
'''simple docstring'''





import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VivitConfig
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


if is_torch_available():
     import torch
     from torch import nn

     from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VivitForVideoClassification, VivitModel
     from transformers.models.vivit.modeling_vivit import VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from transformers import VivitImageProcessor



class  lowerCAmelCase__   :



  '''simple docstring'''






  def __init__(self  : int	, lowerCAmelCase_  : Any	, lowerCAmelCase_  : Optional[int]=2	, lowerCAmelCase_  : Tuple=True	, lowerCAmelCase_  : List[str]=True	, lowerCAmelCase_  : Union[str, Any]=10	, lowerCAmelCase_  : Any=10	, lowerCAmelCase_  : int=8	, lowerCAmelCase_  : Optional[int]=[2, 4, 4]	, lowerCAmelCase_  : List[str]=3	, lowerCAmelCase_  : List[str]=7_68	, lowerCAmelCase_  : Union[str, Any]=5	, lowerCAmelCase_  : Any=4	, lowerCAmelCase_  : Dict=37	, lowerCAmelCase_  : Union[str, Any]="gelu_fast"	, lowerCAmelCase_  : List[Any]=0.0	, lowerCAmelCase_  : int=0.0	, lowerCAmelCase_  : List[str]=0.02	, lowerCAmelCase_  : Optional[Any]=1E-06	, lowerCAmelCase_  : List[Any]=True	, lowerCAmelCase_  : List[str]=None	, )   ->       List[Any]:
        snake_case_						       =							parent
        snake_case_						       =							batch_size
        snake_case_						       =							is_training
        snake_case_						       =							use_labels
        snake_case_						       =							num_labels
        snake_case_						       =							image_size
        snake_case_						       =							num_frames
        snake_case_						       =							tubelet_size
        snake_case_						       =							num_channels
        snake_case_						       =							hidden_size
        snake_case_						       =							num_hidden_layers
        snake_case_						       =							num_attention_heads
        snake_case_						       =							intermediate_size
        snake_case_						       =							hidden_act
        snake_case_						       =							hidden_dropout_prob
        snake_case_						       =							attention_probs_dropout_prob
        snake_case_						       =							initializer_range
        snake_case_						       =							layer_norm_eps
        snake_case_						       =							qkv_bias
        snake_case_						       =							scope

        snake_case_						       =							(
            (self.image_size // self.tubelet_size[2])
            * (self.image_size // self.tubelet_size[1])
            * (self.num_frames // self.tubelet_size[0])
        ) + 1  # CLS token

  def 		_lowerCamelCase	(self  : str    )   ->       int:
        snake_case_						       =							floats_tensor(
            [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]    )

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

        snake_case_						       =							self.get_config()

        return config, pixel_values, labels

  def 		_lowerCamelCase	(self  : Dict    )   ->       Any:
        snake_case_						       =							VivitConfig(
            num_frames=self.num_frames	, image_size=self.image_size	, tubelet_size=self.tubelet_size	, num_channels=self.num_channels	, 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	, layer_norm_eps=self.layer_norm_eps	, qkv_bias=self.qkv_bias	, )
        snake_case_						       =							self.num_labels
        return config

  def 		_lowerCamelCase	(self  : Tuple	, lowerCAmelCase_  : List[Any]	, lowerCAmelCase_  : List[str]	, lowerCAmelCase_  : Optional[int]    )   ->       Dict:
        snake_case_						       =							VivitModel(config=lowerCAmelCase_    )
        model.to(lowerCAmelCase_    )
        model.eval()
        snake_case_						       =							model(lowerCAmelCase_    )
        self.parent.assertEqual(result.last_hidden_state.shape	, (self.batch_size, self.seq_length, self.hidden_size)    )

  def 		_lowerCamelCase	(self  : List[Any]	, lowerCAmelCase_  : Any	, lowerCAmelCase_  : Union[str, Any]	, lowerCAmelCase_  : str    )   ->       str:
        snake_case_						       =							VivitForVideoClassification(lowerCAmelCase_    )
        model.to(lowerCAmelCase_    )
        model.eval()

        snake_case_						       =							model(lowerCAmelCase_    )

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


  def 		_lowerCamelCase	(self  : Optional[int]    )   ->       int:
        snake_case_						       =							self.prepare_config_and_inputs()
        snake_case_       ,	snake_case_       ,	snake_case_						       =							config_and_inputs
        snake_case_						       =							{"pixel_values": pixel_values}
        return config, inputs_dict



@require_torch
class  lowerCAmelCase__   (       UpperCamelCase					,							unittest.TestCase       ):



  '''simple docstring'''






  _UpperCamelCase						= (VivitModel, VivitForVideoClassification) if is_torch_available() else ()

  _UpperCamelCase						= False
  _UpperCamelCase						= False
  _UpperCamelCase						= False
  _UpperCamelCase						= False

  def 		_lowerCamelCase	(self  : str    )   ->       Any:
        snake_case_						       =							VivitModelTester(self    )
        snake_case_						       =							ConfigTester(self	, config_class=lowerCAmelCase_	, has_text_modality=lowerCAmelCase_	, hidden_size=37    )

  def 		_lowerCamelCase	(self  : Any	, lowerCAmelCase_  : List[str]	, lowerCAmelCase_  : str	, lowerCAmelCase_  : Union[str, Any]=False    )   ->       Optional[int]:
        snake_case_						       =							copy.deepcopy(lowerCAmelCase_    )

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

        return inputs_dict

  def 		_lowerCamelCase	(self  : Optional[Any]    )   ->       Optional[int]:
        self.config_tester.run_common_tests()

  @unittest.skip(reason="Vivit does not use inputs_embeds"    )
  def 		_lowerCamelCase	(self  : Tuple    )   ->       Optional[int]:
        pass

  def 		_lowerCamelCase	(self  : int    )   ->       Optional[Any]:
        snake_case_       ,	snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()

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

  def 		_lowerCamelCase	(self  : Any    )   ->       List[str]:
        snake_case_       ,	snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()

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

              snake_case_						       =							["pixel_values", "head_mask"]
              self.assertListEqual(arg_names[:2]	, lowerCAmelCase_    )

  def 		_lowerCamelCase	(self  : Any    )   ->       Tuple:
        snake_case_						       =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase_    )

  def 		_lowerCamelCase	(self  : int    )   ->       Optional[int]:
        snake_case_						       =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_video_classification(*lowerCAmelCase_    )

  @slow
  def 		_lowerCamelCase	(self  : Dict    )   ->       Union[str, Any]:
        for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case_						       =							VivitModel.from_pretrained(lowerCAmelCase_    )
              self.assertIsNotNone(lowerCAmelCase_    )

  def 		_lowerCamelCase	(self  : Dict    )   ->       Dict:
        snake_case_       ,	snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()
        snake_case_						       =							True

        for model_class in self.all_model_classes:
              snake_case_						       =							self.model_tester.seq_length

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

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

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

              snake_case_						       =							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 		_lowerCamelCase	(self  : Optional[int]    )   ->       Optional[int]:
        def check_hidden_states_output(lowerCAmelCase_  : int	, lowerCAmelCase_  : Union[str, Any]	, lowerCAmelCase_  : List[str]    ):
              snake_case_						       =							model_class(lowerCAmelCase_    )
              model.to(lowerCAmelCase_    )
              model.eval()

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

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

              snake_case_						       =							self.model_tester.seq_length

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

        snake_case_       ,	snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()

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

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              snake_case_						       =							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 		_lowerCamelCase	(self  : Union[str, Any]    )   ->       str:
        pass

def   snake_case_       (							):
      snake_case_						       =							hf_hub_download(
          repo_id="hf-internal-testing/spaghetti-video" ,					filename="eating_spaghetti_32_frames.npy" ,					repo_type="dataset"			)
      snake_case_						       =							np.load(_a			)
      return list(_a			)




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



  '''simple docstring'''






  @cached_property
  def 		_lowerCamelCase	(self  : Dict    )   ->       Tuple:
        return VivitImageProcessor() if is_vision_available() else None


  @slow
  def 		_lowerCamelCase	(self  : Union[str, Any]    )   ->       List[str]:
        snake_case_						       =							VivitForVideoClassification.from_pretrained("google/vivit-b-16x2-kinetics400"    ).to(lowerCAmelCase_    )

        snake_case_						       =							self.default_image_processor
        snake_case_						       =							prepare_video()
        snake_case_						       =							image_processor(lowerCAmelCase_	, return_tensors="pt"    ).to(lowerCAmelCase_    )

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

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

        # taken from original model
        snake_case_						       =							torch.tensor([-1.0_543, 2.0_764, -0.2_104, 0.4_439, -0.9_658]    ).to(lowerCAmelCase_    )

        self.assertTrue(torch.allclose(outputs.logits[0, :5]	, lowerCAmelCase_	, atol=1E-4    )    )



'''simple docstring'''





from __future__ import annotations

A           =   [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]



class  lowerCAmelCase__   :



  '''simple docstring'''






  def __init__(self  : Optional[int]	, lowerCAmelCase_  : int    )   ->       None:
        snake_case_						       =							-1
        snake_case_						       =							-1
        snake_case_						       =							[-1] * length
        snake_case_						       =							None
        snake_case_						       =							None


  def __repr__(self  : List[Any]    )   ->       str:
        return f'''Node(min_value={self.minn} max_value={self.maxx})'''

def   snake_case_       (							_a     :							list[int]			):
      snake_case_						       =							Node(len(_a			)			)
      snake_case_       ,	snake_case_						       =							min(_a			), max(_a			)
      # Leaf node case where the node contains only one unique value
      if root.minn == root.maxx:
            return root
      snake_case_						       =							(root.minn + root.maxx) // 2

      snake_case_						       =							[]
      snake_case_						       =							[]

      for index, num in enumerate(_a			):
            if num <= pivot:
                  left_arr.append(_a			)
            else:
                  right_arr.append(_a			)
            snake_case_						       =							len(_a			)
      snake_case_						       =							build_tree(_a			)
      snake_case_						       =							build_tree(_a			)
      return root
def   snake_case_       (							_a     :							Node | None ,					_a     :							int ,					_a     :							int			):
      if index < 0 or node is None:
            return 0
      # Leaf node cases
      if node.minn == node.maxx:
            return index + 1 if node.minn == num else 0
      snake_case_						       =							(node.minn + node.maxx) // 2
      if num <= pivot:
            # go the left subtree and map index to the left subtree
            return rank_till_index(node.left ,					_a ,					node.map_left[index] - 1			)
      else:
            # go to the right subtree and map index to the right subtree
            return rank_till_index(node.right ,					_a ,					index - node.map_left[index]			)
def   snake_case_       (							_a     :							Node | None ,					_a     :							int ,					_a     :							int ,					_a     :							int			):
      if start > end:
            return 0
      snake_case_						       =							rank_till_index(_a ,					_a ,					_a			)
      snake_case_						       =							rank_till_index(_a ,					_a ,					start - 1			)
      return rank_till_end - rank_before_start
def   snake_case_       (							_a     :							Node | None ,					_a     :							int ,					_a     :							int ,					_a     :							int			):
      if index > (end - start) or start > end or node is None:
            return -1
      # Leaf node case
      if node.minn == node.maxx:
            return node.minn
      # Number of elements in the left subtree in interval [start, end]
      snake_case_						       =							node.map_left[end] - (
          node.map_left[start - 1] if start else 0
      )
      if num_elements_in_left_tree > index:
            return quantile(
                node.left ,					_a ,					(node.map_left[start - 1] if start else 0) ,					node.map_left[end] - 1 ,					)
      else:
            return quantile(
                node.right ,					index - num_elements_in_left_tree ,					start - (node.map_left[start - 1] if start else 0) ,					end - node.map_left[end] ,					)
def   snake_case_       (							_a     :							Node | None ,					_a     :							int ,					_a     :							int ,					_a     :							int ,					_a     :							int			):
      if (
          start > end
          or node is None
          or start_num > end_num
          or node.minn > end_num
          or node.maxx < start_num
      ):
            return 0
      if start_num <= node.minn and node.maxx <= end_num:
            return end - start + 1
      snake_case_						       =							range_counting(
          node.left ,					(node.map_left[start - 1] if start else 0) ,					node.map_left[end] - 1 ,					_a ,					_a ,					)
      snake_case_						       =							range_counting(
          node.right ,					start - (node.map_left[start - 1] if start else 0) ,					end - node.map_left[end] ,					_a ,					_a ,					)
      return left + right


if __name__ == "__main__":
     import doctest

     doctest.testmod()

