






"""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

			lowerCamelCase_       			=   True
except ImportError:
			lowerCamelCase_       			=   False

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

def        snake_case   (     A__		):
			return AddNewModelCommand(args.testing			,args.testing_file			,path=args.path		)







class     UpperCamelCase_ (__A  ):





	@staticmethod
	def   _SCREAMING_SNAKE_CASE   (   lowerCAmelCase_      :				ArgumentParser		)		->    Union[str, Any]:
				UpperCAmelCase_  :				int      =      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=lowerCAmelCase_			,			help="Configuration file on which to run."		)
				add_new_model_parser.add_argument(
				    "--path"			,			type=lowerCAmelCase_			,			help="Path to cookiecutter. Should only be used for testing purposes."		)
				add_new_model_parser.set_defaults(func=lowerCAmelCase_		)





	def __init__(   self      :				Union[str, Any]			,			lowerCAmelCase_      :				bool			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				Optional[int]=None			,			*lowerCAmelCase_      :				Dict		)		->    List[Any]:
				UpperCAmelCase_  :				str      =      testing
				UpperCAmelCase_  :				str      =      testing_file
				UpperCAmelCase_  :				List[str]      =      path





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Dict:
				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_  :				Dict      =      [directory for directory in os.listdir() if "cookiecutter-template-" == directory[:22]]
				if len(lowerCAmelCase_		) > 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_  :				Optional[Any]      =      (
				    Path(lowerCAmelCase_		).parent.parent.parent.parent if self._path is None else Path(self._path		).parent.parent
				)
				UpperCAmelCase_  :				List[str]      =      path_to_transformer_root / "templates" / "adding_a_new_model"

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

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

				UpperCAmelCase_  :				Dict      =      [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_  :				Tuple      =      json.load(lowerCAmelCase_		)

				UpperCAmelCase_  :				int      =      configuration["lowercase_modelname"]
				UpperCAmelCase_  :				Optional[Any]      =      configuration["generate_tensorflow_pytorch_and_flax"]
				os.remove(f"""{directory}/configuration.json"""		)

				UpperCAmelCase_  :				Optional[int]      =      "PyTorch" in generate_tensorflow_pytorch_and_flax
				UpperCAmelCase_  :				Dict      =      "TensorFlow" in generate_tensorflow_pytorch_and_flax
				UpperCAmelCase_  :				Union[str, Any]      =      "Flax" in generate_tensorflow_pytorch_and_flax

				UpperCAmelCase_  :				List[Any]      =      f"""{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"""
				os.makedirs(lowerCAmelCase_			,			exist_ok=lowerCAmelCase_		)
				os.makedirs(f"""{path_to_transformer_root}/tests/models/{lowercase_model_name}"""			,			exist_ok=lowerCAmelCase_		)

				# 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(lowerCAmelCase_      :				Optional[int]		):
							with open(lowerCAmelCase_			,			"r"		) as f:
										UpperCAmelCase_  :				Tuple      =      f.readlines()
							with open(lowerCAmelCase_			,			"w"		) as f:
										for line in lines:
													if "# Copied from transformers." not in line:
																f.write(lowerCAmelCase_		)

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

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

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

																UpperCAmelCase_  :				List[str]      =      []
													elif "# Replace with" in line and "##" not in line:
																UpperCAmelCase_  :				List[Any]      =      []
													elif "##" not in line:
																lines_to_copy.append(lowerCAmelCase_		)

							remove(lowerCAmelCase_		)

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











"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey

def        snake_case   (     A__			,A__			,A__			,A__			,A__			,A__		):

			# prepare kernel
			# the kernel size have to be odd
			if (ksize % 2) == 0:
						UpperCAmelCase_  :				List[Any]      =      ksize + 1
			UpperCAmelCase_  :				Optional[Any]      =      np.zeros((ksize, ksize)			,dtype=np.floataa		)

			# each value
			for y in range(A__		):
						for x in range(A__		):
									# distance from center
									UpperCAmelCase_  :				Tuple      =      x - ksize // 2
									UpperCAmelCase_  :				Any      =      y - ksize // 2

									# degree to radiant
									UpperCAmelCase_  :				int      =      theta / 1_80 * np.pi
									UpperCAmelCase_  :				Optional[int]      =      np.cos(_theta		)
									UpperCAmelCase_  :				Union[str, Any]      =      np.sin(_theta		)

									# get kernel x
									UpperCAmelCase_  :				Tuple      =      cos_theta * px + sin_theta * py

									# get kernel y
									UpperCAmelCase_  :				List[str]      =      -sin_theta * px + cos_theta * py

									# fill kernel
									UpperCAmelCase_  :				Dict      =      np.exp(
									    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)		) * np.cos(2 * np.pi * _x / lambd + psi		)

			return gabor


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			# read original image
			lowerCamelCase_       			=   imread('''../image_data/lena.jpg''')
			# turn image in gray scale value
			lowerCamelCase_       			=   cvtColor(img, COLOR_BGR2GRAY)

			# Apply multiple Kernel to detect edges
			lowerCamelCase_       			=   np.zeros(gray.shape[:2])
			for theta in [0, 30, 60, 90, 120, 150]:
						lowerCamelCase_       			=   gabor_filter_kernel(10, 8, theta, 10, 0, 0)
						out += filteraD(gray, CV_8UC3, kernel_aa)
			lowerCamelCase_       			=   out / out.max() * 255
			lowerCamelCase_       			=   out.astype(np.uinta)

			imshow('''Original''', gray)
			imshow('''Gabor filter with 20x20 mask and 6 directions''', out)

			waitKey(0)



