




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 	lowerCamelCase_							:


	'''simple docstring'''







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

							# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
							SCREAMING_SNAKE_CASE_     						=      (image_size // patch_size) ** 2
							SCREAMING_SNAKE_CASE_     						=      (num_frames) * self.num_patches_per_frame + 1



	def 				lowerCAmelCase_  (							self  :		List[str]						):
							SCREAMING_SNAKE_CASE_     						=      floats_tensor(
							    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]						)

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

							SCREAMING_SNAKE_CASE_     						=      self.get_config()

							return config, pixel_values, labels



	def 				lowerCAmelCase_  (							self  :		Any						):
							SCREAMING_SNAKE_CASE_     						=      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    ,    )
							SCREAMING_SNAKE_CASE_     						=      self.num_labels
							return config



	def 				lowerCAmelCase_  (							self  :		List[Any]    ,    _lowerCAmelCase  :		Tuple    ,    _lowerCAmelCase  :		Tuple    ,    _lowerCAmelCase  :		Dict						):
							SCREAMING_SNAKE_CASE_     						=      TimesformerModel(config=_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()
							SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase						)
							self.parent.assertEqual(result.last_hidden_state.shape    ,    (self.batch_size, self.seq_length, self.hidden_size)						)



	def 				lowerCAmelCase_  (							self  :		Any    ,    _lowerCAmelCase  :		Optional[int]    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		Optional[int]						):
							SCREAMING_SNAKE_CASE_     						=      TimesformerForVideoClassification(_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()

							SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase						)

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




	def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
							SCREAMING_SNAKE_CASE_     						=      self.prepare_config_and_inputs()
							SCREAMING_SNAKE_CASE_     						=      config_and_inputs
							SCREAMING_SNAKE_CASE_     						=      {"""pixel_values""": pixel_values}
							return config, inputs_dict





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


	'''simple docstring'''







	lowercase_       =   (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
	lowercase_       =   (
	    {'''feature-extraction''': TimesformerModel, '''video-classification''': TimesformerForVideoClassification}
	    if is_torch_available()
	    else {}
	)

	lowercase_       =   False
	lowercase_       =   False
	lowercase_       =   False
	lowercase_       =   False



	def 				lowerCAmelCase_  (							self  :		Any						):
							SCREAMING_SNAKE_CASE_     						=      TimesformerModelTester(self						)
							SCREAMING_SNAKE_CASE_     						=      ConfigTester(
							    self    ,    config_class=_lowerCAmelCase    ,    has_text_modality=_lowerCAmelCase    ,    hidden_size=37						)



	def 				lowerCAmelCase_  (							self  :		Any    ,    _lowerCAmelCase  :		Tuple    ,    _lowerCAmelCase  :		List[str]    ,    _lowerCAmelCase  :		Union[str, Any]=False						):
							SCREAMING_SNAKE_CASE_     						=      copy.deepcopy(_lowerCAmelCase						)

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

							return inputs_dict



	def 				lowerCAmelCase_  (							self  :		Tuple						):
							self.config_tester.run_common_tests()



	@unittest.skip(reason='TimeSformer does not use inputs_embeds'						)
	def 				lowerCAmelCase_  (							self  :		List[str]						):
							pass



	def 				lowerCAmelCase_  (							self  :		str						):
							SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()

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



	def 				lowerCAmelCase_  (							self  :		Any						):
							SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()

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

													SCREAMING_SNAKE_CASE_     						=      ["""pixel_values"""]
													self.assertListEqual(arg_names[:1]    ,    _lowerCAmelCase						)



	def 				lowerCAmelCase_  (							self  :		str						):
							SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_lowerCAmelCase						)



	def 				lowerCAmelCase_  (							self  :		int						):
							SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_video_classification(*_lowerCAmelCase						)



	@slow
	def 				lowerCAmelCase_  (							self  :		List[Any]						):

							for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE_     						=      TimesformerModel.from_pretrained(_lowerCAmelCase						)
													self.assertIsNotNone(_lowerCAmelCase						)



	def 				lowerCAmelCase_  (							self  :		Dict						):

							if not self.has_attentions:
													pass

							else:
													SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE_     						=      True

													for model_class in self.all_model_classes:
																			SCREAMING_SNAKE_CASE_     						=      self.model_tester.seq_length
																			SCREAMING_SNAKE_CASE_     						=      self.model_tester.num_frames

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

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

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

																			SCREAMING_SNAKE_CASE_     						=      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  :		Dict						):

							def check_hidden_states_output(_lowerCAmelCase  :		List[str]    ,    _lowerCAmelCase  :		List[str]    ,    _lowerCAmelCase  :		Optional[Any]						):
													SCREAMING_SNAKE_CASE_     						=      model_class(_lowerCAmelCase						)
													model.to(_lowerCAmelCase						)
													model.eval()

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

													SCREAMING_SNAKE_CASE_     						=      outputs.hidden_states
													SCREAMING_SNAKE_CASE_     						=      self.model_tester.num_hidden_layers + 1
													self.assertEqual(len(_lowerCAmelCase						)    ,    _lowerCAmelCase						)

													SCREAMING_SNAKE_CASE_     						=      self.model_tester.seq_length

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

							SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													SCREAMING_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"]
													SCREAMING_SNAKE_CASE_     						=      True

													check_hidden_states_output(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase						)


def  UpperCAmelCase_							(				) ->			List[Any]:
						SCREAMING_SNAKE_CASE_     						=      hf_hub_download(
						    repo_id='hf-internal-testing/spaghetti-video'      ,      filename='eating_spaghetti.npy'      ,      repo_type='dataset'							)
						SCREAMING_SNAKE_CASE_     						=      np.load(_lowerCamelCase							)
						return list(_lowerCamelCase							)





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


	'''simple docstring'''







	@cached_property
	def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
							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  :		Optional[int]						):
							SCREAMING_SNAKE_CASE_     						=      TimesformerForVideoClassification.from_pretrained('facebook/timesformer-base-finetuned-k400'						).to(
							    _lowerCAmelCase						)

							SCREAMING_SNAKE_CASE_     						=      self.default_image_processor
							SCREAMING_SNAKE_CASE_     						=      prepare_video()
							SCREAMING_SNAKE_CASE_     						=      image_processor(video[:8]    ,    return_tensors='pt'						).to(_lowerCAmelCase						)

							# forward pass
							with torch.no_grad():
													SCREAMING_SNAKE_CASE_     						=      model(**_lowerCAmelCase						)

							# verify the logits
							SCREAMING_SNAKE_CASE_     						=      torch.Size((1, 400)						)
							self.assertEqual(outputs.logits.shape    ,    _lowerCAmelCase						)

							SCREAMING_SNAKE_CASE_     						=      torch.tensor([-0.3016, -0.7713, -0.4205]						).to(_lowerCAmelCase						)

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





import cva
import numpy as np





class 	lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Optional[Any]    ,    _lowerCAmelCase  :		float    ,    _lowerCAmelCase  :		int						):

       if k in (0.04, 0.06):
             SCREAMING_SNAKE_CASE_     						=      k
             SCREAMING_SNAKE_CASE_     						=      window_size
       else:
             raise ValueError('invalid k value'						)



 def __str__(							self  :		Tuple						):
       return str(self.k						)




 def 				lowerCAmelCase_  (							self  :		int    ,    _lowerCAmelCase  :		str						):
       SCREAMING_SNAKE_CASE_     						=      cva.imread(_lowerCAmelCase    ,    0						)
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      img.shape
       SCREAMING_SNAKE_CASE_     						=      []
       SCREAMING_SNAKE_CASE_     						=      img.copy()
       SCREAMING_SNAKE_CASE_     						=      cva.cvtColor(_lowerCAmelCase    ,    cva.COLOR_GRAY2RGB						)
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      np.gradient(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      dx**2
       SCREAMING_SNAKE_CASE_     						=      dy**2
       SCREAMING_SNAKE_CASE_     						=      dx * dy
       SCREAMING_SNAKE_CASE_     						=      0.04
       SCREAMING_SNAKE_CASE_     						=      self.window_size // 2
       for y in range(_lowerCAmelCase    ,    h - offset						):
             for x in range(_lowerCAmelCase    ,    w - offset						):
                   SCREAMING_SNAKE_CASE_     						=      ixx[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   SCREAMING_SNAKE_CASE_     						=      iyy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   SCREAMING_SNAKE_CASE_     						=      ixy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()

                   SCREAMING_SNAKE_CASE_     						=      (wxx * wyy) - (wxy**2)
                   SCREAMING_SNAKE_CASE_     						=      wxx + wyy
                   SCREAMING_SNAKE_CASE_     						=      det - k * (trace**2)
                   # Can change the value
                   if r > 0.5:
                         corner_list.append([x, y, r]						)
                         color_img.itemset((y, x, 0)    ,    0						)
                         color_img.itemset((y, x, 1)    ,    0						)
                         color_img.itemset((y, x, 2)    ,    255						)
       return color_img, corner_list


if __name__ == "__main__":
   lowerCamelCase__			:       Optional[int]         =							HarrisCorner(0.04, 3)
   lowerCamelCase__    ,							lowerCamelCase__			:       str         =							edge_detect.detect('path_to_image')
   cva.imwrite('detect.png', color_img)