

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  lowerCamelCase__ (					_a):
	return AddNewModelCommand(args.testing							,			args.testing_file							,			path=args.path)






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			@staticmethod
			def 							__UpperCamelCase (			a							: ArgumentParser	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       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=a      ,      help="Configuration file on which to run."	)
				add_new_model_parser.add_argument(
				    "--path"      ,      type=a      ,      help="Path to cookiecutter. Should only be used for testing purposes."	)
				add_new_model_parser.set_defaults(func=a	)
			def __init__(			self							: List[Any]      ,      a							: bool      ,      a							: str      ,      a							: int=None      ,      *a							: str	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       testing
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       testing_file
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       path






			def 							__UpperCamelCase (			self							: Dict	)							->       str:

				"""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
				SCREAMING_SNAKE_CASE   :      List[str]				     =       [directory for directory in os.listdir() if "cookiecutter-template-" == directory[:22]]
				if len(a	) > 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."	)

				SCREAMING_SNAKE_CASE   :      Any				     =       (
				    Path(a	).parent.parent.parent.parent if self._path is None else Path(self._path	).parent.parent
				)
				SCREAMING_SNAKE_CASE   :      Any				     =       path_to_transformer_root / "templates" / "adding_a_new_model"

				# Execute cookiecutter
				if not self._testing:
					cookiecutter(str(a	)	)
				else:
					with open(self._testing_file      ,      "r"	) as configuration_file:
						SCREAMING_SNAKE_CASE   :      int				     =       json.load(a	)

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

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [directory for directory in os.listdir() if "cookiecutter-template-" in directory[:22]][0]

				# Retrieve configuration
				with open(directory + "/configuration.json"      ,      "r"	) as configuration_file:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       json.load(a	)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       configuration["lowercase_modelname"]
				SCREAMING_SNAKE_CASE   :      Dict				     =       configuration["generate_tensorflow_pytorch_and_flax"]
				os.remove(F"{directory}/configuration.json"	)

				SCREAMING_SNAKE_CASE   :      Any				     =       "PyTorch" in generate_tensorflow_pytorch_and_flax
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       "TensorFlow" in generate_tensorflow_pytorch_and_flax
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       "Flax" in generate_tensorflow_pytorch_and_flax

				SCREAMING_SNAKE_CASE   :      str				     =       F"{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"
				os.makedirs(a      ,      exist_ok=a	)
				os.makedirs(F"{path_to_transformer_root}/tests/models/{lowercase_model_name}"      ,      exist_ok=a	)

				# 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(a							: Tuple	):
					with open(a      ,      "r"	) as f:
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       f.readlines()
					with open(a      ,      "w"	) as f:
						for line in lines:
							if "# Copied from transformers." not in line:
								f.write(a	)

				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(a							: str      ,      a							: str      ,      a							: List[str]	):
					# Create temp file
					SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[Any]				     =       mkstemp()
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       False
					with fdopen(a      ,      "w"	) as new_file:
						with open(a	) as old_file:
							for line in old_file:
								new_file.write(a	)
								if line_to_copy_below in line:
									SCREAMING_SNAKE_CASE   :      List[Any]				     =       True
									for line_to_copy in lines_to_copy:
										new_file.write(a	)

					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(a      ,      a	)
					# Remove original file
					remove(a	)
					# Move new file
					move(a      ,      a	)

				def skip_units(a							: Tuple	):
					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(a							: List[str]	):
					with open(a	) as datafile:
						SCREAMING_SNAKE_CASE   :      Dict				     =       []
						SCREAMING_SNAKE_CASE   :      Any				     =       False
						SCREAMING_SNAKE_CASE   :      str				     =       False
						for line in datafile:
							if "# To replace in: " in line and "##" not in line:
								SCREAMING_SNAKE_CASE   :      Any				     =       line.split("\""	)[1]
								SCREAMING_SNAKE_CASE   :      str				     =       skip_units(a	)
							elif "# Below: " in line and "##" not in line:
								SCREAMING_SNAKE_CASE   :      str				     =       line.split("\""	)[1]
								SCREAMING_SNAKE_CASE   :      int				     =       skip_units(a	)
							elif "# End." in line and "##" not in line:
								if not skip_file and not skip_snippet:
									replace(a      ,      a      ,      a	)

								SCREAMING_SNAKE_CASE   :      List[str]				     =       []
							elif "# Replace with" in line and "##" not in line:
								SCREAMING_SNAKE_CASE   :      Optional[int]				     =       []
							elif "##" not in line:
								lines_to_copy.append(a	)

					remove(a	)

				replace_in_files(F"{directory}/to_replace_{lowercase_model_name}.py"	)
				os.rmdir(a	)


def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	global f  # a global dp table for knapsack
	if f[i][j] < 0:
		if j < wt[i - 1]:
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       mf_knapsack(i - 1							,			_a							,			_a							,			_a)
		else:
			SCREAMING_SNAKE_CASE   :      Any				     =       max(
			    mf_knapsack(i - 1							,			_a							,			_a							,			_a)							,			mf_knapsack(i - 1							,			_a							,			_a							,			j - wt[i - 1]) + val[i - 1]							,			)
		SCREAMING_SNAKE_CASE   :      Tuple				     =       val
	return f[i][j]

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [[0] * (w + 1) for _ in range(n + 1)]

	for i in range(1							,			n + 1):
		for w_ in range(1							,			w + 1):
			if wt[i - 1] <= w_:
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]							,			dp[i - 1][w_])
			else:
				SCREAMING_SNAKE_CASE   :      int				     =       dp[i - 1][w_]

	return dp[n][w_], dp

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	if not (isinstance(_a							,			(list, tuple)) and isinstance(_a							,			(list, tuple))):
		raise ValueError(
		    "Both the weights and values vectors must be either lists or tuples")

	SCREAMING_SNAKE_CASE   :      int				     =       len(_a)
	if num_items != len(_a):
		SCREAMING_SNAKE_CASE   :      List[str]				     =       (
		    "The number of weights must be the same as the number of values.\n"
		    f"But got {num_items} weights and {len(_a)} values"
		)
		raise ValueError(_a)
	for i in range(_a):
		if not isinstance(wt[i]							,			_a):
			SCREAMING_SNAKE_CASE   :      int				     =       (
			    "All weights must be integers but got weight of "
			    f"type {type(wt[i])} at index {i}"
			)
			raise TypeError(_a)

	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       knapsack(_a							,			_a							,			_a							,			_a)
	SCREAMING_SNAKE_CASE   :      set				     =       set()
	_construct_solution(_a							,			_a							,			_a							,			_a							,			_a)

	return optimal_val, example_optional_set

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a							,			_a):
	# for the current item i at a maximum weight j to be part of an optimal subset,
	# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
	# where i - 1 means considering only the previous items at the given maximum weight
	if i > 0 and j > 0:
		if dp[i - 1][j] == dp[i][j]:
			_construct_solution(_a							,			_a							,			i - 1							,			_a							,			_a)
		else:
			optimal_set.add(_a)
			_construct_solution(_a							,			_a							,			i - 1							,			j - wt[i - 1]							,			_a)


if __name__ == "__main__":
			a_						     =    [3, 2, 4, 4]
			a_						     =    [4, 3, 2, 3]
			a_						     =    4
			a_						     =    6
			a_						     =    [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
			a_     ,	a_						     =    knapsack(w, wt, val, n)
			print(optimal_solution)
			print(mf_knapsack(n, wt, val, w))  # switched the n and w

			# testing the dynamic programming problem with example
			# the optimal subset for the above example are items 3 and 4
			a_     ,	a_						     =    knapsack_with_example_solution(w, wt, val)
			assert optimal_solution == 8
			assert optimal_subset == {3, 4}
			print('optimal_value = ', optimal_solution)
			print('An optimal subset corresponding to the optimal value', optimal_subset)