import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
    import numpy as np
    import torch

    from . import (
        AlbertForPreTraining,
        BartForConditionalGeneration,
        BertForPreTraining,
        BertForQuestionAnswering,
        BertForSequenceClassification,
        CamembertForMaskedLM,
        CTRLLMHeadModel,
        DistilBertForMaskedLM,
        DistilBertForQuestionAnswering,
        DPRContextEncoder,
        DPRQuestionEncoder,
        DPRReader,
        ElectraForPreTraining,
        FlaubertWithLMHeadModel,
        GPTaLMHeadModel,
        LayoutLMForMaskedLM,
        LxmertForPreTraining,
        LxmertVisualFeatureEncoder,
        OpenAIGPTLMHeadModel,
        RobertaForMaskedLM,
        RobertaForSequenceClassification,
        TaForConditionalGeneration,
        TransfoXLLMHeadModel,
        XLMRobertaForMaskedLM,
        XLMWithLMHeadModel,
        XLNetLMHeadModel,
    )


logging.set_verbosity_info()

__lowercase											=      {
    '''bart''': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''bert''': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-cased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-base-cased-finetuned-mrpc''': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''dpr''': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''gpt2''': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlnet''': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm''': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm-roberta''': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''transfo-xl''': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''openai-gpt''': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''roberta''': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''layoutlm''': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''roberta-large-mnli''': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''camembert''': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''flaubert''': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert''': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert-base-distilled-squad''': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert''': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert-visual-feature-encoder''': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''ctrl''': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''albert''': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''t5''': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''electra''': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''wav2vec2''': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=False						,    SCREAMING_SNAKE_CASE=True			):
    if model_type not in MODEL_CLASSES:
        raise ValueError(f"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()			)}."""			)

    __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :List[Any]    						=	MODEL_CLASSES[model_type]

    # Initialise TF model
    if config_file in aws_config_map:
        __UpperCamelCase  :str    						=	cached_file(lowerCamelCase__						,    lowerCamelCase__						,    force_download=not use_cached_models			)
    __UpperCamelCase  :Any    						=	config_class.from_json_file(lowerCamelCase__			)
    __UpperCamelCase  :Tuple    						=	True
    __UpperCamelCase  :List[str]    						=	True
    print(f"""Building TensorFlow model from configuration: {config}"""			)
    __UpperCamelCase  :Tuple    						=	model_class(lowerCamelCase__			)

    # Load weights from tf checkpoint
    if pytorch_checkpoint_path in aws_config_map.keys():
        __UpperCamelCase  :Any    						=	cached_file(
            lowerCamelCase__						,    lowerCamelCase__						,    force_download=not use_cached_models			)
    # Load PyTorch checkpoint in tf2 model:
    __UpperCamelCase  :Tuple    						=	load_pytorch_checkpoint_in_tfa_model(lowerCamelCase__						,    lowerCamelCase__			)

    if compare_with_pt_model:
        __UpperCamelCase  :List[Any]    						=	tf_model(tf_model.dummy_inputs						,    training=lowerCamelCase__			)  # build the network

        __UpperCamelCase  :Union[str, Any]    						=	torch.load(lowerCamelCase__						,    map_location='''cpu'''			)
        __UpperCamelCase  :str    						=	pt_model_class.from_pretrained(
            pretrained_model_name_or_path=lowerCamelCase__						,    config=lowerCamelCase__						,    state_dict=lowerCamelCase__			)

        with torch.no_grad():
            __UpperCamelCase  :Any    						=	pt_model(**pt_model.dummy_inputs			)

        __UpperCamelCase  :List[Any]    						=	pto[0].numpy()
        __UpperCamelCase  :Dict    						=	tfo[0].numpy()
        __UpperCamelCase  :Dict    						=	np.amax(np.abs(np_pt - np_tf			)			)
        print(f"""Max absolute difference between models outputs {diff}"""			)
        assert diff <= 2e-2, f"""Error, model absolute difference is >2e-2: {diff}"""

    # Save pytorch-model
    print(f"""Save TensorFlow model to {tf_dump_path}"""			)
    tf_model.save_weights(lowerCamelCase__						,    save_format='''h5'''			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=False						,    SCREAMING_SNAKE_CASE=False						,    SCREAMING_SNAKE_CASE=False						,    SCREAMING_SNAKE_CASE=False						,    ):
    if args_model_type is None:
        __UpperCamelCase  :Optional[Any]    						=	list(MODEL_CLASSES.keys()			)
    else:
        __UpperCamelCase  :Any    						=	[args_model_type]

    for j, model_type in enumerate(lowerCamelCase__						,    start=1			):
        print('''=''' * 100			)
        print(f""" Converting model type {j}/{len(lowerCamelCase__			)}: {model_type}"""			)
        print('''=''' * 100			)
        if model_type not in MODEL_CLASSES:
            raise ValueError(f"""Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()			)}."""			)

        __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :List[str]    						=	MODEL_CLASSES[model_type]

        if model_shortcut_names_or_path is None:
            __UpperCamelCase  :Dict    						=	list(aws_model_maps.keys()			)
        if config_shortcut_names_or_path is None:
            __UpperCamelCase  :int    						=	model_shortcut_names_or_path

        for i, (model_shortcut_name, config_shortcut_name) in enumerate(
            zip(lowerCamelCase__						,    lowerCamelCase__			)						,    start=1			):
            print('''-''' * 100			)
            if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                if not only_convert_finetuned_models:
                    print(f"""    Skipping finetuned checkpoint {model_shortcut_name}"""			)
                    continue
                __UpperCamelCase  :int    						=	model_shortcut_name
            elif only_convert_finetuned_models:
                print(f"""    Skipping not finetuned checkpoint {model_shortcut_name}"""			)
                continue
            print(
                f"""    Converting checkpoint {i}/{len(lowerCamelCase__			)}: {model_shortcut_name} - model_type {model_type}"""			)
            print('''-''' * 100			)

            if config_shortcut_name in aws_config_map:
                __UpperCamelCase  :Any    						=	cached_file(lowerCamelCase__						,    lowerCamelCase__						,    force_download=not use_cached_models			)
            else:
                __UpperCamelCase  :List[Any]    						=	config_shortcut_name

            if model_shortcut_name in aws_model_maps:
                __UpperCamelCase  :Any    						=	cached_file(lowerCamelCase__						,    lowerCamelCase__						,    force_download=not use_cached_models			)
            else:
                __UpperCamelCase  :str    						=	model_shortcut_name

            if os.path.isfile(lowerCamelCase__			):
                __UpperCamelCase  :List[str]    						=	'''converted_model'''

            convert_pt_checkpoint_to_tf(
                model_type=lowerCamelCase__						,    pytorch_checkpoint_path=lowerCamelCase__						,    config_file=lowerCamelCase__						,    tf_dump_path=os.path.join(lowerCamelCase__						,    model_shortcut_name + '''-tf_model.h5'''			)						,    compare_with_pt_model=lowerCamelCase__						,    )
            if remove_cached_files:
                os.remove(lowerCamelCase__			)
                os.remove(lowerCamelCase__			)


if __name__ == "__main__":
    __lowercase											=      argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_dump_path''', default=None, type=str, required=True, help='''Path to the output Tensorflow dump file.'''
    )
    parser.add_argument(
        '''--model_type''',
        default=None,
        type=str,
        help=(
            F'Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '
            '''convert all the models from AWS.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_checkpoint_path''',
        default=None,
        type=str,
        help=(
            '''Path to the PyTorch checkpoint path or shortcut name to download from AWS. '''
            '''If not given, will download and convert all the checkpoints from AWS.'''
        ),
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        help=(
            '''The config json file corresponding to the pre-trained model. \n'''
            '''This specifies the model architecture. If not given and '''
            '''--pytorch_checkpoint_path is not given or is a shortcut name '''
            '''use the configuration associated to the shortcut name on the AWS'''
        ),
    )
    parser.add_argument(
        '''--compare_with_pt_model''', action='''store_true''', help='''Compare Tensorflow and PyTorch model predictions.'''
    )
    parser.add_argument(
        '''--use_cached_models''',
        action='''store_true''',
        help='''Use cached models if possible instead of updating to latest checkpoint versions.''',
    )
    parser.add_argument(
        '''--remove_cached_files''',
        action='''store_true''',
        help='''Remove pytorch models after conversion (save memory when converting in batches).''',
    )
    parser.add_argument('''--only_convert_finetuned_models''', action='''store_true''', help='''Only convert finetuned models.''')
    __lowercase											=      parser.parse_args()

    # if args.pytorch_checkpoint_path is not None:
    #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
    #                                 args.pytorch_checkpoint_path,
    #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
    #                                 args.tf_dump_path,
    #                                 compare_with_pt_model=args.compare_with_pt_model,
    #                                 use_cached_models=args.use_cached_models)
    # else:
    convert_all_pt_checkpoints_to_tf(
        args.model_type.lower() if args.model_type is not None else None,
        args.tf_dump_path,
        model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
        if args.pytorch_checkpoint_path is not None
        else None,
        config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
        compare_with_pt_model=args.compare_with_pt_model,
        use_cached_models=args.use_cached_models,
        remove_cached_files=args.remove_cached_files,
        only_convert_finetuned_models=args.only_convert_finetuned_models,
    )






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

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



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	cn.convert_to_negative(SCREAMING_SNAKE_CASE			)
    # assert negative_img array for at least one True
    assert negative_img.any()



def lowerCamelCase						(     ):
    '''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(SCREAMING_SNAKE_CASE						,    110			)			).startswith(
            '''<PIL.Image.Image image mode=RGB size=100x100 at'''			)



def lowerCamelCase						(     ):
    '''simple docstring'''







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



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	imread('''digital_image_processing/image_data/lena_small.jpg'''						,    0			)
    # assert ambiguous array for all == True
    assert canny_img.all()
    __UpperCamelCase  :str    						=	canny.canny(SCREAMING_SNAKE_CASE			)
    # assert canny array for at least one True
    assert canny_array.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







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



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]			)
    __UpperCamelCase  :Optional[Any]    						=	conv.img_convolve(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			).astype(SCREAMING_SNAKE_CASE			)
    assert res.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







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



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase						, __UpperCamelCase  :Dict    						=	sob.sobel_filter(SCREAMING_SNAKE_CASE			)
    assert grad.any() and theta.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	sp.make_sepia(SCREAMING_SNAKE_CASE						,    20			)
    assert sepia.all()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE = "digital_image_processing/image_data/lena_small.jpg"			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	bs.Burkes(imread(SCREAMING_SNAKE_CASE						,    1			)						,    120			)
    burkes.process()
    assert burkes.output_img.any()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE = "digital_image_processing/image_data/lena_small.jpg"						,    ):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	rs.NearestNeighbour(imread(SCREAMING_SNAKE_CASE						,    1			)						,    400						,    200			)
    nn.process()
    assert nn.output.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Union[str, Any]    						=	'''digital_image_processing/image_data/lena.jpg'''

    # Reading the image and converting it to grayscale.
    __UpperCamelCase  :Optional[int]    						=	imread(SCREAMING_SNAKE_CASE						,    0			)

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

    __UpperCamelCase  :int    						=	lbp.get_neighbors_pixel(
        SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_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
    __UpperCamelCase  :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]			):
            __UpperCamelCase  :Optional[int]    						=	lbp.local_binary_value(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    assert lbp_image.any()





