

# 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__      ( __A    :List[str]		):


       """simple docstring"""




       __snake_case       	=							botoa.client("""iam"""		)

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







def 						lowerCamelCase__      ( __A    :str		):


       """simple docstring"""




       __snake_case       	=							botoa.client("""iam"""		)
       return iam_client.get_role(RoleName=__A		)["Role"]["Arn"]







def 						lowerCamelCase__      ( ):


       """simple docstring"""




       __snake_case       	=							_ask_options(
           """How do you want to authorize?"""	,["""AWS Profile""", """Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) """]	,__A	,)
       __snake_case       	=							None
       if credentials_configuration == 0:
              __snake_case       	=							_ask_field("""Enter your AWS Profile name: [default] """	,default="""default"""		)
              __snake_case       	=							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`"""		)
              __snake_case       	=							_ask_field("""AWS Access Key ID: """		)
              __snake_case       	=							aws_access_key_id

              __snake_case       	=							_ask_field("""AWS Secret Access Key: """		)
              __snake_case       	=							aws_secret_access_key

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

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

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

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

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

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

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

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

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




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import VivitImageProcessor

class     __snake_case    (				unittest.TestCase  ):
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase=7      , _UpperCamelCase=3      , _UpperCamelCase=10      , _UpperCamelCase=18      , _UpperCamelCase=30      , _UpperCamelCase=4_00      , _UpperCamelCase=True      , _UpperCamelCase=None      , _UpperCamelCase=True      , _UpperCamelCase=[0.5, 0.5, 0.5]      , _UpperCamelCase=[0.5, 0.5, 0.5]      , _UpperCamelCase=None      , )      ->							str:






              """simple docstring"""


              __snake_case       	=							size if size is not None else {"""shortest_edge""": 18}
              __snake_case       	=							crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

              __snake_case       	=							parent
              __snake_case       	=							batch_size
              __snake_case       	=							num_channels
              __snake_case       	=							num_frames
              __snake_case       	=							image_size
              __snake_case       	=							min_resolution
              __snake_case       	=							max_resolution
              __snake_case       	=							do_resize
              __snake_case       	=							size
              __snake_case       	=							do_normalize
              __snake_case       	=							image_mean
              __snake_case       	=							image_std
              __snake_case       	=							crop_size






       def 			a		(		self   )      ->							Optional[Any]:






              """simple docstring"""


              return {
                  "image_mean": self.image_mean,
                  "image_std": self.image_std,
                  "do_normalize": self.do_normalize,
                  "do_resize": self.do_resize,
                  "size": self.size,
                  "crop_size": self.crop_size,
              }






@require_torch
@require_vision
class     __snake_case    (				snake_case__			,	unittest.TestCase  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							VivitImageProcessor if is_vision_available() else None


       def 			a		(		self   )      ->							int:






              """simple docstring"""


              __snake_case       	=							VivitImageProcessingTester(self   )


       @property
       def 			a		(		self   )      ->							str:






              """simple docstring"""


              return self.image_processor_tester.prepare_image_processor_dict()


       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              __snake_case       	=							self.image_processing_class(**self.image_processor_dict   )
              self.assertTrue(hasattr(_UpperCamelCase      , """image_mean"""   )   )
              self.assertTrue(hasattr(_UpperCamelCase      , """image_std"""   )   )
              self.assertTrue(hasattr(_UpperCamelCase      , """do_normalize"""   )   )
              self.assertTrue(hasattr(_UpperCamelCase      , """do_resize"""   )   )
              self.assertTrue(hasattr(_UpperCamelCase      , """do_center_crop"""   )   )
              self.assertTrue(hasattr(_UpperCamelCase      , """size"""   )   )


       def 			a		(		self   )      ->							str:






              """simple docstring"""


              __snake_case       	=							self.image_processing_class.from_dict(self.image_processor_dict   )
              self.assertEqual(image_processor.size      , {"""shortest_edge""": 18}   )
              self.assertEqual(image_processor.crop_size      , {"""height""": 18, """width""": 18}   )

              __snake_case       	=							self.image_processing_class.from_dict(self.image_processor_dict      , size=42      , crop_size=84   )
              self.assertEqual(image_processor.size      , {"""shortest_edge""": 42}   )
              self.assertEqual(image_processor.crop_size      , {"""height""": 84, """width""": 84}   )


       def 			a		(		self   )      ->							List[str]:






              """simple docstring"""


              __snake_case       	=							self.image_processing_class(**self.image_processor_dict   )
              # create random PIL videos
              __snake_case       	=							prepare_video_inputs(self.image_processor_tester      , equal_resolution=_UpperCamelCase   )
              for video in video_inputs:
                     self.assertIsInstance(_UpperCamelCase      , _UpperCamelCase   )
                     self.assertIsInstance(video[0]      , Image.Image   )

              # Test not batched input
              __snake_case       	=							image_processing(video_inputs[0]      , return_tensors="""pt"""   ).pixel_values
              self.assertEqual(
                  encoded_videos.shape      , (
                      1,
                      self.image_processor_tester.num_frames,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["""height"""],
                      self.image_processor_tester.crop_size["""width"""],
                  )      , )

              # Test batched
              __snake_case       	=							image_processing(_UpperCamelCase      , return_tensors="""pt"""   ).pixel_values
              self.assertEqual(
                  encoded_videos.shape      , (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_frames,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["""height"""],
                      self.image_processor_tester.crop_size["""width"""],
                  )      , )


       def 			a		(		self   )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case       	=							self.image_processing_class(**self.image_processor_dict   )
              # create random numpy tensors
              __snake_case       	=							prepare_video_inputs(self.image_processor_tester      , equal_resolution=_UpperCamelCase      , numpify=_UpperCamelCase   )
              for video in video_inputs:
                     self.assertIsInstance(_UpperCamelCase      , _UpperCamelCase   )
                     self.assertIsInstance(video[0]      , np.ndarray   )

              # Test not batched input
              __snake_case       	=							image_processing(video_inputs[0]      , return_tensors="""pt"""   ).pixel_values
              self.assertEqual(
                  encoded_videos.shape      , (
                      1,
                      self.image_processor_tester.num_frames,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["""height"""],
                      self.image_processor_tester.crop_size["""width"""],
                  )      , )

              # Test batched
              __snake_case       	=							image_processing(_UpperCamelCase      , return_tensors="""pt"""   ).pixel_values
              self.assertEqual(
                  encoded_videos.shape      , (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_frames,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["""height"""],
                      self.image_processor_tester.crop_size["""width"""],
                  )      , )






       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              __snake_case       	=							self.image_processing_class(**self.image_processor_dict   )
              # create random PyTorch tensors
              __snake_case       	=							prepare_video_inputs(self.image_processor_tester      , equal_resolution=_UpperCamelCase      , torchify=_UpperCamelCase   )
              for video in video_inputs:
                     self.assertIsInstance(_UpperCamelCase      , _UpperCamelCase   )
                     self.assertIsInstance(video[0]      , torch.Tensor   )

              # Test not batched input
              __snake_case       	=							image_processing(video_inputs[0]      , return_tensors="""pt"""   ).pixel_values
              self.assertEqual(
                  encoded_videos.shape      , (
                      1,
                      self.image_processor_tester.num_frames,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["""height"""],
                      self.image_processor_tester.crop_size["""width"""],
                  )      , )

              # Test batched
              __snake_case       	=							image_processing(_UpperCamelCase      , return_tensors="""pt"""   ).pixel_values
              self.assertEqual(
                  encoded_videos.shape      , (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_frames,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["""height"""],
                      self.image_processor_tester.crop_size["""width"""],
                  )      , )

