



'''simple docstring'''






import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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 (
         MODEL_FOR_PRETRAINING_MAPPING,
         AlbertForMaskedLM,
         AlbertForMultipleChoice,
         AlbertForPreTraining,
         AlbertForQuestionAnswering,
         AlbertForSequenceClassification,
         AlbertForTokenClassification,
         AlbertModel,
     )
     from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST

class 							a__:







    def __init__(   self				:						str		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						Dict=13		,    __snake_case				:						Dict=7		,    __snake_case				:						List[str]=True		,    __snake_case				:						Tuple=True		,    __snake_case				:						Union[str, Any]=True		,    __snake_case				:						Tuple=True		,    __snake_case				:						Union[str, Any]=99		,    __snake_case				:						Tuple=16		,    __snake_case				:						str=36		,    __snake_case				:						List[str]=6		,    __snake_case				:						Optional[int]=6		,    __snake_case				:						Optional[int]=6		,    __snake_case				:						Tuple=37		,    __snake_case				:						Dict="gelu"		,    __snake_case				:						Tuple=0.1		,    __snake_case				:						Any=0.1		,    __snake_case				:						str=5_12		,    __snake_case				:						List[Any]=16		,    __snake_case				:						Tuple=2		,    __snake_case				:						Dict=0.02		,    __snake_case				:						int=3		,    __snake_case				:						str=4		,    __snake_case				:						Union[str, Any]=None		,    ):
      a		:      List[str]       =  parent
      a		:      Tuple       =  batch_size
      a		:      Optional[Any]       =  seq_length
      a		:      Optional[Any]       =  is_training
      a		:      str       =  use_input_mask
      a		:      Optional[Any]       =  use_token_type_ids
      a		:      str       =  use_labels
      a		:      List[str]       =  vocab_size
      a		:      str       =  embedding_size
      a		:      List[str]       =  hidden_size
      a		:      Tuple       =  num_hidden_layers
      a		:      Optional[Any]       =  num_hidden_groups
      a		:      Dict       =  num_attention_heads
      a		:      Optional[Any]       =  intermediate_size
      a		:      str       =  hidden_act
      a		:      List[Any]       =  hidden_dropout_prob
      a		:      Tuple       =  attention_probs_dropout_prob
      a		:      Any       =  max_position_embeddings
      a		:      Optional[Any]       =  type_vocab_size
      a		:      List[Any]       =  type_sequence_label_size
      a		:      int       =  initializer_range
      a		:      Tuple       =  num_labels
      a		:      List[Any]       =  num_choices
      a		:      Optional[Any]       =  scope







    def     lowercase_ (   self				:						str			):
      a		:      Union[str, Any]       =  ids_tensor([self.batch_size, self.seq_length]		,    self.vocab_size			)

      a		:      Any       =  None
      if self.use_input_mask:
        a		:      List[str]       =  random_attention_mask([self.batch_size, self.seq_length]			)

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

      a		:      Dict       =  None
      a		:      Optional[int]       =  None
      a		:      int       =  None
      if self.use_labels:
        a		:      Tuple       =  ids_tensor([self.batch_size]		,    self.type_sequence_label_size			)
        a		:      Any       =  ids_tensor([self.batch_size, self.seq_length]		,    self.num_labels			)
        a		:      Any       =  ids_tensor([self.batch_size]		,    self.num_choices			)

      a		:      Optional[int]       =  self.get_config()

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







    def     lowercase_ (   self				:						List[Any]			):
      return AlbertConfig(
          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		,    initializer_range=self.initializer_range		,    num_hidden_groups=self.num_hidden_groups		,    )







    def     lowercase_ (   self				:						str		,    __snake_case				:						List[Any]		,    __snake_case				:						Optional[int]		,    __snake_case				:						int		,    __snake_case				:						Any		,    __snake_case				:						List[Any]		,    __snake_case				:						List[Any]		,    __snake_case				:						Any			):
      a		:      Dict       =  AlbertModel(config=__snake_case			)
      model.to(__snake_case			)
      model.eval()
      a		:      Any       =  model(__snake_case		,    attention_mask=__snake_case		,    token_type_ids=__snake_case			)
      a		:      Optional[Any]       =  model(__snake_case		,    token_type_ids=__snake_case			)
      a		:      Tuple       =  model(__snake_case			)
      self.parent.assertEqual(result.last_hidden_state.shape		,    (self.batch_size, self.seq_length, self.hidden_size)			)
      self.parent.assertEqual(result.pooler_output.shape		,    (self.batch_size, self.hidden_size)			)







    def     lowercase_ (   self				:						Optional[int]		,    __snake_case				:						Optional[int]		,    __snake_case				:						Optional[int]		,    __snake_case				:						List[str]		,    __snake_case				:						Optional[int]		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						int		,    __snake_case				:						Dict			):
      a		:      Dict       =  AlbertForPreTraining(config=__snake_case			)
      model.to(__snake_case			)
      model.eval()
      a		:      str       =  model(
          __snake_case		,    attention_mask=__snake_case		,    token_type_ids=__snake_case		,    labels=__snake_case		,    sentence_order_label=__snake_case		,    )
      self.parent.assertEqual(result.prediction_logits.shape		,    (self.batch_size, self.seq_length, self.vocab_size)			)
      self.parent.assertEqual(result.sop_logits.shape		,    (self.batch_size, config.num_labels)			)







    def     lowercase_ (   self				:						Any		,    __snake_case				:						List[Any]		,    __snake_case				:						Optional[int]		,    __snake_case				:						Tuple		,    __snake_case				:						Tuple		,    __snake_case				:						Optional[int]		,    __snake_case				:						Tuple		,    __snake_case				:						int			):
      a		:      str       =  AlbertForMaskedLM(config=__snake_case			)
      model.to(__snake_case			)
      model.eval()
      a		:      Tuple       =  model(__snake_case		,    attention_mask=__snake_case		,    token_type_ids=__snake_case		,    labels=__snake_case			)
      self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.seq_length, self.vocab_size)			)







    def     lowercase_ (   self				:						Tuple		,    __snake_case				:						int		,    __snake_case				:						List[str]		,    __snake_case				:						Optional[int]		,    __snake_case				:						str		,    __snake_case				:						Any		,    __snake_case				:						List[Any]		,    __snake_case				:						Dict			):
      a		:      str       =  AlbertForQuestionAnswering(config=__snake_case			)
      model.to(__snake_case			)
      model.eval()
      a		:      Any       =  model(
          __snake_case		,    attention_mask=__snake_case		,    token_type_ids=__snake_case		,    start_positions=__snake_case		,    end_positions=__snake_case		,    )
      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     lowercase_ (   self				:						Optional[int]		,    __snake_case				:						List[Any]		,    __snake_case				:						Optional[int]		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						Optional[int]		,    __snake_case				:						str		,    __snake_case				:						Tuple			):
      a		:      Union[str, Any]       =  self.num_labels
      a		:      List[Any]       =  AlbertForSequenceClassification(__snake_case			)
      model.to(__snake_case			)
      model.eval()
      a		:      Optional[int]       =  model(__snake_case		,    attention_mask=__snake_case		,    token_type_ids=__snake_case		,    labels=__snake_case			)
      self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_labels)			)







    def     lowercase_ (   self				:						Optional[int]		,    __snake_case				:						Dict		,    __snake_case				:						int		,    __snake_case				:						Dict		,    __snake_case				:						Optional[int]		,    __snake_case				:						List[str]		,    __snake_case				:						List[Any]		,    __snake_case				:						Any			):
      a		:      Dict       =  self.num_labels
      a		:      str       =  AlbertForTokenClassification(config=__snake_case			)
      model.to(__snake_case			)
      model.eval()
      a		:      List[Any]       =  model(__snake_case		,    attention_mask=__snake_case		,    token_type_ids=__snake_case		,    labels=__snake_case			)
      self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.seq_length, self.num_labels)			)







    def     lowercase_ (   self				:						int		,    __snake_case				:						Dict		,    __snake_case				:						Optional[Any]		,    __snake_case				:						List[str]		,    __snake_case				:						Tuple		,    __snake_case				:						List[str]		,    __snake_case				:						List[Any]		,    __snake_case				:						Union[str, Any]			):
      a		:      Any       =  self.num_choices
      a		:      List[str]       =  AlbertForMultipleChoice(config=__snake_case			)
      model.to(__snake_case			)
      model.eval()
      a		:      Any       =  input_ids.unsqueeze(1			).expand(-1		,    self.num_choices		,    -1			).contiguous()
      a		:      str       =  token_type_ids.unsqueeze(1			).expand(-1		,    self.num_choices		,    -1			).contiguous()
      a		:      Optional[Any]       =  input_mask.unsqueeze(1			).expand(-1		,    self.num_choices		,    -1			).contiguous()
      a		:      Optional[int]       =  model(
          __snake_case		,    attention_mask=__snake_case		,    token_type_ids=__snake_case		,    labels=__snake_case		,    )
      self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_choices)			)







    def     lowercase_ (   self				:						Optional[Any]			):
      a		:      List[Any]       =  self.prepare_config_and_inputs()
      (
          (
          a
      )     ,			(
          a
      )     ,			(
          a
      )     ,			(
          a
      )     ,			(
          a
      )     ,			(
          a
      )     ,			(
          a
      )     ,			
      )		:      Dict       =  config_and_inputs
      a		:      Tuple       =  {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
      return config, inputs_dict

@require_torch
class 							a__(      lowerCamelCase__	,				lowerCamelCase__	,				unittest.TestCase			):
    lowercase__				=					(
        (
            AlbertModel,
            AlbertForPreTraining,
            AlbertForMaskedLM,
            AlbertForMultipleChoice,
            AlbertForSequenceClassification,
            AlbertForTokenClassification,
            AlbertForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    lowercase__				=					(
        {
            """feature-extraction""": AlbertModel,
            """fill-mask""": AlbertForMaskedLM,
            """question-answering""": AlbertForQuestionAnswering,
            """text-classification""": AlbertForSequenceClassification,
            """token-classification""": AlbertForTokenClassification,
            """zero-shot""": AlbertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowercase__				=					True







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						str		,    __snake_case				:						int		,    __snake_case				:						Tuple=False			):
      a		:      Union[str, Any]       =  super()._prepare_for_class(__snake_case		,    __snake_case		,    return_labels=__snake_case			)

      if return_labels:
        if model_class in get_values(__snake_case			):
          a		:      Optional[Any]       =  torch.zeros(
              (self.model_tester.batch_size, self.model_tester.seq_length)		,    dtype=torch.long		,    device=__snake_case			)
          a		:      Tuple       =  torch.zeros(
              self.model_tester.batch_size		,    dtype=torch.long		,    device=__snake_case			)
      return inputs_dict







    def     lowercase_ (   self				:						List[str]			):
      a		:      List[str]       =  AlbertModelTester(self			)
      a		:      Optional[Any]       =  ConfigTester(self		,    config_class=__snake_case		,    hidden_size=37			)







    def     lowercase_ (   self				:						int			):
      self.config_tester.run_common_tests()







    def     lowercase_ (   self				:						str			):
      a		:      List[Any]       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__snake_case			)







    def     lowercase_ (   self				:						Tuple			):
      a		:      Dict       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_pretraining(*__snake_case			)







    def     lowercase_ (   self				:						int			):
      a		:      Optional[Any]       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__snake_case			)







    def     lowercase_ (   self				:						Optional[int]			):
      a		:      int       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*__snake_case			)







    def     lowercase_ (   self				:						List[str]			):
      a		:      str       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__snake_case			)







    def     lowercase_ (   self				:						List[Any]			):
      a		:      Optional[int]       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__snake_case			)







    def     lowercase_ (   self				:						Dict			):
      a		:      Union[str, Any]       =  self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
        a		:      List[Any]       =  type
        self.model_tester.create_and_check_model(*__snake_case			)







    @slow
    def     lowercase_ (   self				:						str			):
      for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a		:      int       =  AlbertModel.from_pretrained(__snake_case			)
        self.assertIsNotNone(__snake_case			)

@require_torch
class 							a__(      unittest.TestCase			):







    @slow
    def     lowercase_ (   self				:						int			):
      a		:      List[Any]       =  AlbertModel.from_pretrained('albert-base-v2'			)
      a		:      List[str]       =  torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]			)
      a		:      List[Any]       =  torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
      with torch.no_grad():
        a		:      Dict       =  model(__snake_case		,    attention_mask=__snake_case			)[0]
      a		:      Dict       =  torch.Size((1, 11, 7_68)			)
      self.assertEqual(output.shape		,    __snake_case			)
      a		:      Union[str, Any]       =  torch.tensor(
          [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]			)

      self.assertTrue(torch.allclose(output[:, 1:4, 1:4]		,    __snake_case		,    atol=1e-4			)			)




'''simple docstring'''






class 							a__:







    def __init__(   self				:						Dict		,    __snake_case				:						Optional[int]		,    __snake_case				:						Any		,    __snake_case				:						Tuple			):
      a		:      List[str]       =  name
      a		:      Dict       =  value
      a		:      List[str]       =  weight







    def __repr__(   self				:						int			):
      return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""







    def     lowercase_ (   self				:						Optional[int]			):
      return self.value







    def     lowercase_ (   self				:						List[str]			):
      return self.name







    def     lowercase_ (   self				:						int			):
      return self.weight







    def     lowercase_ (   self				:						List[str]			):
      return self.value / self.weight
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Optional[int]       =  []
  for i in range(len(_A       )       ):
    menu.append(Things(name[i]				,    value[i]				,    weight[i]       )       )
  return menu
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Optional[Any]       =  sorted(_A				,    key=_A				,    reverse=_A       )
  a		:      Optional[int]       =  []
  a     ,			a		:      str       =  0.0, 0.0
  for i in range(len(_A       )       ):
    if (total_cost + items_copy[i].get_weight()) <= max_cost:
      result.append(items_copy[i]       )
      total_cost += items_copy[i].get_weight()
      total_value += items_copy[i].get_value()
  return (result, total_value)
def    lowerCamelCase__ (		):
  pass


if __name__ == "__main__":
     import doctest

     doctest.testmod()