






'''simple docstring'''



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int    )  ->			int:

   """simple docstring"""
   __a				     =    'huggingface/label-files'
   __a				     =    'imagenet-1k-id2label.json'
   __a				     =    json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__,					repo_type='dataset'    ),					'r'    )    )
   __a				     =    {int(SCREAMING_SNAKE_CASE__    ): v for k, v in idalabel.items()}
   __a				     =    {v: k for k, v in idalabel.items()}

   __a				     =    'std_conv' if 'bit' in model_name else False

   # note that when using BiT as backbone for ViT-hybrid checkpoints,
   # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
   # config.conv_layer = "std_conv_same"
   __a				     =    BitConfig(
       conv_layer=SCREAMING_SNAKE_CASE__,					num_labels=1000,					idalabel=SCREAMING_SNAKE_CASE__,					labelaid=SCREAMING_SNAKE_CASE__,					)

   return config



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

   """simple docstring"""
   if "stem.conv" in name:
      __a				     =    name.replace('stem.conv',					'bit.embedder.convolution'    )
   if "blocks" in name:
      __a				     =    name.replace('blocks',					'layers'    )
   if "head.fc" in name:
      __a				     =    name.replace('head.fc',					'classifier.1'    )
   if name.startswith('norm'    ):
      __a				     =    'bit.' + name
   if "bit" not in name and "classifier" not in name:
      __a				     =    'bit.encoder.' + name

   return name



def    __UpperCAmelCase	(				)  ->			Optional[int]:

   """simple docstring"""
   __a				     =    'http://images.cocodataset.org/val2017/000000039769.jpg'
   __a				     =    Image.open(requests.get(SCREAMING_SNAKE_CASE__,					stream=SCREAMING_SNAKE_CASE__    ).raw    )
   return im



@torch.no_grad()
def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int,					SCREAMING_SNAKE_CASE__:	Optional[Any],					SCREAMING_SNAKE_CASE__:	Dict=False    )  ->			Tuple:

   """simple docstring"""
   __a				     =    get_config(SCREAMING_SNAKE_CASE__    )

   # load original model from timm
   __a				     =    create_model(SCREAMING_SNAKE_CASE__,					pretrained=SCREAMING_SNAKE_CASE__    )
   timm_model.eval()

   # load state_dict of original model
   __a				     =    timm_model.state_dict()
   for key in state_dict.copy().keys():
      __a				     =    state_dict.pop(SCREAMING_SNAKE_CASE__    )
      __a				     =    val.squeeze() if 'head' in key else val

   # load HuggingFace model
   __a				     =    BitForImageClassification(SCREAMING_SNAKE_CASE__    )
   model.eval()
   model.load_state_dict(SCREAMING_SNAKE_CASE__    )

   # create image processor
   __a				     =    create_transform(**resolve_data_config({},					model=SCREAMING_SNAKE_CASE__    )    )
   __a				     =    transform.transforms

   __a				     =    {
       'bilinear': PILImageResampling.BILINEAR,
       'bicubic': PILImageResampling.BICUBIC,
       'nearest': PILImageResampling.NEAREST,
   }

   __a				     =    BitImageProcessor(
       do_resize=SCREAMING_SNAKE_CASE__,					size={'shortest_edge': timm_transforms[0].size},					resample=pillow_resamplings[timm_transforms[0].interpolation.value],					do_center_crop=SCREAMING_SNAKE_CASE__,					crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]},					do_normalize=SCREAMING_SNAKE_CASE__,					image_mean=timm_transforms[-1].mean.tolist(),					image_std=timm_transforms[-1].std.tolist(),					)

   __a				     =    prepare_img()
   __a				     =    transform(SCREAMING_SNAKE_CASE__    ).unsqueeze(0    )
   __a				     =    processor(SCREAMING_SNAKE_CASE__,					return_tensors='pt'    ).pixel_values

   # verify pixel values
   assert torch.allclose(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    )

   # verify logits
   with torch.no_grad():
      __a				     =    model(SCREAMING_SNAKE_CASE__    )
      __a				     =    outputs.logits

   print('Logits:',					logits[0, :3]    )
   print('Predicted class:',					model.config.idalabel[logits.argmax(-1    ).item()]    )
   __a				     =    timm_model(SCREAMING_SNAKE_CASE__    )
   assert timm_logits.shape == outputs.logits.shape
   assert torch.allclose(SCREAMING_SNAKE_CASE__,					outputs.logits,					atol=1e-3    )
   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} and processor to {pytorch_dump_folder_path}"""    )
      model.save_pretrained(SCREAMING_SNAKE_CASE__    )
      processor.save_pretrained(SCREAMING_SNAKE_CASE__    )

   if push_to_hub:
      print(f"""Pushing model {model_name} and processor to the hub"""    )
      model.push_to_hub(f"""ybelkada/{model_name}"""    )
      processor.push_to_hub(f"""ybelkada/{model_name}"""    )


if __name__ == "__main__":
 __UpperCamelCase      :       int      			=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default="""resnetv2_50x1_bitm""",
     type=str,
     help="""Name of the BiT timm 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 to push the model to the hub.""",
 )

 __UpperCamelCase      :       Tuple      			=      parser.parse_args()
 convert_bit_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__:	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__,					)