import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__magic_name__    							=							'src/transformers'
__magic_name__    							=							'docs/source/en/tasks'






def 							UpperCAmelCase__(					__UpperCAmelCase			:     int     ,				__UpperCAmelCase			:     Optional[int]     ,				__UpperCAmelCase			:     Dict       ):



   with open(UpperCAmelCase__     ,				'r'     ,				encoding='utf-8'     ,				newline='\n'       ) as f:
      __snake_case   :			Optional[Any]										=						f.readlines()
   # Find the start prompt.
   __snake_case   :			str										=						0
   while not lines[start_index].startswith(UpperCAmelCase__       ):
      start_index += 1
   start_index += 1

   __snake_case   :			Any										=						start_index
   while not lines[end_index].startswith(UpperCAmelCase__       ):
      end_index += 1
   end_index -= 1

   while len(lines[start_index]       ) <= 1:
      start_index += 1
   while len(lines[end_index]       ) <= 1:
      end_index -= 1
   end_index += 1
   return "".join(lines[start_index:end_index]       ), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
__magic_name__    							=							direct_transformers_import(TRANSFORMERS_PATH)

__magic_name__    							=							{
    'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
__magic_name__    							=							{
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}






def 							UpperCAmelCase__(					__UpperCAmelCase			:     List[str]       ):
   __snake_case   :			List[Any]										=						TASK_GUIDE_TO_MODELS[task_guide]
   __snake_case   :			Tuple										=						SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCAmelCase__     ,				set()       )
   __snake_case   :			List[Any]										=						{
       code: name
       for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
       if (code in model_maping_names or code in special_model_types)
   }
   return ", ".join([F"""[{name}](../model_doc/{code})""" for code, name in model_names.items()]       ) + "\n"






def 							UpperCAmelCase__(					__UpperCAmelCase			:     Tuple     ,				__UpperCAmelCase			:     Any=False       ):
   __snake_case						,       __snake_case						,       __snake_case						,       __snake_case   :			int										=						_find_text_in_file(
       filename=os.path.join(UpperCAmelCase__     ,				UpperCAmelCase__       )     ,				start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'     ,				end_prompt='<!--End of the generated tip-->'     ,				)

   __snake_case   :			str										=						get_model_list_for_task(UpperCAmelCase__       )

   if current_list != new_list:
      if overwrite:
         with open(os.path.join(UpperCAmelCase__     ,				UpperCAmelCase__       )     ,				'w'     ,				encoding='utf-8'     ,				newline='\n'       ) as f:
            f.writelines(lines[:start_index] + [new_list] + lines[end_index:]       )
      else:
         raise ValueError(
             F"""The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"""
             ' to fix this.'       )


if __name__ == "__main__":
 __magic_name__    							=							argparse.ArgumentParser()
 parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
 __magic_name__    							=							parser.parse_args()

 for task_guide in TASK_GUIDE_TO_MODELS.keys():
  check_model_list_for_task(task_guide, args.fix_and_overwrite)








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""")