

import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_        =			logging.get_logger(__name__)

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Dict     ,					SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   Dict      ):



     '''simple docstring'''





     lowercase__   :       int				=	os.path.abspath(SCREAMING_SNAKE_CASE_      )
     logger.info(f"""Converting TensorFlow checkpoint from {tf_path}"""      )
     # Load weights from TF model
     lowercase__   :       Optional[Any]				=	tf.train.list_variables(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Union[str, Any]				=	[]
     lowercase__   :       Any				=	[]
     lowercase__   :       str				=	[]
     for full_name, shape in init_vars:
          # logger.info(f"Loading TF weight {name} with shape {shape}")
          lowercase__   :       Dict				=	full_name.split('/'      )
          if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
               logger.info(f"""Skipping non-model layer {full_name}"""      )
               continue
          if "optimizer" in full_name:
               logger.info(f"""Skipping optimization layer {full_name}"""      )
               continue
          if name[0] == "model":
               # ignore initial 'model'
               lowercase__   :       Dict				=	name[1:]
          # figure out how many levels deep the name is
          lowercase__   :       str				=	0
          for _name in name:
               if _name.startswith('layer_with_weights'      ):
                    depth += 1
               else:
                    break
          layer_depth.append(SCREAMING_SNAKE_CASE_      )
          # read data
          lowercase__   :       Tuple				=	tf.train.load_variable(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
          names.append('/'.join(SCREAMING_SNAKE_CASE_      )      )
          arrays.append(SCREAMING_SNAKE_CASE_      )
     logger.info(f"""Read a total of {len(SCREAMING_SNAKE_CASE_      ):,} layers"""      )

     # Sanity check
     if len(set(SCREAMING_SNAKE_CASE_      )      ) != 1:
          raise ValueError(f"""Found layer names with different depths (layer depth {list(set(SCREAMING_SNAKE_CASE_      )      )})"""      )
     lowercase__   :       Optional[Any]				=	list(set(SCREAMING_SNAKE_CASE_      )      )[0]
     if layer_depth != 1:
          raise ValueError(
              'The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'
              ' heads.'      )

     # convert layers
     logger.info('Converting weights...'      )
     for full_name, array in zip(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      ):
          lowercase__   :       List[str]				=	full_name.split('/'      )
          lowercase__   :       Optional[Any]				=	model
          lowercase__   :       Dict				=	[]
          for i, m_name in enumerate(SCREAMING_SNAKE_CASE_      ):
               if m_name == ".ATTRIBUTES":
                    # variable names end with .ATTRIBUTES/VARIABLE_VALUE
                    break
               if m_name.startswith('layer_with_weights'      ):
                    lowercase__   :       Optional[Any]				=	int(m_name.split('-'      )[-1]      )
                    if layer_num <= 2:
                         # embedding layers
                         # layer_num 0: word_embeddings
                         # layer_num 1: position_embeddings
                         # layer_num 2: token_type_embeddings
                         continue
                    elif layer_num == 3:
                         # embedding LayerNorm
                         trace.extend(['embeddings', 'LayerNorm']      )
                         lowercase__   :       Union[str, Any]				=	getattr(SCREAMING_SNAKE_CASE_     ,					'embeddings'      )
                         lowercase__   :       Optional[int]				=	getattr(SCREAMING_SNAKE_CASE_     ,					'LayerNorm'      )
                    elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
                         # encoder layers
                         trace.extend(['encoder', 'layer', str(layer_num - 4      )]      )
                         lowercase__   :       Any				=	getattr(SCREAMING_SNAKE_CASE_     ,					'encoder'      )
                         lowercase__   :       Tuple				=	getattr(SCREAMING_SNAKE_CASE_     ,					'layer'      )
                         lowercase__   :       Any				=	pointer[layer_num - 4]
                    elif layer_num == config.num_hidden_layers + 4:
                         # pooler layer
                         trace.extend(['pooler', 'dense']      )
                         lowercase__   :       Any				=	getattr(SCREAMING_SNAKE_CASE_     ,					'pooler'      )
                         lowercase__   :       List[Any]				=	getattr(SCREAMING_SNAKE_CASE_     ,					'dense'      )
               elif m_name == "embeddings":
                    trace.append('embeddings'      )
                    lowercase__   :       Tuple				=	getattr(SCREAMING_SNAKE_CASE_     ,					'embeddings'      )
                    if layer_num == 0:
                         trace.append('word_embeddings'      )
                         lowercase__   :       Optional[Any]				=	getattr(SCREAMING_SNAKE_CASE_     ,					'word_embeddings'      )
                    elif layer_num == 1:
                         trace.append('position_embeddings'      )
                         lowercase__   :       str				=	getattr(SCREAMING_SNAKE_CASE_     ,					'position_embeddings'      )
                    elif layer_num == 2:
                         trace.append('token_type_embeddings'      )
                         lowercase__   :       List[str]				=	getattr(SCREAMING_SNAKE_CASE_     ,					'token_type_embeddings'      )
                    else:
                         raise ValueError(f"""Unknown embedding layer with name {full_name}"""      )
                    trace.append('weight'      )
                    lowercase__   :       List[Any]				=	getattr(SCREAMING_SNAKE_CASE_     ,					'weight'      )
               elif m_name == "_attention_layer":
                    # self-attention layer
                    trace.extend(['attention', 'self']      )
                    lowercase__   :       List[str]				=	getattr(SCREAMING_SNAKE_CASE_     ,					'attention'      )
                    lowercase__   :       Any				=	getattr(SCREAMING_SNAKE_CASE_     ,					'self'      )
               elif m_name == "_attention_layer_norm":
                    # output attention norm
                    trace.extend(['attention', 'output', 'LayerNorm']      )
                    lowercase__   :       Optional[Any]				=	getattr(SCREAMING_SNAKE_CASE_     ,					'attention'      )
                    lowercase__   :       int				=	getattr(SCREAMING_SNAKE_CASE_     ,					'output'      )
                    lowercase__   :       Union[str, Any]				=	getattr(SCREAMING_SNAKE_CASE_     ,					'LayerNorm'      )
               elif m_name == "_attention_output_dense":
                    # output attention dense
                    trace.extend(['attention', 'output', 'dense']      )
                    lowercase__   :       Optional[int]				=	getattr(SCREAMING_SNAKE_CASE_     ,					'attention'      )
                    lowercase__   :       Tuple				=	getattr(SCREAMING_SNAKE_CASE_     ,					'output'      )
                    lowercase__   :       Optional[Any]				=	getattr(SCREAMING_SNAKE_CASE_     ,					'dense'      )
               elif m_name == "_output_dense":
                    # output dense
                    trace.extend(['output', 'dense']      )
                    lowercase__   :       List[Any]				=	getattr(SCREAMING_SNAKE_CASE_     ,					'output'      )
                    lowercase__   :       List[str]				=	getattr(SCREAMING_SNAKE_CASE_     ,					'dense'      )
               elif m_name == "_output_layer_norm":
                    # output dense
                    trace.extend(['output', 'LayerNorm']      )
                    lowercase__   :       List[str]				=	getattr(SCREAMING_SNAKE_CASE_     ,					'output'      )
                    lowercase__   :       Union[str, Any]				=	getattr(SCREAMING_SNAKE_CASE_     ,					'LayerNorm'      )
               elif m_name == "_key_dense":
                    # attention key
                    trace.append('key'      )
                    lowercase__   :       str				=	getattr(SCREAMING_SNAKE_CASE_     ,					'key'      )
               elif m_name == "_query_dense":
                    # attention query
                    trace.append('query'      )
                    lowercase__   :       Tuple				=	getattr(SCREAMING_SNAKE_CASE_     ,					'query'      )
               elif m_name == "_value_dense":
                    # attention value
                    trace.append('value'      )
                    lowercase__   :       Optional[Any]				=	getattr(SCREAMING_SNAKE_CASE_     ,					'value'      )
               elif m_name == "_intermediate_dense":
                    # attention intermediate dense
                    trace.extend(['intermediate', 'dense']      )
                    lowercase__   :       Any				=	getattr(SCREAMING_SNAKE_CASE_     ,					'intermediate'      )
                    lowercase__   :       str				=	getattr(SCREAMING_SNAKE_CASE_     ,					'dense'      )
               elif m_name == "_output_layer_norm":
                    # output layer norm
                    trace.append('output'      )
                    lowercase__   :       Tuple				=	getattr(SCREAMING_SNAKE_CASE_     ,					'output'      )
               # weights & biases
               elif m_name in ["bias", "beta"]:
                    trace.append('bias'      )
                    lowercase__   :       Optional[Any]				=	getattr(SCREAMING_SNAKE_CASE_     ,					'bias'      )
               elif m_name in ["kernel", "gamma"]:
                    trace.append('weight'      )
                    lowercase__   :       List[str]				=	getattr(SCREAMING_SNAKE_CASE_     ,					'weight'      )
               else:
                    logger.warning(f"""Ignored {m_name}"""      )
        # for certain layers reshape is necessary
          lowercase__   :       Tuple				=	'.'.join(SCREAMING_SNAKE_CASE_      )
          if re.match(R'(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'     ,					SCREAMING_SNAKE_CASE_      ) or re.match(
              R'(\S+)\.attention\.output\.dense\.weight'     ,					SCREAMING_SNAKE_CASE_      ):
               lowercase__   :       List[str]				=	array.reshape(pointer.data.shape      )
          if "kernel" in full_name:
               lowercase__   :       Any				=	array.transpose()
          if pointer.shape == array.shape:
               lowercase__   :       Tuple				=	torch.from_numpy(SCREAMING_SNAKE_CASE_      )
          else:
               raise ValueError(
                   f"""Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"""
                   f""" {array.shape}"""      )
          logger.info(f"""Successfully set variable {full_name} to PyTorch layer {trace}"""      )
     return model

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[Any]     ,					SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   Any      ):



     '''simple docstring'''





     logger.info(f"""Loading model based on config from {config_path}..."""      )
     lowercase__   :       Optional[Any]				=	BertConfig.from_json_file(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Any				=	BertModel(SCREAMING_SNAKE_CASE_      )

     # Load weights from checkpoint
     logger.info(f"""Loading weights from checkpoint {tf_checkpoint_path}..."""      )
     load_tfa_weights_in_bert(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )

     # Save pytorch-model
     logger.info(f"""Saving PyTorch model to {pytorch_dump_path}..."""      )
     torch.save(model.state_dict()     ,					SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
       snake_case_        =			argparse.ArgumentParser()
       parser.add_argument(
           '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow 2.x checkpoint path.'''
       )
       parser.add_argument(
           '''--bert_config_file''',
           type=str,
           required=True,
           help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
       )
       parser.add_argument(
           '''--pytorch_dump_path''',
           type=str,
           required=True,
           help='''Path to the output PyTorch model (must include filename).''',
       )
       snake_case_        =			parser.parse_args()
       convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




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


snake_case_        =			logging.get_logger(__name__)

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

snake_case_        =			{
    '''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'''
        ),
    }
}

snake_case_        =			{
    '''junnyu/roformer_chinese_small''': 1_536,
    '''junnyu/roformer_chinese_base''': 1_536,
    '''junnyu/roformer_chinese_char_small''': 512,
    '''junnyu/roformer_chinese_char_base''': 512,
    '''junnyu/roformer_small_discriminator''': 128,
    '''junnyu/roformer_small_generator''': 128,
}


snake_case_        =			{
    '''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 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Union[str, Any]														=					VOCAB_FILES_NAMES
    __lowerCamelCase						:	int														=					PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:	Any														=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:	int														=					PRETRAINED_INIT_CONFIGURATION
    __lowerCamelCase						:	Tuple														=					RoFormerTokenizer





    def __init__(				self						,       a=None						,       a=None						,       a=True						,       a="[UNK]"						,       a="[SEP]"						,       a="[PAD]"						,       a="[CLS]"						,       a="[MASK]"						,       a=True						,       a=None						,       **a						,       ):
         super().__init__(
             a						,       tokenizer_file=a						,       do_lower_case=a						,       unk_token=a						,       sep_token=a						,       pad_token=a						,       cls_token=a						,       mask_token=a						,       tokenize_chinese_chars=a						,       strip_accents=a						,       **a						,       )

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

         lowercase__   :       Optional[int]				=	do_lower_case





    def __getstate__(				self):
         lowercase__   :       str				=	self.__dict__.copy()
         lowercase__   :       Any				=	BertPreTokenizer()
         return state





    def __setstate__(				self						,       a):
         lowercase__   :       Union[str, Any]				=	d
         lowercase__   :       int				=	self.__dict__['_tokenizer'].get_vocab()
         lowercase__   :       List[str]				=	PreTokenizer.custom(JiebaPreTokenizer(a))





    def 				snake_case_    (				self						,       a						,       a=None):
         lowercase__   :       Union[str, Any]				=	[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 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Tuple				=	[self.sep_token_id]
         lowercase__   :       Optional[int]				=	[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 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Optional[Any]				=	self._tokenizer.model.save(a						,       name=a)
         return tuple(a)





    def 				snake_case_    (				self						,       a						,       a=None						,       a=None						,       a=False						,       **a						,       ):
         lowercase__   :       List[str]				=	BertPreTokenizer()
         return super().save_pretrained(a						,       a						,       a						,       a						,       **a)

