
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    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[int]  ,   lowerCAmelCase__							:				Optional[int]  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				Optional[int]="attention"  ):
 __a :  Union[str, Any]						=  params[f"{prefix}/layers_{i}/{layer_name}/key/kernel"]
 __a :  Optional[int]						=  params[f"{prefix}/layers_{i}/{layer_name}/out/kernel"]
 __a :  Optional[Any]						=  params[f"{prefix}/layers_{i}/{layer_name}/query/kernel"]
 __a :  str						=  params[f"{prefix}/layers_{i}/{layer_name}/value/kernel"]
 return k, o, q, v




def    __UpperCamelCase     (				lowerCAmelCase__							:				Dict  ,   lowerCAmelCase__							:				Any  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				str=False  ):
 if split_mlp_wi:
  __a :  Optional[int]						=  params[f"{prefix}/layers_{i}/mlp/wi_0/kernel"]
  __a :  str						=  params[f"{prefix}/layers_{i}/mlp/wi_1/kernel"]
  __a :  Any						=  (wi_a, wi_a)
 else:
  __a :  List[Any]						=  params[f"{prefix}/layers_{i}/mlp/wi/kernel"]

 __a :  Union[str, Any]						=  params[f"{prefix}/layers_{i}/mlp/wo/kernel"]
 return wi, wo




def    __UpperCamelCase     (				lowerCAmelCase__							:				Tuple  ,   lowerCAmelCase__							:				Any  ,   lowerCAmelCase__							:				Dict  ,   lowerCAmelCase__							:				List[Any]  ):
 return params[f"{prefix}/layers_{i}/{layer_name}/scale"]




def    __UpperCamelCase     (				lowerCAmelCase__							:				dict  ,   *, lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				bool  ):
 __a :  Any						=  traverse_util.flatten_dict(variables['''target''']  )
 __a :  Any						=  {'''/'''.join(lowerCAmelCase__  ): v for k, v in old.items()}

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

 __a :  Union[str, Any]						=  collections.OrderedDict()

 # Shared embeddings.
 __a :  Any						=  old['''token_embedder/embedding''']

 # Encoder.
 for i in range(lowerCAmelCase__  ):
  # Block i, layer 0 (Self Attention).
  __a :  Union[str, Any]						=  tax_layer_norm_lookup(lowerCAmelCase__  ,   lowerCAmelCase__  ,   '''encoder'''  ,   '''pre_attention_layer_norm'''  )
  __a				,       __a				,       __a				,       __a :  Any						=  tax_attention_lookup(lowerCAmelCase__  ,   lowerCAmelCase__  ,   '''encoder'''  ,   '''attention'''  )
  __a :  Tuple						=  layer_norm
  __a :  Optional[Any]						=  k.T
  __a :  List[str]						=  o.T
  __a :  Optional[Any]						=  q.T
  __a :  Optional[int]						=  v.T

  # Block i, layer 1 (MLP).
  __a :  int						=  tax_layer_norm_lookup(lowerCAmelCase__  ,   lowerCAmelCase__  ,   '''encoder'''  ,   '''pre_mlp_layer_norm'''  )
  __a				,       __a :  List[Any]						=  tax_mlp_lookup(lowerCAmelCase__  ,   lowerCAmelCase__  ,   '''encoder'''  ,   lowerCAmelCase__  )
  __a :  List[str]						=  layer_norm
  if split_mlp_wi:
   __a :  List[str]						=  wi[0].T
   __a :  str						=  wi[1].T
  else:
   __a :  str						=  wi.T
  __a :  str						=  wo.T

 __a :  Optional[int]						=  old[
     '''encoder/relpos_bias/rel_embedding'''
 ].T
 __a :  Tuple						=  old['''encoder/encoder_norm/scale''']

 if not is_encoder_only:
  # Decoder.
  for i in range(lowerCAmelCase__  ):
   # Block i, layer 0 (Self Attention).
   __a :  List[Any]						=  tax_layer_norm_lookup(lowerCAmelCase__  ,   lowerCAmelCase__  ,   '''decoder'''  ,   '''pre_self_attention_layer_norm'''  )
   __a				,       __a				,       __a				,       __a :  Tuple						=  tax_attention_lookup(lowerCAmelCase__  ,   lowerCAmelCase__  ,   '''decoder'''  ,   '''self_attention'''  )
   __a :  List[Any]						=  layer_norm
   __a :  Union[str, Any]						=  k.T
   __a :  Tuple						=  o.T
   __a :  Union[str, Any]						=  q.T
   __a :  Dict						=  v.T

   # Block i, layer 1 (Cross Attention).
   __a :  List[str]						=  tax_layer_norm_lookup(lowerCAmelCase__  ,   lowerCAmelCase__  ,   '''decoder'''  ,   '''pre_cross_attention_layer_norm'''  )
   __a				,       __a				,       __a				,       __a :  Tuple						=  tax_attention_lookup(lowerCAmelCase__  ,   lowerCAmelCase__  ,   '''decoder'''  ,   '''encoder_decoder_attention'''  )
   __a :  List[str]						=  layer_norm
   __a :  Optional[int]						=  k.T
   __a :  Optional[Any]						=  o.T
   __a :  str						=  q.T
   __a :  Optional[int]						=  v.T

   # Block i, layer 2 (MLP).
   __a :  Union[str, Any]						=  tax_layer_norm_lookup(lowerCAmelCase__  ,   lowerCAmelCase__  ,   '''decoder'''  ,   '''pre_mlp_layer_norm'''  )
   __a				,       __a :  str						=  tax_mlp_lookup(lowerCAmelCase__  ,   lowerCAmelCase__  ,   '''decoder'''  ,   lowerCAmelCase__  )
   __a :  List[Any]						=  layer_norm
   if split_mlp_wi:
    __a :  List[str]						=  wi[0].T
    __a :  int						=  wi[1].T
   else:
    __a :  Optional[Any]						=  wi.T
   __a :  Tuple						=  wo.T

  __a :  List[str]						=  old['''decoder/decoder_norm/scale''']
  __a :  int						=  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:
   __a :  Tuple						=  old['''decoder/logits_dense/kernel'''].T

 return new




def    __UpperCamelCase     (				lowerCAmelCase__							:				Union[str, Any]  ,   lowerCAmelCase__							:				bool  ):
 __a :  int						=  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:
  __a :  List[str]						=  state_dict['''shared.weight''']

 if not is_encoder_only:
  if "decoder.embed_tokens.weight" not in state_dict:
   __a :  List[str]						=  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.'''  )
   __a :  Any						=  state_dict['''shared.weight''']

 return state_dict




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[Any]  ,   lowerCAmelCase__							:				Optional[int]  ,   lowerCAmelCase__							:				Dict  ,   lowerCAmelCase__							:				Tuple  ):
 __a :  List[str]						=  checkpoints.load_tax_checkpoint(lowerCAmelCase__  )
 __a :  str						=  convert_tax_to_pytorch(lowerCAmelCase__  ,   num_layers=config.num_layers  ,   is_encoder_only=lowerCAmelCase__  )
 __a :  int						=  make_state_dict(lowerCAmelCase__  ,   lowerCAmelCase__  )
 model.load_state_dict(lowerCAmelCase__  ,   strict=lowerCAmelCase__  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				Tuple  ,   lowerCAmelCase__							:				List[Any]  ,   lowerCAmelCase__							:				bool = False  ):
 __a :  int						=  TaConfig.from_json_file(lowerCAmelCase__  )
 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:
  __a :  Dict						=  TaEncoderModel(lowerCAmelCase__  )
 else:
  __a :  Union[str, Any]						=  TaForConditionalGeneration(lowerCAmelCase__  )

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

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

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


if __name__ == "__main__":
      lowercase__       =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
      )
      lowercase__       =parser.parse_args()
      convert_tax_checkpoint_to_pytorch(
          args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
      )


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


lowercase__       =logging.get_logger(__name__)

lowercase__       ={'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase__       ={
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

lowercase__       ={
    'junnyu/roformer_chinese_small': 1536,
    'junnyu/roformer_chinese_base': 1536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


lowercase__       ={
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}



class 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE      :							List[Any]	       =      PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE      :							List[str]	       =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      PRETRAINED_INIT_CONFIGURATION
   _SCREAMING_SNAKE_CASE      :							Optional[int]	       =      RoFormerTokenizer
   def __init__(self :					List[str]					,				snake_case_ :					Optional[int]=None					,				snake_case_ :					str=None					,				snake_case_ :					Optional[Any]=True					,				snake_case_ :					str="[UNK]"					,				snake_case_ :					Dict="[SEP]"					,				snake_case_ :					Any="[PAD]"					,				snake_case_ :					str="[CLS]"					,				snake_case_ :					List[Any]="[MASK]"					,				snake_case_ :					Any=True					,				snake_case_ :					List[str]=None					,				**snake_case_ :					Optional[int]					,				):
    super().__init__(
        snake_case_					,				tokenizer_file=snake_case_					,				do_lower_case=snake_case_					,				unk_token=snake_case_					,				sep_token=snake_case_					,				pad_token=snake_case_					,				cls_token=snake_case_					,				mask_token=snake_case_					,				tokenize_chinese_chars=snake_case_					,				strip_accents=snake_case_					,				**snake_case_					,				)

    __a :  Optional[int]						=  json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
    if (
        pre_tok_state.get('''lowercase'''					,				snake_case_   ) != do_lower_case
        or pre_tok_state.get('''strip_accents'''					,				snake_case_   ) != strip_accents
    ):
     __a :  List[str]						=  getattr(snake_case_					,				pre_tok_state.pop('''type'''   )   )
     __a :  Optional[Any]						=  do_lower_case
     __a :  Optional[int]						=  strip_accents
     __a :  List[str]						=  pre_tok_class(**snake_case_   )

    __a :  Optional[Any]						=  do_lower_case
   def __getstate__(self :					Union[str, Any]   ):
    __a :  Any						=  self.__dict__.copy()
    __a :  Union[str, Any]						=  BertPreTokenizer()
    return state
   def __setstate__(self :					Tuple					,				snake_case_ :					Optional[Any]   ):
    __a :  Dict						=  d
    __a :  str						=  self.__dict__['''_tokenizer'''].get_vocab()
    __a :  Optional[Any]						=  PreTokenizer.custom(JiebaPreTokenizer(snake_case_   )   )
   def       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					List[Any]					,				snake_case_ :					Optional[Any]=None   ):
    __a :  Optional[int]						=  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

    if token_ids_a:
     output += token_ids_a + [self.sep_token_id]

    return output
   def       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					List[int]					,				snake_case_ :					Optional[List[int]] = None   ):
    __a :  int						=  [self.sep_token_id]
    __a :  List[Any]						=  [self.cls_token_id]
    if token_ids_a is None:
     return len(cls + token_ids_a + sep   ) * [0]
    return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]
   def       lowerCAmelCase			(self :					int					,				snake_case_ :					str					,				snake_case_ :					Optional[str] = None   ):
    __a :  Optional[Any]						=  self._tokenizer.model.save(snake_case_					,				name=snake_case_   )
    return tuple(snake_case_   )







   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					Dict					,				snake_case_ :					Tuple=None					,				snake_case_ :					Optional[Any]=None					,				snake_case_ :					Union[str, Any]=False					,				**snake_case_ :					Tuple					,				):
    __a :  List[str]						=  BertPreTokenizer()
    return super().save_pretrained(snake_case_					,				snake_case_					,				snake_case_					,				snake_case_					,				**snake_case_   )
