code: 
  
  
  
  '''simple docstring'''
  
  
  
  import warnings
  
  from ...utils import logging
  from .image_processing_maskformer import MaskFormerImageProcessor
  
  
  a__						:			Any										=			logging.get_logger(__name__)
  class        UpperCAmelCase__  (    UpperCAmelCase_):
  
  
  
   def __init__(  self		,	*lowercase		,	**lowercase			)  ->							None:
    warnings.warn(
        """The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
        """ Please use MaskFormerImageProcessor instead."""		,	lowercase		,	)
    super().__init__(*lowercase		,	**lowercase			)
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  import unittest
  
  from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
  
  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 (
       MODEL_FOR_PRETRAINING_MAPPING,
       FunnelBaseModel,
       FunnelForMaskedLM,
       FunnelForMultipleChoice,
       FunnelForPreTraining,
       FunnelForQuestionAnswering,
       FunnelForSequenceClassification,
       FunnelForTokenClassification,
       FunnelModel,
   )
  class        UpperCAmelCase__  :
  
  
  
   def __init__(  self		,	lowercase		,	lowercase=1_3		,	lowercase=7		,	lowercase=True		,	lowercase=True		,	lowercase=True		,	lowercase=True		,	lowercase=9_9		,	lowercase=[1, 1, 2]		,	lowercase=1		,	lowercase=3_2		,	lowercase=4		,	lowercase=8		,	lowercase=3_7		,	lowercase="gelu_new"		,	lowercase=0.1		,	lowercase=0.1		,	lowercase=0.0		,	lowercase=5_1_2		,	lowercase=3		,	lowercase=0.02		,	lowercase=3		,	lowercase=4		,	lowercase=None		,	lowercase=False		,	)  ->							Any:
    __UpperCamelCase				      =  parent
    __UpperCamelCase				      =  batch_size
    __UpperCamelCase				      =  seq_length
    __UpperCamelCase				      =  is_training
    __UpperCamelCase				      =  use_input_mask
    __UpperCamelCase				      =  use_token_type_ids
    __UpperCamelCase				      =  use_labels
    __UpperCamelCase				      =  vocab_size
    __UpperCamelCase				      =  block_sizes
    __UpperCamelCase				      =  num_decoder_layers
    __UpperCamelCase				      =  d_model
    __UpperCamelCase				      =  n_head
    __UpperCamelCase				      =  d_head
    __UpperCamelCase				      =  d_inner
    __UpperCamelCase				      =  hidden_act
    __UpperCamelCase				      =  hidden_dropout
    __UpperCamelCase				      =  attention_dropout
    __UpperCamelCase				      =  activation_dropout
    __UpperCamelCase				      =  max_position_embeddings
    __UpperCamelCase				      =  type_vocab_size
    __UpperCamelCase				      =  2
    __UpperCamelCase				      =  num_labels
    __UpperCamelCase				      =  num_choices
    __UpperCamelCase				      =  scope
    __UpperCamelCase				      =  initializer_std
  
    # Used in the tests to check the size of the first attention layer
    __UpperCamelCase				      =  n_head
    # Used in the tests to check the size of the first hidden state
    __UpperCamelCase				      =  self.d_model
    # Used in the tests to check the number of output hidden states/attentions
    __UpperCamelCase				      =  sum(self.block_sizes			) + (0 if base else self.num_decoder_layers)
    # FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
    # the last hidden state of the first block (which is the first hidden state of the decoder).
    if not base:
     __UpperCamelCase				      =  self.num_hidden_layers + 2
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Tuple:
    __UpperCamelCase				      =  ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size			)
  
    __UpperCamelCase				      =  None
    if self.use_input_mask:
     __UpperCamelCase				      =  ids_tensor([self.batch_size, self.seq_length]		,	vocab_size=2			)
  
    __UpperCamelCase				      =  None
    if self.use_token_type_ids:
     __UpperCamelCase				      =  ids_tensor([self.batch_size, self.seq_length]		,	self.type_vocab_size			)
  
    __UpperCamelCase				      =  None
    __UpperCamelCase				      =  None
    __UpperCamelCase				      =  None
    if self.use_labels:
     __UpperCamelCase				      =  ids_tensor([self.batch_size]		,	self.type_sequence_label_size			)
     __UpperCamelCase				      =  ids_tensor([self.batch_size, self.seq_length]		,	self.num_labels			)
     __UpperCamelCase				      =  ids_tensor([self.batch_size]		,	self.num_choices			)
     __UpperCamelCase				      =  ids_tensor([self.batch_size, self.seq_length]		,	1			)
  
    __UpperCamelCase				      =  self.get_config()
  
    return (
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        fake_token_labels,
    )
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Any:
    return FunnelConfig(
        vocab_size=self.vocab_size		,	block_sizes=self.block_sizes		,	num_decoder_layers=self.num_decoder_layers		,	d_model=self.d_model		,	n_head=self.n_head		,	d_head=self.d_head		,	d_inner=self.d_inner		,	hidden_act=self.hidden_act		,	hidden_dropout=self.hidden_dropout		,	attention_dropout=self.attention_dropout		,	activation_dropout=self.activation_dropout		,	max_position_embeddings=self.max_position_embeddings		,	type_vocab_size=self.type_vocab_size		,	initializer_std=self.initializer_std		,	)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	)  ->							Any:
    __UpperCamelCase				      =  FunnelModel(config=lowercase			)
    model.to(lowercase			)
    model.eval()
    __UpperCamelCase				      =  model(lowercase		,	attention_mask=lowercase		,	token_type_ids=lowercase			)
    __UpperCamelCase				      =  model(lowercase		,	token_type_ids=lowercase			)
    __UpperCamelCase				      =  model(lowercase			)
    self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.d_model)			)
  
    __UpperCamelCase				      =  False
    __UpperCamelCase				      =  model(lowercase			)
    self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.d_model)			)
  
    __UpperCamelCase				      =  False
    __UpperCamelCase				      =  model(lowercase			)
    self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.d_model)			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	)  ->							str:
    __UpperCamelCase				      =  FunnelBaseModel(config=lowercase			)
    model.to(lowercase			)
    model.eval()
    __UpperCamelCase				      =  model(lowercase		,	attention_mask=lowercase		,	token_type_ids=lowercase			)
    __UpperCamelCase				      =  model(lowercase		,	token_type_ids=lowercase			)
    __UpperCamelCase				      =  model(lowercase			)
    self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, 2, self.d_model)			)
  
    __UpperCamelCase				      =  False
    __UpperCamelCase				      =  model(lowercase			)
    self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, 3, self.d_model)			)
  
    __UpperCamelCase				      =  False
    __UpperCamelCase				      =  model(lowercase			)
    self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, 2, self.d_model)			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	)  ->							Optional[Any]:
    __UpperCamelCase				      =  self.num_labels
    __UpperCamelCase				      =  FunnelForPreTraining(config=lowercase			)
    model.to(lowercase			)
    model.eval()
    __UpperCamelCase				      =  model(lowercase		,	attention_mask=lowercase		,	token_type_ids=lowercase		,	labels=lowercase			)
    self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length)			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	)  ->							Optional[Any]:
    __UpperCamelCase				      =  FunnelForMaskedLM(config=lowercase			)
    model.to(lowercase			)
    model.eval()
    __UpperCamelCase				      =  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		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	)  ->							Any:
    __UpperCamelCase				      =  self.num_labels
    __UpperCamelCase				      =  FunnelForSequenceClassification(lowercase			)
    model.to(lowercase			)
    model.eval()
    __UpperCamelCase				      =  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		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	)  ->							int:
    __UpperCamelCase				      =  self.num_choices
    __UpperCamelCase				      =  FunnelForMultipleChoice(config=lowercase			)
    model.to(lowercase			)
    model.eval()
    __UpperCamelCase				      =  input_ids.unsqueeze(1			).expand(-1		,	self.num_choices		,	-1			).contiguous()
    __UpperCamelCase				      =  token_type_ids.unsqueeze(1			).expand(-1		,	self.num_choices		,	-1			).contiguous()
    __UpperCamelCase				      =  input_mask.unsqueeze(1			).expand(-1		,	self.num_choices		,	-1			).contiguous()
    __UpperCamelCase				      =  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		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	)  ->							Optional[Any]:
    __UpperCamelCase				      =  self.num_labels
    __UpperCamelCase				      =  FunnelForTokenClassification(config=lowercase			)
    model.to(lowercase			)
    model.eval()
    __UpperCamelCase				      =  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		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	)  ->							Any:
    __UpperCamelCase				      =  FunnelForQuestionAnswering(config=lowercase			)
    model.to(lowercase			)
    model.eval()
    __UpperCamelCase				      =  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			)  ->							Any:
    __UpperCamelCase				      =  self.prepare_config_and_inputs()
    (
        (
        __UpperCamelCase
    )      ,		(
        __UpperCamelCase
    )      ,		(
        __UpperCamelCase
    )      ,		(
        __UpperCamelCase
    )      ,		(
        __UpperCamelCase
    )      ,		(
        __UpperCamelCase
    )      ,		(
        __UpperCamelCase
    )      ,		(
        __UpperCamelCase
    )      ,		
    )				      =  config_and_inputs
    __UpperCamelCase				      =  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
    return config, inputs_dict
  
  
  @require_torch
  class        UpperCAmelCase__  (    UpperCAmelCase_							,			UpperCAmelCase_							,			unittest.TestCase):
   __SCREAMING_SNAKE_CASE					      =				False
   __SCREAMING_SNAKE_CASE					      =				False
   __SCREAMING_SNAKE_CASE					      =				(
       (
           FunnelModel,
           FunnelForMaskedLM,
           FunnelForPreTraining,
           FunnelForQuestionAnswering,
           FunnelForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   __SCREAMING_SNAKE_CASE					      =				(
       {
           '''feature-extraction''': (FunnelBaseModel, FunnelModel),
           '''fill-mask''': FunnelForMaskedLM,
           '''question-answering''': FunnelForQuestionAnswering,
           '''text-classification''': FunnelForSequenceClassification,
           '''token-classification''': FunnelForTokenClassification,
           '''zero-shot''': FunnelForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase=False			)  ->							Tuple:
    __UpperCamelCase				      =  super()._prepare_for_class(lowercase		,	lowercase		,	return_labels=lowercase			)
  
    if return_labels:
     if model_class in get_values(lowercase			):
      __UpperCamelCase				      =  torch.zeros(
          (self.model_tester.batch_size, self.model_tester.seq_length)		,	dtype=torch.long		,	device=lowercase			)
    return inputs_dict
  
  
  
   def    __lowerCamelCase				(  self			)  ->							List[str]:
    __UpperCamelCase				      =  FunnelModelTester(self			)
    __UpperCamelCase				      =  ConfigTester(self		,	config_class=lowercase			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Tuple:
    self.config_tester.run_common_tests()
  
  
  
   def    __lowerCamelCase				(  self			)  ->							List[str]:
    __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowercase			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Optional[int]:
    __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_pretraining(*lowercase			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							str:
    __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*lowercase			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Dict:
    __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*lowercase			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Tuple:
    __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*lowercase			)
  
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase			)  ->							int:
    if hasattr(lowercase		,	"""weight"""			) and module.weight is not None:
     module.weight.data.fill_(3			)
    if hasattr(lowercase		,	"""bias"""			) and module.bias is not None:
     module.bias.data.fill_(3			)
  
    for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
     if hasattr(lowercase		,	lowercase			) and getattr(lowercase		,	lowercase			) is not None:
      __UpperCamelCase				      =  getattr(lowercase		,	lowercase			)
      weight.data.fill_(3			)
  
  
  @require_torch
  class        UpperCAmelCase__  (    UpperCAmelCase_							,			unittest.TestCase):
   __SCREAMING_SNAKE_CASE					      =				False
   __SCREAMING_SNAKE_CASE					      =				False
   __SCREAMING_SNAKE_CASE					      =				(
       (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
   )
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Optional[int]:
    __UpperCamelCase				      =  FunnelModelTester(self		,	base=lowercase			)
    __UpperCamelCase				      =  ConfigTester(self		,	config_class=lowercase			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Any:
    self.config_tester.run_common_tests()
  
  
  
   def    __lowerCamelCase				(  self			)  ->							str:
    __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_base_model(*lowercase			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Any:
    __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*lowercase			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Any:
    __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_multiple_choice(*lowercase			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							str:
    __UpperCamelCase      ,		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs_for_common()
    __UpperCamelCase				      =  True
  
    for model_class in self.all_model_classes:
     if model_class.__name__ == "FunnelBaseModel":
      continue
     __UpperCamelCase				      =  model_class(lowercase			)
     model.to(lowercase			)
     model.train()
     __UpperCamelCase				      =  self._prepare_for_class(lowercase		,	lowercase		,	return_labels=lowercase			)
     __UpperCamelCase				      =  model(**lowercase			).loss
     loss.backward()
  
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase			)  ->							List[Any]:
    if hasattr(lowercase		,	"""weight"""			) and module.weight is not None:
     module.weight.data.fill_(3			)
    if hasattr(lowercase		,	"""bias"""			) and module.bias is not None:
     module.bias.data.fill_(3			)
  
    for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
     if hasattr(lowercase		,	lowercase			) and getattr(lowercase		,	lowercase			) is not None:
      __UpperCamelCase				      =  getattr(lowercase		,	lowercase			)
      weight.data.fill_(3			)
  
  
  @require_torch
  @require_sentencepiece
  @require_tokenizers
  class        UpperCAmelCase__  (    unittest.TestCase):
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Tuple:
    __UpperCamelCase				      =  1_3
    __UpperCamelCase				      =  7
    __UpperCamelCase				      =  torch.arange(0		,	batch_size * sequence_length			).long().reshape(lowercase		,	lowercase			)
    __UpperCamelCase				      =  [0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
    __UpperCamelCase				      =  torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths]			)
  
    __UpperCamelCase				      =  FunnelModel.from_pretrained("""sgugger/funnel-random-tiny"""			)
    __UpperCamelCase				      =  model(lowercase		,	token_type_ids=lowercase			)[0].abs()
  
    __UpperCamelCase				      =  torch.tensor(2_344.8_352			)
    __UpperCamelCase				      =  torch.tensor(0.8_052			)
    self.assertTrue(torch.allclose(output.sum()		,	lowercase		,	atol=1E-4			)			)
    self.assertTrue(torch.allclose(output.mean()		,	lowercase		,	atol=1E-4			)			)
  
    __UpperCamelCase				      =  torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]]			)
    __UpperCamelCase				      =  model(lowercase		,	attention_mask=lowercase		,	token_type_ids=lowercase			)[0].abs()
  
    __UpperCamelCase				      =  torch.tensor(2_343.8_425			)
    __UpperCamelCase				      =  torch.tensor(0.8_049			)
    self.assertTrue(torch.allclose(output.sum()		,	lowercase		,	atol=1E-4			)			)
    self.assertTrue(torch.allclose(output.mean()		,	lowercase		,	atol=1E-4			)			)
  
  
  
  
   @slow
   def    __lowerCamelCase				(  self			)  ->							Optional[int]:
    __UpperCamelCase				      =  FunnelTokenizer.from_pretrained("""huggingface/funnel-small"""			)
    __UpperCamelCase				      =  FunnelModel.from_pretrained("""huggingface/funnel-small"""			)
    __UpperCamelCase				      =  tokenizer("""Hello! I am the Funnel Transformer model."""		,	return_tensors="""pt"""			)
    __UpperCamelCase				      =  model(**lowercase			)[0]
  
    __UpperCamelCase				      =  torch.tensor(235.7_246			)
    __UpperCamelCase				      =  torch.tensor(0.0_256			)
    self.assertTrue(torch.allclose(output.sum()		,	lowercase		,	atol=1E-4			)			)
    self.assertTrue(torch.allclose(output.mean()		,	lowercase		,	atol=1E-4			)			)
  
  
  
  
  
  