



"""simple docstring"""




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  int						=					['image_processor', 'tokenizer']
      __SCREAMING_SNAKE_CASE		:  Any						=					'LayoutLMv3ImageProcessor'
      __SCREAMING_SNAKE_CASE		:  Union[str, Any]						=					('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')



      def __init__(	self	:      Any	,      A	:      Optional[int]=None	,      A	:      Tuple=None	,      **A	:      List[str]		):
           _UpperCAmelCase     :      Dict														=       None
           if "feature_extractor" in kwargs:
                warnings.warn(
                    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                    " instead."	,      A	,      )
                _UpperCAmelCase     :      List[Any]														=       kwargs.pop("feature_extractor"		)

           _UpperCAmelCase     :      Tuple														=       image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
                raise ValueError("You need to specify an `image_processor`."		)
           if tokenizer is None:
                raise ValueError("You need to specify a `tokenizer`."		)

           super().__init__(A	,      A		)



      def __call__(	self	:      Optional[int]	,      A	:      Optional[int]	,      A	:      Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,      A	:      Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None	,      A	:      Union[List[List[int]], List[List[List[int]]]] = None	,      A	:      Optional[Union[List[int], List[List[int]]]] = None	,      A	:      bool = True	,      A	:      Union[bool, str, PaddingStrategy] = False	,      A	:      Union[bool, str, TruncationStrategy] = None	,      A	:      Optional[int] = None	,      A	:      int = 0	,      A	:      Optional[int] = None	,      A	:      Optional[bool] = None	,      A	:      Optional[bool] = None	,      A	:      bool = False	,      A	:      bool = False	,      A	:      bool = False	,      A	:      bool = False	,      A	:      bool = True	,      A	:      Optional[Union[str, TensorType]] = None	,      **A	:      str	,      ):
           # verify input
           if self.image_processor.apply_ocr and (boxes is not None):
                raise ValueError(
                    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."		)

           if self.image_processor.apply_ocr and (word_labels is not None):
                raise ValueError(
                    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."		)

           # first, apply the image processor
           _UpperCAmelCase     :      Union[str, Any]														=       self.image_processor(images=A	,      return_tensors=A		)

           # second, apply the tokenizer
           if text is not None and self.image_processor.apply_ocr and text_pair is None:
                if isinstance(A	,      A		):
                     _UpperCAmelCase     :      Dict														=       [text]  # add batch dimension (as the image processor always adds a batch dimension)
                _UpperCAmelCase     :      List[Any]														=       features["words"]

           _UpperCAmelCase     :      int														=       self.tokenizer(
               text=text if text is not None else features["words"]	,      text_pair=text_pair if text_pair is not None else None	,      boxes=boxes if boxes is not None else features["boxes"]	,      word_labels=A	,      add_special_tokens=A	,      padding=A	,      truncation=A	,      max_length=A	,      stride=A	,      pad_to_multiple_of=A	,      return_token_type_ids=A	,      return_attention_mask=A	,      return_overflowing_tokens=A	,      return_special_tokens_mask=A	,      return_offsets_mapping=A	,      return_length=A	,      verbose=A	,      return_tensors=A	,      **A	,      )

           # add pixel values
           _UpperCAmelCase     :      Any														=       features.pop("pixel_values"		)
           if return_overflowing_tokens is True:
                _UpperCAmelCase     :      Any														=       self.get_overflowing_images(A	,      encoded_inputs["overflow_to_sample_mapping"]		)
           _UpperCAmelCase     :      Union[str, Any]														=       images

           return encoded_inputs



      def 		snake_case_   (	self	:      int	,      A	:      Optional[Any]	,      A	:      str		):
           # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
           _UpperCAmelCase     :      Optional[int]														=       []
           for sample_idx in overflow_to_sample_mapping:
                images_with_overflow.append(images[sample_idx]		)

           if len(A		) != len(A		):
                raise ValueError(
                    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
                    f' {len(A		)} and {len(A		)}'		)

           return images_with_overflow



      def 		snake_case_   (	self	:      str	,      *A	:      List[Any]	,      **A	:      Any		):
           return self.tokenizer.batch_decode(*A	,      **A		)



      def 		snake_case_   (	self	:      Union[str, Any]	,      *A	:      List[str]	,      **A	:      str		):
           return self.tokenizer.decode(*A	,      **A		)



      @property
      def 		snake_case_   (	self	:      Optional[int]		):
           return ["input_ids", "bbox", "attention_mask", "pixel_values"]



      @property
      def 		snake_case_   (	self	:      Any		):
           warnings.warn(
               "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."	,      A	,      )
           return self.image_processor_class



      @property
      def 		snake_case_   (	self	:      Tuple		):
           warnings.warn(
               "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."	,      A	,      )
           return self.image_processor





"""simple docstring"""




import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  List[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      List[Any]														=       SwinConfig(image_size=192							)

     if "base" in model_name:
          _UpperCAmelCase     :      Tuple														=       6
          _UpperCAmelCase     :      Optional[Any]														=       128
          _UpperCAmelCase     :      Dict														=       (2, 2, 18, 2)
          _UpperCAmelCase     :      List[Any]														=       (4, 8, 16, 32)
     elif "large" in model_name:
          _UpperCAmelCase     :      int														=       12
          _UpperCAmelCase     :      Optional[int]														=       192
          _UpperCAmelCase     :      Optional[Any]														=       (2, 2, 18, 2)
          _UpperCAmelCase     :      str														=       (6, 12, 24, 48)
     else:
          raise ValueError("Model not supported, only supports base and large variants"							)

     _UpperCAmelCase     :      Optional[int]														=       window_size
     _UpperCAmelCase     :      Optional[int]														=       embed_dim
     _UpperCAmelCase     :      List[Any]														=       depths
     _UpperCAmelCase     :      Any														=       num_heads

     return config

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Dict							)    ->  str:





     '''simple docstring'''

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

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

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

     return name

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  Optional[Any]:





     '''simple docstring'''

     for key in orig_state_dict.copy().keys():
          _UpperCAmelCase     :      Dict														=       orig_state_dict.pop(SCREAMING_SNAKE_CASE__							)

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

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

     return orig_state_dict

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		List[Any]		,    SCREAMING_SNAKE_CASE__      :		Any		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  List[str]:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       torch.load(SCREAMING_SNAKE_CASE__		,    map_location="cpu"							)["model"]

     _UpperCAmelCase     :      Union[str, Any]														=       get_swin_config(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      str														=       SwinForMaskedImageModeling(SCREAMING_SNAKE_CASE__							)
     model.eval()

     _UpperCAmelCase     :      Optional[Any]														=       convert_state_dict(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
     model.load_state_dict(SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      Optional[int]														=       "http://images.cocodataset.org/val2017/000000039769.jpg"

     _UpperCAmelCase     :      List[Any]														=       ViTImageProcessor(size={"height": 192, "width": 192}							)
     _UpperCAmelCase     :      int														=       Image.open(requests.get(SCREAMING_SNAKE_CASE__		,    stream=SCREAMING_SNAKE_CASE__							).raw							)
     _UpperCAmelCase     :      str														=       image_processor(images=SCREAMING_SNAKE_CASE__		,    return_tensors="pt"							)

     with torch.no_grad():
          _UpperCAmelCase     :      List[str]														=       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							:   str				=				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							:   str				=				parser.parse_args()
 convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
