






"""simple docstring"""
import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
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,
							    TimesformerForVideoClassification,
							    TimesformerModel,
							)
							from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from transformers import VideoMAEImageProcessor



class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=13     ,     lowerCAmelCase_=10     ,     lowerCAmelCase_=3     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=32     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=37     ,     lowerCAmelCase_="gelu"     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=10     ,     lowerCAmelCase_=0.02     ,     lowerCAmelCase_="divided_space_time"     ,     lowerCAmelCase_=None     ,     ):
		A_		:						Optional[Any]	    =   parent
		A_		:						Optional[int]	    =   batch_size
		A_		:						str	    =   image_size
		A_		:						str	    =   num_channels
		A_		:						List[Any]	    =   patch_size
		A_		:						Any	    =   num_frames
		A_		:						Optional[Any]	    =   is_training
		A_		:						Any	    =   use_labels
		A_		:						Optional[Any]	    =   hidden_size
		A_		:						Dict	    =   num_hidden_layers
		A_		:						Tuple	    =   num_attention_heads
		A_		:						Optional[int]	    =   intermediate_size
		A_		:						Optional[int]	    =   hidden_act
		A_		:						Union[str, Any]	    =   hidden_dropout_prob
		A_		:						int	    =   attention_probs_dropout_prob
		A_		:						Any	    =   attention_type
		A_		:						List[str]	    =   initializer_range
		A_		:						Dict	    =   scope
		A_		:						Union[str, Any]	    =   num_labels

		# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
		A_		:						Union[str, Any]	    =   (image_size // patch_size) ** 2
		A_		:						Tuple	    =   (num_frames) * self.num_patches_per_frame + 1





	def    lowerCamelCase(self							):
		A_		:						str	    =   floats_tensor(
		    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]							)

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

		A_		:						Dict	    =   self.get_config()

		return config, pixel_values, labels





	def    lowerCamelCase(self							):
		A_		:						List[Any]	    =   TimesformerConfig(
		    image_size=self.image_size     ,     patch_size=self.patch_size     ,     num_channels=self.num_channels     ,     num_frames=self.num_frames     ,     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     ,     attention_type=self.attention_type     ,     )
		A_		:						int	    =   self.num_labels
		return config





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						Tuple	    =   TimesformerModel(config=lowerCAmelCase_							)
		model.to(lowerCAmelCase_							)
		model.eval()
		A_		:						Dict	    =   model(lowerCAmelCase_							)
		self.parent.assertEqual(result.last_hidden_state.shape     ,     (self.batch_size, self.seq_length, self.hidden_size)							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						Any	    =   TimesformerForVideoClassification(lowerCAmelCase_							)
		model.to(lowerCAmelCase_							)
		model.eval()

		A_		:						Optional[int]	    =   model(lowerCAmelCase_							)

		# verify the logits shape
		A_		:						Union[str, Any]	    =   torch.Size((self.batch_size, self.num_labels)							)
		self.parent.assertEqual(result.logits.shape     ,     lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		A_		:						List[Any]	    =   self.prepare_config_and_inputs()
		A_    ,     A_    ,     A_		:						Any	    =   config_and_inputs
		A_		:						str	    =   {"""pixel_values""": pixel_values}
		return config, inputs_dict






@require_torch
class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				str    =				(TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
	_A       :				Optional[Any]    =				(
	    {"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
	    if is_torch_available()
	    else {}
	)

	_A       :				int    =				False
	_A       :				Dict    =				False
	_A       :				Any    =				False
	_A       :				str    =				False





	def    lowerCamelCase(self							):
		A_		:						List[Any]	    =   TimesformerModelTester(self							)
		A_		:						Optional[Any]	    =   ConfigTester(
		    self     ,     config_class=lowerCAmelCase_     ,     has_text_modality=lowerCAmelCase_     ,     hidden_size=37							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_=False							):
		A_		:						Tuple	    =   copy.deepcopy(lowerCAmelCase_							)

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

		return inputs_dict





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





	@unittest.skip(reason="""TimeSformer does not use inputs_embeds"""							)
	def    lowerCamelCase(self							):
		pass





	def    lowerCamelCase(self							):
		A_    ,     A_		:						int	    =   self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			A_		:						Tuple	    =   model_class(lowerCAmelCase_							)
			self.assertIsInstance(model.get_input_embeddings()     ,     (nn.Module)							)
			A_		:						List[str]	    =   model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(lowerCAmelCase_     ,     nn.Linear							)							)





	def    lowerCamelCase(self							):
		A_    ,     A_		:						Optional[Any]	    =   self.model_tester.prepare_config_and_inputs_for_common()

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

			A_		:						List[Any]	    =   ["""pixel_values"""]
			self.assertListEqual(arg_names[:1]     ,     lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		A_		:						Union[str, Any]	    =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		A_		:						Union[str, Any]	    =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_video_classification(*lowerCAmelCase_							)





	@slow
	def    lowerCamelCase(self							):
		for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			A_		:						Tuple	    =   TimesformerModel.from_pretrained(lowerCAmelCase_							)
			self.assertIsNotNone(lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		if not self.has_attentions:
			pass

		else:
			A_    ,     A_		:						Optional[Any]	    =   self.model_tester.prepare_config_and_inputs_for_common()
			A_		:						List[str]	    =   True

			for model_class in self.all_model_classes:
				A_		:						int	    =   self.model_tester.seq_length
				A_		:						Union[str, Any]	    =   self.model_tester.num_frames

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

				# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
				self.assertListEqual(
				    list(attentions[0].shape[-3:]							)     ,     [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]     ,     )
				A_		:						Dict	    =   len(lowerCAmelCase_							)

				# Check attention is always last and order is fine
				A_		:						str	    =   True
				A_		:						List[Any]	    =   True
				A_		:						Dict	    =   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_		:						List[Any]	    =   outputs.attentions

				self.assertEqual(len(lowerCAmelCase_							)     ,     self.model_tester.num_hidden_layers							)

				# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
				self.assertListEqual(
				    list(self_attentions[0].shape[-3:]							)     ,     [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]     ,     )





	def    lowerCamelCase(self							):
		def check_hidden_states_output(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
			A_		:						Optional[int]	    =   model_class(lowerCAmelCase_							)
			model.to(lowerCAmelCase_							)
			model.eval()

			with torch.no_grad():
				A_		:						List[str]	    =   model(**self._prepare_for_class(lowerCAmelCase_     ,     lowerCAmelCase_							)							)

			A_		:						List[str]	    =   outputs.hidden_states
			A_		:						Optional[int]	    =   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_		:						Optional[int]	    =   True
			check_hidden_states_output(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							)

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

			check_hidden_states_output(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							)






def 			__UpperCamelCase	(		):
	A_		:						Dict	    =   hf_hub_download(
	    repo_id="""hf-internal-testing/spaghetti-video"""   ,   filename="""eating_spaghetti.npy"""   ,   repo_type="""dataset"""					)
	A_		:						Optional[int]	    =   np.load(snake_case__					)
	return list(snake_case__					)




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





	"""simple docstring"""




	@cached_property
	def    lowerCamelCase(self							):
		# logits were tested with a different mean and std, so we use the same here
		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    lowerCamelCase(self							):
		A_		:						str	    =   TimesformerForVideoClassification.from_pretrained("""facebook/timesformer-base-finetuned-k400"""							).to(
		    lowerCAmelCase_							)

		A_		:						List[str]	    =   self.default_image_processor
		A_		:						str	    =   prepare_video()
		A_		:						str	    =   image_processor(video[:8]     ,     return_tensors="""pt"""							).to(lowerCAmelCase_							)

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

		# verify the logits
		A_		:						Optional[Any]	    =   torch.Size((1, 400)							)
		self.assertEqual(outputs.logits.shape     ,     lowerCAmelCase_							)

		A_		:						Tuple	    =   torch.tensor([-0.3016, -0.7713, -0.4205]							).to(lowerCAmelCase_							)

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









"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						List[str]	    =   prime_factors(snake_case__					)
	if is_square_free(snake_case__					):
		return -1 if len(snake_case__					) % 2 else 1
	return 0


if __name__ == "__main__":
							import doctest

							doctest.testmod()

