



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
A__     				=	logging.get_logger(__name__)

A__     				=	{
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """attention.self.query_proj""": """encoder.encoder.layer.*.attention.self.query_proj""",
    """attention.self.key_proj""": """encoder.encoder.layer.*.attention.self.key_proj""",
    """attention.self.value_proj""": """encoder.encoder.layer.*.attention.self.value_proj""",
    """attention.output.dense""": """encoder.encoder.layer.*.attention.output.dense""",
    """attention.output.LayerNorm""": """encoder.encoder.layer.*.attention.output.LayerNorm""",
    """intermediate.dense""": """encoder.encoder.layer.*.intermediate.dense""",
    """output.dense""": """encoder.encoder.layer.*.output.dense""",
    """output.LayerNorm""": """encoder.encoder.layer.*.output.LayerNorm""",
    """encoder.encoder.rel_embeddings""": """encoder.encoder.rel_embeddings""",
    """encoder.encoder.LayerNorm""": """encoder.encoder.LayerNorm""",
    """encoder.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case     ,						snake_case     ,						snake_case      ):





					"""simple docstring"""

					for attribute in key.split("""."""      ):
										_lowerCAmelCase      =   getattr(snake_case     ,						snake_case      )

					if weight_type is not None:
										_lowerCAmelCase      =   getattr(snake_case     ,						snake_case      ).shape
					else:
										_lowerCAmelCase      =   hf_pointer.shape

					assert hf_shape == value.shape, (
					    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
					    F' {value.shape} for {full_name}'
					)

					if weight_type == "weight":
										_lowerCAmelCase      =   value
					elif weight_type == "weight_g":
										_lowerCAmelCase      =   value
					elif weight_type == "weight_v":
										_lowerCAmelCase      =   value
					elif weight_type == "bias":
										_lowerCAmelCase      =   value
					else:
										_lowerCAmelCase      =   value

					logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'      )


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   []
					_lowerCAmelCase      =   fairseq_model.state_dict()

					_lowerCAmelCase      =   hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

					for name, value in fairseq_dict.items():
										_lowerCAmelCase      =   False
										if "conv_layers" in name:
															load_conv_layer(
															    snake_case     ,						snake_case     ,						snake_case     ,						snake_case     ,						hf_model.config.feat_extract_norm == """group"""     ,						)
															_lowerCAmelCase      =   True
										else:
															for key, mapped_key in MAPPING.items():
																				_lowerCAmelCase      =   """sew_d.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

																				if key in name or key.split("""w2v_model."""      )[-1] == name.split("""."""      )[0]:
																									_lowerCAmelCase      =   True
																									if "*" in mapped_key:
																														_lowerCAmelCase      =   name.split(snake_case      )[0].split("""."""      )[-2]
																														if not layer_index.isnumeric():
																																			continue
																														_lowerCAmelCase      =   mapped_key.replace("""*"""     ,						snake_case      )
																									if "weight_g" in name:
																														_lowerCAmelCase      =   """weight_g"""
																									elif "weight_v" in name:
																														_lowerCAmelCase      =   """weight_v"""
																									elif "weight" in name:
																														_lowerCAmelCase      =   """weight"""
																									elif "bias" in name:
																														_lowerCAmelCase      =   """bias"""
																									else:
																														_lowerCAmelCase      =   None
																									set_recursively(snake_case     ,						snake_case     ,						snake_case     ,						snake_case     ,						snake_case      )
																				continue
										if not is_used:
															unused_weights.append(snake_case      )

					logger.warning(F'Unused weights: {unused_weights}'      )


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case     ,						snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   full_name.split("""conv_layers."""      )[-1]
					_lowerCAmelCase      =   name.split("""."""      )
					_lowerCAmelCase      =   int(items[0]      )
					_lowerCAmelCase      =   int(items[1]      )

					if type_id == 0:
										if "bias" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
															    F'{full_name} has size {value.shape}, but'
															    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
															)
															_lowerCAmelCase      =   value
															logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'      )
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
															    F'{full_name} has size {value.shape}, but'
															    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
															)
															_lowerCAmelCase      =   value
															logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'      )
					elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
										if "bias" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
															    F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
															    " found."
															)
															_lowerCAmelCase      =   value
															logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'      )
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
															    F'{full_name} has size {value.shape}, but'
															    F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
															)
															_lowerCAmelCase      =   value
															logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'      )
					else:
										unused_weights.append(snake_case      )


def 		_UpperCAmelCase    (				snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   SEWDConfig()
					if is_finetuned:
										_lowerCAmelCase      =   model.wav_encoder.wav_model.cfg
					else:
										_lowerCAmelCase      =   model.cfg

					_lowerCAmelCase      =   fs_config.conv_bias
					_lowerCAmelCase      =   eval(fs_config.conv_feature_layers      )
					_lowerCAmelCase      =   [x[0] for x in conv_layers]
					_lowerCAmelCase      =   [x[1] for x in conv_layers]
					_lowerCAmelCase      =   [x[2] for x in conv_layers]
					_lowerCAmelCase      =   """gelu"""
					_lowerCAmelCase      =   """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
					_lowerCAmelCase      =   0.0
					_lowerCAmelCase      =   fs_config.activation_fn.name
					_lowerCAmelCase      =   fs_config.encoder_embed_dim
					_lowerCAmelCase      =   0.02
					_lowerCAmelCase      =   fs_config.encoder_ffn_embed_dim
					_lowerCAmelCase      =   1E-5
					_lowerCAmelCase      =   fs_config.encoder_layerdrop
					_lowerCAmelCase      =   fs_config.encoder_attention_heads
					_lowerCAmelCase      =   fs_config.conv_pos_groups
					_lowerCAmelCase      =   fs_config.conv_pos
					_lowerCAmelCase      =   len(snake_case      )
					_lowerCAmelCase      =   fs_config.encoder_layers
					_lowerCAmelCase      =   fs_config.squeeze_factor
					# DeBERTa-specific parameters:
					_lowerCAmelCase      =   fs_config.max_position_embeddings
					_lowerCAmelCase      =   fs_config.position_buckets
					_lowerCAmelCase      =   fs_config.share_att_key
					_lowerCAmelCase      =   fs_config.relative_attention
					_lowerCAmelCase      =   fs_config.position_biased_input
					_lowerCAmelCase      =   tuple(fs_config.pos_att_type.split("""|"""      )      )
					_lowerCAmelCase      =   fs_config.norm_rel_ebd

					# take care of any params that are overridden by the Wav2VecCtc model
					if is_finetuned:
										_lowerCAmelCase      =   model.cfg
										_lowerCAmelCase      =   fs_config.final_dropout
										_lowerCAmelCase      =   fs_config.layerdrop
					_lowerCAmelCase      =   fs_config.activation_dropout
					_lowerCAmelCase      =   fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
					_lowerCAmelCase      =   fs_config.attention_dropout
					_lowerCAmelCase      =   fs_config.dropout_input
					_lowerCAmelCase      =   fs_config.dropout
					_lowerCAmelCase      =   fs_config.mask_channel_length
					_lowerCAmelCase      =   fs_config.mask_channel_prob
					_lowerCAmelCase      =   fs_config.mask_length
					_lowerCAmelCase      =   fs_config.mask_prob

					_lowerCAmelCase      =   """Wav2Vec2FeatureExtractor"""
					_lowerCAmelCase      =   """Wav2Vec2CTCTokenizer"""

					return config


@torch.no_grad()
def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case=None     ,						snake_case=None     ,						snake_case=True      ):





					"""simple docstring"""

					if is_finetuned:
										_lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase      =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
										    [checkpoint_path]     ,						arg_overrides={"""data""": """/""".join(dict_path.split("""/"""      )[:-1]      )}      )
					else:
										_lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase      =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      )

					if config_path is not None:
										_lowerCAmelCase      =   SEWDConfig.from_pretrained(snake_case      )
					else:
										_lowerCAmelCase      =   convert_config(model[0]     ,						snake_case      )
					_lowerCAmelCase      =   model[0].eval()

					_lowerCAmelCase      =   True if config.feat_extract_norm == """layer""" else False
					_lowerCAmelCase      =   WavaVecaFeatureExtractor(
					    feature_size=1     ,						sampling_rate=1_60_00     ,						padding_value=0     ,						do_normalize=snake_case     ,						return_attention_mask=snake_case     ,						)

					if is_finetuned:
										if dict_path:
															_lowerCAmelCase      =   Dictionary.load(snake_case      )

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															_lowerCAmelCase      =   target_dict.pad_index
															_lowerCAmelCase      =   target_dict.bos_index
															_lowerCAmelCase      =   target_dict.pad_index
															_lowerCAmelCase      =   target_dict.bos_index
															_lowerCAmelCase      =   target_dict.eos_index
															_lowerCAmelCase      =   len(target_dict.symbols      )
															_lowerCAmelCase      =   os.path.join(snake_case     ,						"""vocab.json"""      )
															if not os.path.isdir(snake_case      ):
																				logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(snake_case      )      )
																				return
															os.makedirs(snake_case     ,						exist_ok=snake_case      )
															with open(snake_case     ,						"""w"""     ,						encoding="""utf-8"""      ) as vocab_handle:
																				json.dump(target_dict.indices     ,						snake_case      )
															_lowerCAmelCase      =   WavaVecaCTCTokenizer(
															    snake_case     ,						unk_token=target_dict.unk_word     ,						pad_token=target_dict.pad_word     ,						bos_token=target_dict.bos_word     ,						eos_token=target_dict.eos_word     ,						word_delimiter_token="""|"""     ,						do_lower_case=snake_case     ,						)
															_lowerCAmelCase      =   WavaVecaProcessor(feature_extractor=snake_case     ,						tokenizer=snake_case      )
															processor.save_pretrained(snake_case      )

										_lowerCAmelCase      =   SEWDForCTC(snake_case      )
					else:
										_lowerCAmelCase      =   SEWDModel(snake_case      )
										feature_extractor.save_pretrained(snake_case      )

					recursively_load_weights(snake_case     ,						snake_case     ,						snake_case      )

					hf_model.save_pretrained(snake_case      )


if __name__ == "__main__":
	A__     				=	argparse.ArgumentParser()
	parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
	parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
	parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
	parser.add_argument(
	    """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
	)
	A__     				=	parser.parse_args()
	convert_sew_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
	)








from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

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


def 		_UpperCAmelCase    (				snake_case     ,						snake_case      ):





					"""simple docstring"""

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

										_lowerCAmelCase      =   BASE_URL.format(snake_case     ,						snake_case      )
										_lowerCAmelCase      =   get(snake_case      )

										# Is the response ok?
										response.raise_for_status()

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

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

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

																				# Get price of the drug.
																				_lowerCAmelCase      =   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__     				=	input("""Enter drug name: """).strip()
	A__     				=	input("""Enter zip code: """).strip()

	A__     				=	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__     				=	pharmacy_price["""pharmacy_name"""]
			A__     				=	pharmacy_price["""price"""]

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



