



'''simple docstring'''





import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   from transformers.models.bert.modeling_flax_bert import (
       FlaxBertForMaskedLM,
       FlaxBertForMultipleChoice,
       FlaxBertForNextSentencePrediction,
       FlaxBertForPreTraining,
       FlaxBertForQuestionAnswering,
       FlaxBertForSequenceClassification,
       FlaxBertForTokenClassification,
       FlaxBertModel,
   )






class    lowerCamelCase_       (							unittest.TestCase	):
  """simple docstring"""




  def __init__(					self :      Optional[Any]     ,      _a :      int     ,      _a :      List[str]=13     ,      _a :      Dict=7     ,      _a :      Optional[int]=True     ,      _a :      int=True     ,      _a :      List[Any]=True     ,      _a :      List[str]=True     ,      _a :      str=99     ,      _a :      List[Any]=32     ,      _a :      str=5     ,      _a :      int=4     ,      _a :      int=37     ,      _a :      Optional[Any]="gelu"     ,      _a :      Union[str, Any]=0.1     ,      _a :      List[str]=0.1     ,      _a :      Any=512     ,      _a :      Tuple=16     ,      _a :      str=2     ,      _a :      Optional[int]=0.02     ,      _a :      int=4     ,      )   ->   List[str]:
    __lowerCamelCase			:			Any       			=					parent
    __lowerCamelCase			:			Optional[int]       			=					batch_size
    __lowerCamelCase			:			int       			=					seq_length
    __lowerCamelCase			:			int       			=					is_training
    __lowerCamelCase			:			str       			=					use_attention_mask
    __lowerCamelCase			:			Dict       			=					use_token_type_ids
    __lowerCamelCase			:			List[Any]       			=					use_labels
    __lowerCamelCase			:			List[str]       			=					vocab_size
    __lowerCamelCase			:			List[Any]       			=					hidden_size
    __lowerCamelCase			:			Union[str, Any]       			=					num_hidden_layers
    __lowerCamelCase			:			List[Any]       			=					num_attention_heads
    __lowerCamelCase			:			str       			=					intermediate_size
    __lowerCamelCase			:			Optional[int]       			=					hidden_act
    __lowerCamelCase			:			List[Any]       			=					hidden_dropout_prob
    __lowerCamelCase			:			Any       			=					attention_probs_dropout_prob
    __lowerCamelCase			:			Optional[int]       			=					max_position_embeddings
    __lowerCamelCase			:			int       			=					type_vocab_size
    __lowerCamelCase			:			Optional[int]       			=					type_sequence_label_size
    __lowerCamelCase			:			Tuple       			=					initializer_range
    __lowerCamelCase			:			List[Any]       			=					num_choices




  def 		_lowercase	(					self :      Any  )   ->   Optional[Any]:
    __lowerCamelCase			:			Optional[int]       			=					ids_tensor([self.batch_size, self.seq_length]     ,      self.vocab_size  )

    __lowerCamelCase			:			str       			=					None
    if self.use_attention_mask:
      __lowerCamelCase			:			int       			=					random_attention_mask([self.batch_size, self.seq_length]  )

    __lowerCamelCase			:			List[Any]       			=					None
    if self.use_token_type_ids:
      __lowerCamelCase			:			Dict       			=					ids_tensor([self.batch_size, self.seq_length]     ,      self.type_vocab_size  )

    __lowerCamelCase			:			Any       			=					BertConfig(
        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=_a     ,      initializer_range=self.initializer_range     ,      )

    return config, input_ids, token_type_ids, attention_mask




  def 		_lowercase	(					self :      List[Any]  )   ->   Optional[Any]:
    __lowerCamelCase			:			Tuple       			=					self.prepare_config_and_inputs()
    __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Optional[int]       			=					config_and_inputs
    __lowerCamelCase			:			List[Any]       			=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
    return config, inputs_dict




  def 		_lowercase	(					self :      Optional[Any]  )   ->   Union[str, Any]:
    __lowerCamelCase			:			Tuple       			=					self.prepare_config_and_inputs()
    __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Union[str, Any]       			=					config_and_inputs

    __lowerCamelCase			:			List[Any]       			=					True
    __lowerCamelCase			:			Dict       			=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
    __lowerCamelCase			:			str       			=					ids_tensor([self.batch_size, self.seq_length]     ,      vocab_size=2  )

    return (
        config,
        input_ids,
        attention_mask,
        encoder_hidden_states,
        encoder_attention_mask,
    )






@require_flax
class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_  ,  unittest.TestCase	):
  """simple docstring"""




  a_				   =True

  a_				   =(
      (
          FlaxBertModel,
          FlaxBertForPreTraining,
          FlaxBertForMaskedLM,
          FlaxBertForMultipleChoice,
          FlaxBertForQuestionAnswering,
          FlaxBertForNextSentencePrediction,
          FlaxBertForSequenceClassification,
          FlaxBertForTokenClassification,
          FlaxBertForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )




  def 		_lowercase	(					self :      Tuple  )   ->   Dict:
    __lowerCamelCase			:			Dict       			=					FlaxBertModelTester(self  )




  @slow
  def 		_lowercase	(					self :      Optional[Any]  )   ->   str:
    # Only check this for base model, not necessary for all model classes.
    # This will also help speed-up tests.
    __lowerCamelCase			:			Union[str, Any]       			=					FlaxBertModel.from_pretrained('bert-base-cased'  )
    __lowerCamelCase			:			Optional[int]       			=					model(np.ones((1, 1)  )  )
    self.assertIsNotNone(_a  )











'''simple docstring'''





import functools

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> int:

  # Validation
  if not isinstance(_lowerCAmelCase			,_lowerCAmelCase			) or not all(isinstance(_lowerCAmelCase			,_lowerCAmelCase			) for day in days			):
    raise ValueError('The parameter days should be a list of integers'			)

  if len(_lowerCAmelCase			) != 3 or not all(isinstance(_lowerCAmelCase			,_lowerCAmelCase			) for cost in costs			):
    raise ValueError('The parameter costs should be a list of three integers'			)

  if len(_lowerCAmelCase			) == 0:
    return 0

  if min(_lowerCAmelCase			) <= 0:
    raise ValueError('All days elements should be greater than 0'			)

  if max(_lowerCAmelCase			) >= 366:
    raise ValueError('All days elements should be less than 366'			)

  __lowerCamelCase			:			int       			=					set(_lowerCAmelCase			)

  @functools.cache
  def dynamic_programming(_lowerCAmelCase			) -> int:
    if index > 365:
      return 0

    if index not in days_set:
      return dynamic_programming(index + 1			)

    return min(
        costs[0] + dynamic_programming(index + 1			)			,costs[1] + dynamic_programming(index + 7			)			,costs[2] + dynamic_programming(index + 30			)			,)

  return dynamic_programming(1			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()






