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

# 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 ...utils import CONFIG_NAME, 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,
)


__lowerCAmelCase												=	logging.get_logger(__name__)

__lowerCAmelCase												=	OrderedDict(
    [
        ('audio-spectrogram-transformer', 'ASTFeatureExtractor'),
        ('beit', 'BeitFeatureExtractor'),
        ('chinese_clip', 'ChineseCLIPFeatureExtractor'),
        ('clap', 'ClapFeatureExtractor'),
        ('clip', 'CLIPFeatureExtractor'),
        ('clipseg', 'ViTFeatureExtractor'),
        ('conditional_detr', 'ConditionalDetrFeatureExtractor'),
        ('convnext', 'ConvNextFeatureExtractor'),
        ('cvt', 'ConvNextFeatureExtractor'),
        ('data2vec-audio', 'Wav2Vec2FeatureExtractor'),
        ('data2vec-vision', 'BeitFeatureExtractor'),
        ('deformable_detr', 'DeformableDetrFeatureExtractor'),
        ('deit', 'DeiTFeatureExtractor'),
        ('detr', 'DetrFeatureExtractor'),
        ('dinat', 'ViTFeatureExtractor'),
        ('donut-swin', 'DonutFeatureExtractor'),
        ('dpt', 'DPTFeatureExtractor'),
        ('encodec', 'EncodecFeatureExtractor'),
        ('flava', 'FlavaFeatureExtractor'),
        ('glpn', 'GLPNFeatureExtractor'),
        ('groupvit', 'CLIPFeatureExtractor'),
        ('hubert', 'Wav2Vec2FeatureExtractor'),
        ('imagegpt', 'ImageGPTFeatureExtractor'),
        ('layoutlmv2', 'LayoutLMv2FeatureExtractor'),
        ('layoutlmv3', 'LayoutLMv3FeatureExtractor'),
        ('levit', 'LevitFeatureExtractor'),
        ('maskformer', 'MaskFormerFeatureExtractor'),
        ('mctct', 'MCTCTFeatureExtractor'),
        ('mobilenet_v1', 'MobileNetV1FeatureExtractor'),
        ('mobilenet_v2', 'MobileNetV2FeatureExtractor'),
        ('mobilevit', 'MobileViTFeatureExtractor'),
        ('nat', 'ViTFeatureExtractor'),
        ('owlvit', 'OwlViTFeatureExtractor'),
        ('perceiver', 'PerceiverFeatureExtractor'),
        ('poolformer', 'PoolFormerFeatureExtractor'),
        ('regnet', 'ConvNextFeatureExtractor'),
        ('resnet', 'ConvNextFeatureExtractor'),
        ('segformer', 'SegformerFeatureExtractor'),
        ('sew', 'Wav2Vec2FeatureExtractor'),
        ('sew-d', 'Wav2Vec2FeatureExtractor'),
        ('speech_to_text', 'Speech2TextFeatureExtractor'),
        ('speecht5', 'SpeechT5FeatureExtractor'),
        ('swiftformer', 'ViTFeatureExtractor'),
        ('swin', 'ViTFeatureExtractor'),
        ('swinv2', 'ViTFeatureExtractor'),
        ('table-transformer', 'DetrFeatureExtractor'),
        ('timesformer', 'VideoMAEFeatureExtractor'),
        ('tvlt', 'TvltFeatureExtractor'),
        ('unispeech', 'Wav2Vec2FeatureExtractor'),
        ('unispeech-sat', 'Wav2Vec2FeatureExtractor'),
        ('van', 'ConvNextFeatureExtractor'),
        ('videomae', 'VideoMAEFeatureExtractor'),
        ('vilt', 'ViltFeatureExtractor'),
        ('vit', 'ViTFeatureExtractor'),
        ('vit_mae', 'ViTFeatureExtractor'),
        ('vit_msn', 'ViTFeatureExtractor'),
        ('wav2vec2', 'Wav2Vec2FeatureExtractor'),
        ('wav2vec2-conformer', 'Wav2Vec2FeatureExtractor'),
        ('wavlm', 'Wav2Vec2FeatureExtractor'),
        ('whisper', 'WhisperFeatureExtractor'),
        ('xclip', 'CLIPFeatureExtractor'),
        ('yolos', 'YolosFeatureExtractor'),
    ]
)

__lowerCAmelCase												=	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
														if class_name in extractors:
																					_snake_case       =  model_type_to_module_name(_A				)

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

							for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
														if getattr(_A							, """__name__"""							, _A				) == 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.
							_snake_case       =  importlib.import_module("""transformers"""				)
							if hasattr(_A							, _A				):
														return getattr(_A							, _A				)

							return None






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE = None							, _SCREAMING_SNAKE_CASE = False							, _SCREAMING_SNAKE_CASE = False							, _SCREAMING_SNAKE_CASE = None							, _SCREAMING_SNAKE_CASE = None							, _SCREAMING_SNAKE_CASE = None							, _SCREAMING_SNAKE_CASE = False							, **_SCREAMING_SNAKE_CASE							, ):
							_snake_case       =  get_file_from_repo(
							    _A							, _A							, cache_dir=_A							, force_download=_A							, resume_download=_A							, proxies=_A							, use_auth_token=_A							, revision=_A							, local_files_only=_A							, )
							if resolved_config_file is None:
														logger.info(
														    """Could not locate the feature extractor configuration file, will try to use the model config instead."""				)
														return {}

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


class       _lowerCAmelCase   :







							'''simple docstring'''

							def __init__(self  )					->       Union[str, Any]:
														raise EnvironmentError(
														    """AutoFeatureExtractor is designed to be instantiated """
														    """using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."""  )

							@classmethod
							@replace_list_option_in_docstrings(__snake_case  )
							def 	lowercase							(cls  ,  UpperCAmelCase  ,  **UpperCAmelCase  )					->       List[Any]:
														_snake_case       =  kwargs.pop("""config"""  ,  __snake_case  )
														_snake_case       =  kwargs.pop("""trust_remote_code"""  ,  __snake_case  )
														_snake_case       =  True

														_snake_case       =  FeatureExtractionMixin.get_feature_extractor_dict(__snake_case  ,  **__snake_case  )
														_snake_case       =  config_dict.get("""feature_extractor_type"""  ,  __snake_case  )
														_snake_case       =  None
														if "AutoFeatureExtractor" in config_dict.get("""auto_map"""  ,  {}  ):
																					_snake_case       =  config_dict['auto_map']['AutoFeatureExtractor']

														# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
														if feature_extractor_class is None and feature_extractor_auto_map is None:
																					if not isinstance(__snake_case  ,  __snake_case  ):
																												_snake_case       =  AutoConfig.from_pretrained(__snake_case  ,  **__snake_case  )
																					# It could be in `config.feature_extractor_type``
																					_snake_case       =  getattr(__snake_case  ,  """feature_extractor_type"""  ,  __snake_case  )
																					if hasattr(__snake_case  ,  """auto_map"""  ) and "AutoFeatureExtractor" in config.auto_map:
																												_snake_case       =  config.auto_map['AutoFeatureExtractor']

														if feature_extractor_class is not None:
																					_snake_case       =  feature_extractor_class_from_name(__snake_case  )

														_snake_case       =  feature_extractor_auto_map is not None
														_snake_case       =  feature_extractor_class is not None or type(__snake_case  ) in FEATURE_EXTRACTOR_MAPPING
														_snake_case       =  resolve_trust_remote_code(
														    __snake_case  ,  __snake_case  ,  __snake_case  ,  __snake_case  )

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

														raise ValueError(
														    f"""Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a """
														    f"""`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following """
														    f"""`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()  )}"""  )





							@staticmethod
							def 	lowercase							(UpperCAmelCase  ,  UpperCAmelCase  )					->       Union[str, Any]:
														FEATURE_EXTRACTOR_MAPPING.register(__snake_case  ,  __snake_case  )

'''simple docstring'''
import qiskit






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  qiskit.Aer.get_backend("""aer_simulator"""				)

							# Create a Quantum Circuit acting on the q register
							_snake_case       =  qiskit.QuantumCircuit(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)

							# Apply X (NOT) Gate to Qubits 0 & 1
							circuit.x(0				)
							circuit.x(1				)

							# Map the quantum measurement to the classical bits
							circuit.measure([0, 1]							, [0, 1]				)

							# Execute the circuit on the qasm simulator
							_snake_case       =  qiskit.execute(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, shots=1000				)

							# Return the histogram data of the results of the experiment.
							return job.result().get_counts(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
				__lowerCAmelCase												=	single_qubit_measure(2, 2)
				print(f'''Total count for various states are: {counts}''')