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		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=2		,      __SCREAMING_SNAKE_CASE=True		,      __SCREAMING_SNAKE_CASE=True		,      __SCREAMING_SNAKE_CASE=10		,      __SCREAMING_SNAKE_CASE=10		,      __SCREAMING_SNAKE_CASE=8		,      __SCREAMING_SNAKE_CASE=[2, 4, 4]		,      __SCREAMING_SNAKE_CASE=3		,      __SCREAMING_SNAKE_CASE=768		,      __SCREAMING_SNAKE_CASE=5		,      __SCREAMING_SNAKE_CASE=4		,      __SCREAMING_SNAKE_CASE=37		,      __SCREAMING_SNAKE_CASE="gelu_fast"		,      __SCREAMING_SNAKE_CASE=0.0		,      __SCREAMING_SNAKE_CASE=0.0		,      __SCREAMING_SNAKE_CASE=0.0_2		,      __SCREAMING_SNAKE_CASE=1e-06		,      __SCREAMING_SNAKE_CASE=True		,      __SCREAMING_SNAKE_CASE=None		,      )						->List[str]:
								lowerCAmelCase     		=		parent
								lowerCAmelCase     		=		batch_size
								lowerCAmelCase     		=		is_training
								lowerCAmelCase     		=		use_labels
								lowerCAmelCase     		=		num_labels
								lowerCAmelCase     		=		image_size
								lowerCAmelCase     		=		num_frames
								lowerCAmelCase     		=		tubelet_size
								lowerCAmelCase     		=		num_channels
								lowerCAmelCase     		=		hidden_size
								lowerCAmelCase     		=		num_hidden_layers
								lowerCAmelCase     		=		num_attention_heads
								lowerCAmelCase     		=		intermediate_size
								lowerCAmelCase     		=		hidden_act
								lowerCAmelCase     		=		hidden_dropout_prob
								lowerCAmelCase     		=		attention_probs_dropout_prob
								lowerCAmelCase     		=		initializer_range
								lowerCAmelCase     		=		layer_norm_eps
								lowerCAmelCase     		=		qkv_bias
								lowerCAmelCase     		=		scope

								lowerCAmelCase     		=		(
								    (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       SCREAMING_SNAKE_CASE_    (							self    )						->List[str]:
								lowerCAmelCase     		=		floats_tensor(
								    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]    )

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

								lowerCAmelCase     		=		self.get_config()

								return config, pixel_values, labels


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Union[str, Any]:
								lowerCAmelCase     		=		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     		=		self.num_labels
								return config


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Dict:
								lowerCAmelCase     		=		VivitModel(config=__SCREAMING_SNAKE_CASE    )
								model.to(__SCREAMING_SNAKE_CASE    )
								model.eval()
								lowerCAmelCase     		=		model(__SCREAMING_SNAKE_CASE    )
								self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, self.seq_length, self.hidden_size)    )


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Tuple:
								lowerCAmelCase     		=		VivitForVideoClassification(__SCREAMING_SNAKE_CASE    )
								model.to(__SCREAMING_SNAKE_CASE    )
								model.eval()

								lowerCAmelCase     		=		model(__SCREAMING_SNAKE_CASE    )

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




	def       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[int]:
								lowerCAmelCase     		=		self.prepare_config_and_inputs()
								lowerCAmelCase		,				lowerCAmelCase		,				lowerCAmelCase     		=		config_and_inputs
								lowerCAmelCase     		=		{'''pixel_values''': pixel_values}
								return config, inputs_dict




@require_torch
class        lowercase_  (  UpperCamelCase_				,					unittest.TestCase						):



	"""simple docstring"""





	UpperCAmelCase_  : Tuple       =    (VivitModel, VivitForVideoClassification) if is_torch_available() else ()

	UpperCAmelCase_  : Any       =    False
	UpperCAmelCase_  : Union[str, Any]       =    False
	UpperCAmelCase_  : List[Any]       =    False
	UpperCAmelCase_  : Dict       =    False


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Any:
								lowerCAmelCase     		=		VivitModelTester(self    )
								lowerCAmelCase     		=		ConfigTester(self		,      config_class=__SCREAMING_SNAKE_CASE		,      has_text_modality=__SCREAMING_SNAKE_CASE		,      hidden_size=37    )


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=False    )						->Optional[Any]:
								lowerCAmelCase     		=		copy.deepcopy(__SCREAMING_SNAKE_CASE    )

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

								return inputs_dict


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[int]:
								self.config_tester.run_common_tests()


	@unittest.skip(reason='''Vivit does not use inputs_embeds'''    )
	def       SCREAMING_SNAKE_CASE_    (							self    )						->Tuple:
								pass


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Tuple:
								lowerCAmelCase		,				lowerCAmelCase     		=		self.model_tester.prepare_config_and_inputs_for_common()

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


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Tuple:
								lowerCAmelCase		,				lowerCAmelCase     		=		self.model_tester.prepare_config_and_inputs_for_common()

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

															lowerCAmelCase     		=		['''pixel_values''', '''head_mask''']
															self.assertListEqual(arg_names[:2]		,      __SCREAMING_SNAKE_CASE    )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->int:
								lowerCAmelCase     		=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE    )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Union[str, Any]:
								lowerCAmelCase     		=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_video_classification(*__SCREAMING_SNAKE_CASE    )


	@slow
	def       SCREAMING_SNAKE_CASE_    (							self    )						->Union[str, Any]:
								for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															lowerCAmelCase     		=		VivitModel.from_pretrained(__SCREAMING_SNAKE_CASE    )
															self.assertIsNotNone(__SCREAMING_SNAKE_CASE    )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->int:
								lowerCAmelCase		,				lowerCAmelCase     		=		self.model_tester.prepare_config_and_inputs_for_common()
								lowerCAmelCase     		=		True

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

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

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

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

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

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

															lowerCAmelCase     		=		outputs.attentions

															self.assertEqual(len(__SCREAMING_SNAKE_CASE    )		,      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       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[Any]:
								def check_hidden_states_output(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    ):
															lowerCAmelCase     		=		model_class(__SCREAMING_SNAKE_CASE    )
															model.to(__SCREAMING_SNAKE_CASE    )
															model.eval()

															with torch.no_grad():
																						lowerCAmelCase     		=		model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )    )

															lowerCAmelCase     		=		outputs.hidden_states
															lowerCAmelCase     		=		self.model_tester.num_hidden_layers + 1
															self.assertEqual(len(__SCREAMING_SNAKE_CASE    )		,      __SCREAMING_SNAKE_CASE    )

															lowerCAmelCase     		=		self.model_tester.seq_length

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

								lowerCAmelCase		,				lowerCAmelCase     		=		self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															lowerCAmelCase     		=		True
															check_hidden_states_output(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )

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

															check_hidden_states_output(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )




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

def        SCREAMING_SNAKE_CASE_					(						)					->     Union[str, Any]:
							lowerCAmelCase     		=		hf_hub_download(
							    repo_id='''hf-internal-testing/spaghetti-video'''       ,  filename='''eating_spaghetti_32_frames.npy'''       ,  repo_type='''dataset'''							)
							lowerCAmelCase     		=		np.load(snake_case__							)
							return list(snake_case__							)






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



	"""simple docstring"""





	@cached_property
	def       SCREAMING_SNAKE_CASE_    (							self    )						->Any:
								return VivitImageProcessor() if is_vision_available() else None




	@slow
	def       SCREAMING_SNAKE_CASE_    (							self    )						->Dict:
								lowerCAmelCase     		=		VivitForVideoClassification.from_pretrained('''google/vivit-b-16x2-kinetics400'''    ).to(__SCREAMING_SNAKE_CASE    )

								lowerCAmelCase     		=		self.default_image_processor
								lowerCAmelCase     		=		prepare_video()
								lowerCAmelCase     		=		image_processor(__SCREAMING_SNAKE_CASE		,      return_tensors='''pt'''    ).to(__SCREAMING_SNAKE_CASE    )

								# forward pass
								with torch.no_grad():
															lowerCAmelCase     		=		model(**__SCREAMING_SNAKE_CASE    )

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

								# taken from original model
								lowerCAmelCase     		=		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(__SCREAMING_SNAKE_CASE    )

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




from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
lowercase__      :    int          =     load_iris()
iris.keys()


print(f'Target names: \n {iris.target_names} ')
print(f'\n Features: \n {iris.feature_names}')

# Train set e Test set
lowercase__     ,   lowercase__     ,   lowercase__     ,   lowercase__      :    Any          =     train_test_split(
    iris['''data'''], iris['''target'''], random_state=4
)

# KNN

lowercase__      :    int          =     KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
lowercase__      :    List[str]          =     [[1, 2, 1, 4], [2, 3, 4, 5]]

lowercase__      :    int          =     knn.predict(X_new)

print(
    f'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    f' {iris["target_names"][prediction]}'
)



