


'''simple docstring'''

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 					a__ :







				"""simple docstring"""



				def __init__(self				,	__lowercase				,	__lowercase=13				,	__lowercase=10				,	__lowercase=3				,	__lowercase=2				,	__lowercase=2				,	__lowercase=True				,	__lowercase=True				,	__lowercase=32				,	__lowercase=5				,	__lowercase=4				,	__lowercase=37				,	__lowercase="gelu"				,	__lowercase=0.1				,	__lowercase=0.1				,	__lowercase=10				,	__lowercase=0.0_2				,	__lowercase="divided_space_time"				,	__lowercase=None				,	):
										__lowerCAmelCase											= parent
										__lowerCAmelCase											= batch_size
										__lowerCAmelCase											= image_size
										__lowerCAmelCase											= num_channels
										__lowerCAmelCase											= patch_size
										__lowerCAmelCase											= num_frames
										__lowerCAmelCase											= is_training
										__lowerCAmelCase											= use_labels
										__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											= attention_type
										__lowerCAmelCase											= initializer_range
										__lowerCAmelCase											= scope
										__lowerCAmelCase											= num_labels

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




				def     _snake_case     (self						):
										__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     _snake_case     (self						):
										__lowerCAmelCase											= 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				,	)
										__lowerCAmelCase											= self.num_labels
										return config




				def     _snake_case     (self				,	__lowercase				,	__lowercase				,	__lowercase						):
										__lowerCAmelCase											= TimesformerModel(config=__lowerCAmelCase						)
										model.to(__lowerCAmelCase						)
										model.eval()
										__lowerCAmelCase											= model(__lowerCAmelCase						)
										self.parent.assertEqual(result.last_hidden_state.shape				,	(self.batch_size, self.seq_length, self.hidden_size)						)




				def     _snake_case     (self				,	__lowercase				,	__lowercase				,	__lowercase						):
										__lowerCAmelCase											= TimesformerForVideoClassification(__lowerCAmelCase						)
										model.to(__lowerCAmelCase						)
										model.eval()

										__lowerCAmelCase											= model(__lowerCAmelCase						)

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




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


@require_torch
class 					a__ (       a__							,  a__							,  unittest.TestCase					):







				"""simple docstring"""



				__UpperCamelCase			:						Any		=		(TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
				__UpperCamelCase			:						Tuple		=		(
				    {'feature-extraction': TimesformerModel, 'video-classification': TimesformerForVideoClassification}
				    if is_torch_available()
				    else {}
				)

				__UpperCamelCase			:						Union[str, Any]		=		False
				__UpperCamelCase			:						str		=		False
				__UpperCamelCase			:						Optional[Any]		=		False
				__UpperCamelCase			:						Any		=		False




				def     _snake_case     (self						):
										__lowerCAmelCase											= TimesformerModelTester(self						)
										__lowerCAmelCase											= ConfigTester(
										    self				,	config_class=__lowerCAmelCase				,	has_text_modality=__lowerCAmelCase				,	hidden_size=37						)




				def     _snake_case     (self				,	__lowercase				,	__lowercase				,	__lowercase=False						):
										__lowerCAmelCase											= copy.deepcopy(__lowerCAmelCase						)

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

										return inputs_dict




				def     _snake_case     (self						):
										self.config_tester.run_common_tests()




				@unittest.skip(reason='''TimeSformer does not use inputs_embeds'''						)
				def     _snake_case     (self						):
										pass




				def     _snake_case     (self						):
										__lowerCAmelCase											= self.model_tester.prepare_config_and_inputs_for_common()

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




				def     _snake_case     (self						):
										__lowerCAmelCase											= self.model_tester.prepare_config_and_inputs_for_common()

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

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




				def     _snake_case     (self						):
										__lowerCAmelCase											= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__lowerCAmelCase						)




				def     _snake_case     (self						):
										__lowerCAmelCase											= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_video_classification(*__lowerCAmelCase						)




				@slow
				def     _snake_case     (self						):
										for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase											= TimesformerModel.from_pretrained(__lowerCAmelCase						)
																self.assertIsNotNone(__lowerCAmelCase						)




				def     _snake_case     (self						):
										if not self.has_attentions:
																pass

										else:
																__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											= self.model_tester.num_frames

																						__lowerCAmelCase											= True
																						__lowerCAmelCase											= False
																						__lowerCAmelCase											= True
																						__lowerCAmelCase											= model_class(__lowerCAmelCase						)
																						model.to(__lowerCAmelCase						)
																						model.eval()
																						with torch.no_grad():
																												__lowerCAmelCase											= model(**self._prepare_for_class(__lowerCAmelCase				,	__lowerCAmelCase						)						)
																						__lowerCAmelCase											= 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											= True
																						__lowerCAmelCase											= model_class(__lowerCAmelCase						)
																						model.to(__lowerCAmelCase						)
																						model.eval()
																						with torch.no_grad():
																												__lowerCAmelCase											= model(**self._prepare_for_class(__lowerCAmelCase				,	__lowerCAmelCase						)						)
																						__lowerCAmelCase											= 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]				,	)
																						__lowerCAmelCase											= len(__lowerCAmelCase						)

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

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

																						__lowerCAmelCase											= 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     _snake_case     (self						):
										def check_hidden_states_output(__lowercase				,	__lowercase				,	__lowercase						):
																__lowerCAmelCase											= model_class(__lowerCAmelCase						)
																model.to(__lowerCAmelCase						)
																model.eval()

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

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

																__lowerCAmelCase											= self.model_tester.seq_length

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

										__lowerCAmelCase											= self.model_tester.prepare_config_and_inputs_for_common()

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

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

																check_hidden_states_output(__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase						)

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




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







				"""simple docstring"""



				@cached_property
				def     _snake_case     (self						):
										# logits were tested with a different mean and std, so we use the same here
										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     _snake_case     (self						):
										__lowerCAmelCase											= TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400'''						).to(
										    __lowerCAmelCase						)

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

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

										# verify the logits
										__lowerCAmelCase											= torch.Size((1, 4_00)						)
										self.assertEqual(outputs.logits.shape				,	__lowerCAmelCase						)

										__lowerCAmelCase											= torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]						).to(__lowerCAmelCase						)

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


'''simple docstring'''

def 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__ ,							lowercase__				:    Optional[int]  =					[], []
 while len(UpperCAmelCase     ) > 1:
  lowercase__ ,							lowercase__				:    List[str]  =					min(UpperCAmelCase     ), max(UpperCAmelCase     )
  start.append(UpperCAmelCase     )
  end.append(UpperCAmelCase     )
  collection.remove(UpperCAmelCase     )
  collection.remove(UpperCAmelCase     )
 end.reverse()
 return start + collection + end


if __name__ == "__main__":
  __a: Optional[int]          =   input("""Enter numbers separated by a comma:\n""").strip()
  __a: Tuple          =   [int(item) for item in user_input.split(""",""")]
  print(*merge_sort(unsorted), sep=""",""")

