






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
   from PIL import Image


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Optional[Any]			=	(
         '''This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'''
         '''It takes two arguments named `image` which should be the original image, and `label` which should be a text '''
         '''describing the elements what should be identified in the segmentation mask. The tool returns the mask.'''
     )
     UpperCamelCase_							:		int			=	'''CIDAS/clipseg-rd64-refined'''
     UpperCamelCase_							:		Optional[Any]			=	'''image_segmenter'''
     UpperCamelCase_							:		Tuple			=	CLIPSegForImageSegmentation

     UpperCamelCase_							:		Tuple			=	['''image''', '''text''']
     UpperCamelCase_							:		int			=	['''image''']


     def __init__(				self       :	Dict					, *UpperCAmelCase_       :	str					, **UpperCAmelCase_       :	Optional[int]      ):
       requires_backends(self					, ["vision"]      )
       super().__init__(*UpperCAmelCase_					, **UpperCAmelCase_      )


     def 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	"Image"					, UpperCAmelCase_       :	str      ):
       return self.pre_processor(text=[label]					, images=[image]					, padding=UpperCAmelCase_					, return_tensors="pt"      )


     def 				_A       (				self       :	List[str]					, UpperCAmelCase_       :	Optional[int]      ):
       with torch.no_grad():
         SCREAMING_SNAKE_CASE       :      Dict							=    self.model(**UpperCAmelCase_      ).logits
       return logits






     def 				_A       (				self       :	str					, UpperCAmelCase_       :	Any      ):
       SCREAMING_SNAKE_CASE       :      Any							=    outputs.cpu().detach().numpy()
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    0
       SCREAMING_SNAKE_CASE       :      List[Any]							=    1
       return Image.fromarray((array * 255).astype(np.uinta      )      )














import qiskit


def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      List[str]							=    qiskit.Aer.get_backend("aer_simulator"				)

  # Create a Quantum Circuit acting on the q register
  SCREAMING_SNAKE_CASE       :      Dict							=    qiskit.QuantumCircuit(lowercase						,     lowercase				)

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

  # Execute the circuit on the simulator
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    qiskit.execute(lowercase						,     lowercase						,     shots=1000				)

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


if __name__ == "__main__":
   print(F"""Total count for various states are: {single_qubit_measure(1, 1)}""")






