


import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Dict:




      """simple docstring"""

      return params[f'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE="attention" )->  Optional[int]:




      """simple docstring"""

      snake_case_	=				snake_case_	=				np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :] )
      snake_case_	=				k_tmp.reshape(k_tmp.shape[0] , k_tmp.shape[1] * k_tmp.shape[2] )
      snake_case_	=				np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :] )
      snake_case_	=				o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] , o_tmp.shape[2] )
      snake_case_	=				np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :] )
      snake_case_	=				q_tmp.reshape(q_tmp.shape[0] , q_tmp.shape[1] * q_tmp.shape[2] )
      snake_case_	=				np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :] )
      snake_case_	=				v_tmp.reshape(v_tmp.shape[0] , v_tmp.shape[1] * v_tmp.shape[2] )
      return k, o, q, v






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False )->  str:




      """simple docstring"""

      if split_mlp_wi:
            snake_case_	=				params[f'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
            snake_case_	=				params[f'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
            snake_case_	=				(wi_a, wi_a)
      else:
            snake_case_	=				params[f'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

      snake_case_	=				params[f'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
      return wi, wo






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Tuple:




      """simple docstring"""

      return params[f'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , *, SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = False )->  Tuple:




      """simple docstring"""

      snake_case_	=				traverse_util.flatten_dict(variables['''target'''] )
      snake_case_	=				{'''/'''.join(SCREAMING_SNAKE_CASE ): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      snake_case_	=				'''encoder/encoder/mlp/wi_0/kernel''' in old
      print('''Split MLP:''' , SCREAMING_SNAKE_CASE )

      snake_case_	=				collections.OrderedDict()

      # Shared embeddings.
      snake_case_	=				old['''token_embedder/embedding''']

      # Encoder.
      for i in range(SCREAMING_SNAKE_CASE ):
            # Block i, layer 0 (Self Attention).
            snake_case_	=				tax_layer_norm_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''encoder''' , '''pre_attention_layer_norm''' )
            snake_case_	,   snake_case_	,   snake_case_	,   snake_case_	=				tax_attention_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''encoder''' , '''attention''' )
            snake_case_	=				layer_norm
            snake_case_	=				k.T
            snake_case_	=				o.T
            snake_case_	=				q.T
            snake_case_	=				v.T

            # Block i, layer 1 (MLP).
            snake_case_	=				tax_layer_norm_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''encoder''' , '''pre_mlp_layer_norm''' )
            snake_case_	,   snake_case_	=				tax_mlp_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''encoder''' , SCREAMING_SNAKE_CASE )
            snake_case_	=				layer_norm
            if split_mlp_wi:
                  snake_case_	=				wi[0].T
                  snake_case_	=				wi[1].T
            else:
                  snake_case_	=				wi.T
            snake_case_	=				wo.T
            if scalable_attention:
                  # convert the rel_embedding of each layer
                  snake_case_	=				tax_relpos_bias_lookup(
                      SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''encoder''' ).T

      snake_case_	=				old['''encoder/encoder_norm/scale''']

      if not scalable_attention:
            snake_case_	=				tax_relpos_bias_lookup(
                SCREAMING_SNAKE_CASE , 0 , '''encoder''' ).T
            snake_case_	=				tax_relpos_bias_lookup(
                SCREAMING_SNAKE_CASE , 0 , '''decoder''' ).T

      if not is_encoder_only:
            # Decoder.
            for i in range(SCREAMING_SNAKE_CASE ):
                  # Block i, layer 0 (Self Attention).
                  snake_case_	=				tax_layer_norm_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''decoder''' , '''pre_self_attention_layer_norm''' )
                  snake_case_	,   snake_case_	,   snake_case_	,   snake_case_	=				tax_attention_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''decoder''' , '''self_attention''' )
                  snake_case_	=				layer_norm
                  snake_case_	=				k.T
                  snake_case_	=				o.T
                  snake_case_	=				q.T
                  snake_case_	=				v.T

                  # Block i, layer 1 (Cross Attention).
                  snake_case_	=				tax_layer_norm_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''decoder''' , '''pre_cross_attention_layer_norm''' )
                  snake_case_	,   snake_case_	,   snake_case_	,   snake_case_	=				tax_attention_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''decoder''' , '''encoder_decoder_attention''' )
                  snake_case_	=				layer_norm
                  snake_case_	=				k.T
                  snake_case_	=				o.T
                  snake_case_	=				q.T
                  snake_case_	=				v.T

                  # Block i, layer 2 (MLP).
                  snake_case_	=				tax_layer_norm_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''decoder''' , '''pre_mlp_layer_norm''' )
                  snake_case_	,   snake_case_	=				tax_mlp_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''decoder''' , SCREAMING_SNAKE_CASE )
                  snake_case_	=				layer_norm
                  if split_mlp_wi:
                        snake_case_	=				wi[0].T
                        snake_case_	=				wi[1].T
                  else:
                        snake_case_	=				wi.T
                  snake_case_	=				wo.T

                  if scalable_attention:
                        # convert the rel_embedding of each layer
                        snake_case_	=				tax_relpos_bias_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''decoder''' ).T

            snake_case_	=				old['''decoder/decoder_norm/scale''']

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  snake_case_	=				old['''decoder/logits_dense/kernel'''].T

      return new






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Optional[int]:




      """simple docstring"""

      snake_case_	=				collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )

      # Add what is missing.
      if "encoder.embed_tokens.weight" not in state_dict:
            snake_case_	=				state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  snake_case_	=				state_dict['''shared.weight''']

            if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                  print('''Using shared word embeddings as lm_head.''' )
                  snake_case_	=				state_dict['''shared.weight''']

      return state_dict






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  List[str]:




      """simple docstring"""

      snake_case_	=				checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE )
      snake_case_	=				convert_tax_to_pytorch(
          SCREAMING_SNAKE_CASE , num_layers=config.num_layers , is_encoder_only=SCREAMING_SNAKE_CASE , scalable_attention=SCREAMING_SNAKE_CASE )
      snake_case_	=				make_state_dict(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
      model.load_state_dict(SCREAMING_SNAKE_CASE , strict=SCREAMING_SNAKE_CASE )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = False , SCREAMING_SNAKE_CASE = False , )->  Optional[int]:




      """simple docstring"""

      snake_case_	=				MTaConfig.from_json_file(SCREAMING_SNAKE_CASE )
      print(f'''Building PyTorch model from configuration: {config}''' )
      # Non-v1.1 checkpoints could also use T5Model, but this works for all.
      # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
      if is_encoder_only:
            snake_case_	=				UMTaEncoderModel(SCREAMING_SNAKE_CASE )
      else:
            snake_case_	=				UMTaForConditionalGeneration(SCREAMING_SNAKE_CASE )

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )

      # Save pytorch-model
      print(f'''Save PyTorch model to {pytorch_dump_path}''' )
      model.save_pretrained(SCREAMING_SNAKE_CASE )

      # Verify that we can load the checkpoint.
      model.from_pretrained(SCREAMING_SNAKE_CASE )
      print('''Done''' )


if __name__ == "__main__":
   UpperCAmelCase      =   argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
   # Required parameters
   parser.add_argument(
       """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
   )
   parser.add_argument(
       """--config_file""",
       default=None,
       type=str,
       required=True,
       help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   parser.add_argument(
       """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
   )
   parser.add_argument(
       """--scalable_attention""",
       action="""store_true""",
       help="""Whether the model uses scaled attention (umt5 model)""",
       default=False,
   )
   UpperCAmelCase      =   parser.parse_args()
   convert_tax_checkpoint_to_pytorch(
       args.tax_checkpoint_path,
       args.config_file,
       args.pytorch_dump_path,
       args.is_encoder_only,
       args.scalable_attention,
   )



import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Tuple:




      """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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  List[Any]:




      """simple docstring"""

      for char in word:
            snake_case_	=				ord(SCREAMING_SNAKE_CASE )
            if not _is_chinese_char(SCREAMING_SNAKE_CASE ):
                  return 0
      return 1






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  str:




      """simple docstring"""

      snake_case_	=				set()

      for token in tokens:
            snake_case_	=				len(SCREAMING_SNAKE_CASE ) > 1 and is_chinese(SCREAMING_SNAKE_CASE )
            if chinese_word:
                  word_set.add(SCREAMING_SNAKE_CASE )
      snake_case_	=				list(SCREAMING_SNAKE_CASE )
      return word_list






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  List[str]:




      """simple docstring"""

      if not chinese_word_set:
            return bert_tokens
      snake_case_	=				max([len(SCREAMING_SNAKE_CASE ) for w in chinese_word_set] )

      snake_case_	=				bert_tokens
      snake_case_	,   snake_case_	=				0, len(SCREAMING_SNAKE_CASE )
      while start < end:
            snake_case_	=				True
            if is_chinese(bert_word[start] ):
                  snake_case_	=				min(end - start , SCREAMING_SNAKE_CASE )
                  for i in range(SCREAMING_SNAKE_CASE , 1 , -1 ):
                        snake_case_	=				''''''.join(bert_word[start : start + i] )
                        if whole_word in chinese_word_set:
                              for j in range(start + 1 , start + i ):
                                    snake_case_	=				'''##''' + bert_word[j]
                              snake_case_	=				start + i
                              snake_case_	=				False
                              break
            if single_word:
                  start += 1
      return bert_word






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      snake_case_	=				[]

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

      snake_case_	=				[]
      for i in range(0 , len(SCREAMING_SNAKE_CASE ) , 100 ):
            snake_case_	=				bert_tokenizer(lines[i : i + 100] , add_special_tokens=SCREAMING_SNAKE_CASE , truncation=SCREAMING_SNAKE_CASE , max_length=512 )
            bert_res.extend(res['''input_ids'''] )
      assert len(SCREAMING_SNAKE_CASE ) == len(SCREAMING_SNAKE_CASE )

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

      assert len(SCREAMING_SNAKE_CASE ) == len(SCREAMING_SNAKE_CASE )

      return ref_ids






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Tuple:




      """simple docstring"""

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

      snake_case_	=				prepare_ref(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )

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


if __name__ == "__main__":
   UpperCAmelCase      =   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      =   parser.parse_args()
   main(args)