




import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VideoMAEConfig
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,
           VideoMAEForPreTraining,
           VideoMAEForVideoClassification,
           VideoMAEModel,
       )
       from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from transformers import VideoMAEImageProcessor





class     lowerCamelCase__:
      def __init__(				self: Tuple    ,       UpperCamelCase_: Union[str, Any]    ,       UpperCamelCase_: Union[str, Any]=13    ,       UpperCamelCase_: Union[str, Any]=10    ,       UpperCamelCase_: str=3    ,       UpperCamelCase_: Optional[Any]=2    ,       UpperCamelCase_: Tuple=2    ,       UpperCamelCase_: str=2    ,       UpperCamelCase_: Any=True    ,       UpperCamelCase_: Union[str, Any]=True    ,       UpperCamelCase_: Tuple=32    ,       UpperCamelCase_: Any=5    ,       UpperCamelCase_: str=4    ,       UpperCamelCase_: Optional[Any]=37    ,       UpperCamelCase_: Dict="gelu"    ,       UpperCamelCase_: Any=0.1    ,       UpperCamelCase_: Dict=0.1    ,       UpperCamelCase_: List[str]=10    ,       UpperCamelCase_: Union[str, Any]=0.02    ,       UpperCamelCase_: Dict=0.9    ,       UpperCamelCase_: Optional[Any]=None    ,       ):
             __lowerCamelCase   =						parent
             __lowerCamelCase   =						batch_size
             __lowerCamelCase   =						image_size
             __lowerCamelCase   =						num_channels
             __lowerCamelCase   =						patch_size
             __lowerCamelCase   =						tubelet_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   =						type_sequence_label_size
             __lowerCamelCase   =						initializer_range
             __lowerCamelCase   =						mask_ratio
             __lowerCamelCase   =						scope

             # in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
             __lowerCamelCase   =						(image_size // patch_size) ** 2
             __lowerCamelCase   =						(num_frames // tubelet_size) * self.num_patches_per_frame

             # use this variable to define bool_masked_pos
             __lowerCamelCase   =						int(mask_ratio * self.seq_length   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __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.type_sequence_label_size   )

             __lowerCamelCase   =						self.get_config()

             return config, pixel_values, labels
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             return VideoMAEConfig(
                 image_size=self.image_size    ,       patch_size=self.patch_size    ,       num_channels=self.num_channels    ,       num_frames=self.num_frames    ,       tubelet_size=self.tubelet_size    ,       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    ,       is_decoder=_a    ,       initializer_range=self.initializer_range    ,       )
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: List[Any]    ,       UpperCamelCase_: str    ,       UpperCamelCase_: Dict   ):
             __lowerCamelCase   =						VideoMAEModel(config=_a   )
             model.to(_a   )
             model.eval()
             __lowerCamelCase   =						model(_a   )
             self.parent.assertEqual(result.last_hidden_state.shape    ,       (self.batch_size, self.seq_length, self.hidden_size)   )
      def 			lowerCAmelCase__						(				self: Any    ,       UpperCamelCase_: List[str]    ,       UpperCamelCase_: str    ,       UpperCamelCase_: str   ):
             __lowerCamelCase   =						VideoMAEForPreTraining(_a   )
             model.to(_a   )
             model.eval()
             # important: each video needs to have the same number of masked patches
             # hence we define a single mask, which we then repeat for each example in the batch
             __lowerCamelCase   =						torch.ones((self.num_masks,)   )
             __lowerCamelCase   =						torch.cat([mask, torch.zeros(self.seq_length - mask.size(0   )   )]   )
             __lowerCamelCase   =						mask.expand(self.batch_size    ,       -1   ).bool()

             __lowerCamelCase   =						model(_a    ,       _a   )
             # model only returns predictions for masked patches
             __lowerCamelCase   =						mask.sum().item()
             __lowerCamelCase   =						3 * self.tubelet_size * self.patch_size**2
             self.parent.assertEqual(result.logits.shape    ,       (self.batch_size, num_masked_patches, decoder_num_labels)   )




      def 			lowerCAmelCase__						(				self: 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     lowerCamelCase__(					UpperCAmelCase__ ,     UpperCAmelCase__ ,     unittest.TestCase):
      UpperCAmelCase__      :	Optional[Any]    		=   (
          (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
      )
      UpperCAmelCase__      :	Optional[int]    		=   (
          {'feature-extraction': VideoMAEModel, 'video-classification': VideoMAEForVideoClassification}
          if is_torch_available()
          else {}
      )

      UpperCAmelCase__      :	List[Any]    		=   False
      UpperCAmelCase__      :	str    		=   False
      UpperCAmelCase__      :	Union[str, Any]    		=   False
      UpperCAmelCase__      :	Optional[Any]    		=   False
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						VideoMAEModelTester(self   )
             __lowerCamelCase   =						ConfigTester(self    ,       config_class=_a    ,       has_text_modality=_a    ,       hidden_size=37   )
      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: str    ,       UpperCamelCase_: Union[str, Any]    ,       UpperCamelCase_: Any=False   ):
             __lowerCamelCase   =						copy.deepcopy(_a   )

             if model_class == VideoMAEForPreTraining:
                    # important: each video needs to have the same number of masked patches
                    # hence we define a single mask, which we then repeat for each example in the batch
                    __lowerCamelCase   =						torch.ones((self.model_tester.num_masks,)   )
                    __lowerCamelCase   =						torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0   )   )]   )
                    __lowerCamelCase   =						mask.expand(self.model_tester.batch_size    ,       -1   ).bool()
                    __lowerCamelCase   =						bool_masked_pos.to(_a   )

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

             return inputs_dict
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             self.config_tester.run_common_tests()
      @unittest.skip(reason="""VideoMAE does not use inputs_embeds"""   )
      def 			lowerCAmelCase__						(				self: int   ):
             pass
      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase,   __lowerCamelCase   =						self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    __lowerCamelCase   =						model_class(_a   )
                    self.assertIsInstance(model.get_input_embeddings()    ,       (nn.Module)   )
                    __lowerCamelCase   =						model.get_output_embeddings()
                    self.assertTrue(x is None or isinstance(_a    ,       nn.Linear   )   )
      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase,   __lowerCamelCase   =						self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    __lowerCamelCase   =						model_class(_a   )
                    __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]    ,       _a   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*_a   )
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_pretraining(*_a   )
      @slow
      def 			lowerCAmelCase__						(				self: str   ):
             for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    __lowerCamelCase   =						VideoMAEModel.from_pretrained(_a   )
                    self.assertIsNotNone(_a   )
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             if not self.has_attentions:
                    pass

             else:
                    __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 - self.model_tester.num_masks
                           __lowerCamelCase   =						(
                               num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
                           )

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

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

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

                           __lowerCamelCase   =						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 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             def check_hidden_states_output(UpperCamelCase_: str    ,       UpperCamelCase_: Optional[Any]    ,       UpperCamelCase_: int   ):
                    __lowerCamelCase   =						model_class(_a   )
                    model.to(_a   )
                    model.eval()

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

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

                    __lowerCamelCase   =						self.model_tester.seq_length - self.model_tester.num_masks
                    __lowerCamelCase   =						num_visible_patches if model_class == VideoMAEForPreTraining else 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(_a    ,       _a    ,       _a   )

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    __lowerCamelCase   =						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 			lowerCAmelCase__						(				self: Any   ):
             pass




def 					lowerCamelCase__       (						):





       '''simple docstring'''



       __lowerCamelCase   =						hf_hub_download(
           repo_id="""hf-internal-testing/spaghetti-video"""  ,			filename="""eating_spaghetti.npy"""  ,			repo_type="""dataset"""	)
       __lowerCamelCase   =						np.load(snake_case__	)
       return list(snake_case__	)





@require_torch
@require_vision
class     lowerCamelCase__(					unittest.TestCase):
      @cached_property
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             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[str]   ):
             __lowerCamelCase   =						VideoMAEForVideoClassification.from_pretrained("""MCG-NJU/videomae-base-finetuned-kinetics"""   ).to(
                 _a   )

             __lowerCamelCase   =						self.default_image_processor
             __lowerCamelCase   =						prepare_video()
             __lowerCamelCase   =						image_processor(_a    ,       return_tensors="""pt"""   ).to(_a   )

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

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

             __lowerCamelCase   =						torch.tensor([0.3669, -0.0688, -0.2421]   ).to(_a   )

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




      @slow
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						VideoMAEForPreTraining.from_pretrained("""MCG-NJU/videomae-base-short"""   ).to(_a   )

             __lowerCamelCase   =						self.default_image_processor
             __lowerCamelCase   =						prepare_video()
             __lowerCamelCase   =						image_processor(_a    ,       return_tensors="""pt"""   ).to(_a   )

             # add boolean mask, indicating which patches to mask
             __lowerCamelCase   =						hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos"""    ,       filename="""bool_masked_pos.pt"""   )
             __lowerCamelCase   =						torch.load(_a   )

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

             # verify the logits
             __lowerCamelCase   =						torch.Size([1, 14_08, 15_36]   )
             __lowerCamelCase   =						torch.tensor(
                 [[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]    ,       device=_a   )
             self.assertEqual(outputs.logits.shape    ,       _a   )
             self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]    ,       _a    ,       atol=1E-4   )   )

             # verify the loss (`config.norm_pix_loss` = `True`)
             __lowerCamelCase   =						torch.tensor([0.5142]    ,       device=_a   )
             self.assertTrue(torch.allclose(outputs.loss    ,       _a    ,       atol=1E-4   )   )

             # verify the loss (`config.norm_pix_loss` = `False`)
             __lowerCamelCase   =						VideoMAEForPreTraining.from_pretrained("""MCG-NJU/videomae-base-short"""    ,       norm_pix_loss=_a   ).to(
                 _a   )

             with torch.no_grad():
                    __lowerCamelCase   =						model(**_a   )

             __lowerCamelCase   =						torch.tensor(torch.tensor([0.6469]   )    ,       device=_a   )
             self.assertTrue(torch.allclose(outputs.loss    ,       _a    ,       atol=1E-4   )   )







"""simple docstring"""




def     a__ (				snake_case__				)						->					list:

			if len(snake_case__				) < 2:
						return collection

			def circle_sort_util(snake_case__       , snake_case__       , snake_case__				) -> bool:
						lowerCamelCase			      =					False

						if low == high:
									return swapped

						lowerCamelCase			      =					low
						lowerCamelCase			      =					high

						while left < right:
									if collection[left] > collection[right]:
												lowerCamelCase      ,   lowerCamelCase			      =					(
												    collection[right],
												    collection[left],
												)
												lowerCamelCase			      =					True

									left += 1
									right -= 1

						if left == right and collection[left] > collection[right + 1]:
									lowerCamelCase      ,   lowerCamelCase			      =					(
									    collection[right + 1],
									    collection[left],
									)

									lowerCamelCase			      =					True

						lowerCamelCase			      =					low + int((high - low) / 2				)
						lowerCamelCase			      =					circle_sort_util(snake_case__       , snake_case__       , snake_case__				)
						lowerCamelCase			      =					circle_sort_util(snake_case__       , mid + 1       , snake_case__				)

						return swapped or left_swap or right_swap

			lowerCamelCase			      =					True

			while is_not_sorted is True:
						lowerCamelCase			      =					circle_sort_util(snake_case__       , 0       , len(snake_case__				) - 1				)

			return collection


if __name__ == "__main__":
					lowerCAmelCase							:			Tuple     							=							input("""Enter numbers separated by a comma:\n""").strip()
					lowerCAmelCase							:			List[Any]     							=							[int(item) for item in user_input.split(""",""")]
					print(circle_sort(unsorted))



