

import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Tuple					)	->					Union[str, Any]:




				'''simple docstring'''





				A__						=	SwinConfig(image_size=1_9_2					)

				if "base" in model_name:
								A__						=	6
								A__						=	1_2_8
								A__						=	(2, 2, 1_8, 2)
								A__						=	(4, 8, 1_6, 3_2)
				elif "large" in model_name:
								A__						=	1_2
								A__						=	1_9_2
								A__						=	(2, 2, 1_8, 2)
								A__						=	(6, 1_2, 2_4, 4_8)
				else:
								raise ValueError("Model not supported, only supports base and large variants"					)

				A__						=	window_size
				A__						=	embed_dim
				A__						=	depths
				A__						=	num_heads

				return config



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[Any]					)	->					Any:




				'''simple docstring'''





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

				if name == "encoder.norm.weight":
								A__						=	"layernorm.weight"
				if name == "encoder.norm.bias":
								A__						=	"layernorm.bias"

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

				return name



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Dict		,				SCREAMING_SNAKE_CASE_:							Union[str, Any]					)	->					List[str]:




				'''simple docstring'''





				for key in orig_state_dict.copy().keys():
								A__						=	orig_state_dict.pop(SCREAMING_SNAKE_CASE_					)

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

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

				return orig_state_dict



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[int]		,				SCREAMING_SNAKE_CASE_:							int		,				SCREAMING_SNAKE_CASE_:							Tuple		,				SCREAMING_SNAKE_CASE_:							Optional[Any]					)	->					List[str]:




				'''simple docstring'''





				A__						=	torch.load(SCREAMING_SNAKE_CASE_		,				map_location="cpu"					)["model"]

				A__						=	get_swin_config(SCREAMING_SNAKE_CASE_					)
				A__						=	SwinForMaskedImageModeling(SCREAMING_SNAKE_CASE_					)
				model.eval()

				A__						=	convert_state_dict(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					)
				model.load_state_dict(SCREAMING_SNAKE_CASE_					)

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

				A__						=	ViTImageProcessor(size={"height": 1_9_2, "width": 1_9_2}					)
				A__						=	Image.open(requests.get(SCREAMING_SNAKE_CASE_		,				stream=SCREAMING_SNAKE_CASE_					).raw					)
				A__						=	image_processor(images=SCREAMING_SNAKE_CASE_		,				return_tensors="pt"					)

				with torch.no_grad():
								A__						=	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__":
	lowerCAmelCase__		      =       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."""
	)

	lowerCAmelCase__		      =       parser.parse_args()
	convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_ : Dict    		=       {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Tuple    		=       ["MBartTokenizer"]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : str    		=       ["MBartTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[Any]    		=       [
        "MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MBartForCausalLM",
        "MBartForConditionalGeneration",
        "MBartForQuestionAnswering",
        "MBartForSequenceClassification",
        "MBartModel",
        "MBartPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Any    		=       [
        "TFMBartForConditionalGeneration",
        "TFMBartModel",
        "TFMBartPreTrainedModel",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[str]    		=       [
        "FlaxMBartForConditionalGeneration",
        "FlaxMBartForQuestionAnswering",
        "FlaxMBartForSequenceClassification",
        "FlaxMBartModel",
        "FlaxMBartPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mbart import MBartTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mbart_fast import MBartTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mbart import (
            MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
            MBartForCausalLM,
            MBartForConditionalGeneration,
            MBartForQuestionAnswering,
            MBartForSequenceClassification,
            MBartModel,
            MBartPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_mbart import (
            FlaxMBartForConditionalGeneration,
            FlaxMBartForQuestionAnswering,
            FlaxMBartForSequenceClassification,
            FlaxMBartModel,
            FlaxMBartPreTrainedModel,
        )

else:
    import sys

    snake_case_ : Any    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






