import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
  import torch

  from transformers import (
      MraForMaskedLM,
      MraForMultipleChoice,
      MraForQuestionAnswering,
      MraForSequenceClassification,
      MraForTokenClassification,
      MraModel,
  )
  from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST




class  __magic_name__  :





   """simple docstring"""




   def __init__(	self					:  List[str]		,  _lowercase					:  List[str]		,  _lowercase					:  List[str]=2		,  _lowercase					:  List[str]=8		,  _lowercase					:  str=True		,  _lowercase					:  List[Any]=True		,  _lowercase					:  Optional[Any]=True		,  _lowercase					:  Optional[Any]=True		,  _lowercase					:  List[str]=99		,  _lowercase					:  Optional[Any]=16		,  _lowercase					:  Tuple=5		,  _lowercase					:  Dict=2		,  _lowercase					:  Optional[int]=36		,  _lowercase					:  Tuple="gelu"		,  _lowercase					:  List[Any]=0.0		,  _lowercase					:  Dict=0.0		,  _lowercase					:  List[str]=512		,  _lowercase					:  List[Any]=16		,  _lowercase					:  Optional[Any]=2		,  _lowercase					:  List[Any]=0.02		,  _lowercase					:  Tuple=3		,  _lowercase					:  Dict=4		,  _lowercase					:  Tuple=None		,  ):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= parent
    _UpperCamelCase:     Dict							= batch_size
    _UpperCamelCase:     int							= seq_length
    _UpperCamelCase:     Optional[int]							= is_training
    _UpperCamelCase:     Any							= use_input_mask
    _UpperCamelCase:     Dict							= use_token_type_ids
    _UpperCamelCase:     Tuple							= use_labels
    _UpperCamelCase:     str							= vocab_size
    _UpperCamelCase:     int							= hidden_size
    _UpperCamelCase:     Any							= num_hidden_layers
    _UpperCamelCase:     int							= num_attention_heads
    _UpperCamelCase:     int							= intermediate_size
    _UpperCamelCase:     Union[str, Any]							= hidden_act
    _UpperCamelCase:     str							= hidden_dropout_prob
    _UpperCamelCase:     Any							= attention_probs_dropout_prob
    _UpperCamelCase:     List[Any]							= max_position_embeddings
    _UpperCamelCase:     Tuple							= type_vocab_size
    _UpperCamelCase:     List[str]							= type_sequence_label_size
    _UpperCamelCase:     Any							= initializer_range
    _UpperCamelCase:     Union[str, Any]							= num_labels
    _UpperCamelCase:     Tuple							= num_choices
    _UpperCamelCase:     str							= scope



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    _UpperCamelCase:     str							= ids_tensor([self.batch_size, self.seq_length]		,  self.vocab_size							)

    _UpperCamelCase:     Optional[int]							= None
    if self.use_input_mask:
     _UpperCamelCase:     Optional[int]							= random_attention_mask([self.batch_size, self.seq_length]							)

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

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

    _UpperCamelCase:     Dict							= self.get_config()

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



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    return MraConfig(
        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=_lowercase		,  initializer_range=self.initializer_range		,  )



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= self.get_config()
    _UpperCamelCase:     Any							= 300
    return config



   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    (
        (
        _UpperCamelCase
    )	,							(
        _UpperCamelCase
    )	,							(
        _UpperCamelCase
    )	,							(
        _UpperCamelCase
    )	,							(
        _UpperCamelCase
    )	,							(
        _UpperCamelCase
    )	,							(
        _UpperCamelCase
    )	,							
    ):     str							= self.prepare_config_and_inputs()

    _UpperCamelCase:     Optional[int]							= True
    _UpperCamelCase:     Tuple							= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
    _UpperCamelCase:     Any							= ids_tensor([self.batch_size, self.seq_length]		,  vocab_size=2							)

    return (
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    )



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  Tuple		,  _lowercase					:  str		,  _lowercase					:  Optional[Any]		,  _lowercase					:  Optional[Any]		,  _lowercase					:  int		,  _lowercase					:  Optional[int]		,  _lowercase					:  Tuple							):



    """simple docstring"""



    _UpperCamelCase:     str							= MraModel(config=_lowercase							)
    model.to(_lowercase							)
    model.eval()
    _UpperCamelCase:     Optional[Any]							= model(_lowercase		,  attention_mask=_lowercase		,  token_type_ids=_lowercase							)
    _UpperCamelCase:     Optional[int]							= model(_lowercase		,  token_type_ids=_lowercase							)
    _UpperCamelCase:     Optional[int]							= model(_lowercase							)
    self.parent.assertEqual(result.last_hidden_state.shape		,  (self.batch_size, self.seq_length, self.hidden_size)							)



   def   lowerCAmelCase						(	self					:  List[str]		,  _lowercase					:  Optional[int]		,  _lowercase					:  Optional[int]		,  _lowercase					:  Optional[Any]		,  _lowercase					:  Dict		,  _lowercase					:  List[Any]		,  _lowercase					:  List[str]		,  _lowercase					:  Any		,  _lowercase					:  int		,  _lowercase					:  List[Any]		,  ):



    """simple docstring"""



    _UpperCamelCase:     Any							= True
    _UpperCamelCase:     Tuple							= MraModel(_lowercase							)
    model.to(_lowercase							)
    model.eval()
    _UpperCamelCase:     int							= model(
        _lowercase		,  attention_mask=_lowercase		,  token_type_ids=_lowercase		,  encoder_hidden_states=_lowercase		,  encoder_attention_mask=_lowercase		,  )
    _UpperCamelCase:     Tuple							= model(
        _lowercase		,  attention_mask=_lowercase		,  token_type_ids=_lowercase		,  encoder_hidden_states=_lowercase		,  )
    _UpperCamelCase:     int							= model(_lowercase		,  attention_mask=_lowercase		,  token_type_ids=_lowercase							)
    self.parent.assertEqual(result.last_hidden_state.shape		,  (self.batch_size, self.seq_length, self.hidden_size)							)



   def   lowerCAmelCase						(	self					:  List[str]		,  _lowercase					:  List[Any]		,  _lowercase					:  int		,  _lowercase					:  Dict		,  _lowercase					:  Optional[int]		,  _lowercase					:  Dict		,  _lowercase					:  str		,  _lowercase					:  Union[str, Any]							):



    """simple docstring"""



    _UpperCamelCase:     int							= MraForMaskedLM(config=_lowercase							)
    model.to(_lowercase							)
    model.eval()
    _UpperCamelCase:     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.vocab_size)							)



   def   lowerCAmelCase						(	self					:  Dict		,  _lowercase					:  Tuple		,  _lowercase					:  Optional[Any]		,  _lowercase					:  str		,  _lowercase					:  List[str]		,  _lowercase					:  str		,  _lowercase					:  Tuple		,  _lowercase					:  Optional[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Any							= MraForQuestionAnswering(config=_lowercase							)
    model.to(_lowercase							)
    model.eval()
    _UpperCamelCase:     List[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   lowerCAmelCase						(	self					:  Tuple		,  _lowercase					:  str		,  _lowercase					:  Optional[Any]		,  _lowercase					:  Optional[Any]		,  _lowercase					:  List[str]		,  _lowercase					:  Union[str, Any]		,  _lowercase					:  Union[str, Any]		,  _lowercase					:  Optional[Any]							):



    """simple docstring"""



    _UpperCamelCase:     str							= self.num_labels
    _UpperCamelCase:     str							= MraForSequenceClassification(_lowercase							)
    model.to(_lowercase							)
    model.eval()
    _UpperCamelCase:     Union[str, 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   lowerCAmelCase						(	self					:  Dict		,  _lowercase					:  str		,  _lowercase					:  Dict		,  _lowercase					:  Optional[int]		,  _lowercase					:  Dict		,  _lowercase					:  int		,  _lowercase					:  Dict		,  _lowercase					:  Tuple							):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= self.num_labels
    _UpperCamelCase:     List[Any]							= MraForTokenClassification(config=_lowercase							)
    model.to(_lowercase							)
    model.eval()
    _UpperCamelCase:     Optional[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   lowerCAmelCase						(	self					:  Optional[Any]		,  _lowercase					:  Any		,  _lowercase					:  Any		,  _lowercase					:  Optional[int]		,  _lowercase					:  Optional[Any]		,  _lowercase					:  List[Any]		,  _lowercase					:  str		,  _lowercase					:  Tuple							):



    """simple docstring"""



    _UpperCamelCase:     Any							= self.num_choices
    _UpperCamelCase:     int							= MraForMultipleChoice(config=_lowercase							)
    model.to(_lowercase							)
    model.eval()
    _UpperCamelCase:     int							= input_ids.unsqueeze(1							).expand(-1		,  self.num_choices		,  -1							).contiguous()
    _UpperCamelCase:     List[str]							= token_type_ids.unsqueeze(1							).expand(-1		,  self.num_choices		,  -1							).contiguous()
    _UpperCamelCase:     List[Any]							= input_mask.unsqueeze(1							).expand(-1		,  self.num_choices		,  -1							).contiguous()
    _UpperCamelCase:     Dict							= model(
        _lowercase		,  attention_mask=_lowercase		,  token_type_ids=_lowercase		,  labels=_lowercase		,  )
    self.parent.assertEqual(result.logits.shape		,  (self.batch_size, self.num_choices)							)



   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= self.prepare_config_and_inputs()
    (
        (
        _UpperCamelCase
    )	,							(
        _UpperCamelCase
    )	,							(
        _UpperCamelCase
    )	,							(
        _UpperCamelCase
    )	,							(
        _UpperCamelCase
    )	,							(
        _UpperCamelCase
    )	,							(
        _UpperCamelCase
    )	,							
    ):     Dict							= config_and_inputs
    _UpperCamelCase:     Optional[int]							= {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
    return config, inputs_dict




@require_torch
class  __magic_name__  (		__a      ,	unittest.TestCase    ):





   """simple docstring"""




   lowerCAmelCase      :    int  			=   (
       (
           MraModel,
           MraForMaskedLM,
           MraForMultipleChoice,
           MraForQuestionAnswering,
           MraForSequenceClassification,
           MraForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   lowerCAmelCase      :    List[Any]  			=   False
   lowerCAmelCase      :    str  			=   False
   lowerCAmelCase      :    Optional[Any]  			=   False
   lowerCAmelCase      :    Optional[int]  			=   False

   lowerCAmelCase      :    int  			=   ()



   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= MraModelTester(self							)
    _UpperCamelCase:     Optional[Any]							= ConfigTester(self		,  config_class=_lowercase		,  hidden_size=37							)



   def   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



    self.config_tester.run_common_tests()



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    _UpperCamelCase:     int							= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_lowercase							)



   def   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
     _UpperCamelCase:     int							= type
     self.model_tester.create_and_check_model(*_lowercase							)



   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*_lowercase							)



   def   lowerCAmelCase						(	self					:  List[str]							):



    """simple docstring"""



    _UpperCamelCase:     Dict							= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_multiple_choice(*_lowercase							)



   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*_lowercase							)



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    _UpperCamelCase:     int							= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*_lowercase							)



   def   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*_lowercase							)



   @slow
   def   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



    for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _UpperCamelCase:     List[str]							= MraModel.from_pretrained(_lowercase							)
     self.assertIsNotNone(_lowercase							)



   @unittest.skip(reason='''MRA does not output attentions'''							)
   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    return




@require_torch
class  __magic_name__  (		unittest.TestCase    ):





   """simple docstring"""




   @slow
   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Any							= MraModel.from_pretrained('''uw-madison/mra-base-512-4'''							)
    _UpperCamelCase:     Union[str, Any]							= torch.arange(256							).unsqueeze(0							)

    with torch.no_grad():
     _UpperCamelCase:     List[str]							= model(_lowercase							)[0]

    _UpperCamelCase:     Optional[Any]							= torch.Size((1, 256, 768)							)
    self.assertEqual(output.shape		,  _lowercase							)

    _UpperCamelCase:     Optional[Any]							= torch.tensor(
        [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]]							)

    self.assertTrue(torch.allclose(output[:, :3, :3]		,  _lowercase		,  atol=1E-4							)							)



   @slow
   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4'''							)
    _UpperCamelCase:     str							= torch.arange(256							).unsqueeze(0							)

    with torch.no_grad():
     _UpperCamelCase:     List[Any]							= model(_lowercase							)[0]

    _UpperCamelCase:     List[str]							= 50_265

    _UpperCamelCase:     Union[str, Any]							= torch.Size((1, 256, vocab_size)							)
    self.assertEqual(output.shape		,  _lowercase							)

    _UpperCamelCase:     str							= torch.tensor(
        [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]]							)

    self.assertTrue(torch.allclose(output[:, :3, :3]		,  _lowercase		,  atol=1E-4							)							)



   @slow
   def   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3'''							)
    _UpperCamelCase:     Optional[Any]							= torch.arange(4_096							).unsqueeze(0							)

    with torch.no_grad():
     _UpperCamelCase:     Optional[Any]							= model(_lowercase							)[0]

    _UpperCamelCase:     str							= 50_265

    _UpperCamelCase:     int							= torch.Size((1, 4_096, vocab_size)							)
    self.assertEqual(output.shape		,  _lowercase							)

    _UpperCamelCase:     Any							= torch.tensor(
        [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]]							)

    self.assertTrue(torch.allclose(output[:, :3, :3]		,  _lowercase		,  atol=1E-4							)							)
def       lowerCAmelCase_     (		lowercase:							int = 10**9		)    ->				int:







 '''simple docstring'''
 _UpperCamelCase:     List[Any]							= 1
 _UpperCamelCase:     Dict							= 2

 _UpperCamelCase:     Tuple							= 0
 _UpperCamelCase:     int							= 0
 _UpperCamelCase:     Dict							= 0
 while perimeter <= max_perimeter:
  perimeters_sum += perimeter

  prev_value += 2 * value
  value += prev_value

  _UpperCamelCase:     str							= 2 * value + 2 if i % 2 == 0 else 2 * value - 2
  i += 1

 return perimeters_sum


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