(snake_case__						)

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

							lowerCAmelCase_												=				(
							    '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(snake_case__	,   snake_case__						)







def        __UpperCAmelCase						(							lowerCamelCase_ :     str  )       ->  bool:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      0
			for ch in input_str:
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      ord(lowerCamelCase_  )
						SCREAMING_SNAKE_CASE_   : Tuple					  =      pow(2  ,							lowerCamelCase_  )

						# If we already turned on bit for current character's unicode
						if bitmap >> ch_unicode & 1 == 1:
									return False
						bitmap |= ch_bit_index_on
			return True


if __name__ == "__main__":
						import doctest

						doctest.testmod()



