


'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor






def 					_A			(			snake_case__    :			Optional[int]				):
	snake_case__			:			Tuple             =		SwinConfig(image_size=1_92				)

	if "base" in model_name:
		snake_case__			:			Optional[Any]             =		6
		snake_case__			:			Dict             =		1_28
		snake_case__			:			int             =		(2, 2, 18, 2)
		snake_case__			:			Tuple             =		(4, 8, 16, 32)
	elif "large" in model_name:
		snake_case__			:			List[Any]             =		12
		snake_case__			:			Union[str, Any]             =		1_92
		snake_case__			:			Dict             =		(2, 2, 18, 2)
		snake_case__			:			Tuple             =		(6, 12, 24, 48)
	else:
		raise ValueError('''Model not supported, only supports base and large variants'''				)

	snake_case__			:			List[Any]             =		window_size
	snake_case__			:			Optional[Any]             =		embed_dim
	snake_case__			:			Tuple             =		depths
	snake_case__			:			List[str]             =		num_heads

	return config






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

	if name == "encoder.norm.weight":
		snake_case__			:			Dict             =		"""layernorm.weight"""
	if name == "encoder.norm.bias":
		snake_case__			:			Tuple             =		"""layernorm.bias"""

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

	return name






def 					_A			(			snake_case__    :			List[str]						,						snake_case__    :			List[Any]				):
	for key in orig_state_dict.copy().keys():
		snake_case__			:			str             =		orig_state_dict.pop(__lowercase				)

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

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

	return orig_state_dict






def 					_A			(			snake_case__    :			Tuple						,						snake_case__    :			Union[str, Any]						,						snake_case__    :			Optional[int]						,						snake_case__    :			Optional[Any]				):
	snake_case__			:			Optional[int]             =		torch.load(__lowercase						,						map_location='''cpu'''				)["""model"""]

	snake_case__			:			Tuple             =		get_swin_config(__lowercase				)
	snake_case__			:			Optional[int]             =		SwinForMaskedImageModeling(__lowercase				)
	model.eval()

	snake_case__			:			Any             =		convert_state_dict(__lowercase						,						__lowercase				)
	model.load_state_dict(__lowercase				)

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

	snake_case__			:			List[Any]             =		ViTImageProcessor(size={'''height''': 1_92, '''width''': 1_92}				)
	snake_case__			:			List[Any]             =		Image.open(requests.get(__lowercase						,						stream=__lowercase				).raw				)
	snake_case__			:			List[str]             =		image_processor(images=__lowercase						,						return_tensors='''pt'''				)

	with torch.no_grad():
		snake_case__			:			List[Any]             =		model(**__lowercase				).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(__lowercase				)

		print(f'''Saving image processor to {pytorch_dump_folder_path}'''				)
		image_processor.save_pretrained(__lowercase				)

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











'''simple docstring'''







from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
      import PIL

# soft dependency
if is_pytesseract_available():
      import pytesseract

lowercase      :   Optional[Any]      =       logging.get_logger(__name__)



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase				):
     return [
         int(1_000 * (box[0] / width)				),
         int(1_000 * (box[1] / height)				),
         int(1_000 * (box[2] / width)				),
         int(1_000 * (box[3] / height)				),
     ]



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase				):
     snake_case			:      List[Any]        =			to_pil_image(__lowercase				)
     snake_case ,		snake_case			:      Union[str, Any]        =			pil_image.size
     snake_case			:      Optional[Any]        =			pytesseract.image_to_data(__lowercase     ,   lang=__lowercase     ,   output_type="""dict"""     ,   config=__lowercase				)
     snake_case ,		snake_case ,		snake_case ,		snake_case ,		snake_case			:      Dict        =			data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

     # filter empty words and corresponding coordinates
     snake_case			:      Tuple        =			[idx for idx, word in enumerate(__lowercase				) if not word.strip()]
     snake_case			:      Tuple        =			[word for idx, word in enumerate(__lowercase				) if idx not in irrelevant_indices]
     snake_case			:      str        =			[coord for idx, coord in enumerate(__lowercase				) if idx not in irrelevant_indices]
     snake_case			:      Tuple        =			[coord for idx, coord in enumerate(__lowercase				) if idx not in irrelevant_indices]
     snake_case			:      Optional[Any]        =			[coord for idx, coord in enumerate(__lowercase				) if idx not in irrelevant_indices]
     snake_case			:      Optional[Any]        =			[coord for idx, coord in enumerate(__lowercase				) if idx not in irrelevant_indices]

     # turn coordinates into (left, top, left+width, top+height) format
     snake_case			:      Dict        =			[]
     for x, y, w, h in zip(__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				):
          snake_case			:      Tuple        =			[x, y, x + w, y + h]
          actual_boxes.append(__lowercase				)

     # finally, normalize the bounding boxes
     snake_case			:      str        =			[]
     for box in actual_boxes:
          normalized_boxes.append(normalize_box(__lowercase     ,   __lowercase     ,   __lowercase				)				)

     assert len(__lowercase				) == len(__lowercase				), "Not as many words as there are bounding boxes"

     return words, normalized_boxes







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Dict          =					["""pixel_values"""]







     def __init__(				self							,__a = True							,__a = None							,__a = PILImageResampling.BILINEAR							,__a = True							,__a = 1 / 255							,__a = True							,__a = None							,__a = None							,__a = True							,__a = None							,__a = ""							,**__a							,)      ->				None:
          super().__init__(**__a							)
          snake_case			:      int        =			size if size is not None else {"""height""": 224, """width""": 224}
          snake_case			:      Union[str, Any]        =			get_size_dict(__a							)

          snake_case			:      List[str]        =			do_resize
          snake_case			:      Dict        =			size
          snake_case			:      Dict        =			resample
          snake_case			:      Union[str, Any]        =			do_rescale
          snake_case			:      Union[str, Any]        =			rescale_value
          snake_case			:      List[str]        =			do_normalize
          snake_case			:      int        =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          snake_case			:      int        =			image_std if image_std is not None else IMAGENET_STANDARD_STD
          snake_case			:      List[Any]        =			apply_ocr
          snake_case			:      Any        =			ocr_lang
          snake_case			:      Optional[int]        =			tesseract_config







     def 			snake_case_      (				self							,__a							,__a							,__a = PILImageResampling.BILINEAR							,__a = None							,**__a							,)      ->				np.ndarray:
          snake_case			:      List[str]        =			get_size_dict(__a							)
          if "height" not in size or "width" not in size:
               raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''							)
          snake_case			:      Tuple        =			(size["""height"""], size["""width"""])
          return resize(__a							,size=__a							,resample=__a							,data_format=__a							,**__a							)







     def 			snake_case_      (				self							,__a							,__a							,__a = None							,**__a							,)      ->				np.ndarray:
          return rescale(__a							,scale=__a							,data_format=__a							,**__a							)







     def 			snake_case_      (				self							,__a							,__a							,__a							,__a = None							,**__a							,)      ->				np.ndarray:
          return normalize(__a							,mean=__a							,std=__a							,data_format=__a							,**__a							)







     def 			snake_case_      (				self							,__a							,__a = None							,__a = None							,__a=None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = ChannelDimension.FIRST							,**__a							,)      ->				PIL.Image.Image:
          snake_case			:      int        =			do_resize if do_resize is not None else self.do_resize
          snake_case			:      List[Any]        =			size if size is not None else self.size
          snake_case			:      Any        =			get_size_dict(__a							)
          snake_case			:      Any        =			resample if resample is not None else self.resample
          snake_case			:      str        =			do_rescale if do_rescale is not None else self.do_rescale
          snake_case			:      List[str]        =			rescale_factor if rescale_factor is not None else self.rescale_factor
          snake_case			:      Dict        =			do_normalize if do_normalize is not None else self.do_normalize
          snake_case			:      Optional[int]        =			image_mean if image_mean is not None else self.image_mean
          snake_case			:      Tuple        =			image_std if image_std is not None else self.image_std
          snake_case			:      List[str]        =			apply_ocr if apply_ocr is not None else self.apply_ocr
          snake_case			:      str        =			ocr_lang if ocr_lang is not None else self.ocr_lang
          snake_case			:      Dict        =			tesseract_config if tesseract_config is not None else self.tesseract_config

          snake_case			:      Any        =			make_list_of_images(__a							)

          if not valid_images(__a							):
               raise ValueError(
                   """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                   """torch.Tensor, tf.Tensor or jax.ndarray."""							)

          if do_resize and size is None:
               raise ValueError("""Size must be specified if do_resize is True."""							)

          if do_rescale and rescale_factor is None:
               raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

          if do_normalize and (image_mean is None or image_std is None):
               raise ValueError("""If do_normalize is True, image_mean and image_std must be specified."""							)

          # All transformations expect numpy arrays.
          snake_case			:      Union[str, Any]        =			[to_numpy_array(__a							) for image in images]

          # Tesseract OCR to get words + normalized bounding boxes
          if apply_ocr:
               requires_backends(self							,"""pytesseract"""							)
               snake_case			:      int        =			[]
               snake_case			:      Optional[Any]        =			[]
               for image in images:
                    snake_case ,		snake_case			:      Tuple        =			apply_tesseract(__a							,__a							,__a							)
                    words_batch.append(__a							)
                    boxes_batch.append(__a							)

          if do_resize:
               snake_case			:      Any        =			[self.resize(image=__a							,size=__a							,resample=__a							) for image in images]

          if do_rescale:
               snake_case			:      Tuple        =			[self.rescale(image=__a							,scale=__a							) for image in images]

          if do_normalize:
               snake_case			:      Optional[Any]        =			[self.normalize(image=__a							,mean=__a							,std=__a							) for image in images]

          snake_case			:      Dict        =			[to_channel_dimension_format(__a							,__a							) for image in images]

          snake_case			:      List[Any]        =			BatchFeature(data={"""pixel_values""": images}							,tensor_type=__a							)

          if apply_ocr:
               snake_case			:      Optional[Any]        =			words_batch
               snake_case			:      Optional[int]        =			boxes_batch
          return data



