
'''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 			__A					:
 def __init__( self      :     Dict					, lowerCamelCase      :     Optional[int]					, lowerCamelCase      :     Union[str, Any]=2					, lowerCamelCase      :     List[Any]=True					, lowerCamelCase      :     Dict=True					, lowerCamelCase      :     List[Any]=10					, lowerCamelCase      :     Union[str, Any]=10					, lowerCamelCase      :     List[Any]=8					, lowerCamelCase      :     List[Any]=[2, 4, 4]					, lowerCamelCase      :     Union[str, Any]=3					, lowerCamelCase      :     Dict=7_68					, lowerCamelCase      :     List[str]=5					, lowerCamelCase      :     List[str]=4					, lowerCamelCase      :     List[str]=37					, lowerCamelCase      :     List[str]="gelu_fast"					, lowerCamelCase      :     Dict=0.0					, lowerCamelCase      :     Union[str, Any]=0.0					, lowerCamelCase      :     List[str]=0.02					, lowerCamelCase      :     str=1e-0_6					, lowerCamelCase      :     Optional[int]=True					, lowerCamelCase      :     int=None					, ):



   """simple docstring"""





   __A		:  List[str]       				=		parent
   __A		:  Tuple       				=		batch_size
   __A		:  int       				=		is_training
   __A		:  Optional[Any]       				=		use_labels
   __A		:  Optional[int]       				=		num_labels
   __A		:  List[Any]       				=		image_size
   __A		:  List[Any]       				=		num_frames
   __A		:  Optional[Any]       				=		tubelet_size
   __A		:  List[str]       				=		num_channels
   __A		:  Optional[Any]       				=		hidden_size
   __A		:  Optional[Any]       				=		num_hidden_layers
   __A		:  Dict       				=		num_attention_heads
   __A		:  Union[str, Any]       				=		intermediate_size
   __A		:  int       				=		hidden_act
   __A		:  Any       				=		hidden_dropout_prob
   __A		:  Any       				=		attention_probs_dropout_prob
   __A		:  Optional[int]       				=		initializer_range
   __A		:  Optional[int]       				=		layer_norm_eps
   __A		:  Optional[int]       				=		qkv_bias
   __A		:  List[str]       				=		scope

   __A		:  Union[str, Any]       				=		(
       (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 		lowercase_( self      :     Any							):



   """simple docstring"""





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

   __A		:  Optional[int]       				=		None
   if self.use_labels:
     __A		:  str       				=		ids_tensor([self.batch_size]					, self.num_labels							)

   __A		:  Union[str, Any]       				=		self.get_config()

   return config, pixel_values, labels
 def 		lowercase_( self      :     Tuple							):



   """simple docstring"""





   __A		:  Dict       				=		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					, )
   __A		:  Tuple       				=		self.num_labels
   return config
 def 		lowercase_( self      :     str					, lowerCamelCase      :     str					, lowerCamelCase      :     List[str]					, lowerCamelCase      :     List[Any]							):



   """simple docstring"""





   __A		:  int       				=		VivitModel(config=lowerCamelCase							)
   model.to(lowerCamelCase							)
   model.eval()
   __A		:  List[Any]       				=		model(lowerCamelCase							)
   self.parent.assertEqual(result.last_hidden_state.shape					, (self.batch_size, self.seq_length, self.hidden_size)							)
 def 		lowercase_( self      :     List[str]					, lowerCamelCase      :     List[str]					, lowerCamelCase      :     Union[str, Any]					, lowerCamelCase      :     Dict							):



   """simple docstring"""





   __A		:  int       				=		VivitForVideoClassification(lowerCamelCase							)
   model.to(lowerCamelCase							)
   model.eval()

   __A		:  int       				=		model(lowerCamelCase							)

   # verify the logits shape
   __A		:  Optional[int]       				=		torch.Size((self.batch_size, self.num_labels)							)
   self.parent.assertEqual(result.logits.shape					, lowerCamelCase							)






 def 		lowercase_( self      :     Union[str, Any]							):



   """simple docstring"""





   __A		:  str       				=		self.prepare_config_and_inputs()
   __A				,       __A				,       __A		:  Tuple       				=		config_and_inputs
   __A		:  Any       				=		{"""pixel_values""": pixel_values}
   return config, inputs_dict







@require_torch
class 			__A					(			_SCREAMING_SNAKE_CASE     ,    unittest.TestCase      ):
 lowerCamelCase												=(VivitModel, VivitForVideoClassification) if is_torch_available() else ()

 lowerCamelCase												=False
 lowerCamelCase												=False
 lowerCamelCase												=False
 lowerCamelCase												=False
 def 		lowercase_( self      :     List[str]							):



   """simple docstring"""





   __A		:  Dict       				=		VivitModelTester(self							)
   __A		:  Tuple       				=		ConfigTester(self					, config_class=lowerCamelCase					, has_text_modality=lowerCamelCase					, hidden_size=37							)
 def 		lowercase_( self      :     Union[str, Any]					, lowerCamelCase      :     Union[str, Any]					, lowerCamelCase      :     List[Any]					, lowerCamelCase      :     Optional[Any]=False							):



   """simple docstring"""





   __A		:  Dict       				=		copy.deepcopy(lowerCamelCase							)

   if return_labels:
     if model_class in get_values(lowerCamelCase							):
       __A		:  List[Any]       				=		torch.zeros(
           self.model_tester.batch_size					, dtype=torch.long					, device=lowerCamelCase							)

   return inputs_dict
 def 		lowercase_( self      :     Any							):



   """simple docstring"""





   self.config_tester.run_common_tests()
 @unittest.skip(reason="""Vivit does not use inputs_embeds"""							)
 def 		lowercase_( self      :     str							):



   """simple docstring"""





   pass
 def 		lowercase_( self      :     List[str]							):



   """simple docstring"""





   __A				,       __A		:  int       				=		self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     __A		:  Dict       				=		model_class(lowerCamelCase							)
     self.assertIsInstance(model.get_input_embeddings()					, (nn.Module)							)
     __A		:  str       				=		model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(lowerCamelCase					, nn.Linear							)							)
 def 		lowercase_( self      :     Dict							):



   """simple docstring"""





   __A				,       __A		:  Any       				=		self.model_tester.prepare_config_and_inputs_for_common()

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

     __A		:  List[str]       				=		["""pixel_values""", """head_mask"""]
     self.assertListEqual(arg_names[:2]					, lowerCamelCase							)
 def 		lowercase_( self      :     Optional[int]							):



   """simple docstring"""





   __A		:  Any       				=		self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowerCamelCase							)
 def 		lowercase_( self      :     str							):



   """simple docstring"""





   __A		:  Any       				=		self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_video_classification(*lowerCamelCase							)
 @slow
 def 		lowercase_( self      :     List[str]							):



   """simple docstring"""





   for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     __A		:  int       				=		VivitModel.from_pretrained(lowerCamelCase							)
     self.assertIsNotNone(lowerCamelCase							)
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   __A				,       __A		:  str       				=		self.model_tester.prepare_config_and_inputs_for_common()
   __A		:  Tuple       				=		True

   for model_class in self.all_model_classes:
     __A		:  Tuple       				=		self.model_tester.seq_length

     __A		:  Tuple       				=		True
     __A		:  Optional[Any]       				=		False
     __A		:  Tuple       				=		True
     __A		:  Optional[Any]       				=		model_class(lowerCamelCase							)
     model.to(lowerCamelCase							)
     model.eval()
     with torch.no_grad():
       __A		:  Optional[Any]       				=		model(**self._prepare_for_class(lowerCamelCase					, lowerCamelCase							)							)
     __A		:  Union[str, Any]       				=		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		:  int       				=		True
     __A		:  Optional[Any]       				=		model_class(lowerCamelCase							)
     model.to(lowerCamelCase							)
     model.eval()
     with torch.no_grad():
       __A		:  Optional[int]       				=		model(**self._prepare_for_class(lowerCamelCase					, lowerCamelCase							)							)
     __A		:  str       				=		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		:  Any       				=		len(lowerCamelCase							)

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

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

     __A		:  Dict       				=		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 		lowercase_( self      :     Union[str, Any]							):



   """simple docstring"""





   def check_hidden_states_output(lowerCamelCase      :     Tuple					, lowerCamelCase      :     Union[str, Any]					, lowerCamelCase      :     Optional[int]							):
     __A		:  Tuple       				=		model_class(lowerCamelCase							)
     model.to(lowerCamelCase							)
     model.eval()

     with torch.no_grad():
       __A		:  Optional[int]       				=		model(**self._prepare_for_class(lowerCamelCase					, lowerCamelCase							)							)

     __A		:  Tuple       				=		outputs.hidden_states
     __A		:  Any       				=		self.model_tester.num_hidden_layers + 1
     self.assertEqual(len(lowerCamelCase							)					, lowerCamelCase							)

     __A		:  List[str]       				=		self.model_tester.seq_length

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

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

   for model_class in self.all_model_classes:
     __A		:  Tuple       				=		True
     check_hidden_states_output(lowerCamelCase					, lowerCamelCase					, lowerCamelCase							)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     __A		:  Tuple       				=		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 		lowercase_( self      :     Union[str, Any]							):



   """simple docstring"""





   pass







def 							A_  (    )					->							Any:





  """simple docstring"""

  __A		:  str       				=		hf_hub_download(
      repo_id="""hf-internal-testing/spaghetti-video"""      ,  filename="""eating_spaghetti_32_frames.npy"""      ,  repo_type="""dataset"""			)
  __A		:  Optional[Any]       				=		np.load(__SCREAMING_SNAKE_CASE			)
  return list(__SCREAMING_SNAKE_CASE			)







@require_torch
@require_vision
class 			__A					(			unittest.TestCase      ):
 @cached_property
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   return VivitImageProcessor() if is_vision_available() else None






 @slow
 def 		lowercase_( self      :     List[str]							):



   """simple docstring"""





   __A		:  List[str]       				=		VivitForVideoClassification.from_pretrained("""google/vivit-b-16x2-kinetics400"""							).to(lowerCamelCase							)

   __A		:  List[str]       				=		self.default_image_processor
   __A		:  Optional[Any]       				=		prepare_video()
   __A		:  str       				=		image_processor(lowerCamelCase					, return_tensors="""pt"""							).to(lowerCamelCase							)

   # forward pass
   with torch.no_grad():
     __A		:  Optional[int]       				=		model(**lowerCamelCase							)

   # verify the logits
   __A		:  Union[str, Any]       				=		torch.Size((1, 4_00)							)
   self.assertEqual(outputs.logits.shape					, lowerCamelCase							)

   # taken from original model
   __A		:  str       				=		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







def 							A_  (    __SCREAMING_SNAKE_CASE    :	float | int      ,  __SCREAMING_SNAKE_CASE    :	float | int      ,  __SCREAMING_SNAKE_CASE    :	float | int      ,  )					->							list[float | int]:





  """simple docstring"""

  if not all((nth_term, start_term_a, common_ratio_r)			):
    return []
  __A		:  list[float | int]       				=		[]
  __A		:  Union[str, Any]       				=		1
  __A		:  List[str]       				=		common_ratio_r
  for _ in range(int(__SCREAMING_SNAKE_CASE			)			):
    if not series:
      series.append(__SCREAMING_SNAKE_CASE			)
    else:
      power += 1
      series.append(float(start_term_a * multiple			)			)
      __A		:  List[str]       				=		pow(float(__SCREAMING_SNAKE_CASE			)      ,  __SCREAMING_SNAKE_CASE			)
  return series


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     A__ :		Optional[int]					=float(input('Enter the last number (n term) of the Geometric Series'))
     A__ :		List[Any]					=float(input('Enter the starting term (a) of the Geometric Series'))
     A__ :		List[Any]					=float(
         input('Enter the common ratio between two terms (r) of the Geometric Series')
     )
     print('Formula of Geometric Series => a + ar + ar^2 ... +ar^n')
     print(geometric_series(nth_term, start_term_a, common_ratio_r))



