"""simple docstring"""





import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
_UpperCamelCase   :						Any      =    logging.getLogger(__name__)





def 				a_     (      ):





       '''simple docstring'''

       lowercase__			:		Union[str, Any]       =	argparse.ArgumentParser(
           description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'     )
       parser.add_argument('--file_path'   , type=_lowerCAmelCase   , default='data/dump.txt'   , help='The path to the data.'     )
       parser.add_argument('--tokenizer_type'   , type=_lowerCAmelCase   , default='bert'   , choices=['bert', 'roberta', 'gpt2']     )
       parser.add_argument('--tokenizer_name'   , type=_lowerCAmelCase   , default='bert-base-uncased'   , help='The tokenizer to use.'     )
       parser.add_argument('--dump_file'   , type=_lowerCAmelCase   , default='data/dump'   , help='The dump file prefix.'     )
       lowercase__			:		Any       =	parser.parse_args()

       logger.info(f"""Loading Tokenizer ({args.tokenizer_name})"""     )
       if args.tokenizer_type == "bert":
              lowercase__			:		Tuple       =	BertTokenizer.from_pretrained(args.tokenizer_name     )
              lowercase__			:		Optional[int]       =	tokenizer.special_tokens_map['cls_token']  # `[CLS]`
              lowercase__			:		List[str]       =	tokenizer.special_tokens_map['sep_token']  # `[SEP]`
       elif args.tokenizer_type == "roberta":
              lowercase__			:		Optional[int]       =	RobertaTokenizer.from_pretrained(args.tokenizer_name     )
              lowercase__			:		Dict       =	tokenizer.special_tokens_map['cls_token']  # `<s>`
              lowercase__			:		Any       =	tokenizer.special_tokens_map['sep_token']  # `</s>`
       elif args.tokenizer_type == "gpt2":
              lowercase__			:		List[str]       =	GPTaTokenizer.from_pretrained(args.tokenizer_name     )
              lowercase__			:		Any       =	tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
              lowercase__			:		Tuple       =	tokenizer.special_tokens_map['eos_token']  # `<|endoftext|>`

       logger.info(f"""Loading text from {args.file_path}"""     )
       with open(args.file_path   , 'r'   , encoding='utf8'     ) as fp:
              lowercase__			:		int       =	fp.readlines()

       logger.info('Start encoding'     )
       logger.info(f"""{len(_lowerCAmelCase     )} examples to process."""     )

       lowercase__			:		Optional[Any]       =	[]
       lowercase__			:		Optional[int]       =	0
       lowercase__			:		List[Any]       =	1_0000
       lowercase__			:		int       =	time.time()
       for text in data:
              lowercase__			:		Any       =	f"""{bos} {text.strip()} {sep}"""
              lowercase__			:		Optional[int]       =	tokenizer.encode(_lowerCAmelCase   , add_special_tokens=_lowerCAmelCase     )
              rslt.append(_lowerCAmelCase     )

              iter += 1
              if iter % interval == 0:
                     lowercase__			:		List[str]       =	time.time()
                     logger.info(f"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""     )
                     lowercase__			:		List[Any]       =	time.time()
       logger.info('Finished binarization'     )
       logger.info(f"""{len(_lowerCAmelCase     )} examples processed."""     )

       lowercase__			:		Tuple       =	f"""{args.dump_file}.{args.tokenizer_name}.pickle"""
       lowercase__			:		Any       =	tokenizer.vocab_size
       if vocab_size < (1 << 16):
              lowercase__			:		Tuple       =	[np.uintaa(_lowerCAmelCase     ) for d in rslt]
       else:
              lowercase__			:		Dict       =	[np.intaa(_lowerCAmelCase     ) for d in rslt]
       random.shuffle(rslt_     )
       logger.info(f"""Dump to {dp_file}"""     )
       with open(_lowerCAmelCase   , 'wb'     ) as handle:
              pickle.dump(rslt_   , _lowerCAmelCase   , protocol=pickle.HIGHEST_PROTOCOL     )


if __name__ == "__main__":
       main()








import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						SwinConfig()
      SCREAMING_SNAKE_CASE		=						swin_name.split("""_"""	)

      SCREAMING_SNAKE_CASE		=						name_split[1]
      SCREAMING_SNAKE_CASE		=						int(name_split[4]	)
      SCREAMING_SNAKE_CASE		=						int(name_split[3][-1]	)

      if model_size == "tiny":
            SCREAMING_SNAKE_CASE		=						96
            SCREAMING_SNAKE_CASE		=						(2, 2, 6, 2)
            SCREAMING_SNAKE_CASE		=						(3, 6, 12, 24)
      elif model_size == "small":
            SCREAMING_SNAKE_CASE		=						96
            SCREAMING_SNAKE_CASE		=						(2, 2, 18, 2)
            SCREAMING_SNAKE_CASE		=						(3, 6, 12, 24)
      elif model_size == "base":
            SCREAMING_SNAKE_CASE		=						1_28
            SCREAMING_SNAKE_CASE		=						(2, 2, 18, 2)
            SCREAMING_SNAKE_CASE		=						(4, 8, 16, 32)
      else:
            SCREAMING_SNAKE_CASE		=						1_92
            SCREAMING_SNAKE_CASE		=						(2, 2, 18, 2)
            SCREAMING_SNAKE_CASE		=						(6, 12, 24, 48)

      if "in22k" in swin_name:
            SCREAMING_SNAKE_CASE		=						2_18_41
      else:
            SCREAMING_SNAKE_CASE		=						10_00
            SCREAMING_SNAKE_CASE		=						"""huggingface/label-files"""
            SCREAMING_SNAKE_CASE		=						"""imagenet-1k-id2label.json"""
            SCREAMING_SNAKE_CASE		=						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						repo_type="""dataset"""	) ,						"""r"""	)	)
            SCREAMING_SNAKE_CASE		=						{int(_SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE		=						idalabel
            SCREAMING_SNAKE_CASE		=						{v: k for k, v in idalabel.items()}

      SCREAMING_SNAKE_CASE		=						img_size
      SCREAMING_SNAKE_CASE		=						num_classes
      SCREAMING_SNAKE_CASE		=						embed_dim
      SCREAMING_SNAKE_CASE		=						depths
      SCREAMING_SNAKE_CASE		=						num_heads
      SCREAMING_SNAKE_CASE		=						window_size

      return config



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   Dict:


      '''simple docstring'''


      if "patch_embed.proj" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""patch_embed.proj""" ,						"""embeddings.patch_embeddings.projection"""	)
      if "patch_embed.norm" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""patch_embed.norm""" ,						"""embeddings.norm"""	)
      if "layers" in name:
            SCREAMING_SNAKE_CASE		=						"""encoder.""" + name
      if "attn.proj" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""attn.proj""" ,						"""attention.output.dense"""	)
      if "attn" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""attn""" ,						"""attention.self"""	)
      if "norm1" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""norm1""" ,						"""layernorm_before"""	)
      if "norm2" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""norm2""" ,						"""layernorm_after"""	)
      if "mlp.fc1" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""mlp.fc1""" ,						"""intermediate.dense"""	)
      if "mlp.fc2" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""mlp.fc2""" ,						"""output.dense"""	)

      if name == "norm.weight":
            SCREAMING_SNAKE_CASE		=						"""layernorm.weight"""
      if name == "norm.bias":
            SCREAMING_SNAKE_CASE		=						"""layernorm.bias"""

      if "head" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""head""" ,						"""classifier"""	)
      else:
            SCREAMING_SNAKE_CASE		=						"""swin.""" + name

      return name



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Any:


      '''simple docstring'''


      for key in orig_state_dict.copy().keys():
            SCREAMING_SNAKE_CASE		=						orig_state_dict.pop(_SCREAMING_SNAKE_CASE	)

            if "mask" in key:
                  continue
            elif "qkv" in key:
                  SCREAMING_SNAKE_CASE		=						key.split("""."""	)
                  SCREAMING_SNAKE_CASE		=						int(key_split[1]	)
                  SCREAMING_SNAKE_CASE		=						int(key_split[3]	)
                  SCREAMING_SNAKE_CASE		=						model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        SCREAMING_SNAKE_CASE		=						val[:dim, :]
                        SCREAMING_SNAKE_CASE		=						val[
                            dim : dim * 2, :
                        ]
                        SCREAMING_SNAKE_CASE		=						val[-dim:, :]
                  else:
                        SCREAMING_SNAKE_CASE		=						val[
                            :dim
                        ]
                        SCREAMING_SNAKE_CASE		=						val[
                            dim : dim * 2
                        ]
                        SCREAMING_SNAKE_CASE		=						val[
                            -dim:
                        ]
            else:
                  SCREAMING_SNAKE_CASE		=						val

      return orig_state_dict



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						timm.create_model(_SCREAMING_SNAKE_CASE ,						pretrained=_SCREAMING_SNAKE_CASE	)
      timm_model.eval()

      SCREAMING_SNAKE_CASE		=						get_swin_config(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						SwinForImageClassification(_SCREAMING_SNAKE_CASE	)
      model.eval()

      SCREAMING_SNAKE_CASE		=						convert_state_dict(timm_model.state_dict() ,						_SCREAMING_SNAKE_CASE	)
      model.load_state_dict(_SCREAMING_SNAKE_CASE	)

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

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

      SCREAMING_SNAKE_CASE		=						timm_model(inputs["""pixel_values"""]	)
      SCREAMING_SNAKE_CASE		=						model(**_SCREAMING_SNAKE_CASE	).logits

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

      print(F"""Saving model {swin_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 __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						   =       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--swin_name""",
          default="""swin_tiny_patch4_window7_224""",
          type=str,
          help="""Name of the Swin 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."""
      )

      SCREAMING_SNAKE_CASE_						   =       parser.parse_args()
      convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)


