


from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame







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


if __name__ == "__main__":
   lowerCamelCase__							:						Optional[Any]							     =   """headphones"""
   get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__							:						Tuple							     =   {
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						Tuple							     =   ["""RobertaTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						Optional[int]							     =   [
       """ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """RobertaForCausalLM""",
       """RobertaForMaskedLM""",
       """RobertaForMultipleChoice""",
       """RobertaForQuestionAnswering""",
       """RobertaForSequenceClassification""",
       """RobertaForTokenClassification""",
       """RobertaModel""",
       """RobertaPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						List[str]							     =   [
       """TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFRobertaForCausalLM""",
       """TFRobertaForMaskedLM""",
       """TFRobertaForMultipleChoice""",
       """TFRobertaForQuestionAnswering""",
       """TFRobertaForSequenceClassification""",
       """TFRobertaForTokenClassification""",
       """TFRobertaMainLayer""",
       """TFRobertaModel""",
       """TFRobertaPreTrainedModel""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						str							     =   [
       """FlaxRobertaForCausalLM""",
       """FlaxRobertaForMaskedLM""",
       """FlaxRobertaForMultipleChoice""",
       """FlaxRobertaForQuestionAnswering""",
       """FlaxRobertaForSequenceClassification""",
       """FlaxRobertaForTokenClassification""",
       """FlaxRobertaModel""",
       """FlaxRobertaPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
   from .tokenization_roberta import RobertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_roberta_fast import RobertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roberta import (
          ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          RobertaForCausalLM,
          RobertaForMaskedLM,
          RobertaForMultipleChoice,
          RobertaForQuestionAnswering,
          RobertaForSequenceClassification,
          RobertaForTokenClassification,
          RobertaModel,
          RobertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roberta import (
          TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRobertaForCausalLM,
          TFRobertaForMaskedLM,
          TFRobertaForMultipleChoice,
          TFRobertaForQuestionAnswering,
          TFRobertaForSequenceClassification,
          TFRobertaForTokenClassification,
          TFRobertaMainLayer,
          TFRobertaModel,
          TFRobertaPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roberta import (
          FlaxRobertaForCausalLM,
          FlaxRobertaForMaskedLM,
          FlaxRobertaForMultipleChoice,
          FlaxRobertaForQuestionAnswering,
          FlaxRobertaForSequenceClassification,
          FlaxRobertaForTokenClassification,
          FlaxRobertaModel,
          FlaxRobertaPreTrainedModel,
      )

else:
   import sys

   lowerCamelCase__							:						List[Any]							     =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

