


'''simple docstring'''


import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__			=      logging.get_logger(__name__)




def      lowerCamelCase					(      lowerCamelCase  :      Optional[int]							,						lowerCamelCase  :      int							,						lowerCamelCase  :      Tuple							,						lowerCamelCase  :      List[str]):
      A_						:  List[Any]          =				original_name.split(""".""")[0]
      A_						:  str          =				key.split(""".""")
      A_						:  List[Any]          =				int(key_list[key_list.index(lowerCamelCase) - 2])
      A_						:  List[Any]          =				int(key_list[key_list.index(lowerCamelCase) - 1])
      A_						:  List[Any]          =				orig_block_num - offset

      A_						:  Optional[Any]          =				key.replace(F'{orig_block_num}.{layer_num}.{original_name}'							,						F'block.{new_block_num}.{layer_num}.{new_name}')
      return key




def      lowerCamelCase					(      lowerCamelCase  :      Any):
      A_						:  str          =				OrderedDict()
      A_   ,					A_						:  Tuple          =				0, 0
      for key, value in state_dict.items():
            if key.startswith("""network"""):
                  A_						:  List[str]          =				key.replace("""network"""							,						"""poolformer.encoder""")
            if "proj" in key:
                  # Works for the first embedding as well as the internal embedding layers
                  if key.endswith("""bias""") and "patch_embed" not in key:
                        patch_emb_offset += 1
                  A_						:  Optional[int]          =				key[: key.find("""proj""")]
                  A_						:  List[str]          =				key.replace(lowerCamelCase							,						F'patch_embeddings.{total_embed_found}.')
                  A_						:  Tuple          =				key.replace("""proj"""							,						"""projection""")
                  if key.endswith("""bias"""):
                        total_embed_found += 1
            if "patch_embeddings" in key:
                  A_						:  Tuple          =				"""poolformer.encoder.""" + key
            if "mlp.fc1" in key:
                  A_						:  Any          =				replace_key_with_offset(lowerCamelCase							,						lowerCamelCase							,						"""mlp.fc1"""							,						"""output.conv1""")
            if "mlp.fc2" in key:
                  A_						:  str          =				replace_key_with_offset(lowerCamelCase							,						lowerCamelCase							,						"""mlp.fc2"""							,						"""output.conv2""")
            if "norm1" in key:
                  A_						:  Dict          =				replace_key_with_offset(lowerCamelCase							,						lowerCamelCase							,						"""norm1"""							,						"""before_norm""")
            if "norm2" in key:
                  A_						:  Any          =				replace_key_with_offset(lowerCamelCase							,						lowerCamelCase							,						"""norm2"""							,						"""after_norm""")
            if "layer_scale_1" in key:
                  A_						:  Any          =				replace_key_with_offset(lowerCamelCase							,						lowerCamelCase							,						"""layer_scale_1"""							,						"""layer_scale_1""")
            if "layer_scale_2" in key:
                  A_						:  List[Any]          =				replace_key_with_offset(lowerCamelCase							,						lowerCamelCase							,						"""layer_scale_2"""							,						"""layer_scale_2""")
            if "head" in key:
                  A_						:  int          =				key.replace("""head"""							,						"""classifier""")
            A_						:  Optional[int]          =				value
      return new_state_dict




def      lowerCamelCase					(      ):
      A_						:  str          =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      A_						:  Dict          =				Image.open(requests.get(lowerCamelCase							,						stream=lowerCamelCase).raw)

      return image




@torch.no_grad()
def      lowerCamelCase					(      lowerCamelCase  :      int							,						lowerCamelCase  :      Any							,						lowerCamelCase  :      Dict):
      A_						:  List[str]          =				PoolFormerConfig()

      # set attributes based on model_name
      A_						:  Tuple          =				"""huggingface/label-files"""
      A_						:  List[Any]          =				model_name[-3:]
      A_						:  int          =				1000
      A_						:  Tuple          =				"""imagenet-1k-id2label.json"""
      A_						:  str          =				(1, 1000)

      # set config attributes
      A_						:  Tuple          =				json.load(open(hf_hub_download(lowerCamelCase							,						lowerCamelCase							,						repo_type="""dataset""")							,						"""r"""))
      A_						:  List[str]          =				{int(lowerCamelCase): v for k, v in idalabel.items()}
      A_						:  Tuple          =				idalabel
      A_						:  Union[str, Any]          =				{v: k for k, v in idalabel.items()}
      if size == "s12":
            A_						:  List[str]          =				[2, 2, 6, 2]
            A_						:  str          =				[64, 128, 320, 512]
            A_						:  Optional[Any]          =				4.0
            A_						:  Tuple          =				0.9
      elif size == "s24":
            A_						:  str          =				[4, 4, 12, 4]
            A_						:  Any          =				[64, 128, 320, 512]
            A_						:  Any          =				4.0
            A_						:  Union[str, Any]          =				0.9
      elif size == "s36":
            A_						:  Optional[int]          =				[6, 6, 18, 6]
            A_						:  List[Any]          =				[64, 128, 320, 512]
            A_						:  Optional[Any]          =				4.0
            A_						:  str          =				1E-6
            A_						:  Optional[int]          =				0.9
      elif size == "m36":
            A_						:  Optional[int]          =				[6, 6, 18, 6]
            A_						:  Optional[int]          =				[96, 192, 384, 768]
            A_						:  List[Any]          =				4.0
            A_						:  List[str]          =				1E-6
            A_						:  List[Any]          =				0.95
      elif size == "m48":
            A_						:  Optional[int]          =				[8, 8, 24, 8]
            A_						:  Optional[int]          =				[96, 192, 384, 768]
            A_						:  Dict          =				4.0
            A_						:  List[str]          =				1E-6
            A_						:  Tuple          =				0.95
      else:
            raise ValueError(F'Size {size} not supported')

      # load image processor
      A_						:  str          =				PoolFormerImageProcessor(crop_pct=lowerCamelCase)

      # Prepare image
      A_						:  Union[str, Any]          =				prepare_img()
      A_						:  Tuple          =				image_processor(images=lowerCamelCase							,						return_tensors="""pt""").pixel_values

      logger.info(F'Converting model {model_name}...')

      # load original state dict
      A_						:  Optional[int]          =				torch.load(lowerCamelCase							,						map_location=torch.device("""cpu"""))

      # rename keys
      A_						:  Union[str, Any]          =				rename_keys(lowerCamelCase)

      # create HuggingFace model and load state dict
      A_						:  Optional[int]          =				PoolFormerForImageClassification(lowerCamelCase)
      model.load_state_dict(lowerCamelCase)
      model.eval()

      # Define image processor
      A_						:  int          =				PoolFormerImageProcessor(crop_pct=lowerCamelCase)
      A_						:  List[Any]          =				image_processor(images=prepare_img()							,						return_tensors="""pt""").pixel_values

      # forward pass
      A_						:  str          =				model(lowerCamelCase)
      A_						:  Optional[int]          =				outputs.logits

      # define expected logit slices for different models
      if size == "s12":
            A_						:  List[str]          =				torch.tensor([-0.3045, -0.6758, -0.4869])
      elif size == "s24":
            A_						:  Optional[int]          =				torch.tensor([0.4402, -0.1374, -0.8045])
      elif size == "s36":
            A_						:  Tuple          =				torch.tensor([-0.6080, -0.5133, -0.5898])
      elif size == "m36":
            A_						:  str          =				torch.tensor([0.3952, 0.2263, -1.2668])
      elif size == "m48":
            A_						:  Union[str, Any]          =				torch.tensor([0.1167, -0.0656, -0.3423])
      else:
            raise ValueError(F'Size {size} not supported')

      # verify logits
      assert logits.shape == expected_shape
      assert torch.allclose(logits[0, :3]							,						lowerCamelCase							,						atol=1E-2)

      # finally, save model and image processor
      logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...')
      Path(lowerCamelCase).mkdir(exist_ok=lowerCamelCase)
      model.save_pretrained(lowerCamelCase)
      print(F'Saving image processor to {pytorch_dump_folder_path}')
      image_processor.save_pretrained(lowerCamelCase)


if __name__ == "__main__":
     __magic_name__			=      argparse.ArgumentParser()

     parser.add_argument(
         '--model_name',
         default='poolformer_s12',
         type=str,
         help='Name of the model you\'d like to convert.',
     )
     parser.add_argument(
         '--checkpoint_path', default=None, 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 folder to output PyTorch model.'
     )
     __magic_name__			=      parser.parse_args()
     convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)




'''simple docstring'''


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer




def      lowerCamelCase					(      lowerCamelCase  :      Optional[Any]):
      # This defines a "chinese character" as anything in the CJK Unicode block:
      #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
      #
      # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
      # despite its name. The modern Korean Hangul alphabet is a different block,
      # as is Japanese Hiragana and Katakana. Those alphabets are used to write
      # space-separated words, so they are not treated specially and handled
      # like the all of the other languages.
      if (
          (cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
          or (cp >= 0X3_4_0_0 and cp <= 0X4_D_B_F)  #
          or (cp >= 0X2_0_0_0_0 and cp <= 0X2_A_6_D_F)  #
          or (cp >= 0X2_A_7_0_0 and cp <= 0X2_B_7_3_F)  #
          or (cp >= 0X2_B_7_4_0 and cp <= 0X2_B_8_1_F)  #
          or (cp >= 0X2_B_8_2_0 and cp <= 0X2_C_E_A_F)  #
          or (cp >= 0XF_9_0_0 and cp <= 0XF_A_F_F)
          or (cp >= 0X2_F_8_0_0 and cp <= 0X2_F_A_1_F)  #
      ):  #
            return True

      return False




def      lowerCamelCase					(      lowerCamelCase  :      str):
      # word like '180' or '身高' or '神'
      for char in word:
            A_						:  Optional[Any]          =				ord(lowerCamelCase)
            if not _is_chinese_char(lowerCamelCase):
                  return 0
      return 1




def      lowerCamelCase					(      lowerCamelCase  :      List[str]):
      A_						:  Any          =				set()

      for token in tokens:
            A_						:  str          =				len(lowerCamelCase) > 1 and is_chinese(lowerCamelCase)
            if chinese_word:
                  word_set.add(lowerCamelCase)
      A_						:  Any          =				list(lowerCamelCase)
      return word_list




def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      set()):
      if not chinese_word_set:
            return bert_tokens
      A_						:  Any          =				max([len(lowerCamelCase) for w in chinese_word_set])

      A_						:  str          =				bert_tokens
      A_   ,					A_						:  Any          =				0, len(lowerCamelCase)
      while start < end:
            A_						:  Tuple          =				True
            if is_chinese(bert_word[start]):
                  A_						:  List[str]          =				min(end - start							,						lowerCamelCase)
                  for i in range(lowerCamelCase							,						1							,						-1):
                        A_						:  Tuple          =				"""""".join(bert_word[start : start + i])
                        if whole_word in chinese_word_set:
                              for j in range(start + 1							,						start + i):
                                    A_						:  Dict          =				"""##""" + bert_word[j]
                              A_						:  str          =				start + i
                              A_						:  Dict          =				False
                              break
            if single_word:
                  start += 1
      return bert_word




def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      LTP							,						lowerCamelCase  :      BertTokenizer):
      A_						:  Union[str, Any]          =				[]

      for i in range(0							,						len(lowerCamelCase)							,						100):
            A_						:  List[Any]          =				ltp_tokenizer.pipeline(lines[i : i + 100]							,						tasks=["""cws"""]).cws
            A_						:  int          =				[get_chinese_word(lowerCamelCase) for r in res]
            ltp_res.extend(lowerCamelCase)
      assert len(lowerCamelCase) == len(lowerCamelCase)

      A_						:  List[Any]          =				[]
      for i in range(0							,						len(lowerCamelCase)							,						100):
            A_						:  Dict          =				bert_tokenizer(lines[i : i + 100]							,						add_special_tokens=lowerCamelCase							,						truncation=lowerCamelCase							,						max_length=512)
            bert_res.extend(res["""input_ids"""])
      assert len(lowerCamelCase) == len(lowerCamelCase)

      A_						:  Union[str, Any]          =				[]
      for input_ids, chinese_word in zip(lowerCamelCase							,						lowerCamelCase):
            A_						:  List[Any]          =				[]
            for id in input_ids:
                  A_						:  List[Any]          =				bert_tokenizer._convert_id_to_token(lowerCamelCase)
                  input_tokens.append(lowerCamelCase)
            A_						:  int          =				add_sub_symbol(lowerCamelCase							,						lowerCamelCase)
            A_						:  str          =				[]
            # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
            for i, token in enumerate(lowerCamelCase):
                  if token[:2] == "##":
                        A_						:  Optional[Any]          =				token[2:]
                        # save chinese tokens' pos
                        if len(lowerCamelCase) == 1 and _is_chinese_char(ord(lowerCamelCase)):
                              ref_id.append(lowerCamelCase)
            ref_ids.append(lowerCamelCase)

      assert len(lowerCamelCase) == len(lowerCamelCase)

      return ref_ids




def      lowerCamelCase					(      lowerCamelCase  :      Tuple):
      # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
      # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
      with open(args.file_name							,						"""r"""							,						encoding="""utf-8""") as f:
            A_						:  Optional[int]          =				f.readlines()
      A_						:  Union[str, Any]          =				[line.strip() for line in data if len(lowerCamelCase) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
      A_						:  Optional[Any]          =				LTP(args.ltp)  # faster in GPU device
      A_						:  Dict          =				BertTokenizer.from_pretrained(args.bert)

      A_						:  str          =				prepare_ref(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase)

      with open(args.save_path							,						"""w"""							,						encoding="""utf-8""") as f:
            A_						:  Optional[Any]          =				[json.dumps(lowerCamelCase) + """\n""" for ref in ref_ids]
            f.writelines(lowerCamelCase)


if __name__ == "__main__":
     __magic_name__			=      argparse.ArgumentParser(description='prepare_chinese_ref')
     parser.add_argument(
         '--file_name',
         required=False,
         type=str,
         default='./resources/chinese-demo.txt',
         help='file need process, same as training data in lm',
     )
     parser.add_argument(
         '--ltp',
         required=False,
         type=str,
         default='./resources/ltp',
         help='resources for LTP tokenizer, usually a path',
     )
     parser.add_argument(
         '--bert',
         required=False,
         type=str,
         default='./resources/robert',
         help='resources for Bert tokenizer',
     )
     parser.add_argument(
         '--save_path',
         required=False,
         type=str,
         default='./resources/ref.txt',
         help='path to save res',
     )

     __magic_name__			=      parser.parse_args()
     main(args)
