

"""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					:						Dict	,	__SCREAMING_SNAKE_CASE					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						List[Any]=1_3	,	__SCREAMING_SNAKE_CASE					:						List[str]=7	,	__SCREAMING_SNAKE_CASE					:						Optional[int]=True	,	__SCREAMING_SNAKE_CASE					:						str=True	,	__SCREAMING_SNAKE_CASE					:						Tuple=True	,	__SCREAMING_SNAKE_CASE					:						List[Any]=True	,	__SCREAMING_SNAKE_CASE					:						List[str]=9_9	,	__SCREAMING_SNAKE_CASE					:						Any=[1, 1, 2]	,	__SCREAMING_SNAKE_CASE					:						int=1	,	__SCREAMING_SNAKE_CASE					:						List[str]=3_2	,	__SCREAMING_SNAKE_CASE					:						List[str]=4	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]=8	,	__SCREAMING_SNAKE_CASE					:						List[str]=3_7	,	__SCREAMING_SNAKE_CASE					:						Tuple="gelu_new"	,	__SCREAMING_SNAKE_CASE					:						List[Any]=0.1	,	__SCREAMING_SNAKE_CASE					:						str=0.1	,	__SCREAMING_SNAKE_CASE					:						Dict=0.0	,	__SCREAMING_SNAKE_CASE					:						List[str]=5_1_2	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]=3	,	__SCREAMING_SNAKE_CASE					:						List[Any]=0.0_2	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]=3	,	__SCREAMING_SNAKE_CASE					:						Optional[int]=4	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]=None	,	__SCREAMING_SNAKE_CASE					:						Tuple=False	,	):
							A     =   parent
							A     =   batch_size
							A     =   seq_length
							A     =   is_training
							A     =   use_input_mask
							A     =   use_token_type_ids
							A     =   use_labels
							A     =   vocab_size
							A     =   block_sizes
							A     =   num_decoder_layers
							A     =   d_model
							A     =   n_head
							A     =   d_head
							A     =   d_inner
							A     =   hidden_act
							A     =   hidden_dropout
							A     =   attention_dropout
							A     =   activation_dropout
							A     =   max_position_embeddings
							A     =   type_vocab_size
							A     =   2
							A     =   num_labels
							A     =   num_choices
							A     =   scope
							A     =   initializer_std

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


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							A     =   ids_tensor([self.batch_size, self.seq_length]	,	self.vocab_size)

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

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

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

							A     =   self.get_config()

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


		def    SCREAMING_SNAKE_CASE__  (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    SCREAMING_SNAKE_CASE__  (self					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						int	,	__SCREAMING_SNAKE_CASE					:						Dict	,	__SCREAMING_SNAKE_CASE					:						Dict	,	__SCREAMING_SNAKE_CASE					:						Optional[int]	,	__SCREAMING_SNAKE_CASE					:						Optional[int]	,	__SCREAMING_SNAKE_CASE					:						Optional[int]	,	__SCREAMING_SNAKE_CASE					:						Any	,	__SCREAMING_SNAKE_CASE					:						List[str]	,	):
							A     =   FunnelModel(config=__SCREAMING_SNAKE_CASE)
							model.to(__SCREAMING_SNAKE_CASE)
							model.eval()
							A     =   model(__SCREAMING_SNAKE_CASE	,	attention_mask=__SCREAMING_SNAKE_CASE	,	token_type_ids=__SCREAMING_SNAKE_CASE)
							A     =   model(__SCREAMING_SNAKE_CASE	,	token_type_ids=__SCREAMING_SNAKE_CASE)
							A     =   model(__SCREAMING_SNAKE_CASE)
							self.parent.assertEqual(result.last_hidden_state.shape	,	(self.batch_size, self.seq_length, self.d_model))

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

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


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						int	,	__SCREAMING_SNAKE_CASE					:						int	,	__SCREAMING_SNAKE_CASE					:						Any	,	__SCREAMING_SNAKE_CASE					:						Dict	,	__SCREAMING_SNAKE_CASE					:						Dict	,	__SCREAMING_SNAKE_CASE					:						Dict	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]	,	__SCREAMING_SNAKE_CASE					:						List[Any]	,	):
							A     =   FunnelBaseModel(config=__SCREAMING_SNAKE_CASE)
							model.to(__SCREAMING_SNAKE_CASE)
							model.eval()
							A     =   model(__SCREAMING_SNAKE_CASE	,	attention_mask=__SCREAMING_SNAKE_CASE	,	token_type_ids=__SCREAMING_SNAKE_CASE)
							A     =   model(__SCREAMING_SNAKE_CASE	,	token_type_ids=__SCREAMING_SNAKE_CASE)
							A     =   model(__SCREAMING_SNAKE_CASE)
							self.parent.assertEqual(result.last_hidden_state.shape	,	(self.batch_size, 2, self.d_model))

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

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


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						str	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]	,	__SCREAMING_SNAKE_CASE					:						Optional[int]	,	__SCREAMING_SNAKE_CASE					:						Optional[int]	,	__SCREAMING_SNAKE_CASE					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						Dict	,	__SCREAMING_SNAKE_CASE					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						Tuple	,	):
							A     =   self.num_labels
							A     =   FunnelForPreTraining(config=__SCREAMING_SNAKE_CASE)
							model.to(__SCREAMING_SNAKE_CASE)
							model.eval()
							A     =   model(__SCREAMING_SNAKE_CASE	,	attention_mask=__SCREAMING_SNAKE_CASE	,	token_type_ids=__SCREAMING_SNAKE_CASE	,	labels=__SCREAMING_SNAKE_CASE)
							self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.seq_length))


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						int	,	__SCREAMING_SNAKE_CASE					:						str	,	__SCREAMING_SNAKE_CASE					:						Tuple	,	):
							A     =   FunnelForMaskedLM(config=__SCREAMING_SNAKE_CASE)
							model.to(__SCREAMING_SNAKE_CASE)
							model.eval()
							A     =   model(__SCREAMING_SNAKE_CASE	,	attention_mask=__SCREAMING_SNAKE_CASE	,	token_type_ids=__SCREAMING_SNAKE_CASE	,	labels=__SCREAMING_SNAKE_CASE)
							self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.seq_length, self.vocab_size))


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						str	,	__SCREAMING_SNAKE_CASE					:						str	,	__SCREAMING_SNAKE_CASE					:						int	,	__SCREAMING_SNAKE_CASE					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						Any	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]	,	__SCREAMING_SNAKE_CASE					:						Optional[int]	,	):
							A     =   self.num_labels
							A     =   FunnelForSequenceClassification(__SCREAMING_SNAKE_CASE)
							model.to(__SCREAMING_SNAKE_CASE)
							model.eval()
							A     =   model(__SCREAMING_SNAKE_CASE	,	attention_mask=__SCREAMING_SNAKE_CASE	,	token_type_ids=__SCREAMING_SNAKE_CASE	,	labels=__SCREAMING_SNAKE_CASE)
							self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.num_labels))


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


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]	,	__SCREAMING_SNAKE_CASE					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						Any	,	__SCREAMING_SNAKE_CASE					:						Optional[int]	,	__SCREAMING_SNAKE_CASE					:						int	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]	,	__SCREAMING_SNAKE_CASE					:						List[Any]	,	):
							A     =   self.num_labels
							A     =   FunnelForTokenClassification(config=__SCREAMING_SNAKE_CASE)
							model.to(__SCREAMING_SNAKE_CASE)
							model.eval()
							A     =   model(__SCREAMING_SNAKE_CASE	,	attention_mask=__SCREAMING_SNAKE_CASE	,	token_type_ids=__SCREAMING_SNAKE_CASE	,	labels=__SCREAMING_SNAKE_CASE)
							self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.seq_length, self.num_labels))


		def    SCREAMING_SNAKE_CASE__  (self					:						Dict	,	__SCREAMING_SNAKE_CASE					:						Any	,	__SCREAMING_SNAKE_CASE					:						Optional[int]	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						int	,	__SCREAMING_SNAKE_CASE					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						int	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]	,	):
							A     =   FunnelForQuestionAnswering(config=__SCREAMING_SNAKE_CASE)
							model.to(__SCREAMING_SNAKE_CASE)
							model.eval()
							A     =   model(
							    __SCREAMING_SNAKE_CASE	,	attention_mask=__SCREAMING_SNAKE_CASE	,	token_type_ids=__SCREAMING_SNAKE_CASE	,	start_positions=__SCREAMING_SNAKE_CASE	,	end_positions=__SCREAMING_SNAKE_CASE	,	)
							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    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							A     =   self.prepare_config_and_inputs()
							(
							    (
							    A
							)     ,       (
							    A
							)     ,       (
							    A
							)     ,       (
							    A
							)     ,       (
							    A
							)     ,       (
							    A
							)     ,       (
							    A
							)     ,       (
							    A
							)     ,       
							)     =   config_and_inputs
							A     =   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
							return config, inputs_dict





@require_torch
class      __UpperCamelCase					(  _A	,						_A	,						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    SCREAMING_SNAKE_CASE__  (self					:						Dict	,	__SCREAMING_SNAKE_CASE					:						Optional[int]	,	__SCREAMING_SNAKE_CASE					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						Any=False):
							A     =   super()._prepare_for_class(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE	,	return_labels=__SCREAMING_SNAKE_CASE)

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


		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							A     =   FunnelModelTester(self)
							A     =   ConfigTester(self	,	config_class=__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							self.config_tester.run_common_tests()


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							A     =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							A     =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]):
							A     =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]):
							A     =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						Tuple):
							if hasattr(__SCREAMING_SNAKE_CASE	,	"weight") and module.weight is not None:
												module.weight.data.fill_(3)
							if hasattr(__SCREAMING_SNAKE_CASE	,	"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(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE) and getattr(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE) is not None:
																	A     =   getattr(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)
																	weight.data.fill_(3)





@require_torch
class      __UpperCamelCase					(  _A	,						unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       False
		SCREAMING_SNAKE_CASE														=       False
		SCREAMING_SNAKE_CASE														=       (
		    (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
		)


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     =   FunnelModelTester(self	,	base=__SCREAMING_SNAKE_CASE)
							A     =   ConfigTester(self	,	config_class=__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							self.config_tester.run_common_tests()


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							A     =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_base_model(*__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     ,       A     =   self.model_tester.prepare_config_and_inputs_for_common()
							A     =   True

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


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						Tuple):
							if hasattr(__SCREAMING_SNAKE_CASE	,	"weight") and module.weight is not None:
												module.weight.data.fill_(3)
							if hasattr(__SCREAMING_SNAKE_CASE	,	"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(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE) and getattr(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE) is not None:
																	A     =   getattr(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)
																	weight.data.fill_(3)





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


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   1_3
							A     =   7
							A     =   torch.arange(0	,	batch_size * sequence_length).long().reshape(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)
							A     =   [0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
							A     =   torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths])

							A     =   FunnelModel.from_pretrained("sgugger/funnel-random-tiny")
							A     =   model(__SCREAMING_SNAKE_CASE	,	token_type_ids=__SCREAMING_SNAKE_CASE)[0].abs()

							A     =   torch.tensor(2_3_4_4.8_3_5_2)
							A     =   torch.tensor(0.8_0_5_2)
							self.assertTrue(torch.allclose(output.sum()	,	__SCREAMING_SNAKE_CASE	,	atol=1E-4))
							self.assertTrue(torch.allclose(output.mean()	,	__SCREAMING_SNAKE_CASE	,	atol=1E-4))

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

							A     =   torch.tensor(2_3_4_3.8_4_2_5)
							A     =   torch.tensor(0.8_0_4_9)
							self.assertTrue(torch.allclose(output.sum()	,	__SCREAMING_SNAKE_CASE	,	atol=1E-4))
							self.assertTrue(torch.allclose(output.mean()	,	__SCREAMING_SNAKE_CASE	,	atol=1E-4))


		@slow
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]):
							A     =   FunnelTokenizer.from_pretrained("huggingface/funnel-small")
							A     =   FunnelModel.from_pretrained("huggingface/funnel-small")
							A     =   tokenizer("Hello! I am the Funnel Transformer model."	,	return_tensors="pt")
							A     =   model(**__SCREAMING_SNAKE_CASE)[0]

							A     =   torch.tensor(2_3_5.7_2_4_6)
							A     =   torch.tensor(0.0_2_5_6)
							self.assertTrue(torch.allclose(output.sum()	,	__SCREAMING_SNAKE_CASE	,	atol=1E-4))
							self.assertTrue(torch.allclose(output.mean()	,	__SCREAMING_SNAKE_CASE	,	atol=1E-4))






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__   			=      {
    'configuration_mpnet': ['MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MPNetConfig'],
    'tokenization_mpnet': ['MPNetTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      ['MPNetTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MPNetForMaskedLM',
     'MPNetForMultipleChoice',
     'MPNetForQuestionAnswering',
     'MPNetForSequenceClassification',
     'MPNetForTokenClassification',
     'MPNetLayer',
     'MPNetModel',
     'MPNetPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFMPNetEmbeddings',
     'TFMPNetForMaskedLM',
     'TFMPNetForMultipleChoice',
     'TFMPNetForQuestionAnswering',
     'TFMPNetForSequenceClassification',
     'TFMPNetForTokenClassification',
     'TFMPNetMainLayer',
     'TFMPNetModel',
     'TFMPNetPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
 from .tokenization_mpnet import MPNetTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mpnet_fast import MPNetTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mpnet import (
      MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      MPNetForMaskedLM,
      MPNetForMultipleChoice,
      MPNetForQuestionAnswering,
      MPNetForSequenceClassification,
      MPNetForTokenClassification,
      MPNetLayer,
      MPNetModel,
      MPNetPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mpnet import (
      TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMPNetEmbeddings,
      TFMPNetForMaskedLM,
      TFMPNetForMultipleChoice,
      TFMPNetForQuestionAnswering,
      TFMPNetForSequenceClassification,
      TFMPNetForTokenClassification,
      TFMPNetMainLayer,
      TFMPNetModel,
      TFMPNetPreTrainedModel,
  )

else:
 import sys

 lowercase__   			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


