


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
   from ...onnx.config import PatchingSpec
   from ...tokenization_utils_base import PreTrainedTokenizerBase


lowerCamelCase__							:						Tuple							     =   logging.get_logger(__name__)

lowerCamelCase__							:						Optional[int]							     =   {
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Dict	   =       'longformer'

     def __init__(					self:str     ,		_a:Union[List[int], int] = 5_12     ,		_a:int = 2     ,		_a:int = 1     ,		_a:int = 0     ,		_a:int = 2     ,		_a:int = 3_05_22     ,		_a:int = 7_68     ,		_a:int = 12     ,		_a:int = 12     ,		_a:int = 30_72     ,		_a:str = "gelu"     ,		_a:float = 0.1     ,		_a:float = 0.1     ,		_a:int = 5_12     ,		_a:int = 2     ,		_a:float = 0.02     ,		_a:float = 1e-12     ,		_a:bool = False     ,		**_a:Any     ,		):
       super().__init__(pad_token_id=_a     ,		**_a   )

       snake_case__						= attention_window
       snake_case__						= sep_token_id
       snake_case__						= bos_token_id
       snake_case__						= eos_token_id
       snake_case__						= vocab_size
       snake_case__						= hidden_size
       snake_case__						= num_hidden_layers
       snake_case__						= num_attention_heads
       snake_case__						= hidden_act
       snake_case__						= intermediate_size
       snake_case__						= hidden_dropout_prob
       snake_case__						= attention_probs_dropout_prob
       snake_case__						= max_position_embeddings
       snake_case__						= type_vocab_size
       snake_case__						= initializer_range
       snake_case__						= layer_norm_eps
       snake_case__						= onnx_export




class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     def __init__(					self:Tuple     ,		_a:"PretrainedConfig"     ,		_a:str = "default"     ,		_a:"List[PatchingSpec]" = None   ):
       super().__init__(_a     ,		_a     ,		_a   )
       snake_case__						= True

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       if self.task == "multiple-choice":
         snake_case__						= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
         snake_case__						= {0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ('''input_ids''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
               ('''global_attention_mask''', dynamic_axis),
           ]   )

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       snake_case__						= super().outputs
       if self.task == "default":
         snake_case__						= {0: '''batch'''}
       return outputs

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       return 1e-4

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       # needs to be >= 14 to support tril operator
       return max(super().default_onnx_opset     ,		14   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:"PreTrainedTokenizerBase"     ,		_a:int = -1     ,		_a:int = -1     ,		_a:bool = False     ,		_a:Optional[TensorType] = None     ,		):
       snake_case__						= super().generate_dummy_inputs(
           preprocessor=_a     ,		batch_size=_a     ,		seq_length=_a     ,		is_pair=_a     ,		framework=_a   )
       import torch

       # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
       # makes the export fail randomly
       snake_case__						= torch.zeros_like(inputs['''input_ids''']   )
       # make every second token global
       snake_case__						= 1

       return inputs






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""")