




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

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

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

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


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











from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments





