

import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import (
						    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
						    TimesformerForVideoClassification,
						    TimesformerModel,
						)
						from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from transformers import VideoMAEImageProcessor






class 	__magic_name__    :





			def __init__(		self					,  __snake_case					,  __snake_case=13					,  __snake_case=10					,  __snake_case=3					,  __snake_case=2					,  __snake_case=2					,  __snake_case=True					,  __snake_case=True					,  __snake_case=32					,  __snake_case=5					,  __snake_case=4					,  __snake_case=37					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=10					,  __snake_case=0.02					,  __snake_case="divided_space_time"					,  __snake_case=None					,  )   -> Any:




						'''simple docstring'''
						__a        =parent
						__a        =batch_size
						__a        =image_size
						__a        =num_channels
						__a        =patch_size
						__a        =num_frames
						__a        =is_training
						__a        =use_labels
						__a        =hidden_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =intermediate_size
						__a        =hidden_act
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =attention_type
						__a        =initializer_range
						__a        =scope
						__a        =num_labels

						# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
						__a        =(image_size // patch_size) ** 2
						__a        =(num_frames) * self.num_patches_per_frame + 1





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




						'''simple docstring'''
						__a        =floats_tensor(
						    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]					)

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

						__a        =self.get_config()

						return config, pixel_values, labels





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




						'''simple docstring'''
						__a        =TimesformerConfig(
						    image_size=self.image_size					,  patch_size=self.patch_size					,  num_channels=self.num_channels					,  num_frames=self.num_frames					,  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					,  attention_type=self.attention_type					,  )
						__a        =self.num_labels
						return config





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					)   -> Tuple:




						'''simple docstring'''
						__a        =TimesformerModel(config=lowerCamelCase__					)
						model.to(lowerCamelCase__					)
						model.eval()
						__a        =model(lowerCamelCase__					)
						self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size)					)





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




						'''simple docstring'''
						__a        =TimesformerForVideoClassification(lowerCamelCase__					)
						model.to(lowerCamelCase__					)
						model.eval()

						__a        =model(lowerCamelCase__					)

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





			def 		__magic_name__    (		self					)   -> Tuple:




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






@require_torch
class 	__magic_name__    (			lowerCAmelCase_  ,    lowerCAmelCase_  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						(TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
			SCREAMING_SNAKE_CASE							   =						(
			    {'feature-extraction': TimesformerModel, 'video-classification': TimesformerForVideoClassification}
			    if is_torch_available()
			    else {}
			)

			SCREAMING_SNAKE_CASE							   =						False
			SCREAMING_SNAKE_CASE							   =						False
			SCREAMING_SNAKE_CASE							   =						False
			SCREAMING_SNAKE_CASE							   =						False





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




						'''simple docstring'''
						__a        =TimesformerModelTester(self					)
						__a        =ConfigTester(
						    self					,  config_class=lowerCamelCase__					,  has_text_modality=lowerCamelCase__					,  hidden_size=37					)





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case=False					)   -> int:




						'''simple docstring'''
						__a        =copy.deepcopy(lowerCamelCase__					)

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

						return inputs_dict





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




						'''simple docstring'''
						self.config_tester.run_common_tests()





			@unittest.skip(reason='TimeSformer does not use inputs_embeds'					)
			def 		__magic_name__    (		self					)   -> List[str]:




						'''simple docstring'''
						pass





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




						'''simple docstring'''
						__a      ,    __a        =self.model_tester.prepare_config_and_inputs_for_common()

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





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




						'''simple docstring'''
						__a      ,    __a        =self.model_tester.prepare_config_and_inputs_for_common()

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

									__a        =['pixel_values']
									self.assertListEqual(arg_names[:1]					,  lowerCamelCase__					)





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




						'''simple docstring'''
						__a        =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowerCamelCase__					)





			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						__a        =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_video_classification(*lowerCamelCase__					)





			@slow
			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__a        =TimesformerModel.from_pretrained(lowerCamelCase__					)
									self.assertIsNotNone(lowerCamelCase__					)





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						if not self.has_attentions:
									pass

						else:
									__a      ,    __a        =self.model_tester.prepare_config_and_inputs_for_common()
									__a        =True

									for model_class in self.all_model_classes:
												__a        =self.model_tester.seq_length
												__a        =self.model_tester.num_frames

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

												# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
												self.assertListEqual(
												    list(attentions[0].shape[-3:]					)					,  [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]					,  )
												__a        =len(lowerCamelCase__					)

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

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

												__a        =outputs.attentions

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

												# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
												self.assertListEqual(
												    list(self_attentions[0].shape[-3:]					)					,  [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]					,  )





			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						def check_hidden_states_output(__snake_case					,  __snake_case					,  __snake_case					):
									__a        =model_class(lowerCamelCase__					)
									model.to(lowerCamelCase__					)
									model.eval()

									with torch.no_grad():
												__a        =model(**self._prepare_for_class(lowerCamelCase__					,  lowerCamelCase__					)					)

									__a        =outputs.hidden_states
									__a        =self.model_tester.num_hidden_layers + 1
									self.assertEqual(len(lowerCamelCase__					)					,  lowerCamelCase__					)

									__a        =self.model_tester.seq_length

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

						__a      ,    __a        =self.model_tester.prepare_config_and_inputs_for_common()

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

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

									check_hidden_states_output(lowerCamelCase__					,  lowerCamelCase__					,  lowerCamelCase__					)






def        UpperCamelCase_(							):




			"""simple docstring"""






			__a        =hf_hub_download(
			    repo_id='hf-internal-testing/spaghetti-video'       ,     filename='eating_spaghetti.npy'       ,     repo_type='dataset'	)
			__a        =np.load(UpperCamelCase__	)
			return list(UpperCamelCase__	)






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





			@cached_property
			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						return (
						    VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]					,  image_std=[0.5, 0.5, 0.5]					)
						    if is_vision_available()
						    else None
						)





			@slow
			def 		__magic_name__    (		self					)   -> List[str]:




						'''simple docstring'''
						__a        =TimesformerForVideoClassification.from_pretrained('facebook/timesformer-base-finetuned-k400'					).to(
						    lowerCamelCase__					)

						__a        =self.default_image_processor
						__a        =prepare_video()
						__a        =image_processor(video[:8]					,  return_tensors='pt'					).to(lowerCamelCase__					)

						# forward pass
						with torch.no_grad():
									__a        =model(**lowerCamelCase__					)

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

						__a        =torch.tensor([-0.3016, -0.7713, -0.4205]					).to(lowerCamelCase__					)

						self.assertTrue(torch.allclose(outputs.logits[0, :3]					,  lowerCamelCase__					,  atol=1e-4					)					)












from math import sqrt







def 			lowerCamelCase_		(       UpperCamelCase__				:      int      )     ->       bool:





				"""simple docstring"""


				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5 ,     int(sqrt(UpperCamelCase__      ) + 1      ) ,     6      ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True







def 			lowerCamelCase_		(       UpperCamelCase__				:      int = 1_0001      )     ->       int:





				"""simple docstring"""


				__lowerCamelCase		       =    0
				__lowerCamelCase		       =    1
				while count != nth and number < 3:
								number += 1
								if is_prime(UpperCamelCase__      ):
												count += 1
				while count != nth:
								number += 2
								if is_prime(UpperCamelCase__      ):
												count += 1
				return number


if __name__ == "__main__":
					print(f'''{solution() = }''')


