


'''simple docstring'''



import argparse
import json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A__						:   str				=    logging.get_logger(__name__)

A__						:   Tuple				=    torch.device("""cpu""")



def      UpperCAmelCase__   (      )	->						Any:
     __lowerCamelCase  :      List[str]														=						'http://images.cocodataset.org/val2017/000000039769.jpg'
     __lowerCamelCase  :      int														=						Image.open(requests.get(UpperCAmelCase_ ,       stream=UpperCAmelCase_				).raw				)
     return im



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[Any]				)	->						Tuple:
     if swiftformer_name == "swiftformer_xs":
          return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01]				)

     elif swiftformer_name == "swiftformer_s":
          return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01]				)

     elif swiftformer_name == "swiftformer_l1":
          return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02]				)

     elif swiftformer_name == "swiftformer_l3":
          return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02]				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							List[Any] ,       UpperCAmelCase_       :							List[str] ,       UpperCAmelCase_       :							List[str]				)	->						int:
     __lowerCamelCase  :      Optional[Any]														=						dct.pop(UpperCAmelCase_				)
     __lowerCamelCase  :      List[str]														=						val



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Tuple				)	->						Dict:
     __lowerCamelCase  :      str														=						[]
     for k in state_dict.keys():
          __lowerCamelCase  :      Optional[Any]														=						k
          if ".pwconv" in k:
               __lowerCamelCase  :      int														=						k_new.replace('.pwconv' ,       '.point_wise_conv'				)
          if ".dwconv" in k:
               __lowerCamelCase  :      Any														=						k_new.replace('.dwconv' ,       '.depth_wise_conv'				)
          if ".Proj." in k:
               __lowerCamelCase  :      List[Any]														=						k_new.replace('.Proj.' ,       '.proj.'				)
          if "patch_embed" in k_new:
               __lowerCamelCase  :      Union[str, Any]														=						k_new.replace('patch_embed' ,       'swiftformer.patch_embed.patch_embedding'				)
          if "network" in k_new:
               __lowerCamelCase  :      Optional[Any]														=						k_new.split('.'				)
               if ls[2].isdigit():
                    __lowerCamelCase  :      int														=						'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]				)
               else:
                    __lowerCamelCase  :      List[Any]														=						k_new.replace('network' ,       'swiftformer.encoder.network'				)
          rename_keys.append((k, k_new)				)
     return rename_keys



@torch.no_grad()
def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[Any] ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							Any				)	->						Optional[int]:
     __lowerCamelCase  :      Any														=						SwiftFormerConfig()

     # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
     __lowerCamelCase  :      Optional[Any]														=						10_00
     __lowerCamelCase  :      Optional[Any]														=						'huggingface/label-files'
     __lowerCamelCase  :      Optional[int]														=						'imagenet-1k-id2label.json'
     __lowerCamelCase  :      Any														=						json.load(open(hf_hub_download(UpperCAmelCase_ ,       UpperCAmelCase_ ,       repo_type='dataset'				) ,       'r'				)				)
     __lowerCamelCase  :      Optional[Any]														=						{int(UpperCAmelCase_				): v for k, v in idalabel.items()}
     __lowerCamelCase  :      Any														=						idalabel
     __lowerCamelCase  :      Dict														=						{v: k for k, v in idalabel.items()}

     # size of the architecture
     if swiftformer_name == "swiftformer_xs":
          __lowerCamelCase  :      Tuple														=						[3, 3, 6, 4]
          __lowerCamelCase  :      Optional[Any]														=						[48, 56, 1_12, 2_20]

     elif swiftformer_name == "swiftformer_s":
          __lowerCamelCase  :      Any														=						[3, 3, 9, 6]
          __lowerCamelCase  :      Any														=						[48, 64, 1_68, 2_24]

     elif swiftformer_name == "swiftformer_l1":
          __lowerCamelCase  :      List[str]														=						[4, 3, 10, 5]
          __lowerCamelCase  :      Union[str, Any]														=						[48, 96, 1_92, 3_84]

     elif swiftformer_name == "swiftformer_l3":
          __lowerCamelCase  :      Union[str, Any]														=						[4, 4, 12, 6]
          __lowerCamelCase  :      Dict														=						[64, 1_28, 3_20, 5_12]

     # load state_dict of original model, remove and rename some keys
     if original_ckpt:
          if original_ckpt.startswith('https'				):
               __lowerCamelCase  :      int														=						torch.hub.load_state_dict_from_url(UpperCAmelCase_ ,       map_location='cpu' ,       check_hash=UpperCAmelCase_				)
          else:
               __lowerCamelCase  :      Tuple														=						torch.load(UpperCAmelCase_ ,       map_location='cpu'				)
     __lowerCamelCase  :      Tuple														=						checkpoint

     __lowerCamelCase  :      Any														=						create_rename_keys(UpperCAmelCase_				)
     for rename_key_src, rename_key_dest in rename_keys:
          rename_key(UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_				)

     # load HuggingFace model
     __lowerCamelCase  :      List[str]														=						SwiftFormerForImageClassification(UpperCAmelCase_				).eval()
     hf_model.load_state_dict(UpperCAmelCase_				)

     # prepare test inputs
     __lowerCamelCase  :      Dict														=						prepare_img()
     __lowerCamelCase  :      Tuple														=						ViTImageProcessor.from_pretrained('preprocessor_config'				)
     __lowerCamelCase  :      Any														=						processor(images=UpperCAmelCase_ ,       return_tensors='pt'				)

     # compare outputs from both models
     __lowerCamelCase  :      List[Any]														=						get_expected_output(UpperCAmelCase_				)
     __lowerCamelCase  :      Dict														=						hf_model(inputs['pixel_values']				).logits

     assert hf_logits.shape == torch.Size([1, 10_00]				)
     assert torch.allclose(hf_logits[0, 0:5] ,       UpperCAmelCase_ ,       atol=1e-3				)

     Path(UpperCAmelCase_				).mkdir(exist_ok=UpperCAmelCase_				)
     print(F'Saving model {swiftformer_name} to {pytorch_dump_folder_path}'				)
     hf_model.save_pretrained(UpperCAmelCase_				)


if __name__ == "__main__":
      A__						:   List[Any]				=    argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--swiftformer_name""",
          default="""swiftformer_xs""",
          choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
          type=str,
          help="""Name of the SwiftFormer model you'd like to convert.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default="""./converted_outputs/""",
          type=str,
          help="""Path to the output PyTorch model directory.""",
      )
      parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")

      A__						:   Union[str, Any]				=    parser.parse_args()
      convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)









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        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    ["""image_processor""", """tokenizer"""]
       lowercase_						=    """LayoutLMv2ImageProcessor"""
       lowercase_						=    ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")

       def __init__(							self ,   lowercase__=None ,   lowercase__=None ,   **lowercase__     ):







             '''simple docstring'''



             if "feature_extractor" in kwargs:
                   warnings.warn(
                       '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                       ''' instead.''' ,   lowercase__ ,   )
                   __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__(lowercase__ ,   lowercase__     )

       def __call__(							self ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = True ,   lowercase__ = False ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = 0 ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = False ,   lowercase__ = False ,   lowercase__ = False ,   lowercase__ = False ,   lowercase__ = True ,   lowercase__ = None ,   **lowercase__ ,   ):







             '''simple docstring'''



             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=lowercase__ ,   return_tensors=lowercase__     )

             # second, apply the tokenizer
             if text is not None and self.image_processor.apply_ocr and text_pair is None:
                   if isinstance(lowercase__ ,   lowercase__     ):
                         __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=lowercase__ ,   add_special_tokens=lowercase__ ,   padding=lowercase__ ,   truncation=lowercase__ ,   max_length=lowercase__ ,   stride=lowercase__ ,   pad_to_multiple_of=lowercase__ ,   return_token_type_ids=lowercase__ ,   return_attention_mask=lowercase__ ,   return_overflowing_tokens=lowercase__ ,   return_special_tokens_mask=lowercase__ ,   return_offsets_mapping=lowercase__ ,   return_length=lowercase__ ,   verbose=lowercase__ ,   return_tensors=lowercase__ ,   **lowercase__ ,   )

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

             return encoded_inputs

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =[]
             for sample_idx in overflow_to_sample_mapping:
                   images_with_overflow.append(images[sample_idx]     )

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

             return images_with_overflow

       def     __UpperCamelCase     (							self ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



             return self.tokenizer.batch_decode(*lowercase__ ,   **lowercase__     )

       def     __UpperCamelCase     (							self ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



             return self.tokenizer.decode(*lowercase__ ,   **lowercase__     )

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return ["input_ids", "bbox", "attention_mask", "image"]

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             warnings.warn(
                 '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,   lowercase__ ,   )
             return self.image_processor_class


       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             warnings.warn(
                 '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,   lowercase__ ,   )
             return self.image_processor


