





"""simple docstring"""


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer




def        lowercase__     (       lowercase_      )       ->		List[str]:




      """simple docstring"""

      if (
          (cp >= 0X4e00 and cp <= 0X9fff)
          or (cp >= 0X3400 and cp <= 0X4dbf)  #
          or (cp >= 0X2_0000 and cp <= 0X2_a6df)  #
          or (cp >= 0X2_a700 and cp <= 0X2_b73f)  #
          or (cp >= 0X2_b740 and cp <= 0X2_b81f)  #
          or (cp >= 0X2_b820 and cp <= 0X2_ceaf)  #
          or (cp >= 0Xf900 and cp <= 0Xfaff)
          or (cp >= 0X2_f800 and cp <= 0X2_fa1f)  #
      ):  #
            return True

      return False




def        lowercase__     (       lowercase_      )       ->		str:




      """simple docstring"""

      for char in word:
            _UpperCamelCase   :						Tuple    =							ord(__UpperCamelCase      )
            if not _is_chinese_char(__UpperCamelCase      ):
                  return 0
      return 1




def        lowercase__     (       lowercase_      )       ->		str:




      """simple docstring"""

      _UpperCamelCase   :						List[Any]    =							set()

      for token in tokens:
            _UpperCamelCase   :						List[Any]    =							len(__UpperCamelCase      ) > 1 and is_chinese(__UpperCamelCase      )
            if chinese_word:
                  word_set.add(__UpperCamelCase      )
      _UpperCamelCase   :						List[str]    =							list(__UpperCamelCase      )
      return word_list




def        lowercase__     (       lowercase_		,lowercase_      )       ->		str:




      """simple docstring"""

      if not chinese_word_set:
            return bert_tokens
      _UpperCamelCase   :						Tuple    =							max([len(__UpperCamelCase      ) for w in chinese_word_set]      )

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




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_      )       ->		Optional[int]:




      """simple docstring"""

      _UpperCamelCase   :						Optional[int]    =							[]

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

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

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

      assert len(__UpperCamelCase      ) == len(__UpperCamelCase      )

      return ref_ids




def        lowercase__     (       lowercase_      )       ->		List[str]:




      """simple docstring"""

      with open(args.file_name		,"r"		,encoding="utf-8"      ) as f:
            _UpperCamelCase   :						Dict    =							f.readlines()
      _UpperCamelCase   :						Any    =							[line.strip() for line in data if len(__UpperCamelCase      ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
      _UpperCamelCase   :						Any    =							LTP(args.ltp      )  # faster in GPU device
      _UpperCamelCase   :						Optional[Any]    =							BertTokenizer.from_pretrained(args.bert      )

      _UpperCamelCase   :						Tuple    =							prepare_ref(__UpperCamelCase		,__UpperCamelCase		,__UpperCamelCase      )

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


if __name__ == "__main__":
  lowerCamelCase__						= 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",
  )

  lowerCamelCase__						= parser.parse_args()
  main(args)





import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)

os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)


def A    (							__UpperCamelCase       )    ->   Union[str, Any]:
			if hor == 128:
						A__	  =     ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
						A__	  =     (32, 128, 256)
						A__	  =     ('UpResnetBlock1D', 'UpResnetBlock1D')

			elif hor == 32:
						A__	  =     ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
						A__	  =     (32, 64, 128, 256)
						A__	  =     ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
			A__	  =     torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''       )
			A__	  =     model.state_dict()
			A__	  =     {
			    'down_block_types': down_block_types,
			    'block_out_channels': block_out_channels,
			    'up_block_types': up_block_types,
			    'layers_per_block': 1,
			    'use_timestep_embedding': True,
			    'out_block_type': 'OutConv1DBlock',
			    'norm_num_groups': 8,
			    'downsample_each_block': False,
			    'in_channels': 14,
			    'out_channels': 14,
			    'extra_in_channels': 0,
			    'time_embedding_type': 'positional',
			    'flip_sin_to_cos': False,
			    'freq_shift': 1,
			    'sample_size': 65_536,
			    'mid_block_type': 'MidResTemporalBlock1D',
			    'act_fn': 'mish',
			}
			A__	  =     UNetaDModel(**__UpperCamelCase       )
			print(f'''length of state dict: {len(state_dict.keys()       )}'''       )
			print(f'''length of value function dict: {len(hf_value_function.state_dict().keys()       )}'''       )
			A__	  =     dict(zip(model.state_dict().keys()			,						hf_value_function.state_dict().keys()       )       )
			for k, v in mapping.items():
						A__	  =     state_dict.pop(__UpperCamelCase       )
			hf_value_function.load_state_dict(__UpperCamelCase       )

			torch.save(hf_value_function.state_dict()			,						f'''hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin'''       )
			with open(f'''hub/hopper-medium-v2/unet/hor{hor}/config.json'''			,						'w'       ) as f:
						json.dump(__UpperCamelCase			,						__UpperCamelCase       )


def A    (							)    ->   List[str]:
			A__	  =     {
			    'in_channels': 14,
			    'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
			    'up_block_types': (),
			    'out_block_type': 'ValueFunction',
			    'mid_block_type': 'ValueFunctionMidBlock1D',
			    'block_out_channels': (32, 64, 128, 256),
			    'layers_per_block': 1,
			    'downsample_each_block': True,
			    'sample_size': 65_536,
			    'out_channels': 14,
			    'extra_in_channels': 0,
			    'time_embedding_type': 'positional',
			    'use_timestep_embedding': True,
			    'flip_sin_to_cos': False,
			    'freq_shift': 1,
			    'norm_num_groups': 8,
			    'act_fn': 'mish',
			}

			A__	  =     torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'       )
			A__	  =     model
			A__	  =     UNetaDModel(**__UpperCamelCase       )
			print(f'''length of state dict: {len(state_dict.keys()       )}'''       )
			print(f'''length of value function dict: {len(hf_value_function.state_dict().keys()       )}'''       )

			A__	  =     dict(zip(state_dict.keys()			,						hf_value_function.state_dict().keys()       )       )
			for k, v in mapping.items():
						A__	  =     state_dict.pop(__UpperCamelCase       )

			hf_value_function.load_state_dict(__UpperCamelCase       )

			torch.save(hf_value_function.state_dict()			,						'hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin'       )
			with open('hub/hopper-medium-v2/value_function/config.json'			,						'w'       ) as f:
						json.dump(__UpperCamelCase			,						__UpperCamelCase       )


if __name__ == "__main__":
							unet(3_2)
							# unet(128)
							value_function()




