






"""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      lowerCamelCase__       (							_lowerCamelCase       :		int )      ->				Any:
  lowerCamelCase_         =						botoa.client('iam' )

  lowerCamelCase_         =						{
      '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=__UpperCamelCase		,				AssumeRolePolicyDocument=json.dumps(__UpperCamelCase		,				indent=2 ) )
    lowerCamelCase_         =						{
        '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=__UpperCamelCase		,				PolicyName=F'''{role_name}_policy_permission'''		,				PolicyDocument=json.dumps(__UpperCamelCase		,				indent=2 )		,				)
  except iam_client.exceptions.EntityAlreadyExistsException:
    print(F'''role {role_name} already exists. Using existing one''' )







def      lowerCamelCase__       (							_lowerCamelCase       :		Dict )      ->				List[str]:
  lowerCamelCase_         =						botoa.client('iam' )
  return iam_client.get_role(RoleName=__UpperCamelCase )["Role"]["Arn"]







def      lowerCamelCase__       (							)      ->				Any:
  lowerCamelCase_         =						_ask_options(
      'How do you want to authorize?'		,				['AWS Profile', 'Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) ']		,				__UpperCamelCase		,				)
  lowerCamelCase_         =						None
  if credentials_configuration == 0:
    lowerCamelCase_         =						_ask_field('Enter your AWS Profile name: [default] '		,				default='default' )
    lowerCamelCase_         =						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_         =						_ask_field('AWS Access Key ID: ' )
    lowerCamelCase_         =						aws_access_key_id

    lowerCamelCase_         =						_ask_field('AWS Secret Access Key: ' )
    lowerCamelCase_         =						aws_secret_access_key

  lowerCamelCase_         =						_ask_field('Enter your AWS Region: [us-east-1]'		,				default='us-east-1' )
  lowerCamelCase_         =						aws_region

  lowerCamelCase_         =						_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']		,				__UpperCamelCase		,				)
  if role_management == 0:
    lowerCamelCase_         =						_ask_field('Enter your IAM role name: ' )
  else:
    lowerCamelCase_         =						'accelerate_sagemaker_execution_role'
    print(F'''Accelerate will create an iam role "{iam_role_name}" using the provided credentials''' )
    _create_iam_role_for_sagemaker(__UpperCamelCase )

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

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

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

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

    if use_custom_options:
      lowerCamelCase_         =						_ask_options(
          'Which mode do you want to use?'		,				__UpperCamelCase		,				lambda _lowerCamelCase : TORCH_DYNAMO_MODES[int(__UpperCamelCase )]		,				default='default'		,				)
      lowerCamelCase_         =						_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=__UpperCamelCase		,				error_message='Please enter yes or no.'		,				)
      lowerCamelCase_         =						_ask_field(
          'Do you want to enable dynamic shape tracing? [yes/NO]: '		,				_convert_yes_no_to_bool		,				default=__UpperCamelCase		,				error_message='Please enter yes or no.'		,				)
  lowerCamelCase_         =						'Which EC2 instance type you want to use for your training?'
  if distributed_type != SageMakerDistributedType.NO:
    lowerCamelCase_         =						_ask_options(
        __UpperCamelCase		,				__UpperCamelCase		,				lambda _lowerCamelCase : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(__UpperCamelCase )] )
  else:
    eca_instance_query += "? [ml.p3.2xlarge]:"
    lowerCamelCase_         =						_ask_field(__UpperCamelCase		,				lambda _lowerCamelCase : str(__UpperCamelCase ).lower()		,				default='ml.p3.2xlarge' )

  lowerCamelCase_         =						1
  if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
    lowerCamelCase_         =						_ask_field(
        'How many machines do you want use? [1]: '		,				__UpperCamelCase		,				default=1		,				)

  lowerCamelCase_         =						_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=__UpperCamelCase		,				compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER		,				distributed_type=__UpperCamelCase		,				use_cpu=__UpperCamelCase		,				dynamo_config=__UpperCamelCase		,				eca_instance_type=__UpperCamelCase		,				profile=__UpperCamelCase		,				region=__UpperCamelCase		,				iam_role_name=__UpperCamelCase		,				mixed_precision=__UpperCamelCase		,				num_machines=__UpperCamelCase		,				sagemaker_inputs_file=__UpperCamelCase		,				sagemaker_metrics_file=__UpperCamelCase		,				)





from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     k_size // 2
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     mgrid[0 - center : k_size - center, 0 - center : k_size - center]
      SCREAMING_SNAKE_CASE_      =     1 / (2 * pi * sigma) * exp(-(square(__UpperCamelCase ) + square(__UpperCamelCase )) / (2 * square(__UpperCamelCase )) )
      return g


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     image.shape[0], image.shape[1]
      # dst image height and width
      SCREAMING_SNAKE_CASE_      =     height - k_size + 1
      SCREAMING_SNAKE_CASE_      =     width - k_size + 1

      # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
      SCREAMING_SNAKE_CASE_      =     zeros((dst_height * dst_width, k_size * k_size) )
      SCREAMING_SNAKE_CASE_      =     0
      for i, j in product(range(__UpperCamelCase )   ,					range(__UpperCamelCase ) ):
            SCREAMING_SNAKE_CASE_      =     ravel(image[i : i + k_size, j : j + k_size] )
            SCREAMING_SNAKE_CASE_      =     window
            row += 1

      #  turn the kernel into shape(k*k, 1)
      SCREAMING_SNAKE_CASE_      =     gen_gaussian_kernel(__UpperCamelCase   ,					__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     ravel(__UpperCamelCase )

      # reshape and get the dst image
      SCREAMING_SNAKE_CASE_      =     dot(__UpperCamelCase   ,					__UpperCamelCase ).reshape(__UpperCamelCase   ,					__UpperCamelCase ).astype(__UpperCamelCase )

      return dst


if __name__ == "__main__":
      # read original image
      A   :     Tuple       =				imread(r"../image_data/lena.jpg")
      # turn image in gray scale value
      A   :     Optional[int]       =				cvtColor(img, COLOR_BGR2GRAY)

      # get values with two different mask size
      A   :     Tuple       =				gaussian_filter(gray, 3, sigma=1)
      A   :     Optional[int]       =				gaussian_filter(gray, 5, sigma=0.8)

      # show result images
      imshow("gaussian filter with 3x3 mask", gaussianaxa)
      imshow("gaussian filter with 5x5 mask", gaussianaxa)
      waitKey()


