
"""simple docstring"""





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 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):
		with open(UpperCamelCase_  ,				"""r"""  ,				encoding="""utf-8"""  ,				newline="""\n"""				) as f:
				__SCREAMING_SNAKE_CASE            = f.readlines()
		# Find the start prompt.
		__SCREAMING_SNAKE_CASE            = 0
		while not lines[start_index].startswith(UpperCamelCase_				):
				start_index += 1
		start_index += 1

		__SCREAMING_SNAKE_CASE            = 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 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = TASK_GUIDE_TO_MODELS[task_guide]
		__SCREAMING_SNAKE_CASE            = SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCamelCase_  ,				set()				)
		__SCREAMING_SNAKE_CASE            = {
		    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 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_=False				):
		__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = _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-->"""  ,				)

		__SCREAMING_SNAKE_CASE            = 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)






"""simple docstring"""





import requests
from bsa import BeautifulSoup



def 						_lowerCAmelCase				(				UpperCamelCase_ = "AAPL"				):
		__SCREAMING_SNAKE_CASE            = f"https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"
		__SCREAMING_SNAKE_CASE            = BeautifulSoup(requests.get(UpperCamelCase_				).text  ,				"""html.parser"""				)
		__SCREAMING_SNAKE_CASE            = """My(6px) Pos(r) smartphone_Mt(6px)"""
		return soup.find("""div"""  ,				class_=class_				).find("""span"""				).text


if __name__ == "__main__":
							for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
														print(F"""Current {symbol:<4} stock price is {stock_price(symbol):>8}""")




