






'''simple docstring'''



import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase      :       Dict      			=      logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCamelCase      :       str      			=      []
for i in range(6):
 # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
 rename_keys.append(
     (f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", f"""encoder.layers.{i}.self_attn.out_proj.weight""")
 )
 rename_keys.append(
     (f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""")
 )
 rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight"""))
 rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias"""))
 rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight"""))
 rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias"""))
 rename_keys.append(
     (f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""")
 )
 rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
 rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight"""))
 rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias"""))
 # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
 rename_keys.append(
     (f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""decoder.layers.{i}.self_attn.out_proj.weight""")
 )
 rename_keys.append(
     (f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""")
 )
 rename_keys.append(
     (
         f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
         f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
     )
 )
 rename_keys.append(
     (
         f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
         f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
     )
 )
 rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight"""))
 rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias"""))
 rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight"""))
 rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias"""))
 rename_keys.append(
     (f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""")
 )
 rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
 rename_keys.append(
     (f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
 )
 rename_keys.append(
     (f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
 )
 rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight"""))
 rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias"""))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
        ("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
    ]
)



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Union[str, Any],					SCREAMING_SNAKE_CASE__:	Any,					SCREAMING_SNAKE_CASE__:	Any    )  ->			Union[str, Any]:

   """simple docstring"""
   __a				     =    state_dict.pop(SCREAMING_SNAKE_CASE__    )
   __a				     =    val



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	str    )  ->			int:

   """simple docstring"""
   __a				     =    OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         __a				     =    key.replace('backbone.0.body',					'backbone.conv_encoder.model'    )
         __a				     =    value
      else:
         __a				     =    value

   return new_state_dict



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Tuple    )  ->			Dict:

   """simple docstring"""
   __a				     =    ''

   # first: transformer encoder
   for i in range(6    ):
      # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
      __a				     =    state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""    )
      __a				     =    state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""    )
      # next, add query, keys and values (in that order) to the state dict
      __a				     =    in_proj_weight[:256, :]
      __a				     =    in_proj_bias[:256]
      __a				     =    in_proj_weight[256:512, :]
      __a				     =    in_proj_bias[256:512]
      __a				     =    in_proj_weight[-256:, :]
      __a				     =    in_proj_bias[-256:]
   # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
   for i in range(6    ):
      # read in weights + bias of input projection layer of self-attention
      __a				     =    state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""    )
      __a				     =    state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""    )
      # next, add query, keys and values (in that order) to the state dict
      __a				     =    in_proj_weight[:256, :]
      __a				     =    in_proj_bias[:256]
      __a				     =    in_proj_weight[256:512, :]
      __a				     =    in_proj_bias[256:512]
      __a				     =    in_proj_weight[-256:, :]
      __a				     =    in_proj_bias[-256:]
      # read in weights + bias of input projection layer of cross-attention
      __a				     =    state_dict.pop(
          f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""    )
      __a				     =    state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""    )
      # next, add query, keys and values (in that order) of cross-attention to the state dict
      __a				     =    in_proj_weight_cross_attn[:256, :]
      __a				     =    in_proj_bias_cross_attn[:256]
      __a				     =    in_proj_weight_cross_attn[256:512, :]
      __a				     =    in_proj_bias_cross_attn[256:512]
      __a				     =    in_proj_weight_cross_attn[-256:, :]
      __a				     =    in_proj_bias_cross_attn[-256:]



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Union[str, Any],					SCREAMING_SNAKE_CASE__:	Optional[int]    )  ->			Any:

   """simple docstring"""
   __a     ,			__a				     =    image.size
   __a				     =    max(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    )
   __a				     =    800 if 'detection' in checkpoint_url else 1000
   __a				     =    target_max_size / current_max_size
   __a				     =    image.resize((int(round(scale * width    )    ), int(round(scale * height    )    ))    )

   return resized_image



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int    )  ->			str:

   """simple docstring"""
   __a				     =    F.to_tensor(SCREAMING_SNAKE_CASE__    )
   __a				     =    F.normalize(SCREAMING_SNAKE_CASE__,					mean=[0.4_8_5, 0.4_5_6, 0.4_0_6],					std=[0.2_2_9, 0.2_2_4, 0.2_2_5]    )
   return image



@torch.no_grad()
def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Tuple,					SCREAMING_SNAKE_CASE__:	Any,					SCREAMING_SNAKE_CASE__:	Any    )  ->			Tuple:

   """simple docstring"""
   logger.info('Converting model...'    )

   # load original state dict
   __a				     =    torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__,					map_location='cpu'    )
   # rename keys
   for src, dest in rename_keys:
      rename_key(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    )
   __a				     =    rename_backbone_keys(SCREAMING_SNAKE_CASE__    )
   # query, key and value matrices need special treatment
   read_in_q_k_v(SCREAMING_SNAKE_CASE__    )
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   __a				     =    'model.'
   for key in state_dict.copy().keys():
      if not key.startswith('class_labels_classifier'    ) and not key.startswith('bbox_predictor'    ):
         __a				     =    state_dict.pop(SCREAMING_SNAKE_CASE__    )
         __a				     =    val
    # create HuggingFace model and load state dict
   __a				     =    TableTransformerConfig(
       backbone='resnet18',					mask_loss_coefficient=1,					dice_loss_coefficient=1,					ce_loss_coefficient=1,					bbox_loss_coefficient=5,					giou_loss_coefficient=2,					eos_coefficient=0.4,					class_cost=1,					bbox_cost=5,					giou_cost=2,					)

   if "detection" in checkpoint_url:
      __a				     =    15
      __a				     =    2
      __a				     =    {0: 'table', 1: 'table rotated'}
      __a				     =    idalabel
      __a				     =    {v: k for k, v in idalabel.items()}
   else:
      __a				     =    125
      __a				     =    6
      __a				     =    {
          0: 'table',
          1: 'table column',
          2: 'table row',
          3: 'table column header',
          4: 'table projected row header',
          5: 'table spanning cell',
      }
      __a				     =    idalabel
      __a				     =    {v: k for k, v in idalabel.items()}

   __a				     =    DetrImageProcessor(
       format='coco_detection',					max_size=800 if 'detection' in checkpoint_url else 1000    )
   __a				     =    TableTransformerForObjectDetection(SCREAMING_SNAKE_CASE__    )
   model.load_state_dict(SCREAMING_SNAKE_CASE__    )
   model.eval()

   # verify our conversion
   __a				     =    'example_pdf.png' if 'detection' in checkpoint_url else 'example_table.png'
   __a				     =    hf_hub_download(repo_id='nielsr/example-pdf',					repo_type='dataset',					filename=SCREAMING_SNAKE_CASE__    )
   __a				     =    Image.open(SCREAMING_SNAKE_CASE__    ).convert('RGB'    )
   __a				     =    normalize(resize(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    )    ).unsqueeze(0    )

   __a				     =    model(SCREAMING_SNAKE_CASE__    )

   if "detection" in checkpoint_url:
      __a				     =    (1, 15, 3)
      __a				     =    torch.tensor(
          [[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]]    )
      __a				     =    torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]]    )

   else:
      __a				     =    (1, 125, 7)
      __a				     =    torch.tensor(
          [[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]]    )
      __a				     =    torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]]    )

   assert outputs.logits.shape == expected_shape
   assert torch.allclose(outputs.logits[0, :3, :3],					SCREAMING_SNAKE_CASE__,					atol=1e-4    )
   assert torch.allclose(outputs.pred_boxes[0, :3, :3],					SCREAMING_SNAKE_CASE__,					atol=1e-4    )
   print('Looks ok!'    )

   if pytorch_dump_folder_path is not None:
      # Save model and image processor
      logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""    )
      Path(SCREAMING_SNAKE_CASE__    ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__    )
      model.save_pretrained(SCREAMING_SNAKE_CASE__    )
      image_processor.save_pretrained(SCREAMING_SNAKE_CASE__    )

   if push_to_hub:
      # Push model to HF hub
      logger.info('Pushing model to the hub...'    )
      __a				     =    (
          'microsoft/table-transformer-detection'
          if 'detection' in checkpoint_url
          else 'microsoft/table-transformer-structure-recognition'
      )
      model.push_to_hub(SCREAMING_SNAKE_CASE__    )
      image_processor.push_to_hub(SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
 __UpperCamelCase      :       Optional[int]      			=      argparse.ArgumentParser()

 parser.add_argument(
     """--checkpoint_url""",
     default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
     type=str,
     choices=[
         """https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
         """https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
     ],
     help="""URL of the Table Transformer checkpoint you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
 )
 parser.add_argument(
     """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
 )
 __UpperCamelCase      :       Tuple      			=      parser.parse_args()
 convert_table_transformer_checkpoint(args.checkpoint_url, 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__:	Union[str, Any]    )  ->			List[str]:

   """simple docstring"""
   __a				     =    botoa.client('iam'    )

   __a				     =    {
       '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    )    )
      __a				     =    {
          '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__:	List[str]    )  ->			Optional[Any]:

   """simple docstring"""
   __a				     =    botoa.client('iam'    )
   return iam_client.get_role(RoleName=SCREAMING_SNAKE_CASE__    )["Role"]["Arn"]



def    __UpperCAmelCase	(				)  ->			Any:

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

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

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

   __a				     =    _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:
      __a				     =    _ask_field('Enter your IAM role name: '    )
   else:
      __a				     =    '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__    )

   __a				     =    _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.',					)
   __a				     =    None
   if is_custom_docker_image:
      __a				     =    _ask_field('Enter your Docker image: ',					lambda SCREAMING_SNAKE_CASE__    : str(SCREAMING_SNAKE_CASE__    ).lower()    )

   __a				     =    _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.',					)
   __a				     =    None
   if is_sagemaker_inputs_enabled:
      __a				     =    _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(),					)

   __a				     =    _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.',					)
   __a				     =    None
   if is_sagemaker_metrics_enabled:
      __a				     =    _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(),					)

   __a				     =    _ask_options(
       'What is the distributed mode?',					['No distributed training', 'Data parallelism'],					_convert_sagemaker_distributed_mode,					)
   __a				     =    {}
   __a				     =    _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:
      __a				     =    'dynamo_'
      __a				     =    _ask_options(
          'Which dynamo backend would you like to use?',					[x.lower() for x in DYNAMO_BACKENDS],					_convert_dynamo_backend,					default=2,					)
      __a				     =    _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:
         __a				     =    _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',					)
         __a				     =    _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.',					)
         __a				     =    _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.',					)
   __a				     =    'Which EC2 instance type you want to use for your training?'
   if distributed_type != SageMakerDistributedType.NO:
      __a				     =    _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]:"
      __a				     =    _ask_field(SCREAMING_SNAKE_CASE__,					lambda SCREAMING_SNAKE_CASE__    : str(SCREAMING_SNAKE_CASE__    ).lower(),					default='ml.p3.2xlarge'    )

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

   __a				     =    _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__,					)