



'''simple docstring'''




import argparse
import json
from pathlib import Path

import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download

from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging


logging.set_verbosity_info()
_A								=   logging.get_logger(__name__)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[Any]     				=					ASTConfig()

     if "10-10" in model_name:
          pass
     elif "speech-commands" in model_name:
          lowercase_					:       List[Any]     				=					128
     elif "12-12" in model_name:
          lowercase_					:       Tuple     				=					12
          lowercase_					:       List[Any]     				=					12
     elif "14-14" in model_name:
          lowercase_					:       List[str]     				=					14
          lowercase_					:       Optional[Any]     				=					14
     elif "16-16" in model_name:
          lowercase_					:       Union[str, Any]     				=					16
          lowercase_					:       List[str]     				=					16
     else:
          raise ValueError('Model not supported'							)

     lowercase_					:       Optional[Any]     				=					'huggingface/label-files'
     if "speech-commands" in model_name:
          lowercase_					:       List[str]     				=					35
          lowercase_					:       int     				=					'speech-commands-v2-id2label.json'
     else:
          lowercase_					:       Union[str, Any]     				=					527
          lowercase_					:       int     				=					'audioset-id2label.json'

     lowercase_					:       Union[str, Any]     				=					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       repo_type='dataset'							)  ,       'r'							)							)
     lowercase_					:       Union[str, Any]     				=					{int(SCREAMING_SNAKE_CASE_							): v for k, v in idalabel.items()}
     lowercase_					:       Optional[int]     				=					idalabel
     lowercase_					:       Optional[int]     				=					{v: k for k, v in idalabel.items()}

     return config

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     if "module.v" in name:
          lowercase_					:       Dict     				=					name.replace('module.v'  ,       'audio_spectrogram_transformer'							)
     if "cls_token" in name:
          lowercase_					:       Optional[Any]     				=					name.replace('cls_token'  ,       'embeddings.cls_token'							)
     if "dist_token" in name:
          lowercase_					:       Any     				=					name.replace('dist_token'  ,       'embeddings.distillation_token'							)
     if "pos_embed" in name:
          lowercase_					:       List[str]     				=					name.replace('pos_embed'  ,       'embeddings.position_embeddings'							)
     if "patch_embed.proj" in name:
          lowercase_					:       int     				=					name.replace('patch_embed.proj'  ,       'embeddings.patch_embeddings.projection'							)
     # transformer blocks
     if "blocks" in name:
          lowercase_					:       Optional[Any]     				=					name.replace('blocks'  ,       'encoder.layer'							)
     if "attn.proj" in name:
          lowercase_					:       Optional[int]     				=					name.replace('attn.proj'  ,       'attention.output.dense'							)
     if "attn" in name:
          lowercase_					:       Dict     				=					name.replace('attn'  ,       'attention.self'							)
     if "norm1" in name:
          lowercase_					:       int     				=					name.replace('norm1'  ,       'layernorm_before'							)
     if "norm2" in name:
          lowercase_					:       Optional[int]     				=					name.replace('norm2'  ,       'layernorm_after'							)
     if "mlp.fc1" in name:
          lowercase_					:       Optional[int]     				=					name.replace('mlp.fc1'  ,       'intermediate.dense'							)
     if "mlp.fc2" in name:
          lowercase_					:       int     				=					name.replace('mlp.fc2'  ,       'output.dense'							)
     # final layernorm
     if "audio_spectrogram_transformer.norm" in name:
          lowercase_					:       int     				=					name.replace('audio_spectrogram_transformer.norm'  ,       'audio_spectrogram_transformer.layernorm'							)
     # classifier head
     if "module.mlp_head.0" in name:
          lowercase_					:       Dict     				=					name.replace('module.mlp_head.0'  ,       'classifier.layernorm'							)
     if "module.mlp_head.1" in name:
          lowercase_					:       List[Any]     				=					name.replace('module.mlp_head.1'  ,       'classifier.dense'							)

     return name

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     for key in orig_state_dict.copy().keys():
          lowercase_					:       List[str]     				=					orig_state_dict.pop(SCREAMING_SNAKE_CASE_							)

          if "qkv" in key:
               lowercase_					:       List[str]     				=					key.split('.'							)
               lowercase_					:       int     				=					int(key_split[3]							)
               lowercase_					:       Tuple     				=					config.hidden_size
               if "weight" in key:
                    lowercase_					:       Tuple     				=					val[:dim, :]
                    lowercase_					:       Union[str, Any]     				=					val[dim : dim * 2, :]
                    lowercase_					:       Optional[int]     				=					val[-dim:, :]
               else:
                    lowercase_					:       Optional[Any]     				=					val[:dim]
                    lowercase_					:       Any     				=					val[dim : dim * 2]
                    lowercase_					:       Tuple     				=					val[-dim:]
          else:
               lowercase_					:       Optional[Any]     				=					val

     return orig_state_dict

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[Any]     				=					[
         'module.v.head.weight',
         'module.v.head.bias',
         'module.v.head_dist.weight',
         'module.v.head_dist.bias',
     ]
     for k in ignore_keys:
          state_dict.pop(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

@torch.no_grad()
def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=False							):
     lowercase_					:       Dict     				=					get_audio_spectrogram_transformer_config(SCREAMING_SNAKE_CASE_							)

     lowercase_					:       Optional[int]     				=					{
         'ast-finetuned-audioset-10-10-0.4593': (
             'https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'
         ),
         'ast-finetuned-audioset-10-10-0.450': (
             'https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'
         ),
         'ast-finetuned-audioset-10-10-0.448': (
             'https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'
         ),
         'ast-finetuned-audioset-10-10-0.448-v2': (
             'https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'
         ),
         'ast-finetuned-audioset-12-12-0.447': (
             'https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'
         ),
         'ast-finetuned-audioset-14-14-0.443': (
             'https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'
         ),
         'ast-finetuned-audioset-16-16-0.442': (
             'https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'
         ),
         'ast-finetuned-speech-commands-v2': (
             'https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'
         ),
     }

     # load original state_dict
     lowercase_					:       Dict     				=					model_name_to_url[model_name]
     lowercase_					:       Optional[Any]     				=					torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_  ,       map_location='cpu'							)
     # remove some keys
     remove_keys(SCREAMING_SNAKE_CASE_							)
     # rename some keys
     lowercase_					:       str     				=					convert_state_dict(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

     # load 🤗 model
     lowercase_					:       Optional[Any]     				=					ASTForAudioClassification(SCREAMING_SNAKE_CASE_							)
     model.eval()

     model.load_state_dict(SCREAMING_SNAKE_CASE_							)

     # verify outputs on dummy input
     # source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
     lowercase_					:       Tuple     				=					-4.267_7393 if 'speech-commands' not in model_name else -6.84_5978
     lowercase_					:       str     				=					4.568_9974 if 'speech-commands' not in model_name else 5.565_4526
     lowercase_					:       str     				=					1_024 if 'speech-commands' not in model_name else 128
     lowercase_					:       Dict     				=					ASTFeatureExtractor(mean=SCREAMING_SNAKE_CASE_  ,       std=SCREAMING_SNAKE_CASE_  ,       max_length=SCREAMING_SNAKE_CASE_							)

     if "speech-commands" in model_name:
          lowercase_					:       Optional[Any]     				=					load_dataset('speech_commands'  ,       'v0.02'  ,       split='validation'							)
          lowercase_					:       Any     				=					dataset[0]['audio']['array']
     else:
          lowercase_					:       Any     				=					hf_hub_download(
              repo_id='nielsr/audio-spectogram-transformer-checkpoint'  ,       filename='sample_audio.flac'  ,       repo_type='dataset'  ,       )

          lowercase_					:       Union[str, Any]     				=					torchaudio.load(SCREAMING_SNAKE_CASE_							)
          lowercase_					:       str     				=					waveform.squeeze().numpy()

     lowercase_					:       str     				=					feature_extractor(SCREAMING_SNAKE_CASE_  ,       sampling_rate=16_000  ,       return_tensors='pt'							)

     # forward pass
     lowercase_					:       Tuple     				=					model(**SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Tuple     				=					outputs.logits

     if model_name == "ast-finetuned-audioset-10-10-0.4593":
          lowercase_					:       int     				=					torch.tensor([-0.8760, -7.0042, -8.6602]							)
     elif model_name == "ast-finetuned-audioset-10-10-0.450":
          lowercase_					:       Optional[int]     				=					torch.tensor([-1.1986, -7.0903, -8.2718]							)
     elif model_name == "ast-finetuned-audioset-10-10-0.448":
          lowercase_					:       Optional[Any]     				=					torch.tensor([-2.6128, -8.0080, -9.4344]							)
     elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
          lowercase_					:       List[str]     				=					torch.tensor([-1.5080, -7.4534, -8.8917]							)
     elif model_name == "ast-finetuned-audioset-12-12-0.447":
          lowercase_					:       List[str]     				=					torch.tensor([-0.5050, -6.5833, -8.0843]							)
     elif model_name == "ast-finetuned-audioset-14-14-0.443":
          lowercase_					:       Any     				=					torch.tensor([-0.3826, -7.0336, -8.2413]							)
     elif model_name == "ast-finetuned-audioset-16-16-0.442":
          lowercase_					:       List[str]     				=					torch.tensor([-1.2113, -6.9101, -8.3470]							)
     elif model_name == "ast-finetuned-speech-commands-v2":
          lowercase_					:       Optional[Any]     				=					torch.tensor([6.1589, -8.0566, -8.7984]							)
     else:
          raise ValueError('Unknown model name'							)
     if not torch.allclose(logits[0, :3]  ,       SCREAMING_SNAKE_CASE_  ,       atol=1e-4							):
          raise ValueError('Logits don\'t match'							)
     print('Looks ok!'							)

     if pytorch_dump_folder_path is not None:
          Path(SCREAMING_SNAKE_CASE_							).mkdir(exist_ok=SCREAMING_SNAKE_CASE_							)
          print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''							)
          model.save_pretrained(SCREAMING_SNAKE_CASE_							)
          print(f'''Saving feature extractor to {pytorch_dump_folder_path}'''							)
          feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_							)

     if push_to_hub:
          print('Pushing model and feature extractor to the hub...'							)
          model.push_to_hub(f'''MIT/{model_name}'''							)
          feature_extractor.push_to_hub(f'''MIT/{model_name}'''							)


if __name__ == "__main__":
 _A								=   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--model_name',
     default='ast-finetuned-audioset-10-10-0.4593',
     type=str,
     help='Name of the Audio Spectrogram Transformer model you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
 )
 parser.add_argument(
     '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
 )

 _A								=   parser.parse_args()
 convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





'''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 _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       str     				=					botoa.client('iam'							)

     lowercase_					:       Optional[int]     				=					{
         '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=SCREAMING_SNAKE_CASE_  ,       AssumeRolePolicyDocument=json.dumps(SCREAMING_SNAKE_CASE_  ,       indent=2							)							)
          lowercase_					:       Optional[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=SCREAMING_SNAKE_CASE_  ,       PolicyName=f'''{role_name}_policy_permission'''  ,       PolicyDocument=json.dumps(SCREAMING_SNAKE_CASE_  ,       indent=2							)  ,       )
     except iam_client.exceptions.EntityAlreadyExistsException:
          print(f'''role {role_name} already exists. Using existing one'''							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[Any]     				=					botoa.client('iam'							)
     return iam_client.get_role(RoleName=SCREAMING_SNAKE_CASE_							)["Role"]["Arn"]






def _UpperCamelCase   (       ):
     lowercase_					:       Dict     				=					_ask_options(
         'How do you want to authorize?'  ,       ['AWS Profile', 'Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) ']  ,       SCREAMING_SNAKE_CASE_  ,       )
     lowercase_					:       List[Any]     				=					None
     if credentials_configuration == 0:
          lowercase_					:       List[Any]     				=					_ask_field('Enter your AWS Profile name: [default] '  ,       default='default'							)
          lowercase_					:       Optional[int]     				=					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`'							)
          lowercase_					:       Optional[Any]     				=					_ask_field('AWS Access Key ID: '							)
          lowercase_					:       Optional[int]     				=					aws_access_key_id

          lowercase_					:       Optional[Any]     				=					_ask_field('AWS Secret Access Key: '							)
          lowercase_					:       Optional[int]     				=					aws_secret_access_key

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

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

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

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

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

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

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

     lowercase_					:       str     				=					1
     if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
          lowercase_					:       Union[str, Any]     				=					_ask_field(
              'How many machines do you want use? [1]: '  ,       SCREAMING_SNAKE_CASE_  ,       default=1  ,       )

     lowercase_					:       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=SCREAMING_SNAKE_CASE_  ,       compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER  ,       distributed_type=SCREAMING_SNAKE_CASE_  ,       use_cpu=SCREAMING_SNAKE_CASE_  ,       dynamo_config=SCREAMING_SNAKE_CASE_  ,       eca_instance_type=SCREAMING_SNAKE_CASE_  ,       profile=SCREAMING_SNAKE_CASE_  ,       region=SCREAMING_SNAKE_CASE_  ,       iam_role_name=SCREAMING_SNAKE_CASE_  ,       mixed_precision=SCREAMING_SNAKE_CASE_  ,       num_machines=SCREAMING_SNAKE_CASE_  ,       sagemaker_inputs_file=SCREAMING_SNAKE_CASE_  ,       sagemaker_metrics_file=SCREAMING_SNAKE_CASE_  ,       )




