



"""simple docstring"""
import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


UpperCAmelCase  :						Union[str, Any]	=       logging.get_logger(__name__)


UpperCAmelCase  :						Union[str, Any]	=       {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase  :						Tuple	=       {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

UpperCAmelCase  :						int	=       {
    "allenai/led-base-16384": 1_6384,
}


class       SCREAMING_SNAKE_CASE__       (							__lowercase      ):
    lowercase__          =      VOCAB_FILES_NAMES
    lowercase__          =      PRETRAINED_VOCAB_FILES_MAP
    lowercase__          =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__          =      LEDTokenizer
    lowercase__          =      ["input_ids", "attention_mask"]

    def __init__( self	: Tuple       ,     lowerCAmelCase_	: Optional[int]=None       ,     lowerCAmelCase_	: Any=None       ,     lowerCAmelCase_	: List[str]=None       ,     lowerCAmelCase_	: Union[str, Any]="replace"       ,     lowerCAmelCase_	: Any="<s>"       ,     lowerCAmelCase_	: str="</s>"       ,     lowerCAmelCase_	: List[str]="</s>"       ,     lowerCAmelCase_	: str="<s>"       ,     lowerCAmelCase_	: Tuple="<unk>"       ,     lowerCAmelCase_	: str="<pad>"       ,     lowerCAmelCase_	: Union[str, Any]="<mask>"       ,     lowerCAmelCase_	: Optional[Any]=False       ,     lowerCAmelCase_	: str=True       ,     **lowerCAmelCase_	: Dict       ,     ):







      """simple docstring"""







      super().__init__(
          snake_case_       ,     snake_case_       ,     tokenizer_file=snake_case_       ,     errors=snake_case_       ,     bos_token=snake_case_       ,     eos_token=snake_case_       ,     sep_token=snake_case_       ,     cls_token=snake_case_       ,     unk_token=snake_case_       ,     pad_token=snake_case_       ,     mask_token=snake_case_       ,     add_prefix_space=snake_case_       ,     trim_offsets=snake_case_       ,     **snake_case_       ,     )

      lowercase_	    =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
      if pre_tok_state.get("""add_prefix_space"""       ,     snake_case_) != add_prefix_space:
        lowercase_	    =       getattr(snake_case_       ,     pre_tok_state.pop("""type"""))
        lowercase_	    =       add_prefix_space
        lowercase_	    =       pre_tok_class(**snake_case_)

      lowercase_	    =       add_prefix_space

      # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
      lowercase_	    =       '''post_processor'''
      lowercase_	    =       getattr(self.backend_tokenizer       ,     snake_case_       ,     snake_case_)
      if tokenizer_component_instance:
        lowercase_	    =       json.loads(tokenizer_component_instance.__getstate__())

        # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
        if "sep" in state:
          lowercase_	    =       tuple(state["""sep"""])
        if "cls" in state:
          lowercase_	    =       tuple(state["""cls"""])

        lowercase_	    =       False

        if state.get("""add_prefix_space"""       ,     snake_case_) != add_prefix_space:
          lowercase_	    =       add_prefix_space
          lowercase_	    =       True

        if state.get("""trim_offsets"""       ,     snake_case_) != trim_offsets:
          lowercase_	    =       trim_offsets
          lowercase_	    =       True

        if changes_to_apply:
          lowercase_	    =       getattr(snake_case_       ,     state.pop("""type"""))
          lowercase_	    =       component_class(**snake_case_)
          setattr(self.backend_tokenizer       ,     snake_case_       ,     snake_case_)

    @property
    # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
    def     _UpperCAmelCase		( self	: Optional[int]):







      """simple docstring"""







      if self._mask_token is None:
        if self.verbose:
          logger.error("""Using mask_token, but it is not set yet.""")
        return None
      return str(self._mask_token)

    @mask_token.setter
    def     _UpperCAmelCase		( self	: Optional[Any]       ,     lowerCAmelCase_	: Any):







      """simple docstring"""







      lowercase_	    =       AddedToken(snake_case_       ,     lstrip=snake_case_       ,     rstrip=snake_case_) if isinstance(snake_case_       ,     snake_case_) else value
      lowercase_	    =       value

    def     _UpperCAmelCase		( self	: Dict       ,     *lowerCAmelCase_	: Dict       ,     **lowerCAmelCase_	: Dict):







      """simple docstring"""







      lowercase_	    =       kwargs.get("""is_split_into_words"""       ,     snake_case_)

      if is_split_into_words and not self.add_prefix_space:
        raise ValueError(
            F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            """to use it with pretokenized inputs.""")

      return super()._batch_encode_plus(*snake_case_       ,     **snake_case_)

    def     _UpperCAmelCase		( self	: List[Any]       ,     *lowerCAmelCase_	: Optional[int]       ,     **lowerCAmelCase_	: List[Any]):







      """simple docstring"""







      lowercase_	    =       kwargs.get("""is_split_into_words"""       ,     snake_case_)

      if is_split_into_words and not self.add_prefix_space:
        raise ValueError(
            F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            """to use it with pretokenized inputs.""")

      return super()._encode_plus(*snake_case_       ,     **snake_case_)

    def     _UpperCAmelCase		( self	: List[Any]       ,     lowerCAmelCase_	: str       ,     lowerCAmelCase_	: Optional[str] = None):







      """simple docstring"""







      lowercase_	    =       self._tokenizer.model.save(snake_case_       ,     name=snake_case_)
      return tuple(snake_case_)

    def     _UpperCAmelCase		( self	: str       ,     lowerCAmelCase_	: Dict       ,     lowerCAmelCase_	: Tuple=None):







      """simple docstring"""







      lowercase_	    =       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
        return output

      return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]

    def     _UpperCAmelCase		( self	: Union[str, Any]       ,     lowerCAmelCase_	: List[int]       ,     lowerCAmelCase_	: Optional[List[int]] = None):







      """simple docstring"""







      lowercase_	    =       [self.sep_token_id]
      lowercase_	    =       [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 + sep + token_ids_a + sep) * [0]




    def     _UpperCAmelCase		( self	: List[str]       ,     lowerCAmelCase_	: Union[Dict[str, EncodedInput], BatchEncoding]       ,     lowerCAmelCase_	: Optional[int] = None       ,     lowerCAmelCase_	: PaddingStrategy = PaddingStrategy.DO_NOT_PAD       ,     lowerCAmelCase_	: Optional[int] = None       ,     lowerCAmelCase_	: Optional[bool] = None       ,     ):







      """simple docstring"""







      lowercase_	    =       super()._pad(
          encoded_inputs=snake_case_       ,     max_length=snake_case_       ,     padding_strategy=snake_case_       ,     pad_to_multiple_of=snake_case_       ,     return_attention_mask=snake_case_       ,     )

      # Load from model defaults
      if return_attention_mask is None:
        lowercase_	    =       '''attention_mask''' in self.model_input_names

      if return_attention_mask and "global_attention_mask" in encoded_inputs:
        lowercase_	    =       encoded_inputs[self.model_input_names[0]]
        # `global_attention_mask` need to have the same length as other (sequential) inputs.
        lowercase_	    =       len(encoded_inputs["""global_attention_mask"""]) != len(snake_case_)

        if needs_to_be_padded:
          lowercase_	    =       len(snake_case_) - len(encoded_inputs["""global_attention_mask"""])

          if self.padding_side == "right":
            # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
            lowercase_	    =       (
                encoded_inputs['''global_attention_mask'''] + [-1] * difference
            )
          elif self.padding_side == "left":
            lowercase_	    =       [-1] * difference + encoded_inputs[
                '''global_attention_mask'''
            ]
          else:
            raise ValueError("""Invalid padding strategy:""" + str(self.padding_side))

      return encoded_inputs



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)
