

"""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))









import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VivitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
    import torch
    from torch import nn

    from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VivitForVideoClassification, VivitModel
    from transformers.models.vivit.modeling_vivit import VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from transformers import VivitImageProcessor







class      _lowercase					:


       """simple docstring"""




       def __init__(  self				:     Union[str, Any]   , __lowerCamelCase				:     List[Any]   , __lowerCamelCase				:     int=2   , __lowerCamelCase				:     Any=True   , __lowerCamelCase				:     Union[str, Any]=True   , __lowerCamelCase				:     int=10   , __lowerCamelCase				:     str=10   , __lowerCamelCase				:     int=8   , __lowerCamelCase				:     Optional[int]=[2, 4, 4]   , __lowerCamelCase				:     List[Any]=3   , __lowerCamelCase				:     str=768   , __lowerCamelCase				:     int=5   , __lowerCamelCase				:     Any=4   , __lowerCamelCase				:     int=37   , __lowerCamelCase				:     int="gelu_fast"   , __lowerCamelCase				:     int=0.0   , __lowerCamelCase				:     Optional[int]=0.0   , __lowerCamelCase				:     Union[str, Any]=0.0_2   , __lowerCamelCase				:     Any=1E-0_6   , __lowerCamelCase				:     Optional[int]=True   , __lowerCamelCase				:     Tuple=None   , ):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[Any]       =    parent
             lowerCamelCase__						:							int       =    batch_size
             lowerCamelCase__						:							List[Any]       =    is_training
             lowerCamelCase__						:							Optional[Any]       =    use_labels
             lowerCamelCase__						:							List[str]       =    num_labels
             lowerCamelCase__						:							Union[str, Any]       =    image_size
             lowerCamelCase__						:							Tuple       =    num_frames
             lowerCamelCase__						:							Union[str, Any]       =    tubelet_size
             lowerCamelCase__						:							Optional[int]       =    num_channels
             lowerCamelCase__						:							Any       =    hidden_size
             lowerCamelCase__						:							Optional[Any]       =    num_hidden_layers
             lowerCamelCase__						:							List[str]       =    num_attention_heads
             lowerCamelCase__						:							List[Any]       =    intermediate_size
             lowerCamelCase__						:							Union[str, Any]       =    hidden_act
             lowerCamelCase__						:							Any       =    hidden_dropout_prob
             lowerCamelCase__						:							Union[str, Any]       =    attention_probs_dropout_prob
             lowerCamelCase__						:							List[Any]       =    initializer_range
             lowerCamelCase__						:							Optional[int]       =    layer_norm_eps
             lowerCamelCase__						:							Tuple       =    qkv_bias
             lowerCamelCase__						:							List[Any]       =    scope

             lowerCamelCase__						:							Union[str, Any]       =    (
                 (self.image_size // self.tubelet_size[2])
                 * (self.image_size // self.tubelet_size[1])
                 * (self.num_frames // self.tubelet_size[0])
             ) + 1  # CLS token





       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							int       =    floats_tensor(
                 [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]			)

             lowerCamelCase__						:							Optional[int]       =    None
             if self.use_labels:
                   lowerCamelCase__						:							Optional[Any]       =    ids_tensor([self.batch_size]   , self.num_labels			)

             lowerCamelCase__						:							Any       =    self.get_config()

             return config, pixel_values, labels





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


             '''simple docstring'''



             lowerCamelCase__						:							Dict       =    VivitConfig(
                 num_frames=self.num_frames   , image_size=self.image_size   , tubelet_size=self.tubelet_size   , num_channels=self.num_channels   , 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   , initializer_range=self.initializer_range   , layer_norm_eps=self.layer_norm_eps   , qkv_bias=self.qkv_bias   , )
             lowerCamelCase__						:							Union[str, Any]       =    self.num_labels
             return config





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


             '''simple docstring'''



             lowerCamelCase__						:							Tuple       =    VivitModel(config=__lowerCamelCase			)
             model.to(__lowerCamelCase			)
             model.eval()
             lowerCamelCase__						:							Any       =    model(__lowerCamelCase			)
             self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, self.seq_length, self.hidden_size)			)





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


             '''simple docstring'''



             lowerCamelCase__						:							Dict       =    VivitForVideoClassification(__lowerCamelCase			)
             model.to(__lowerCamelCase			)
             model.eval()

             lowerCamelCase__						:							List[str]       =    model(__lowerCamelCase			)

             # verify the logits shape
             lowerCamelCase__						:							Optional[int]       =    torch.Size((self.batch_size, self.num_labels)			)
             self.parent.assertEqual(result.logits.shape   , __lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Optional[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[int]       =    self.prepare_config_and_inputs()
             lowerCamelCase__       ,  lowerCamelCase__       ,  lowerCamelCase__						:							Any       =    config_and_inputs
             lowerCamelCase__						:							int       =    {"pixel_values": pixel_values}
             return config, inputs_dict







@require_torch
class      _lowercase					(				lowercase__				,    unittest.TestCase):


       """simple docstring"""




       A__   		=     (VivitModel, VivitForVideoClassification) if is_torch_available() else ()

       A__   		=     False
       A__   		=     False
       A__   		=     False
       A__   		=     False





       def 		lowerCAmelCase	(  self				:     Optional[int]			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    VivitModelTester(self			)
             lowerCamelCase__						:							Any       =    ConfigTester(self   , config_class=__lowerCamelCase   , has_text_modality=__lowerCamelCase   , hidden_size=37			)





       def 		lowerCAmelCase	(  self				:     int   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     Dict   , __lowerCamelCase				:     Union[str, Any]=False			):


             '''simple docstring'''



             lowerCamelCase__						:							int       =    copy.deepcopy(__lowerCamelCase			)

             if return_labels:
                   if model_class in get_values(__lowerCamelCase			):
                         lowerCamelCase__						:							List[Any]       =    torch.zeros(
                             self.model_tester.batch_size   , dtype=torch.long   , device=__lowerCamelCase			)

             return inputs_dict





       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             self.config_tester.run_common_tests()





       @unittest.skip(reason="Vivit does not use inputs_embeds"			)
       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             pass





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


             '''simple docstring'''



             lowerCamelCase__       ,  lowerCamelCase__						:							int       =    self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   lowerCamelCase__						:							str       =    model_class(__lowerCamelCase			)
                   self.assertIsInstance(model.get_input_embeddings()   , (nn.Module)			)
                   lowerCamelCase__						:							Dict       =    model.get_output_embeddings()
                   self.assertTrue(x is None or isinstance(__lowerCamelCase   , nn.Linear			)			)





       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__       ,  lowerCamelCase__						:							str       =    self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   lowerCamelCase__						:							Tuple       =    model_class(__lowerCamelCase			)
                   lowerCamelCase__						:							List[Any]       =    inspect.signature(model.forward			)
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   lowerCamelCase__						:							int       =    [*signature.parameters.keys()]

                   lowerCamelCase__						:							Any       =    ["pixel_values", "head_mask"]
                   self.assertListEqual(arg_names[:2]   , __lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             lowerCamelCase__						:							str       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Tuple			):


             '''simple docstring'''



             lowerCamelCase__						:							int       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_video_classification(*__lowerCamelCase			)





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


             '''simple docstring'''



             for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   lowerCamelCase__						:							str       =    VivitModel.from_pretrained(__lowerCamelCase			)
                   self.assertIsNotNone(__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Tuple			):


             '''simple docstring'''



             lowerCamelCase__       ,  lowerCamelCase__						:							Dict       =    self.model_tester.prepare_config_and_inputs_for_common()
             lowerCamelCase__						:							List[str]       =    True

             for model_class in self.all_model_classes:
                   lowerCamelCase__						:							Any       =    self.model_tester.seq_length

                   lowerCamelCase__						:							List[str]       =    True
                   lowerCamelCase__						:							List[str]       =    False
                   lowerCamelCase__						:							Tuple       =    True
                   lowerCamelCase__						:							Dict       =    model_class(__lowerCamelCase			)
                   model.to(__lowerCamelCase			)
                   model.eval()
                   with torch.no_grad():
                         lowerCamelCase__						:							str       =    model(**self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase			)			)
                   lowerCamelCase__						:							Dict       =    outputs.attentions
                   self.assertEqual(len(__lowerCamelCase			)   , self.model_tester.num_hidden_layers			)

                   # check that output_attentions also work using config
                   del inputs_dict["output_attentions"]
                   lowerCamelCase__						:							List[str]       =    True
                   lowerCamelCase__						:							Union[str, Any]       =    model_class(__lowerCamelCase			)
                   model.to(__lowerCamelCase			)
                   model.eval()
                   with torch.no_grad():
                         lowerCamelCase__						:							List[str]       =    model(**self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase			)			)
                   lowerCamelCase__						:							Optional[int]       =    outputs.attentions
                   self.assertEqual(len(__lowerCamelCase			)   , self.model_tester.num_hidden_layers			)

                   self.assertListEqual(
                       list(attentions[0].shape[-3:]			)   , [self.model_tester.num_attention_heads, seq_len, seq_len]   , )
                   lowerCamelCase__						:							int       =    len(__lowerCamelCase			)

                   # Check attention is always last and order is fine
                   lowerCamelCase__						:							Optional[int]       =    True
                   lowerCamelCase__						:							str       =    True
                   lowerCamelCase__						:							Optional[int]       =    model_class(__lowerCamelCase			)
                   model.to(__lowerCamelCase			)
                   model.eval()
                   with torch.no_grad():
                         lowerCamelCase__						:							Union[str, Any]       =    model(**self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase			)			)

                   self.assertEqual(out_len + 1   , len(__lowerCamelCase			)			)

                   lowerCamelCase__						:							Any       =    outputs.attentions

                   self.assertEqual(len(__lowerCamelCase			)   , self.model_tester.num_hidden_layers			)

                   self.assertListEqual(
                       list(self_attentions[0].shape[-3:]			)   , [self.model_tester.num_attention_heads, seq_len, seq_len]   , )





       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             def check_hidden_states_output(__lowerCamelCase				:     str   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     Tuple			):
                   lowerCamelCase__						:							Union[str, Any]       =    model_class(__lowerCamelCase			)
                   model.to(__lowerCamelCase			)
                   model.eval()

                   with torch.no_grad():
                         lowerCamelCase__						:							Optional[Any]       =    model(**self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase			)			)

                   lowerCamelCase__						:							List[Any]       =    outputs.hidden_states
                   lowerCamelCase__						:							Optional[int]       =    self.model_tester.num_hidden_layers + 1
                   self.assertEqual(len(__lowerCamelCase			)   , __lowerCamelCase			)

                   lowerCamelCase__						:							Optional[int]       =    self.model_tester.seq_length

                   self.assertListEqual(
                       list(hidden_states[0].shape[-2:]			)   , [seq_length, self.model_tester.hidden_size]   , )

             lowerCamelCase__       ,  lowerCamelCase__						:							Optional[Any]       =    self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   lowerCamelCase__						:							Tuple       =    True
                   check_hidden_states_output(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase			)

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   lowerCamelCase__						:							int       =    True

                   check_hidden_states_output(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase			)





       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."			)
       def 		lowerCAmelCase	(  self				:     Tuple			):


             '''simple docstring'''



             pass



def  lowercase_						(						):

      """simple docstring"""




      lowerCamelCase__						:							str       =    hf_hub_download(
          repo_id="hf-internal-testing/spaghetti-video"       ,   filename="eating_spaghetti_32_frames.npy"       ,   repo_type="dataset"		)
      lowerCamelCase__						:							Union[str, Any]       =    np.load(_A		)
      return list(_A		)







@require_torch
@require_vision
class      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       @cached_property
       def 		lowerCAmelCase	(  self				:     str			):


             '''simple docstring'''



             return VivitImageProcessor() if is_vision_available() else None





       @slow
       def 		lowerCAmelCase	(  self				:     Tuple			):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[int]       =    VivitForVideoClassification.from_pretrained("google/vivit-b-16x2-kinetics400"			).to(__lowerCamelCase			)

             lowerCamelCase__						:							Tuple       =    self.default_image_processor
             lowerCamelCase__						:							List[str]       =    prepare_video()
             lowerCamelCase__						:							Optional[int]       =    image_processor(__lowerCamelCase   , return_tensors="pt"			).to(__lowerCamelCase			)

             # forward pass
             with torch.no_grad():
                   lowerCamelCase__						:							Optional[int]       =    model(**__lowerCamelCase			)

             # verify the logits
             lowerCamelCase__						:							str       =    torch.Size((1, 400)			)
             self.assertEqual(outputs.logits.shape   , __lowerCamelCase			)

             # taken from original model
             lowerCamelCase__						:							str       =    torch.tensor([-1.0_5_4_3, 2.0_7_6_4, -0.2_1_0_4, 0.4_4_3_9, -0.9_6_5_8]			).to(__lowerCamelCase			)

             self.assertTrue(torch.allclose(outputs.logits[0, :5]   , __lowerCamelCase   , atol=1E-4			)			)


