
'''simple docstring'''
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Dict     			=						SwinConfig(image_size=1_9_2       )

    if "base" in model_name:
        snake_case_ :    Dict     			=						6
        snake_case_ :    Optional[Any]     			=						1_2_8
        snake_case_ :    str     			=						(2, 2, 1_8, 2)
        snake_case_ :    Dict     			=						(4, 8, 1_6, 3_2)
    elif "large" in model_name:
        snake_case_ :    str     			=						1_2
        snake_case_ :    Optional[int]     			=						1_9_2
        snake_case_ :    Any     			=						(2, 2, 1_8, 2)
        snake_case_ :    int     			=						(6, 1_2, 2_4, 4_8)
    else:
        raise ValueError("Model not supported, only supports base and large variants"       )

    snake_case_ :    Any     			=						window_size
    snake_case_ :    Any     			=						embed_dim
    snake_case_ :    Dict     			=						depths
    snake_case_ :    Union[str, Any]     			=						num_heads

    return config

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if "encoder.mask_token" in name:
        snake_case_ :    int     			=						name.replace("encoder.mask_token",		"embeddings.mask_token"       )
    if "encoder.patch_embed.proj" in name:
        snake_case_ :    List[str]     			=						name.replace("encoder.patch_embed.proj",		"embeddings.patch_embeddings.projection"       )
    if "encoder.patch_embed.norm" in name:
        snake_case_ :    Tuple     			=						name.replace("encoder.patch_embed.norm",		"embeddings.norm"       )
    if "attn.proj" in name:
        snake_case_ :    str     			=						name.replace("attn.proj",		"attention.output.dense"       )
    if "attn" in name:
        snake_case_ :    List[str]     			=						name.replace("attn",		"attention.self"       )
    if "norm1" in name:
        snake_case_ :    int     			=						name.replace("norm1",		"layernorm_before"       )
    if "norm2" in name:
        snake_case_ :    List[Any]     			=						name.replace("norm2",		"layernorm_after"       )
    if "mlp.fc1" in name:
        snake_case_ :    Optional[Any]     			=						name.replace("mlp.fc1",		"intermediate.dense"       )
    if "mlp.fc2" in name:
        snake_case_ :    int     			=						name.replace("mlp.fc2",		"output.dense"       )

    if name == "encoder.norm.weight":
        snake_case_ :    List[Any]     			=						"layernorm.weight"
    if name == "encoder.norm.bias":
        snake_case_ :    Optional[Any]     			=						"layernorm.bias"

    if "decoder" in name:
        pass
    else:
        snake_case_ :    str     			=						"swin." + name

    return name

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    for key in orig_state_dict.copy().keys():
        snake_case_ :    Optional[int]     			=						orig_state_dict.pop(__SCREAMING_SNAKE_CASE       )

        if "attn_mask" in key:
            pass
        elif "qkv" in key:
            snake_case_ :    str     			=						key.split("."       )
            snake_case_ :    int     			=						int(key_split[2]       )
            snake_case_ :    str     			=						int(key_split[4]       )
            snake_case_ :    int     			=						model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                snake_case_ :    Optional[Any]     			=						val[:dim, :]
                snake_case_ :    Any     			=						val[
                    dim : dim * 2, :
                ]
                snake_case_ :    List[str]     			=						val[-dim:, :]
            else:
                snake_case_ :    Tuple     			=						val[
                    :dim
                ]
                snake_case_ :    int     			=						val[
                    dim : dim * 2
                ]
                snake_case_ :    Any     			=						val[
                    -dim:
                ]
        else:
            snake_case_ :    int     			=						val

    return orig_state_dict

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Union[str, Any]     			=						torch.load(__SCREAMING_SNAKE_CASE,		map_location="cpu"       )["model"]

    snake_case_ :    Any     			=						get_swin_config(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Optional[int]     			=						SwinForMaskedImageModeling(__SCREAMING_SNAKE_CASE       )
    model.eval()

    snake_case_ :    Tuple     			=						convert_state_dict(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    model.load_state_dict(__SCREAMING_SNAKE_CASE       )

    snake_case_ :    Tuple     			=						"http://images.cocodataset.org/val2017/000000039769.jpg"

    snake_case_ :    Optional[int]     			=						ViTImageProcessor(size={"height": 1_9_2, "width": 1_9_2}       )
    snake_case_ :    Any     			=						Image.open(requests.get(__SCREAMING_SNAKE_CASE,		stream=__SCREAMING_SNAKE_CASE       ).raw       )
    snake_case_ :    Optional[int]     			=						image_processor(images=__SCREAMING_SNAKE_CASE,		return_tensors="pt"       )

    with torch.no_grad():
        snake_case_ :    Tuple     			=						model(**__SCREAMING_SNAKE_CASE       ).logits

    print(outputs.keys()       )
    print("Looks ok!"       )

    if pytorch_dump_folder_path is not None:
        print(f'Saving model {model_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 push_to_hub:
        print(f'Pushing model and image processor for {model_name} to hub'       )
        model.push_to_hub(f'microsoft/{model_name}'       )
        image_processor.push_to_hub(f'microsoft/{model_name}'       )


if __name__ == "__main__":
  a_											=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="swin-base-simmim-window6-192",
      type=str,
      choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"],
      help="Name of the Swin SimMIM model you'd like to convert.",
  )
  parser.add_argument(
      "--checkpoint_path",
      default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth",
      type=str,
      help="Path to the original PyTorch checkpoint (.pth file).",
  )
  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_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''
import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


a_											=						logging.get_logger(__name__)






class    UpperCAmelCase_				:
 UpperCAmelCase_		    =						None


@experimental
def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if ParallelBackendConfig.backend_name is None:
        return _map_with_multiprocessing_pool(
            __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

    return _map_with_joblib(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    str     			=						num_proc if num_proc <= len(__SCREAMING_SNAKE_CASE       ) else len(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Optional[int]     			=						[]  # We organize the splits ourselve (contiguous splits)
    for index in range(__SCREAMING_SNAKE_CASE       ):
        snake_case_ :    List[str]     			=						len(__SCREAMING_SNAKE_CASE       ) // num_proc
        snake_case_ :    Optional[Any]     			=						len(__SCREAMING_SNAKE_CASE       ) % num_proc
        snake_case_ :    Optional[Any]     			=						div * index + min(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
        snake_case_ :    Union[str, Any]     			=						start + div + (1 if index < mod else 0)
        split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)       )

    if len(__SCREAMING_SNAKE_CASE       ) != sum(len(i[1]       ) for i in split_kwds       ):
        raise ValueError(
            f'Error dividing inputs iterable among processes. '
            f'Total number of objects {len(__SCREAMING_SNAKE_CASE       )}, '
            f'length: {sum(len(i[1]       ) for i in split_kwds       )}'       )

    logger.info(
        f'Spawning {num_proc} processes for {len(__SCREAMING_SNAKE_CASE       )} objects in slices of {[len(i[1]       ) for i in split_kwds]}'       )
    snake_case_			, snake_case_ :    Tuple     			=						None, None
    if not disable_tqdm:
        snake_case_			, snake_case_ :    int     			=						(RLock(),), tqdm.set_lock
    with Pool(__SCREAMING_SNAKE_CASE,		initargs=__SCREAMING_SNAKE_CASE,		initializer=__SCREAMING_SNAKE_CASE       ) as pool:
        snake_case_ :    Union[str, Any]     			=						pool.map(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    logger.info(f'Finished {num_proc} processes'       )
    snake_case_ :    str     			=						[obj for proc_res in mapped for obj in proc_res]
    logger.info(f'Unpacked {len(__SCREAMING_SNAKE_CASE       )} objects'       )

    return mapped

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    import joblib

    with joblib.parallel_backend(ParallelBackendConfig.backend_name,		n_jobs=__SCREAMING_SNAKE_CASE       ):
        return joblib.Parallel()(
            joblib.delayed(__SCREAMING_SNAKE_CASE       )((function, obj, types, None, True, None)       ) for obj in iterable       )


@experimental
@contextlib.contextmanager
def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Union[str, Any]     			=						backend_name

    if backend_name == "spark":
        from joblibspark import register_spark

        register_spark()

        # TODO: call create_cache_and_write_probe if "download" in steps
        # TODO: raise NotImplementedError when Dataset.map etc is called

    try:
        yield
    finally:
        snake_case_ :    Any     			=						None




