


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


_lowerCAmelCase:	int      							=					logging.get_logger(__name__)

_lowerCAmelCase:	List[str]      							=					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'),
    ]
)

_lowerCAmelCase:	Dict      							=					_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)






def      _lowercase(				__a							:	Union[str, Any]    ):
							for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
														if class_name in processors:
																					a__     						=model_type_to_module_name(_UpperCAmelCase    )

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

							for processor in PROCESSOR_MAPPING._extra_content.values():
														if getattr(_UpperCAmelCase						,    '__name__'						,    _UpperCAmelCase    ) == 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.
							a__     						=importlib.import_module('transformers'    )
							if hasattr(_UpperCAmelCase						,    _UpperCAmelCase    ):
														return getattr(_UpperCAmelCase						,    _UpperCAmelCase    )

							return None







class 			lowercase_				:





				def __init__(							self)     ->   int:
											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(SCREAMING_SNAKE_CASE_)
				def 	__UpperCamelCase						(							cls					,     lowercase_					,     **lowercase_)     ->   List[str]:
											a__     						=kwargs.pop('config'					,     SCREAMING_SNAKE_CASE_)
											a__     						=kwargs.pop('trust_remote_code'					,     SCREAMING_SNAKE_CASE_)
											a__     						=True

											a__     						=None
											a__     						=None

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

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

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

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

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

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

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

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

											# 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(
																		    SCREAMING_SNAKE_CASE_					,     trust_remote_code=SCREAMING_SNAKE_CASE_					,     **SCREAMING_SNAKE_CASE_)
											except Exception:
																		try:
																									return AutoImageProcessor.from_pretrained(
																									    SCREAMING_SNAKE_CASE_					,     trust_remote_code=SCREAMING_SNAKE_CASE_					,     **SCREAMING_SNAKE_CASE_)
																		except Exception:
																									pass

																		try:
																									return AutoFeatureExtractor.from_pretrained(
																									    SCREAMING_SNAKE_CASE_					,     trust_remote_code=SCREAMING_SNAKE_CASE_					,     **SCREAMING_SNAKE_CASE_)
																		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 	__UpperCamelCase						(							lowercase_					,     lowercase_)     ->   Tuple:
											PROCESSOR_MAPPING.register(SCREAMING_SNAKE_CASE_					,     SCREAMING_SNAKE_CASE_)








import numpy as np
from PIL import Image






def      _lowercase(				__a							:	np.array    ):
							a__						,    a__						,    a__     						=rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
							return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b






def      _lowercase(				__a							:	np.array    ):
							return (gray > 127) & (gray <= 255)






def      _lowercase(				__a							:	np.array						,    __a							:	np.array    ):
							a__     						=np.zeros_like(__a    )
							a__     						=np.zeros(
							    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)    )

							# Copy image to padded image
							a__     						=image

							# Iterate over image & apply kernel
							for x in range(image.shape[1]    ):
														for y in range(image.shape[0]    ):
																					a__     						=(
																					    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
																					).sum()
																					a__     						=int(summation == 5    )
							return output


# kernel to be applied
_lowerCAmelCase:	Dict      							=					np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
	# read original image
	_lowerCAmelCase:	Optional[int]      							=					np.array(Image.open(R'..\image_data\lena.jpg'))
	# Apply erosion operation to a binary image
	_lowerCAmelCase:	Any      							=					erosion(grayabinary(rgbagray(image)), structuring_element)
	# Save the output image
	_lowerCAmelCase:	Dict      							=					Image.fromarray(output).convert('RGB')
	pil_img.save('result_erosion.png')

