


from __future__ import annotations







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	tuple[float, list[float]]:

		'''simple docstring'''







		__lowercase=			list(range(len(lowercase__	)	)	)
		__lowercase=			[v / w for v, w in zip(lowercase__   , lowercase__	)]
		index.sort(key=lambda lowercase__	: ratio[i]   , reverse=lowercase__	)

		__lowercase=			0
		__lowercase=			[0] * len(lowercase__	)
		for i in index:
				if weight[i] <= capacity:
						__lowercase=			1
						max_value += value[i]
						capacity -= weight[i]
				else:
						__lowercase=			capacity / weight[i]
						max_value += value[i] * capacity / weight[i]
						break

		return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()










import unittest

from transformers import 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
	    OpenAIGPTConfig,
	    OpenAIGPTDoubleHeadsModel,
	    OpenAIGPTForSequenceClassification,
	    OpenAIGPTLMHeadModel,
	    OpenAIGPTModel,
	)





class       A    :


							def __init__(self      ,				lowerCAmelCase      ,				lowerCAmelCase=1_3      ,				lowerCAmelCase=7      ,				lowerCAmelCase=True      ,				lowerCAmelCase=True      ,				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=0.02      ,				lowerCAmelCase=3      ,				lowerCAmelCase=4      ,				lowerCAmelCase=None      ,				):
									__lowercase=			parent
									__lowercase=			batch_size
									__lowercase=			seq_length
									__lowercase=			is_training
									__lowercase=			use_token_type_ids
									__lowercase=			use_labels
									__lowercase=			vocab_size
									__lowercase=			hidden_size
									__lowercase=			num_hidden_layers
									__lowercase=			num_attention_heads
									__lowercase=			intermediate_size
									__lowercase=			hidden_act
									__lowercase=			hidden_dropout_prob
									__lowercase=			attention_probs_dropout_prob
									__lowercase=			max_position_embeddings
									__lowercase=			type_vocab_size
									__lowercase=			type_sequence_label_size
									__lowercase=			initializer_range
									__lowercase=			num_labels
									__lowercase=			num_choices
									__lowercase=			scope
									__lowercase=			self.vocab_size - 1


							def 			_A       (self					):
									__lowercase=			ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size					)

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

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

									__lowercase=			OpenAIGPTConfig(
									    vocab_size=self.vocab_size      ,				n_embd=self.hidden_size      ,				n_layer=self.num_hidden_layers      ,				n_head=self.num_attention_heads      ,				n_positions=self.max_position_embeddings      ,				pad_token_id=self.pad_token_id      ,				)

									__lowercase=			ids_tensor([self.num_hidden_layers, self.num_attention_heads]      ,				2					)

									return (
									    config,
									    input_ids,
									    head_mask,
									    token_type_ids,
									    sequence_labels,
									    token_labels,
									    choice_labels,
									)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				*lowerCAmelCase					):
									__lowercase=			OpenAIGPTModel(config=lowerCAmelCase					)
									model.to(lowerCAmelCase					)
									model.eval()

									__lowercase=			model(lowerCAmelCase      ,				token_type_ids=lowerCAmelCase      ,				head_mask=lowerCAmelCase					)
									__lowercase=			model(lowerCAmelCase      ,				token_type_ids=lowerCAmelCase					)
									__lowercase=			model(lowerCAmelCase					)

									self.parent.assertEqual(result.last_hidden_state.shape      ,				(self.batch_size, self.seq_length, self.hidden_size)					)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				*lowerCAmelCase					):
									__lowercase=			OpenAIGPTLMHeadModel(lowerCAmelCase					)
									model.to(lowerCAmelCase					)
									model.eval()

									__lowercase=			model(lowerCAmelCase      ,				token_type_ids=lowerCAmelCase      ,				labels=lowerCAmelCase					)
									self.parent.assertEqual(result.loss.shape      ,				()					)
									self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.seq_length, self.vocab_size)					)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				*lowerCAmelCase					):
									__lowercase=			OpenAIGPTDoubleHeadsModel(lowerCAmelCase					)
									model.to(lowerCAmelCase					)
									model.eval()

									__lowercase=			model(lowerCAmelCase      ,				token_type_ids=lowerCAmelCase      ,				labels=lowerCAmelCase					)
									self.parent.assertEqual(result.loss.shape      ,				()					)
									self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.seq_length, self.vocab_size)					)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				*lowerCAmelCase					):
									__lowercase=			self.num_labels
									__lowercase=			OpenAIGPTForSequenceClassification(lowerCAmelCase					)
									model.to(lowerCAmelCase					)
									model.eval()

									__lowercase=			ids_tensor([self.batch_size]      ,				self.type_sequence_label_size					)
									__lowercase=			model(lowerCAmelCase      ,				token_type_ids=lowerCAmelCase      ,				labels=lowerCAmelCase					)
									self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_labels)					)







							def 			_A       (self					):
									__lowercase=			self.prepare_config_and_inputs()
									(
									    (
									    __lowercase
									),							(
									    __lowercase
									),							(
									    __lowercase
									),							(
									    __lowercase
									),							(
									    __lowercase
									),							(
									    __lowercase
									),							(
									    __lowercase
									),							
									)=			config_and_inputs
									__lowercase=			{
									    'input_ids': input_ids,
									    'token_type_ids': token_type_ids,
									    'head_mask': head_mask,
									}

									return config, inputs_dict





@require_torch
class       A    (   A_					,				A_					,				A_					,				unittest.TestCase ):
							UpperCamelCase_   :							Optional[Any]       =(
							    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
							    if is_torch_available()
							    else ()
							)
							UpperCamelCase_   :							Tuple       =(
							    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
							)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
							UpperCamelCase_   :							List[str]       =(
							    {
							        '''feature-extraction''': OpenAIGPTModel,
							        '''text-classification''': OpenAIGPTForSequenceClassification,
							        '''text-generation''': OpenAIGPTLMHeadModel,
							        '''zero-shot''': OpenAIGPTForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
											# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
											# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
											# tiny config could not be created.
											return True

									return False


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase=False					):
									__lowercase=			super()._prepare_for_class(lowerCAmelCase      ,				lowerCAmelCase      ,				return_labels=lowerCAmelCase					)

									if return_labels:
											if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
													__lowercase=			torch.zeros(
													    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)      ,				dtype=torch.long      ,				device=lowerCAmelCase      ,				)
													__lowercase=			inputs_dict['labels']
													__lowercase=			inputs_dict['labels']
													__lowercase=			torch.zeros(
													    (self.model_tester.batch_size, self.model_tester.num_choices)      ,				dtype=torch.long      ,				device=lowerCAmelCase      ,				)
													__lowercase=			torch.zeros(
													    self.model_tester.batch_size      ,				dtype=torch.long      ,				device=lowerCAmelCase					)
									return inputs_dict


							def 			_A       (self					):
									__lowercase=			OpenAIGPTModelTester(self					)
									__lowercase=			ConfigTester(self      ,				config_class=lowerCAmelCase      ,				n_embd=3_7					)


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


							def 			_A       (self					):
									__lowercase=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_openai_gpt_model(*lowerCAmelCase					)


							def 			_A       (self					):
									__lowercase=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_lm_head_model(*lowerCAmelCase					)


							def 			_A       (self					):
									__lowercase=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_double_lm_head_model(*lowerCAmelCase					)


							def 			_A       (self					):
									__lowercase=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowerCAmelCase					)







							@slow
							def 			_A       (self					):
									for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowercase=			OpenAIGPTModel.from_pretrained(lowerCAmelCase					)
											self.assertIsNotNone(lowerCAmelCase					)





@require_torch
class       A    (   unittest.TestCase ):


							@slow
							def 			_A       (self					):
									__lowercase=			OpenAIGPTLMHeadModel.from_pretrained('openai-gpt'					)
									model.to(lowerCAmelCase					)
									__lowercase=			torch.tensor([[4_8_1, 4_7_3_5, 5_4_4]]      ,				dtype=torch.long      ,				device=lowerCAmelCase					)  # the president is
									__lowercase=			[
									    4_8_1,
									    4_7_3_5,
									    5_4_4,
									    2_4_6,
									    9_6_3,
									    8_7_0,
									    7_6_2,
									    2_3_9,
									    2_4_4,
									    4_0_4_7_7,
									    2_4_4,
									    2_4_9,
									    7_1_9,
									    8_8_1,
									    4_8_7,
									    5_4_4,
									    2_4_0,
									    2_4_4,
									    6_0_3,
									    4_8_1,
									]  # the president is a very good man. " \n " i\'m sure he is, " said the

									__lowercase=			model.generate(lowerCAmelCase      ,				do_sample=lowerCAmelCase					)
									self.assertListEqual(output_ids[0].tolist()      ,				lowerCAmelCase					)






