"""simple docstring"""




# Copyright 2021 The HuggingFace 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 json
import os

from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
    DYNAMO_BACKENDS,
    _ask_field,
    _ask_options,
    _convert_dynamo_backend,
    _convert_mixed_precision,
    _convert_sagemaker_distributed_mode,
    _convert_yes_no_to_bool,
)


if is_botoa_available():
     import botoa  # noqa: F401





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Tuple	)       ->      Optional[Any]:
  _lowerCAmelCase						:       List[Any]  			=     botoa.client("""iam"""	)

  _lowerCAmelCase						:       Dict  			=     {
      """Version""": """2012-10-17""",
      """Statement""": [
          {"""Effect""": """Allow""", """Principal""": {"""Service""": """sagemaker.amazonaws.com"""}, """Action""": """sts:AssumeRole"""}
      ],
  }
  try:
    # create the role, associated with the chosen trust policy
    iam_client.create_role(
        RoleName=_lowerCamelCase			,AssumeRolePolicyDocument=json.dumps(_lowerCamelCase			,indent=2	)	)
    _lowerCAmelCase						:       Union[str, Any]  			=     {
        """Version""": """2012-10-17""",
        """Statement""": [
            {
                """Effect""": """Allow""",
                """Action""": [
                    """sagemaker:*""",
                    """ecr:GetDownloadUrlForLayer""",
                    """ecr:BatchGetImage""",
                    """ecr:BatchCheckLayerAvailability""",
                    """ecr:GetAuthorizationToken""",
                    """cloudwatch:PutMetricData""",
                    """cloudwatch:GetMetricData""",
                    """cloudwatch:GetMetricStatistics""",
                    """cloudwatch:ListMetrics""",
                    """logs:CreateLogGroup""",
                    """logs:CreateLogStream""",
                    """logs:DescribeLogStreams""",
                    """logs:PutLogEvents""",
                    """logs:GetLogEvents""",
                    """s3:CreateBucket""",
                    """s3:ListBucket""",
                    """s3:GetBucketLocation""",
                    """s3:GetObject""",
                    """s3:PutObject""",
                ],
                """Resource""": """*""",
            }
        ],
    }
    # attach policy to role
    iam_client.put_role_policy(
        RoleName=_lowerCamelCase			,PolicyName=f"{role_name}_policy_permission"			,PolicyDocument=json.dumps(_lowerCamelCase			,indent=2	)			,)
  except iam_client.exceptions.EntityAlreadyExistsException:
    print(f"role {role_name} already exists. Using existing one"	)





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int	)       ->      Optional[int]:
  _lowerCAmelCase						:       List[str]  			=     botoa.client("""iam"""	)
  return iam_client.get_role(RoleName=_lowerCamelCase	)["Role"]["Arn"]






def 							SCREAMING_SNAKE_CASE     (			)       ->      int:
  _lowerCAmelCase						:       List[Any]  			=     _ask_options(
      """How do you want to authorize?"""			,["""AWS Profile""", """Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) """]			,_lowerCamelCase			,)
  _lowerCAmelCase						:       Optional[Any]  			=     None
  if credentials_configuration == 0:
    _lowerCAmelCase						:       Union[str, Any]  			=     _ask_field("""Enter your AWS Profile name: [default] """			,default="""default"""	)
    _lowerCAmelCase						:       List[Any]  			=     aws_profile
  else:
    print(
        """Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,"""
        """`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`"""	)
    _lowerCAmelCase						:       str  			=     _ask_field("""AWS Access Key ID: """	)
    _lowerCAmelCase						:       List[str]  			=     aws_access_key_id

    _lowerCAmelCase						:       List[Any]  			=     _ask_field("""AWS Secret Access Key: """	)
    _lowerCAmelCase						:       int  			=     aws_secret_access_key

  _lowerCAmelCase						:       Any  			=     _ask_field("""Enter your AWS Region: [us-east-1]"""			,default="""us-east-1"""	)
  _lowerCAmelCase						:       int  			=     aws_region

  _lowerCAmelCase						:       Union[str, Any]  			=     _ask_options(
      """Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?"""			,["""Provide IAM Role name""", """Create new IAM role using credentials"""]			,_lowerCamelCase			,)
  if role_management == 0:
    _lowerCAmelCase						:       Any  			=     _ask_field("""Enter your IAM role name: """	)
  else:
    _lowerCAmelCase						:       Optional[int]  			=     """accelerate_sagemaker_execution_role"""
    print(f"Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials"	)
    _create_iam_role_for_sagemaker(_lowerCamelCase	)

  _lowerCAmelCase						:       int  			=     _ask_field(
      """Do you want to use custom Docker image? [yes/NO]: """			,_convert_yes_no_to_bool			,default=_lowerCamelCase			,error_message="""Please enter yes or no."""			,)
  _lowerCAmelCase						:       Tuple  			=     None
  if is_custom_docker_image:
    _lowerCAmelCase						:       Optional[int]  			=     _ask_field("""Enter your Docker image: """			,lambda _lowerCamelCase	: str(_lowerCamelCase	).lower()	)

  _lowerCAmelCase						:       Dict  			=     _ask_field(
      """Do you want to provide SageMaker input channels with data locations? [yes/NO]: """			,_convert_yes_no_to_bool			,default=_lowerCamelCase			,error_message="""Please enter yes or no."""			,)
  _lowerCAmelCase						:       Tuple  			=     None
  if is_sagemaker_inputs_enabled:
    _lowerCAmelCase						:       str  			=     _ask_field(
        """Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): """			,lambda _lowerCamelCase	: str(_lowerCamelCase	).lower()			,)

  _lowerCAmelCase						:       Dict  			=     _ask_field(
      """Do you want to enable SageMaker metrics? [yes/NO]: """			,_convert_yes_no_to_bool			,default=_lowerCamelCase			,error_message="""Please enter yes or no."""			,)
  _lowerCAmelCase						:       Union[str, Any]  			=     None
  if is_sagemaker_metrics_enabled:
    _lowerCAmelCase						:       Dict  			=     _ask_field(
        """Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): """			,lambda _lowerCamelCase	: str(_lowerCamelCase	).lower()			,)

  _lowerCAmelCase						:       List[Any]  			=     _ask_options(
      """What is the distributed mode?"""			,["""No distributed training""", """Data parallelism"""]			,_convert_sagemaker_distributed_mode			,)
  _lowerCAmelCase						:       List[Any]  			=     {}
  _lowerCAmelCase						:       Union[str, Any]  			=     _ask_field(
      """Do you wish to optimize your script with torch dynamo?[yes/NO]:"""			,_convert_yes_no_to_bool			,default=_lowerCamelCase			,error_message="""Please enter yes or no."""			,)
  if use_dynamo:
    _lowerCAmelCase						:       Tuple  			=     """dynamo_"""
    _lowerCAmelCase						:       List[Any]  			=     _ask_options(
        """Which dynamo backend would you like to use?"""			,[x.lower() for x in DYNAMO_BACKENDS]			,_convert_dynamo_backend			,default=2			,)
    _lowerCAmelCase						:       int  			=     _ask_field(
        """Do you want to customize the defaults sent to torch.compile? [yes/NO]: """			,_convert_yes_no_to_bool			,default=_lowerCamelCase			,error_message="""Please enter yes or no."""			,)

    if use_custom_options:
      _lowerCAmelCase						:       Dict  			=     _ask_options(
          """Which mode do you want to use?"""			,_lowerCamelCase			,lambda _lowerCamelCase	: TORCH_DYNAMO_MODES[int(_lowerCamelCase	)]			,default="""default"""			,)
      _lowerCAmelCase						:       List[Any]  			=     _ask_field(
          """Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: """			,_convert_yes_no_to_bool			,default=_lowerCamelCase			,error_message="""Please enter yes or no."""			,)
      _lowerCAmelCase						:       Dict  			=     _ask_field(
          """Do you want to enable dynamic shape tracing? [yes/NO]: """			,_convert_yes_no_to_bool			,default=_lowerCamelCase			,error_message="""Please enter yes or no."""			,)
  _lowerCAmelCase						:       Any  			=     """Which EC2 instance type you want to use for your training?"""
  if distributed_type != SageMakerDistributedType.NO:
    _lowerCAmelCase						:       List[Any]  			=     _ask_options(
        _lowerCamelCase			,_lowerCamelCase			,lambda _lowerCamelCase	: SAGEMAKER_PARALLEL_EC2_INSTANCES[int(_lowerCamelCase	)]	)
  else:
    eca_instance_query += "? [ml.p3.2xlarge]:"
    _lowerCAmelCase						:       int  			=     _ask_field(_lowerCamelCase			,lambda _lowerCamelCase	: str(_lowerCamelCase	).lower()			,default="""ml.p3.2xlarge"""	)

  _lowerCAmelCase						:       int  			=     1
  if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
    _lowerCAmelCase						:       List[Any]  			=     _ask_field(
        """How many machines do you want use? [1]: """			,_lowerCamelCase			,default=1			,)

  _lowerCAmelCase						:       Optional[Any]  			=     _ask_options(
      """Do you wish to use FP16 or BF16 (mixed precision)?"""			,["""no""", """fp16""", """bf16""", """fp8"""]			,_convert_mixed_precision			,)

  if use_dynamo and mixed_precision == "no":
    print(
        """Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts."""	)

  return SageMakerConfig(
      image_uri=_lowerCamelCase			,compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER			,distributed_type=_lowerCamelCase			,use_cpu=_lowerCamelCase			,dynamo_config=_lowerCamelCase			,eca_instance_type=_lowerCamelCase			,profile=_lowerCamelCase			,region=_lowerCamelCase			,iam_role_name=_lowerCamelCase			,mixed_precision=_lowerCamelCase			,num_machines=_lowerCamelCase			,sagemaker_inputs_file=_lowerCamelCase			,sagemaker_metrics_file=_lowerCamelCase			,)





"""simple docstring"""




import numpy as np
import qiskit





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int = 8			,_lowerCamelCase						:       int | None = None	)       ->      str:
  _lowerCAmelCase						:       int  			=     np.random.default_rng(seed=_lowerCamelCase	)

  # Roughly 25% of the qubits will contribute to the key.
  # So we take more than we need.
  _lowerCAmelCase						:       Tuple  			=     6 * key_len
  # Measurement basis for Alice's qubits.
  _lowerCAmelCase						:       Dict  			=     rng.integers(2			,size=_lowerCamelCase	)
  # The set of states Alice will prepare.
  _lowerCAmelCase						:       Tuple  			=     rng.integers(2			,size=_lowerCamelCase	)
  # Measurement basis for Bob's qubits.
  _lowerCAmelCase						:       Union[str, Any]  			=     rng.integers(2			,size=_lowerCamelCase	)

  # Quantum Circuit to simulate BB84
  _lowerCAmelCase						:       Dict  			=     qiskit.QuantumCircuit(_lowerCamelCase			,name="""BB84"""	)

  # Alice prepares her qubits according to rules above.
  for index, _ in enumerate(_lowerCamelCase	):
    if alice_state[index] == 1:
      bbaa_circ.x(_lowerCamelCase	)
    if alice_basis[index] == 1:
      bbaa_circ.h(_lowerCamelCase	)
  bbaa_circ.barrier()

  # Bob measures the received qubits according to rules above.
  for index, _ in enumerate(_lowerCamelCase	):
    if bob_basis[index] == 1:
      bbaa_circ.h(_lowerCamelCase	)

  bbaa_circ.barrier()
  bbaa_circ.measure_all()

  # Simulate the quantum circuit.
  _lowerCAmelCase						:       int  			=     qiskit.Aer.get_backend("""aer_simulator"""	)
  # We only need to run one shot because the key is unique.
  # Multiple shots will produce the same key.
  _lowerCAmelCase						:       List[str]  			=     qiskit.execute(_lowerCamelCase			,_lowerCamelCase			,shots=1			,seed_simulator=_lowerCamelCase	)
  # Returns the result of measurement.
  _lowerCAmelCase						:       List[Any]  			=     job.result().get_counts(_lowerCamelCase	).most_frequent()

  # Extracting the generated key from the simulation results.
  # Only keep measurement results where Alice and Bob chose the same basis.
  _lowerCAmelCase						:       str  			=     """""".join(
      [
          result_bit
          for alice_basis_bit, bob_basis_bit, result_bit in zip(
              _lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)
          if alice_basis_bit == bob_basis_bit
      ]	)

  # Get final key. Pad with 0 if too short, otherwise truncate.
  _lowerCAmelCase						:       List[Any]  			=     gen_key[:key_len] if len(_lowerCamelCase	) >= key_len else gen_key.ljust(_lowerCamelCase			,"""0"""	)
  return key


if __name__ == "__main__":
     print(F"""The generated key is : {bbaa(8, seed=0)}""")
     from doctest import testmod

     testmod()




