




'''simple docstring'''







import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
   from cookiecutter.main import cookiecutter

   A     =True
except ImportError:
   A     =False

A     =logging.get_logger(__name__)  # pylint: disable=invalid-name





def 						snake_case_							(_a	:							Namespace     ):
    return AddNewModelCommand(args.testing    ,   args.testing_file    ,   path=args.path     )







class 			_a  (	__a ):
       @staticmethod
       def     A      (	lowercase						:	ArgumentParser				):



           '''simple docstring'''

           UpperCAmelCase			       =		parser.add_parser('''add-new-model'''				)
           add_new_model_parser.add_argument('''--testing'''   ,				action='''store_true'''   ,				help='''If in testing mode.'''				)
           add_new_model_parser.add_argument('''--testing_file'''   ,				type=lowercase   ,				help='''Configuration file on which to run.'''				)
           add_new_model_parser.add_argument(
               '''--path'''   ,				type=lowercase   ,				help='''Path to cookiecutter. Should only be used for testing purposes.'''				)
           add_new_model_parser.set_defaults(func=lowercase				)
       def __init__(	self						:	List[Any]   ,				lowercase						:	bool   ,				lowercase						:	str   ,				lowercase						:	Dict=None   ,				*lowercase						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		testing
           UpperCAmelCase			       =		testing_file
           UpperCAmelCase			       =		path







       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           warnings.warn(
               '''The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. '''
               '''It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality '''
               '''checks, you should use `transformers-cli add-new-model-like` instead.'''				)
           if not _has_cookiecutter:
               raise ImportError(
                   '''Model creation dependencies are required to use the `add_new_model` command. Install them by running '''
                   '''the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n'''				)
           # Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
           UpperCAmelCase			       =		[directory for directory in os.listdir() if '''cookiecutter-template-''' == directory[:22]]
           if len(lowercase				) > 0:
               raise ValueError(
                   '''Several directories starting with `cookiecutter-template-` in current working directory. '''
                   '''Please clean your directory by removing all folders starting with `cookiecutter-template-` or '''
                   '''change your working directory.'''				)

           UpperCAmelCase			       =		(
               Path(lowercase				).parent.parent.parent.parent if self._path is None else Path(self._path				).parent.parent
           )
           UpperCAmelCase			       =		path_to_transformer_root / '''templates''' / '''adding_a_new_model'''

           # Execute cookiecutter
           if not self._testing:
               cookiecutter(str(lowercase				)				)
           else:
               with open(self._testing_file   ,				'''r'''				) as configuration_file:
                   UpperCAmelCase			       =		json.load(lowercase				)

               cookiecutter(
                   str(path_to_cookiecutter if self._path is None else self._path				)   ,				no_input=lowercase   ,				extra_context=lowercase   ,				)

           UpperCAmelCase			       =		[directory for directory in os.listdir() if '''cookiecutter-template-''' in directory[:22]][0]

           # Retrieve configuration
           with open(directory + '''/configuration.json'''   ,				'''r'''				) as configuration_file:
               UpperCAmelCase			       =		json.load(lowercase				)

           UpperCAmelCase			       =		configuration['''lowercase_modelname''']
           UpperCAmelCase			       =		configuration['''generate_tensorflow_pytorch_and_flax''']
           os.remove(f"{directory}/configuration.json"				)

           UpperCAmelCase			       =		'''PyTorch''' in generate_tensorflow_pytorch_and_flax
           UpperCAmelCase			       =		'''TensorFlow''' in generate_tensorflow_pytorch_and_flax
           UpperCAmelCase			       =		'''Flax''' in generate_tensorflow_pytorch_and_flax

           UpperCAmelCase			       =		f"{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"
           os.makedirs(lowercase   ,				exist_ok=lowercase				)
           os.makedirs(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}"   ,				exist_ok=lowercase				)

           # Tests require submodules as they have parent imports
           with open(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py"   ,				'''w'''				):
               pass

           shutil.move(
               f"{directory}/__init__.py"   ,				f"{model_dir}/__init__.py"   ,				)
           shutil.move(
               f"{directory}/configuration_{lowercase_model_name}.py"   ,				f"{model_dir}/configuration_{lowercase_model_name}.py"   ,				)

           def remove_copy_lines(lowercase						:	Union[str, Any]				):
               with open(lowercase   ,				'''r'''				) as f:
                   UpperCAmelCase			       =		f.readlines()
               with open(lowercase   ,				'''w'''				) as f:
                   for line in lines:
                       if "# Copied from transformers." not in line:
                           f.write(lowercase				)

           if output_pytorch:
               if not self._testing:
                   remove_copy_lines(f"{directory}/modeling_{lowercase_model_name}.py"				)

               shutil.move(
                   f"{directory}/modeling_{lowercase_model_name}.py"   ,				f"{model_dir}/modeling_{lowercase_model_name}.py"   ,				)

               shutil.move(
                   f"{directory}/test_modeling_{lowercase_model_name}.py"   ,				f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py"   ,				)
           else:
               os.remove(f"{directory}/modeling_{lowercase_model_name}.py"				)
               os.remove(f"{directory}/test_modeling_{lowercase_model_name}.py"				)

           if output_tensorflow:
               if not self._testing:
                   remove_copy_lines(f"{directory}/modeling_tf_{lowercase_model_name}.py"				)

               shutil.move(
                   f"{directory}/modeling_tf_{lowercase_model_name}.py"   ,				f"{model_dir}/modeling_tf_{lowercase_model_name}.py"   ,				)

               shutil.move(
                   f"{directory}/test_modeling_tf_{lowercase_model_name}.py"   ,				f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py"   ,				)
           else:
               os.remove(f"{directory}/modeling_tf_{lowercase_model_name}.py"				)
               os.remove(f"{directory}/test_modeling_tf_{lowercase_model_name}.py"				)

           if output_flax:
               if not self._testing:
                   remove_copy_lines(f"{directory}/modeling_flax_{lowercase_model_name}.py"				)

               shutil.move(
                   f"{directory}/modeling_flax_{lowercase_model_name}.py"   ,				f"{model_dir}/modeling_flax_{lowercase_model_name}.py"   ,				)

               shutil.move(
                   f"{directory}/test_modeling_flax_{lowercase_model_name}.py"   ,				f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py"   ,				)
           else:
               os.remove(f"{directory}/modeling_flax_{lowercase_model_name}.py"				)
               os.remove(f"{directory}/test_modeling_flax_{lowercase_model_name}.py"				)

           shutil.move(
               f"{directory}/{lowercase_model_name}.md"   ,				f"{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md"   ,				)

           shutil.move(
               f"{directory}/tokenization_{lowercase_model_name}.py"   ,				f"{model_dir}/tokenization_{lowercase_model_name}.py"   ,				)

           shutil.move(
               f"{directory}/tokenization_fast_{lowercase_model_name}.py"   ,				f"{model_dir}/tokenization_{lowercase_model_name}_fast.py"   ,				)

           from os import fdopen, remove
           from shutil import copymode, move
           from tempfile import mkstemp

           def replace(lowercase						:	str   ,				lowercase						:	str   ,				lowercase						:	List[str]				):
               # Create temp file
               UpperCAmelCase       ,     UpperCAmelCase			       =		mkstemp()
               UpperCAmelCase			       =		False
               with fdopen(lowercase   ,				'''w'''				) as new_file:
                   with open(lowercase				) as old_file:
                       for line in old_file:
                           new_file.write(lowercase				)
                           if line_to_copy_below in line:
                               UpperCAmelCase			       =		True
                               for line_to_copy in lines_to_copy:
                                   new_file.write(lowercase				)

               if not line_found:
                   raise ValueError(f"Line {line_to_copy_below} was not found in file."				)

               # Copy the file permissions from the old file to the new file
               copymode(lowercase   ,				lowercase				)
               # Remove original file
               remove(lowercase				)
               # Move new file
               move(lowercase   ,				lowercase				)

           def skip_units(lowercase						:	List[Any]				):
               return (
                   ("generating PyTorch" in line and not output_pytorch)
                   or ("generating TensorFlow" in line and not output_tensorflow)
                   or ("generating Flax" in line and not output_flax)
               )

           def replace_in_files(lowercase						:	Tuple				):
               with open(lowercase				) as datafile:
                   UpperCAmelCase			       =		[]
                   UpperCAmelCase			       =		False
                   UpperCAmelCase			       =		False
                   for line in datafile:
                       if "# To replace in: " in line and "##" not in line:
                           UpperCAmelCase			       =		line.split('''"'''				)[1]
                           UpperCAmelCase			       =		skip_units(lowercase				)
                       elif "# Below: " in line and "##" not in line:
                           UpperCAmelCase			       =		line.split('''"'''				)[1]
                           UpperCAmelCase			       =		skip_units(lowercase				)
                       elif "# End." in line and "##" not in line:
                           if not skip_file and not skip_snippet:
                               replace(lowercase   ,				lowercase   ,				lowercase				)

                           UpperCAmelCase			       =		[]
                       elif "# Replace with" in line and "##" not in line:
                           UpperCAmelCase			       =		[]
                       elif "##" not in line:
                           lines_to_copy.append(lowercase				)

               remove(lowercase				)

           replace_in_files(f"{directory}/to_replace_{lowercase_model_name}.py"				)
           os.rmdir(lowercase				)









from __future__ import annotations

from math import ceil, floor, sqrt




def 	__UpperCamelCase	(  _lowerCAmelCase = 200_0000		)       ->  int:



    """simple docstring"""






    A :       list[int]          =   [0]
    A :       int

    for idx in range(1      ,     ceil(sqrt(target * 2		) * 1.1		)		):
        triangle_numbers.append(triangle_numbers[-1] + idx		)

    # we want this to be as close as possible to target
    A :       int          =   0
    # the area corresponding to the grid that gives the product closest to target
    A :       int          =   0
    # an estimate of b, using the quadratic formula
    A :       float
    # the largest integer less than b_estimate
    A :       int
    # the largest integer less than b_estimate
    A :       int
    # the triangle number corresponding to b_floor
    A :       int
    # the triangle number corresponding to b_ceil
    A :       int

    for idx_a, triangle_a in enumerate(triangle_numbers[1:]      ,     1		):
        A :       Union[str, Any]          =   (-1 + sqrt(1 + 8 * target / triangle_a		)) / 2
        A :       List[Any]          =   floor(_lowerCAmelCase		)
        A :       Tuple          =   ceil(_lowerCAmelCase		)
        A :       int          =   triangle_numbers[b_floor]
        A :       Dict          =   triangle_numbers[b_ceil]

        if abs(target - triangle_b_first_guess * triangle_a		) < abs(
            target - best_product		):
            A :       Optional[int]          =   triangle_b_first_guess * triangle_a
            A :       Optional[int]          =   idx_a * b_floor

        if abs(target - triangle_b_second_guess * triangle_a		) < abs(
            target - best_product		):
            A :       Tuple          =   triangle_b_second_guess * triangle_a
            A :       Tuple          =   idx_a * b_ceil

    return area


if __name__ == "__main__":
     print(F"""{solution() = }""")


