"""simple docstring"""


import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 					UpperCamelCase   (							lowerCAmelCase__  ):
       SCREAMING_SNAKE_CASE_						      = (EulerAncestralDiscreteScheduler,)
       SCREAMING_SNAKE_CASE_						      = 1_0







       def 						a_		(					self,				**lowerCAmelCase__) -> int:
            snake_case_   			=   {
                'num_train_timesteps': 1100,
                'beta_start': 0.0001,
                'beta_end': 0.02,
                'beta_schedule': 'linear',
            }

            config.update(**lowerCAmelCase__)
            return config







       def 						a_		(					self) -> Tuple:
            for timesteps in [10, 50, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=lowerCAmelCase__)







       def 						a_		(					self) -> str:
            for beta_start, beta_end in zip([0.00001, 0.0001, 0.001],				[0.0002, 0.002, 0.02]):
                 self.check_over_configs(beta_start=lowerCAmelCase__,				beta_end=lowerCAmelCase__)







       def 						a_		(					self) -> Tuple:
            for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=lowerCAmelCase__)







       def 						a_		(					self) -> Optional[Any]:
            for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=lowerCAmelCase__)







       def 						a_		(					self) -> List[Any]:
            snake_case_   			=   self.scheduler_classes[0]
            snake_case_   			=   self.get_scheduler_config()
            snake_case_   			=   scheduler_class(**lowerCAmelCase__)

            scheduler.set_timesteps(self.num_inference_steps)

            snake_case_   			=   torch.manual_seed(0)

            snake_case_   			=   self.dummy_model()
            snake_case_   			=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
            snake_case_   			=   sample.to(lowerCAmelCase__)

            for i, t in enumerate(scheduler.timesteps):
                 snake_case_   			=   scheduler.scale_model_input(lowerCAmelCase__,				lowerCAmelCase__)

                 snake_case_   			=   model(lowerCAmelCase__,				lowerCAmelCase__)

                 snake_case_   			=   scheduler.step(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				generator=lowerCAmelCase__)
                 snake_case_   			=   output.prev_sample

            snake_case_   			=   torch.sum(torch.abs(lowerCAmelCase__))
            snake_case_   			=   torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_sum.item() - 152.3192) < 1e-2
            assert abs(result_mean.item() - 0.1983) < 1e-3







       def 						a_		(					self) -> Tuple:
            snake_case_   			=   self.scheduler_classes[0]
            snake_case_   			=   self.get_scheduler_config(prediction_type='v_prediction')
            snake_case_   			=   scheduler_class(**lowerCAmelCase__)

            scheduler.set_timesteps(self.num_inference_steps)

            snake_case_   			=   torch.manual_seed(0)

            snake_case_   			=   self.dummy_model()
            snake_case_   			=   self.dummy_sample_deter * scheduler.init_noise_sigma
            snake_case_   			=   sample.to(lowerCAmelCase__)

            for i, t in enumerate(scheduler.timesteps):
                 snake_case_   			=   scheduler.scale_model_input(lowerCAmelCase__,				lowerCAmelCase__)

                 snake_case_   			=   model(lowerCAmelCase__,				lowerCAmelCase__)

                 snake_case_   			=   scheduler.step(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				generator=lowerCAmelCase__)
                 snake_case_   			=   output.prev_sample

            snake_case_   			=   torch.sum(torch.abs(lowerCAmelCase__))
            snake_case_   			=   torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_sum.item() - 108.4439) < 1e-2
            assert abs(result_mean.item() - 0.1412) < 1e-3







       def 						a_		(					self) -> List[str]:
            snake_case_   			=   self.scheduler_classes[0]
            snake_case_   			=   self.get_scheduler_config()
            snake_case_   			=   scheduler_class(**lowerCAmelCase__)

            scheduler.set_timesteps(self.num_inference_steps,				device=lowerCAmelCase__)
            snake_case_   			=   torch.manual_seed(0)

            snake_case_   			=   self.dummy_model()
            snake_case_   			=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
            snake_case_   			=   sample.to(lowerCAmelCase__)

            for t in scheduler.timesteps:
                 snake_case_   			=   scheduler.scale_model_input(lowerCAmelCase__,				lowerCAmelCase__)

                 snake_case_   			=   model(lowerCAmelCase__,				lowerCAmelCase__)

                 snake_case_   			=   scheduler.step(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				generator=lowerCAmelCase__)
                 snake_case_   			=   output.prev_sample

            snake_case_   			=   torch.sum(torch.abs(lowerCAmelCase__))
            snake_case_   			=   torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_sum.item() - 152.3192) < 1e-2
            assert abs(result_mean.item() - 0.1983) < 1e-3


"""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,				lowerCAmelCase__,				lowerCAmelCase__=13,				lowerCAmelCase__=7,				lowerCAmelCase__=True,				lowerCAmelCase__=True,				lowerCAmelCase__=True,				lowerCAmelCase__=True,				lowerCAmelCase__=99,				lowerCAmelCase__=[1, 1, 2],				lowerCAmelCase__=1,				lowerCAmelCase__=32,				lowerCAmelCase__=4,				lowerCAmelCase__=8,				lowerCAmelCase__=37,				lowerCAmelCase__="gelu_new",				lowerCAmelCase__=0.1,				lowerCAmelCase__=0.1,				lowerCAmelCase__=0.0,				lowerCAmelCase__=512,				lowerCAmelCase__=3,				lowerCAmelCase__=0.02,				lowerCAmelCase__=3,				lowerCAmelCase__=4,				lowerCAmelCase__=None,				lowerCAmelCase__=False,				) -> str:
            snake_case_   			=   parent
            snake_case_   			=   batch_size
            snake_case_   			=   seq_length
            snake_case_   			=   is_training
            snake_case_   			=   use_input_mask
            snake_case_   			=   use_token_type_ids
            snake_case_   			=   use_labels
            snake_case_   			=   vocab_size
            snake_case_   			=   block_sizes
            snake_case_   			=   num_decoder_layers
            snake_case_   			=   d_model
            snake_case_   			=   n_head
            snake_case_   			=   d_head
            snake_case_   			=   d_inner
            snake_case_   			=   hidden_act
            snake_case_   			=   hidden_dropout
            snake_case_   			=   attention_dropout
            snake_case_   			=   activation_dropout
            snake_case_   			=   max_position_embeddings
            snake_case_   			=   type_vocab_size
            snake_case_   			=   2
            snake_case_   			=   num_labels
            snake_case_   			=   num_choices
            snake_case_   			=   scope
            snake_case_   			=   initializer_std

            # Used in the tests to check the size of the first attention layer
            snake_case_   			=   n_head
            # Used in the tests to check the size of the first hidden state
            snake_case_   			=   self.d_model
            # Used in the tests to check the number of output hidden states/attentions
            snake_case_   			=   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:
                 snake_case_   			=   self.num_hidden_layers + 2







       def 						a_		(					self) -> Optional[int]:
            snake_case_   			=   ids_tensor([self.batch_size, self.seq_length],				self.vocab_size)

            snake_case_   			=   None
            if self.use_input_mask:
                 snake_case_   			=   ids_tensor([self.batch_size, self.seq_length],				vocab_size=2)

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

            snake_case_   			=   None
            snake_case_   			=   None
            snake_case_   			=   None
            if self.use_labels:
                 snake_case_   			=   ids_tensor([self.batch_size],				self.type_sequence_label_size)
                 snake_case_   			=   ids_tensor([self.batch_size, self.seq_length],				self.num_labels)
                 snake_case_   			=   ids_tensor([self.batch_size],				self.num_choices)
                 snake_case_   			=   ids_tensor([self.batch_size, self.seq_length],				1)

            snake_case_   			=   self.get_config()

            return (
                config,
                input_ids,
                token_type_ids,
                input_mask,
                sequence_labels,
                token_labels,
                choice_labels,
                fake_token_labels,
            )







       def 						a_		(					self) -> Dict:
            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 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				) -> Tuple:
            snake_case_   			=   FunnelModel(config=lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_   			=   model(lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__)
            snake_case_   			=   model(lowerCAmelCase__,				token_type_ids=lowerCAmelCase__)
            snake_case_   			=   model(lowerCAmelCase__)
            self.parent.assertEqual(result.last_hidden_state.shape,				(self.batch_size, self.seq_length, self.d_model))

            snake_case_   			=   False
            snake_case_   			=   model(lowerCAmelCase__)
            self.parent.assertEqual(result.last_hidden_state.shape,				(self.batch_size, self.seq_length, self.d_model))

            snake_case_   			=   False
            snake_case_   			=   model(lowerCAmelCase__)
            self.parent.assertEqual(result.last_hidden_state.shape,				(self.batch_size, self.seq_length, self.d_model))







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				) -> Any:
            snake_case_   			=   FunnelBaseModel(config=lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_   			=   model(lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__)
            snake_case_   			=   model(lowerCAmelCase__,				token_type_ids=lowerCAmelCase__)
            snake_case_   			=   model(lowerCAmelCase__)
            self.parent.assertEqual(result.last_hidden_state.shape,				(self.batch_size, 2, self.d_model))

            snake_case_   			=   False
            snake_case_   			=   model(lowerCAmelCase__)
            self.parent.assertEqual(result.last_hidden_state.shape,				(self.batch_size, 3, self.d_model))

            snake_case_   			=   False
            snake_case_   			=   model(lowerCAmelCase__)
            self.parent.assertEqual(result.last_hidden_state.shape,				(self.batch_size, 2, self.d_model))







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				) -> List[Any]:
            snake_case_   			=   self.num_labels
            snake_case_   			=   FunnelForPreTraining(config=lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_   			=   model(lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__,				labels=lowerCAmelCase__)
            self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.seq_length))







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				) -> str:
            snake_case_   			=   FunnelForMaskedLM(config=lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_   			=   model(lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__,				labels=lowerCAmelCase__)
            self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.seq_length, self.vocab_size))







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				) -> Optional[int]:
            snake_case_   			=   self.num_labels
            snake_case_   			=   FunnelForSequenceClassification(lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_   			=   model(lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__,				labels=lowerCAmelCase__)
            self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.num_labels))







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				) -> Optional[int]:
            snake_case_   			=   self.num_choices
            snake_case_   			=   FunnelForMultipleChoice(config=lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_   			=   input_ids.unsqueeze(1).expand(-1,				self.num_choices,				-1).contiguous()
            snake_case_   			=   token_type_ids.unsqueeze(1).expand(-1,				self.num_choices,				-1).contiguous()
            snake_case_   			=   input_mask.unsqueeze(1).expand(-1,				self.num_choices,				-1).contiguous()
            snake_case_   			=   model(
                lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__,				labels=lowerCAmelCase__,				)
            self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.num_choices))







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				) -> Tuple:
            snake_case_   			=   self.num_labels
            snake_case_   			=   FunnelForTokenClassification(config=lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_   			=   model(lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__,				labels=lowerCAmelCase__)
            self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.seq_length, self.num_labels))







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				) -> int:
            snake_case_   			=   FunnelForQuestionAnswering(config=lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_   			=   model(
                lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__,				start_positions=lowerCAmelCase__,				end_positions=lowerCAmelCase__,				)
            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 						a_		(					self) -> Optional[int]:
            snake_case_   			=   self.prepare_config_and_inputs()
            (
                (
                snake_case_
            )			,      (
                snake_case_
            )			,      (
                snake_case_
            )			,      (
                snake_case_
            )			,      (
                snake_case_
            )			,      (
                snake_case_
            )			,      (
                snake_case_
            )			,      (
                snake_case_
            )			,      
            )   			=   config_and_inputs
            snake_case_   			=   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
            return config, inputs_dict




@require_torch
class 					UpperCamelCase   (							lowerCAmelCase__       ,						lowerCAmelCase__       ,						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 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__=False) -> List[Any]:
            snake_case_   			=   super()._prepare_for_class(lowerCAmelCase__,				lowerCAmelCase__,				return_labels=lowerCAmelCase__)

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







       def 						a_		(					self) -> List[Any]:
            snake_case_   			=   FunnelModelTester(self)
            snake_case_   			=   ConfigTester(self,				config_class=lowerCAmelCase__)







       def 						a_		(					self) -> Tuple:
            self.config_tester.run_common_tests()







       def 						a_		(					self) -> Dict:
            snake_case_   			=   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__)







       def 						a_		(					self) -> Optional[Any]:
            snake_case_   			=   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase__)







       def 						a_		(					self) -> List[str]:
            snake_case_   			=   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__)







       def 						a_		(					self) -> int:
            snake_case_   			=   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__)







       def 						a_		(					self) -> Any:
            snake_case_   			=   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__)







       def 						a_		(					self,				lowerCAmelCase__) -> int:
            if hasattr(lowerCAmelCase__,				'weight') and module.weight is not None:
                 module.weight.data.fill_(3)
            if hasattr(lowerCAmelCase__,				'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(lowerCAmelCase__,				lowerCAmelCase__) and getattr(lowerCAmelCase__,				lowerCAmelCase__) is not None:
                      snake_case_   			=   getattr(lowerCAmelCase__,				lowerCAmelCase__)
                      weight.data.fill_(3)




@require_torch
class 					UpperCamelCase   (							lowerCAmelCase__       ,						unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_						      = False
       SCREAMING_SNAKE_CASE_						      = False
       SCREAMING_SNAKE_CASE_						      = (
           (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
       )







       def 						a_		(					self) -> List[Any]:
            snake_case_   			=   FunnelModelTester(self,				base=lowerCAmelCase__)
            snake_case_   			=   ConfigTester(self,				config_class=lowerCAmelCase__)







       def 						a_		(					self) -> Tuple:
            self.config_tester.run_common_tests()







       def 						a_		(					self) -> str:
            snake_case_   			=   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_base_model(*lowerCAmelCase__)







       def 						a_		(					self) -> Optional[Any]:
            snake_case_   			=   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__)







       def 						a_		(					self) -> List[Any]:
            snake_case_   			=   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase__)







       def 						a_		(					self) -> Union[str, Any]:
            snake_case_			,      snake_case_   			=   self.model_tester.prepare_config_and_inputs_for_common()
            snake_case_   			=   True

            for model_class in self.all_model_classes:
                 if model_class.__name__ == "FunnelBaseModel":
                      continue
                 snake_case_   			=   model_class(lowerCAmelCase__)
                 model.to(lowerCAmelCase__)
                 model.train()
                 snake_case_   			=   self._prepare_for_class(lowerCAmelCase__,				lowerCAmelCase__,				return_labels=lowerCAmelCase__)
                 snake_case_   			=   model(**lowerCAmelCase__).loss
                 loss.backward()







       def 						a_		(					self,				lowerCAmelCase__) -> Any:
            if hasattr(lowerCAmelCase__,				'weight') and module.weight is not None:
                 module.weight.data.fill_(3)
            if hasattr(lowerCAmelCase__,				'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(lowerCAmelCase__,				lowerCAmelCase__) and getattr(lowerCAmelCase__,				lowerCAmelCase__) is not None:
                      snake_case_   			=   getattr(lowerCAmelCase__,				lowerCAmelCase__)
                      weight.data.fill_(3)




@require_torch
@require_sentencepiece
@require_tokenizers
class 					UpperCamelCase   (							unittest.TestCase  ):







       def 						a_		(					self) -> Any:
            snake_case_   			=   13
            snake_case_   			=   7
            snake_case_   			=   torch.arange(0,				batch_size * sequence_length).long().reshape(lowerCAmelCase__,				lowerCAmelCase__)
            snake_case_   			=   [0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
            snake_case_   			=   torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths])

            snake_case_   			=   FunnelModel.from_pretrained('sgugger/funnel-random-tiny')
            snake_case_   			=   model(lowerCAmelCase__,				token_type_ids=lowerCAmelCase__)[0].abs()

            snake_case_   			=   torch.tensor(2344.8352)
            snake_case_   			=   torch.tensor(0.8052)
            self.assertTrue(torch.allclose(output.sum(),				lowerCAmelCase__,				atol=1e-4))
            self.assertTrue(torch.allclose(output.mean(),				lowerCAmelCase__,				atol=1e-4))

            snake_case_   			=   torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]])
            snake_case_   			=   model(lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__)[0].abs()

            snake_case_   			=   torch.tensor(2343.8425)
            snake_case_   			=   torch.tensor(0.8049)
            self.assertTrue(torch.allclose(output.sum(),				lowerCAmelCase__,				atol=1e-4))
            self.assertTrue(torch.allclose(output.mean(),				lowerCAmelCase__,				atol=1e-4))







       @slow
       def 						a_		(					self) -> str:
            snake_case_   			=   FunnelTokenizer.from_pretrained('huggingface/funnel-small')
            snake_case_   			=   FunnelModel.from_pretrained('huggingface/funnel-small')
            snake_case_   			=   tokenizer('Hello! I am the Funnel Transformer model.',				return_tensors='pt')
            snake_case_   			=   model(**lowerCAmelCase__)[0]

            snake_case_   			=   torch.tensor(235.7246)
            snake_case_   			=   torch.tensor(0.0256)
            self.assertTrue(torch.allclose(output.sum(),				lowerCAmelCase__,				atol=1e-4))
            self.assertTrue(torch.allclose(output.mean(),				lowerCAmelCase__,				atol=1e-4))

