


'''simple docstring'''

import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()





def __snake_case					(      UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						Tuple="attention"		):
 lowerCamelCase_      =  params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
 lowerCamelCase_      =  params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
 lowerCamelCase_      =  params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
 lowerCamelCase_      =  params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
 return k, o, q, v





def __snake_case					(      UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						Any=False		):
 if split_mlp_wi:
  lowerCamelCase_      =  params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
  lowerCamelCase_      =  params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
  lowerCamelCase_      =  (wi_a, wi_a)
 else:
  lowerCamelCase_      =  params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

 lowerCamelCase_      =  params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
 return wi, wo





def __snake_case					(      UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						int		):
 return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']





def __snake_case					(      UpperCAmelCase_						:						dict					,				*, UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						bool		):
 lowerCamelCase_      =  traverse_util.flatten_dict(variables["target"]		)
 lowerCamelCase_      =  {"/".join(UpperCAmelCase_		): v for k, v in old.items()}

 # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
 lowerCamelCase_      =  "encoder/layers_0/mlp/wi_0/kernel" in old
 print("Split MLP:"					,				UpperCAmelCase_		)

 lowerCamelCase_      =  collections.OrderedDict()

 # Shared embeddings.
 lowerCamelCase_      =  old["token_embedder/embedding"]

 # Encoder.
 for i in range(UpperCAmelCase_		):
  # Block i, layer 0 (Self Attention).
  lowerCamelCase_      =  tax_layer_norm_lookup(UpperCAmelCase_					,				UpperCAmelCase_					,				"encoder"					,				"pre_attention_layer_norm"		)
  lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  tax_attention_lookup(UpperCAmelCase_					,				UpperCAmelCase_					,				"encoder"					,				"attention"		)
  lowerCamelCase_      =  layer_norm
  lowerCamelCase_      =  k.T
  lowerCamelCase_      =  o.T
  lowerCamelCase_      =  q.T
  lowerCamelCase_      =  v.T

  # Block i, layer 1 (MLP).
  lowerCamelCase_      =  tax_layer_norm_lookup(UpperCAmelCase_					,				UpperCAmelCase_					,				"encoder"					,				"pre_mlp_layer_norm"		)
  lowerCamelCase_ ,lowerCamelCase_      =  tax_mlp_lookup(UpperCAmelCase_					,				UpperCAmelCase_					,				"encoder"					,				UpperCAmelCase_		)
  lowerCamelCase_      =  layer_norm
  if split_mlp_wi:
   lowerCamelCase_      =  wi[0].T
   lowerCamelCase_      =  wi[1].T
  else:
   lowerCamelCase_      =  wi.T
  lowerCamelCase_      =  wo.T

 lowerCamelCase_      =  old[
     "encoder/relpos_bias/rel_embedding"
 ].T
 lowerCamelCase_      =  old["encoder/encoder_norm/scale"]

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

   # Block i, layer 1 (Cross Attention).
   lowerCamelCase_      =  tax_layer_norm_lookup(UpperCAmelCase_					,				UpperCAmelCase_					,				"decoder"					,				"pre_cross_attention_layer_norm"		)
   lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  tax_attention_lookup(UpperCAmelCase_					,				UpperCAmelCase_					,				"decoder"					,				"encoder_decoder_attention"		)
   lowerCamelCase_      =  layer_norm
   lowerCamelCase_      =  k.T
   lowerCamelCase_      =  o.T
   lowerCamelCase_      =  q.T
   lowerCamelCase_      =  v.T

   # Block i, layer 2 (MLP).
   lowerCamelCase_      =  tax_layer_norm_lookup(UpperCAmelCase_					,				UpperCAmelCase_					,				"decoder"					,				"pre_mlp_layer_norm"		)
   lowerCamelCase_ ,lowerCamelCase_      =  tax_mlp_lookup(UpperCAmelCase_					,				UpperCAmelCase_					,				"decoder"					,				UpperCAmelCase_		)
   lowerCamelCase_      =  layer_norm
   if split_mlp_wi:
    lowerCamelCase_      =  wi[0].T
    lowerCamelCase_      =  wi[1].T
   else:
    lowerCamelCase_      =  wi.T
   lowerCamelCase_      =  wo.T

  lowerCamelCase_      =  old["decoder/decoder_norm/scale"]
  lowerCamelCase_      =  old[
      "decoder/relpos_bias/rel_embedding"
  ].T

  # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
  if "decoder/logits_dense/kernel" in old:
   lowerCamelCase_      =  old["decoder/logits_dense/kernel"].T

 return new





def __snake_case					(      UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						bool		):
 lowerCamelCase_      =  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:
  lowerCamelCase_      =  state_dict["shared.weight"]

 if not is_encoder_only:
  if "decoder.embed_tokens.weight" not in state_dict:
   lowerCamelCase_      =  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."		)
   lowerCamelCase_      =  state_dict["shared.weight"]

 return state_dict





def __snake_case					(      UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						Dict					,				UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						Tuple		):
 lowerCamelCase_      =  checkpoints.load_tax_checkpoint(UpperCAmelCase_		)
 lowerCamelCase_      =  convert_tax_to_pytorch(UpperCAmelCase_					,				num_layers=config.num_layers					,				is_encoder_only=UpperCAmelCase_		)
 lowerCamelCase_      =  make_state_dict(UpperCAmelCase_					,				UpperCAmelCase_		)
 model.load_state_dict(UpperCAmelCase_					,				strict=UpperCAmelCase_		)





def __snake_case					(      UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						Dict					,				UpperCAmelCase_						:						bool = False		):
 lowerCamelCase_      =  TaConfig.from_json_file(UpperCAmelCase_		)
 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:
  lowerCamelCase_      =  TaEncoderModel(UpperCAmelCase_		)
 else:
  lowerCamelCase_      =  TaForConditionalGeneration(UpperCAmelCase_		)

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

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

 # Verify that we can load the checkpoint.
 model.from_pretrained(UpperCAmelCase_		)
 print("Done"		)


if __name__ == "__main__":
     a_  :				str						= 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
     )
     a_  :				List[str]						= parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
     )







'''simple docstring'''

import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


a_  :				Any						= logging.get_logger(__name__)

a_  :				Optional[Any]						= {"""vocab_file""": """spiece.model"""}

a_  :				Tuple						= {
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    }
}





class      snake_case    (    lowercase			):





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase=False			,  UpperCamelCase=True			,  UpperCamelCase=False			,  UpperCamelCase="<s>"			,  UpperCamelCase="</s>"			,  UpperCamelCase="<unk>"			,  UpperCamelCase="<sep>"			,  UpperCamelCase="<pad>"			,  UpperCamelCase="<cls>"			,  UpperCamelCase="<mask>"			,  UpperCamelCase=["<eop>", "<eod>"]			,  UpperCamelCase = None			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  AddedToken(UpperCamelCase			,  lstrip=UpperCamelCase			,  rstrip=UpperCamelCase     ) if isinstance(UpperCamelCase			,  UpperCamelCase     ) else mask_token

   lowerCamelCase_      =  {} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       do_lower_case=UpperCamelCase			,  remove_space=UpperCamelCase			,  keep_accents=UpperCamelCase			,  bos_token=UpperCamelCase			,  eos_token=UpperCamelCase			,  unk_token=UpperCamelCase			,  sep_token=UpperCamelCase			,  pad_token=UpperCamelCase			,  cls_token=UpperCamelCase			,  mask_token=UpperCamelCase			,  additional_special_tokens=UpperCamelCase			,  sp_model_kwargs=self.sp_model_kwargs			,  **UpperCamelCase			,  )

   lowerCamelCase_      =  3

   lowerCamelCase_      =  do_lower_case
   lowerCamelCase_      =  remove_space
   lowerCamelCase_      =  keep_accents
   lowerCamelCase_      =  vocab_file

   lowerCamelCase_      =  spm.SentencePieceProcessor(**self.sp_model_kwargs     )
   self.sp_model.Load(UpperCamelCase     )

   try:
    import jieba
   except ModuleNotFoundError as error:
    raise error.__class__(
        "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
        "See https://pypi.org/project/jieba/ for installation."     )
   lowerCamelCase_      =  jieba
   lowerCamelCase_      =  str.maketrans(" \n"			,  "\u2582\u2583"     )






  @property
  # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
  def   snake_case		(    self     ):







   """simple docstring"""
   return len(self.sp_model     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  {self.convert_ids_to_tokens(UpperCamelCase     ): i for i in range(self.vocab_size     )}
   vocab.update(self.added_tokens_encoder     )
   return vocab






  def __getstate__(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.__dict__.copy()
   lowerCamelCase_      =  None
   return state






  def __setstate__(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  d

   # for backward compatibility
   if not hasattr(self			,  "sp_model_kwargs"     ):
    lowerCamelCase_      =  {}

   lowerCamelCase_      =  spm.SentencePieceProcessor(**self.sp_model_kwargs     )
   self.sp_model.Load(self.vocab_file     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   if self.remove_space:
    lowerCamelCase_      =  " ".join(inputs.strip().split()     )
   else:
    lowerCamelCase_      =  inputs
   lowerCamelCase_      =  outputs.replace("``"			,  "\""     ).replace("''"			,  "\""     )

   if not self.keep_accents:
    lowerCamelCase_      =  unicodedata.normalize("NFKD"			,  UpperCamelCase     )
    lowerCamelCase_      =  "".join([c for c in outputs if not unicodedata.combining(UpperCamelCase     )]     )
   if self.do_lower_case:
    lowerCamelCase_      =  outputs.lower()

   return outputs






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.preprocess_text(UpperCamelCase     )
   lowerCamelCase_      =  self.sp_model.encode(UpperCamelCase			,  out_type=UpperCamelCase     )
   lowerCamelCase_      =  []
   for piece in pieces:
    if len(UpperCamelCase     ) > 1 and piece[-1] == str(","     ) and piece[-2].isdigit():
     lowerCamelCase_      =  self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCamelCase			,  ""     )     )
     if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
      if len(cur_pieces[0]     ) == 1:
       lowerCamelCase_      =  cur_pieces[1:]
      else:
       lowerCamelCase_      =  cur_pieces[0][1:]
     cur_pieces.append(piece[-1]     )
     new_pieces.extend(UpperCamelCase     )
    else:
     new_pieces.append(UpperCamelCase     )

   return new_pieces






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   return self.sp_model.PieceToId(UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   return self.sp_model.IdToPiece(UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  "".join(UpperCamelCase     ).replace(UpperCamelCase			,  " "     ).strip()
   return out_string






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







   """simple docstring"""
   lowerCamelCase_      =  [self.sep_token_id]
   lowerCamelCase_      =  [self.cls_token_id]
   if token_ids_a is None:
    return token_ids_a + sep + cls
   return token_ids_a + sep + token_ids_a + sep + cls






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None			,  UpperCamelCase = False     ):







   """simple docstring"""

   if already_has_special_tokens:
    return super().get_special_tokens_mask(
        token_ids_a=UpperCamelCase			,  token_ids_a=UpperCamelCase			,  already_has_special_tokens=UpperCamelCase     )

   if token_ids_a is not None:
    return ([0] * len(UpperCamelCase     )) + [1] + ([0] * len(UpperCamelCase     )) + [1, 1]
   return ([0] * len(UpperCamelCase     )) + [1, 1]






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







   """simple docstring"""
   lowerCamelCase_      =  [self.sep_token_id]
   lowerCamelCase_      =  [2]

   if token_ids_a is None:
    return len(token_ids_a + sep     ) * [0] + cls_segment_id
   return len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1] + cls_segment_id






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







   """simple docstring"""
   if not os.path.isdir(UpperCamelCase     ):
    logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
    return
   lowerCamelCase_      =  os.path.join(
       UpperCamelCase			,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

   if os.path.abspath(self.vocab_file     ) != os.path.abspath(UpperCamelCase     ) and os.path.isfile(self.vocab_file     ):
    copyfile(self.vocab_file			,  UpperCamelCase     )
   elif not os.path.isfile(self.vocab_file     ):
    with open(UpperCamelCase			,  "wb"     ) as fi:
     lowerCamelCase_      =  self.sp_model.serialized_model_proto()
     fi.write(UpperCamelCase     )

   return (out_vocab_file,)







  def   snake_case		(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  super()._decode(*UpperCamelCase			,  **UpperCamelCase     )
   lowerCamelCase_      =  text.replace(" "			,  ""     ).replace("\u2582"			,  " "     ).replace("\u2583"			,  "\n"     )
   return text



