

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase													={
    "configuration_clip": [
        "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPConfig",
        "CLIPOnnxConfig",
        "CLIPTextConfig",
        "CLIPVisionConfig",
    ],
    "processing_clip": ["CLIPProcessor"],
    "tokenization_clip": ["CLIPTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase													=["CLIPTokenizerFast"]

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase													=["CLIPFeatureExtractor"]
  UpperCAmelCase													=["CLIPImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase													=[
      "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
      "CLIPModel",
      "CLIPPreTrainedModel",
      "CLIPTextModel",
      "CLIPTextModelWithProjection",
      "CLIPVisionModel",
      "CLIPVisionModelWithProjection",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase													=[
      "TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFCLIPModel",
      "TFCLIPPreTrainedModel",
      "TFCLIPTextModel",
      "TFCLIPVisionModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase													=[
      "FlaxCLIPModel",
      "FlaxCLIPPreTrainedModel",
      "FlaxCLIPTextModel",
      "FlaxCLIPTextPreTrainedModel",
      "FlaxCLIPVisionModel",
      "FlaxCLIPVisionPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_clip import (
      CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
      CLIPConfig,
      CLIPOnnxConfig,
      CLIPTextConfig,
      CLIPVisionConfig,
  )
  from .processing_clip import CLIPProcessor
  from .tokenization_clip import CLIPTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_clip_fast import CLIPTokenizerFast

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_clip import CLIPFeatureExtractor
    from .image_processing_clip import CLIPImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_clip import (
        CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        CLIPModel,
        CLIPPreTrainedModel,
        CLIPTextModel,
        CLIPTextModelWithProjection,
        CLIPVisionModel,
        CLIPVisionModelWithProjection,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_clip import (
        TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFCLIPModel,
        TFCLIPPreTrainedModel,
        TFCLIPTextModel,
        TFCLIPVisionModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_clip import (
        FlaxCLIPModel,
        FlaxCLIPPreTrainedModel,
        FlaxCLIPTextModel,
        FlaxCLIPTextPreTrainedModel,
        FlaxCLIPVisionModel,
        FlaxCLIPVisionPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase													=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







"""simple docstring"""


import qiskit



def   _A					(	_a	:						int     ,   _a	:						int				):



      """simple docstring"""






      A				  =		qiskit.Aer.get_backend("""aer_simulator"""				)

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

      # 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
      A				  =		qiskit.execute(_a     ,   _a     ,   shots=1_0_0_0				)

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


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




