


"""simple docstring"""



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						,	UpperCamelCase__						,	UpperCamelCase__=13						,	UpperCamelCase__=10						,	UpperCamelCase__=3						,	UpperCamelCase__=2						,	UpperCamelCase__=2						,	UpperCamelCase__=2						,	UpperCamelCase__=True						,	UpperCamelCase__=True						,	UpperCamelCase__=32						,	UpperCamelCase__=5						,	UpperCamelCase__=4						,	UpperCamelCase__=37						,	UpperCamelCase__="gelu"						,	UpperCamelCase__=0.1						,	UpperCamelCase__=0.1						,	UpperCamelCase__=10						,	UpperCamelCase__=0.02						,	UpperCamelCase__=0.9						,	UpperCamelCase__=None						,	)	->		Optional[Any]:

       '''simple docstring'''




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

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

       # use this variable to define bool_masked_pos
       snake_case    :      Optional[int]      					=    int(mask_ratio * self.seq_length						)







   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       snake_case    :      Dict      					=    floats_tensor(
           [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]						)

       snake_case    :      Optional[Any]      					=    None
       if self.use_labels:
           snake_case    :      Any      					=    ids_tensor([self.batch_size]						,	self.type_sequence_label_size						)

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

       return config, pixel_values, labels







   def  lowerCamelCase		(	self						)	->		Dict:

       '''simple docstring'''




       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=UpperCamelCase__						,	initializer_range=self.initializer_range						,	)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Any:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    VideoMAEModel(config=UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       model.eval()
       snake_case    :      int      					=    model(UpperCamelCase__						)
       self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)						)







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

       '''simple docstring'''




       snake_case    :      Dict      					=    VideoMAEForPreTraining(UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       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
       snake_case    :      List[str]      					=    torch.ones((self.num_masks,)						)
       snake_case    :      int      					=    torch.cat([mask, torch.zeros(self.seq_length - mask.size(0						)						)]						)
       snake_case    :      Optional[int]      					=    mask.expand(self.batch_size						,	-1						).bool()

       snake_case    :      int      					=    model(UpperCamelCase__						,	UpperCamelCase__						)
       # model only returns predictions for masked patches
       snake_case    :      Dict      					=    mask.sum().item()
       snake_case    :      List[str]      					=    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						)	->		Dict:

       '''simple docstring'''




       snake_case    :      Optional[int]      					=    self.prepare_config_and_inputs()
       snake_case					,snake_case					,snake_case    :      Dict      					=    config_and_inputs
       snake_case    :      Optional[Any]      					=    {"pixel_values": pixel_values}
       return config, inputs_dict







@require_torch
class _lowerCAmelCase				(      snake_case_    ,							snake_case_    ,							unittest.TestCase	):
   __UpperCAmelCase :       Tuple							  =  (
       (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
   )
   __UpperCAmelCase :       Any							  =  (
       {'''feature-extraction''': VideoMAEModel, '''video-classification''': VideoMAEForVideoClassification}
       if is_torch_available()
       else {}
   )

   __UpperCAmelCase :       Optional[int]							  =  False
   __UpperCAmelCase :       int							  =  False
   __UpperCAmelCase :       Dict							  =  False
   __UpperCAmelCase :       Optional[Any]							  =  False







   def  lowerCamelCase		(	self						)	->		Dict:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    VideoMAEModelTester(self						)
       snake_case    :      str      					=    ConfigTester(self						,	config_class=UpperCamelCase__						,	has_text_modality=UpperCamelCase__						,	hidden_size=37						)







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

       '''simple docstring'''




       snake_case    :      List[Any]      					=    copy.deepcopy(UpperCamelCase__						)

       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
           snake_case    :      Optional[Any]      					=    torch.ones((self.model_tester.num_masks,)						)
           snake_case    :      str      					=    torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0						)						)]						)
           snake_case    :      List[Any]      					=    mask.expand(self.model_tester.batch_size						,	-1						).bool()
           snake_case    :      str      					=    bool_masked_pos.to(UpperCamelCase__						)

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

       return inputs_dict







   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       self.config_tester.run_common_tests()







   @unittest.skip(reason="VideoMAE does not use inputs_embeds"						)
   def  lowerCamelCase		(	self						)	->		Optional[int]:

       '''simple docstring'''




       pass







   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case					,snake_case    :      Dict      					=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           snake_case    :      Optional[Any]      					=    model_class(UpperCamelCase__						)
           self.assertIsInstance(model.get_input_embeddings()						,	(nn.Module)						)
           snake_case    :      Any      					=    model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(UpperCamelCase__						,	nn.Linear						)						)







   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    :      Any      					=    model_class(UpperCamelCase__						)
           snake_case    :      Tuple      					=    inspect.signature(model.forward						)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           snake_case    :      List[Any]      					=    [*signature.parameters.keys()]

           snake_case    :      List[Any]      					=    ["pixel_values"]
           self.assertListEqual(arg_names[:1]						,	UpperCamelCase__						)







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

       '''simple docstring'''




       snake_case    :      Tuple      					=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      List[str]      					=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__						)







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

       '''simple docstring'''




       for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           snake_case    :      List[Any]      					=    VideoMAEModel.from_pretrained(UpperCamelCase__						)
           self.assertIsNotNone(UpperCamelCase__						)







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

           for model_class in self.all_model_classes:
               snake_case    :      str      					=    self.model_tester.seq_length - self.model_tester.num_masks
               snake_case    :      Optional[int]      					=    (
                   num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
               )

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

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

               self.assertListEqual(
                   list(attentions[0].shape[-3:]						)						,	[self.model_tester.num_attention_heads, seq_len, seq_len]						,	)
               snake_case    :      List[Any]      					=    len(UpperCamelCase__						)

               # Check attention is always last and order is fine
               snake_case    :      Union[str, Any]      					=    True
               snake_case    :      List[Any]      					=    True
               snake_case    :      Optional[int]      					=    model_class(UpperCamelCase__						)
               model.to(UpperCamelCase__						)
               model.eval()
               with torch.no_grad():
                   snake_case    :      Any      					=    model(**self._prepare_for_class(UpperCamelCase__						,	UpperCamelCase__						)						)

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

               snake_case    :      Optional[int]      					=    outputs.attentions

               self.assertEqual(len(UpperCamelCase__						)						,	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						)	->		List[str]:

       '''simple docstring'''




       def check_hidden_states_output(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						):
           snake_case    :      str      					=    model_class(UpperCamelCase__						)
           model.to(UpperCamelCase__						)
           model.eval()

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

           snake_case    :      Union[str, Any]      					=    outputs.hidden_states
           snake_case    :      str      					=    self.model_tester.num_hidden_layers + 1
           self.assertEqual(len(UpperCamelCase__						)						,	UpperCamelCase__						)

           snake_case    :      Optional[int]      					=    self.model_tester.seq_length - self.model_tester.num_masks
           snake_case    :      Optional[int]      					=    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]						,	)

       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    :      List[Any]      					=    True
           check_hidden_states_output(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)

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

           check_hidden_states_output(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)







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

       '''simple docstring'''




       pass







def   __lowerCAmelCase						(						)	->						Dict:


    """simple docstring"""





    snake_case    :      Union[str, Any]      					=    hf_hub_download(
        repo_id="hf-internal-testing/spaghetti-video"						,							filename="eating_spaghetti.npy"						,							repo_type="dataset" )
    snake_case    :      Any      					=    np.load(lowercase )
    return list(lowercase )







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







   @cached_property
   def  lowerCamelCase		(	self						)	->		int:

       '''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						)	->		Optional[Any]:

       '''simple docstring'''




       snake_case    :      str      					=    VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics"						).to(
           UpperCamelCase__						)

       snake_case    :      Tuple      					=    self.default_image_processor
       snake_case    :      Tuple      					=    prepare_video()
       snake_case    :      int      					=    image_processor(UpperCamelCase__						,	return_tensors="pt"						).to(UpperCamelCase__						)

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

       # verify the logits
       snake_case    :      str      					=    torch.Size((1, 400)						)
       self.assertEqual(outputs.logits.shape						,	UpperCamelCase__						)

       snake_case    :      List[Any]      					=    torch.tensor([0.3669, -0.0688, -0.2421]						).to(UpperCamelCase__						)

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







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

       '''simple docstring'''




       snake_case    :      List[Any]      					=    VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short"						).to(UpperCamelCase__						)

       snake_case    :      List[Any]      					=    self.default_image_processor
       snake_case    :      str      					=    prepare_video()
       snake_case    :      Any      					=    image_processor(UpperCamelCase__						,	return_tensors="pt"						).to(UpperCamelCase__						)

       # add boolean mask, indicating which patches to mask
       snake_case    :      List[str]      					=    hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos"						,	filename="bool_masked_pos.pt"						)
       snake_case    :      str      					=    torch.load(UpperCamelCase__						)

       # forward pass
       with torch.no_grad():
           snake_case    :      List[str]      					=    model(**UpperCamelCase__						)

       # verify the logits
       snake_case    :      List[str]      					=    torch.Size([1, 1408, 1536]						)
       snake_case    :      Optional[Any]      					=    torch.tensor(
           [[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]						,	device=UpperCamelCase__						)
       self.assertEqual(outputs.logits.shape						,	UpperCamelCase__						)
       self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]						,	UpperCamelCase__						,	atol=1e-4						)						)

       # verify the loss (`config.norm_pix_loss` = `True`)
       snake_case    :      Any      					=    torch.tensor([0.5142]						,	device=UpperCamelCase__						)
       self.assertTrue(torch.allclose(outputs.loss						,	UpperCamelCase__						,	atol=1e-4						)						)

       # verify the loss (`config.norm_pix_loss` = `False`)
       snake_case    :      Optional[Any]      					=    VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short"						,	norm_pix_loss=UpperCamelCase__						).to(
           UpperCamelCase__						)

       with torch.no_grad():
           snake_case    :      Dict      					=    model(**UpperCamelCase__						)

       snake_case    :      Dict      					=    torch.tensor(torch.tensor([0.6469]						)						,	device=UpperCamelCase__						)
       self.assertTrue(torch.allclose(outputs.loss						,	UpperCamelCase__						,	atol=1e-4						)						)









"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  Union[str, Any] )	->						List[str]:


    """simple docstring"""





    snake_case    :      List[str]      					=    len(lowercase )
    for i in range(length - 1 ):
        snake_case    :      List[str]      					=    i
        for k in range(i + 1						,							lowercase ):
            if collection[k] < collection[least]:
                snake_case    :      List[str]      					=    k
        if least != i:
            snake_case					,snake_case    :      Union[str, Any]      					=    (collection[i], collection[least])
    return collection


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





