
from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame







def 						_snake_case(  SCREAMING_SNAKE_CASE__ = "laptop"    )			->   DataFrame:
       lowercase							:					Tuple	     =				f"https://www.amazon.in/laptop/s?k={product}"
       lowercase							:					int	     =				{
           """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""",
       }
       lowercase							:					Optional[int]	     =				BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE__ ,     headers=SCREAMING_SNAKE_CASE__    ).text    )
       # Initialize a Pandas dataframe with the column titles
       lowercase							:					int	     =				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:
                     lowercase							:					str	     =				item.ha.text
                     lowercase							:					Tuple	     =				"""https://www.amazon.in/""" + item.ha.a["""href"""]
                     lowercase							:					List[Any]	     =				item.find("""span""" ,     attrs={"""class""": """a-offscreen"""}    ).text
                     try:
                            lowercase							:					Dict	     =				item.find("""span""" ,     attrs={"""class""": """a-icon-alt"""}    ).text
                     except AttributeError:
                            lowercase							:					List[Any]	     =				"""Not available"""
                     try:
                            lowercase							:					Union[str, Any]	     =				(
                                """₹"""
                                + item.find(
                                    """span""" ,     attrs={"""class""": """a-price a-text-price"""}    ).text.split("""₹"""    )[1]
                            )
                     except AttributeError:
                            lowercase							:					int	     =				""""""
                     try:
                            lowercase							:					int	     =				float(
                                (
                                    (
                                        float(product_mrp.strip("""₹"""    ).replace(""",""" ,     """"""    )    )
                                        - float(product_price.strip("""₹"""    ).replace(""",""" ,     """"""    )    )
                                    )
                                    / float(product_mrp.strip("""₹"""    ).replace(""",""" ,     """"""    )    )
                                )
                                * 100    )
                     except ValueError:
                            lowercase							:					Union[str, Any]	     =				float("""nan"""    )
              except AttributeError:
                     pass
              lowercase							:					List[Any]	     =				[
                  product_title,
                  product_link,
                  product_price,
                  product_rating,
                  product_mrp,
                  discount,
              ]
       lowercase							:					Dict	     =				""" """
       lowercase							:					Optional[int]	     =				""" """
       data_frame.index += 1
       return data_frame


if __name__ == "__main__":
  lowercase  : Any     =							"""headphones"""
  get_amazon_product_data(product).to_csv(F'''Amazon Product Data for {product}.csv''')


import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken







@torch.no_grad()
def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   int:
       # Load configuration defined in the metadata file
       with open(SCREAMING_SNAKE_CASE__    ) as metadata_file:
              lowercase							:					Union[str, Any]	     =				json.load(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Tuple	     =				LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE__ ,     **metadata["""model_config"""]    )

       # Load in the weights from the checkpoint_path
       lowercase							:					Tuple	     =				torch.load(SCREAMING_SNAKE_CASE__ ,     map_location="""cpu"""    )["""module"""]

       # Load the entity vocab file
       lowercase							:					str	     =				load_original_entity_vocab(SCREAMING_SNAKE_CASE__    )
       # add an entry for [MASK2]
       lowercase							:					Any	     =				max(entity_vocab.values()    ) + 1
       config.entity_vocab_size += 1

       lowercase							:					Dict	     =				XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]    )

       # Add special tokens to the token vocabulary for downstream tasks
       lowercase							:					List[Any]	     =				AddedToken("""<ent>""" ,     lstrip=SCREAMING_SNAKE_CASE__ ,     rstrip=SCREAMING_SNAKE_CASE__    )
       lowercase							:					int	     =				AddedToken("""<ent2>""" ,     lstrip=SCREAMING_SNAKE_CASE__ ,     rstrip=SCREAMING_SNAKE_CASE__    )
       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(SCREAMING_SNAKE_CASE__    )
       with open(os.path.join(SCREAMING_SNAKE_CASE__ ,     """tokenizer_config.json"""    ) ,     """r"""    ) as f:
              lowercase							:					List[str]	     =				json.load(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Optional[Any]	     =				"""MLukeTokenizer"""
       with open(os.path.join(SCREAMING_SNAKE_CASE__ ,     """tokenizer_config.json"""    ) ,     """w"""    ) as f:
              json.dump(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       with open(os.path.join(SCREAMING_SNAKE_CASE__ ,     MLukeTokenizer.vocab_files_names["""entity_vocab_file"""]    ) ,     """w"""    ) as f:
              json.dump(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       lowercase							:					int	     =				MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )

       # Initialize the embeddings of the special tokens
       lowercase							:					Dict	     =				tokenizer.convert_tokens_to_ids(["""@"""]    )[0]
       lowercase							:					Dict	     =				tokenizer.convert_tokens_to_ids(["""#"""]    )[0]

       lowercase							:					int	     =				state_dict["""embeddings.word_embeddings.weight"""]
       lowercase							:					Union[str, Any]	     =				word_emb[ent_init_index].unsqueeze(0    )
       lowercase							:					List[str]	     =				word_emb[enta_init_index].unsqueeze(0    )
       lowercase							:					str	     =				torch.cat([word_emb, ent_emb, enta_emb]    )
       # add special tokens for 'entity_predictions.bias'
       for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
              lowercase							:					List[Any]	     =				state_dict[bias_name]
              lowercase							:					Any	     =				decoder_bias[ent_init_index].unsqueeze(0    )
              lowercase							:					Tuple	     =				decoder_bias[enta_init_index].unsqueeze(0    )
              lowercase							:					int	     =				torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]    )

       # 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"]:
                     lowercase							:					Union[str, Any]	     =				f"encoder.layer.{layer_index}.attention.self."
                     lowercase							:					List[str]	     =				state_dict[prefix + matrix_name]
                     lowercase							:					Any	     =				state_dict[prefix + matrix_name]
                     lowercase							:					str	     =				state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
       lowercase							:					Any	     =				state_dict["""entity_embeddings.entity_embeddings.weight"""]
       lowercase							:					Tuple	     =				entity_emb[entity_vocab["""[MASK]"""]].unsqueeze(0    )
       lowercase							:					Optional[Any]	     =				torch.cat([entity_emb, entity_mask_emb]    )
       # add [MASK2] for 'entity_predictions.bias'
       lowercase							:					Optional[Any]	     =				state_dict["""entity_predictions.bias"""]
       lowercase							:					str	     =				entity_prediction_bias[entity_vocab["""[MASK]"""]].unsqueeze(0    )
       lowercase							:					List[str]	     =				torch.cat([entity_prediction_bias, entity_mask_bias]    )

       lowercase							:					List[str]	     =				LukeForMaskedLM(config=SCREAMING_SNAKE_CASE__    ).eval()

       state_dict.pop("""entity_predictions.decoder.weight"""    )
       state_dict.pop("""lm_head.decoder.weight"""    )
       state_dict.pop("""lm_head.decoder.bias"""    )
       lowercase							:					List[str]	     =				OrderedDict()
       for key, value in state_dict.items():
              if not (key.startswith("""lm_head"""    ) or key.startswith("""entity_predictions"""    )):
                     lowercase							:					List[Any]	     =				state_dict[key]
              else:
                     lowercase							:					Union[str, Any]	     =				state_dict[key]

       lowercase     ,      lowercase							:					int	     =				model.load_state_dict(SCREAMING_SNAKE_CASE__ ,     strict=SCREAMING_SNAKE_CASE__    )

       if set(SCREAMING_SNAKE_CASE__    ) != {"luke.embeddings.position_ids"}:
              raise ValueError(f"Unexpected unexpected_keys: {unexpected_keys}"    )
       if set(SCREAMING_SNAKE_CASE__    ) != {
           "lm_head.decoder.weight",
           "lm_head.decoder.bias",
           "entity_predictions.decoder.weight",
       }:
              raise ValueError(f"Unexpected missing_keys: {missing_keys}"    )

       model.tie_weights()
       assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
       assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

       # Check outputs
       lowercase							:					str	     =				MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ ,     task="""entity_classification"""    )

       lowercase							:					str	     =				"""ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."""
       lowercase							:					str	     =				(0, 9)
       lowercase							:					Dict	     =				tokenizer(SCREAMING_SNAKE_CASE__ ,     entity_spans=[span] ,     return_tensors="""pt"""    )

       lowercase							:					Any	     =				model(**SCREAMING_SNAKE_CASE__    )

       # Verify word hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              lowercase							:					List[Any]	     =				torch.Size((1, 33, 768)    )
              lowercase							:					str	     =				torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]    )

       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] ,     SCREAMING_SNAKE_CASE__ ,     atol=1e-4    ):
              raise ValueError

       # Verify entity hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              lowercase							:					Optional[int]	     =				torch.Size((1, 1, 768)    )
              lowercase							:					List[Any]	     =				torch.tensor([[-0.1482, 0.0609, 0.0322]]    )

       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] ,     SCREAMING_SNAKE_CASE__ ,     atol=1e-4    ):
              raise ValueError

       # Verify masked word/entity prediction
       lowercase							:					Any	     =				MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )
       lowercase							:					List[Any]	     =				"""Tokyo is the capital of <mask>."""
       lowercase							:					List[Any]	     =				(24, 30)
       lowercase							:					int	     =				tokenizer(SCREAMING_SNAKE_CASE__ ,     entity_spans=[span] ,     return_tensors="""pt"""    )

       lowercase							:					Dict	     =				model(**SCREAMING_SNAKE_CASE__    )

       lowercase							:					Union[str, Any]	     =				encoding["""input_ids"""][0].tolist()
       lowercase							:					List[Any]	     =				input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""    )    )
       lowercase							:					Dict	     =				outputs.logits[0][mask_position_id].argmax(dim=-1    )
       assert "Japan" == tokenizer.decode(SCREAMING_SNAKE_CASE__    )

       lowercase							:					Union[str, Any]	     =				outputs.entity_logits[0][0].argmax().item()
       lowercase							:					int	     =				[
           entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
       ]
       assert [e for e in multilingual_predicted_entities if e.startswith("""en:"""    )][0] == "en:Japan"

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







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   Dict:
       lowercase							:					Optional[int]	     =				["""[MASK]""", """[PAD]""", """[UNK]"""]

       lowercase							:					List[str]	     =				[json.loads(SCREAMING_SNAKE_CASE__    ) for line in open(SCREAMING_SNAKE_CASE__    )]

       lowercase							:					int	     =				{}
       for entry in data:
              lowercase							:					Optional[Any]	     =				entry["""id"""]
              for entity_name, language in entry["entities"]:
                     if entity_name in SPECIAL_TOKENS:
                            lowercase							:					Optional[Any]	     =				entity_id
                            break
                     lowercase							:					List[Any]	     =				f"{language}:{entity_name}"
                     lowercase							:					Union[str, Any]	     =				entity_id
       return new_mapping


if __name__ == "__main__":
  lowercase  : Any						   =							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."""
  )
  lowercase  : str						   =							parser.parse_args()
  convert_luke_checkpoint(
      args.checkpoint_path,
      args.metadata_path,
      args.entity_vocab_path,
      args.pytorch_dump_folder_path,
      args.model_size,
  )
