





'''simple docstring'''







from __future__ import annotations

from random import random
from typing import Generic, TypeVar

snake_case_     :       List[str]						=  TypeVar("KT")
snake_case_     :       int						=  TypeVar("VT")


class 					__a      (Generic[KT, VT]     ):

							def __init__(							self							:     Union[str, Any]       ,     __magic_name__							:     KT | str = "root"       ,     __magic_name__							:     VT | None = None   )					->       str:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  key
													UpperCAmelCase_			:      Dict     						=  value
													UpperCAmelCase_			:      list[Node[KT, VT]]     						=  []

							def __repr__(							self							:     Optional[Any]   )					->       str:







													"""simple docstring"""
													return F"""Node({self.key}: {self.value})"""



							@property
							def     UpperCAmelCase__	(							self							:     Any   )					->       int:







													"""simple docstring"""
													return len(self.forward   )


class 					__a      (Generic[KT, VT]     ):

							def __init__(							self							:     str       ,     __magic_name__							:     float = 0.5       ,     __magic_name__							:     int = 16   )					->       Any:







													"""simple docstring"""
													UpperCAmelCase_			:      Node[KT, VT]     						=  Node[KT, VT]()
													UpperCAmelCase_			:      Dict     						=  0
													UpperCAmelCase_			:      Tuple     						=  p
													UpperCAmelCase_			:      Tuple     						=  max_level

							def __str__(							self							:     Optional[Any]   )					->       str:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  list(self   )

													if len(__magic_name__   ) == 0:
																			return F"""SkipList(level={self.level})"""

													UpperCAmelCase_			:      Tuple     						=  max((len(str(__magic_name__   )   ) for item in items)       ,     default=4   )
													UpperCAmelCase_			:      Tuple     						=  max(__magic_name__       ,     4   ) + 4

													UpperCAmelCase_			:      Dict     						=  self.head
													UpperCAmelCase_			:      str     						=  []

													UpperCAmelCase_			:      int     						=  node.forward.copy()
													lines.append(F"""[{node.key}]""".ljust(__magic_name__       ,     '''-'''   ) + '''* ''' * len(__magic_name__   )   )
													lines.append(''' ''' * label_size + '''| ''' * len(__magic_name__   )   )

													while len(node.forward   ) != 0:
																			UpperCAmelCase_			:      str     						=  node.forward[0]

																			lines.append(
																			    F"""[{node.key}]""".ljust(__magic_name__       ,     '''-'''   )
																			    + ''' '''.join(str(n.key   ) if n.key == node.key else '''|''' for n in forwards   )   )
																			lines.append(''' ''' * label_size + '''| ''' * len(__magic_name__   )   )
																			UpperCAmelCase_			:      List[Any]     						=  node.forward

													lines.append('''None'''.ljust(__magic_name__   ) + '''* ''' * len(__magic_name__   )   )
													return F"""SkipList(level={self.level})\n""" + "\n".join(__magic_name__   )

							def __iter__(							self							:     Optional[Any]   )					->       Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  self.head

													while len(node.forward   ) != 0:
																			yield node.forward[0].key
																			UpperCAmelCase_			:      Any     						=  node.forward[0]

							def     UpperCAmelCase__	(							self							:     Any   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  1
													while random() < self.p and level < self.max_level:
																			level += 1

													return level

							def     UpperCAmelCase__	(							self							:     Optional[Any]       ,     __magic_name__							:     str   )					->       tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  []

													UpperCAmelCase_			:      str     						=  self.head

													for i in reversed(range(self.level   )   ):
																			# i < node.level - When node level is lesser than `i` decrement `i`.
																			# node.forward[i].key < key - Jumping to node with key value higher
																			#                             or equal to searched key would result
																			#                             in skipping searched key.
																			while i < node.level and node.forward[i].key < key:
																									UpperCAmelCase_			:      int     						=  node.forward[i]
																			# Each leftmost node (relative to searched node) will potentially have to
																			# be updated.
																			update_vector.append(__magic_name__   )

													update_vector.reverse()  # Note that we were inserting values in reverse order.

													# len(node.forward) != 0 - If current node doesn't contain any further
													#                          references then searched key is not present.
													# node.forward[0].key == key - Next node key should be equal to search key
													#                              if key is present.
													if len(node.forward   ) != 0 and node.forward[0].key == key:
																			return node.forward[0], update_vector
													else:
																			return None, update_vector

							def     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     KT   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  self._locate_node(__magic_name__   )

													if node is not None:
																			for i, update_node in enumerate(__magic_name__   ):
																									# Remove or replace all references to removed node.
																									if update_node.level > i and update_node.forward[i].key == key:
																															if node.level > i:
																																					UpperCAmelCase_			:      Optional[int]     						=  node.forward[i]
																															else:
																																					UpperCAmelCase_			:      List[Any]     						=  update_node.forward[:i]

							def     UpperCAmelCase__	(							self							:     str       ,     __magic_name__							:     KT       ,     __magic_name__							:     VT   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  self._locate_node(__magic_name__   )
													if node is not None:
																			UpperCAmelCase_			:      str     						=  value
													else:
																			UpperCAmelCase_			:      Optional[Any]     						=  self.random_level()

																			if level > self.level:
																									# After level increase we have to add additional nodes to head.
																									for _ in range(self.level - 1       ,     __magic_name__   ):
																															update_vector.append(self.head   )
																									UpperCAmelCase_			:      int     						=  level

																			UpperCAmelCase_			:      Optional[int]     						=  Node(__magic_name__       ,     __magic_name__   )

																			for i, update_node in enumerate(update_vector[:level]   ):
																									# Change references to pass through new node.
																									if update_node.level > i:
																															new_node.forward.append(update_node.forward[i]   )

																									if update_node.level < i + 1:
																															update_node.forward.append(__magic_name__   )
																									else:
																															UpperCAmelCase_			:      Tuple     						=  new_node



							def     UpperCAmelCase__	(							self							:     Any       ,     __magic_name__							:     VT   )					->       VT | None:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  self._locate_node(__magic_name__   )

													if node is not None:
																			return node.value

													return None





def        lowerCamelCase_      (  ) ->	List[Any]:
						UpperCAmelCase_			:      List[Any]     						=  SkipList()
						skip_list.insert('''Key1''',			3   )
						skip_list.insert('''Key2''',			12   )
						skip_list.insert('''Key3''',			41   )
						skip_list.insert('''Key4''',			-19   )

						UpperCAmelCase_			:      Dict     						=  skip_list.head
						UpperCAmelCase_			:      Any     						=  {}
						while node.level != 0:
												UpperCAmelCase_			:      List[str]     						=  node.forward[0]
												UpperCAmelCase_			:      Any     						=  node.value

						assert len(SCREAMING_SNAKE_CASE__   ) == 4
						assert all_values["Key1"] == 3
						assert all_values["Key2"] == 12
						assert all_values["Key3"] == 41
						assert all_values["Key4"] == -19





def        lowerCamelCase_      (  ) ->	Any:
						UpperCAmelCase_			:      List[str]     						=  SkipList()
						skip_list.insert('''Key1''',			10   )
						skip_list.insert('''Key1''',			12   )

						skip_list.insert('''Key5''',			7   )
						skip_list.insert('''Key7''',			10   )
						skip_list.insert('''Key10''',			5   )

						skip_list.insert('''Key7''',			7   )
						skip_list.insert('''Key5''',			5   )
						skip_list.insert('''Key10''',			10   )

						UpperCAmelCase_			:      Optional[Any]     						=  skip_list.head
						UpperCAmelCase_			:      Dict     						=  {}
						while node.level != 0:
												UpperCAmelCase_			:      List[Any]     						=  node.forward[0]
												UpperCAmelCase_			:      Dict     						=  node.value

						if len(SCREAMING_SNAKE_CASE__   ) != 4:
												print()
						assert len(SCREAMING_SNAKE_CASE__   ) == 4
						assert all_values["Key1"] == 12
						assert all_values["Key7"] == 7
						assert all_values["Key5"] == 5
						assert all_values["Key10"] == 10





def        lowerCamelCase_      (  ) ->	Union[str, Any]:
						UpperCAmelCase_			:      Any     						=  SkipList()
						assert skip_list.find('''Some key'''   ) is None





def        lowerCamelCase_      (  ) ->	Optional[Any]:
						UpperCAmelCase_			:      Optional[Any]     						=  SkipList()

						skip_list.insert('''Key2''',			20   )
						assert skip_list.find('''Key2'''   ) == 20

						skip_list.insert('''Some Key''',			10   )
						skip_list.insert('''Key2''',			8   )
						skip_list.insert('''V''',			13   )

						assert skip_list.find('''Y'''   ) is None
						assert skip_list.find('''Key2'''   ) == 8
						assert skip_list.find('''Some Key'''   ) == 10
						assert skip_list.find('''V'''   ) == 13





def        lowerCamelCase_      (  ) ->	Optional[int]:
						UpperCAmelCase_			:      int     						=  SkipList()
						skip_list.delete('''Some key'''   )

						assert len(skip_list.head.forward   ) == 0





def        lowerCamelCase_      (  ) ->	List[str]:
						UpperCAmelCase_			:      Optional[Any]     						=  SkipList()

						skip_list.insert('''Key1''',			12   )
						skip_list.insert('''V''',			13   )
						skip_list.insert('''X''',			14   )
						skip_list.insert('''Key2''',			15   )

						skip_list.delete('''V'''   )
						skip_list.delete('''Key2'''   )

						assert skip_list.find('''V'''   ) is None
						assert skip_list.find('''Key2'''   ) is None





def        lowerCamelCase_      (  ) ->	Optional[Any]:
						UpperCAmelCase_			:      int     						=  SkipList()

						skip_list.insert('''Key1''',			12   )
						skip_list.insert('''V''',			13   )
						skip_list.insert('''X''',			14   )
						skip_list.insert('''Key2''',			15   )

						skip_list.delete('''V'''   )
						assert skip_list.find('''V'''   ) is None
						assert skip_list.find('''X'''   ) == 14
						assert skip_list.find('''Key1'''   ) == 12
						assert skip_list.find('''Key2'''   ) == 15

						skip_list.delete('''X'''   )
						assert skip_list.find('''V'''   ) is None
						assert skip_list.find('''X'''   ) is None
						assert skip_list.find('''Key1'''   ) == 12
						assert skip_list.find('''Key2'''   ) == 15

						skip_list.delete('''Key1'''   )
						assert skip_list.find('''V'''   ) is None
						assert skip_list.find('''X'''   ) is None
						assert skip_list.find('''Key1'''   ) is None
						assert skip_list.find('''Key2'''   ) == 15

						skip_list.delete('''Key2'''   )
						assert skip_list.find('''V'''   ) is None
						assert skip_list.find('''X'''   ) is None
						assert skip_list.find('''Key1'''   ) is None
						assert skip_list.find('''Key2'''   ) is None





def        lowerCamelCase_      (  ) ->	Tuple:
						UpperCAmelCase_			:      Tuple     						=  SkipList()

						skip_list.insert('''Key1''',			12   )
						skip_list.insert('''V''',			13   )
						skip_list.insert('''X''',			142   )
						skip_list.insert('''Key2''',			15   )

						skip_list.delete('''X'''   )

						def traverse_keys(SCREAMING_SNAKE_CASE__      :			Optional[Any]   ):
												yield node.key
												for forward_node in node.forward:
																		yield from traverse_keys(SCREAMING_SNAKE_CASE__   )

						assert len(set(traverse_keys(skip_list.head   )   )   ) == 4





def        lowerCamelCase_      (  ) ->	Union[str, Any]:
						def is_sorted(SCREAMING_SNAKE_CASE__      :			Tuple   ):
												return all(next_item >= item for item, next_item in zip(SCREAMING_SNAKE_CASE__,			lst[1:]   )   )

						UpperCAmelCase_			:      Union[str, Any]     						=  SkipList()
						for i in range(10   ):
												skip_list.insert(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
						assert is_sorted(list(SCREAMING_SNAKE_CASE__   )   )
						skip_list.delete(5   )
						skip_list.delete(8   )
						skip_list.delete(2   )
						assert is_sorted(list(SCREAMING_SNAKE_CASE__   )   )
						skip_list.insert(-12,			-12   )
						skip_list.insert(77,			77   )
						assert is_sorted(list(SCREAMING_SNAKE_CASE__   )   )





def        lowerCamelCase_      (  ) ->	Any:
						for _ in range(100   ):
												# Repeat test 100 times due to the probabilistic nature of skip list
												# random values == random bugs
												test_insert()
												test_insert_overrides_existing_value()

												test_searching_empty_list_returns_none()
												test_search()

												test_deleting_item_from_empty_list_do_nothing()
												test_deleted_items_are_not_founded_by_find_method()
												test_delete_removes_only_given_key()
												test_delete_doesnt_leave_dead_nodes()

												test_iter_always_yields_sorted_values()





def        lowerCamelCase_      (  ) ->	Any:
						UpperCAmelCase_			:      Dict     						=  SkipList()
						skip_list.insert(2,			'''2'''   )
						skip_list.insert(4,			'''4'''   )
						skip_list.insert(6,			'''4'''   )
						skip_list.insert(4,			'''5'''   )
						skip_list.insert(8,			'''4'''   )
						skip_list.insert(9,			'''4'''   )

						skip_list.delete(4   )

						print(SCREAMING_SNAKE_CASE__   )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()












'''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 					__a      :

							def __init__(							self							:     List[str]       ,     __magic_name__							:     List[str]       ,     __magic_name__							:     str=13       ,     __magic_name__							:     Union[str, Any]=7       ,     __magic_name__							:     Union[str, Any]=True       ,     __magic_name__							:     Any=True       ,     __magic_name__							:     List[str]=False       ,     __magic_name__							:     Optional[int]=True       ,     __magic_name__							:     Dict=99       ,     __magic_name__							:     Tuple=32       ,     __magic_name__							:     int=5       ,     __magic_name__							:     Dict=4       ,     __magic_name__							:     Tuple=37       ,     __magic_name__							:     Optional[int]="gelu"       ,     __magic_name__							:     List[str]=0.1       ,     __magic_name__							:     Union[str, Any]=0.1       ,     __magic_name__							:     str=5_12       ,     __magic_name__							:     Union[str, Any]=16       ,     __magic_name__							:     int=2       ,     __magic_name__							:     List[Any]=0.0_2       ,     __magic_name__							:     Tuple=3       ,     __magic_name__							:     Union[str, Any]=4       ,     __magic_name__							:     Optional[int]=None       ,     )					->       str:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  parent
													UpperCAmelCase_			:      Union[str, Any]     						=  batch_size
													UpperCAmelCase_			:      List[Any]     						=  seq_length
													UpperCAmelCase_			:      str     						=  is_training
													UpperCAmelCase_			:      Any     						=  use_input_mask
													UpperCAmelCase_			:      List[str]     						=  use_token_type_ids
													UpperCAmelCase_			:      Union[str, Any]     						=  use_labels
													UpperCAmelCase_			:      Dict     						=  vocab_size
													UpperCAmelCase_			:      Optional[Any]     						=  hidden_size
													UpperCAmelCase_			:      Dict     						=  num_hidden_layers
													UpperCAmelCase_			:      List[Any]     						=  num_attention_heads
													UpperCAmelCase_			:      Optional[int]     						=  intermediate_size
													UpperCAmelCase_			:      Union[str, Any]     						=  hidden_act
													UpperCAmelCase_			:      str     						=  hidden_dropout_prob
													UpperCAmelCase_			:      Optional[Any]     						=  attention_probs_dropout_prob
													UpperCAmelCase_			:      Any     						=  max_position_embeddings
													UpperCAmelCase_			:      str     						=  type_vocab_size
													UpperCAmelCase_			:      Optional[Any]     						=  type_sequence_label_size
													UpperCAmelCase_			:      List[Any]     						=  initializer_range
													UpperCAmelCase_			:      int     						=  num_labels
													UpperCAmelCase_			:      Optional[int]     						=  num_choices
													UpperCAmelCase_			:      Tuple     						=  scope

							def     UpperCAmelCase__	(							self							:     Union[str, Any]   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size   )

													UpperCAmelCase_			:      Union[str, Any]     						=  None
													if self.use_input_mask:
																			UpperCAmelCase_			:      Optional[int]     						=  random_attention_mask([self.batch_size, self.seq_length]   )

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

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

													UpperCAmelCase_			:      int     						=  self.get_config()

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

							def     UpperCAmelCase__	(							self							:     Any   )					->       List[Any]:







													"""simple docstring"""
													return BioGptConfig(
													    vocab_size=self.vocab_size       ,     hidden_size=self.hidden_size       ,     num_hidden_layers=self.num_hidden_layers       ,     num_attention_heads=self.num_attention_heads       ,     intermediate_size=self.intermediate_size       ,     hidden_act=self.hidden_act       ,     hidden_dropout_prob=self.hidden_dropout_prob       ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,     max_position_embeddings=self.max_position_embeddings       ,     type_vocab_size=self.type_vocab_size       ,     is_decoder=__magic_name__       ,     initializer_range=self.initializer_range       ,     )

							def     UpperCAmelCase__	(							self							:     Union[str, Any]       ,     __magic_name__							:     List[str]       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     str       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     List[Any]       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Optional[int]   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  BioGptModel(config=__magic_name__   )
													model.to(__magic_name__   )
													model.eval()
													UpperCAmelCase_			:      Dict     						=  model(__magic_name__       ,     attention_mask=__magic_name__   )
													UpperCAmelCase_			:      Optional[Any]     						=  model(__magic_name__   )
													self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)   )

							def     UpperCAmelCase__	(							self							:     Optional[Any]       ,     __magic_name__							:     Dict       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     Any       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     List[str]       ,     __magic_name__							:     Optional[int]       ,     )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  BioGptForCausalLM(config=__magic_name__   )
													model.to(__magic_name__   )
													model.eval()
													UpperCAmelCase_			:      List[Any]     						=  model(__magic_name__       ,     attention_mask=__magic_name__       ,     token_type_ids=__magic_name__       ,     labels=__magic_name__   )
													self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.vocab_size)   )

							def     UpperCAmelCase__	(							self							:     str       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     List[str]       ,     __magic_name__							:     int       ,     __magic_name__							:     str       ,     *__magic_name__							:     Any   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  BioGptModel(config=__magic_name__   )
													model.to(__magic_name__   )
													model.eval()

													# create attention mask
													UpperCAmelCase_			:      Optional[Any]     						=  torch.ones(input_ids.shape       ,     dtype=torch.long       ,     device=__magic_name__   )
													UpperCAmelCase_			:      Any     						=  self.seq_length // 2
													UpperCAmelCase_			:      Tuple     						=  0

													# first forward pass
													UpperCAmelCase_						,       UpperCAmelCase_			:      Dict     						=  model(__magic_name__       ,     attention_mask=__magic_name__   ).to_tuple()

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

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

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

													# get two different outputs
													UpperCAmelCase_			:      Any     						=  model(__magic_name__       ,     attention_mask=__magic_name__   )['''last_hidden_state''']
													UpperCAmelCase_			:      int     						=  model(__magic_name__       ,     past_key_values=__magic_name__       ,     attention_mask=__magic_name__   )['''last_hidden_state''']

													# select random slice
													UpperCAmelCase_			:      Optional[int]     						=  ids_tensor((1,)       ,     output_from_past.shape[-1]   ).item()
													UpperCAmelCase_			:      Union[str, Any]     						=  output_from_no_past[:, -1, random_slice_idx].detach()
													UpperCAmelCase_			:      Dict     						=  output_from_past[:, 0, random_slice_idx].detach()

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

							def     UpperCAmelCase__	(							self							:     Dict       ,     __magic_name__							:     Dict       ,     __magic_name__							:     List[Any]       ,     __magic_name__							:     Dict       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     List[Any]       ,     *__magic_name__							:     str   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  BioGptModel(config=__magic_name__   ).to(__magic_name__   ).eval()

													UpperCAmelCase_			:      Optional[int]     						=  torch.ones(input_ids.shape       ,     dtype=torch.long       ,     device=__magic_name__   )

													# first forward pass
													UpperCAmelCase_			:      Union[str, Any]     						=  model(__magic_name__       ,     attention_mask=__magic_name__       ,     use_cache=__magic_name__   )

													UpperCAmelCase_						,       UpperCAmelCase_			:      int     						=  outputs.to_tuple()

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

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

													UpperCAmelCase_			:      Any     						=  model(__magic_name__       ,     attention_mask=__magic_name__   )['''last_hidden_state''']
													UpperCAmelCase_			:      Optional[Any]     						=  model(__magic_name__       ,     attention_mask=__magic_name__       ,     past_key_values=__magic_name__   )[
													    '''last_hidden_state'''
													]

													# select random slice
													UpperCAmelCase_			:      Tuple     						=  ids_tensor((1,)       ,     output_from_past.shape[-1]   ).item()
													UpperCAmelCase_			:      str     						=  output_from_no_past[:, -3:, random_slice_idx].detach()
													UpperCAmelCase_			:      Optional[int]     						=  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(__magic_name__       ,     __magic_name__       ,     atol=1E-3   )   )

							def     UpperCAmelCase__	(							self							:     List[str]       ,     __magic_name__							:     str       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     str       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     Optional[int]       ,     *__magic_name__							:     Any       ,     __magic_name__							:     List[Any]=False   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  BioGptForCausalLM(__magic_name__   )
													model.to(__magic_name__   )
													if gradient_checkpointing:
																			model.gradient_checkpointing_enable()

													UpperCAmelCase_			:      List[str]     						=  model(__magic_name__       ,     labels=__magic_name__   )
													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							:     List[str]       ,     __magic_name__							:     Optional[int]       ,     *__magic_name__							:     List[str]   )					->       str:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  BioGptModel(__magic_name__   )
													UpperCAmelCase_			:      Dict     						=  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     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     Dict       ,     __magic_name__							:     List[Any]       ,     __magic_name__							:     Union[str, Any]       ,     __magic_name__							:     Optional[Any]       ,     *__magic_name__							:     Any   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  self.num_labels
													UpperCAmelCase_			:      Any     						=  BioGptForTokenClassification(__magic_name__   )
													model.to(__magic_name__   )
													model.eval()
													UpperCAmelCase_			:      Any     						=  model(__magic_name__       ,     attention_mask=__magic_name__       ,     token_type_ids=__magic_name__   )
													self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.num_labels)   )



							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       str:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  self.prepare_config_and_inputs()
													(
													    (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       
													)			:      int     						=  config_and_inputs
													UpperCAmelCase_			:      Any     						=  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
													return config, inputs_dict





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

							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  BioGptModelTester(self   )
													UpperCAmelCase_			:      Optional[Any]     						=  ConfigTester(self       ,     config_class=__magic_name__       ,     hidden_size=37   )

							def     UpperCAmelCase__	(							self							:     Union[str, Any]   )					->       Tuple:







													"""simple docstring"""
													self.config_tester.run_common_tests()

							def     UpperCAmelCase__	(							self							:     List[str]   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  self.model_tester.prepare_config_and_inputs()
													for type in ["absolute", "relative_key", "relative_key_query"]:
																			UpperCAmelCase_			:      str     						=  type
																			self.model_tester.create_and_check_model(*__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_forward_and_backwards(*__magic_name__       ,     gradient_checkpointing=__magic_name__   )

							def     UpperCAmelCase__	(							self							:     str   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Dict   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_weight_initialization(*__magic_name__   )

							def     UpperCAmelCase__	(							self							:     List[Any]   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_for_token_classification(*__magic_name__   )

							@slow
							def     UpperCAmelCase__	(							self							:     List[Any]   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''   )
													model.to(__magic_name__   )
													UpperCAmelCase_			:      List[str]     						=  BioGptTokenizer.from_pretrained('''microsoft/biogpt'''   )

													UpperCAmelCase_			:      Tuple     						=  '''left'''

													# Define PAD Token = EOS Token = 50256
													UpperCAmelCase_			:      List[Any]     						=  tokenizer.eos_token
													UpperCAmelCase_			:      List[Any]     						=  model.config.eos_token_id

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

													UpperCAmelCase_			:      Optional[Any]     						=  tokenizer(__magic_name__       ,     return_tensors='''pt'''       ,     padding=__magic_name__   )
													UpperCAmelCase_			:      Optional[Any]     						=  inputs['''input_ids'''].to(__magic_name__   )

													UpperCAmelCase_			:      Any     						=  model.generate(
													    input_ids=__magic_name__       ,     attention_mask=inputs['''attention_mask'''].to(__magic_name__   )       ,     )

													UpperCAmelCase_			:      Union[str, Any]     						=  tokenizer(sentences[0]       ,     return_tensors='''pt'''   ).input_ids.to(__magic_name__   )
													UpperCAmelCase_			:      Tuple     						=  model.generate(input_ids=__magic_name__   )

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

													UpperCAmelCase_			:      int     						=  tokenizer.batch_decode(__magic_name__       ,     skip_special_tokens=__magic_name__   )
													UpperCAmelCase_			:      Dict     						=  tokenizer.decode(output_non_padded[0]       ,     skip_special_tokens=__magic_name__   )
													UpperCAmelCase_			:      Union[str, Any]     						=  tokenizer.decode(output_padded[0]       ,     skip_special_tokens=__magic_name__   )

													UpperCAmelCase_			:      Optional[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(__magic_name__       ,     __magic_name__   )
													self.assertListEqual(__magic_name__       ,     [non_padded_sentence, padded_sentence]   )

							@slow
							def     UpperCAmelCase__	(							self							:     str   )					->       Optional[Any]:







													"""simple docstring"""
													for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			UpperCAmelCase_			:      List[Any]     						=  BioGptModel.from_pretrained(__magic_name__   )
																			self.assertIsNotNone(__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       str:







													"""simple docstring"""
													UpperCAmelCase_						,       UpperCAmelCase_			:      str     						=  self.model_tester.prepare_config_and_inputs_for_common()
													UpperCAmelCase_			:      List[str]     						=  3
													UpperCAmelCase_			:      Tuple     						=  input_dict['''input_ids''']
													UpperCAmelCase_			:      Dict     						=  input_ids.ne(1   ).to(__magic_name__   )
													UpperCAmelCase_			:      List[str]     						=  ids_tensor([self.model_tester.batch_size]       ,     self.model_tester.type_sequence_label_size   )
													UpperCAmelCase_			:      Dict     						=  BioGptForSequenceClassification(__magic_name__   )
													model.to(__magic_name__   )
													model.eval()
													UpperCAmelCase_			:      int     						=  model(__magic_name__       ,     attention_mask=__magic_name__       ,     labels=__magic_name__   )
													self.assertEqual(result.logits.shape       ,     (self.model_tester.batch_size, self.model_tester.num_labels)   )



							def     UpperCAmelCase__	(							self							:     List[Any]   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[int]     						=  self.model_tester.prepare_config_and_inputs_for_common()
													UpperCAmelCase_			:      List[Any]     						=  3
													UpperCAmelCase_			:      Optional[int]     						=  '''multi_label_classification'''
													UpperCAmelCase_			:      int     						=  input_dict['''input_ids''']
													UpperCAmelCase_			:      str     						=  input_ids.ne(1   ).to(__magic_name__   )
													UpperCAmelCase_			:      Any     						=  ids_tensor(
													    [self.model_tester.batch_size, config.num_labels]       ,     self.model_tester.type_sequence_label_size   ).to(torch.float   )
													UpperCAmelCase_			:      Union[str, Any]     						=  BioGptForSequenceClassification(__magic_name__   )
													model.to(__magic_name__   )
													model.eval()
													UpperCAmelCase_			:      str     						=  model(__magic_name__       ,     attention_mask=__magic_name__       ,     labels=__magic_name__   )
													self.assertEqual(result.logits.shape       ,     (self.model_tester.batch_size, self.model_tester.num_labels)   )





@require_torch
class 					__a      (unittest.TestCase     ):

							@slow
							def     UpperCAmelCase__	(							self							:     List[Any]   )					->       str:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''   )
													UpperCAmelCase_			:      List[str]     						=  torch.tensor([[2, 48_05, 9, 6_56, 21]]   )
													UpperCAmelCase_			:      str     						=  model(__magic_name__   )[0]

													UpperCAmelCase_			:      Optional[int]     						=  4_23_84

													UpperCAmelCase_			:      Tuple     						=  torch.Size((1, 5, vocab_size)   )
													self.assertEqual(output.shape       ,     __magic_name__   )

													UpperCAmelCase_			:      List[Any]     						=  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]       ,     __magic_name__       ,     atol=1E-4   )   )



							@slow
							def     UpperCAmelCase__	(							self							:     Any   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  BioGptTokenizer.from_pretrained('''microsoft/biogpt'''   )
													UpperCAmelCase_			:      str     						=  BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''   )
													model.to(__magic_name__   )

													torch.manual_seed(0   )
													UpperCAmelCase_			:      Optional[Any]     						=  tokenizer('''COVID-19 is'''       ,     return_tensors='''pt'''   ).to(__magic_name__   )
													UpperCAmelCase_			:      Optional[int]     						=  model.generate(
													    **__magic_name__       ,     min_length=1_00       ,     max_length=10_24       ,     num_beams=5       ,     early_stopping=__magic_name__       ,     )
													UpperCAmelCase_			:      int     						=  tokenizer.decode(output_ids[0]       ,     skip_special_tokens=__magic_name__   )

													UpperCAmelCase_			:      Optional[Any]     						=  (
													    '''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(__magic_name__       ,     __magic_name__   )





