



"""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 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__=1_3				,       lowerCamelCase__=7				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=False				,       lowerCamelCase__=True				,       lowerCamelCase__=9_9				,       lowerCamelCase__=3_2				,       lowerCamelCase__=5				,       lowerCamelCase__=4				,       lowerCamelCase__=3_7				,       lowerCamelCase__="gelu"				,       lowerCamelCase__=0.1				,       lowerCamelCase__=0.1				,       lowerCamelCase__=5_1_2				,       lowerCamelCase__=1_6				,       lowerCamelCase__=2				,       lowerCamelCase__=0.0_2				,       lowerCamelCase__=3				,       lowerCamelCase__=4				,       lowerCamelCase__=None				,       ):
									_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 			snake_case__      (    self   ):
									_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 			snake_case__      (    self   ):
									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=lowerCamelCase__				,       initializer_range=self.initializer_range				,       )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				BioGptModel(config=lowerCamelCase__   )
									model.to(lowerCamelCase__   )
									model.eval()
									_lowerCamelCase        =				model(lowerCamelCase__				,       attention_mask=lowerCamelCase__   )
									_lowerCamelCase        =				model(lowerCamelCase__   )
									self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       ):
									_lowerCamelCase        =				BioGptForCausalLM(config=lowerCamelCase__   )
									model.to(lowerCamelCase__   )
									model.eval()
									_lowerCamelCase        =				model(lowerCamelCase__				,       attention_mask=lowerCamelCase__				,       token_type_ids=lowerCamelCase__				,       labels=lowerCamelCase__   )
									self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       *lowerCamelCase__   ):
									_lowerCamelCase        =				BioGptModel(config=lowerCamelCase__   )
									model.to(lowerCamelCase__   )
									model.eval()

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

									# first forward pass
									_lowerCamelCase  ,     _lowerCamelCase        =				model(lowerCamelCase__				,       attention_mask=lowerCamelCase__   ).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,)				,       lowerCamelCase__   ).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=lowerCamelCase__   )]				,       dim=1				,       )

									# get two different outputs
									_lowerCamelCase        =				model(lowerCamelCase__				,       attention_mask=lowerCamelCase__   )['''last_hidden_state''']
									_lowerCamelCase        =				model(lowerCamelCase__				,       past_key_values=lowerCamelCase__				,       attention_mask=lowerCamelCase__   )['''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(lowerCamelCase__				,       lowerCamelCase__				,       atol=1e-3   )   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       *lowerCamelCase__   ):
									_lowerCamelCase        =				BioGptModel(config=lowerCamelCase__   ).to(lowerCamelCase__   ).eval()

									_lowerCamelCase        =				torch.ones(input_ids.shape				,       dtype=torch.long				,       device=lowerCamelCase__   )

									# first forward pass
									_lowerCamelCase        =				model(lowerCamelCase__				,       attention_mask=lowerCamelCase__				,       use_cache=lowerCamelCase__   )

									_lowerCamelCase  ,     _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(lowerCamelCase__				,       attention_mask=lowerCamelCase__   )['''last_hidden_state''']
									_lowerCamelCase        =				model(lowerCamelCase__				,       attention_mask=lowerCamelCase__				,       past_key_values=lowerCamelCase__   )[
									    '''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(lowerCamelCase__				,       lowerCamelCase__				,       atol=1e-3   )   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       *lowerCamelCase__				,       lowerCamelCase__=False   ):
									_lowerCamelCase        =				BioGptForCausalLM(lowerCamelCase__   )
									model.to(lowerCamelCase__   )
									if gradient_checkpointing:
													model.gradient_checkpointing_enable()

									_lowerCamelCase        =				model(lowerCamelCase__				,       labels=lowerCamelCase__   )
									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				,       lowerCamelCase__				,       *lowerCamelCase__   ):
									_lowerCamelCase        =				BioGptModel(lowerCamelCase__   )
									_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 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       *lowerCamelCase__   ):
									_lowerCamelCase        =				self.num_labels
									_lowerCamelCase        =				BioGptForTokenClassification(lowerCamelCase__   )
									model.to(lowerCamelCase__   )
									model.eval()
									_lowerCamelCase        =				model(lowerCamelCase__				,       attention_mask=lowerCamelCase__				,       token_type_ids=lowerCamelCase__   )
									self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.num_labels)   )





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


@require_torch
class 							lowerCamelCase_(   A__,				A__,				A__,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       Optional[int]										= (
					    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
					    if is_torch_available()
					    else ()
					)
					lowercase__   :       List[str]										= (BioGptForCausalLM,) if is_torch_available() else ()
					lowercase__   :       Union[str, Any]										= (
					    {
					        'feature-extraction': BioGptModel,
					        'text-classification': BioGptForSequenceClassification,
					        'text-generation': BioGptForCausalLM,
					        'token-classification': BioGptForTokenClassification,
					        'zero-shot': BioGptForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					lowercase__   :       Dict										= False

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				BioGptModelTester(self   )
									_lowerCamelCase        =				ConfigTester(self				,       config_class=lowerCamelCase__				,       hidden_size=3_7   )

					def 			snake_case__      (    self   ):
									self.config_tester.run_common_tests()

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_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(*lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_forward_and_backwards(*lowerCamelCase__				,       gradient_checkpointing=lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_biogpt_weight_initialization(*lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_biogpt_for_token_classification(*lowerCamelCase__   )

					@slow
					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''   )
									model.to(lowerCamelCase__   )
									_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(lowerCamelCase__				,       return_tensors='''pt'''				,       padding=lowerCamelCase__   )
									_lowerCamelCase        =				inputs['''input_ids'''].to(lowerCamelCase__   )

									_lowerCamelCase        =				model.generate(
									    input_ids=lowerCamelCase__				,       attention_mask=inputs['''attention_mask'''].to(lowerCamelCase__   )				,       )

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

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

									_lowerCamelCase        =				tokenizer.batch_decode(lowerCamelCase__				,       skip_special_tokens=lowerCamelCase__   )
									_lowerCamelCase        =				tokenizer.decode(output_non_padded[0]				,       skip_special_tokens=lowerCamelCase__   )
									_lowerCamelCase        =				tokenizer.decode(output_padded[0]				,       skip_special_tokens=lowerCamelCase__   )

									_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(lowerCamelCase__				,       lowerCamelCase__   )
									self.assertListEqual(lowerCamelCase__				,       [non_padded_sentence, padded_sentence]   )

					@slow
					def 			snake_case__      (    self   ):
									for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_lowerCamelCase        =				BioGptModel.from_pretrained(lowerCamelCase__   )
													self.assertIsNotNone(lowerCamelCase__   )

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





					def 			snake_case__      (    self   ):
									_lowerCamelCase  ,     _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(lowerCamelCase__   )
									_lowerCamelCase        =				ids_tensor(
									    [self.model_tester.batch_size, config.num_labels]				,       self.model_tester.type_sequence_label_size   ).to(torch.float   )
									_lowerCamelCase        =				BioGptForSequenceClassification(lowerCamelCase__   )
									model.to(lowerCamelCase__   )
									model.eval()
									_lowerCamelCase        =				model(lowerCamelCase__				,       attention_mask=lowerCamelCase__				,       labels=lowerCamelCase__   )
									self.assertEqual(result.logits.shape				,       (self.model_tester.batch_size, self.model_tester.num_labels)   )


@require_torch
class 							lowerCamelCase_(   unittest.TestCase    ):





					'''simple docstring'''



					@slow
					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''   )
									_lowerCamelCase        =				torch.tensor([[2, 4_8_0_5, 9, 6_5_6, 2_1]]   )
									_lowerCamelCase        =				model(lowerCamelCase__   )[0]

									_lowerCamelCase        =				4_2_3_8_4

									_lowerCamelCase        =				torch.Size((1, 5, vocab_size)   )
									self.assertEqual(output.shape				,       lowerCamelCase__   )

									_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]				,       lowerCamelCase__				,       atol=1e-4   )   )





					@slow
					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				BioGptTokenizer.from_pretrained('''microsoft/biogpt'''   )
									_lowerCamelCase        =				BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''   )
									model.to(lowerCamelCase__   )

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

									_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(lowerCamelCase__				,       lowerCamelCase__   )





"""simple docstring"""
from __future__ import annotations

from typing import Any



def        lowerCAmelCase_(   lowercase_     :							list[Any]							)	->       None:
				create_state_space_tree(lowercase_						,   []						,   0							)



def        lowerCAmelCase_(   lowercase_     :							list[Any]						,   lowercase_     :							list[Any]						,   lowercase_     :							int							)	->       None:

				if index == len(lowercase_							):
								print(lowercase_							)
								return

				create_state_space_tree(lowercase_						,   lowercase_						,   index + 1							)
				current_subsequence.append(sequence[index]							)
				create_state_space_tree(lowercase_						,   lowercase_						,   index + 1							)
				current_subsequence.pop()


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						list[Any]    							=      [3, 1, 2, 4]
			generate_all_subsequences(seq)

			seq.clear()
			seq.extend(['''A''', '''B''', '''C'''])
			generate_all_subsequences(seq)
