



import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor



class        __snake_case		(  unittest.TestCase		):




							def __init__( self  : Tuple       ,       A_  : int       ,       A_  : Optional[int]=3       ,       A_  : int=3_2       ,       A_  : List[str]=3       ,       A_  : Optional[int]=1_0       ,       A_  : str=[1_0, 2_0, 3_0, 4_0]       ,       A_  : List[str]=[1, 1, 2, 1]       ,       A_  : List[str]=True       ,       A_  : Dict=True       ,       A_  : int="relu"       ,       A_  : Optional[Any]=3       ,       A_  : List[str]=None       ,       ):
									lowerCAmelCase_    :  Optional[int]             =				parent
									lowerCAmelCase_    :  Optional[int]             =				batch_size
									lowerCAmelCase_    :  Dict             =				image_size
									lowerCAmelCase_    :  str             =				num_channels
									lowerCAmelCase_    :  int             =				embeddings_size
									lowerCAmelCase_    :  List[str]             =				hidden_sizes
									lowerCAmelCase_    :  List[Any]             =				depths
									lowerCAmelCase_    :  List[Any]             =				is_training
									lowerCAmelCase_    :  Optional[int]             =				use_labels
									lowerCAmelCase_    :  List[Any]             =				hidden_act
									lowerCAmelCase_    :  int             =				num_labels
									lowerCAmelCase_    :  Tuple             =				scope
									lowerCAmelCase_    :  Tuple             =				len(A_)




							def      UpperCAmelCase__    ( self  : Any):
									lowerCAmelCase_    :  int             =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

									lowerCAmelCase_    :  Tuple             =				self.get_config()

									return config, pixel_values




							def      UpperCAmelCase__    ( self  : List[Any]):
									return ResNetConfig(
									    num_channels=self.num_channels       ,       embeddings_size=self.embeddings_size       ,       hidden_sizes=self.hidden_sizes       ,       depths=self.depths       ,       hidden_act=self.hidden_act       ,       num_labels=self.num_labels       ,       image_size=self.image_size       ,       )




							def      UpperCAmelCase__    ( self  : Tuple       ,       A_  : Optional[int]       ,       A_  : int):
									lowerCAmelCase_    :  Optional[int]             =				FlaxResNetModel(config=A_)
									lowerCAmelCase_    :  List[str]             =				model(A_)

									# Output shape (b, c, h, w)
									self.parent.assertEqual(
									    result.last_hidden_state.shape       ,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)       ,       )




							def      UpperCAmelCase__    ( self  : str       ,       A_  : Dict       ,       A_  : Union[str, Any]):
									lowerCAmelCase_    :  Tuple             =				self.num_labels
									lowerCAmelCase_    :  Tuple             =				FlaxResNetForImageClassification(config=A_)
									lowerCAmelCase_    :  Tuple             =				model(A_)
									self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.num_labels))







							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									lowerCAmelCase_    :  List[str]             =				self.prepare_config_and_inputs()
									lowerCAmelCase_      ,						lowerCAmelCase_    :  Union[str, Any]             =				config_and_inputs
									lowerCAmelCase_    :  List[Any]             =				{'''pixel_values''': pixel_values}
									return config, inputs_dict





@require_flax
class        __snake_case		(  UpperCamelCase_		,unittest.TestCase		):
							_a       =				(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

							_a       =				False
							_a       =				False
							_a       =				False




							def      UpperCAmelCase__    ( self  : Any):
									lowerCAmelCase_    :  Optional[int]             =				FlaxResNetModelTester(self)
									lowerCAmelCase_    :  Optional[int]             =				ConfigTester(self       ,       config_class=A_       ,       has_text_modality=A_)




							def      UpperCAmelCase__    ( self  : List[Any]):
									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      UpperCAmelCase__    ( self  : Tuple):
									return




							def      UpperCAmelCase__    ( self  : Dict):
									lowerCAmelCase_    :  Optional[Any]             =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*A_)




							def      UpperCAmelCase__    ( self  : Any):
									lowerCAmelCase_    :  Tuple             =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*A_)




							@unittest.skip(reason='''ResNet does not use inputs_embeds''')
							def      UpperCAmelCase__    ( self  : Dict):
									pass




							@unittest.skip(reason='''ResNet does not support input and output embeddings''')
							def      UpperCAmelCase__    ( self  : Optional[Any]):
									pass




							def      UpperCAmelCase__    ( self  : List[Any]):
									lowerCAmelCase_      ,						lowerCAmelCase_    :  str             =				self.model_tester.prepare_config_and_inputs_for_common()

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

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




							def      UpperCAmelCase__    ( self  : Tuple):
									def check_hidden_states_output(A_  : Dict       ,       A_  : Tuple       ,       A_  : Any):
											lowerCAmelCase_    :  List[Any]             =				model_class(A_)

											lowerCAmelCase_    :  int             =				model(**self._prepare_for_class(A_       ,       A_))

											lowerCAmelCase_    :  List[Any]             =				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

											lowerCAmelCase_    :  str             =				self.model_tester.num_stages
											self.assertEqual(len(A_)       ,       expected_num_stages + 1)




							@unittest.skip(reason='''ResNet does not use feedforward chunking''')
							def      UpperCAmelCase__    ( self  : int):
									pass







							def      UpperCAmelCase__    ( self  : Tuple):
									lowerCAmelCase_      ,						lowerCAmelCase_    :  Optional[int]             =				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__):
													lowerCAmelCase_    :  List[str]             =				self._prepare_for_class(A_       ,       A_)
													lowerCAmelCase_    :  Optional[int]             =				model_class(A_)

													@jax.jit
													def model_jitted(A_  : Union[str, Any]       ,       **A_  : List[str]):
															return model(pixel_values=A_       ,       **A_)

													with self.subTest('''JIT Enabled'''):
															lowerCAmelCase_    :  Any             =				model_jitted(**A_).to_tuple()

													with self.subTest('''JIT Disabled'''):
															with jax.disable_jit():
																	lowerCAmelCase_    :  Union[str, Any]             =				model_jitted(**A_).to_tuple()

													self.assertEqual(len(A_)       ,       len(A_))
													for jitted_output, output in zip(A_       ,       A_):
															self.assertEqual(jitted_output.shape       ,       output.shape)





def     UpperCamelCase(						):
		lowerCAmelCase_    :  Tuple             =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
		return image





@require_flax
class        __snake_case		(  unittest.TestCase		):




							@cached_property
							def      UpperCAmelCase__    ( self  : Optional[int]):
									return AutoImageProcessor.from_pretrained('''microsoft/resnet-50''') if is_vision_available() else None







							@slow
							def      UpperCAmelCase__    ( self  : Tuple):
									lowerCAmelCase_    :  Union[str, Any]             =				FlaxResNetForImageClassification.from_pretrained('''microsoft/resnet-50''')

									lowerCAmelCase_    :  Tuple             =				self.default_image_processor
									lowerCAmelCase_    :  List[Any]             =				prepare_img()
									lowerCAmelCase_    :  Any             =				image_processor(images=A_       ,       return_tensors='''np''')

									lowerCAmelCase_    :  List[str]             =				model(**A_)

									# verify the logits
									lowerCAmelCase_    :  Union[str, Any]             =				(1, 1_0_0_0)
									self.assertEqual(outputs.logits.shape       ,       A_)

									lowerCAmelCase_    :  str             =				jnp.array([-11.1069, -9.7877, -8.3777])

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










from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

A__						:						int          =      '''https://www.wellrx.com/prescriptions/{0}/{1}/?freshSearch=true'''


def     UpperCamelCase(						__UpperCamelCase							:	str  ,__UpperCamelCase							:	str				):

		try:
				# Has user provided both inputs?
				if not drug_name or not zip_code:
						return None

				lowerCAmelCase_    :  Union[str, Any]             =				BASE_URL.format(__UpperCamelCase  ,__UpperCamelCase				)
				lowerCAmelCase_    :  Optional[int]             =				get(__UpperCamelCase				)

				# Is the response ok?
				response.raise_for_status()

				# Scrape the data using bs4
				lowerCAmelCase_    :  Optional[int]             =				BeautifulSoup(response.text  ,'''html.parser'''				)

				# This list will store the name and price.
				lowerCAmelCase_    :  List[Any]             =				[]

				# Fetch all the grids that contains the items.
				lowerCAmelCase_    :  Union[str, Any]             =				soup.find_all('''div'''  ,{'''class''': '''grid-x pharmCard'''}				)
				if grid_list and len(__UpperCamelCase				) > 0:
						for grid in grid_list:
								# Get the pharmacy price.
								lowerCAmelCase_    :  int             =				grid.find('''p'''  ,{'''class''': '''list-title'''}				).text

								# Get price of the drug.
								lowerCAmelCase_    :  List[Any]             =				grid.find('''span'''  ,{'''p''', '''price price-large'''}				).text

								pharmacy_price_list.append(
								    {
								        '''pharmacy_name''': pharmacy_name,
								        '''price''': price,
								    }				)

				return pharmacy_price_list

		except (HTTPError, exceptions.RequestException, ValueError):
				return None


if __name__ == "__main__":
						# Enter a drug name and a zip code
						A__						:						Dict          =      input('''Enter drug name: ''').strip()
						A__						:						int          =      input('''Enter zip code: ''').strip()

						A__						:						list | None          =      fetch_pharmacy_and_price_list(
						    drug_name, zip_code
						)

						if pharmacy_price_list:
												print(F'''\nSearch results for {drug_name} at location {zip_code}:''')
												for pharmacy_price in pharmacy_price_list:
																		A__						:						Union[str, Any]          =      pharmacy_price['''pharmacy_name''']
																		A__						:						Tuple          =      pharmacy_price['''price''']

																		print(F'''Pharmacy: {name} Price: {price}''')
						else:
												print(F'''No results found for {drug_name}''')





