





import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__lowerCamelCase       :     Dict   =     logging.getLogger(__name__)



@dataclass
class 					A__   :
      _UpperCAmelCase	:str
      _UpperCAmelCase	:List[str]
      _UpperCAmelCase	:Optional[List[str]]



@dataclass
class 					A__   :
      _UpperCAmelCase	:List[int]
      _UpperCAmelCase	:List[int]
      _UpperCAmelCase	:Optional[List[int]]						    =   None
      _UpperCAmelCase	:Optional[List[int]]						    =   None



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:List[str]						    =   'train'
      _UpperCAmelCase	:Dict						    =   'dev'
      _UpperCAmelCase	:str						    =   'test'



class 					A__   :





      @staticmethod
      def        __UpperCamelCase( A_       ,							A_   ):
       '''simple docstring'''




       raise NotImplementedError





      @staticmethod
      def        __UpperCamelCase( A_   ):
       '''simple docstring'''




       raise NotImplementedError





      @staticmethod
      def        __UpperCamelCase( A_       ,							A_       ,							A_       ,							A_       ,							A_=False       ,							A_="[CLS]"       ,							A_=1       ,							A_="[SEP]"       ,							A_=False       ,							A_=False       ,							A_=0       ,							A_=0       ,							A_=-100       ,							A_=0       ,							A_=True       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   {label: i for i, label in enumerate(A_   )}

       UpperCamelCase	:					List[str]       	=   []
       for ex_index, example in enumerate(A_   ):
        if ex_index % 1_0000 == 0:
         logger.info("Writing example %d of %d"       ,							A_       ,							len(A_   )   )

        UpperCamelCase	:					Optional[int]       	=   []
        UpperCamelCase	:					Optional[Any]       	=   []
        for word, label in zip(example.words       ,							example.labels   ):
         UpperCamelCase	:					str       	=   tokenizer.tokenize(A_   )

         # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
         if len(A_   ) > 0:
          tokens.extend(A_   )
          # Use the real label id for the first token of the word, and padding ids for the remaining tokens
          label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(A_   ) - 1)   )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
        UpperCamelCase	:					Tuple       	=   tokenizer.num_special_tokens_to_add()
        if len(A_   ) > max_seq_length - special_tokens_count:
         UpperCamelCase	:					str       	=   tokens[: (max_seq_length - special_tokens_count)]
         UpperCamelCase	:					str       	=   label_ids[: (max_seq_length - special_tokens_count)]

        # The convention in BERT is:
        # (a) For sequence pairs:
        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
        #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
        # (b) For single sequences:
        #  tokens:   [CLS] the dog is hairy . [SEP]
        #  type_ids:   0   0   0   0  0     0   0
        #
        # Where "type_ids" are used to indicate whether this is the first
        # sequence or the second sequence. The embedding vectors for `type=0` and
        # `type=1` were learned during pre-training and are added to the wordpiece
        # embedding vector (and position vector). This is not *strictly* necessary
        # since the [SEP] token unambiguously separates the sequences, but it makes
        # it easier for the model to learn the concept of sequences.
        #
        # For classification tasks, the first vector (corresponding to [CLS]) is
        # used as the "sentence vector". Note that this only makes sense because
        # the entire model is fine-tuned.
        tokens += [sep_token]
        label_ids += [pad_token_label_id]
        if sep_token_extra:
         # roberta uses an extra separator b/w pairs of sentences
         tokens += [sep_token]
         label_ids += [pad_token_label_id]
        UpperCamelCase	:					Optional[int]       	=   [sequence_a_segment_id] * len(A_   )

        if cls_token_at_end:
         tokens += [cls_token]
         label_ids += [pad_token_label_id]
         segment_ids += [cls_token_segment_id]
        else:
         UpperCamelCase	:					Optional[Any]       	=   [cls_token] + tokens
         UpperCamelCase	:					Optional[Any]       	=   [pad_token_label_id] + label_ids
         UpperCamelCase	:					Any       	=   [cls_token_segment_id] + segment_ids

        UpperCamelCase	:					Any       	=   tokenizer.convert_tokens_to_ids(A_   )

        # The mask has 1 for real tokens and 0 for padding tokens. Only real
        # tokens are attended to.
        UpperCamelCase	:					List[str]       	=   [1 if mask_padding_with_zero else 0] * len(A_   )

        # Zero-pad up to the sequence length.
        UpperCamelCase	:					Tuple       	=   max_seq_length - len(A_   )
        if pad_on_left:
         UpperCamelCase	:					str       	=   ([pad_token] * padding_length) + input_ids
         UpperCamelCase	:					Dict       	=   ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
         UpperCamelCase	:					Optional[Any]       	=   ([pad_token_segment_id] * padding_length) + segment_ids
         UpperCamelCase	:					int       	=   ([pad_token_label_id] * padding_length) + label_ids
        else:
         input_ids += [pad_token] * padding_length
         input_mask += [0 if mask_padding_with_zero else 1] * padding_length
         segment_ids += [pad_token_segment_id] * padding_length
         label_ids += [pad_token_label_id] * padding_length

        assert len(A_   ) == max_seq_length
        assert len(A_   ) == max_seq_length
        assert len(A_   ) == max_seq_length
        assert len(A_   ) == max_seq_length

        if ex_index < 5:
         logger.info("*** Example ***"   )
         logger.info("guid: %s"       ,							example.guid   )
         logger.info("tokens: %s"       ,							" ".join([str(A_   ) for x in tokens]   )   )
         logger.info("input_ids: %s"       ,							" ".join([str(A_   ) for x in input_ids]   )   )
         logger.info("input_mask: %s"       ,							" ".join([str(A_   ) for x in input_mask]   )   )
         logger.info("segment_ids: %s"       ,							" ".join([str(A_   ) for x in segment_ids]   )   )
         logger.info("label_ids: %s"       ,							" ".join([str(A_   ) for x in label_ids]   )   )

        if "token_type_ids" not in tokenizer.model_input_names:
         UpperCamelCase	:					Tuple       	=   None

        features.append(
            InputFeatures(
                input_ids=A_       ,							attention_mask=A_       ,							token_type_ids=A_       ,							label_ids=A_   )   )
       return features


if is_torch_available():
    import torch
    from torch import nn
    from torch.utils.data import Dataset



    class 					A__   (       __snake_case			):
          _UpperCAmelCase	:List[InputFeatures]
          _UpperCAmelCase	:int						    =   nn.CrossEntropyLoss().ignore_index





          def __init__( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_ = None       ,							A_=False       ,							A_ = Split.train       ,							):
           '''simple docstring'''




           UpperCamelCase	:					Union[str, Any]       	=   os.path.join(
               A_       ,							"cached_{}_{}_{}".format(mode.value       ,							tokenizer.__class__.__name__       ,							str(A_   )   )       ,							)

           # Make sure only the first process in distributed training processes the dataset,
           # and the others will use the cache.
           UpperCamelCase	:					List[Any]       	=   cached_features_file + ".lock"
           with FileLock(A_   ):
            if os.path.exists(A_   ) and not overwrite_cache:
             logger.info(F"""Loading features from cached file {cached_features_file}"""   )
             UpperCamelCase	:					Dict       	=   torch.load(A_   )
            else:
             logger.info(F"""Creating features from dataset file at {data_dir}"""   )
             UpperCamelCase	:					List[Any]       	=   token_classification_task.read_examples_from_file(A_       ,							A_   )
             # TODO clean up all this to leverage built-in features of tokenizers
             UpperCamelCase	:					int       	=   token_classification_task.convert_examples_to_features(
                 A_       ,							A_       ,							A_       ,							A_       ,							cls_token_at_end=bool(model_type in ["xlnet"]   )       ,							cls_token=tokenizer.cls_token       ,							cls_token_segment_id=2 if model_type in ["xlnet"] else 0       ,							sep_token=tokenizer.sep_token       ,							sep_token_extra=A_       ,							pad_on_left=bool(tokenizer.padding_side == "left"   )       ,							pad_token=tokenizer.pad_token_id       ,							pad_token_segment_id=tokenizer.pad_token_type_id       ,							pad_token_label_id=self.pad_token_label_id       ,							)
             logger.info(F"""Saving features into cached file {cached_features_file}"""   )
             torch.save(self.features       ,							A_   )





          def __len__( self   ):
           '''simple docstring'''




           return len(self.features   )





          def __getitem__( self       ,							A_   ):
           '''simple docstring'''




           return self.features[i]


if is_tf_available():
    import tensorflow as tf



    class 					A__   :
          _UpperCAmelCase	:List[InputFeatures]
          _UpperCAmelCase	:int						    =   -1_0_0





          def __init__( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_ = None       ,							A_=False       ,							A_ = Split.train       ,							):
           '''simple docstring'''




           UpperCamelCase	:					str       	=   token_classification_task.read_examples_from_file(A_       ,							A_   )
           # TODO clean up all this to leverage built-in features of tokenizers
           UpperCamelCase	:					str       	=   token_classification_task.convert_examples_to_features(
               A_       ,							A_       ,							A_       ,							A_       ,							cls_token_at_end=bool(model_type in ["xlnet"]   )       ,							cls_token=tokenizer.cls_token       ,							cls_token_segment_id=2 if model_type in ["xlnet"] else 0       ,							sep_token=tokenizer.sep_token       ,							sep_token_extra=A_       ,							pad_on_left=bool(tokenizer.padding_side == "left"   )       ,							pad_token=tokenizer.pad_token_id       ,							pad_token_segment_id=tokenizer.pad_token_type_id       ,							pad_token_label_id=self.pad_token_label_id       ,							)

           def gen():
            for ex in self.features:
             if ex.token_type_ids is None:
              yield (
                  {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                  ex.label_ids,
              )
             else:
              yield (
                  {
                      "input_ids": ex.input_ids,
                      "attention_mask": ex.attention_mask,
                      "token_type_ids": ex.token_type_ids,
                  },
                  ex.label_ids,
              )

           if "token_type_ids" not in tokenizer.model_input_names:
            UpperCamelCase	:					Optional[int]       	=   tf.data.Dataset.from_generator(
                A_       ,							({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)       ,							(
                    {"input_ids": tf.TensorShape([None]   ), "attention_mask": tf.TensorShape([None]   )},
                    tf.TensorShape([None]   ),
                )       ,							)
           else:
            UpperCamelCase	:					Optional[int]       	=   tf.data.Dataset.from_generator(
                A_       ,							({"input_ids": tf.intaa, "attention_mask": tf.intaa, "token_type_ids": tf.intaa}, tf.intaa)       ,							(
                    {
                        "input_ids": tf.TensorShape([None]   ),
                        "attention_mask": tf.TensorShape([None]   ),
                        "token_type_ids": tf.TensorShape([None]   ),
                    },
                    tf.TensorShape([None]   ),
                )       ,							)





          def        __UpperCamelCase( self   ):
           '''simple docstring'''




           UpperCamelCase	:					List[str]       	=   self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features   )   )   )

           return self.dataset





          def __len__( self   ):
           '''simple docstring'''




           return len(self.features   )





          def __getitem__( self       ,							A_   ):
           '''simple docstring'''




           return self.features[i]


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()
lowercase__       =logging.get_logger(__name__)




def    __UpperCamelCase     (				lowerCAmelCase__							:				Union[str, Any]  ,   lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				Dict  ):
 __a :  List[str]						=  os.path.abspath(lowerCAmelCase__  )
 logger.info(f"Converting TensorFlow checkpoint from {tf_path}"  )
 # Load weights from TF model
 __a :  Tuple						=  tf.train.list_variables(lowerCAmelCase__  )
 __a :  Optional[Any]						=  []
 __a :  Union[str, Any]						=  []
 __a :  str						=  []
 for full_name, shape in init_vars:
  # logger.info(f"Loading TF weight {name} with shape {shape}")
  __a :  Any						=  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'
   __a :  Any						=  name[1:]
  # figure out how many levels deep the name is
  __a :  List[Any]						=  0
  for _name in name:
   if _name.startswith('''layer_with_weights'''  ):
    depth += 1
   else:
    break
  layer_depth.append(lowerCAmelCase__  )
  # read data
  __a :  Tuple						=  tf.train.load_variable(lowerCAmelCase__  ,   lowerCAmelCase__  )
  names.append('''/'''.join(lowerCAmelCase__  )  )
  arrays.append(lowerCAmelCase__  )
 logger.info(f"Read a total of {len(lowerCAmelCase__  ):,} layers"  )

 # Sanity check
 if len(set(lowerCAmelCase__  )  ) != 1:
  raise ValueError(f"Found layer names with different depths (layer depth {list(set(lowerCAmelCase__  )  )})"  )
 __a :  int						=  list(set(lowerCAmelCase__  )  )[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(lowerCAmelCase__  ,   lowerCAmelCase__  ):
  __a :  int						=  full_name.split('''/'''  )
  __a :  Tuple						=  model
  __a :  Dict						=  []
  for i, m_name in enumerate(lowerCAmelCase__  ):
   if m_name == ".ATTRIBUTES":
    # variable names end with .ATTRIBUTES/VARIABLE_VALUE
    break
   if m_name.startswith('''layer_with_weights'''  ):
    __a :  Union[str, 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''']  )
     __a :  Union[str, Any]						=  getattr(lowerCAmelCase__  ,   '''embeddings'''  )
     __a :  List[str]						=  getattr(lowerCAmelCase__  ,   '''LayerNorm'''  )
    elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
     # encoder layers
     trace.extend(['''encoder''', '''layer''', str(layer_num - 4  )]  )
     __a :  Dict						=  getattr(lowerCAmelCase__  ,   '''encoder'''  )
     __a :  Union[str, Any]						=  getattr(lowerCAmelCase__  ,   '''layer'''  )
     __a :  Any						=  pointer[layer_num - 4]
    elif layer_num == config.num_hidden_layers + 4:
     # pooler layer
     trace.extend(['''pooler''', '''dense''']  )
     __a :  Any						=  getattr(lowerCAmelCase__  ,   '''pooler'''  )
     __a :  Optional[int]						=  getattr(lowerCAmelCase__  ,   '''dense'''  )
   elif m_name == "embeddings":
    trace.append('''embeddings'''  )
    __a :  int						=  getattr(lowerCAmelCase__  ,   '''embeddings'''  )
    if layer_num == 0:
     trace.append('''word_embeddings'''  )
     __a :  Optional[int]						=  getattr(lowerCAmelCase__  ,   '''word_embeddings'''  )
    elif layer_num == 1:
     trace.append('''position_embeddings'''  )
     __a :  List[str]						=  getattr(lowerCAmelCase__  ,   '''position_embeddings'''  )
    elif layer_num == 2:
     trace.append('''token_type_embeddings'''  )
     __a :  Optional[Any]						=  getattr(lowerCAmelCase__  ,   '''token_type_embeddings'''  )
    else:
     raise ValueError(f"Unknown embedding layer with name {full_name}"  )
    trace.append('''weight'''  )
    __a :  Tuple						=  getattr(lowerCAmelCase__  ,   '''weight'''  )
   elif m_name == "_attention_layer":
    # self-attention layer
    trace.extend(['''attention''', '''self''']  )
    __a :  Optional[Any]						=  getattr(lowerCAmelCase__  ,   '''attention'''  )
    __a :  Union[str, Any]						=  getattr(lowerCAmelCase__  ,   '''self'''  )
   elif m_name == "_attention_layer_norm":
    # output attention norm
    trace.extend(['''attention''', '''output''', '''LayerNorm''']  )
    __a :  int						=  getattr(lowerCAmelCase__  ,   '''attention'''  )
    __a :  List[Any]						=  getattr(lowerCAmelCase__  ,   '''output'''  )
    __a :  List[Any]						=  getattr(lowerCAmelCase__  ,   '''LayerNorm'''  )
   elif m_name == "_attention_output_dense":
    # output attention dense
    trace.extend(['''attention''', '''output''', '''dense''']  )
    __a :  Optional[int]						=  getattr(lowerCAmelCase__  ,   '''attention'''  )
    __a :  Optional[Any]						=  getattr(lowerCAmelCase__  ,   '''output'''  )
    __a :  Any						=  getattr(lowerCAmelCase__  ,   '''dense'''  )
   elif m_name == "_output_dense":
    # output dense
    trace.extend(['''output''', '''dense''']  )
    __a :  Tuple						=  getattr(lowerCAmelCase__  ,   '''output'''  )
    __a :  str						=  getattr(lowerCAmelCase__  ,   '''dense'''  )
   elif m_name == "_output_layer_norm":
    # output dense
    trace.extend(['''output''', '''LayerNorm''']  )
    __a :  int						=  getattr(lowerCAmelCase__  ,   '''output'''  )
    __a :  str						=  getattr(lowerCAmelCase__  ,   '''LayerNorm'''  )
   elif m_name == "_key_dense":
    # attention key
    trace.append('''key'''  )
    __a :  Union[str, Any]						=  getattr(lowerCAmelCase__  ,   '''key'''  )
   elif m_name == "_query_dense":
    # attention query
    trace.append('''query'''  )
    __a :  Union[str, Any]						=  getattr(lowerCAmelCase__  ,   '''query'''  )
   elif m_name == "_value_dense":
    # attention value
    trace.append('''value'''  )
    __a :  Union[str, Any]						=  getattr(lowerCAmelCase__  ,   '''value'''  )
   elif m_name == "_intermediate_dense":
    # attention intermediate dense
    trace.extend(['''intermediate''', '''dense''']  )
    __a :  Optional[Any]						=  getattr(lowerCAmelCase__  ,   '''intermediate'''  )
    __a :  Optional[int]						=  getattr(lowerCAmelCase__  ,   '''dense'''  )
   elif m_name == "_output_layer_norm":
    # output layer norm
    trace.append('''output'''  )
    __a :  int						=  getattr(lowerCAmelCase__  ,   '''output'''  )
   # weights & biases
   elif m_name in ["bias", "beta"]:
    trace.append('''bias'''  )
    __a :  Dict						=  getattr(lowerCAmelCase__  ,   '''bias'''  )
   elif m_name in ["kernel", "gamma"]:
    trace.append('''weight'''  )
    __a :  List[Any]						=  getattr(lowerCAmelCase__  ,   '''weight'''  )
   else:
    logger.warning(f"Ignored {m_name}"  )
        # for certain layers reshape is necessary
  __a :  List[str]						=  '''.'''.join(lowerCAmelCase__  )
  if re.match(R'''(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'''  ,   lowerCAmelCase__  ) or re.match(
      R'''(\S+)\.attention\.output\.dense\.weight'''  ,   lowerCAmelCase__  ):
   __a :  str						=  array.reshape(pointer.data.shape  )
  if "kernel" in full_name:
   __a :  Optional[Any]						=  array.transpose()
  if pointer.shape == array.shape:
   __a :  str						=  torch.from_numpy(lowerCAmelCase__  )
  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    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[Any]  ,   lowerCAmelCase__							:				Union[str, Any]  ,   lowerCAmelCase__							:				Optional[Any]  ):
 # Instantiate model
 logger.info(f"Loading model based on config from {config_path}..."  )
 __a :  Dict						=  BertConfig.from_json_file(lowerCAmelCase__  )
 __a :  int						=  BertModel(lowerCAmelCase__  )

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

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


if __name__ == "__main__":
      lowercase__       =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).',
      )
      lowercase__       =parser.parse_args()
      convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
