'''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			:







      def __init__(  self  ,    lowercase  ,    lowercase=2  ,    lowercase=True  ,    lowercase=True  ,    lowercase=10  ,    lowercase=10  ,    lowercase=8  ,    lowercase=[2, 4, 4]  ,    lowercase=3  ,    lowercase=768  ,    lowercase=5  ,    lowercase=4  ,    lowercase=37  ,    lowercase="gelu_fast"  ,    lowercase=0.0  ,    lowercase=0.0  ,    lowercase=0.02  ,    lowercase=1e-06  ,    lowercase=True  ,    lowercase=None  ,    ):
        lowercase				:							int									=   parent
        lowercase				:							Dict									=   batch_size
        lowercase				:							List[str]									=   is_training
        lowercase				:							List[str]									=   use_labels
        lowercase				:							List[Any]									=   num_labels
        lowercase				:							Any									=   image_size
        lowercase				:							List[str]									=   num_frames
        lowercase				:							List[str]									=   tubelet_size
        lowercase				:							Optional[int]									=   num_channels
        lowercase				:							str									=   hidden_size
        lowercase				:							Any									=   num_hidden_layers
        lowercase				:							Optional[int]									=   num_attention_heads
        lowercase				:							Optional[int]									=   intermediate_size
        lowercase				:							int									=   hidden_act
        lowercase				:							Tuple									=   hidden_dropout_prob
        lowercase				:							Union[str, Any]									=   attention_probs_dropout_prob
        lowercase				:							List[str]									=   initializer_range
        lowercase				:							Tuple									=   layer_norm_eps
        lowercase				:							Union[str, Any]									=   qkv_bias
        lowercase				:							Dict									=   scope

        lowercase				:							int									=   (
            (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     ):
        lowercase				:							Tuple									=   floats_tensor(
            [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]     )

        lowercase				:							Tuple									=   None
        if self.use_labels:
          lowercase				:							str									=   ids_tensor([self.batch_size]  ,    self.num_labels     )

        lowercase				:							Dict									=   self.get_config()

        return config, pixel_values, labels







      def      lowerCamelCase__      (  self     ):
        lowercase				:							List[str]									=   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  ,    )
        lowercase				:							Optional[Any]									=   self.num_labels
        return config







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase     ):
        lowercase				:							Dict									=   VivitModel(config=lowercase     )
        model.to(lowercase     )
        model.eval()
        lowercase				:							Dict									=   model(lowercase     )
        self.parent.assertEqual(result.last_hidden_state.shape  ,    (self.batch_size, self.seq_length, self.hidden_size)     )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase     ):
        lowercase				:							int									=   VivitForVideoClassification(lowercase     )
        model.to(lowercase     )
        model.eval()

        lowercase				:							List[Any]									=   model(lowercase     )

        # verify the logits shape
        lowercase				:							Any									=   torch.Size((self.batch_size, self.num_labels)     )
        self.parent.assertEqual(result.logits.shape  ,    lowercase     )







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Any									=   self.prepare_config_and_inputs()
        lowercase				,      lowercase				,      lowercase				:							Optional[int]									=   config_and_inputs
        lowercase				:							Union[str, Any]									=   {'''pixel_values''': pixel_values}
        return config, inputs_dict





@require_torch
class _lowerCAmelCase			(	snake_case							,   unittest.TestCase					):
      UpperCAmelCase_	:						List[str]	 =       (VivitModel, VivitForVideoClassification) if is_torch_available() else ()

      UpperCAmelCase_	:						Union[str, Any]	 =       False
      UpperCAmelCase_	:						Union[str, Any]	 =       False
      UpperCAmelCase_	:						Tuple	 =       False
      UpperCAmelCase_	:						Any	 =       False







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Optional[Any]									=   VivitModelTester(self     )
        lowercase				:							str									=   ConfigTester(self  ,    config_class=lowercase  ,    has_text_modality=lowercase  ,    hidden_size=37     )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase=False     ):
        lowercase				:							int									=   copy.deepcopy(lowercase     )

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

        return inputs_dict







      def      lowerCamelCase__      (  self     ):
        self.config_tester.run_common_tests()







      @unittest.skip(reason='''Vivit does not use inputs_embeds'''     )
      def      lowerCamelCase__      (  self     ):
        pass







      def      lowerCamelCase__      (  self     ):
        lowercase				,      lowercase				:							List[Any]									=   self.model_tester.prepare_config_and_inputs_for_common()

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







      def      lowerCamelCase__      (  self     ):
        lowercase				,      lowercase				:							Any									=   self.model_tester.prepare_config_and_inputs_for_common()

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

          lowercase				:							int									=   ['''pixel_values''', '''head_mask''']
          self.assertListEqual(arg_names[:2]  ,    lowercase     )







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Optional[int]									=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase     )







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Union[str, Any]									=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_video_classification(*lowercase     )







      @slow
      def      lowerCamelCase__      (  self     ):
        for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase				:							Union[str, Any]									=   VivitModel.from_pretrained(lowercase     )
          self.assertIsNotNone(lowercase     )







      def      lowerCamelCase__      (  self     ):
        lowercase				,      lowercase				:							Tuple									=   self.model_tester.prepare_config_and_inputs_for_common()
        lowercase				:							List[Any]									=   True

        for model_class in self.all_model_classes:
          lowercase				:							Optional[int]									=   self.model_tester.seq_length

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

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

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

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

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

          lowercase				:							str									=   outputs.attentions

          self.assertEqual(len(lowercase     )  ,    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     ):
        def check_hidden_states_output(lowercase  ,    lowercase  ,    lowercase     ):
          lowercase				:							int									=   model_class(lowercase     )
          model.to(lowercase     )
          model.eval()

          with torch.no_grad():
            lowercase				:							Optional[Any]									=   model(**self._prepare_for_class(lowercase  ,    lowercase     )     )

          lowercase				:							Dict									=   outputs.hidden_states
          lowercase				:							List[str]									=   self.model_tester.num_hidden_layers + 1
          self.assertEqual(len(lowercase     )  ,    lowercase     )

          lowercase				:							Tuple									=   self.model_tester.seq_length

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

        lowercase				,      lowercase				:							Tuple									=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          lowercase				:							List[Any]									=   True
          check_hidden_states_output(lowercase  ,    lowercase  ,    lowercase     )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          lowercase				:							Union[str, Any]									=   True

          check_hidden_states_output(lowercase  ,    lowercase  ,    lowercase     )







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







def 				__UpperCamelCase (					)							->	Tuple:
  lowercase				:							Optional[Any]									=   hf_hub_download(
      repo_id='''hf-internal-testing/spaghetti-video''' ,   filename='''eating_spaghetti_32_frames.npy''' ,   repo_type='''dataset'''			)
  lowercase				:							str									=   np.load(__UpperCAmelCase			)
  return list(__UpperCAmelCase			)






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







      @cached_property
      def      lowerCamelCase__      (  self     ):
        return VivitImageProcessor() if is_vision_available() else None







      @slow
      def      lowerCamelCase__      (  self     ):
        lowercase				:							Any									=   VivitForVideoClassification.from_pretrained('''google/vivit-b-16x2-kinetics400'''     ).to(lowercase     )

        lowercase				:							Tuple									=   self.default_image_processor
        lowercase				:							int									=   prepare_video()
        lowercase				:							Optional[Any]									=   image_processor(lowercase  ,    return_tensors='''pt'''     ).to(lowercase     )

        # forward pass
        with torch.no_grad():
          lowercase				:							Tuple									=   model(**lowercase     )

        # verify the logits
        lowercase				:							Dict									=   torch.Size((1, 400)     )
        self.assertEqual(outputs.logits.shape  ,    lowercase     )

        # taken from original model
        lowercase				:							Any									=   torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]     ).to(lowercase     )

        self.assertTrue(torch.allclose(outputs.logits[0, :5]  ,    lowercase  ,    atol=1e-4     )     )
'''simple docstring'''
import math







def 				__UpperCamelCase (					__UpperCAmelCase  :					str			)							->	Optional[int]:
  lowercase				:							Tuple									=   0
  if n % 2 == 0:
    count += 1
    while n % 2 == 0:
      lowercase				:							Optional[int]									=   int(n / 2			)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

  lowercase				:							Dict									=   3

  while i <= int(math.sqrt(__UpperCAmelCase			)			):
    if n % i == 0:
      count += 1
      while n % i == 0:
        lowercase				:							Dict									=   int(n / i			)
    lowercase				:							List[Any]									=   i + 2

  # this condition checks the prime
  # number n is greater than 2

  if n > 2:
    count += 1
  return count


if __name__ == "__main__":
 _A		:      List[Any]						   =5_1_2_4_2_1_8_3
 print(f"""The number of distinct prime factors is/are {exact_prime_factor_count(n)}""")
 print(f"""The value of log(log(n)) is {math.log(math.log(n)):.4f}""")