




import numpy as np
import qiskit




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		Union[str, Any]:

   """simple docstring"""


   __UpperCamelCase											=  qiskit.QuantumCircuit(num_qubits + 1 )

   # First, let's deal with the case in which oracle is balanced
   if case == "balanced":
      # First generate a random number that tells us which CNOTs to
      # wrap in X-gates:
      __UpperCamelCase											=  np.random.randint(1   ,       2**num_qubits )
      # Next, format 'b' as a binary string of length 'n', padded with zeros:
      __UpperCamelCase											=  format(lowercase_   ,       F"0{num_qubits}b" )
      # Next, we place the first X-gates. Each digit in our binary string
      # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
      # we apply an X-gate to that qubit:
      for index, bit in enumerate(lowercase_ ):
         if bit == "1":
            oracle_qc.x(lowercase_ )
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
      for index in range(lowercase_ ):
         oracle_qc.cx(lowercase_   ,       lowercase_ )
      # Next, place the final X-gates
      for index, bit in enumerate(lowercase_ ):
         if bit == "1":
            oracle_qc.x(lowercase_ )

    # Case in which oracle is constant
   if case == "constant":
      # First decide what the fixed output of the oracle will be
      # (either always 0 or always 1)
      __UpperCamelCase											=  np.random.randint(2 )
      if output == 1:
         oracle_qc.x(lowercase_ )

   __UpperCamelCase											=  oracle_qc.to_gate()
   __UpperCamelCase											=  "Oracle"  # To show when we display the circuit
   return oracle_gate




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		Union[str, Any]:

   """simple docstring"""


   __UpperCamelCase											=  qiskit.QuantumCircuit(num_qubits + 1   ,       lowercase_ )
   # Set up the output qubit:
   dj_circuit.x(lowercase_ )
   dj_circuit.h(lowercase_ )
   # And set up the input register:
   for qubit in range(lowercase_ ):
      dj_circuit.h(lowercase_ )
   # Let's append the oracle gate to our circuit:
   dj_circuit.append(lowercase_   ,       range(num_qubits + 1 ) )
   # Finally, perform the H-gates again and measure:
   for qubit in range(lowercase_ ):
      dj_circuit.h(lowercase_ )

   for i in range(lowercase_ ):
      dj_circuit.measure(lowercase_   ,       lowercase_ )

   return dj_circuit




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		int:

   """simple docstring"""


   __UpperCamelCase											=  qiskit.Aer.get_backend('''aer_simulator''' )

   __UpperCamelCase											=  dj_oracle(lowercase_   ,       lowercase_ )
   __UpperCamelCase											=  dj_algorithm(lowercase_   ,       lowercase_ )

   # Execute the circuit on the simulator
   __UpperCamelCase											=  qiskit.execute(lowercase_   ,       lowercase_   ,       shots=10_00 )

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


if __name__ == "__main__":
 print(f"""Deutsch Jozsa - Constant Oracle: {deutsch_jozsa('constant', 3)}""")
 print(f"""Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa('balanced', 3)}""")












# 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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


a_										=       "Here is a text containing a lot of information: '''{text}'''.\n\nCan you answer this question about the text: '{question}'"

class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      lowerCAmelCase__     :	Union[str, Any]      =   "google/flan-t5-base"
      lowerCAmelCase__     :	Union[str, Any]      =   (
          "This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the "
          "text where to find the answer, and `question`, which is the question, and returns the answer to the question."
      )
      lowerCAmelCase__     :	List[Any]      =   "text_qa"
      lowerCAmelCase__     :	int      =   AutoTokenizer
      lowerCAmelCase__     :	Optional[Any]      =   AutoModelForSeqaSeqLM

      lowerCAmelCase__     :	Tuple      =   ["text", "text"]
      lowerCAmelCase__     :	Any      =   ["text"]







      def  snake_case		(       self   :	Union[str, Any]	,    snake_case   :	str	,    snake_case   :	str			):
          __UpperCamelCase											=  QA_PROMPT.format(text=snake_case	,    question=snake_case			)
          return self.pre_processor(snake_case	,    return_tensors='''pt'''			)







      def  snake_case		(       self   :	Any	,    snake_case   :	Optional[int]			):
          __UpperCamelCase											=  self.model.generate(**snake_case			)

          __UpperCamelCase						, __UpperCamelCase											=  inputs['''input_ids'''].shape
          __UpperCamelCase											=  output_ids.shape[0]

          return output_ids.reshape(snake_case	,    out_b // in_b	,    *output_ids.shape[1:]			)[0][0]







      def  snake_case		(       self   :	Tuple	,    snake_case   :	Optional[Any]			):
          return self.pre_processor.decode(snake_case	,    skip_special_tokens=snake_case	,    clean_up_tokenization_spaces=snake_case			)






