




"""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       __lowercase       :


				'''simple docstring'''


				def __init__(   self   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     Dict      ,SCREAMING_SNAKE_CASE_   :     Tuple=2      ,SCREAMING_SNAKE_CASE_   :     Dict=True      ,SCREAMING_SNAKE_CASE_   :     Any=True      ,SCREAMING_SNAKE_CASE_   :     Dict=1_0      ,SCREAMING_SNAKE_CASE_   :     int=1_0      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]=8      ,SCREAMING_SNAKE_CASE_   :     Dict=[2, 4, 4]      ,SCREAMING_SNAKE_CASE_   :     List[str]=3      ,SCREAMING_SNAKE_CASE_   :     Tuple=7_6_8      ,SCREAMING_SNAKE_CASE_   :     str=5      ,SCREAMING_SNAKE_CASE_   :     Any=4      ,SCREAMING_SNAKE_CASE_   :     Optional[int]=3_7      ,SCREAMING_SNAKE_CASE_   :     List[Any]="gelu_fast"      ,SCREAMING_SNAKE_CASE_   :     List[Any]=0.0      ,SCREAMING_SNAKE_CASE_   :     List[Any]=0.0      ,SCREAMING_SNAKE_CASE_   :     Tuple=0.02      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]=1E-06      ,SCREAMING_SNAKE_CASE_   :     List[str]=True      ,SCREAMING_SNAKE_CASE_   :     Dict=None      ,):
										snake_case__       :					Tuple										=					parent
										snake_case__       :					Optional[Any]										=					batch_size
										snake_case__       :					str										=					is_training
										snake_case__       :					Optional[int]										=					use_labels
										snake_case__       :					Tuple										=					num_labels
										snake_case__       :					int										=					image_size
										snake_case__       :					str										=					num_frames
										snake_case__       :					List[str]										=					tubelet_size
										snake_case__       :					Union[str, Any]										=					num_channels
										snake_case__       :					Any										=					hidden_size
										snake_case__       :					int										=					num_hidden_layers
										snake_case__       :					Any										=					num_attention_heads
										snake_case__       :					Optional[Any]										=					intermediate_size
										snake_case__       :					Optional[Any]										=					hidden_act
										snake_case__       :					List[Any]										=					hidden_dropout_prob
										snake_case__       :					List[Any]										=					attention_probs_dropout_prob
										snake_case__       :					Union[str, Any]										=					initializer_range
										snake_case__       :					Optional[Any]										=					layer_norm_eps
										snake_case__       :					Union[str, Any]										=					qkv_bias
										snake_case__       :					Tuple										=					scope

										snake_case__       :					Optional[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       _lowerCamelCase					(   self   :     Optional[Any]      ):
										snake_case__       :					Dict										=					floats_tensor(
										    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]      )

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

										snake_case__       :					List[str]										=					self.get_config()

										return config, pixel_values, labels

				def       _lowerCamelCase					(   self   :     List[Any]      ):
										snake_case__       :					Optional[Any]										=					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__       :					str										=					self.num_labels
										return config

				def       _lowerCamelCase					(   self   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     Tuple      ,SCREAMING_SNAKE_CASE_   :     str      ,SCREAMING_SNAKE_CASE_   :     Tuple      ):
										snake_case__       :					Union[str, Any]										=					VivitModel(config=SCREAMING_SNAKE_CASE_      )
										model.to(SCREAMING_SNAKE_CASE_      )
										model.eval()
										snake_case__       :					Dict										=					model(SCREAMING_SNAKE_CASE_      )
										self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.hidden_size)      )

				def       _lowerCamelCase					(   self   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     int      ,SCREAMING_SNAKE_CASE_   :     int      ,SCREAMING_SNAKE_CASE_   :     Optional[int]      ):
										snake_case__       :					Optional[Any]										=					VivitForVideoClassification(SCREAMING_SNAKE_CASE_      )
										model.to(SCREAMING_SNAKE_CASE_      )
										model.eval()

										snake_case__       :					int										=					model(SCREAMING_SNAKE_CASE_      )

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





				def       _lowerCamelCase					(   self   :     Optional[int]      ):
										snake_case__       :					Optional[Any]										=					self.prepare_config_and_inputs()
										snake_case__       :					Optional[int]										=					config_and_inputs
										snake_case__       :					Dict										=					{'''pixel_values''': pixel_values}
										return config, inputs_dict







@require_torch
class       __lowercase       (			snake_case_						,   unittest.TestCase	):


				'''simple docstring'''


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

				_lowerCAmelCase				=False
				_lowerCAmelCase				=False
				_lowerCAmelCase				=False
				_lowerCAmelCase				=False

				def       _lowerCamelCase					(   self   :     str      ):
										snake_case__       :					Optional[int]										=					VivitModelTester(self      )
										snake_case__       :					str										=					ConfigTester(self      ,config_class=SCREAMING_SNAKE_CASE_      ,has_text_modality=SCREAMING_SNAKE_CASE_      ,hidden_size=3_7      )

				def       _lowerCamelCase					(   self   :     Dict      ,SCREAMING_SNAKE_CASE_   :     int      ,SCREAMING_SNAKE_CASE_   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     int=False      ):
										snake_case__       :					Any										=					copy.deepcopy(SCREAMING_SNAKE_CASE_      )

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

										return inputs_dict

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

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

				def       _lowerCamelCase					(   self   :     int      ):
										snake_case__       :					Optional[int]										=					self.model_tester.prepare_config_and_inputs_for_common()

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

				def       _lowerCamelCase					(   self   :     Tuple      ):
										snake_case__       :					str										=					self.model_tester.prepare_config_and_inputs_for_common()

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

																snake_case__       :					Dict										=					['''pixel_values''', '''head_mask''']
																self.assertListEqual(arg_names[:2]      ,SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     List[str]      ):
										snake_case__       :					Any										=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     Tuple      ):
										snake_case__       :					Dict										=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_video_classification(*SCREAMING_SNAKE_CASE_      )

				@slow
				def       _lowerCamelCase					(   self   :     Optional[int]      ):
										for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																snake_case__       :					Optional[int]										=					VivitModel.from_pretrained(SCREAMING_SNAKE_CASE_      )
																self.assertIsNotNone(SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     Optional[Any]      ):
										snake_case__       :					str										=					self.model_tester.prepare_config_and_inputs_for_common()
										snake_case__       :					Tuple										=					True

										for model_class in self.all_model_classes:
																snake_case__       :					List[Any]										=					self.model_tester.seq_length

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

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

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

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

																snake_case__       :					int										=					outputs.attentions

																self.assertEqual(len(SCREAMING_SNAKE_CASE_      )      ,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   :     Tuple      ):
										def check_hidden_states_output(SCREAMING_SNAKE_CASE_   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]      ,SCREAMING_SNAKE_CASE_   :     Optional[int]      ):
																snake_case__       :					Optional[Any]										=					model_class(SCREAMING_SNAKE_CASE_      )
																model.to(SCREAMING_SNAKE_CASE_      )
																model.eval()

																with torch.no_grad():
																						snake_case__       :					str										=					model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      )      )

																snake_case__       :					Tuple										=					outputs.hidden_states
																snake_case__       :					Optional[int]										=					self.model_tester.num_hidden_layers + 1
																self.assertEqual(len(SCREAMING_SNAKE_CASE_      )      ,SCREAMING_SNAKE_CASE_      )

																snake_case__       :					Union[str, Any]										=					self.model_tester.seq_length

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

										snake_case__       :					Any										=					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																snake_case__       :					Dict										=					True
																check_hidden_states_output(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      )

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

																check_hidden_states_output(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      )





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







def 		lowerCAmelCase							( ):
						'''simple docstring'''


						snake_case__       :					Dict										=					hf_hub_download(
						    repo_id='''hf-internal-testing/spaghetti-video'''      ,			filename='''eating_spaghetti_32_frames.npy'''      ,			repo_type='''dataset'''		)
						snake_case__       :					int										=					np.load(lowerCamelCase		)
						return list(lowerCamelCase		)

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


				'''simple docstring'''


				@cached_property
				def       _lowerCamelCase					(   self   :     Optional[int]      ):
										return VivitImageProcessor() if is_vision_available() else None





				@slow
				def       _lowerCamelCase					(   self   :     str      ):
										snake_case__       :					Any										=					VivitForVideoClassification.from_pretrained('''google/vivit-b-16x2-kinetics400'''      ).to(SCREAMING_SNAKE_CASE_      )

										snake_case__       :					Any										=					self.default_image_processor
										snake_case__       :					Dict										=					prepare_video()
										snake_case__       :					int										=					image_processor(SCREAMING_SNAKE_CASE_      ,return_tensors='''pt'''      ).to(SCREAMING_SNAKE_CASE_      )

										# forward pass
										with torch.no_grad():
																snake_case__       :					Optional[Any]										=					model(**SCREAMING_SNAKE_CASE_      )

										# verify the logits
										snake_case__       :					Tuple										=					torch.Size((1, 4_0_0)      )
										self.assertEqual(outputs.logits.shape      ,SCREAMING_SNAKE_CASE_      )

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

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








"""simple docstring"""





from __future__ import annotations

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			):
						'''simple docstring'''


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


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							_lowercase	      =    float(input("""Enter the last number (n term) of the Geometric Series"""))
							_lowercase	      =    float(input("""Enter the starting term (a) of the Geometric Series"""))
							_lowercase	      =    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))


