





"""simple docstring"""


import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


A_     :      Union[str, Any]								=logging.get_logger(__name__)

A_     :      Dict								=OrderedDict(
    [
        ("""align""", """AlignProcessor"""),
        ("""altclip""", """AltCLIPProcessor"""),
        ("""bark""", """BarkProcessor"""),
        ("""blip""", """BlipProcessor"""),
        ("""blip-2""", """Blip2Processor"""),
        ("""bridgetower""", """BridgeTowerProcessor"""),
        ("""chinese_clip""", """ChineseCLIPProcessor"""),
        ("""clap""", """ClapProcessor"""),
        ("""clip""", """CLIPProcessor"""),
        ("""clipseg""", """CLIPSegProcessor"""),
        ("""flava""", """FlavaProcessor"""),
        ("""git""", """GitProcessor"""),
        ("""groupvit""", """CLIPProcessor"""),
        ("""hubert""", """Wav2Vec2Processor"""),
        ("""instructblip""", """InstructBlipProcessor"""),
        ("""layoutlmv2""", """LayoutLMv2Processor"""),
        ("""layoutlmv3""", """LayoutLMv3Processor"""),
        ("""markuplm""", """MarkupLMProcessor"""),
        ("""mctct""", """MCTCTProcessor"""),
        ("""mgp-str""", """MgpstrProcessor"""),
        ("""oneformer""", """OneFormerProcessor"""),
        ("""owlvit""", """OwlViTProcessor"""),
        ("""pix2struct""", """Pix2StructProcessor"""),
        ("""sam""", """SamProcessor"""),
        ("""sew""", """Wav2Vec2Processor"""),
        ("""sew-d""", """Wav2Vec2Processor"""),
        ("""speech_to_text""", """Speech2TextProcessor"""),
        ("""speech_to_text_2""", """Speech2Text2Processor"""),
        ("""speecht5""", """SpeechT5Processor"""),
        ("""trocr""", """TrOCRProcessor"""),
        ("""tvlt""", """TvltProcessor"""),
        ("""unispeech""", """Wav2Vec2Processor"""),
        ("""unispeech-sat""", """Wav2Vec2Processor"""),
        ("""vilt""", """ViltProcessor"""),
        ("""vision-text-dual-encoder""", """VisionTextDualEncoderProcessor"""),
        ("""wav2vec2""", """Wav2Vec2Processor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2Processor"""),
        ("""wavlm""", """Wav2Vec2Processor"""),
        ("""whisper""", """WhisperProcessor"""),
        ("""xclip""", """XCLIPProcessor"""),
    ]
)

A_     :      Optional[Any]								=_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)





def SCREAMING_SNAKE_CASE_   (       snake_case   :					str		)->		Tuple:
       for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
              if class_name in processors:
                     _lowerCamelCase    	=					model_type_to_module_name(snake_case		)

                     _lowerCamelCase    	=					importlib.import_module(f'.{module_name}'						,  'transformers.models'		)
                     try:
                            return getattr(snake_case						,  snake_case		)
                     except AttributeError:
                            continue

       for processor in PROCESSOR_MAPPING._extra_content.values():
              if getattr(snake_case						,  '__name__'						,  snake_case		) == class_name:
                     return processor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
       _lowerCamelCase    	=					importlib.import_module('transformers'		)
       if hasattr(snake_case						,  snake_case		):
              return getattr(snake_case						,  snake_case		)

       return None







class 	__a   :



     def __init__(       self   ):
            raise EnvironmentError(
                'AutoProcessor is designed to be instantiated '
                'using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method.'   )



     @classmethod
     @replace_list_option_in_docstrings(a__   )
     def 						snake_case_			(       cls					,     a__					,     **a__   ):
            _lowerCamelCase    	=					kwargs.pop('config'					,     a__   )
            _lowerCamelCase    	=					kwargs.pop('trust_remote_code'					,     a__   )
            _lowerCamelCase    	=					True

            _lowerCamelCase    	=					None
            _lowerCamelCase    	=					None

            # First, let's see if we have a preprocessor config.
            # Filter the kwargs for `get_file_from_repo`.
            _lowerCamelCase    	=					{
                key: kwargs[key] for key in inspect.signature(a__   ).parameters.keys() if key in kwargs
            }
            # Let's start by checking whether the processor class is saved in an image processor
            _lowerCamelCase    	=					get_file_from_repo(
                a__					,     a__					,     **a__   )
            if preprocessor_config_file is not None:
                   _lowerCamelCase	,	_lowerCamelCase    	=					ImageProcessingMixin.get_image_processor_dict(a__					,     **a__   )
                   _lowerCamelCase    	=					config_dict.get('processor_class'					,     a__   )
                   if "AutoProcessor" in config_dict.get('auto_map'					,     {}   ):
                          _lowerCamelCase    	=					config_dict['auto_map']['AutoProcessor']

        # If not found, let's check whether the processor class is saved in a feature extractor config
            if preprocessor_config_file is not None and processor_class is None:
                   _lowerCamelCase	,	_lowerCamelCase    	=					FeatureExtractionMixin.get_feature_extractor_dict(a__					,     **a__   )
                   _lowerCamelCase    	=					config_dict.get('processor_class'					,     a__   )
                   if "AutoProcessor" in config_dict.get('auto_map'					,     {}   ):
                          _lowerCamelCase    	=					config_dict['auto_map']['AutoProcessor']

            if processor_class is None:
                   # Next, let's check whether the processor class is saved in a tokenizer
                   _lowerCamelCase    	=					get_file_from_repo(
                       a__					,     a__					,     **a__   )
                   if tokenizer_config_file is not None:
                          with open(a__					,     encoding='utf-8'   ) as reader:
                                 _lowerCamelCase    	=					json.load(a__   )

                          _lowerCamelCase    	=					config_dict.get('processor_class'					,     a__   )
                          if "AutoProcessor" in config_dict.get('auto_map'					,     {}   ):
                                 _lowerCamelCase    	=					config_dict['auto_map']['AutoProcessor']

            if processor_class is None:
                   # Otherwise, load config, if it can be loaded.
                   if not isinstance(a__					,     a__   ):
                          _lowerCamelCase    	=					AutoConfig.from_pretrained(
                              a__					,     trust_remote_code=a__					,     **a__   )

                   # And check if the config contains the processor class.
                   _lowerCamelCase    	=					getattr(a__					,     'processor_class'					,     a__   )
                   if hasattr(a__					,     'auto_map'   ) and "AutoProcessor" in config.auto_map:
                          _lowerCamelCase    	=					config.auto_map['AutoProcessor']

            if processor_class is not None:
                   _lowerCamelCase    	=					processor_class_from_name(a__   )

            _lowerCamelCase    	=					processor_auto_map is not None
            _lowerCamelCase    	=					processor_class is not None or type(a__   ) in PROCESSOR_MAPPING
            _lowerCamelCase    	=					resolve_trust_remote_code(
                a__					,     a__					,     a__					,     a__   )

            if has_remote_code and trust_remote_code:
                   _lowerCamelCase    	=					get_class_from_dynamic_module(
                       a__					,     a__					,     **a__   )
                   _lowerCamelCase    	=					kwargs.pop('code_revision'					,     a__   )
                   if os.path.isdir(a__   ):
                          processor_class.register_for_auto_class()
                   return processor_class.from_pretrained(
                       a__					,     trust_remote_code=a__					,     **a__   )
            elif processor_class is not None:
                   return processor_class.from_pretrained(
                       a__					,     trust_remote_code=a__					,     **a__   )
            # Last try: we use the PROCESSOR_MAPPING.
            elif type(a__   ) in PROCESSOR_MAPPING:
                   return PROCESSOR_MAPPING[type(a__   )].from_pretrained(a__					,     **a__   )

            # At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
            # tokenizer.
            try:
                   return AutoTokenizer.from_pretrained(
                       a__					,     trust_remote_code=a__					,     **a__   )
            except Exception:
                   try:
                          return AutoImageProcessor.from_pretrained(
                              a__					,     trust_remote_code=a__					,     **a__   )
                   except Exception:
                          pass

                   try:
                          return AutoFeatureExtractor.from_pretrained(
                              a__					,     trust_remote_code=a__					,     **a__   )
                   except Exception:
                          pass

            raise ValueError(
                F'Unrecognized processing class in {pretrained_model_name_or_path}. Can\'t instantiate a processor, a '
                'tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'
                'the files of at least one of those processing classes.'   )



     @staticmethod
     def 						snake_case_			(       a__					,     a__   ):
            PROCESSOR_MAPPING.register(a__					,     a__   )













"""simple docstring"""


import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

A_     :      Any								=1_8_0





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Dict						,  snake_case   :					Any		)->		Tuple:
       _lowerCamelCase    	=					k_size // 2
       _lowerCamelCase	,	_lowerCamelCase    	=					np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       _lowerCamelCase    	=					(
           1
           / (2 * np.pi * sigma)
           * np.exp(-(np.square(snake_case		) + np.square(snake_case		)) / (2 * np.square(snake_case		))		)
       )
       return g





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Optional[int]						,  snake_case   :					int						,  snake_case   :					Dict		)->		Optional[Any]:
       _lowerCamelCase    	=					np.zeros(snake_case		)

       for row in range(1						,  image_shape[0] - 1		):
              for col in range(1						,  image_shape[1] - 1		):
                     _lowerCamelCase    	=					gradient_direction[row, col]

                     if (
                         0 <= direction < PI / 8
                         or 15 * PI / 8 <= direction <= 2 * PI
                         or 7 * PI / 8 <= direction <= 9 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row, col - 1]
                            _lowerCamelCase    	=					sobel_grad[row, col + 1]
                            if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

                     elif (
                         PI / 8 <= direction < 3 * PI / 8
                         or 9 * PI / 8 <= direction < 11 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row + 1, col - 1]
                            _lowerCamelCase    	=					sobel_grad[row - 1, col + 1]
                            if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

                     elif (
                         3 * PI / 8 <= direction < 5 * PI / 8
                         or 11 * PI / 8 <= direction < 13 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row - 1, col]
                            _lowerCamelCase    	=					sobel_grad[row + 1, col]
                            if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

                     elif (
                         5 * PI / 8 <= direction < 7 * PI / 8
                         or 13 * PI / 8 <= direction < 15 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row - 1, col - 1]
                            _lowerCamelCase    	=					sobel_grad[row + 1, col + 1]
                            if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

       return destination





def SCREAMING_SNAKE_CASE_   (       snake_case   :					List[Any]						,  snake_case   :					List[Any]						,  snake_case   :					int						,  snake_case   :					int						,  snake_case   :					List[str]						,  snake_case   :					str		)->		Tuple:
       for row in range(1						,  image_shape[0] - 1		):
              for col in range(1						,  image_shape[1] - 1		):
                     if destination[row, col] >= threshold_high:
                            _lowerCamelCase    	=					strong
                     elif destination[row, col] <= threshold_low:
                            _lowerCamelCase    	=					0
                     else:
                            _lowerCamelCase    	=					weak





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Union[str, Any]						,  snake_case   :					Optional[int]						,  snake_case   :					Dict						,  snake_case   :					Tuple		)->		Optional[int]:
       for row in range(1						,  image_shape[0]		):
              for col in range(1						,  image_shape[1]		):
                     if destination[row, col] == weak:
                            if 255 in (
                                destination[row, col + 1],
                                destination[row, col - 1],
                                destination[row - 1, col],
                                destination[row + 1, col],
                                destination[row - 1, col - 1],
                                destination[row + 1, col - 1],
                                destination[row - 1, col + 1],
                                destination[row + 1, col + 1],
                            ):
                                   _lowerCamelCase    	=					strong
                            else:
                                   _lowerCamelCase    	=					0





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Dict						,  snake_case   :					Union[str, Any]=15						,  snake_case   :					Optional[int]=30						,  snake_case   :					int=128						,  snake_case   :					str=255		)->		List[Any]:
       # gaussian_filter
       _lowerCamelCase    	=					img_convolve(snake_case						,  gen_gaussian_kernel(9						,  sigma=1.4		)		)
       # get the gradient and degree by sobel_filter
       _lowerCamelCase	,	_lowerCamelCase    	=					sobel_filter(snake_case		)
       _lowerCamelCase    	=					PI + np.radadeg(snake_case		)

       _lowerCamelCase    	=					suppress_non_maximum(image.shape						,  snake_case						,  snake_case		)

       detect_high_low_threshold(
           image.shape						,  snake_case						,  snake_case						,  snake_case						,  snake_case						,  snake_case		)

       track_edge(image.shape						,  snake_case						,  snake_case						,  snake_case		)

       return destination


if __name__ == "__main__":
      # read original image in gray mode
      A_     :      Optional[Any]								=cva.imread(R"""../image_data/lena.jpg""", 0)
      # canny edge detection
      A_     :      Optional[Any]								=canny(lena)
      cva.imshow("""canny""", canny_destination)
      cva.waitKey(0)






