



import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


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

      from transformers.modeling_flax_pytorch_utils import (
          convert_pytorch_state_dict_to_flax,
          load_flax_weights_in_pytorch_model,
      )
      from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
      import torch



@require_flax
class        __snake_case		:




       def __init__( self  : int       ,       A_  : str       ,       A_  : Optional[Any]=1_4       ,       A_  : int=7       ,       A_  : List[Any]=True       ,       A_  : str=True       ,       A_  : Union[str, Any]=True       ,       A_  : List[Any]=9_9       ,       A_  : int=3_2       ,       A_  : Optional[int]=5       ,       A_  : Optional[Any]=4       ,       A_  : Optional[Any]=3_7       ,       A_  : Union[str, Any]="gelu"       ,       A_  : List[str]=0.1       ,       A_  : int=0.1       ,       A_  : Optional[int]=5_1_2       ,       A_  : List[str]=0.02       ,       A_  : Any=None       ,       ):
         lowerCAmelCase_    :  int             =				parent
         lowerCAmelCase_    :  List[Any]             =				batch_size
         lowerCAmelCase_    :  Optional[Any]             =				seq_length
         lowerCAmelCase_    :  Union[str, Any]             =				is_training
         lowerCAmelCase_    :  Tuple             =				use_input_mask
         lowerCAmelCase_    :  List[Any]             =				use_labels
         lowerCAmelCase_    :  Optional[int]             =				vocab_size
         lowerCAmelCase_    :  Tuple             =				d_model
         lowerCAmelCase_    :  str             =				num_hidden_layers
         lowerCAmelCase_    :  Tuple             =				num_attention_heads
         lowerCAmelCase_    :  Dict             =				ffn_dim
         lowerCAmelCase_    :  int             =				activation_function
         lowerCAmelCase_    :  Optional[Any]             =				activation_dropout
         lowerCAmelCase_    :  str             =				attention_dropout
         lowerCAmelCase_    :  Optional[int]             =				max_position_embeddings
         lowerCAmelCase_    :  Optional[Any]             =				initializer_range
         lowerCAmelCase_    :  Union[str, Any]             =				None
         lowerCAmelCase_    :  Optional[int]             =				0
         lowerCAmelCase_    :  Optional[Any]             =				2
         lowerCAmelCase_    :  str             =				1




       def      UpperCAmelCase__    ( self  : int):
         lowerCAmelCase_    :  str             =				np.clip(ids_tensor([self.batch_size, self.seq_length]       ,       self.vocab_size)       ,       3       ,       self.vocab_size)

         lowerCAmelCase_    :  str             =				None
         if self.use_input_mask:
           lowerCAmelCase_    :  Tuple             =				random_attention_mask([self.batch_size, self.seq_length])

         lowerCAmelCase_    :  Optional[int]             =				XGLMConfig(
             vocab_size=self.vocab_size       ,       d_model=self.hidden_size       ,       num_layers=self.num_hidden_layers       ,       attention_heads=self.num_attention_heads       ,       ffn_dim=self.ffn_dim       ,       activation_function=self.activation_function       ,       activation_dropout=self.activation_dropout       ,       attention_dropout=self.attention_dropout       ,       max_position_embeddings=self.max_position_embeddings       ,       initializer_range=self.initializer_range       ,       use_cache=A_       ,       bos_token_id=self.bos_token_id       ,       eos_token_id=self.eos_token_id       ,       pad_token_id=self.pad_token_id       ,       )

         return (config, input_ids, input_mask)




       def      UpperCAmelCase__    ( self  : Dict):
         lowerCAmelCase_    :  Any             =				self.prepare_config_and_inputs()
         lowerCAmelCase_    :  Any             =				config_and_inputs
         lowerCAmelCase_    :  Optional[int]             =				{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
         return config, inputs_dict




       def      UpperCAmelCase__    ( self  : Tuple):
         lowerCAmelCase_    :  Optional[int]             =				self.prepare_config_and_inputs()

         lowerCAmelCase_    :  Any             =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
         lowerCAmelCase_    :  Union[str, Any]             =				ids_tensor([self.batch_size, self.seq_length]       ,       vocab_size=2)

         return (
             config,
             input_ids,
             attention_mask,
             encoder_hidden_states,
             encoder_attention_mask,
         )




       def      UpperCAmelCase__    ( self  : Union[str, Any]       ,       A_  : str       ,       A_  : List[str]       ,       A_  : Optional[int]       ,       A_  : Optional[Any]):
         lowerCAmelCase_    :  Optional[int]             =				2_0
         lowerCAmelCase_    :  Any             =				model_class_name(A_)

         lowerCAmelCase_    :  Tuple             =				model.init_cache(input_ids.shape[0]       ,       A_)
         lowerCAmelCase_    :  int             =				jnp.ones((input_ids.shape[0], max_decoder_length)       ,       dtype='''i4''')

         lowerCAmelCase_    :  List[str]             =				jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1)[None, :]       ,       (input_ids.shape[0], input_ids.shape[-1] - 1))
         lowerCAmelCase_    :  Any             =				model(
             input_ids[:, :-1]       ,       attention_mask=A_       ,       past_key_values=A_       ,       position_ids=A_       ,       )

         lowerCAmelCase_    :  str             =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,       dtype='''i4''')
         lowerCAmelCase_    :  List[Any]             =				model(
             input_ids[:, -1:]       ,       attention_mask=A_       ,       past_key_values=outputs_cache.past_key_values       ,       position_ids=A_       ,       )

         lowerCAmelCase_    :  Any             =				model(A_)

         lowerCAmelCase_    :  Dict             =				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
         self.parent.assertTrue(diff < 1e-3       ,       msg=F"""Max diff is {diff}""")







       def      UpperCAmelCase__    ( self  : Any       ,       A_  : Dict       ,       A_  : Optional[Any]       ,       A_  : Union[str, Any]       ,       A_  : Optional[Any]):
         lowerCAmelCase_    :  Dict             =				2_0
         lowerCAmelCase_    :  List[Any]             =				model_class_name(A_)

         lowerCAmelCase_    :  Any             =				jnp.concatenate(
             [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))]       ,       axis=-1       ,       )

         lowerCAmelCase_    :  str             =				model.init_cache(input_ids.shape[0]       ,       A_)
         lowerCAmelCase_    :  Tuple             =				jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1)[None, :]       ,       (input_ids.shape[0], input_ids.shape[-1] - 1))

         lowerCAmelCase_    :  Union[str, Any]             =				model(
             input_ids[:, :-1]       ,       attention_mask=A_       ,       past_key_values=A_       ,       position_ids=A_       ,       )
         lowerCAmelCase_    :  List[str]             =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,       dtype='''i4''')
         lowerCAmelCase_    :  List[Any]             =				model(
             input_ids[:, -1:]       ,       past_key_values=outputs_cache.past_key_values       ,       attention_mask=A_       ,       position_ids=A_       ,       )

         lowerCAmelCase_    :  Optional[int]             =				model(A_       ,       attention_mask=A_)
         lowerCAmelCase_    :  Dict             =				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
         self.parent.assertTrue(diff < 1e-3       ,       msg=F"""Max diff is {diff}""")





@require_sentencepiece
@require_flax
class        __snake_case		(  UpperCamelCase_		,UpperCamelCase_		,unittest.TestCase		):
       _a       =				(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
       _a       =				(FlaxXGLMForCausalLM,) if is_flax_available() else ()




       def      UpperCAmelCase__    ( self  : str):
         lowerCAmelCase_    :  Optional[int]             =				FlaxXGLMModelTester(self)




       def      UpperCAmelCase__    ( self  : str):
         for model_class_name in self.all_model_classes:
           lowerCAmelCase_    :  List[str]             =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward(A_       ,       A_       ,       A_       ,       A_)




       def      UpperCAmelCase__    ( self  : List[str]):
         for model_class_name in self.all_model_classes:
           lowerCAmelCase_    :  Union[str, Any]             =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward_with_attn_mask(
               A_       ,       A_       ,       A_       ,       A_)




       @slow
       def      UpperCAmelCase__    ( self  : str):
         lowerCAmelCase_    :  int             =				XGLMTokenizer.from_pretrained('''XGLM'''       ,       padding_side='''left''')
         lowerCAmelCase_    :  int             =				tokenizer(['''Hello this is a long string''', '''Hey''']       ,       return_tensors='''np'''       ,       padding=A_       ,       truncation=A_)

         lowerCAmelCase_    :  Union[str, Any]             =				FlaxXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''')
         lowerCAmelCase_    :  List[str]             =				1
         lowerCAmelCase_    :  Any             =				False

         lowerCAmelCase_    :  Union[str, Any]             =				jax.jit(model.generate)

         lowerCAmelCase_    :  Union[str, Any]             =				jit_generate(inputs['''input_ids''']       ,       attention_mask=inputs['''attention_mask''']).sequences

         lowerCAmelCase_    :  List[Any]             =				tokenizer.batch_decode(A_       ,       skip_special_tokens=A_)

         lowerCAmelCase_    :  Union[str, Any]             =				[
             '''Hello this is a long string of questions, but I\'m not sure if I\'m''',
             '''Hey, I\'m a newbie to the forum and I\'''',
         ]

         self.assertListEqual(A_       ,       A_)




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

         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__):
             # prepare inputs
             lowerCAmelCase_    :  Tuple             =				self._prepare_for_class(A_       ,       A_)
             lowerCAmelCase_    :  Any             =				{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             lowerCAmelCase_    :  Tuple             =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowerCAmelCase_    :  Tuple             =				getattr(A_       ,       A_)

             lowerCAmelCase_    :  Union[str, Any]             =				pt_inputs['''input_ids'''].shape
             lowerCAmelCase_    :  Any             =				np.random.randint(0       ,       seq_length - 1       ,       size=(batch_size,))
             for batch_idx, start_index in enumerate(A_):
               lowerCAmelCase_    :  List[Any]             =				0
               lowerCAmelCase_    :  List[Any]             =				1
               lowerCAmelCase_    :  Optional[Any]             =				0
               lowerCAmelCase_    :  List[Any]             =				1
             lowerCAmelCase_    :  int             =				pt_model_class(A_).eval()
             # Flax models don't use the `use_cache` option and cache is not returned as a default.
             # So we disable `use_cache` here for PyTorch model.
             lowerCAmelCase_    :  Optional[Any]             =				False
             lowerCAmelCase_    :  List[str]             =				model_class(A_       ,       dtype=jnp.floataa)
             lowerCAmelCase_    :  Union[str, Any]             =				convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,       A_)
             lowerCAmelCase_    :  int             =				fx_state

             with torch.no_grad():
               lowerCAmelCase_    :  Optional[int]             =				pt_model(**A_).to_tuple()

             lowerCAmelCase_    :  Union[str, Any]             =				fx_model(**A_).to_tuple()
             self.assertEqual(len(A_)       ,       len(A_)       ,       '''Output lengths differ between Flax and PyTorch''')
             for fx_output, pt_output in zip(A_       ,       A_):
               self.assert_almost_equals(fx_output[:, -1]       ,       pt_output[:, -1].numpy()       ,       4e-2)

             with tempfile.TemporaryDirectory() as tmpdirname:
               pt_model.save_pretrained(A_)
               lowerCAmelCase_    :  Any             =				model_class.from_pretrained(A_       ,       from_pt=A_)

             lowerCAmelCase_    :  List[Any]             =				fx_model_loaded(**A_).to_tuple()
             self.assertEqual(
                 len(A_)       ,       len(A_)       ,       '''Output lengths differ between Flax and PyTorch''')
             for fx_output_loaded, pt_output in zip(A_       ,       A_):
               self.assert_almost_equals(fx_output_loaded[:, -1]       ,       pt_output[:, -1].numpy()       ,       4e-2)




       @is_pt_flax_cross_test
       def      UpperCAmelCase__    ( self  : Dict):
         lowerCAmelCase_    :  List[Any]             =				self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__):
             # prepare inputs
             lowerCAmelCase_    :  str             =				self._prepare_for_class(A_       ,       A_)
             lowerCAmelCase_    :  List[str]             =				{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             lowerCAmelCase_    :  str             =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowerCAmelCase_    :  List[str]             =				getattr(A_       ,       A_)

             lowerCAmelCase_    :  List[Any]             =				pt_model_class(A_).eval()
             lowerCAmelCase_    :  int             =				False
             lowerCAmelCase_    :  List[str]             =				model_class(A_       ,       dtype=jnp.floataa)

             lowerCAmelCase_    :  Tuple             =				load_flax_weights_in_pytorch_model(A_       ,       fx_model.params)
             lowerCAmelCase_    :  Union[str, Any]             =				pt_inputs['''input_ids'''].shape
             lowerCAmelCase_    :  Any             =				np.random.randint(0       ,       seq_length - 1       ,       size=(batch_size,))
             for batch_idx, start_index in enumerate(A_):
               lowerCAmelCase_    :  Any             =				0
               lowerCAmelCase_    :  Optional[Any]             =				1
               lowerCAmelCase_    :  List[Any]             =				0
               lowerCAmelCase_    :  Any             =				1

             # make sure weights are tied in PyTorch
             pt_model.tie_weights()

             with torch.no_grad():
               lowerCAmelCase_    :  Dict             =				pt_model(**A_).to_tuple()

             lowerCAmelCase_    :  Optional[int]             =				fx_model(**A_).to_tuple()
             self.assertEqual(len(A_)       ,       len(A_)       ,       '''Output lengths differ between Flax and PyTorch''')
             for fx_output, pt_output in zip(A_       ,       A_):
               self.assert_almost_equals(fx_output[:, -1]       ,       pt_output[:, -1].numpy()       ,       4e-2)

             with tempfile.TemporaryDirectory() as tmpdirname:
               fx_model.save_pretrained(A_)
               lowerCAmelCase_    :  Optional[int]             =				pt_model_class.from_pretrained(A_       ,       from_flax=A_)

             with torch.no_grad():
               lowerCAmelCase_    :  Dict             =				pt_model_loaded(**A_).to_tuple()

             self.assertEqual(
                 len(A_)       ,       len(A_)       ,       '''Output lengths differ between Flax and PyTorch''')
             for fx_output, pt_output in zip(A_       ,       A_):
               self.assert_almost_equals(fx_output[:, -1]       ,       pt_output[:, -1].numpy()       ,       4e-2)







       @slow
       def      UpperCAmelCase__    ( self  : Union[str, Any]):
         for model_class_name in self.all_model_classes:
           lowerCAmelCase_    :  Union[str, Any]             =				model_class_name.from_pretrained('''facebook/xglm-564M''')
           lowerCAmelCase_    :  List[Any]             =				model(np.ones((1, 1)))
           self.assertIsNotNone(A_)










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}''')





