
import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
		import torch

		from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
		import pyanvml.pyanvml as nvml


A		:   Union[str, Any]         =    logging.get_logger(__name__)



class       a_   ( _a						):
			a  : PyTorchBenchmarkArguments
			a  : PretrainedConfig
			a  : str				      =       "PyTorch"






			@property
			def       UpperCamelCase_							(			self     ):
									return torch.__version__






			def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase     ):
									_lowercase									=				self._prepare_inference_func(__UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase     )
									return self._measure_speed(_inference     )






			def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase     ):
									_lowercase									=				self._prepare_inference_func(__UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase     )
									return self._measure_memory(_inference     )






			def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase     ):
									_lowercase									=				self._prepare_train_func(__UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase     )
									return self._measure_speed(_train     )






			def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase     ):
									_lowercase									=				self._prepare_train_func(__UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase     )
									return self._measure_memory(_train     )






			def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase     ):
									_lowercase									=				self.config_dict[model_name]

									if self.args.torchscript:
															_lowercase									=				True

									_lowercase									=				(
									    hasattr(__UpperCamelCase							, """architectures"""     )
									    and isinstance(config.architectures							, __UpperCamelCase     )
									    and len(config.architectures     ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																					_lowercase									=				config.architectures[0]
																					_lowercase									=				__import__("""transformers"""							, fromlist=[model_class]     )
																					_lowercase									=				getattr(__UpperCamelCase							, __UpperCamelCase     )
																					_lowercase									=				model_cls(__UpperCamelCase     )
															except ImportError:
																					raise ImportError(
																					    f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
																					    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""     )
									else:
															_lowercase									=				MODEL_MAPPING[config.__class__](__UpperCamelCase     )

									model.eval()
									model.to(self.args.device     )

									# encoder-decoder has vocab size saved differently
									_lowercase									=				config.vocab_size if hasattr(__UpperCamelCase							, """vocab_size"""     ) else config.encoder.vocab_size
									_lowercase									=				torch.randint(__UpperCamelCase							, (batch_size, sequence_length)							, dtype=torch.long							, device=self.args.device     )

									if self.args.fpaa:
															logger.info("""Running training in Mixed Precision..."""     )
															if not self.args.is_gpu:
																					raise ValueError("""Mixed precision is possible only for GPU."""     )
															# amp seems to have memory leaks so that memory usage
															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
															model.half()

									if self.args.torchscript:
															with torch.no_grad():
																					_lowercase									=				torch.jit.trace(__UpperCamelCase							, __UpperCamelCase     )
									else:
															_lowercase									=				model

									def encoder_decoder_forward():
															with torch.no_grad():
																					_lowercase									=				inference_model(__UpperCamelCase							, decoder_input_ids=__UpperCamelCase     )
															return outputs

									def encoder_forward():
															with torch.no_grad():
																					_lowercase									=				inference_model(__UpperCamelCase     )
															return outputs

									_lowercase									=				encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
									return _forward






			def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase     ):
									_lowercase									=				self.config_dict[model_name]

									_lowercase									=				(
									    hasattr(__UpperCamelCase							, """architectures"""     )
									    and isinstance(config.architectures							, __UpperCamelCase     )
									    and len(config.architectures     ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																					_lowercase									=				config.architectures[0]
																					_lowercase									=				__import__("""transformers"""							, fromlist=[model_class]     )
																					_lowercase									=				getattr(__UpperCamelCase							, __UpperCamelCase     )
																					_lowercase									=				model_cls(__UpperCamelCase     )
															except ImportError:
																					raise ImportError(
																					    f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
																					    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""     )
									else:
															_lowercase									=				MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__UpperCamelCase     )

									if self.args.torchscript:
															raise NotImplementedError("""Training for torchscript is currently not implemented"""     )
									else:
															_lowercase									=				model

									model.train()
									model.to(self.args.device     )

									# encoder-decoder has vocab size saved differently
									_lowercase									=				config.vocab_size if hasattr(__UpperCamelCase							, """vocab_size"""     ) else config.encoder.vocab_size
									_lowercase									=				torch.randint(__UpperCamelCase							, (batch_size, sequence_length)							, dtype=torch.long							, device=self.args.device     )

									if self.args.fpaa:
															logger.info("""Running training in Mixed Precision..."""     )
															if not self.args.is_gpu:
																					raise ValueError("""Mixed precision is possible only for GPU."""     )

															# amp seems to have memory leaks so that memory usage
															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
															model.half()

									def compute_loss_and_backprob_encoder():
															_lowercase									=				train_model(__UpperCamelCase							, labels=__UpperCamelCase     )[0]
															loss.backward()
															return loss

									def compute_loss_and_backprob_encoder_decoder():
															_lowercase									=				train_model(__UpperCamelCase							, decoder_input_ids=__UpperCamelCase							, labels=__UpperCamelCase     )[0]
															loss.backward()
															return loss

									_lowercase									=				(
									    compute_loss_and_backprob_encoder_decoder
									    if config.is_encoder_decoder
									    else compute_loss_and_backprob_encoder
									)
									return _train






			def       UpperCamelCase_							(			self							, __UpperCamelCase     ):
									try:
															if self.args.is_tpu or self.args.torchscript:
																					# run additional 10 times to stabilize compilation for tpu and torchscript
																					logger.info("""Do inference on TPU or torchscript. Running model 5 times to stabilize compilation"""     )
																					timeit.repeat(
																					    __UpperCamelCase							, repeat=1							, number=5							, )

															# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
															_lowercase									=				timeit.repeat(
															    __UpperCamelCase							, repeat=self.args.repeat							, number=10							, )

															if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
																					import torch_xla.debug.metrics as met

																					self.print_fn(met.metrics_report()     )

															return min(__UpperCamelCase     ) / 10.0
									except RuntimeError as e:
															self.print_fn(f"""Doesn't fit on GPU. {e}"""     )
															return "N/A"






			def       UpperCamelCase_							(			self							, __UpperCamelCase     ):
									try:
															if self.args.trace_memory_line_by_line:
																					_lowercase									=				start_memory_tracing("""transformers"""     )

															if self.args.is_tpu:
																					# tpu
																					raise NotImplementedError(
																					    """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with"""
																					    """ `--no-memory` or `args.memory=False`"""     )
															elif self.args.is_gpu:
																					if not is_pyanvml_available():
																											logger.warning(
																											    """py3nvml not installed, we won't log GPU memory usage. """
																											    """Install py3nvml (pip install py3nvml) to log information about GPU."""     )
																											_lowercase									=				"""N/A"""
																					else:
																											logger.info(
																											    """Measuring total GPU usage on GPU device. Make sure to not have additional processes running"""
																											    """ on the same GPU."""     )
																											# init nvml
																											nvml.nvmlInit()
																											func()
																											_lowercase									=				nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx     )
																											_lowercase									=				nvml.nvmlDeviceGetMemoryInfo(__UpperCamelCase     )
																											_lowercase									=				meminfo.used
																											_lowercase									=				Memory(__UpperCamelCase     )
																											# shutdown nvml
																											nvml.nvmlShutdown()
															else:
																					# cpu
																					_lowercase									=				measure_peak_memory_cpu(__UpperCamelCase     )
																					_lowercase									=				Memory(__UpperCamelCase     ) if isinstance(__UpperCamelCase							, __UpperCamelCase     ) else memory_bytes

															if self.args.trace_memory_line_by_line:
																					_lowercase									=				stop_memory_tracing(__UpperCamelCase     )
															else:
																					_lowercase									=				None

															return memory, summary
									except RuntimeError as e:
															self.print_fn(f"""Doesn't fit on GPU. {e}"""     )
															return "N/A", None

from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

A		:   Union[str, Any]         =    '''https://www.wellrx.com/prescriptions/{0}/{1}/?freshSearch=true'''




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  str		,      SCREAMING_SNAKE_CASE_  :  str					)     ->    list | None:

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

												_lowercase									=				BASE_URL.format(SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_					)
												_lowercase									=				get(SCREAMING_SNAKE_CASE_					)

												# Is the response ok?
												response.raise_for_status()

												# Scrape the data using bs4
												_lowercase									=				BeautifulSoup(response.text		,      """html.parser"""					)

												# This list will store the name and price.
												_lowercase									=				[]

												# Fetch all the grids that contains the items.
												_lowercase									=				soup.find_all("""div"""		,      {"""class""": """grid-x pharmCard"""}					)
												if grid_list and len(SCREAMING_SNAKE_CASE_					) > 0:
																		for grid in grid_list:
																								# Get the pharmacy price.
																								_lowercase									=				grid.find("""p"""		,      {"""class""": """list-title"""}					).text

																								# Get price of the drug.
																								_lowercase									=				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		:   Union[str, Any]         =    input('''Enter drug name: ''').strip()
		A		:   Dict         =    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		:   List[str]         =    pharmacy_price['''pharmacy_name''']
						A		:   Dict         =    pharmacy_price['''price''']

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