





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

		_lowerCamelCase :    Tuple  							= True
except ImportError:
		_lowerCamelCase :    Optional[Any]  							= False

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




def _a (  SCREAMING_SNAKE_CASE__      :  Union[str, Any]       )	->    Optional[int]:

							'''simple docstring'''






							return AddNewModelCommand(args.testing						,				args.testing_file						,				path=args.path       )







class 						lowerCamelCase       (__snake_case      ):







							"""simple docstring"""




							@staticmethod
							def  A_     ( _UpperCAmelCase     :					List[str]						)				->			List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						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=_UpperCAmelCase,			help="Configuration file on which to run."						)
														add_new_model_parser.add_argument(
														    "--path",			type=_UpperCAmelCase,			help="Path to cookiecutter. Should only be used for testing purposes."						)
														add_new_model_parser.set_defaults(func=_UpperCAmelCase						)






							def __init__( self     :					Union[str, Any],			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					Optional[int]=None,			*_UpperCAmelCase     :					Optional[Any]						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						testing
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						testing_file
														SCREAMING_SNAKE_CASE__       :      int     =						path






							def  A_     ( self     :					List[Any]						)				->			Dict:





														"""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__       :      int     =						[directory for directory in os.listdir() if "cookiecutter-template-" == directory[:2_2]]
														if len(_UpperCAmelCase						) > 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__       :      List[str]     =						(
														    Path(_UpperCAmelCase						).parent.parent.parent.parent if self._path is None else Path(self._path						).parent.parent
														)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						path_to_transformer_root / "templates" / "adding_a_new_model"

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

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

														SCREAMING_SNAKE_CASE__       :      int     =						[directory for directory in os.listdir() if "cookiecutter-template-" in directory[:2_2]][0]

														# Retrieve configuration
														with open(directory + "/configuration.json",			"r"						) as configuration_file:
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						json.load(_UpperCAmelCase						)

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

														SCREAMING_SNAKE_CASE__       :      int     =						"PyTorch" in generate_tensorflow_pytorch_and_flax
														SCREAMING_SNAKE_CASE__       :      Tuple     =						"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(_UpperCAmelCase,			exist_ok=_UpperCAmelCase						)
														os.makedirs(F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}''',			exist_ok=_UpperCAmelCase						)

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

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

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

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

																																										SCREAMING_SNAKE_CASE__       :      Dict     =						[]
																																			elif "# Replace with" in line and "##" not in line:
																																										SCREAMING_SNAKE_CASE__       :      Dict     =						[]
																																			elif "##" not in line:
																																										lines_to_copy.append(_UpperCAmelCase						)

																					remove(_UpperCAmelCase						)

														replace_in_files(F'''{directory}/to_replace_{lowercase_model_name}.py'''						)
														os.rmdir(_UpperCAmelCase						)













from __future__ import annotations

import time

_lowerCamelCase :    Tuple  							= list[tuple[int, int]]

_lowerCamelCase :    int  							= [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

_lowerCamelCase :    str  							= [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Optional[int],			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					Node | None						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						pos_x
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						pos_y
														SCREAMING_SNAKE_CASE__       :      str     =						(pos_y, pos_x)
														SCREAMING_SNAKE_CASE__       :      Dict     =						goal_x
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						goal_y
														SCREAMING_SNAKE_CASE__       :      str     =						parent


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Dict,			_UpperCAmelCase     :					tuple[int, int],			_UpperCAmelCase     :					tuple[int, int]						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						Node(start[1],			start[0],			goal[1],			goal[0],			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						Node(goal[1],			goal[0],			goal[1],			goal[0],			_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						[self.start]
														SCREAMING_SNAKE_CASE__       :      str     =						False






							def  A_     ( self     :					Dict						)				->			Path | None:





														"""simple docstring"""
														while self.node_queue:
																					SCREAMING_SNAKE_CASE__       :      int     =						self.node_queue.pop(0						)

																					if current_node.pos == self.target.pos:
																												SCREAMING_SNAKE_CASE__       :      Dict     =						True
																												return self.retrace_path(_UpperCAmelCase						)

																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.get_successors(_UpperCAmelCase						)

																					for node in successors:
																												self.node_queue.append(_UpperCAmelCase						)

														if not self.reached:
																					return [self.start.pos]
														return None






							def  A_     ( self     :					Any,			_UpperCAmelCase     :					Node						)				->			list[Node]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						[]
														for action in delta:
																					SCREAMING_SNAKE_CASE__       :      str     =						parent.pos_x + action[1]
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						parent.pos_y + action[0]
																					if not (0 <= pos_x <= len(grid[0]						) - 1 and 0 <= pos_y <= len(_UpperCAmelCase						) - 1):
																												continue

																					if grid[pos_y][pos_x] != 0:
																												continue

																					successors.append(
																					    Node(_UpperCAmelCase,			_UpperCAmelCase,			self.target.pos_y,			self.target.pos_x,			_UpperCAmelCase						)						)
														return successors






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					Node | None						)				->			Path:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						node
														SCREAMING_SNAKE_CASE__       :      Tuple     =						[]
														while current_node is not None:
																					path.append((current_node.pos_y, current_node.pos_x)						)
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						current_node.parent
														path.reverse()
														return path


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					List[str],			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					List[str]						)				->			List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						BreadthFirstSearch(_UpperCAmelCase,			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						BreadthFirstSearch(_UpperCAmelCase,			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Dict     =						False






							def  A_     ( self     :					List[str]						)				->			Path | None:





														"""simple docstring"""
														while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
																					SCREAMING_SNAKE_CASE__       :      int     =						self.fwd_bfs.node_queue.pop(0						)
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.bwd_bfs.node_queue.pop(0						)

																					if current_bwd_node.pos == current_fwd_node.pos:
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						True
																												return self.retrace_bidirectional_path(
																												    _UpperCAmelCase,			_UpperCAmelCase						)

																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						current_bwd_node
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						current_fwd_node

																					SCREAMING_SNAKE_CASE__       :      List[str]     =						{
																					    self.fwd_bfs: self.fwd_bfs.get_successors(_UpperCAmelCase						),
																					    self.bwd_bfs: self.bwd_bfs.get_successors(_UpperCAmelCase						),
																					}

																					for bfs in [self.fwd_bfs, self.bwd_bfs]:
																												for node in successors[bfs]:
																																			bfs.node_queue.append(_UpperCAmelCase						)

														if not self.reached:
																					return [self.fwd_bfs.start.pos]
														return None






							def  A_     ( self     :					int,			_UpperCAmelCase     :					Node,			_UpperCAmelCase     :					Node						)				->			Path:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						self.fwd_bfs.retrace_path(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Any     =						self.bwd_bfs.retrace_path(_UpperCAmelCase						)
														bwd_path.pop()
														bwd_path.reverse()
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						fwd_path + bwd_path
														return path


if __name__ == "__main__":
		# all coordinates are given in format [y,x]
		import doctest

		doctest.testmod()
		_lowerCamelCase :    Optional[Any]  							= (0, 0)
		_lowerCamelCase :    Dict  							= (len(grid) - 1, len(grid[0]) - 1)
		for elem in grid:
				print(elem)

		_lowerCamelCase :    Dict  							= time.time()
		_lowerCamelCase :    List[Any]  							= BreadthFirstSearch(init, goal)
		_lowerCamelCase :    Optional[Any]  							= bfs.search()
		_lowerCamelCase :    Dict  							= time.time() - start_bfs_time

		print('''Unidirectional BFS computation time : ''', bfs_time)

		_lowerCamelCase :    str  							= time.time()
		_lowerCamelCase :    Dict  							= BidirectionalBreadthFirstSearch(init, goal)
		_lowerCamelCase :    Optional[Any]  							= bd_bfs.search()
		_lowerCamelCase :    Optional[Any]  							= time.time() - start_bd_bfs_time

		print('''Bidirectional BFS computation time : ''', bd_bfs_time)






