






import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def 				snake_case__							(							UpperCAmelCase							:  Dict       ):
    lowerCAmelCase__   :int      							=				SwinConfig(image_size=1_9_2       )

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

    lowerCAmelCase__   :Any      							=				window_size
    lowerCAmelCase__   :Optional[Any]      							=				embed_dim
    lowerCAmelCase__   :Optional[Any]      							=				depths
    lowerCAmelCase__   :int      							=				num_heads

    return config


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

    if name == "encoder.norm.weight":
        lowerCAmelCase__   :str      							=				"layernorm.weight"
    if name == "encoder.norm.bias":
        lowerCAmelCase__   :List[str]      							=				"layernorm.bias"

    if "decoder" in name:
        pass
    else:
        lowerCAmelCase__   :List[str]      							=				"swin." + name

    return name


def 				snake_case__							(							UpperCAmelCase							:  Dict    ,			UpperCAmelCase							:  Union[str, Any]       ):
    for key in orig_state_dict.copy().keys():
        lowerCAmelCase__   :Dict      							=				orig_state_dict.pop(UpperCAmelCase       )

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

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

    return orig_state_dict


def 				snake_case__							(							UpperCAmelCase							:  List[str]    ,			UpperCAmelCase							:  Any    ,			UpperCAmelCase							:  List[Any]    ,			UpperCAmelCase							:  str       ):
    lowerCAmelCase__   :List[str]      							=				torch.load(UpperCAmelCase    ,			map_location="cpu"       )["model"]

    lowerCAmelCase__   :List[Any]      							=				get_swin_config(UpperCAmelCase       )
    lowerCAmelCase__   :Tuple      							=				SwinForMaskedImageModeling(UpperCAmelCase       )
    model.eval()

    lowerCAmelCase__   :List[Any]      							=				convert_state_dict(UpperCAmelCase    ,			UpperCAmelCase       )
    model.load_state_dict(UpperCAmelCase       )

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

    lowerCAmelCase__   :Union[str, Any]      							=				ViTImageProcessor(size={"height": 1_9_2, "width": 1_9_2}       )
    lowerCAmelCase__   :Union[str, Any]      							=				Image.open(requests.get(UpperCAmelCase    ,			stream=UpperCAmelCase       ).raw       )
    lowerCAmelCase__   :Any      							=				image_processor(images=UpperCAmelCase    ,			return_tensors="pt"       )

    with torch.no_grad():
        lowerCAmelCase__   :Optional[Any]      							=				model(**UpperCAmelCase       ).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(UpperCAmelCase       )

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

    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    :   List[Any]        =			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    :   Any        =			parser.parse_args()
 convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)











from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean


def 				snake_case__							(							UpperCAmelCase							:  int       ):

    # A local function to see if a dot lands in the circle.
    def is_in_circle(UpperCAmelCase							:  float    ,			UpperCAmelCase							:  float       ) -> bool:
        lowerCAmelCase__   :Optional[int]      							=				sqrt((x**2) + (y**2)       )
        # Our circle has a radius of 1, so a distance
        # greater than 1 would land outside the circle.
        return distance_from_centre <= 1

    # The proportion of guesses that landed in the circle
    lowerCAmelCase__   :Union[str, Any]      							=				mean(
        int(is_in_circle(uniform(-1.0    ,			1.0       )    ,			uniform(-1.0    ,			1.0       )       )       )
        for _ in range(UpperCAmelCase       )       )
    # The ratio of the area for circle to square is pi/4.
    lowerCAmelCase__   :List[Any]      							=				proportion * 4
    print(F'''The estimated value of pi is {pi_estimate}'''       )
    print(F'''The numpy value of pi is {pi}'''       )
    print(F'''The total error is {abs(pi - pi_estimate       )}'''       )


def 				snake_case__							(							UpperCAmelCase							:  int    ,			UpperCAmelCase							:  Callable[[float], float]    ,			UpperCAmelCase							:  float = 0.0    ,			UpperCAmelCase							:  float = 1.0    ,			):
    return mean(
        function_to_integrate(uniform(UpperCAmelCase    ,			UpperCAmelCase       )       ) for _ in range(UpperCAmelCase       )       ) * (max_value - min_value)


def 				snake_case__							(							UpperCAmelCase							:  int    ,			UpperCAmelCase							:  float = 0.0    ,			UpperCAmelCase							:  float = 1.0       ):

    def identity_function(UpperCAmelCase							:  float       ) -> float:
        return x

    lowerCAmelCase__   :List[str]      							=				area_under_curve_estimator(
        UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase       )
    lowerCAmelCase__   :Tuple      							=				(max_value * max_value - min_value * min_value) / 2

    print("******************"       )
    print(F'''Estimating area under y=x where x varies from {min_value} to {max_value}'''       )
    print(F'''Estimated value is {estimated_value}'''       )
    print(F'''Expected value is {expected_value}'''       )
    print(F'''Total error is {abs(estimated_value - expected_value       )}'''       )
    print("******************"       )


def 				snake_case__							(							UpperCAmelCase							:  int       ):

    def function_to_integrate(UpperCAmelCase							:  float       ) -> float:
        return sqrt(4.0 - x * x       )

    lowerCAmelCase__   :int      							=				area_under_curve_estimator(
        UpperCAmelCase    ,			UpperCAmelCase    ,			0.0    ,			2.0       )

    print("******************"       )
    print("Estimating pi using area_under_curve_estimator"       )
    print(F'''Estimated value is {estimated_value}'''       )
    print(F'''Expected value is {pi}'''       )
    print(F'''Total error is {abs(estimated_value - pi       )}'''       )
    print("******************"       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()



