


'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers.models.distilbert.modeling_tf_distilbert import (
					    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFDistilBertForMaskedLM,
					    TFDistilBertForMultipleChoice,
					    TFDistilBertForQuestionAnswering,
					    TFDistilBertForSequenceClassification,
					    TFDistilBertForTokenClassification,
					    TFDistilBertModel,
					)
class 						a__					:





			'''simple docstring'''

			def __init__(  self							:			str			,    lowerCAmelCase_							:			str			,    )      ->		Optional[int]:
						__A=	parent
						__A=	13
						__A=	7
						__A=	True
						__A=	True
						__A=	False
						__A=	True
						__A=	99
						__A=	32
						__A=	2
						__A=	4
						__A=	37
						__A=	'gelu'
						__A=	0.1
						__A=	0.1
						__A=	512
						__A=	16
						__A=	2
						__A=	0.02
						__A=	3
						__A=	4
						__A=	None
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		str:
						__A=	ids_tensor([self.batch_size, self.seq_length]			,    self.vocab_size	)

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

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

						__A=	DistilBertConfig(
						    vocab_size=self.vocab_size			,    dim=self.hidden_size			,    n_layers=self.num_hidden_layers			,    n_heads=self.num_attention_heads			,    hidden_dim=self.intermediate_size			,    hidden_act=self.hidden_act			,    dropout=self.hidden_dropout_prob			,    attention_dropout=self.attention_probs_dropout_prob			,    max_position_embeddings=self.max_position_embeddings			,    initializer_range=self.initializer_range			,    )

						return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Any			,    lowerCAmelCase_							:			int			,    lowerCAmelCase_							:			int			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			Any			,    lowerCAmelCase_							:			Optional[Any]	)      ->		Any:
						__A=	TFDistilBertModel(config=lowerCAmelCase_	)
						__A=	{'input_ids': input_ids, 'attention_mask': input_mask}

						__A=	model(lowerCAmelCase_	)

						__A=	[input_ids, input_mask]

						__A=	model(lowerCAmelCase_	)

						self.parent.assertEqual(result.last_hidden_state.shape			,    (self.batch_size, self.seq_length, self.hidden_size)	)
			def 					lowerCAmelCase     (  self							:			Optional[int]			,    lowerCAmelCase_							:			Optional[Any]			,    lowerCAmelCase_							:			List[Any]			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			Tuple	)      ->		Optional[int]:
						__A=	TFDistilBertForMaskedLM(config=lowerCAmelCase_	)
						__A=	{'input_ids': input_ids, 'attention_mask': input_mask}
						__A=	model(lowerCAmelCase_	)
						self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.vocab_size)	)
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			Optional[Any]			,    lowerCAmelCase_							:			Tuple			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			List[str]	)      ->		int:
						__A=	TFDistilBertForQuestionAnswering(config=lowerCAmelCase_	)
						__A=	{
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						}
						__A=	model(lowerCAmelCase_	)
						self.parent.assertEqual(result.start_logits.shape			,    (self.batch_size, self.seq_length)	)
						self.parent.assertEqual(result.end_logits.shape			,    (self.batch_size, self.seq_length)	)
			def 					lowerCAmelCase     (  self							:			List[str]			,    lowerCAmelCase_							:			Any			,    lowerCAmelCase_							:			Optional[int]			,    lowerCAmelCase_							:			Tuple			,    lowerCAmelCase_							:			int			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			List[str]	)      ->		Union[str, Any]:
						__A=	self.num_labels
						__A=	TFDistilBertForSequenceClassification(lowerCAmelCase_	)
						__A=	{'input_ids': input_ids, 'attention_mask': input_mask}
						__A=	model(lowerCAmelCase_	)
						self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.num_labels)	)
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Any			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			List[Any]			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			int	)      ->		Optional[Any]:
						__A=	self.num_choices
						__A=	TFDistilBertForMultipleChoice(lowerCAmelCase_	)
						__A=	tf.tile(tf.expand_dims(lowerCAmelCase_			,    1	)			,    (1, self.num_choices, 1)	)
						__A=	tf.tile(tf.expand_dims(lowerCAmelCase_			,    1	)			,    (1, self.num_choices, 1)	)
						__A=	{
						    'input_ids': multiple_choice_inputs_ids,
						    'attention_mask': multiple_choice_input_mask,
						}
						__A=	model(lowerCAmelCase_	)
						self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.num_choices)	)
			def 					lowerCAmelCase     (  self							:			List[Any]			,    lowerCAmelCase_							:			Optional[Any]			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			Optional[int]			,    lowerCAmelCase_							:			List[str]			,    lowerCAmelCase_							:			Optional[Any]	)      ->		Dict:
						__A=	self.num_labels
						__A=	TFDistilBertForTokenClassification(lowerCAmelCase_	)
						__A=	{'input_ids': input_ids, 'attention_mask': input_mask}
						__A=	model(lowerCAmelCase_	)
						self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.num_labels)	)

			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		int:
						__A=	self.prepare_config_and_inputs()
						((__A),	(__A),	(__A),	(__A),	(__A),	(__A))=	config_and_inputs
						__A=	{'input_ids': input_ids, 'attention_mask': input_mask}
						return config, inputs_dict


@require_tf
class 						a__					(					a_      ,  a_      ,  unittest.TestCase   ):





			'''simple docstring'''

			A						:				Optional[Any]					      =			(
			    (
			        TFDistilBertModel,
			        TFDistilBertForMaskedLM,
			        TFDistilBertForQuestionAnswering,
			        TFDistilBertForSequenceClassification,
			        TFDistilBertForTokenClassification,
			        TFDistilBertForMultipleChoice,
			    )
			    if is_tf_available()
			    else None
			)
			A						:				Optional[int]					      =			(
			    {
			        '''feature-extraction''': TFDistilBertModel,
			        '''fill-mask''': TFDistilBertForMaskedLM,
			        '''question-answering''': TFDistilBertForQuestionAnswering,
			        '''text-classification''': TFDistilBertForSequenceClassification,
			        '''token-classification''': TFDistilBertForTokenClassification,
			        '''zero-shot''': TFDistilBertForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			A						:				str					      =			False
			A						:				List[Any]					      =			False
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		Optional[Any]:
						__A=	TFDistilBertModelTester(self	)
						__A=	ConfigTester(self			,    config_class=lowerCAmelCase_			,    dim=37	)
			def 					lowerCAmelCase     (  self							:			Dict	)      ->		Tuple:
						self.config_tester.run_common_tests()
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Optional[int]:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_model(*lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		Optional[Any]:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_masked_lm(*lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			str	)      ->		Any:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_question_answering(*lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		Optional[Any]:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_sequence_classification(*lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Optional[Any]:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_multiple_choice(*lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Any	)      ->		Optional[int]:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_token_classification(*lowerCAmelCase_	)

			@slow
			def 					lowerCAmelCase     (  self							:			int	)      ->		Tuple:
						for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]	):
									__A=	TFDistilBertModel.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)


@require_tf
class 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

			@slow
			def 					lowerCAmelCase     (  self							:			Any	)      ->		List[Any]:
						__A=	TFDistilBertModel.from_pretrained('distilbert-base-uncased'	)
						__A=	tf.constant([[0, 1, 2, 3, 4, 5]]	)
						__A=	model(lowerCAmelCase_	)[0]

						__A=	[1, 6, 768]
						self.assertEqual(output.shape			,    lowerCAmelCase_	)

						__A=	tf.constant(
						    [
						        [
						            [0.19_26_18_85, -0.13_73_29_55, 0.4_11_97_99],
						            [0.22_15_01_56, -0.07_42_26_61, 0.39_03_72_04],
						            [0.22_75_60_18, -0.0_89_64_14, 0.3_70_14_67],
						        ]
						    ]	)
						tf.debugging.assert_near(output[:, :3, :3]			,    lowerCAmelCase_			,    atol=1E-4	)







'''simple docstring'''





from __future__ import annotations

from functools import lru_cache
from math import ceil

UpperCAmelCase__  			=				1_0_0

UpperCAmelCase__  			=				set(range(3, NUM_PRIMES, 2))
primes.add(2)
UpperCAmelCase__  			=				42

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
					if prime not in primes:
										continue
					primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))






@lru_cache(maxsize=100 )
def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			if number_to_partition < 0:
						return set()
			elif number_to_partition == 0:
						return {1}

			__A=	set()
			__A=	42
			__A=	42

			for prime in primes:
						if prime > number_to_partition:
									continue
						for sub in partition(number_to_partition - prime ):
									ret.add(sub * prime )

			return ret






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int = 5000 ):






			"""simple docstring"""




			for number_to_partition in range(1,_SCREAMING_SNAKE_CASE ):
						if len(partition(_SCREAMING_SNAKE_CASE ) ) > number_unique_partitions:
									return number_to_partition
			return None


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



