




"""simple docstring"""


import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

A_						=				imread(r'''digital_image_processing/image_data/lena_small.jpg''')
A_						=				cvtColor(img, COLOR_BGR2GRAY)


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Dict								=  cn.convert_to_negative(snake_case__    )
  # assert negative_img array for at least one True
  assert negative_img.any()


def 					UpperCAmelCase__       ():
  """simple docstring"""

  with Image.open("""digital_image_processing/image_data/lena_small.jpg"""    ) as img:
    # Work around assertion for response
    assert str(cc.change_contrast(snake_case__		,   1_10    )    ).startswith(
        """<PIL.Image.Image image mode=RGB size=100x100 at"""    )


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		List[Any]								=  canny.gen_gaussian_kernel(9		,   sigma=1.4    )
  # Assert ambiguous array
  assert resp.all()


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Optional[int]								=  imread("""digital_image_processing/image_data/lena_small.jpg"""		,   0    )
  # assert ambiguous array for all == True
  assert canny_img.all()
  _snake_case							:		Optional[Any]								=  canny.canny(snake_case__    )
  # assert canny array for at least one True
  assert canny_array.any()


def 					UpperCAmelCase__       ():
  """simple docstring"""

  assert gg.gaussian_filter(snake_case__		,   5		,   sigma=0.9    ).all()


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Any								=  array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]    )
  _snake_case							:		Optional[int]								=  conv.img_convolve(snake_case__		,   snake_case__    ).astype(snake_case__    )
  assert res.any()


def 					UpperCAmelCase__       ():
  """simple docstring"""

  assert med.median_filter(snake_case__		,   3    ).any()


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case		,					_snake_case							:		int								=  sob.sobel_filter(snake_case__    )
  assert grad.any() and theta.any()


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		str								=  sp.make_sepia(snake_case__		,   20    )
  assert sepia.all()


def 					UpperCAmelCase__       (snake_case__							:					str = "digital_image_processing/image_data/lena_small.jpg"    ):
  """simple docstring"""

  _snake_case							:		Any								=  bs.Burkes(imread(snake_case__		,   1    )		,   1_20    )
  burkes.process()
  assert burkes.output_img.any()


def 					UpperCAmelCase__       (snake_case__							:					str = "digital_image_processing/image_data/lena_small.jpg"		,   ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  rs.NearestNeighbour(imread(snake_case__		,   1    )		,   4_00		,   2_00    )
  nn.process()
  assert nn.output.any()






def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Union[str, Any]								=  """digital_image_processing/image_data/lena.jpg"""

  # Reading the image and converting it to grayscale.
  _snake_case							:		List[Any]								=  imread(snake_case__		,   0    )

  # Test for get_neighbors_pixel function() return not None
  _snake_case							:		str								=  0
  _snake_case							:		Union[str, Any]								=  0
  _snake_case							:		Optional[int]								=  image[x_coordinate][y_coordinate]

  _snake_case							:		str								=  lbp.get_neighbors_pixel(
      snake_case__		,   snake_case__		,   snake_case__		,   snake_case__    )

  assert neighbors_pixels is not None

  # Test for local_binary_pattern function()
  # Create a numpy array as the same height and width of read image
  _snake_case							:		Tuple								=  np.zeros((image.shape[0], image.shape[1])    )

  # Iterating through the image and calculating the local binary pattern value
  # for each pixel.
  for i in range(0		,   image.shape[0]    ):
    for j in range(0		,   image.shape[1]    ):
      _snake_case							:		Optional[int]								=  lbp.local_binary_value(snake_case__		,   snake_case__		,   snake_case__    )

  assert lbp_image.any()






"""simple docstring"""




from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def        lowercase							(		lowerCAmelCase__ :     Namespace			) ->      Tuple:
		return ConvertCommand(
		    args.model_type    ,					args.tf_checkpoint    ,					args.pytorch_dump_output    ,					args.config    ,					args.finetuning_task_name			)


lowercase_			     =  "\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"




class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		@staticmethod
		def 			__UpperCAmelCase       (  _a      ):
				__a           =			parser.add_parser(
				    '''convert'''							, help='''CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'''							, )
				train_parser.add_argument('''--model_type'''							, type=_a							, required=_a							, help='''Model\'s type.'''      )
				train_parser.add_argument(
				    '''--tf_checkpoint'''							, type=_a							, required=_a							, help='''TensorFlow checkpoint path or folder.'''      )
				train_parser.add_argument(
				    '''--pytorch_dump_output'''							, type=_a							, required=_a							, help='''Path to the PyTorch saved model output.'''      )
				train_parser.add_argument('''--config'''							, type=_a							, default=''''''							, help='''Configuration file path or folder.'''      )
				train_parser.add_argument(
				    '''--finetuning_task_name'''							, type=_a							, default=_a							, help='''Optional fine-tuning task name if the TF model was a finetuned model.'''							, )
				train_parser.set_defaults(func=_a      )

		def __init__(  self							, _a							, _a							, _a							, _a							, _a							, *_a							, ):
				__a           =			logging.get_logger('''transformers-cli/converting'''      )

				self._logger.info(f'''Loading model {model_type}'''      )
				__a           =			model_type
				__a           =			tf_checkpoint
				__a           =			pytorch_dump_output
				__a           =			config
				__a           =			finetuning_task_name





		def 			__UpperCAmelCase       (  self      ):
				if self._model_type == "albert":
						try:
								from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
								    convert_tf_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "bert":
						try:
								from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
								    convert_tf_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "funnel":
						try:
								from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
								    convert_tf_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "t5":
						try:
								from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
						except ImportError:
								raise ImportError(_a      )

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "gpt":
						from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
						    convert_openai_checkpoint_to_pytorch,
						)

						convert_openai_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "transfo_xl":
						try:
								from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
								    convert_transfo_xl_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						if "ckpt" in self._tf_checkpoint.lower():
								__a           =			self._tf_checkpoint
								__a           =			''''''
						else:
								__a           =			self._tf_checkpoint
								__a           =			''''''
						convert_transfo_xl_checkpoint_to_pytorch(
						    _a							, self._config							, self._pytorch_dump_output							, _a      )
				elif self._model_type == "gpt2":
						try:
								from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
								    convert_gpta_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "xlnet":
						try:
								from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
								    convert_xlnet_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_xlnet_checkpoint_to_pytorch(
						    self._tf_checkpoint							, self._config							, self._pytorch_dump_output							, self._finetuning_task_name      )
				elif self._model_type == "xlm":
						from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
						    convert_xlm_checkpoint_to_pytorch,
						)

						convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint							, self._pytorch_dump_output      )
				elif self._model_type == "lxmert":
						from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
						    convert_lxmert_checkpoint_to_pytorch,
						)

						convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint							, self._pytorch_dump_output      )
				elif self._model_type == "rembert":
						from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
						    convert_rembert_tf_checkpoint_to_pytorch,
						)

						convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				else:
						raise ValueError(
						    '''--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]'''      )






