






'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A     :      Tuple   					={
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     :      Optional[Any]   					=['''XLMRobertaTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     :      Optional[int]   					=['''XLMRobertaTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     :      Any   					=[
			    '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XLMRobertaForCausalLM''',
			    '''XLMRobertaForMaskedLM''',
			    '''XLMRobertaForMultipleChoice''',
			    '''XLMRobertaForQuestionAnswering''',
			    '''XLMRobertaForSequenceClassification''',
			    '''XLMRobertaForTokenClassification''',
			    '''XLMRobertaModel''',
			    '''XLMRobertaPreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     :      Any   					=[
			    '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXLMRobertaForCausalLM''',
			    '''TFXLMRobertaForMaskedLM''',
			    '''TFXLMRobertaForMultipleChoice''',
			    '''TFXLMRobertaForQuestionAnswering''',
			    '''TFXLMRobertaForSequenceClassification''',
			    '''TFXLMRobertaForTokenClassification''',
			    '''TFXLMRobertaModel''',
			    '''TFXLMRobertaPreTrainedModel''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     :      str   					=[
			    '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FlaxXLMRobertaForMaskedLM''',
			    '''FlaxXLMRobertaForCausalLM''',
			    '''FlaxXLMRobertaForMultipleChoice''',
			    '''FlaxXLMRobertaForQuestionAnswering''',
			    '''FlaxXLMRobertaForSequenceClassification''',
			    '''FlaxXLMRobertaForTokenClassification''',
			    '''FlaxXLMRobertaModel''',
			    '''FlaxXLMRobertaPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_xlm_roberta import (
			    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    XLMRobertaConfig,
			    XLMRobertaOnnxConfig,
			)

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_roberta import XLMRobertaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_roberta import (
						    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMRobertaForCausalLM,
						    XLMRobertaForMaskedLM,
						    XLMRobertaForMultipleChoice,
						    XLMRobertaForQuestionAnswering,
						    XLMRobertaForSequenceClassification,
						    XLMRobertaForTokenClassification,
						    XLMRobertaModel,
						    XLMRobertaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlm_roberta import (
						    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLMRobertaForCausalLM,
						    TFXLMRobertaForMaskedLM,
						    TFXLMRobertaForMultipleChoice,
						    TFXLMRobertaForQuestionAnswering,
						    TFXLMRobertaForSequenceClassification,
						    TFXLMRobertaForTokenClassification,
						    TFXLMRobertaModel,
						    TFXLMRobertaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_xlm_roberta import (
						    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaxXLMRobertaForCausalLM,
						    FlaxXLMRobertaForMaskedLM,
						    FlaxXLMRobertaForMultipleChoice,
						    FlaxXLMRobertaForQuestionAnswering,
						    FlaxXLMRobertaForSequenceClassification,
						    FlaxXLMRobertaForTokenClassification,
						    FlaxXLMRobertaModel,
						    FlaxXLMRobertaPreTrainedModel,
						)

else:
			import sys

			_A     :      str   					=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









'''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     :      int   					=imread(r'''digital_image_processing/image_data/lena_small.jpg''')
_A     :      Optional[Any]   					=cvtColor(img, COLOR_BGR2GRAY)





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							lowerCamelCase__ :     int	  =	cn.convert_to_negative(UpperCamelCase				)
							# assert negative_img array for at least one True
							assert negative_img.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Optional[int]:
							with Image.open("""digital_image_processing/image_data/lena_small.jpg"""				) as img:
														# Work around assertion for response
														assert str(cc.change_contrast(UpperCamelCase    ,      110				)				).startswith(
														    """<PIL.Image.Image image mode=RGB size=100x100 at"""				)





def 			SCREAMING_SNAKE_CASE_	()				->	Union[str, Any]:
							lowerCamelCase__ :     Optional[int]	  =	canny.gen_gaussian_kernel(9    ,      sigma=1.4				)
							# Assert ambiguous array
							assert resp.all()





def 			SCREAMING_SNAKE_CASE_	()				->	str:
							lowerCamelCase__ :     int	  =	imread("""digital_image_processing/image_data/lena_small.jpg"""    ,      0				)
							# assert ambiguous array for all == True
							assert canny_img.all()
							lowerCamelCase__ :     Union[str, Any]	  =	canny.canny(UpperCamelCase				)
							# assert canny array for at least one True
							assert canny_array.any()





def 			SCREAMING_SNAKE_CASE_	()				->	str:
							assert gg.gaussian_filter(UpperCamelCase    ,      5    ,      sigma=0.9				).all()





def 			SCREAMING_SNAKE_CASE_	()				->	int:
							# laplace diagonals
							lowerCamelCase__ :     int	  =	array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
							lowerCamelCase__ :     int	  =	conv.img_convolve(UpperCamelCase    ,      UpperCamelCase				).astype(UpperCamelCase				)
							assert res.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							assert med.median_filter(UpperCamelCase    ,      3				).any()





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							lowerCamelCase__  ,		lowerCamelCase__ :     Optional[Any]	  =	sob.sobel_filter(UpperCamelCase				)
							assert grad.any() and theta.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Tuple:
							lowerCamelCase__ :     Union[str, Any]	  =	sp.make_sepia(UpperCamelCase    ,      20				)
							assert sepia.all()





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"				)				->	Optional[Any]:
							lowerCamelCase__ :     Union[str, Any]	  =	bs.Burkes(imread(UpperCamelCase    ,      1				)    ,      120				)
							burkes.process()
							assert burkes.output_img.any()





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"    ,      )				->	Optional[Any]:
							lowerCamelCase__ :     int	  =	rs.NearestNeighbour(imread(UpperCamelCase    ,      1				)    ,      400    ,      200				)
							nn.process()
							assert nn.output.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Optional[int]:
							lowerCamelCase__ :     Union[str, Any]	  =	"""digital_image_processing/image_data/lena.jpg"""

							# Reading the image and converting it to grayscale.
							lowerCamelCase__ :     Tuple	  =	imread(UpperCamelCase    ,      0				)

							# Test for get_neighbors_pixel function() return not None
							lowerCamelCase__ :     int	  =	0
							lowerCamelCase__ :     Dict	  =	0
							lowerCamelCase__ :     Optional[Any]	  =	image[x_coordinate][y_coordinate]

							lowerCamelCase__ :     str	  =	lbp.get_neighbors_pixel(
							    UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							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
							lowerCamelCase__ :     List[str]	  =	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]				):
																					lowerCamelCase__ :     List[str]	  =	lbp.local_binary_value(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							assert lbp_image.any()

