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 							UpperCAmelCase__(					__UpperCAmelCase			:     Any     ,				__UpperCAmelCase			:     Any     ,				__UpperCAmelCase			:     Dict     ,				__UpperCAmelCase			:     int     ,				__UpperCAmelCase			:     Any       ):



   with open(UpperCAmelCase__       ) as metadata_file:
      __snake_case   :			List[Any]										=						json.load(UpperCAmelCase__       )
   __snake_case   :			List[str]										=						LukeConfig(use_entity_aware_attention=UpperCAmelCase__     ,				**metadata['model_config']       )

   # Load in the weights from the checkpoint_path
   __snake_case   :			Dict										=						torch.load(UpperCAmelCase__     ,				map_location='cpu'       )

   # Load the entity vocab file
   __snake_case   :			Any										=						load_entity_vocab(UpperCAmelCase__       )

   __snake_case   :			Any										=						RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']       )

   # Add special tokens to the token vocabulary for downstream tasks
   __snake_case   :			str										=						AddedToken('<ent>'     ,				lstrip=UpperCAmelCase__     ,				rstrip=UpperCAmelCase__       )
   __snake_case   :			Any										=						AddedToken('<ent2>'     ,				lstrip=UpperCAmelCase__     ,				rstrip=UpperCAmelCase__       )
   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(UpperCAmelCase__       )
   with open(os.path.join(UpperCAmelCase__     ,				LukeTokenizer.vocab_files_names['entity_vocab_file']       )     ,				'w'       ) as f:
      json.dump(UpperCAmelCase__     ,				UpperCAmelCase__       )

   __snake_case   :			List[Any]										=						LukeTokenizer.from_pretrained(UpperCAmelCase__       )

   # Initialize the embeddings of the special tokens
   __snake_case   :			int										=						state_dict['embeddings.word_embeddings.weight']
   __snake_case   :			Dict										=						word_emb[tokenizer.convert_tokens_to_ids(['@']       )[0]].unsqueeze(0       )
   __snake_case   :			Optional[int]										=						word_emb[tokenizer.convert_tokens_to_ids(['#']       )[0]].unsqueeze(0       )
   __snake_case   :			List[str]										=						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"]:
         __snake_case   :			Tuple										=						F"""encoder.layer.{layer_index}.attention.self."""
         __snake_case   :			Optional[int]										=						state_dict[prefix + matrix_name]
         __snake_case   :			Optional[int]										=						state_dict[prefix + matrix_name]
         __snake_case   :			Any										=						state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
   __snake_case   :			Any										=						state_dict['entity_embeddings.entity_embeddings.weight']
   __snake_case   :			str										=						entity_emb[entity_vocab['[MASK]']]

   __snake_case   :			Tuple										=						LukeModel(config=UpperCAmelCase__       ).eval()

   __snake_case						,       __snake_case   :			str										=						model.load_state_dict(UpperCAmelCase__     ,				strict=UpperCAmelCase__       )
   if not (len(UpperCAmelCase__       ) == 1 and missing_keys[0] == "embeddings.position_ids"):
      raise ValueError(F"""Missing keys {", ".join(UpperCAmelCase__       )}. 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
   __snake_case   :			Optional[Any]										=						LukeTokenizer.from_pretrained(UpperCAmelCase__     ,				task='entity_classification'       )

   __snake_case   :			List[Any]										=						(
       '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 .'
   )
   __snake_case   :			Dict										=						(39, 42)
   __snake_case   :			List[Any]										=						tokenizer(UpperCAmelCase__     ,				entity_spans=[span]     ,				add_prefix_space=UpperCAmelCase__     ,				return_tensors='pt'       )

   __snake_case   :			str										=						model(**UpperCAmelCase__       )

   # Verify word hidden states
   if model_size == "large":
      __snake_case   :			Union[str, Any]										=						torch.Size((1, 42, 10_24)       )
      __snake_case   :			int										=						torch.tensor(
          [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]       )
   else:  # base
      __snake_case   :			Optional[Any]										=						torch.Size((1, 42, 7_68)       )
      __snake_case   :			str										=						torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]       )

   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]     ,				UpperCAmelCase__     ,				atol=1E-4       ):
      raise ValueError

   # Verify entity hidden states
   if model_size == "large":
      __snake_case   :			Any										=						torch.Size((1, 1, 10_24)       )
      __snake_case   :			Union[str, Any]										=						torch.tensor([[0.0466, -0.0106, -0.0179]]       )
   else:  # base
      __snake_case   :			List[Any]										=						torch.Size((1, 1, 7_68)       )
      __snake_case   :			Union[str, Any]										=						torch.tensor([[0.1457, 0.1044, 0.0174]]       )

   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]     ,				UpperCAmelCase__     ,				atol=1E-4       ):
      raise ValueError

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






def 							UpperCAmelCase__(					__UpperCAmelCase			:     int       ):
   __snake_case   :			Dict										=						{}
   with open(UpperCAmelCase__     ,				'r'     ,				encoding='utf-8'       ) as f:
      for index, line in enumerate(UpperCAmelCase__       ):
         __snake_case						,       __snake_case   :			Dict										=						line.rstrip().split('\t'       )
         __snake_case   :			Tuple										=						index

   return entity_vocab


if __name__ == "__main__":
 __magic_name__    							=							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.'''
 )
 __magic_name__    							=							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      __lowercase(						UpperCAmelCase__ = "laptop"			):


      """simple docstring"""



      lowerCamelCase             =		F"""https://www.amazon.in/laptop/s?k={product}"""
      lowerCamelCase             =		{
          "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36",
          "Accept-Language": "en-US, en;q=0.5",
      }
      lowerCamelCase             =		BeautifulSoup(requests.get(UpperCAmelCase__ , headers=UpperCAmelCase__			).text			)
      # Initialize a Pandas dataframe with the column titles
      lowerCamelCase             =		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:
                  lowerCamelCase             =		item.ha.text
                  lowerCamelCase             =		"https://www.amazon.in/" + item.ha.a["href"]
                  lowerCamelCase             =		item.find("span" , attrs={"class": "a-offscreen"}			).text
                  try:
                        lowerCamelCase             =		item.find("span" , attrs={"class": "a-icon-alt"}			).text
                  except AttributeError:
                        lowerCamelCase             =		"Not available"
                  try:
                        lowerCamelCase             =		(
                            "₹"
                            + item.find(
                                "span" , attrs={"class": "a-price a-text-price"}			).text.split("₹"			)[1]
                        )
                  except AttributeError:
                        lowerCamelCase             =		""
                  try:
                        lowerCamelCase             =		float(
                            (
                                (
                                    float(product_mrp.strip("₹"			).replace("," , ""			)			)
                                    - float(product_price.strip("₹"			).replace("," , ""			)			)
                                )
                                / float(product_mrp.strip("₹"			).replace("," , ""			)			)
                            )
                            * 100			)
                  except ValueError:
                        lowerCamelCase             =		float("nan"			)
            except AttributeError:
                  pass
            lowerCamelCase             =		[
                product_title,
                product_link,
                product_price,
                product_rating,
                product_mrp,
                discount,
            ]
      lowerCamelCase             =		" "
      lowerCamelCase             =		" "
      data_frame.index += 1
      return data_frame


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