


'''simple docstring'''
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,
)


__magic_name__ : str					=logging.get_logger(__name__)

__magic_name__ : Tuple					=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'),
    ]
)

__magic_name__ : str					=_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)




def   __snake_case							(							lowerCamelCase_			:					Tuple    ):
				'''simple docstring'''




				for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
								if class_name in extractors:
												__magic_name__							=			model_type_to_module_name(lowercase__    )

												__magic_name__							=			importlib.import_module(F'.{module_name}'						,	"transformers.models"    )
												try:
																return getattr(lowercase__						,	lowercase__    )
												except AttributeError:
																continue

				for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
								if getattr(lowercase__						,	"__name__"						,	lowercase__    ) == 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.
				__magic_name__							=			importlib.import_module("transformers"    )
				if hasattr(lowercase__						,	lowercase__    ):
								return getattr(lowercase__						,	lowercase__    )

				return None




def   __snake_case							(							lowerCamelCase_			:					Any						,	lowerCamelCase_			:					List[Any] = None						,	lowerCamelCase_			:					Any = False						,	lowerCamelCase_			:					Union[str, Any] = False						,	lowerCamelCase_			:					Union[str, Any] = None						,	lowerCamelCase_			:					Optional[int] = None						,	lowerCamelCase_			:					Tuple = None						,	lowerCamelCase_			:					Union[str, Any] = False						,	**lowerCamelCase_			:					List[Any]						,	):
				'''simple docstring'''




				__magic_name__							=			get_file_from_repo(
				    lowercase__						,	lowercase__						,	cache_dir=lowercase__						,	force_download=lowercase__						,	resume_download=lowercase__						,	proxies=lowercase__						,	use_auth_token=lowercase__						,	revision=lowercase__						,	local_files_only=lowercase__						,	)
				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(lowercase__						,	encoding="utf-8"    ) as reader:
								return json.load(lowercase__    )






class 		UpperCamelCase_		:





						"""simple docstring"""




						def __init__(			self					:       Optional[int]	)				->       List[str]:
										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(_lowerCamelCase	)
						def        __A       (			cls					:       List[str]						,					_lowerCamelCase					:       str						,					**_lowerCamelCase					:       str	)				->       Optional[int]:
										__magic_name__							=			kwargs.pop("config"						,					_lowerCamelCase	)
										__magic_name__							=			kwargs.pop("trust_remote_code"						,					_lowerCamelCase	)
										__magic_name__							=			True

										__magic_name__							=			ImageProcessingMixin.get_image_processor_dict(_lowerCamelCase						,					**_lowerCamelCase	)
										__magic_name__							=			config_dict.get("image_processor_type"						,					_lowerCamelCase	)
										__magic_name__							=			None
										if "AutoImageProcessor" in config_dict.get("auto_map"						,					{}	):
														__magic_name__							=			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:
														__magic_name__							=			config_dict.pop("feature_extractor_type"						,					_lowerCamelCase	)
														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."	)
																		__magic_name__							=			feature_extractor_class.replace("FeatureExtractor"						,					"ImageProcessor"	)
														if "AutoFeatureExtractor" in config_dict.get("auto_map"						,					{}	):
																		__magic_name__							=			config_dict['auto_map']['AutoFeatureExtractor']
																		__magic_name__							=			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(_lowerCamelCase						,					_lowerCamelCase	):
																		__magic_name__							=			AutoConfig.from_pretrained(_lowerCamelCase						,					**_lowerCamelCase	)
														# It could be in `config.image_processor_type``
														__magic_name__							=			getattr(_lowerCamelCase						,					"image_processor_type"						,					_lowerCamelCase	)
														if hasattr(_lowerCamelCase						,					"auto_map"	) and "AutoImageProcessor" in config.auto_map:
																		__magic_name__							=			config.auto_map['AutoImageProcessor']

										if image_processor_class is not None:
														__magic_name__							=			image_processor_class_from_name(_lowerCamelCase	)

										__magic_name__							=			image_processor_auto_map is not None
										__magic_name__							=			image_processor_class is not None or type(_lowerCamelCase	) in IMAGE_PROCESSOR_MAPPING
										__magic_name__							=			resolve_trust_remote_code(
										    _lowerCamelCase						,					_lowerCamelCase						,					_lowerCamelCase						,					_lowerCamelCase	)

										if has_remote_code and trust_remote_code:
														__magic_name__							=			get_class_from_dynamic_module(
														    _lowerCamelCase						,					_lowerCamelCase						,					**_lowerCamelCase	)
														__magic_name__							=			kwargs.pop("code_revision"						,					_lowerCamelCase	)
														if os.path.isdir(_lowerCamelCase	):
																		image_processor_class.register_for_auto_class()
														return image_processor_class.from_dict(_lowerCamelCase						,					**_lowerCamelCase	)
										elif image_processor_class is not None:
														return image_processor_class.from_dict(_lowerCamelCase						,					**_lowerCamelCase	)
										# Last try: we use the IMAGE_PROCESSOR_MAPPING.
										elif type(_lowerCamelCase	) in IMAGE_PROCESSOR_MAPPING:
														__magic_name__							=			IMAGE_PROCESSOR_MAPPING[type(_lowerCamelCase	)]
														return image_processor_class.from_dict(_lowerCamelCase						,					**_lowerCamelCase	)

										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        __A       (			_lowerCamelCase					:       Optional[int]						,					_lowerCamelCase					:       Union[str, Any]	)				->       Tuple:
										IMAGE_PROCESSOR_MAPPING.register(_lowerCamelCase						,					_lowerCamelCase	)









"""simple docstring"""






import qiskit







def 	_snake_case     (							lowercase__ = 2						):
				_lowerCamelCase	:     Optional[Any]       =       qubits

				# Using Aer's simulator
				_lowerCamelCase	:     Dict       =       qiskit.Aer.get_backend('aer_simulator'						)

				# Creating a Quantum Circuit acting on the q register
				_lowerCamelCase	:     Dict       =       qiskit.QuantumCircuit(lowercase__				,       lowercase__						)

				# Adding a H gate on qubit 0 (now q0 in superposition)
				circuit.h(0						)

				for i in range(1				,       lowercase__						):
								# Adding CX (CNOT) gate
								circuit.cx(i - 1				,       lowercase__						)

				# Mapping the quantum measurement to the classical bits
				circuit.measure(list(range(lowercase__						)						)				,       list(range(lowercase__						)						)						)

				# Now measuring any one qubit would affect other qubits to collapse
				# their super position and have same state as the measured one.

				# Executing the circuit on the simulator
				_lowerCamelCase	:     Optional[Any]       =       qiskit.execute(lowercase__				,       lowercase__				,       shots=1000						)

				return job.result().get_counts(lowercase__						)


if __name__ == "__main__":
			print(F"Total count for various states are: {quantum_entanglement(3)}")