






import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_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,
)


__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						OrderedDict(
    [
        ('align', 'EfficientNetImageProcessor'),
        ('beit', 'BeitImageProcessor'),
        ('bit', 'BitImageProcessor'),
        ('blip', 'BlipImageProcessor'),
        ('blip-2', 'BlipImageProcessor'),
        ('bridgetower', 'BridgeTowerImageProcessor'),
        ('chinese_clip', 'ChineseCLIPImageProcessor'),
        ('clip', 'CLIPImageProcessor'),
        ('clipseg', 'ViTImageProcessor'),
        ('conditional_detr', 'ConditionalDetrImageProcessor'),
        ('convnext', 'ConvNextImageProcessor'),
        ('convnextv2', 'ConvNextImageProcessor'),
        ('cvt', 'ConvNextImageProcessor'),
        ('data2vec-vision', 'BeitImageProcessor'),
        ('deformable_detr', 'DeformableDetrImageProcessor'),
        ('deit', 'DeiTImageProcessor'),
        ('deta', 'DetaImageProcessor'),
        ('detr', 'DetrImageProcessor'),
        ('dinat', 'ViTImageProcessor'),
        ('donut-swin', 'DonutImageProcessor'),
        ('dpt', 'DPTImageProcessor'),
        ('efficientformer', 'EfficientFormerImageProcessor'),
        ('efficientnet', 'EfficientNetImageProcessor'),
        ('flava', 'FlavaImageProcessor'),
        ('focalnet', 'BitImageProcessor'),
        ('git', 'CLIPImageProcessor'),
        ('glpn', 'GLPNImageProcessor'),
        ('groupvit', 'CLIPImageProcessor'),
        ('imagegpt', 'ImageGPTImageProcessor'),
        ('instructblip', 'BlipImageProcessor'),
        ('layoutlmv2', 'LayoutLMv2ImageProcessor'),
        ('layoutlmv3', 'LayoutLMv3ImageProcessor'),
        ('levit', 'LevitImageProcessor'),
        ('mask2former', 'Mask2FormerImageProcessor'),
        ('maskformer', 'MaskFormerImageProcessor'),
        ('mgp-str', 'ViTImageProcessor'),
        ('mobilenet_v1', 'MobileNetV1ImageProcessor'),
        ('mobilenet_v2', 'MobileNetV2ImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevitv2', 'MobileViTImageProcessor'),
        ('nat', 'ViTImageProcessor'),
        ('oneformer', 'OneFormerImageProcessor'),
        ('owlvit', 'OwlViTImageProcessor'),
        ('perceiver', 'PerceiverImageProcessor'),
        ('pix2struct', 'Pix2StructImageProcessor'),
        ('poolformer', 'PoolFormerImageProcessor'),
        ('regnet', 'ConvNextImageProcessor'),
        ('resnet', 'ConvNextImageProcessor'),
        ('sam', 'SamImageProcessor'),
        ('segformer', 'SegformerImageProcessor'),
        ('swiftformer', 'ViTImageProcessor'),
        ('swin', 'ViTImageProcessor'),
        ('swin2sr', 'Swin2SRImageProcessor'),
        ('swinv2', 'ViTImageProcessor'),
        ('table-transformer', 'DetrImageProcessor'),
        ('timesformer', 'VideoMAEImageProcessor'),
        ('tvlt', 'TvltImageProcessor'),
        ('upernet', 'SegformerImageProcessor'),
        ('van', 'ConvNextImageProcessor'),
        ('videomae', 'VideoMAEImageProcessor'),
        ('vilt', 'ViltImageProcessor'),
        ('vit', 'ViTImageProcessor'),
        ('vit_hybrid', 'ViTHybridImageProcessor'),
        ('vit_mae', 'ViTImageProcessor'),
        ('vit_msn', 'ViTImageProcessor'),
        ('xclip', 'CLIPImageProcessor'),
        ('yolos', 'YolosImageProcessor'),
    ]
)

__UpperCAmelCase     							=						_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)




def 					lowercase__				(					__snake_case   :							str  ):


					'''simple docstring'''


					for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
										if class_name in extractors:
															UpperCAmelCase_   :				Optional[Any]						=						model_type_to_module_name(__snake_case  )

															UpperCAmelCase_   :				Optional[Any]						=						importlib.import_module(F".{module_name}"					,	'transformers.models'  )
															try:
																				return getattr(__snake_case					,	__snake_case  )
															except AttributeError:
																				continue

					for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
										if getattr(__snake_case					,	'__name__'					,	__snake_case  ) == class_name:
															return extractor

    # 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.
					UpperCAmelCase_   :				Optional[Any]						=						importlib.import_module('transformers'  )
					if hasattr(__snake_case					,	__snake_case  ):
										return getattr(__snake_case					,	__snake_case  )

					return None




def 					lowercase__				(					__snake_case   :							Union[str, os.PathLike]					,	__snake_case   :							Optional[Union[str, os.PathLike]] = None					,	__snake_case   :							bool = False					,	__snake_case   :							bool = False					,	__snake_case   :							Optional[Dict[str, str]] = None					,	__snake_case   :							Optional[Union[bool, str]] = None					,	__snake_case   :							Optional[str] = None					,	__snake_case   :							bool = False					,	**__snake_case   :							List[Any]					,	):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[Any]						=						get_file_from_repo(
					    __snake_case					,	__snake_case					,	cache_dir=__snake_case					,	force_download=__snake_case					,	resume_download=__snake_case					,	proxies=__snake_case					,	use_auth_token=__snake_case					,	revision=__snake_case					,	local_files_only=__snake_case					,	)
					if resolved_config_file is None:
										logger.info(
										    'Could not locate the image processor configuration file, will try to use the model config instead.'  )
										return {}

					with open(__snake_case					,	encoding='utf-8'  ) as reader:
										return json.load(__snake_case  )






class   lowerCamelCase				:







		'''simple docstring'''



		def __init__(							self       )     ->							Optional[Any]:
							raise EnvironmentError(
							    'AutoImageProcessor is designed to be instantiated '
							    'using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'       )



		@classmethod
		@replace_list_option_in_docstrings(_UpperCamelCase       )
		def  __UpperCAmelCase      (							cls    ,		_UpperCamelCase    ,		**_UpperCamelCase       )     ->							str:
							UpperCAmelCase_   :				str						=						kwargs.pop('config'    ,		_UpperCamelCase       )
							UpperCAmelCase_   :				List[str]						=						kwargs.pop('trust_remote_code'    ,		_UpperCamelCase       )
							UpperCAmelCase_   :				List[Any]						=						True

							UpperCAmelCase_   :				Any						=						ImageProcessingMixin.get_image_processor_dict(_UpperCamelCase    ,		**_UpperCamelCase       )
							UpperCAmelCase_   :				Dict						=						config_dict.get('image_processor_type'    ,		_UpperCamelCase       )
							UpperCAmelCase_   :				Union[str, Any]						=						None
							if "AutoImageProcessor" in config_dict.get('auto_map'    ,		{}       ):
												UpperCAmelCase_   :				List[str]						=						config_dict['auto_map']['AutoImageProcessor']

							# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
							# and if so, infer the image processor class from there.
							if image_processor_class is None and image_processor_auto_map is None:
												UpperCAmelCase_   :				Union[str, Any]						=						config_dict.pop('feature_extractor_type'    ,		_UpperCamelCase       )
												if feature_extractor_class is not None:
																	logger.warning(
																	    'Could not find image processor class in the image processor config or the model config. Loading'
																	    ' based on pattern matching with the model\'s feature extractor configuration.'       )
																	UpperCAmelCase_   :				Dict						=						feature_extractor_class.replace('FeatureExtractor'    ,		'ImageProcessor'       )
												if "AutoFeatureExtractor" in config_dict.get('auto_map'    ,		{}       ):
																	UpperCAmelCase_   :				str						=						config_dict['auto_map']['AutoFeatureExtractor']
																	UpperCAmelCase_   :				Optional[Any]						=						feature_extractor_auto_map.replace('FeatureExtractor'    ,		'ImageProcessor'       )
																	logger.warning(
																	    'Could not find image processor auto map in the image processor config or the model config.'
																	    ' Loading based on pattern matching with the model\'s feature extractor configuration.'       )

        # If we don't find the image processor class in the image processor config, let's try the model config.
							if image_processor_class is None and image_processor_auto_map is None:
												if not isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
																	UpperCAmelCase_   :				List[Any]						=						AutoConfig.from_pretrained(_UpperCamelCase    ,		**_UpperCamelCase       )
												# It could be in `config.image_processor_type``
												UpperCAmelCase_   :				Dict						=						getattr(_UpperCamelCase    ,		'image_processor_type'    ,		_UpperCamelCase       )
												if hasattr(_UpperCamelCase    ,		'auto_map'       ) and "AutoImageProcessor" in config.auto_map:
																	UpperCAmelCase_   :				List[str]						=						config.auto_map['AutoImageProcessor']

							if image_processor_class is not None:
												UpperCAmelCase_   :				Union[str, Any]						=						image_processor_class_from_name(_UpperCamelCase       )

							UpperCAmelCase_   :				str						=						image_processor_auto_map is not None
							UpperCAmelCase_   :				Tuple						=						image_processor_class is not None or type(_UpperCamelCase       ) in IMAGE_PROCESSOR_MAPPING
							UpperCAmelCase_   :				Dict						=						resolve_trust_remote_code(
							    _UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )

							if has_remote_code and trust_remote_code:
												UpperCAmelCase_   :				Tuple						=						get_class_from_dynamic_module(
												    _UpperCamelCase    ,		_UpperCamelCase    ,		**_UpperCamelCase       )
												UpperCAmelCase_   :				Optional[Any]						=						kwargs.pop('code_revision'    ,		_UpperCamelCase       )
												if os.path.isdir(_UpperCamelCase       ):
																	image_processor_class.register_for_auto_class()
												return image_processor_class.from_dict(_UpperCamelCase    ,		**_UpperCamelCase       )
							elif image_processor_class is not None:
												return image_processor_class.from_dict(_UpperCamelCase    ,		**_UpperCamelCase       )
							# Last try: we use the IMAGE_PROCESSOR_MAPPING.
							elif type(_UpperCamelCase       ) in IMAGE_PROCESSOR_MAPPING:
												UpperCAmelCase_   :				List[str]						=						IMAGE_PROCESSOR_MAPPING[type(_UpperCamelCase       )]
												return image_processor_class.from_dict(_UpperCamelCase    ,		**_UpperCamelCase       )

							raise ValueError(
							    f"Unrecognized image processor in {pretrained_model_name_or_path}. Should have a "
							    f"`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following "
							    f"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()       )}"       )



		@staticmethod
		def  __UpperCAmelCase      (							_UpperCamelCase    ,		_UpperCamelCase       )     ->							Union[str, Any]:
							IMAGE_PROCESSOR_MAPPING.register(_UpperCamelCase    ,		_UpperCamelCase       )














import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

__UpperCAmelCase     							=						'Usage of script: script_name <size_of_canvas:int>'

__UpperCAmelCase     							=						[0] * 100 + [1] * 10
random.shuffle(choice)




def 					lowercase__				(					__snake_case   :							int  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Any						=						[[False for i in range(__snake_case  )] for j in range(__snake_case  )]
					return canvas




def 					lowercase__				(					__snake_case   :							list[list[bool]]  ):


					'''simple docstring'''


					for i, row in enumerate(__snake_case  ):
										for j, _ in enumerate(__snake_case  ):
															UpperCAmelCase_   :				Tuple						=						bool(random.getrandbits(1  )  )




def 					lowercase__				(					__snake_case   :							list[list[bool]]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				List[Any]						=						np.array(__snake_case  )
					UpperCAmelCase_   :				Any						=						np.array(create_canvas(current_canvas.shape[0]  )  )
					for r, row in enumerate(__snake_case  ):
										for c, pt in enumerate(__snake_case  ):
															UpperCAmelCase_   :				Optional[int]						=						__judge_point(
															    __snake_case					,	current_canvas[r - 1 : r + 2, c - 1 : c + 2]  )

					UpperCAmelCase_   :				List[Any]						=						next_gen_canvas
					del next_gen_canvas  # cleaning memory as we move on.
					UpperCAmelCase_   :				list[list[bool]]						=						current_canvas.tolist()
					return return_canvas




def 					lowercase__				(					__snake_case   :							bool					,	__snake_case   :							list[list[bool]]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[int]						=						0
					UpperCAmelCase_   :				List[Any]						=						0
					# finding dead or alive neighbours count.
					for i in neighbours:
										for status in i:
															if status:
																				alive += 1
															else:
																				dead += 1

    # handling duplicate entry for focus pt.
					if pt:
										alive -= 1
					else:
										dead -= 1

					# running the rules of game here.
					UpperCAmelCase_   :				List[Any]						=						pt
					if pt:
										if alive < 2:
															UpperCAmelCase_   :				str						=						False
										elif alive == 2 or alive == 3:
															UpperCAmelCase_   :				int						=						True
										elif alive > 3:
															UpperCAmelCase_   :				List[Any]						=						False
					else:
										if alive == 3:
															UpperCAmelCase_   :				int						=						True

					return state


if __name__ == "__main__":
		if len(sys.argv) != 2:
				raise Exception(usage_doc)

		__UpperCAmelCase     							=						int(sys.argv[1])
		# main working structure of this module.
		__UpperCAmelCase     							=						create_canvas(canvas_size)
		seed(c)
		__UpperCAmelCase							,       __UpperCAmelCase     							=						plt.subplots()
		fig.show()
		__UpperCAmelCase     							=						ListedColormap(['w', 'k'])
		try:
				while True:
						__UpperCAmelCase     							=						run(c)
						ax.matshow(c, cmap=cmap)
						fig.canvas.draw()
						ax.cla()
		except KeyboardInterrupt:
				# do nothing.
				pass






