





'''simple docstring'''



import requests
from bsa import BeautifulSoup



def  lowercase__(		_UpperCamelCase  : int ,   _UpperCamelCase  : str				)->   str:






					"""simple docstring"""

					_UpperCamelCase											=   (
					    "https://www.horoscope.com/us/horoscopes/general/"
					    f"horoscope-general-daily-{day}.aspx?sign={zodiac_sign}"
					)
					_UpperCamelCase											=   BeautifulSoup(requests.get(_UpperCamelCase				).content ,   "html.parser"				)
					return soup.find("div" ,   class_="main-horoscope"				).p.text


if __name__ == "__main__":
			print('''Daily Horoscope. \n''')
			print(
			    '''enter your Zodiac sign number:\n''',
			    '''1. Aries\n''',
			    '''2. Taurus\n''',
			    '''3. Gemini\n''',
			    '''4. Cancer\n''',
			    '''5. Leo\n''',
			    '''6. Virgo\n''',
			    '''7. Libra\n''',
			    '''8. Scorpio\n''',
			    '''9. Sagittarius\n''',
			    '''10. Capricorn\n''',
			    '''11. Aquarius\n''',
			    '''12. Pisces\n''',
			)
			snake_case_   :  Dict								=      int(input('''number> ''').strip())
			print('''choose some day:\n''', '''yesterday\n''', '''today\n''', '''tomorrow\n''')
			snake_case_   :  Tuple								=      input('''enter the day> ''')
			snake_case_   :  Union[str, Any]								=      horoscope(zodiac_sign, day)
			print(horoscope_text)










'''simple docstring'''



import collections
import inspect
import unittest

from transformers import DinatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import DinatBackbone, DinatForImageClassification, DinatModel
			from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor

class     A_ :

			'''simple docstring'''





			def __init__(					self			,     A_			,     A_=13			,     A_=64			,     A_=4			,     A_=3			,     A_=16			,     A_=[1, 2, 1]			,     A_=[2, 4, 8]			,     A_=3			,     A_=[[3], [1, 2], [1]]			,     A_=2.0			,     A_=True			,     A_=0.0			,     A_=0.0			,     A_=0.1			,     A_="gelu"			,     A_=True			,     A_=0.02			,     A_=1e-5			,     A_=True			,     A_=None			,     A_=True			,     A_=10			,     A_=["stage1", "stage2"]			,     A_=[1, 2]			,     ):
								_UpperCamelCase											=   parent
								_UpperCamelCase											=   batch_size
								_UpperCamelCase											=   image_size
								_UpperCamelCase											=   patch_size
								_UpperCamelCase											=   num_channels
								_UpperCamelCase											=   embed_dim
								_UpperCamelCase											=   depths
								_UpperCamelCase											=   num_heads
								_UpperCamelCase											=   kernel_size
								_UpperCamelCase											=   dilations
								_UpperCamelCase											=   mlp_ratio
								_UpperCamelCase											=   qkv_bias
								_UpperCamelCase											=   hidden_dropout_prob
								_UpperCamelCase											=   attention_probs_dropout_prob
								_UpperCamelCase											=   drop_path_rate
								_UpperCamelCase											=   hidden_act
								_UpperCamelCase											=   patch_norm
								_UpperCamelCase											=   layer_norm_eps
								_UpperCamelCase											=   initializer_range
								_UpperCamelCase											=   is_training
								_UpperCamelCase											=   scope
								_UpperCamelCase											=   use_labels
								_UpperCamelCase											=   num_labels
								_UpperCamelCase											=   out_features
								_UpperCamelCase											=   out_indices




			def       a	(					self   ):
								_UpperCamelCase											=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

								_UpperCamelCase											=   self.get_config()

								return config, pixel_values, labels




			def       a	(					self   ):
								return DinatConfig(
								    num_labels=self.num_labels			,     image_size=self.image_size			,     patch_size=self.patch_size			,     num_channels=self.num_channels			,     embed_dim=self.embed_dim			,     depths=self.depths			,     num_heads=self.num_heads			,     kernel_size=self.kernel_size			,     dilations=self.dilations			,     mlp_ratio=self.mlp_ratio			,     qkv_bias=self.qkv_bias			,     hidden_dropout_prob=self.hidden_dropout_prob			,     attention_probs_dropout_prob=self.attention_probs_dropout_prob			,     drop_path_rate=self.drop_path_rate			,     hidden_act=self.hidden_act			,     patch_norm=self.patch_norm			,     layer_norm_eps=self.layer_norm_eps			,     initializer_range=self.initializer_range			,     out_features=self.out_features			,     out_indices=self.out_indices			,     )




			def       a	(					self			,     A_			,     A_			,     A_   ):
								_UpperCamelCase											=   DinatModel(config=A_   )
								model.to(A_   )
								model.eval()
								_UpperCamelCase											=   model(A_   )

								_UpperCamelCase											=   _UpperCamelCase											=   (config.image_size // config.patch_size) // (2 ** (len(config.depths   ) - 1))
								_UpperCamelCase											=   int(config.embed_dim * 2 ** (len(config.depths   ) - 1)   )

								self.parent.assertEqual(
								    result.last_hidden_state.shape			,     (self.batch_size, expected_height, expected_width, expected_dim)   )




			def       a	(					self			,     A_			,     A_			,     A_   ):
								_UpperCamelCase											=   DinatForImageClassification(A_   )
								model.to(A_   )
								model.eval()
								_UpperCamelCase											=   model(A_			,     labels=A_   )
								self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)   )

								# test greyscale images
								_UpperCamelCase											=   1
								_UpperCamelCase											=   DinatForImageClassification(A_   )
								model.to(A_   )
								model.eval()

								_UpperCamelCase											=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
								_UpperCamelCase											=   model(A_   )
								self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)   )




			def       a	(					self			,     A_			,     A_			,     A_   ):
								_UpperCamelCase											=   DinatBackbone(config=A_   )
								model.to(A_   )
								model.eval()
								_UpperCamelCase											=   model(A_   )

								# verify hidden states
								self.parent.assertEqual(len(result.feature_maps   )			,     len(config.out_features   )   )
								self.parent.assertListEqual(list(result.feature_maps[0].shape   )			,     [self.batch_size, model.channels[0], 16, 16]   )

								# verify channels
								self.parent.assertEqual(len(model.channels   )			,     len(config.out_features   )   )

								# verify backbone works with out_features=None
								_UpperCamelCase											=   None
								_UpperCamelCase											=   DinatBackbone(config=A_   )
								model.to(A_   )
								model.eval()
								_UpperCamelCase											=   model(A_   )

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps   )			,     1   )
								self.parent.assertListEqual(list(result.feature_maps[0].shape   )			,     [self.batch_size, model.channels[-1], 4, 4]   )

								# verify channels
								self.parent.assertEqual(len(model.channels   )			,     1   )




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




@require_natten
@require_torch
class     A_ (  lowerCAmelCase_    ,			lowerCAmelCase_    ,			unittest.TestCase  ):

			'''simple docstring'''





			_lowerCAmelCase						=    (
			    (
			        DinatModel,
			        DinatForImageClassification,
			        DinatBackbone,
			    )
			    if is_torch_available()
			    else ()
			)
			_lowerCAmelCase						=    (
			    {"""feature-extraction""": DinatModel, """image-classification""": DinatForImageClassification}
			    if is_torch_available()
			    else {}
			)
			_lowerCAmelCase						=    False

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




			def       a	(					self   ):
								_UpperCamelCase											=   DinatModelTester(self   )
								_UpperCamelCase											=   ConfigTester(self			,     config_class=A_			,     embed_dim=37   )




			def       a	(					self   ):
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()




			def       a	(					self   ):
								return




			def       a	(					self   ):
								_UpperCamelCase											=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A_   )




			def       a	(					self   ):
								_UpperCamelCase											=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*A_   )




			def       a	(					self   ):
								_UpperCamelCase											=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_backbone(*A_   )




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




			@unittest.skip(reason="Dinat does not use feedforward chunking"   )
			def       a	(					self   ):
								pass




			def       a	(					self   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.model_tester.prepare_config_and_inputs_for_common()

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




			def       a	(					self   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.model_tester.prepare_config_and_inputs_for_common()

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

													_UpperCamelCase											=   ["pixel_values"]
													self.assertListEqual(arg_names[:1]			,     A_   )




			def       a	(					self   ):
								self.skipTest("Dinat's attention operation is handled entirely by NATTEN."   )




			def       a	(					self			,     A_			,     A_			,     A_			,     A_   ):
								_UpperCamelCase											=   model_class(A_   )
								model.to(A_   )
								model.eval()

								with torch.no_grad():
													_UpperCamelCase											=   model(**self._prepare_for_class(A_			,     A_   )   )

								_UpperCamelCase											=   outputs.hidden_states

								_UpperCamelCase											=   getattr(
								    self.model_tester			,     "expected_num_hidden_layers"			,     len(self.model_tester.depths   ) + 1   )
								self.assertEqual(len(A_   )			,     A_   )

								# Dinat has a different seq_length
								_UpperCamelCase											=   (
								    config.patch_size
								    if isinstance(config.patch_size			,     collections.abc.Iterable   )
								    else (config.patch_size, config.patch_size)
								)

								_UpperCamelCase											=   image_size[0] // patch_size[0]
								_UpperCamelCase											=   image_size[1] // patch_size[1]

								self.assertListEqual(
								    list(hidden_states[0].shape[-3:]   )			,     [height, width, self.model_tester.embed_dim]			,     )

								if model_class.__name__ != "DinatBackbone":
													_UpperCamelCase											=   outputs.reshaped_hidden_states
													self.assertEqual(len(A_   )			,     A_   )

													_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   reshaped_hidden_states[0].shape
													_UpperCamelCase											=   (
													    reshaped_hidden_states[0].view(A_			,     A_			,     A_			,     A_   ).permute(0			,     2			,     3			,     1   )
													)
													self.assertListEqual(
													    list(reshaped_hidden_states.shape[-3:]   )			,     [height, width, self.model_tester.embed_dim]			,     )




			def       a	(					self   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.model_tester.prepare_config_and_inputs_for_common()

								_UpperCamelCase											=   (
								    self.model_tester.image_size
								    if isinstance(self.model_tester.image_size			,     collections.abc.Iterable   )
								    else (self.model_tester.image_size, self.model_tester.image_size)
								)

								for model_class in self.all_model_classes:
													_UpperCamelCase											=   True
													self.check_hidden_states_output(A_			,     A_			,     A_			,     A_   )

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

													self.check_hidden_states_output(A_			,     A_			,     A_			,     A_   )




			@slow
			def       a	(					self   ):
								for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_UpperCamelCase											=   DinatModel.from_pretrained(A_   )
													self.assertIsNotNone(A_   )




			def       a	(					self   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.model_tester.prepare_config_and_inputs_for_common()

								_UpperCamelCase											=   _config_zero_init(A_   )
								for model_class in self.all_model_classes:
													_UpperCamelCase											=   model_class(config=A_   )
													for name, param in model.named_parameters():
																		if "embeddings" not in name and param.requires_grad:
																							self.assertIn(
																							    ((param.data.mean() * 1e9).round() / 1e9).item()			,     [0.0, 1.0]			,     msg=F"Parameter {name} of model {model_class} seems not properly initialized"			,     )




@require_natten
@require_vision
@require_torch
class     A_ (  unittest.TestCase  ):

			'''simple docstring'''





			@cached_property
			def       a	(					self   ):
								return AutoImageProcessor.from_pretrained("shi-labs/dinat-mini-in1k-224"   ) if is_vision_available() else None




			@slow
			def       a	(					self   ):
								_UpperCamelCase											=   DinatForImageClassification.from_pretrained("shi-labs/dinat-mini-in1k-224"   ).to(A_   )
								_UpperCamelCase											=   self.default_image_processor

								_UpperCamelCase											=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
								_UpperCamelCase											=   image_processor(images=A_			,     return_tensors="pt"   ).to(A_   )

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

								# verify the logits
								_UpperCamelCase											=   torch.Size((1, 10_00)   )
								self.assertEqual(outputs.logits.shape			,     A_   )
								_UpperCamelCase											=   torch.tensor([-0.1545, -0.7667, 0.4642]   ).to(A_   )
								self.assertTrue(torch.allclose(outputs.logits[0, :3]			,     A_			,     atol=1e-4   )   )




@require_torch
@require_natten
class     A_ (  unittest.TestCase    ,			lowerCAmelCase_  ):

			'''simple docstring'''





			_lowerCAmelCase						=    (DinatBackbone,) if is_torch_available() else ()
			_lowerCAmelCase						=    DinatConfig




			def       a	(					self   ):
								_UpperCamelCase											=   DinatModelTester(self   )



