






'''simple docstring'''







import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def 						A__			(				__lowerCAmelCase		:			Optional[int]						):
						# 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 						A__			(				__lowerCAmelCase		:			str						):
						# word like '180' or '身高' or '神'
						for char in word:
												lowerCamelCase__         =  ord(__lowerCAmelCase						)
												if not _is_chinese_char(__lowerCAmelCase						):
																		return 0
						return 1
def 						A__			(				__lowerCAmelCase		:			List[str]						):
						lowerCamelCase__         =  set()

						for token in tokens:
												lowerCamelCase__         =  len(__lowerCAmelCase						) > 1 and is_chinese(__lowerCAmelCase						)
												if chinese_word:
																		word_set.add(__lowerCAmelCase						)
						lowerCamelCase__         =  list(__lowerCAmelCase						)
						return word_list
def 						A__			(				__lowerCAmelCase		:			List[str]   ,    __lowerCAmelCase		:			set()						):
						if not chinese_word_set:
												return bert_tokens
						lowerCamelCase__         =  max([len(__lowerCAmelCase						) for w in chinese_word_set]						)

						lowerCamelCase__         =  bert_tokens
						lowerCamelCase__       ,			lowerCamelCase__         =  0, len(__lowerCAmelCase						)
						while start < end:
												lowerCamelCase__         =  True
												if is_chinese(bert_word[start]						):
																		lowerCamelCase__         =  min(end - start   ,    __lowerCAmelCase						)
																		for i in range(__lowerCAmelCase   ,    1   ,    -1						):
																								lowerCamelCase__         =  """""".join(bert_word[start : start + i]						)
																								if whole_word in chinese_word_set:
																														for j in range(start + 1   ,    start + i						):
																																				lowerCamelCase__         =  """##""" + bert_word[j]
																														lowerCamelCase__         =  start + i
																														lowerCamelCase__         =  False
																														break
												if single_word:
																		start += 1
						return bert_word
def 						A__			(				__lowerCAmelCase		:			List[str]   ,    __lowerCAmelCase		:			LTP   ,    __lowerCAmelCase		:			BertTokenizer						):
						lowerCamelCase__         =  []

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

						lowerCamelCase__         =  []
						for i in range(0   ,    len(__lowerCAmelCase						)   ,    100						):
												lowerCamelCase__         =  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						)

						lowerCamelCase__         =  []
						for input_ids, chinese_word in zip(__lowerCAmelCase   ,    __lowerCAmelCase						):
												lowerCamelCase__         =  []
												for id in input_ids:
																		lowerCamelCase__         =  bert_tokenizer._convert_id_to_token(__lowerCAmelCase						)
																		input_tokens.append(__lowerCAmelCase						)
												lowerCamelCase__         =  add_sub_symbol(__lowerCAmelCase   ,    __lowerCAmelCase						)
												lowerCamelCase__         =  []
												# 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] == "##":
																								lowerCamelCase__         =  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 						A__			(				__lowerCAmelCase		:			Dict						):
						# 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:
												lowerCamelCase__         =  f.readlines()
						lowerCamelCase__         =  [line.strip() for line in data if len(__lowerCAmelCase						) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
						lowerCamelCase__         =  LTP(args.ltp						)  # faster in GPU device
						lowerCamelCase__         =  BertTokenizer.from_pretrained(args.bert						)

						lowerCamelCase__         =  prepare_ref(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase						)

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


if __name__ == "__main__":
							UpperCamelCase    :      Tuple  			=		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',
							)

							UpperCamelCase    :      List[str]  			=		parser.parse_args()
							main(args)









'''simple docstring'''







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__			(				__lowerCAmelCase		:			Union[str, Any]						):
						if hor == 128:
												lowerCamelCase__         =  ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
												lowerCamelCase__         =  (32, 128, 256)
												lowerCamelCase__         =  ("""UpResnetBlock1D""", """UpResnetBlock1D""")

						elif hor == 32:
												lowerCamelCase__         =  ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
												lowerCamelCase__         =  (32, 64, 128, 256)
												lowerCamelCase__         =  ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
						lowerCamelCase__         =  torch.load(F'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''						)
						lowerCamelCase__         =  model.state_dict()
						lowerCamelCase__         =  {
						    """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""": 6_5536,
						    """mid_block_type""": """MidResTemporalBlock1D""",
						    """act_fn""": """mish""",
						}
						lowerCamelCase__         =  UNetaDModel(**__lowerCAmelCase						)
						print(F'''length of state dict: {len(state_dict.keys()						)}'''						)
						print(F'''length of value function dict: {len(hf_value_function.state_dict().keys()						)}'''						)
						lowerCamelCase__         =  dict(zip(model.state_dict().keys()   ,    hf_value_function.state_dict().keys()						)						)
						for k, v in mapping.items():
												lowerCamelCase__         =  state_dict.pop(__lowerCAmelCase						)
						hf_value_function.load_state_dict(__lowerCAmelCase						)

						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(__lowerCAmelCase   ,    __lowerCAmelCase						)
def 						A__			(				):
						lowerCamelCase__         =  {
						    """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""": 6_5536,
						    """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""",
						}

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

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

						hf_value_function.load_state_dict(__lowerCAmelCase						)

						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(__lowerCAmelCase   ,    __lowerCAmelCase						)


if __name__ == "__main__":
							unet(32)
							# unet(128)
							value_function()

