


'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification






def __UpperCamelCase   (			__lowerCamelCase		: List[Any]				)      ->     List[str]:







     '''simple docstring'''






     _a						=		SwinvaConfig()
     _a						=		swinva_name.split("_"				)

     _a						=		name_split[1]
     if "to" in name_split[3]:
          _a						=		int(name_split[3][-3:]				)
     else:
          _a						=		int(name_split[3]				)
     if "to" in name_split[2]:
          _a						=		int(name_split[2][-2:]				)
     else:
          _a						=		int(name_split[2][6:]				)

     if model_size == "tiny":
          _a						=		96
          _a						=		(2, 2, 6, 2)
          _a						=		(3, 6, 12, 24)
     elif model_size == "small":
          _a						=		96
          _a						=		(2, 2, 18, 2)
          _a						=		(3, 6, 12, 24)
     elif model_size == "base":
          _a						=		128
          _a						=		(2, 2, 18, 2)
          _a						=		(4, 8, 16, 32)
     else:
          _a						=		192
          _a						=		(2, 2, 18, 2)
          _a						=		(6, 12, 24, 48)

     if "to" in swinva_name:
          _a						=		(12, 12, 12, 6)

     if ("22k" in swinva_name) and ("to" not in swinva_name):
          _a						=		2_1841
          _a						=		"huggingface/label-files"
          _a						=		"imagenet-22k-id2label.json"
          _a						=		json.load(open(hf_hub_download(__lowerCamelCase      ,  __lowerCamelCase      ,  repo_type="dataset"				)      ,  "r"				)				)
          _a						=		{int(__lowerCamelCase				): v for k, v in idalabel.items()}
          _a						=		idalabel
          _a						=		{v: k for k, v in idalabel.items()}

     else:
          _a						=		1000
          _a						=		"huggingface/label-files"
          _a						=		"imagenet-1k-id2label.json"
          _a						=		json.load(open(hf_hub_download(__lowerCamelCase      ,  __lowerCamelCase      ,  repo_type="dataset"				)      ,  "r"				)				)
          _a						=		{int(__lowerCamelCase				): v for k, v in idalabel.items()}
          _a						=		idalabel
          _a						=		{v: k for k, v in idalabel.items()}

     _a						=		img_size
     _a						=		num_classes
     _a						=		embed_dim
     _a						=		depths
     _a						=		num_heads
     _a						=		window_size

     return config






def __UpperCamelCase   (			__lowerCamelCase		: str				)      ->     Tuple:







     '''simple docstring'''






     if "patch_embed.proj" in name:
          _a						=		name.replace("patch_embed.proj"      ,  "embeddings.patch_embeddings.projection"				)
     if "patch_embed.norm" in name:
          _a						=		name.replace("patch_embed.norm"      ,  "embeddings.norm"				)
     if "layers" in name:
          _a						=		"encoder." + name
     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 "q_bias" in name:
          _a						=		name.replace("q_bias"      ,  "query.bias"				)
     if "k_bias" in name:
          _a						=		name.replace("k_bias"      ,  "key.bias"				)
     if "v_bias" in name:
          _a						=		name.replace("v_bias"      ,  "value.bias"				)
     if "cpb_mlp" in name:
          _a						=		name.replace("cpb_mlp"      ,  "continuous_position_bias_mlp"				)
     if name == "norm.weight":
          _a						=		"layernorm.weight"
     if name == "norm.bias":
          _a						=		"layernorm.bias"

     if "head" in name:
          _a						=		name.replace("head"      ,  "classifier"				)
     else:
          _a						=		"swinv2." + name

     return name






def __UpperCamelCase   (			__lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: int				)      ->     Any:







     '''simple docstring'''






     for key in orig_state_dict.copy().keys():
          _a						=		orig_state_dict.pop(__lowerCamelCase				)

          if "mask" in key:
               continue
          elif "qkv" in key:
               _a						=		key.split("."				)
               _a						=		int(key_split[1]				)
               _a						=		int(key_split[3]				)
               _a						=		model.swinva.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 __UpperCamelCase   (			__lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: Dict				)      ->     Tuple:







     '''simple docstring'''






     _a						=		timm.create_model(__lowerCamelCase      ,  pretrained=__lowerCamelCase				)
     timm_model.eval()

     _a						=		get_swinva_config(__lowerCamelCase				)
     _a						=		SwinvaForImageClassification(__lowerCamelCase				)
     model.eval()

     _a						=		convert_state_dict(timm_model.state_dict()      ,  __lowerCamelCase				)
     model.load_state_dict(__lowerCamelCase				)

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

     _a						=		AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"      ,  "-"				)				)				)
     _a						=		Image.open(requests.get(__lowerCamelCase      ,  stream=__lowerCamelCase				).raw				)
     _a						=		image_processor(images=__lowerCamelCase      ,  return_tensors="pt"				)

     _a						=		timm_model(inputs["pixel_values"]				)
     _a						=		model(**__lowerCamelCase				).logits

     assert torch.allclose(__lowerCamelCase      ,  __lowerCamelCase      ,  atol=1E-3				)

     print(F"Saving model {swinva_name} to {pytorch_dump_folder_path}"				)
     model.save_pretrained(__lowerCamelCase				)

     print(F"Saving image processor to {pytorch_dump_folder_path}"				)
     image_processor.save_pretrained(__lowerCamelCase				)

     model.push_to_hub(
         repo_path_or_name=Path(__lowerCamelCase      ,  __lowerCamelCase				)      ,  organization="nandwalritik"      ,  commit_message="Add model"      ,  )


if __name__ == "__main__":
   lowercase__				     =		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--swinv2_name",
       default="swinv2_tiny_patch4_window8_256",
       type=str,
       help="Name of the Swinv2 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."
   )

   lowercase__				     =		parser.parse_args()
   convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)





'''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 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				['''image_processor''', '''tokenizer''']
     UpperCAmelCase 					=				'''LayoutLMv2ImageProcessor'''
     UpperCAmelCase 					=				('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')






     def __init__(			self     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							**__UpperCamelCase		)       ->    str:
          if "feature_extractor" in kwargs:
               warnings.warn(
                   "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                   " instead."     ,							__UpperCamelCase     ,							)
               _a						=		kwargs.pop("feature_extractor"		)

          _a						=		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__(__UpperCamelCase     ,							__UpperCamelCase		)






     def __call__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = True     ,							__UpperCamelCase = False     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = 0     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = True     ,							__UpperCamelCase = None     ,							**__UpperCamelCase     ,							)       ->    BatchEncoding:
          # 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."		)

          if return_overflowing_tokens is True and return_offsets_mapping is False:
               raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."		)

          # first, apply the image processor
          _a						=		self.image_processor(images=__UpperCamelCase     ,							return_tensors=__UpperCamelCase		)

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

          _a						=		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=__UpperCamelCase     ,							add_special_tokens=__UpperCamelCase     ,							padding=__UpperCamelCase     ,							truncation=__UpperCamelCase     ,							max_length=__UpperCamelCase     ,							stride=__UpperCamelCase     ,							pad_to_multiple_of=__UpperCamelCase     ,							return_token_type_ids=__UpperCamelCase     ,							return_attention_mask=__UpperCamelCase     ,							return_overflowing_tokens=__UpperCamelCase     ,							return_special_tokens_mask=__UpperCamelCase     ,							return_offsets_mapping=__UpperCamelCase     ,							return_length=__UpperCamelCase     ,							verbose=__UpperCamelCase     ,							return_tensors=__UpperCamelCase     ,							**__UpperCamelCase     ,							)

          # add pixel values
          _a						=		features.pop("pixel_values"		)
          if return_overflowing_tokens is True:
               _a						=		self.get_overflowing_images(__UpperCamelCase     ,							encoded_inputs["overflow_to_sample_mapping"]		)
          _a						=		images

          return encoded_inputs






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Dict:
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          _a						=		[]
          for sample_idx in overflow_to_sample_mapping:
               images_with_overflow.append(images[sample_idx]		)

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

          return images_with_overflow






     def  a_					(			self     ,							*__UpperCamelCase     ,							**__UpperCamelCase		)       ->    Tuple:
          return self.tokenizer.batch_decode(*__UpperCamelCase     ,							**__UpperCamelCase		)






     def  a_					(			self     ,							*__UpperCamelCase     ,							**__UpperCamelCase		)       ->    List[Any]:
          return self.tokenizer.decode(*__UpperCamelCase     ,							**__UpperCamelCase		)






     @property
     def  a_					(			self		)       ->    int:
          return ["input_ids", "bbox", "attention_mask", "image"]






     @property
     def  a_					(			self		)       ->    Dict:
          warnings.warn(
              "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."     ,							__UpperCamelCase     ,							)
          return self.image_processor_class






     @property
     def  a_					(			self		)       ->    int:
          warnings.warn(
              "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,							__UpperCamelCase     ,							)
          return self.image_processor

