



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,
)


__snake_case            =   logging.get_logger(__name__)

__snake_case            =   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"),
    ]
)

__snake_case            =   _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str	):





			"""simple docstring"""




			for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
						if class_name in extractors:
									UpperCamelCase         =					model_type_to_module_name(_lowercase	)

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

			for _, extractor in FEATURE_EXTRACTOR_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.
			UpperCamelCase         =					importlib.import_module("""transformers"""	)
			if hasattr(_lowercase       ,  _lowercase	):
						return getattr(_lowercase       ,  _lowercase	)

			return None






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		List[Any] = None       ,  SCREAMING_SNAKE_CASE_   :		List[Any] = False       ,  SCREAMING_SNAKE_CASE_   :		str = False       ,  SCREAMING_SNAKE_CASE_   :		Any = None       ,  SCREAMING_SNAKE_CASE_   :		Tuple = None       ,  SCREAMING_SNAKE_CASE_   :		Any = None       ,  SCREAMING_SNAKE_CASE_   :		Tuple = False       ,  **SCREAMING_SNAKE_CASE_   :		List[Any]       ,  ):





			"""simple docstring"""




			UpperCamelCase         =					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 feature extractor 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					:




			def __init__(						self							:		Dict						):







						"""simple docstring"""







						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(UpperCamelCase_						)
			def   lowerCamelCase_					(						cls							:		Optional[Any]       ,       __magic_name__							:		Optional[Any]       ,       **__magic_name__							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					kwargs.pop("""config"""       ,       UpperCamelCase_						)
						UpperCamelCase         =					kwargs.pop("""trust_remote_code"""       ,       UpperCamelCase_						)
						UpperCamelCase         =					True

						UpperCamelCase         =					FeatureExtractionMixin.get_feature_extractor_dict(UpperCamelCase_       ,       **UpperCamelCase_						)
						UpperCamelCase         =					config_dict.get("""feature_extractor_type"""       ,       UpperCamelCase_						)
						UpperCamelCase         =					None
						if "AutoFeatureExtractor" in config_dict.get("""auto_map"""       ,       {}						):
									UpperCamelCase         =					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(UpperCamelCase_       ,       UpperCamelCase_						):
												UpperCamelCase         =					AutoConfig.from_pretrained(UpperCamelCase_       ,       **UpperCamelCase_						)
									# It could be in `config.feature_extractor_type``
									UpperCamelCase         =					getattr(UpperCamelCase_       ,       """feature_extractor_type"""       ,       UpperCamelCase_						)
									if hasattr(UpperCamelCase_       ,       """auto_map"""						) and "AutoFeatureExtractor" in config.auto_map:
												UpperCamelCase         =					config.auto_map['AutoFeatureExtractor']

						if feature_extractor_class is not None:
									UpperCamelCase         =					feature_extractor_class_from_name(UpperCamelCase_						)

						UpperCamelCase         =					feature_extractor_auto_map is not None
						UpperCamelCase         =					feature_extractor_class is not None or type(UpperCamelCase_						) in FEATURE_EXTRACTOR_MAPPING
						UpperCamelCase         =					resolve_trust_remote_code(
						    UpperCamelCase_       ,       UpperCamelCase_       ,       UpperCamelCase_       ,       UpperCamelCase_						)

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

						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   lowerCamelCase_					(						__magic_name__							:		Optional[Any]       ,       __magic_name__							:		Tuple						):







						"""simple docstring"""







						FEATURE_EXTRACTOR_MAPPING.register(UpperCamelCase_       ,       UpperCamelCase_						)








import qiskit






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int = 2	):





			"""simple docstring"""




			UpperCamelCase         =					qubits

			# Using Aer's simulator
			UpperCamelCase         =					qiskit.Aer.get_backend("""aer_simulator"""	)

			# Creating a Quantum Circuit acting on the q register
			UpperCamelCase         =					qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

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

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

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

			# 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
			UpperCamelCase         =					qiskit.execute(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  shots=1_000	)

			return job.result().get_counts(SCREAMING_SNAKE_CASE_	)


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




