






"""simple docstring"""





import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


A__					: Tuple=	"Run commands across TPU VMs for initial setup before running `accelerate launch`."







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE=None					)					->	Any:
    """simple docstring"""

    if subparsers is not None:
        UpperCamelCase__         =    subparsers.add_parser('tpu-config' ,	description=_description					)
    else:
        UpperCamelCase__         =    argparse.ArgumentParser('Accelerate tpu-config command' ,	description=_description					)
    # Core arguments
    UpperCamelCase__         =    parser.add_argument_group(
        'Config Arguments' ,	'Arguments that can be configured through `accelerate config`.'					)
    config_args.add_argument(
        '--config_file' ,	type=_UpperCamelCase ,	default=_UpperCamelCase ,	help='Path to the config file to use for accelerate.' ,	)
    config_args.add_argument(
        '--tpu_name' ,	default=_UpperCamelCase ,	help='The name of the TPU to use. If not specified, will use the TPU specified in the config file.' ,	)
    config_args.add_argument(
        '--tpu_zone' ,	default=_UpperCamelCase ,	help='The zone of the TPU to use. If not specified, will use the zone specified in the config file.' ,	)
    UpperCamelCase__         =    parser.add_argument_group('TPU Arguments' ,	'Arguments for options ran inside the TPU.'					)
    pod_args.add_argument(
        '--use_alpha' ,	action='store_true' ,	help='Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.' ,	)
    pod_args.add_argument(
        '--command_file' ,	default=_UpperCamelCase ,	help='The path to the file containing the commands to run on the pod on startup.' ,	)
    pod_args.add_argument(
        '--command' ,	action='append' ,	nargs='+' ,	help='A command to run on the pod. Can be passed multiple times.' ,	)
    pod_args.add_argument(
        '--install_accelerate' ,	action='store_true' ,	help='Whether to install accelerate on the pod. Defaults to False.' ,	)
    pod_args.add_argument(
        '--accelerate_version' ,	default='latest' ,	help='The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.' ,	)
    pod_args.add_argument(
        '--debug' ,	action='store_true' ,	help='If set, will print the command that would be run instead of running it.'					)

    if subparsers is not None:
        parser.set_defaults(func=_UpperCamelCase					)
    return parser







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	str:
    """simple docstring"""

    UpperCamelCase__         =    None

    # Get the default from the config file if it exists.
    if args.config_file is not None or os.path.isfile(_UpperCamelCase					):
        UpperCamelCase__         =    load_config_from_file(args.config_file					)
        if not args.command_file and defaults.command_file is not None and not args.command:
            UpperCamelCase__         =    defaults.command_file
        if not args.command and defaults.commands is not None:
            UpperCamelCase__         =    defaults.commands
        if not args.tpu_name:
            UpperCamelCase__         =    defaults.tpu_name
        if not args.tpu_zone:
            UpperCamelCase__         =    defaults.tpu_zone
    if args.accelerate_version == "dev":
        UpperCamelCase__         =    'git+https://github.com/huggingface/accelerate.git'
    elif args.accelerate_version == "latest":
        UpperCamelCase__         =    'accelerate -U'
    elif isinstance(parse(args.accelerate_version					) ,	_UpperCamelCase					):
        UpperCamelCase__         =    F'accelerate=={args.accelerate_version}'

    if not args.command_file and not args.command:
        raise ValueError('You must specify either a command file or a command to run on the pod.'					)

    if args.command_file:
        with open(args.command_file ,	'r'					) as f:
            UpperCamelCase__         =    [f.read().splitlines()]

    # To turn list of lists into list of strings
    if isinstance(args.command[0] ,	_UpperCamelCase					):
        UpperCamelCase__         =    [line for cmd in args.command for line in cmd]
    # Default to the shared folder and install accelerate
    UpperCamelCase__         =    ['cd /usr/share']
    if args.install_accelerate:
        new_cmd += [F'pip install {args.accelerate_version}']
    new_cmd += args.command
    UpperCamelCase__         =    '; '.join(_UpperCamelCase					)

    # Then send it to gcloud
    # Eventually try to use google-api-core to do this instead of subprocess
    UpperCamelCase__         =    ['gcloud']
    if args.use_alpha:
        cmd += ["alpha"]
    cmd += [
        "compute",
        "tpus",
        "tpu-vm",
        "ssh",
        args.tpu_name,
        "--zone",
        args.tpu_zone,
        "--command",
        args.command,
        "--worker",
        "all",
    ]
    if args.debug:
        print(F'Running {" ".join(_UpperCamelCase					)}'					)
        return
    subprocess.run(_UpperCamelCase					)
    print('Successfully setup pod.'					)







def   lowerCAmelCase_( )					->	Dict:
    """simple docstring"""

    UpperCamelCase__         =    tpu_command_parser()
    UpperCamelCase__         =    parser.parse_args()

    tpu_command_launcher(_UpperCamelCase					)












"""simple docstring"""





import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Tuple:
    """simple docstring"""

    UpperCamelCase__         =    SwinConfig()
    UpperCamelCase__         =    swin_name.split('_'					)

    UpperCamelCase__         =    name_split[1]
    UpperCamelCase__         =    int(name_split[4]					)
    UpperCamelCase__         =    int(name_split[3][-1]					)

    if model_size == "tiny":
        UpperCamelCase__         =    96
        UpperCamelCase__         =    (2, 2, 6, 2)
        UpperCamelCase__         =    (3, 6, 12, 24)
    elif model_size == "small":
        UpperCamelCase__         =    96
        UpperCamelCase__         =    (2, 2, 18, 2)
        UpperCamelCase__         =    (3, 6, 12, 24)
    elif model_size == "base":
        UpperCamelCase__         =    1_28
        UpperCamelCase__         =    (2, 2, 18, 2)
        UpperCamelCase__         =    (4, 8, 16, 32)
    else:
        UpperCamelCase__         =    1_92
        UpperCamelCase__         =    (2, 2, 18, 2)
        UpperCamelCase__         =    (6, 12, 24, 48)

    if "in22k" in swin_name:
        UpperCamelCase__         =    2_18_41
    else:
        UpperCamelCase__         =    10_00
        UpperCamelCase__         =    'huggingface/label-files'
        UpperCamelCase__         =    'imagenet-1k-id2label.json'
        UpperCamelCase__         =    json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	repo_type='dataset'					) ,	'r'					)					)
        UpperCamelCase__         =    {int(SCREAMING_SNAKE_CASE					): v for k, v in idalabel.items()}
        UpperCamelCase__         =    idalabel
        UpperCamelCase__         =    {v: k for k, v in idalabel.items()}

    UpperCamelCase__         =    img_size
    UpperCamelCase__         =    num_classes
    UpperCamelCase__         =    embed_dim
    UpperCamelCase__         =    depths
    UpperCamelCase__         =    num_heads
    UpperCamelCase__         =    window_size

    return config







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    if "patch_embed.proj" in name:
        UpperCamelCase__         =    name.replace('patch_embed.proj' ,	'embeddings.patch_embeddings.projection'					)
    if "patch_embed.norm" in name:
        UpperCamelCase__         =    name.replace('patch_embed.norm' ,	'embeddings.norm'					)
    if "layers" in name:
        UpperCamelCase__         =    'encoder.' + name
    if "attn.proj" in name:
        UpperCamelCase__         =    name.replace('attn.proj' ,	'attention.output.dense'					)
    if "attn" in name:
        UpperCamelCase__         =    name.replace('attn' ,	'attention.self'					)
    if "norm1" in name:
        UpperCamelCase__         =    name.replace('norm1' ,	'layernorm_before'					)
    if "norm2" in name:
        UpperCamelCase__         =    name.replace('norm2' ,	'layernorm_after'					)
    if "mlp.fc1" in name:
        UpperCamelCase__         =    name.replace('mlp.fc1' ,	'intermediate.dense'					)
    if "mlp.fc2" in name:
        UpperCamelCase__         =    name.replace('mlp.fc2' ,	'output.dense'					)

    if name == "norm.weight":
        UpperCamelCase__         =    'layernorm.weight'
    if name == "norm.bias":
        UpperCamelCase__         =    'layernorm.bias'

    if "head" in name:
        UpperCamelCase__         =    name.replace('head' ,	'classifier'					)
    else:
        UpperCamelCase__         =    'swin.' + name

    return name







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	str:
    """simple docstring"""

    for key in orig_state_dict.copy().keys():
        UpperCamelCase__         =    orig_state_dict.pop(SCREAMING_SNAKE_CASE					)

        if "mask" in key:
            continue
        elif "qkv" in key:
            UpperCamelCase__         =    key.split('.'					)
            UpperCamelCase__         =    int(key_split[1]					)
            UpperCamelCase__         =    int(key_split[3]					)
            UpperCamelCase__         =    model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                UpperCamelCase__         =    val[:dim, :]
                UpperCamelCase__         =    val[
                    dim : dim * 2, :
                ]
                UpperCamelCase__         =    val[-dim:, :]
            else:
                UpperCamelCase__         =    val[
                    :dim
                ]
                UpperCamelCase__         =    val[
                    dim : dim * 2
                ]
                UpperCamelCase__         =    val[
                    -dim:
                ]
        else:
            UpperCamelCase__         =    val

    return orig_state_dict







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    timm.create_model(SCREAMING_SNAKE_CASE ,	pretrained=SCREAMING_SNAKE_CASE					)
    timm_model.eval()

    UpperCamelCase__         =    get_swin_config(SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    SwinForImageClassification(SCREAMING_SNAKE_CASE					)
    model.eval()

    UpperCamelCase__         =    convert_state_dict(timm_model.state_dict() ,	SCREAMING_SNAKE_CASE					)
    model.load_state_dict(SCREAMING_SNAKE_CASE					)

    UpperCamelCase__         =    'http://images.cocodataset.org/val2017/000000039769.jpg'

    UpperCamelCase__         =    AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_' ,	'-'					)					)					)
    UpperCamelCase__         =    Image.open(requests.get(SCREAMING_SNAKE_CASE ,	stream=SCREAMING_SNAKE_CASE					).raw					)
    UpperCamelCase__         =    image_processor(images=SCREAMING_SNAKE_CASE ,	return_tensors='pt'					)

    UpperCamelCase__         =    timm_model(inputs['pixel_values']					)
    UpperCamelCase__         =    model(**SCREAMING_SNAKE_CASE					).logits

    assert torch.allclose(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	atol=1E-3					)

    print(F'Saving model {swin_name} to {pytorch_dump_folder_path}'					)
    model.save_pretrained(SCREAMING_SNAKE_CASE					)

    print(F'Saving image processor to {pytorch_dump_folder_path}'					)
    image_processor.save_pretrained(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
  A__					: Optional[Any]=	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--swin_name""",
      default="""swin_tiny_patch4_window7_224""",
      type=str,
      help="""Name of the Swin 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."""
  )

  A__					: Tuple=	parser.parse_args()
  convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)




