





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    __lowerCAmelCase			:






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


							'''simple docstring'''



							snake_case_		:       Tuple           =    parent
							snake_case_		:       Optional[Any]           =    batch_size
							snake_case_		:       Union[str, Any]           =    image_size
							snake_case_		:       Tuple           =    num_channels
							snake_case_		:       int           =    patch_size
							snake_case_		:       str           =    num_frames
							snake_case_		:       str           =    is_training
							snake_case_		:       Dict           =    use_labels
							snake_case_		:       Optional[int]           =    hidden_size
							snake_case_		:       Dict           =    num_hidden_layers
							snake_case_		:       List[str]           =    num_attention_heads
							snake_case_		:       Union[str, Any]           =    intermediate_size
							snake_case_		:       int           =    hidden_act
							snake_case_		:       int           =    hidden_dropout_prob
							snake_case_		:       Any           =    attention_probs_dropout_prob
							snake_case_		:       Tuple           =    attention_type
							snake_case_		:       Any           =    initializer_range
							snake_case_		:       Optional[int]           =    scope
							snake_case_		:       List[str]           =    num_labels

							# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
							snake_case_		:       Any           =    (image_size // patch_size) ** 2
							snake_case_		:       List[str]           =    (num_frames) * self.num_patches_per_frame + 1






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


							'''simple docstring'''



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

							snake_case_		:       List[str]           =    None
							if self.use_labels:
											snake_case_		:       Dict           =    ids_tensor([self.batch_size]   ,				self.num_labels       )

							snake_case_		:       List[Any]           =    self.get_config()

							return config, pixel_values, labels






			def      lowerCamelCase    (self       )	->  Tuple:


							'''simple docstring'''



							snake_case_		:       int           =    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   ,				)
							snake_case_		:       Tuple           =    self.num_labels
							return config






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


							'''simple docstring'''



							snake_case_		:       Dict           =    TimesformerModel(config=__magic_name__       )
							model.to(__magic_name__       )
							model.eval()
							snake_case_		:       List[str]           =    model(__magic_name__       )
							self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)       )






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


							'''simple docstring'''



							snake_case_		:       Optional[int]           =    TimesformerForVideoClassification(__magic_name__       )
							model.to(__magic_name__       )
							model.eval()

							snake_case_		:       Union[str, Any]           =    model(__magic_name__       )

							# verify the logits shape
							snake_case_		:       Tuple           =    torch.Size((self.batch_size, self.num_labels)       )
							self.parent.assertEqual(result.logits.shape   ,				__magic_name__       )






			def      lowerCamelCase    (self       )	->  int:


							'''simple docstring'''



							snake_case_		:       str           =    self.prepare_config_and_inputs()
							snake_case_     ,				snake_case_     ,				snake_case_		:       Union[str, Any]           =    config_and_inputs
							snake_case_		:       str           =    {'''pixel_values''': pixel_values}
							return config, inputs_dict




@require_torch
class    __lowerCAmelCase			(     _a,	_a,	unittest.TestCase	):
			lowerCamelCase_	:	Optional[int]			     = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
			lowerCamelCase_	:	Union[str, Any]			     = (
			    {'''feature-extraction''': TimesformerModel, '''video-classification''': TimesformerForVideoClassification}
			    if is_torch_available()
			    else {}
			)

			lowerCamelCase_	:	List[str]			     = False
			lowerCamelCase_	:	List[Any]			     = False
			lowerCamelCase_	:	Dict			     = False
			lowerCamelCase_	:	List[Any]			     = False






			def      lowerCamelCase    (self       )	->  str:


							'''simple docstring'''



							snake_case_		:       Union[str, Any]           =    TimesformerModelTester(self       )
							snake_case_		:       Tuple           =    ConfigTester(
							    self   ,				config_class=__magic_name__   ,				has_text_modality=__magic_name__   ,				hidden_size=37       )






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


							'''simple docstring'''



							snake_case_		:       Tuple           =    copy.deepcopy(__magic_name__       )

							if return_labels:
											if model_class in get_values(__magic_name__       ):
															snake_case_		:       Union[str, Any]           =    torch.zeros(
															    self.model_tester.batch_size   ,				dtype=torch.long   ,				device=__magic_name__       )

							return inputs_dict






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


							'''simple docstring'''



							self.config_tester.run_common_tests()






			@unittest.skip(reason='''TimeSformer does not use inputs_embeds'''       )
			def      lowerCamelCase    (self       )	->  Tuple:


							'''simple docstring'''



							pass






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


							'''simple docstring'''



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

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






			def      lowerCamelCase    (self       )	->  Dict:


							'''simple docstring'''



							snake_case_     ,				snake_case_		:       Optional[int]           =    self.model_tester.prepare_config_and_inputs_for_common()

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

											snake_case_		:       str           =    ['''pixel_values''']
											self.assertListEqual(arg_names[:1]   ,				__magic_name__       )






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


							'''simple docstring'''



							snake_case_		:       Optional[Any]           =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__magic_name__       )






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


							'''simple docstring'''



							snake_case_		:       str           =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_video_classification(*__magic_name__       )






			@slow
			def      lowerCamelCase    (self       )	->  int:


							'''simple docstring'''



							for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											snake_case_		:       Optional[Any]           =    TimesformerModel.from_pretrained(__magic_name__       )
											self.assertIsNotNone(__magic_name__       )






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


							'''simple docstring'''



							if not self.has_attentions:
											pass

							else:
											snake_case_     ,				snake_case_		:       int           =    self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_		:       Union[str, Any]           =    True

											for model_class in self.all_model_classes:
															snake_case_		:       List[Any]           =    self.model_tester.seq_length
															snake_case_		:       List[Any]           =    self.model_tester.num_frames

															snake_case_		:       List[str]           =    True
															snake_case_		:       Union[str, Any]           =    False
															snake_case_		:       List[str]           =    True
															snake_case_		:       List[Any]           =    model_class(__magic_name__       )
															model.to(__magic_name__       )
															model.eval()
															with torch.no_grad():
																			snake_case_		:       Optional[Any]           =    model(**self._prepare_for_class(__magic_name__   ,				__magic_name__       )       )
															snake_case_		:       Optional[Any]           =    outputs.attentions
															self.assertEqual(len(__magic_name__       )   ,				self.model_tester.num_hidden_layers       )

															# check that output_attentions also work using config
															del inputs_dict["output_attentions"]
															snake_case_		:       Dict           =    True
															snake_case_		:       Optional[int]           =    model_class(__magic_name__       )
															model.to(__magic_name__       )
															model.eval()
															with torch.no_grad():
																			snake_case_		:       Tuple           =    model(**self._prepare_for_class(__magic_name__   ,				__magic_name__       )       )
															snake_case_		:       List[Any]           =    outputs.attentions
															self.assertEqual(len(__magic_name__       )   ,				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]   ,				)
															snake_case_		:       Dict           =    len(__magic_name__       )

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

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

															snake_case_		:       Optional[Any]           =    outputs.attentions

															self.assertEqual(len(__magic_name__       )   ,				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      lowerCamelCase    (self       )	->  str:


							'''simple docstring'''



							def check_hidden_states_output(__magic_name__   ,				__magic_name__   ,				__magic_name__       ):
											snake_case_		:       List[Any]           =    model_class(__magic_name__       )
											model.to(__magic_name__       )
											model.eval()

											with torch.no_grad():
															snake_case_		:       List[Any]           =    model(**self._prepare_for_class(__magic_name__   ,				__magic_name__       )       )

											snake_case_		:       str           =    outputs.hidden_states
											snake_case_		:       List[Any]           =    self.model_tester.num_hidden_layers + 1
											self.assertEqual(len(__magic_name__       )   ,				__magic_name__       )

											snake_case_		:       Optional[Any]           =    self.model_tester.seq_length

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

							snake_case_     ,				snake_case_		:       Union[str, Any]           =    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											snake_case_		:       Optional[Any]           =    True
											check_hidden_states_output(__magic_name__   ,				__magic_name__   ,				__magic_name__       )

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											snake_case_		:       List[str]           =    True

											check_hidden_states_output(__magic_name__   ,				__magic_name__   ,				__magic_name__       )






def 	lowerCamelCase_		(							)						->		Optional[int]:






				"""simple docstring"""


				snake_case_		:       List[Any]           =    hf_hub_download(
				    repo_id='''hf-internal-testing/spaghetti-video'''					,							filename='''eating_spaghetti.npy'''					,							repo_type='''dataset'''					)
				snake_case_		:       Dict           =    np.load(_UpperCamelCase					)
				return list(_UpperCamelCase					)





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






			@cached_property
			def      lowerCamelCase    (self       )	->  Tuple:


							'''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      lowerCamelCase    (self       )	->  List[Any]:


							'''simple docstring'''



							snake_case_		:       Any           =    TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400'''       ).to(
							    __magic_name__       )

							snake_case_		:       List[str]           =    self.default_image_processor
							snake_case_		:       List[str]           =    prepare_video()
							snake_case_		:       int           =    image_processor(video[:8]   ,				return_tensors='''pt'''       ).to(__magic_name__       )

							# forward pass
							with torch.no_grad():
											snake_case_		:       Union[str, Any]           =    model(**__magic_name__       )

							# verify the logits
							snake_case_		:       Any           =    torch.Size((1, 400)       )
							self.assertEqual(outputs.logits.shape   ,				__magic_name__       )

							snake_case_		:       Any           =    torch.tensor([-0.3_016, -0.7_713, -0.4_205]       ).to(__magic_name__       )

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






'''simple docstring'''






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   str:
      snake_case__      ,							snake_case__       :   Optional[int]	      =    [], []
      while len(__SCREAMING_SNAKE_CASE		) > 1:
            snake_case__      ,							snake_case__       :   Tuple	      =    min(__SCREAMING_SNAKE_CASE		), max(__SCREAMING_SNAKE_CASE		)
            start.append(__SCREAMING_SNAKE_CASE		)
            end.append(__SCREAMING_SNAKE_CASE		)
            collection.remove(__SCREAMING_SNAKE_CASE		)
            collection.remove(__SCREAMING_SNAKE_CASE		)
      end.reverse()
      return start + collection + end


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