



import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken

@torch.no_grad()
def 			A_      (	__a     :				str ,		__a     :				Union[str, Any] ,		__a     :				List[str] ,		__a     :				Tuple ,		__a     :				List[Any]		):

				"""simple docstring"""
				# Load configuration defined in the metadata file
				with open(__a		) as metadata_file:
								a__				      =		json.load(__a		)
				a__				      =		LukeConfig(use_entity_aware_attention=__a ,		**metadata["""model_config"""]		)

				# Load in the weights from the checkpoint_path
				a__				      =		torch.load(__a ,		map_location="""cpu"""		)

				# Load the entity vocab file
				a__				      =		load_entity_vocab(__a		)

				a__				      =		RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]		)

				# Add special tokens to the token vocabulary for downstream tasks
				a__				      =		AddedToken("""<ent>""" ,		lstrip=__a ,		rstrip=__a		)
				a__				      =		AddedToken("""<ent2>""" ,		lstrip=__a ,		rstrip=__a		)
				tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]}		)
				config.vocab_size += 2

				print(F'''Saving tokenizer to {pytorch_dump_folder_path}'''		)
				tokenizer.save_pretrained(__a		)
				with open(os.path.join(__a ,		LukeTokenizer.vocab_files_names["""entity_vocab_file"""]		) ,		"""w"""		) as f:
								json.dump(__a ,		__a		)

				a__				      =		LukeTokenizer.from_pretrained(__a		)

				# Initialize the embeddings of the special tokens
				a__				      =		state_dict["""embeddings.word_embeddings.weight"""]
				a__				      =		word_emb[tokenizer.convert_tokens_to_ids(["""@"""]		)[0]].unsqueeze(0		)
				a__				      =		word_emb[tokenizer.convert_tokens_to_ids(["""#"""]		)[0]].unsqueeze(0		)
				a__				      =		torch.cat([word_emb, ent_emb, enta_emb]		)

				# Initialize the query layers of the entity-aware self-attention mechanism
				for layer_index in range(config.num_hidden_layers		):
								for matrix_name in ["query.weight", "query.bias"]:
												a__				      =		F'''encoder.layer.{layer_index}.attention.self.'''
												a__				      =		state_dict[prefix + matrix_name]
												a__				      =		state_dict[prefix + matrix_name]
												a__				      =		state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
				a__				      =		state_dict["""entity_embeddings.entity_embeddings.weight"""]
				a__				      =		entity_emb[entity_vocab["""[MASK]"""]]

				a__				      =		LukeModel(config=__a		).eval()

				a__      ,			a__				      =		model.load_state_dict(__a ,		strict=__a		)
				if not (len(__a		) == 1 and missing_keys[0] == "embeddings.position_ids"):
								raise ValueError(F'''Missing keys {", ".join(__a		)}. Expected only missing embeddings.position_ids'''		)
				if not (all(key.startswith("""entity_predictions"""		) or key.startswith("""lm_head"""		) for key in unexpected_keys		)):
								raise ValueError(
								    """Unexpected keys"""
								    F''' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"		) or key.startswith("lm_head"		))]		)}'''		)

				# Check outputs
				a__				      =		LukeTokenizer.from_pretrained(__a ,		task="""entity_classification"""		)

				a__				      =		(
				    """Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"""
				    """ new world number one avoid a humiliating second- round exit at Wimbledon ."""
				)
				a__				      =		(39, 42)
				a__				      =		tokenizer(__a ,		entity_spans=[span] ,		add_prefix_space=__a ,		return_tensors="""pt"""		)

				a__				      =		model(**__a		)

				# Verify word hidden states
				if model_size == "large":
								a__				      =		torch.Size((1, 42, 1_024)		)
								a__				      =		torch.tensor(
								    [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]		)
				else:  # base
								a__				      =		torch.Size((1, 42, 768)		)
								a__				      =		torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]		)

				if not (outputs.last_hidden_state.shape == expected_shape):
								raise ValueError(
								    F'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'''		)
				if not torch.allclose(outputs.last_hidden_state[0, :3, :3] ,		__a ,		atol=1e-4		):
								raise ValueError

				# Verify entity hidden states
				if model_size == "large":
								a__				      =		torch.Size((1, 1, 1_024)		)
								a__				      =		torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]		)
				else:  # base
								a__				      =		torch.Size((1, 1, 768)		)
								a__				      =		torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]		)

				if not (outputs.entity_last_hidden_state.shape != expected_shape):
								raise ValueError(
								    F'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
								    F''' {expected_shape}'''		)
				if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] ,		__a ,		atol=1e-4		):
								raise ValueError

				# Finally, save our PyTorch model and tokenizer
				print("""Saving PyTorch model to {}""".format(__a		)		)
				model.save_pretrained(__a		)

def 			A_      (	__a     :				Optional[int]		):

				"""simple docstring"""
				a__				      =		{}
				with open(__a ,		"""r""" ,		encoding="""utf-8"""		) as f:
								for index, line in enumerate(__a		):
												a__      ,			a__				      =		line.rstrip().split("""\t"""		)
												a__				      =		index

				return entity_vocab


if __name__ == "__main__":
	UpperCAmelCase							 =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("""--checkpoint_path""", type=str, help="""Path to a pytorch_model.bin file.""")
	parser.add_argument(
	    """--metadata_path""", default=None, type=str, help="""Path to a metadata.json file, defining the configuration."""
	)
	parser.add_argument(
	    """--entity_vocab_path""",
	    default=None,
	    type=str,
	    help="""Path to an entity_vocab.tsv file, containing the entity vocabulary.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to where to dump the output PyTorch model."""
	)
	parser.add_argument(
	    """--model_size""", default="""base""", type=str, choices=["""base""", """large"""], help="""Size of the model to be converted."""
	)
	UpperCAmelCase							 =					parser.parse_args()
	convert_luke_checkpoint(
	    args.checkpoint_path,
	    args.metadata_path,
	    args.entity_vocab_path,
	    args.pytorch_dump_folder_path,
	    args.model_size,
	)






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame

def 			A_      (	__a     :				str = "laptop"		):

				"""simple docstring"""
				a__				      =		F'''https://www.amazon.in/laptop/s?k={product}'''
				a__				      =		{
				    """User-Agent""": """Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36""",
				    """Accept-Language""": """en-US, en;q=0.5""",
				}
				a__				      =		BeautifulSoup(requests.get(__a ,		headers=__a		).text		)
				# Initialize a Pandas dataframe with the column titles
				a__				      =		DataFrame(
				    columns=[
				        """Product Title""",
				        """Product Link""",
				        """Current Price of the product""",
				        """Product Rating""",
				        """MRP of the product""",
				        """Discount""",
				    ]		)
				# Loop through each entry and store them in the dataframe
				for item, _ in zip_longest(
				    soup.find_all(
				        """div""" ,		attrs={"""class""": """s-result-item""", """data-component-type""": """s-search-result"""} ,		) ,		soup.find_all("""div""" ,		attrs={"""class""": """a-row a-size-base a-color-base"""}		) ,		):
								try:
												a__				      =		item.ha.text
												a__				      =		"""https://www.amazon.in/""" + item.ha.a["""href"""]
												a__				      =		item.find("""span""" ,		attrs={"""class""": """a-offscreen"""}		).text
												try:
																a__				      =		item.find("""span""" ,		attrs={"""class""": """a-icon-alt"""}		).text
												except AttributeError:
																a__				      =		"""Not available"""
												try:
																a__				      =		(
																    """₹"""
																    + item.find(
																        """span""" ,		attrs={"""class""": """a-price a-text-price"""}		).text.split("""₹"""		)[1]
																)
												except AttributeError:
																a__				      =		""""""
												try:
																a__				      =		float(
																    (
																        (
																            float(product_mrp.strip("""₹"""		).replace(""",""" ,		""""""		)		)
																            - float(product_price.strip("""₹"""		).replace(""",""" ,		""""""		)		)
																        )
																        / float(product_mrp.strip("""₹"""		).replace(""",""" ,		""""""		)		)
																    )
																    * 100		)
												except ValueError:
																a__				      =		float("""nan"""		)
								except AttributeError:
												pass
								a__				      =		[
								    product_title,
								    product_link,
								    product_price,
								    product_rating,
								    product_mrp,
								    discount,
								]
				a__				      =		""" """
				a__				      =		""" """
				data_frame.index += 1
				return data_frame


if __name__ == "__main__":
	UpperCAmelCase							 =					"""headphones"""
	get_amazon_product_data(product).to_csv(f"""Amazon Product Data for {product}.csv""")

