



"""simple docstring"""


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__A   :							Union[str, Any]													=   logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")

__A   :							List[str]													=   list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__A   :							List[Any]													=   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class 			_a :
			"""simple docstring"""
			UpperCamelCase__			      =	field(
			    default="""cifar10"""			,							metadata={"""help""": """Name of a dataset from the datasets package"""})
			UpperCamelCase__			      =	field(
			    default=lowerCAmelCase			,							metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
			UpperCamelCase__			      =	field(
			    default=lowerCAmelCase			,							metadata={"""help""": """The column name of the images in the files. If not set, will try to use 'image' or 'img'."""}			,							)
			UpperCamelCase__			      =	field(default=lowerCAmelCase			,							metadata={"""help""": """A folder containing the training data."""})
			UpperCamelCase__			      =	field(default=lowerCAmelCase			,							metadata={"""help""": """A folder containing the validation data."""})
			UpperCamelCase__			      =	field(
			    default=0.15			,							metadata={"""help""": """Percent to split off of train for validation."""})
			UpperCamelCase__			      =	field(default=32			,							metadata={"""help""": """The size of the square patches to use for masking."""})
			UpperCamelCase__			      =	field(
			    default=0.6			,							metadata={"""help""": """Percentage of patches to mask."""}			,							)
			UpperCamelCase__			      =	field(
			    default=lowerCAmelCase			,							metadata={
			        """help""": (
			            """For debugging purposes or quicker training, truncate the number of training examples to this """
			            """value if set."""
			        )
			    }			,							)
			UpperCamelCase__			      =	field(
			    default=lowerCAmelCase			,							metadata={
			        """help""": (
			            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
			            """value if set."""
			        )
			    }			,							)



			def  lowercase__						(		self     :    Union[str, Any]   )->Union[str, Any]:
							_UpperCAmelCase					=    {}
							if self.train_dir is not None:
											_UpperCAmelCase					=    self.train_dir
							if self.validation_dir is not None:
											_UpperCAmelCase					=    self.validation_dir
							_UpperCAmelCase					=    data_files if data_files else None



@dataclass
class 			_a :
			"""simple docstring"""
			UpperCamelCase__			      =	field(
			    default=lowerCAmelCase			,							metadata={
			        """help""": (
			            """The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a """
			            """checkpoint identifier on the hub. """
			            """Don't set if you want to train a model from scratch."""
			        )
			    }			,							)
			UpperCamelCase__			      =	field(
			    default=lowerCAmelCase			,							metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(lowerCAmelCase)}			,							)
			UpperCamelCase__			      =	field(
			    default=lowerCAmelCase			,							metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
			UpperCamelCase__			      =	field(
			    default=lowerCAmelCase			,							metadata={
			        """help""": (
			            """Override some existing default config settings when a model is trained from scratch. Example: """
			            """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
			        )
			    }			,							)
			UpperCamelCase__			      =	field(
			    default=lowerCAmelCase			,							metadata={"""help""": """Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"""}			,							)
			UpperCamelCase__			      =	field(
			    default="""main"""			,							metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}			,							)
			UpperCamelCase__			      =	field(default=lowerCAmelCase			,							metadata={"""help""": """Name or path of preprocessor config."""})
			UpperCamelCase__			      =	field(
			    default=lowerCAmelCase			,							metadata={
			        """help""": (
			            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
			            """with private models)."""
			        )
			    }			,							)
			UpperCamelCase__			      =	field(
			    default=lowerCAmelCase			,							metadata={
			        """help""": (
			            """The size (resolution) of each image. If not specified, will use `image_size` of the configuration."""
			        )
			    }			,							)
			UpperCamelCase__			      =	field(
			    default=lowerCAmelCase			,							metadata={
			        """help""": (
			            """The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."""
			        )
			    }			,							)
			UpperCamelCase__			      =	field(
			    default=lowerCAmelCase			,							metadata={"""help""": """Stride to use for the encoder."""}			,							)



class 			_a :
			"""simple docstring"""



			def __init__(		self     :    Dict				,   __UpperCamelCase     :    Tuple=1_9_2				,   __UpperCamelCase     :    Optional[Any]=3_2				,   __UpperCamelCase     :    Tuple=4				,   __UpperCamelCase     :    Tuple=0.6   )->Optional[int]:
							_UpperCAmelCase					=    input_size
							_UpperCAmelCase					=    mask_patch_size
							_UpperCAmelCase					=    model_patch_size
							_UpperCAmelCase					=    mask_ratio

							if self.input_size % self.mask_patch_size != 0:
											raise ValueError('''Input size must be divisible by mask patch size'''   )
							if self.mask_patch_size % self.model_patch_size != 0:
											raise ValueError('''Mask patch size must be divisible by model patch size'''   )

							_UpperCAmelCase					=    self.input_size // self.mask_patch_size
							_UpperCAmelCase					=    self.mask_patch_size // self.model_patch_size

							_UpperCAmelCase					=    self.rand_size**2
							_UpperCAmelCase					=    int(np.ceil(self.token_count * self.mask_ratio   )   )



			def __call__(		self     :    Dict   )->List[str]:
							_UpperCAmelCase					=    np.random.permutation(self.token_count   )[: self.mask_count]
							_UpperCAmelCase					=    np.zeros(self.token_count				,   dtype=__UpperCamelCase   )
							_UpperCAmelCase					=    1

							_UpperCAmelCase					=    mask.reshape((self.rand_size, self.rand_size)   )
							_UpperCAmelCase					=    mask.repeat(self.scale				,   axis=0   ).repeat(self.scale				,   axis=1   )

							return torch.tensor(mask.flatten()   )







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]				):






				'''simple docstring'''



				_UpperCAmelCase					=    torch.stack([example['''pixel_values'''] for example in examples]				)
				_UpperCAmelCase					=    torch.stack([example['''mask'''] for example in examples]				)
				return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def  lowercase	(      ):






				'''simple docstring'''



				_UpperCAmelCase					=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
				if len(sys.argv				) == 2 and sys.argv[1].endswith('''.json'''				):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
				else:
								_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry('''run_mim'''					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

				# Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''					,     datefmt='''%m/%d/%Y %H:%M:%S'''					,     handlers=[logging.StreamHandler(sys.stdout				)]					,     )

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				_UpperCAmelCase					=    training_args.get_process_log_level()
				logger.setLevel(_SCREAMING_SNAKE_CASE				)
				transformers.utils.logging.set_verbosity(_SCREAMING_SNAKE_CASE				)
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
				    + f'distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}'				)
				logger.info(f'Training/evaluation parameters {training_args}'				)

				# Detecting last checkpoint.
				_UpperCAmelCase					=    None
				if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
								_UpperCAmelCase					=    get_last_checkpoint(training_args.output_dir				)
								if last_checkpoint is None and len(os.listdir(training_args.output_dir				)				) > 0:
												raise ValueError(
												    f'Output directory ({training_args.output_dir}) already exists and is not empty. '
												    '''Use --overwrite_output_dir to overcome.'''				)
								elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
												logger.info(
												    f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
												    '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''				)

    # Initialize our dataset.
				_UpperCAmelCase					=    load_dataset(
				    data_args.dataset_name					,     data_args.dataset_config_name					,     data_files=data_args.data_files					,     cache_dir=model_args.cache_dir					,     use_auth_token=True if model_args.use_auth_token else None					,     )

				# If we don't have a validation split, split off a percentage of train as validation.
				_UpperCAmelCase					=    None if '''validation''' in ds.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split					,     _SCREAMING_SNAKE_CASE				) and data_args.train_val_split > 0.0:
								_UpperCAmelCase					=    ds['''train'''].train_test_split(data_args.train_val_split				)
								_UpperCAmelCase					=    split['''train''']
								_UpperCAmelCase					=    split['''test''']

				# Create config
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				_UpperCAmelCase					=    {
				    '''cache_dir''': model_args.cache_dir,
				    '''revision''': model_args.model_revision,
				    '''use_auth_token''': True if model_args.use_auth_token else None,
				}
				if model_args.config_name_or_path:
								_UpperCAmelCase					=    AutoConfig.from_pretrained(model_args.config_name_or_path					,     **_SCREAMING_SNAKE_CASE				)
				elif model_args.model_name_or_path:
								_UpperCAmelCase					=    AutoConfig.from_pretrained(model_args.model_name_or_path					,     **_SCREAMING_SNAKE_CASE				)
				else:
								_UpperCAmelCase					=    CONFIG_MAPPING[model_args.model_type]()
								logger.warning('''You are instantiating a new config instance from scratch.'''				)
								if model_args.config_overrides is not None:
												logger.info(f'Overriding config: {model_args.config_overrides}'				)
												config.update_from_string(model_args.config_overrides				)
												logger.info(f'New config: {config}'				)

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
				if hasattr(_SCREAMING_SNAKE_CASE					,     '''decoder_type'''				):
								_UpperCAmelCase					=    '''simmim'''

				# adapt config
				_UpperCAmelCase					=    model_args.image_size if model_args.image_size is not None else config.image_size
				_UpperCAmelCase					=    model_args.patch_size if model_args.patch_size is not None else config.patch_size
				_UpperCAmelCase					=    (
				    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
				)

				config.update(
				    {
				        '''image_size''': model_args.image_size,
				        '''patch_size''': model_args.patch_size,
				        '''encoder_stride''': model_args.encoder_stride,
				    }				)

				# create image processor
				if model_args.image_processor_name:
								_UpperCAmelCase					=    AutoImageProcessor.from_pretrained(model_args.image_processor_name					,     **_SCREAMING_SNAKE_CASE				)
				elif model_args.model_name_or_path:
								_UpperCAmelCase					=    AutoImageProcessor.from_pretrained(model_args.model_name_or_path					,     **_SCREAMING_SNAKE_CASE				)
				else:
								_UpperCAmelCase					=    {
								    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
								}
								_UpperCAmelCase					=    IMAGE_PROCESSOR_TYPES[model_args.model_type]()

				# create model
				if model_args.model_name_or_path:
								_UpperCAmelCase					=    AutoModelForMaskedImageModeling.from_pretrained(
								    model_args.model_name_or_path					,     from_tf=bool('''.ckpt''' in model_args.model_name_or_path				)					,     config=_SCREAMING_SNAKE_CASE					,     cache_dir=model_args.cache_dir					,     revision=model_args.model_revision					,     use_auth_token=True if model_args.use_auth_token else None					,     )
				else:
								logger.info('''Training new model from scratch'''				)
								_UpperCAmelCase					=    AutoModelForMaskedImageModeling.from_config(_SCREAMING_SNAKE_CASE				)

				if training_args.do_train:
								_UpperCAmelCase					=    ds['''train'''].column_names
				else:
								_UpperCAmelCase					=    ds['''validation'''].column_names

				if data_args.image_column_name is not None:
								_UpperCAmelCase					=    data_args.image_column_name
				elif "image" in column_names:
								_UpperCAmelCase					=    '''image'''
				elif "img" in column_names:
								_UpperCAmelCase					=    '''img'''
				else:
								_UpperCAmelCase					=    column_names[0]

				# transformations as done in original SimMIM paper
				# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
				_UpperCAmelCase					=    Compose(
				    [
				        Lambda(lambda _SCREAMING_SNAKE_CASE				: img.convert('''RGB'''				) if img.mode != "RGB" else img				),
				        RandomResizedCrop(model_args.image_size					,     scale=(0.67, 1.0)					,     ratio=(3.0 / 4.0, 4.0 / 3.0)				),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        Normalize(mean=image_processor.image_mean					,     std=image_processor.image_std				),
				    ]				)

				# create mask generator
				_UpperCAmelCase					=    MaskGenerator(
				    input_size=model_args.image_size					,     mask_patch_size=data_args.mask_patch_size					,     model_patch_size=model_args.patch_size					,     mask_ratio=data_args.mask_ratio					,     )

				def preprocess_images(_SCREAMING_SNAKE_CASE   :						Dict				):
								_UpperCAmelCase					=    [transforms(_SCREAMING_SNAKE_CASE				) for image in examples[image_column_name]]
								_UpperCAmelCase					=    [mask_generator() for i in range(len(examples[image_column_name]				)				)]

								return examples

				if training_args.do_train:
								if "train" not in ds:
												raise ValueError('''--do_train requires a train dataset'''				)
								if data_args.max_train_samples is not None:
												_UpperCAmelCase					=    ds['''train'''].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
								# Set the training transforms
								ds["train"].set_transform(_SCREAMING_SNAKE_CASE				)

				if training_args.do_eval:
								if "validation" not in ds:
												raise ValueError('''--do_eval requires a validation dataset'''				)
								if data_args.max_eval_samples is not None:
												_UpperCAmelCase					=    (
												    ds['''validation'''].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
												)
								# Set the validation transforms
								ds["validation"].set_transform(_SCREAMING_SNAKE_CASE				)

				# Initialize our trainer
				_UpperCAmelCase					=    Trainer(
				    model=_SCREAMING_SNAKE_CASE					,     args=_SCREAMING_SNAKE_CASE					,     train_dataset=ds['''train'''] if training_args.do_train else None					,     eval_dataset=ds['''validation'''] if training_args.do_eval else None					,     tokenizer=_SCREAMING_SNAKE_CASE					,     data_collator=_SCREAMING_SNAKE_CASE					,     )

				# Training
				if training_args.do_train:
								_UpperCAmelCase					=    None
								if training_args.resume_from_checkpoint is not None:
												_UpperCAmelCase					=    training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												_UpperCAmelCase					=    last_checkpoint
								_UpperCAmelCase					=    trainer.train(resume_from_checkpoint=_SCREAMING_SNAKE_CASE				)
								trainer.save_model()
								trainer.log_metrics('''train'''					,     train_result.metrics				)
								trainer.save_metrics('''train'''					,     train_result.metrics				)
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								_UpperCAmelCase					=    trainer.evaluate()
								trainer.log_metrics('''eval'''					,     _SCREAMING_SNAKE_CASE				)
								trainer.save_metrics('''eval'''					,     _SCREAMING_SNAKE_CASE				)

				# Write model card and (optionally) push to hub
				_UpperCAmelCase					=    {
				    '''finetuned_from''': model_args.model_name_or_path,
				    '''tasks''': '''masked-image-modeling''',
				    '''dataset''': data_args.dataset_name,
				    '''tags''': ['''masked-image-modeling'''],
				}
				if training_args.push_to_hub:
								trainer.push_to_hub(**_SCREAMING_SNAKE_CASE				)
				else:
								trainer.create_model_card(**_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
					main()








"""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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]				):






				'''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					=    128
								_UpperCAmelCase					=    (2, 2, 18, 2)
								_UpperCAmelCase					=    (4, 8, 16, 32)
				else:
								_UpperCAmelCase					=    192
								_UpperCAmelCase					=    (2, 2, 18, 2)
								_UpperCAmelCase					=    (6, 12, 24, 48)

				if "in22k" in swin_name:
								_UpperCAmelCase					=    2_1841
				else:
								_UpperCAmelCase					=    1000
								_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  lowercase	(      _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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						Dict				):






				'''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  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _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   :							int										=   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)



