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    A_ :

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

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

   # use this variable to define bool_masked_pos
   __a       =  int(mask_ratio * self.seq_length       )

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   __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.type_sequence_label_size       )

   __a       =  self.get_config()

   return config, pixel_values, labels

  def 		_UpperCAmelCase   (		self :      Union[str, 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=_lowerCamelCase     ,							initializer_range=self.initializer_range     ,							)

  def 		_UpperCAmelCase   (		self :      str     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Any       ):
   __a       =  VideoMAEModel(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 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]       ):
   __a       =  VideoMAEForPreTraining(_lowerCamelCase       )
   model.to(_lowerCamelCase       )
   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
   __a       =  torch.ones((self.num_masks,)       )
   __a       =  torch.cat([mask, torch.zeros(self.seq_length - mask.size(0       )       )]       )
   __a       =  mask.expand(self.batch_size     ,							-1       ).bool()

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

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  self.prepare_config_and_inputs()
   __a       ,     __a       ,     __a       =  config_and_inputs
   __a       =  {"pixel_values": pixel_values}
   return config, inputs_dict
@require_torch
class    A_ (       lowerCAmelCase__			,       lowerCAmelCase__			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			(
      (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
  )
  _SCREAMING_SNAKE_CASE    				=			(
      {"feature-extraction": VideoMAEModel, "video-classification": VideoMAEForVideoClassification}
      if is_torch_available()
      else {}
  )

  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False

  def 		_UpperCAmelCase   (		self :      int       ):
   __a       =  VideoMAEModelTester(self       )
   __a       =  ConfigTester(self     ,							config_class=_lowerCamelCase     ,							has_text_modality=_lowerCamelCase     ,							hidden_size=37       )

  def 		_UpperCAmelCase   (		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      Tuple=False       ):
   __a       =  copy.deepcopy(_lowerCamelCase       )

   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
    __a       =  torch.ones((self.model_tester.num_masks,)       )
    __a       =  torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0       )       )]       )
    __a       =  mask.expand(self.model_tester.batch_size     ,							-1       ).bool()
    __a       =  bool_masked_pos.to(_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 		_UpperCAmelCase   (		self :      List[Any]       ):
   self.config_tester.run_common_tests()

  @unittest.skip(reason="VideoMAE does not use inputs_embeds"       )
  def 		_UpperCAmelCase   (		self :      int       ):
   pass

  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   __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 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   __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 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*_lowerCamelCase       )

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_pretraining(*_lowerCamelCase       )

  @slow
  def 		_UpperCAmelCase   (		self :      str       ):





   for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __a       =  VideoMAEModel.from_pretrained(_lowerCamelCase       )
    self.assertIsNotNone(_lowerCamelCase       )

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):





   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 - self.model_tester.num_masks
     __a       =  (
         num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
     )

     __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       )

     self.assertListEqual(
         list(attentions[0].shape[-3:]       )     ,							[self.model_tester.num_attention_heads, seq_len, seq_len]     ,							)
     __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       )
     self.assertListEqual(
         list(self_attentions[0].shape[-3:]       )     ,							[self.model_tester.num_attention_heads, seq_len, seq_len]     ,							)

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):





   def check_hidden_states_output(__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      List[Any]       ):
    __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.model_tester.num_masks
    __a       =  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]     ,							)

   __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       )

  @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
  def 		_UpperCAmelCase   (		self :      Any       ):
   pass



def   __A		(			):



 """simple docstring"""


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






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

  @cached_property
  def 		_UpperCAmelCase   (		self :      List[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 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       =  VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics"       ).to(
       _lowerCamelCase       )

   __a       =  self.default_image_processor
   __a       =  prepare_video()
   __a       =  image_processor(_lowerCamelCase     ,							return_tensors="pt"       ).to(_lowerCamelCase       )

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

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

   __a       =  torch.tensor([0.36_69, -0.06_88, -0.24_21]       ).to(_lowerCamelCase       )

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

  @slow
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       =  VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short"       ).to(_lowerCamelCase       )

   __a       =  self.default_image_processor
   __a       =  prepare_video()
   __a       =  image_processor(_lowerCamelCase     ,							return_tensors="pt"       ).to(_lowerCamelCase       )

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

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

   # verify the logits
   __a       =  torch.Size([1, 14_08, 15_36]       )
   __a       =  torch.tensor(
       [[0.79_94, 0.96_12, 0.85_08], [0.74_01, 0.89_58, 0.83_02], [0.58_62, 0.74_68, 0.73_25]]     ,							device=_lowerCamelCase       )
   self.assertEqual(outputs.logits.shape     ,							_lowerCamelCase       )
   self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]     ,							_lowerCamelCase     ,							atol=1E-4       )       )

   # verify the loss (`config.norm_pix_loss` = `True`)
   __a       =  torch.tensor([0.51_42]     ,							device=_lowerCamelCase       )
   self.assertTrue(torch.allclose(outputs.loss     ,							_lowerCamelCase     ,							atol=1E-4       )       )

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

   with torch.no_grad():
    __a       =  model(**_lowerCamelCase       )

   __a       =  torch.tensor(torch.tensor([0.64_69]       )     ,							device=_lowerCamelCase       )
   self.assertTrue(torch.allclose(outputs.loss     ,							_lowerCamelCase     ,							atol=1E-4       )       )



class    A_ :

  def __init__(		self :      List[Any]       ):
   __a       =  {}  # Mapping from char to TrieNode
   __a       =  False

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      list[str]       ):
   for word in words:
    self.insert(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self
   for char in word:
    if char not in curr.nodes:
     __a       =  TrieNode()
    __a       =  curr.nodes[char]
   __a       =  True

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self
   for char in word:
    if char not in curr.nodes:
     return False
    __a       =  curr.nodes[char]
   return curr.is_leaf

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):

   def _delete(__SCREAMING_SNAKE_CASE :      TrieNode     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      int       ) -> bool:
    if index == len(__SCREAMING_SNAKE_CASE       ):
     # If word does not exist
     if not curr.is_leaf:
      return False
     __a       =  False
     return len(curr.nodes       ) == 0
    __a       =  word[index]
    __a       =  curr.nodes.get(__SCREAMING_SNAKE_CASE       )
    # If char not in current trie node
    if not char_node:
     return False
    # Flag to check if node can be deleted
    __a       =  _delete(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							index + 1       )
    if delete_curr:
     del curr.nodes[char]
     return len(curr.nodes       ) == 0
    return delete_curr

   _delete(self     ,							__SCREAMING_SNAKE_CASE     ,							0       )



def   __A		(			_A							,				_A					):



 """simple docstring"""


 if node.is_leaf:
  print(_A							,				end=" "					)

 for key, value in node.nodes.items():
  print_words(_A							,				word + key					)



def   __A		(			):



 """simple docstring"""


 __a       =  "banana bananas bandana band apple all beast".split()
 __a       =  TrieNode()
 root.insert_many(_A					)
 # print_words(root, "")
 assert all(root.find(_A					) for word in words					)
 assert root.find("banana"					)
 assert not root.find("bandanas"					)
 assert not root.find("apps"					)
 assert root.find("apple"					)
 assert root.find("all"					)
 root.delete("all"					)
 assert not root.find("all"					)
 root.delete("banana"					)
 assert not root.find("banana"					)
 assert root.find("bananas"					)
 return True



def   __A		(			_A							,				_A					):



 """simple docstring"""


 print(str(_A					)							,				"works!" if passes else "doesn't work :("					)



def   __A		(			):



 """simple docstring"""


 assert test_trie()



def   __A		(			):



 """simple docstring"""


 print_results("Testing trie functionality"							,				test_trie()					)


if __name__ == "__main__":
  main()


