
import inspect
import unittest

from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import YolosForObjectDetection, YolosModel
		from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor



class    a_ :






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase=13				,			__UpperCamelCase=[30, 30]				,			__UpperCamelCase=2				,			__UpperCamelCase=3				,			__UpperCamelCase=True				,			__UpperCamelCase=True				,			__UpperCamelCase=32				,			__UpperCamelCase=5				,			__UpperCamelCase=4				,			__UpperCamelCase=37				,			__UpperCamelCase="gelu"				,			__UpperCamelCase=0.1				,			__UpperCamelCase=0.1				,			__UpperCamelCase=10				,			__UpperCamelCase=0.02				,			__UpperCamelCase=3				,			__UpperCamelCase=None				,			__UpperCamelCase=8				,			__UpperCamelCase=10				,			):
									_lowercase					       =					parent
									_lowercase					       =					batch_size
									_lowercase					       =					image_size
									_lowercase					       =					patch_size
									_lowercase					       =					num_channels
									_lowercase					       =					is_training
									_lowercase					       =					use_labels
									_lowercase					       =					hidden_size
									_lowercase					       =					num_hidden_layers
									_lowercase					       =					num_attention_heads
									_lowercase					       =					intermediate_size
									_lowercase					       =					hidden_act
									_lowercase					       =					hidden_dropout_prob
									_lowercase					       =					attention_probs_dropout_prob
									_lowercase					       =					type_sequence_label_size
									_lowercase					       =					initializer_range
									_lowercase					       =					num_labels
									_lowercase					       =					scope
									_lowercase					       =					n_targets
									_lowercase					       =					num_detection_tokens
									# we set the expected sequence length (which is used in several tests)
									# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
									_lowercase					       =					(image_size[1] // patch_size) * (image_size[0] // patch_size)
									_lowercase					       =					num_patches + 1 + self.num_detection_tokens






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]  )

									_lowercase					       =					None
									if self.use_labels:
															# labels is a list of Dict (each Dict being the labels for a given example in the batch)
															_lowercase					       =					[]
															for i in range(self.batch_size  ):
																					_lowercase					       =					{}
																					_lowercase					       =					torch.randint(
																					    high=self.num_labels				,			size=(self.n_targets,)				,			device=__UpperCamelCase  )
																					_lowercase					       =					torch.rand(self.n_targets				,			4				,			device=__UpperCamelCase  )
																					labels.append(__UpperCamelCase  )

									_lowercase					       =					self.get_config()

									return config, pixel_values, labels






			def    UpperCamelCase_				(      self  ):
									return YolosConfig(
									    image_size=self.image_size				,			patch_size=self.patch_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				,			is_decoder=__UpperCamelCase				,			initializer_range=self.initializer_range				,			num_detection_tokens=self.num_detection_tokens				,			num_labels=self.num_labels				,			)






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase					       =					YolosModel(config=__UpperCamelCase  )
									model.to(__UpperCamelCase  )
									model.eval()
									_lowercase					       =					model(__UpperCamelCase  )
									self.parent.assertEqual(
									    result.last_hidden_state.shape				,			(self.batch_size, self.expected_seq_len, self.hidden_size)  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase					       =					YolosForObjectDetection(__UpperCamelCase  )
									model.to(__UpperCamelCase  )
									model.eval()

									_lowercase					       =					model(pixel_values=__UpperCamelCase  )
									_lowercase					       =					model(__UpperCamelCase  )

									self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.num_detection_tokens, self.num_labels + 1)  )
									self.parent.assertEqual(result.pred_boxes.shape				,			(self.batch_size, self.num_detection_tokens, 4)  )

									_lowercase					       =					model(pixel_values=__UpperCamelCase				,			labels=__UpperCamelCase  )

									self.parent.assertEqual(result.loss.shape				,			()  )
									self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.num_detection_tokens, self.num_labels + 1)  )
									self.parent.assertEqual(result.pred_boxes.shape				,			(self.batch_size, self.num_detection_tokens, 4)  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.prepare_config_and_inputs()
									_lowercase       ,						_lowercase       ,						_lowercase					       =					config_and_inputs
									_lowercase					       =					{"""pixel_values""": pixel_values}
									return config, inputs_dict







@require_torch
class    a_ (			_a					,			_a					,			unittest.TestCase    ):
			a	:			List[str]					   =							(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
			a	:			Optional[Any]					   =							(
			    {'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
			)

			a	:			int					   =							False
			a	:			List[str]					   =							False
			a	:			Any					   =							False
			a	:			Tuple					   =							False






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase=False  ):
									_lowercase					       =					super()._prepare_for_class(__UpperCamelCase				,			__UpperCamelCase				,			return_labels=__UpperCamelCase  )

									if return_labels:
															if model_class.__name__ == "YolosForObjectDetection":
																					_lowercase					       =					[]
																					for i in range(self.model_tester.batch_size  ):
																											_lowercase					       =					{}
																											_lowercase					       =					torch.ones(
																											    size=(self.model_tester.n_targets,)				,			device=__UpperCamelCase				,			dtype=torch.long  )
																											_lowercase					       =					torch.ones(
																											    self.model_tester.n_targets				,			4				,			device=__UpperCamelCase				,			dtype=torch.float  )
																											labels.append(__UpperCamelCase  )
																					_lowercase					       =					labels

									return inputs_dict






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					YolosModelTester(self  )
									_lowercase					       =					ConfigTester(self				,			config_class=__UpperCamelCase				,			has_text_modality=__UpperCamelCase				,			hidden_size=37  )






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






			def    UpperCamelCase_				(      self  ):
									# YOLOS does not use inputs_embeds
									pass






			def    UpperCamelCase_				(      self  ):
									_lowercase       ,						_lowercase					       =					self.model_tester.prepare_config_and_inputs_for_common()

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






			def    UpperCamelCase_				(      self  ):
									_lowercase       ,						_lowercase					       =					self.model_tester.prepare_config_and_inputs_for_common()

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

															_lowercase					       =					["""pixel_values"""]
															self.assertListEqual(arg_names[:1]				,			__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									_lowercase       ,						_lowercase					       =					self.model_tester.prepare_config_and_inputs_for_common()
									_lowercase					       =					True

									# in YOLOS, the seq_len is different
									_lowercase					       =					self.model_tester.expected_seq_len
									for model_class in self.all_model_classes:
															_lowercase					       =					True
															_lowercase					       =					False
															_lowercase					       =					True
															_lowercase					       =					model_class(__UpperCamelCase  )
															model.to(__UpperCamelCase  )
															model.eval()
															with torch.no_grad():
																					_lowercase					       =					model(**self._prepare_for_class(__UpperCamelCase				,			__UpperCamelCase  )  )
															_lowercase					       =					outputs.attentions
															self.assertEqual(len(__UpperCamelCase  )				,			self.model_tester.num_hidden_layers  )

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

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

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

															_lowercase					       =					1
															self.assertEqual(out_len + added_hidden_states				,			len(__UpperCamelCase  )  )

															_lowercase					       =					outputs.attentions

															self.assertEqual(len(__UpperCamelCase  )				,			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    UpperCamelCase_				(      self  ):
									def check_hidden_states_output(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
															_lowercase					       =					model_class(__UpperCamelCase  )
															model.to(__UpperCamelCase  )
															model.eval()

															with torch.no_grad():
																					_lowercase					       =					model(**self._prepare_for_class(__UpperCamelCase				,			__UpperCamelCase  )  )

															_lowercase					       =					outputs.hidden_states

															_lowercase					       =					getattr(
															    self.model_tester				,			"""expected_num_hidden_layers"""				,			self.model_tester.num_hidden_layers + 1  )
															self.assertEqual(len(__UpperCamelCase  )				,			__UpperCamelCase  )

															# YOLOS has a different seq_length
															_lowercase					       =					self.model_tester.expected_seq_len

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

									_lowercase       ,						_lowercase					       =					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															_lowercase					       =					True
															check_hidden_states_output(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  )

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															_lowercase					       =					True

															check_hidden_states_output(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_object_detection(*__UpperCamelCase  )






			@slow
			def    UpperCamelCase_				(      self  ):
									for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_lowercase					       =					YolosModel.from_pretrained(__UpperCamelCase  )
															self.assertIsNotNone(__UpperCamelCase  )







def 	UpperCamelCase__  (     )  ->     Union[str, Any]:
						_lowercase					       =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
						return image



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






			@cached_property
			def    UpperCamelCase_				(      self  ):
									return AutoImageProcessor.from_pretrained("""hustvl/yolos-small"""  ) if is_vision_available() else None






			@slow
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					YolosForObjectDetection.from_pretrained("""hustvl/yolos-small"""  ).to(__UpperCamelCase  )

									_lowercase					       =					self.default_image_processor
									_lowercase					       =					prepare_img()
									_lowercase					       =					image_processor(images=__UpperCamelCase				,			return_tensors="""pt"""  ).to(__UpperCamelCase  )

									# forward pass
									with torch.no_grad():
															_lowercase					       =					model(inputs.pixel_values  )

									# verify outputs
									_lowercase					       =					torch.Size((1, 100, 92)  )
									self.assertEqual(outputs.logits.shape				,			__UpperCamelCase  )

									_lowercase					       =					torch.tensor(
									    [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]				,			device=__UpperCamelCase				,			)
									_lowercase					       =					torch.tensor(
									    [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]				,			device=__UpperCamelCase  )
									self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]				,			__UpperCamelCase				,			atol=1E-4  )  )
									self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]				,			__UpperCamelCase				,			atol=1E-4  )  )

									# verify postprocessing
									_lowercase					       =					image_processor.post_process_object_detection(
									    __UpperCamelCase				,			threshold=0.3				,			target_sizes=[image.size[::-1]]  )[0]
									_lowercase					       =					torch.tensor([0.99_94, 0.97_90, 0.99_64, 0.99_72, 0.98_61]  ).to(__UpperCamelCase  )
									_lowercase					       =					[75, 75, 17, 63, 17]
									_lowercase					       =					torch.tensor([3_35.06_09, 79.38_48, 3_75.42_16, 1_87.24_95]  ).to(__UpperCamelCase  )

									self.assertEqual(len(results["""scores"""]  )				,			5  )
									self.assertTrue(torch.allclose(results["""scores"""]				,			__UpperCamelCase				,			atol=1E-4  )  )
									self.assertSequenceEqual(results["""labels"""].tolist()				,			__UpperCamelCase  )
									self.assertTrue(torch.allclose(results["""boxes"""][0, :]				,			__UpperCamelCase  )  )

import os

# Precomputes a list of the 100 first triangular numbers
A		: List[Any]								=     [int(0.5 * n * (n + 1)) for n in range(1, 101)]




def 	UpperCamelCase__  (     )  ->     str:
						_lowercase					       =					os.path.dirname(os.path.realpath(SCREAMING_SNAKE_CASE_       )       )
						_lowercase					       =					os.path.join(SCREAMING_SNAKE_CASE_		,   """words.txt"""       )

						_lowercase					       =					""""""
						with open(SCREAMING_SNAKE_CASE_       ) as f:
												_lowercase					       =					f.readline()

						_lowercase					       =					[word.strip("""\""""       ) for word in words.strip("""\r\n"""       ).split(""","""       )]
						_lowercase					       =					[
						    word
						    for word in [sum(ord(SCREAMING_SNAKE_CASE_       ) - 64 for x in word       ) for word in words]
						    if word in TRIANGULAR_NUMBERS
						]
						return len(SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
		print(solution())