
'''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    UpperCAmelCase_				:





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





 def        snake_case__		(    self):
     snake_case_ :    int     			=						ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size)

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

     snake_case_ :    Optional[int]     			=						None
     if self.use_token_type_ids:
         snake_case_ :    List[str]     			=						ids_tensor([self.batch_size, self.seq_length] ,       self.type_vocab_size)

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

     snake_case_ :    Any     			=						self.get_config()

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





 def        snake_case__		(    self):
     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        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_):
     snake_case_ :    List[Any]     			=						AlbertModel(config=lowercase_)
     model.to(lowercase_)
     model.eval()
     snake_case_ :    List[str]     			=						model(lowercase_ ,       attention_mask=lowercase_ ,       token_type_ids=lowercase_)
     snake_case_ :    Dict     			=						model(lowercase_ ,       token_type_ids=lowercase_)
     snake_case_ :    List[Any]     			=						model(lowercase_)
     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        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_):
     snake_case_ :    Dict     			=						AlbertForPreTraining(config=lowercase_)
     model.to(lowercase_)
     model.eval()
     snake_case_ :    str     			=						model(
         lowercase_ ,       attention_mask=lowercase_ ,       token_type_ids=lowercase_ ,       labels=lowercase_ ,       sentence_order_label=lowercase_ ,       )
     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        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_):
     snake_case_ :    Dict     			=						AlbertForMaskedLM(config=lowercase_)
     model.to(lowercase_)
     model.eval()
     snake_case_ :    Any     			=						model(lowercase_ ,       attention_mask=lowercase_ ,       token_type_ids=lowercase_ ,       labels=lowercase_)
     self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.seq_length, self.vocab_size))





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





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





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





 def        snake_case__		(    self):
     snake_case_ :    List[Any]     			=						self.prepare_config_and_inputs()
     (
         (
         snake_case_
     )			, (
         snake_case_
     )			, (
         snake_case_
     )			, (
         snake_case_
     )			, (
         snake_case_
     )			, (
         snake_case_
     )			, (
         snake_case_
     )			, 
     ) :    Optional[Any]     			=						config_and_inputs
     snake_case_ :    int     			=						{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
     return config, inputs_dict






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





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_=False):
     snake_case_ :    List[Any]     			=						super()._prepare_for_class(lowercase_ ,       lowercase_ ,       return_labels=lowercase_)

     if return_labels:
         if model_class in get_values(lowercase_):
             snake_case_ :    str     			=						torch.zeros(
                 (self.model_tester.batch_size, self.model_tester.seq_length) ,       dtype=torch.long ,       device=lowercase_)
             snake_case_ :    Dict     			=						torch.zeros(
                 self.model_tester.batch_size ,       dtype=torch.long ,       device=lowercase_)
     return inputs_dict





 def        snake_case__		(    self):
     snake_case_ :    Dict     			=						AlbertModelTester(self)
     snake_case_ :    Any     			=						ConfigTester(self ,       config_class=lowercase_ ,       hidden_size=37)





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





 def        snake_case__		(    self):
     snake_case_ :    Dict     			=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_)





 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_pretraining(*lowercase_)





 def        snake_case__		(    self):
     snake_case_ :    Tuple     			=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*lowercase_)





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*lowercase_)





 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*lowercase_)





 def        snake_case__		(    self):
     snake_case_ :    str     			=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*lowercase_)





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
         snake_case_ :    Optional[Any]     			=						type
         self.model_tester.create_and_check_model(*lowercase_)





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






@require_torch
class    UpperCAmelCase_				(	unittest.TestCase					):





 @slow
 def        snake_case__		(    self):
     snake_case_ :    Tuple     			=						AlbertModel.from_pretrained("albert-base-v2")
     snake_case_ :    str     			=						torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]])
     snake_case_ :    Optional[int]     			=						torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
     with torch.no_grad():
         snake_case_ :    List[str]     			=						model(lowercase_ ,       attention_mask=lowercase_)[0]
     snake_case_ :    Any     			=						torch.Size((1, 11, 7_68))
     self.assertEqual(output.shape ,       lowercase_)
     snake_case_ :    List[str]     			=						torch.tensor(
         [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]])

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






'''simple docstring'''
from __future__ import annotations

import math

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if depth < 0:
        raise ValueError("Depth cannot be less than 0"       )

    if len(__SCREAMING_SNAKE_CASE       ) == 0:
        raise ValueError("Scores cannot be empty"       )

    if depth == height:
        return scores[node_index]

    if is_max:
        return max(
            minimax(depth + 1,		node_index * 2,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ),		minimax(depth + 1,		node_index * 2 + 1,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ),		)

    return min(
        minimax(depth + 1,		node_index * 2,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ),		minimax(depth + 1,		node_index * 2 + 1,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ),		)

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    int     			=						[9_0, 2_3, 6, 3_3, 2_1, 6_5, 1_2_3, 3_4_4_2_3]
    snake_case_ :    Union[str, Any]     			=						math.log(len(__SCREAMING_SNAKE_CASE       ),		2       )
    print("Optimal value : ",		end=""       )
    print(minimax(0,		0,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




