


import unittest
from unittest.util import safe_repr

from transformers import AutoTokenizer, RwkvConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
							    RwkvForCausalLM,
							    RwkvModel,
							)
							from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
else:
							snake_case_  :	List[Any]							  =							False

class   A__							:




						def __init__(				self    :					Dict ,			_a    :					List[str] ,			_a    :					Optional[Any]=14 ,			_a    :					str=7 ,			_a    :					Dict=True ,			_a    :					Optional[Any]=False ,			_a    :					Optional[Any]=True ,			_a    :					Union[str, Any]=True ,			_a    :					Dict=True ,			_a    :					Any=99 ,			_a    :					Optional[Any]=32 ,			_a    :					Dict=5 ,			_a    :					Dict=37 ,			_a    :					Union[str, Any]="gelu" ,			_a    :					int=0.1 ,			_a    :					Any=0.1 ,			_a    :					Tuple=512 ,			_a    :					List[str]=16 ,			_a    :					Tuple=2 ,			_a    :					Optional[Any]=3 ,			_a    :					List[Any]=4 ,			_a    :					str=None ,			)							->   List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =parent
										_SCREAMING_SNAKE_CASE	     =batch_size
										_SCREAMING_SNAKE_CASE	     =seq_length
										_SCREAMING_SNAKE_CASE	     =is_training
										_SCREAMING_SNAKE_CASE	     =use_token_type_ids
										_SCREAMING_SNAKE_CASE	     =use_input_mask
										_SCREAMING_SNAKE_CASE	     =use_labels
										_SCREAMING_SNAKE_CASE	     =use_mc_token_ids
										_SCREAMING_SNAKE_CASE	     =vocab_size
										_SCREAMING_SNAKE_CASE	     =hidden_size
										_SCREAMING_SNAKE_CASE	     =num_hidden_layers
										_SCREAMING_SNAKE_CASE	     =intermediate_size
										_SCREAMING_SNAKE_CASE	     =hidden_act
										_SCREAMING_SNAKE_CASE	     =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE	     =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE	     =max_position_embeddings
										_SCREAMING_SNAKE_CASE	     =type_vocab_size
										_SCREAMING_SNAKE_CASE	     =type_sequence_label_size
										_SCREAMING_SNAKE_CASE	     =num_labels
										_SCREAMING_SNAKE_CASE	     =num_choices
										_SCREAMING_SNAKE_CASE	     =scope
										_SCREAMING_SNAKE_CASE	     =vocab_size - 1
										_SCREAMING_SNAKE_CASE	     =vocab_size - 1
										_SCREAMING_SNAKE_CASE	     =vocab_size - 1




						def 							__UpperCamelCase (				self    :					int     )							->   str:


										"""simple docstring"""





										return RwkvConfig.from_pretrained('''sgugger/rwkv-4-pile-7b'''     )




						def 							__UpperCamelCase (				self    :					Optional[int] ,			_a    :					List[str]=False ,			_a    :					List[Any]=False ,			_a    :					Union[str, Any]=False     )							->   List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size     )

										_SCREAMING_SNAKE_CASE	     =None
										if self.use_input_mask:
														_SCREAMING_SNAKE_CASE	     =random_attention_mask([self.batch_size, self.seq_length]     )

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

										_SCREAMING_SNAKE_CASE	     =None
										if self.use_mc_token_ids:
														_SCREAMING_SNAKE_CASE	     =ids_tensor([self.batch_size, self.num_choices] ,			self.seq_length     )

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

										_SCREAMING_SNAKE_CASE	     =self.get_config(
										    gradient_checkpointing=_a ,			scale_attn_by_inverse_layer_idx=_a ,			reorder_and_upcast_attn=_a ,			)

										return (
										    config,
										    input_ids,
										    input_mask,
										    None,
										    token_type_ids,
										    mc_token_ids,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										)




						def 							__UpperCamelCase (				self    :					Optional[int] ,			_a    :					Union[str, Any]=False ,			_a    :					str=False ,			_a    :					Optional[int]=False     )							->   Optional[Any]:


										"""simple docstring"""





										return RwkvConfig(
										    vocab_size=self.vocab_size ,			hidden_size=self.hidden_size ,			num_hidden_layers=self.num_hidden_layers ,			intermediate_size=self.intermediate_size ,			activation_function=self.hidden_act ,			resid_pdrop=self.hidden_dropout_prob ,			attn_pdrop=self.attention_probs_dropout_prob ,			n_positions=self.max_position_embeddings ,			type_vocab_size=self.type_vocab_size ,			use_cache=_a ,			bos_token_id=self.bos_token_id ,			eos_token_id=self.eos_token_id ,			pad_token_id=self.pad_token_id ,			gradient_checkpointing=_a ,			scale_attn_by_inverse_layer_idx=_a ,			reorder_and_upcast_attn=_a ,			)




						def 							__UpperCamelCase (				self    :					Any     )							->   Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.get_config()
										_SCREAMING_SNAKE_CASE	     =300
										return config




						def 							__UpperCamelCase (				self    :					str     )							->   str:


										"""simple docstring"""





										(
										    (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , 
										)	     =self.prepare_config_and_inputs()

										_SCREAMING_SNAKE_CASE	     =floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
										_SCREAMING_SNAKE_CASE	     =ids_tensor([self.batch_size, self.seq_length] ,			vocab_size=2     )

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




						def 							__UpperCamelCase (				self    :					Optional[int] ,			_a    :					List[Any] ,			_a    :					int ,			_a    :					List[str] ,			_a    :					Dict ,			_a    :					str ,			*_a    :					Tuple     )							->   Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =True
										_SCREAMING_SNAKE_CASE	     =RwkvModel(config=_a     )
										model.to(_a     )
										model.eval()

										_SCREAMING_SNAKE_CASE	     =model(_a     )

										self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)     )
										self.parent.assertEqual(len(result.hidden_states     ) ,			config.num_hidden_layers + 1     )




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					int ,			_a    :					Any ,			_a    :					str ,			_a    :					List[str] ,			_a    :					Tuple ,			*_a    :					List[str]     )							->   List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =RwkvForCausalLM(_a     )
										model.to(_a     )
										model.eval()

										_SCREAMING_SNAKE_CASE	     =model(_a ,			labels=_a     )
										self.parent.assertEqual(result.loss.shape ,			()     )
										self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.vocab_size)     )




						def 							__UpperCamelCase (				self    :					Dict ,			_a    :					str ,			_a    :					List[str] ,			_a    :					Tuple ,			_a    :					int ,			_a    :					Optional[int] ,			*_a    :					List[str]     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =RwkvModel(config=_a     )
										model.to(_a     )
										model.eval()

										_SCREAMING_SNAKE_CASE	     =model(_a     )
										_SCREAMING_SNAKE_CASE	     =outputs.last_hidden_state

										_SCREAMING_SNAKE_CASE	     =model(input_ids[:, :2]     )
										_SCREAMING_SNAKE_CASE	     =outputs.last_hidden_state

										# Using the state computed on the first inputs, we will get the same output
										_SCREAMING_SNAKE_CASE	     =model(input_ids[:, 2:] ,			state=outputs.state     )
										_SCREAMING_SNAKE_CASE	     =outputs.last_hidden_state

										self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two] ,			dim=1     ) ,			_a ,			atol=1E-5     )     )




						def 							__UpperCamelCase (				self    :					Union[str, Any] ,			_a    :					Union[str, Any] ,			_a    :					Optional[int] ,			_a    :					Union[str, Any] ,			_a    :					Union[str, Any] ,			_a    :					int ,			*_a    :					Union[str, Any] ,			_a    :					Optional[Any]=False     )							->   Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =RwkvForCausalLM(_a     )
										model.to(_a     )
										if gradient_checkpointing:
														model.gradient_checkpointing_enable()

										_SCREAMING_SNAKE_CASE	     =model(_a ,			labels=_a     )
										self.parent.assertEqual(result.loss.shape ,			()     )
										self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.vocab_size)     )
										result.loss.backward()




						def 							__UpperCamelCase (				self    :					Any     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.prepare_config_and_inputs()

										(
										    (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , 
										)	     =config_and_inputs

										_SCREAMING_SNAKE_CASE	     ={'''input_ids''': input_ids}

										return config, inputs_dict

@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0      ,   reason="See https://github.com/huggingface/transformers/pull/24204"     )
@require_torch
class   A__							(						UpperCamelCase__      ,   UpperCamelCase__      ,   UpperCamelCase__      ,   unittest.TestCase     ):
						UpperCAmelCase									=  (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
						UpperCAmelCase									=  (
						    {"feature-extraction": RwkvModel, "text-generation": RwkvForCausalLM} if is_torch_available() else {}
						)
						# all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
						UpperCAmelCase									=  False
						UpperCAmelCase									=  False
						UpperCAmelCase									=  False
						UpperCAmelCase									=  False
						UpperCAmelCase									=  False  # Rwkv does not support head masking




						def 							__UpperCamelCase (				self    :					str     )							->   Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =RwkvModelTester(self     )
										_SCREAMING_SNAKE_CASE	     =ConfigTester(
										    self ,			config_class=_a ,			n_embd=37 ,			common_properties=['''hidden_size''', '''num_hidden_layers''']     )




						def 							__UpperCamelCase (				self    :					List[str] ,			_a    :					Any ,			_a    :					Optional[Any] ,			_a    :					str=None     )							->   str:


										"""simple docstring"""





										if isinstance(_a ,			torch.Tensor     ):
														_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =member.max().item(), member.min().item()
										elif isinstance(_a ,			_a     ) or isinstance(_a ,			_a     ):
														_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =max(_a     ), min(_a     )

										if not isinstance(_a ,			_a     ):
														raise TypeError('''container should be a list or tuple'''     )
										elif len(_a     ) != 2:
														raise ValueError('''container should have 2 elements'''     )

										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =container

										_SCREAMING_SNAKE_CASE	     =(min_value >= expected_min) and (max_value <= expected_max)

										if not is_inside_interval:
														_SCREAMING_SNAKE_CASE	     ='''%s not found in %s''' % (safe_repr(_a     ), safe_repr(_a     ))
														self.fail(self._formatMessage(_a ,			_a     )     )




						def 							__UpperCamelCase (				self    :					Optional[int]     )							->   int:


										"""simple docstring"""





										self.config_tester.run_common_tests()




						def 							__UpperCamelCase (				self    :					str     )							->   List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_rwkv_model(*_a     )




						def 							__UpperCamelCase (				self    :					Dict     )							->   List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_causl_lm(*_a     )




						def 							__UpperCamelCase (				self    :					Optional[Any]     )							->   Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_state_equivalency(*_a     )




						def 							__UpperCamelCase (				self    :					Union[str, Any]     )							->   Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE	     =model_class(config=_a     )
														for name, param in model.named_parameters():
																		if "time_decay" in name:
																						if param.requires_grad:
																										self.assertTrue(param.data.max().item() == 3.0     )
																										self.assertTrue(param.data.min().item() == -5.0     )
																		elif "time_first" in name:
																						if param.requires_grad:
																										# check if it's a ones like
																										self.assertTrue(torch.allclose(param.data ,			torch.ones_like(param.data     ) ,			atol=1E-5 ,			rtol=1E-5     )     )
																		elif any(x in name for x in ['''time_mix_key''', '''time_mix_receptance''']     ):
																						if param.requires_grad:
																										self.assertInterval(
																										    param.data ,			[0.0, 1.0] ,			msg=f"Parameter {name} of model {model_class} seems not properly initialized" ,			)
																		elif "time_mix_value" in name:
																						if param.requires_grad:
																										self.assertInterval(
																										    param.data ,			[0.0, 1.3] ,			msg=f"Parameter {name} of model {model_class} seems not properly initialized" ,			)




						def 							__UpperCamelCase (				self    :					Tuple     )							->   List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs_for_common()
										_SCREAMING_SNAKE_CASE	     =True

										_SCREAMING_SNAKE_CASE	     =getattr(self.model_tester ,			'''seq_length''' ,			_a     )

										for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE	     =True
														_SCREAMING_SNAKE_CASE	     =False
														_SCREAMING_SNAKE_CASE	     =True
														_SCREAMING_SNAKE_CASE	     =model_class(_a     )
														model.to(_a     )
														model.eval()

														_SCREAMING_SNAKE_CASE	     =self._prepare_for_class(_a ,			_a     )
														_SCREAMING_SNAKE_CASE	     =inputs['''input_ids'''].shape[0]
														with torch.no_grad():
																		_SCREAMING_SNAKE_CASE	     =model(**_a     )
														_SCREAMING_SNAKE_CASE	     =outputs.attentions
														self.assertEqual(len(_a     ) ,			self.model_tester.num_hidden_layers     )

														# check that output_attentions also work using config
														del inputs_dict["output_attentions"]
														_SCREAMING_SNAKE_CASE	     =True
														_SCREAMING_SNAKE_CASE	     =model_class(_a     )
														model.to(_a     )
														model.eval()

														_SCREAMING_SNAKE_CASE	     =self._prepare_for_class(_a ,			_a     )
														_SCREAMING_SNAKE_CASE	     =inputs['''input_ids'''].shape[0]
														with torch.no_grad():
																		_SCREAMING_SNAKE_CASE	     =model(**_a     )
														_SCREAMING_SNAKE_CASE	     =outputs.attentions
														self.assertEqual(len(_a     ) ,			self.model_tester.num_hidden_layers     )

														self.assertListEqual(
														    list(attentions[0].shape[-3:]     ) ,			[batch_size, seq_len, config.hidden_size] ,			)
														_SCREAMING_SNAKE_CASE	     =len(_a     )

														# Check attention is always last and order is fine
														_SCREAMING_SNAKE_CASE	     =True
														_SCREAMING_SNAKE_CASE	     =True
														_SCREAMING_SNAKE_CASE	     =model_class(_a     )
														model.to(_a     )
														model.eval()

														_SCREAMING_SNAKE_CASE	     =self._prepare_for_class(_a ,			_a     )
														_SCREAMING_SNAKE_CASE	     =inputs['''input_ids'''].shape[0]
														with torch.no_grad():
																		_SCREAMING_SNAKE_CASE	     =model(**_a     )

														_SCREAMING_SNAKE_CASE	     =1
														self.assertEqual(out_len + added_hidden_states ,			len(_a     )     )

														_SCREAMING_SNAKE_CASE	     =outputs.attentions

														self.assertEqual(len(_a     ) ,			self.model_tester.num_hidden_layers     )
														self.assertListEqual(
														    list(self_attentions[0].shape[-3:]     ) ,			[batch_size, seq_len, config.hidden_size] ,			)




						@slow
						def 							__UpperCamelCase (				self    :					Any     )							->   Tuple:


										"""simple docstring"""





										for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_SCREAMING_SNAKE_CASE	     =RwkvModel.from_pretrained(_a     )
														self.assertIsNotNone(_a     )

@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0      ,   reason="See https://github.com/huggingface/transformers/pull/24204"     )
@slow
class   A__							(						unittest.TestCase     ):




						def 							__UpperCamelCase (				self    :					Dict     )							->   Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     ='''RWKV/rwkv-4-169m-pile'''
										_SCREAMING_SNAKE_CASE	     =AutoTokenizer.from_pretrained(self.model_id     )




						def 							__UpperCamelCase (				self    :					int     )							->   Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     ='''Hello my name is Jasmine and I am a newbie to the'''
										_SCREAMING_SNAKE_CASE	     =RwkvForCausalLM.from_pretrained(self.model_id     ).to(_a     )

										_SCREAMING_SNAKE_CASE	     =self.tokenizer('''Hello my name is''' ,			return_tensors='''pt'''     ).input_ids.to(_a     )
										_SCREAMING_SNAKE_CASE	     =model.generate(_a ,			max_new_tokens=10     )
										_SCREAMING_SNAKE_CASE	     =self.tokenizer.decode(output[0].tolist()     )

										self.assertEqual(_a ,			_a     )




						def 							__UpperCamelCase (				self    :					Dict     )							->   Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     ='''Hello my name is Jasmine and I am a newbie to the'''

										_SCREAMING_SNAKE_CASE	     =self.tokenizer('''Hello my name is''' ,			return_tensors='''pt'''     ).input_ids.to(_a     )
										_SCREAMING_SNAKE_CASE	     =RwkvForCausalLM.from_pretrained(self.model_id ,			torch_dtype=torch.bfloataa     ).to(_a     )

										_SCREAMING_SNAKE_CASE	     =model.generate(_a ,			max_new_tokens=10     )
										_SCREAMING_SNAKE_CASE	     =self.tokenizer.decode(output[0].tolist()     )

										self.assertEqual(_a ,			_a     )



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   A__							:




						def __init__(				self    :					str ,			_a    :					List[str] ,			_a    :					List[str]=2 ,			_a    :					Dict=True ,			_a    :					Tuple=True ,			_a    :					Any=10 ,			_a    :					str=10 ,			_a    :					int=8 ,			_a    :					Tuple=[2, 4, 4] ,			_a    :					Optional[int]=3 ,			_a    :					List[str]=768 ,			_a    :					int=5 ,			_a    :					Any=4 ,			_a    :					Tuple=37 ,			_a    :					Tuple="gelu_fast" ,			_a    :					Dict=0.0 ,			_a    :					Optional[Any]=0.0 ,			_a    :					Optional[Any]=0.02 ,			_a    :					str=1E-06 ,			_a    :					Tuple=True ,			_a    :					List[str]=None ,			)							->   Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =parent
										_SCREAMING_SNAKE_CASE	     =batch_size
										_SCREAMING_SNAKE_CASE	     =is_training
										_SCREAMING_SNAKE_CASE	     =use_labels
										_SCREAMING_SNAKE_CASE	     =num_labels
										_SCREAMING_SNAKE_CASE	     =image_size
										_SCREAMING_SNAKE_CASE	     =num_frames
										_SCREAMING_SNAKE_CASE	     =tubelet_size
										_SCREAMING_SNAKE_CASE	     =num_channels
										_SCREAMING_SNAKE_CASE	     =hidden_size
										_SCREAMING_SNAKE_CASE	     =num_hidden_layers
										_SCREAMING_SNAKE_CASE	     =num_attention_heads
										_SCREAMING_SNAKE_CASE	     =intermediate_size
										_SCREAMING_SNAKE_CASE	     =hidden_act
										_SCREAMING_SNAKE_CASE	     =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE	     =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE	     =initializer_range
										_SCREAMING_SNAKE_CASE	     =layer_norm_eps
										_SCREAMING_SNAKE_CASE	     =qkv_bias
										_SCREAMING_SNAKE_CASE	     =scope

										_SCREAMING_SNAKE_CASE	     =(
										    (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 							__UpperCamelCase (				self    :					List[str]     )							->   str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =floats_tensor(
										    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]     )

										_SCREAMING_SNAKE_CASE	     =None
										if self.use_labels:
														_SCREAMING_SNAKE_CASE	     =ids_tensor([self.batch_size] ,			self.num_labels     )

										_SCREAMING_SNAKE_CASE	     =self.get_config()

										return config, pixel_values, labels




						def 							__UpperCamelCase (				self    :					Optional[int]     )							->   List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =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 ,			)
										_SCREAMING_SNAKE_CASE	     =self.num_labels
										return config




						def 							__UpperCamelCase (				self    :					Union[str, Any] ,			_a    :					Optional[int] ,			_a    :					Dict ,			_a    :					Tuple     )							->   Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =VivitModel(config=_a     )
										model.to(_a     )
										model.eval()
										_SCREAMING_SNAKE_CASE	     =model(_a     )
										self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)     )




						def 							__UpperCamelCase (				self    :					Union[str, Any] ,			_a    :					Optional[Any] ,			_a    :					Any ,			_a    :					Any     )							->   int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =VivitForVideoClassification(_a     )
										model.to(_a     )
										model.eval()

										_SCREAMING_SNAKE_CASE	     =model(_a     )

										# verify the logits shape
										_SCREAMING_SNAKE_CASE	     =torch.Size((self.batch_size, self.num_labels)     )
										self.parent.assertEqual(result.logits.shape ,			_a     )




						def 							__UpperCamelCase (				self    :					Tuple     )							->   List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.prepare_config_and_inputs()
										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =config_and_inputs
										_SCREAMING_SNAKE_CASE	     ={'''pixel_values''': pixel_values}
										return config, inputs_dict

@require_torch
class   A__							(						UpperCamelCase__      ,   unittest.TestCase     ):
						UpperCAmelCase									=  (VivitModel, VivitForVideoClassification) if is_torch_available() else ()

						UpperCAmelCase									=  False
						UpperCAmelCase									=  False
						UpperCAmelCase									=  False
						UpperCAmelCase									=  False




						def 							__UpperCamelCase (				self    :					Any     )							->   Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =VivitModelTester(self     )
										_SCREAMING_SNAKE_CASE	     =ConfigTester(self ,			config_class=_a ,			has_text_modality=_a ,			hidden_size=37     )




						def 							__UpperCamelCase (				self    :					List[str] ,			_a    :					List[str] ,			_a    :					Union[str, Any] ,			_a    :					Dict=False     )							->   List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =copy.deepcopy(_a     )

										if return_labels:
														if model_class in get_values(_a     ):
																		_SCREAMING_SNAKE_CASE	     =torch.zeros(
																		    self.model_tester.batch_size ,			dtype=torch.long ,			device=_a     )

										return inputs_dict




						def 							__UpperCamelCase (				self    :					str     )							->   Optional[int]:


										"""simple docstring"""





										self.config_tester.run_common_tests()




						@unittest.skip(reason='''Vivit does not use inputs_embeds'''     )
						def 							__UpperCamelCase (				self    :					Optional[Any]     )							->   Any:


										"""simple docstring"""





										pass




						def 							__UpperCamelCase (				self    :					str     )							->   Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs_for_common()

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




						def 							__UpperCamelCase (				self    :					int     )							->   str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE	     =model_class(_a     )
														_SCREAMING_SNAKE_CASE	     =inspect.signature(model.forward     )
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														_SCREAMING_SNAKE_CASE	     =[*signature.parameters.keys()]

														_SCREAMING_SNAKE_CASE	     =['''pixel_values''', '''head_mask''']
														self.assertListEqual(arg_names[:2] ,			_a     )




						def 							__UpperCamelCase (				self    :					Tuple     )							->   List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_a     )




						def 							__UpperCamelCase (				self    :					str     )							->   Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_video_classification(*_a     )




						@slow
						def 							__UpperCamelCase (				self    :					str     )							->   Optional[Any]:


										"""simple docstring"""





										for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_SCREAMING_SNAKE_CASE	     =VivitModel.from_pretrained(_a     )
														self.assertIsNotNone(_a     )




						def 							__UpperCamelCase (				self    :					List[Any]     )							->   Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs_for_common()
										_SCREAMING_SNAKE_CASE	     =True

										for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE	     =self.model_tester.seq_length

														_SCREAMING_SNAKE_CASE	     =True
														_SCREAMING_SNAKE_CASE	     =False
														_SCREAMING_SNAKE_CASE	     =True
														_SCREAMING_SNAKE_CASE	     =model_class(_a     )
														model.to(_a     )
														model.eval()
														with torch.no_grad():
																		_SCREAMING_SNAKE_CASE	     =model(**self._prepare_for_class(_a ,			_a     )     )
														_SCREAMING_SNAKE_CASE	     =outputs.attentions
														self.assertEqual(len(_a     ) ,			self.model_tester.num_hidden_layers     )

														# check that output_attentions also work using config
														del inputs_dict["output_attentions"]
														_SCREAMING_SNAKE_CASE	     =True
														_SCREAMING_SNAKE_CASE	     =model_class(_a     )
														model.to(_a     )
														model.eval()
														with torch.no_grad():
																		_SCREAMING_SNAKE_CASE	     =model(**self._prepare_for_class(_a ,			_a     )     )
														_SCREAMING_SNAKE_CASE	     =outputs.attentions
														self.assertEqual(len(_a     ) ,			self.model_tester.num_hidden_layers     )

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

														# Check attention is always last and order is fine
														_SCREAMING_SNAKE_CASE	     =True
														_SCREAMING_SNAKE_CASE	     =True
														_SCREAMING_SNAKE_CASE	     =model_class(_a     )
														model.to(_a     )
														model.eval()
														with torch.no_grad():
																		_SCREAMING_SNAKE_CASE	     =model(**self._prepare_for_class(_a ,			_a     )     )

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

														_SCREAMING_SNAKE_CASE	     =outputs.attentions

														self.assertEqual(len(_a     ) ,			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 							__UpperCamelCase (				self    :					Any     )							->   Optional[int]:


										"""simple docstring"""





										def check_hidden_states_output(_a    :					Dict ,			_a    :					Optional[Any] ,			_a    :					List[Any]     ):
														_SCREAMING_SNAKE_CASE	     =model_class(_a     )
														model.to(_a     )
														model.eval()

														with torch.no_grad():
																		_SCREAMING_SNAKE_CASE	     =model(**self._prepare_for_class(_a ,			_a     )     )

														_SCREAMING_SNAKE_CASE	     =outputs.hidden_states
														_SCREAMING_SNAKE_CASE	     =self.model_tester.num_hidden_layers + 1
														self.assertEqual(len(_a     ) ,			_a     )

														_SCREAMING_SNAKE_CASE	     =self.model_tester.seq_length

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

										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE	     =True
														check_hidden_states_output(_a ,			_a ,			_a     )

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														_SCREAMING_SNAKE_CASE	     =True

														check_hidden_states_output(_a ,			_a ,			_a     )




						@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''     )
						def 							__UpperCamelCase (				self    :					Optional[int]     )							->   List[Any]:


										"""simple docstring"""





										pass







def   lowerCamelCase(      ):
				_SCREAMING_SNAKE_CASE	     =hf_hub_download(
				    repo_id='''hf-internal-testing/spaghetti-video'''		,filename='''eating_spaghetti_32_frames.npy'''		,repo_type='''dataset''')
				_SCREAMING_SNAKE_CASE	     =np.load(a__)
				return list(a__)


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




						@cached_property
						def 							__UpperCamelCase (				self    :					Union[str, Any]     )							->   List[Any]:


										"""simple docstring"""





										return VivitImageProcessor() if is_vision_available() else None




						@slow
						def 							__UpperCamelCase (				self    :					Optional[Any]     )							->   str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =VivitForVideoClassification.from_pretrained('''google/vivit-b-16x2-kinetics400'''     ).to(_a     )

										_SCREAMING_SNAKE_CASE	     =self.default_image_processor
										_SCREAMING_SNAKE_CASE	     =prepare_video()
										_SCREAMING_SNAKE_CASE	     =image_processor(_a ,			return_tensors='''pt'''     ).to(_a     )

										# forward pass
										with torch.no_grad():
														_SCREAMING_SNAKE_CASE	     =model(**_a     )

										# verify the logits
										_SCREAMING_SNAKE_CASE	     =torch.Size((1, 400)     )
										self.assertEqual(outputs.logits.shape ,			_a     )

										# taken from original model
										_SCREAMING_SNAKE_CASE	     =torch.tensor([-1.05_43, 2.07_64, -0.21_04, 0.44_39, -0.96_58]     ).to(_a     )

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