






import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig


def   _A   (				SCREAMING_SNAKE_CASE__  :	List[str]     ,  SCREAMING_SNAKE_CASE__  :	Optional[Any]     ,  SCREAMING_SNAKE_CASE__  :	int=0       ):


    if name is None:
        UpperCamelCase       :List[str]								=   None
    else:
        UpperCamelCase       :Union[str, Any]								=   """.""" * max(0     ,  spaces - 2       ) + """# {:""" + str(50 - spaces       ) + """s}"""
        UpperCamelCase       :Union[str, Any]								=   fmt.format(lowerCAmelCase_       )

    # Print and recurse (if needed).
    if isinstance(lowerCAmelCase_     ,  lowerCAmelCase_       ):
        if msg is not None:
            print(lowerCAmelCase_       )
        for k in val.keys():
            recursive_print(lowerCAmelCase_     ,  val[k]     ,  spaces + 2       )
    elif isinstance(lowerCAmelCase_     ,  torch.Tensor       ):
        print(lowerCAmelCase_     ,  ''':'''     ,  val.size()       )
    else:
        print(lowerCAmelCase_     ,  ''':'''     ,  lowerCAmelCase_       )


def   _A   (				SCREAMING_SNAKE_CASE__  :	List[Any]     ,  SCREAMING_SNAKE_CASE__  :	str     ,  SCREAMING_SNAKE_CASE__  :	str     ,  SCREAMING_SNAKE_CASE__  :	Optional[Any]     ,  SCREAMING_SNAKE_CASE__  :	Any       ):
    UpperCamelCase       :Dict								=   param.size()
    if checkpoint_version == 1.0:
        # version 1.0 stores [num_heads * hidden_size * num_splits, :]
        UpperCamelCase       :Dict								=   (num_heads, hidden_size, num_splits) + input_shape[1:]
        UpperCamelCase       :List[str]								=   param.view(*lowerCAmelCase_       )
        UpperCamelCase       :int								=   param.transpose(0     ,  2       )
        UpperCamelCase       :Union[str, Any]								=   param.transpose(1     ,  2       ).contiguous()
    elif checkpoint_version >= 2.0:
        # other versions store [num_heads * num_splits * hidden_size, :]
        UpperCamelCase       :int								=   (num_heads, num_splits, hidden_size) + input_shape[1:]
        UpperCamelCase       :Union[str, Any]								=   param.view(*lowerCAmelCase_       )
        UpperCamelCase       :Union[str, Any]								=   param.transpose(0     ,  1       ).contiguous()
    UpperCamelCase       :List[Any]								=   param.view(*lowerCAmelCase_       )
    return param


def   _A   (				SCREAMING_SNAKE_CASE__  :	Dict     ,  SCREAMING_SNAKE_CASE__  :	Any     ,  SCREAMING_SNAKE_CASE__  :	Dict       ):
    UpperCamelCase       :str								=   {}

    # old versions did not store training args
    UpperCamelCase       :str								=   input_state_dict.get('''args'''     ,  lowerCAmelCase_       )
    if ds_args is not None:
        # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
        # from pprint import pprint
        # pprint(vars(ds_args))

        UpperCamelCase       :Any								=   ds_args.padded_vocab_size
        UpperCamelCase       :List[Any]								=   ds_args.max_position_embeddings
        UpperCamelCase       :List[Any]								=   ds_args.hidden_size
        UpperCamelCase       :Union[str, Any]								=   ds_args.num_layers
        UpperCamelCase       :Dict								=   ds_args.num_attention_heads
        UpperCamelCase       :Any								=   ds_args.ffn_hidden_size
        # pprint(config)

    # The number of heads.
    UpperCamelCase       :str								=   config.n_head
    # The hidden_size per head.
    UpperCamelCase       :Optional[Any]								=   config.n_embd // config.n_head
    # Megatron-LM checkpoint version
    if "checkpoint_version" in input_state_dict.keys():
        UpperCamelCase       :Dict								=   input_state_dict["""checkpoint_version"""]
    else:
        UpperCamelCase       :Dict								=   0.0

    # The model.
    UpperCamelCase       :List[Any]								=   input_state_dict["""model"""]
    # The language model.
    UpperCamelCase       :Tuple								=   model["""language_model"""]
    # The embeddings.
    UpperCamelCase       :List[Any]								=   lm["""embedding"""]

    # The word embeddings.
    UpperCamelCase       :Optional[int]								=   embeddings["""word_embeddings"""]["""weight"""]
    # Truncate the embedding table to vocab_size rows.
    UpperCamelCase       :Union[str, Any]								=   word_embeddings[: config.vocab_size, :]
    UpperCamelCase       :str								=   word_embeddings

    # The position embeddings.
    UpperCamelCase       :int								=   embeddings["""position_embeddings"""]["""weight"""]
    # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
    UpperCamelCase       :Tuple								=   pos_embeddings.size(0       )
    if n_positions != config.n_positions:
        raise ValueError(
            F'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match'''       )
    # Store the position embeddings.
    UpperCamelCase       :Tuple								=   pos_embeddings

    # The transformer.
    UpperCamelCase       :str								=   lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]

    # The regex to extract layer names.
    UpperCamelCase       :str								=   re.compile(R'''layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'''       )

    # The simple map of names for "automated" rules.
    UpperCamelCase       :Dict								=   {
        """attention.dense""": """.attn.c_proj.""",
        """self_attention.dense""": """.attn.c_proj.""",
        """mlp.dense_h_to_4h""": """.mlp.c_fc.""",
        """mlp.dense_4h_to_h""": """.mlp.c_proj.""",
    }

    # Extract the layers.
    for key, val in transformer.items():
        # Match the name.
        UpperCamelCase       :int								=   layer_re.match(lowerCAmelCase_       )

        # Stop if that's not a layer
        if m is None:
            break

        # The index of the layer.
        UpperCamelCase       :Union[str, Any]								=   int(m.group(1       )       )
        # The name of the operation.
        UpperCamelCase       :Dict								=   m.group(2       )
        # Is it a weight or a bias?
        UpperCamelCase       :Union[str, Any]								=   m.group(3       )

        # The name of the layer.
        UpperCamelCase       :List[Any]								=   F'''transformer.h.{layer_idx}'''

        # For layernorm(s), simply store the layer norm.
        if op_name.endswith('''layernorm'''       ):
            UpperCamelCase       :Optional[int]								=   """ln_1""" if op_name.startswith('''input'''       ) else """ln_2"""
            UpperCamelCase       :List[Any]								=   val

        # Transpose the QKV matrix.
        elif (
            op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
        ) and weight_or_bias == "weight":
            # Insert a tensor of 1x1xDxD bias.
            UpperCamelCase       :int								=   torch.tril(torch.ones((n_positions, n_positions)     ,  dtype=torch.floataa       )       ).view(
                1     ,  1     ,  lowerCAmelCase_     ,  lowerCAmelCase_       )
            UpperCamelCase       :List[Any]								=   causal_mask

            # Insert a "dummy" tensor for masked_bias.
            UpperCamelCase       :Any								=   torch.tensor(-1e4     ,  dtype=torch.floataa       )
            UpperCamelCase       :str								=   masked_bias

            UpperCamelCase       :List[str]								=   fix_query_key_value_ordering(lowerCAmelCase_     ,  lowerCAmelCase_     ,  3     ,  lowerCAmelCase_     ,  lowerCAmelCase_       )
            # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
            UpperCamelCase       :int								=   out_val.transpose(0     ,  1       ).contiguous()
            # Store.
            UpperCamelCase       :Dict								=   out_val

        # Transpose the bias.
        elif (
            op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
        ) and weight_or_bias == "bias":
            UpperCamelCase       :Optional[Any]								=   fix_query_key_value_ordering(lowerCAmelCase_     ,  lowerCAmelCase_     ,  3     ,  lowerCAmelCase_     ,  lowerCAmelCase_       )
            # Store. No change of shape.
            UpperCamelCase       :int								=   out_val

        # Transpose the weights.
        elif weight_or_bias == "weight":
            UpperCamelCase       :int								=   megatron_to_transformers[op_name]
            UpperCamelCase       :Optional[int]								=   val.transpose(0     ,  1       )

        # Copy the bias.
        elif weight_or_bias == "bias":
            UpperCamelCase       :Any								=   megatron_to_transformers[op_name]
            UpperCamelCase       :List[Any]								=   val

    # DEBUG.
    assert config.n_layer == layer_idx + 1

    # The final layernorm.
    UpperCamelCase       :List[str]								=   transformer["""final_layernorm.weight"""]
    UpperCamelCase       :Union[str, Any]								=   transformer["""final_layernorm.bias"""]

    # For LM head, transformers' wants the matrix to weight embeddings.
    UpperCamelCase       :str								=   word_embeddings

    # It should be done!
    return output_state_dict


def   _A   (				):
    UpperCamelCase       :Dict								=   argparse.ArgumentParser()
    parser.add_argument('''--print-checkpoint-structure'''     ,  action='''store_true'''       )
    parser.add_argument(
        '''path_to_checkpoint'''     ,  type=lowerCAmelCase_     ,  help='''Path to the checkpoint file (.zip archive or direct .pt file)'''     ,  )
    parser.add_argument(
        '''--config_file'''     ,  default=''''''     ,  type=lowerCAmelCase_     ,  help='''An optional config json file describing the pre-trained model.'''     ,  )
    UpperCamelCase       :List[str]								=   parser.parse_args()

    # Extract the basename.
    UpperCamelCase       :Dict								=   os.path.dirname(args.path_to_checkpoint       )

    # Load the model.
    # the .zip is very optional, let's keep it for backward compatibility
    print(F'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}'''       )
    if args.path_to_checkpoint.endswith('''.zip'''       ):
        with zipfile.ZipFile(args.path_to_checkpoint     ,  '''r'''       ) as checkpoint:
            with checkpoint.open('''release/mp_rank_00/model_optim_rng.pt'''       ) as pytorch_dict:
                UpperCamelCase       :List[Any]								=   torch.load(lowerCAmelCase_     ,  map_location='''cpu'''       )
    else:
        UpperCamelCase       :str								=   torch.load(args.path_to_checkpoint     ,  map_location='''cpu'''       )

    UpperCamelCase       :List[str]								=   input_state_dict.get('''args'''     ,  lowerCAmelCase_       )

    # Read the config, or default to the model released by NVIDIA.
    if args.config_file == "":
        if ds_args is not None:
            if ds_args.bias_gelu_fusion:
                UpperCamelCase       :int								=   """gelu_fast"""
            elif ds_args.openai_gelu:
                UpperCamelCase       :Optional[int]								=   """gelu_new"""
            else:
                UpperCamelCase       :Dict								=   """gelu"""
        else:
            # in the very early days this used to be "gelu_new"
            UpperCamelCase       :str								=   """gelu_new"""

        # Spell out all parameters in case the defaults change.
        UpperCamelCase       :Any								=   GPTaConfig(
            vocab_size=50257     ,  n_positions=1024     ,  n_embd=1024     ,  n_layer=24     ,  n_head=16     ,  n_inner=4096     ,  activation_function=lowerCAmelCase_     ,  resid_pdrop=0.1     ,  embd_pdrop=0.1     ,  attn_pdrop=0.1     ,  layer_norm_epsilon=1e-5     ,  initializer_range=0.02     ,  summary_type='''cls_index'''     ,  summary_use_proj=lowerCAmelCase_     ,  summary_activation=lowerCAmelCase_     ,  summary_proj_to_labels=lowerCAmelCase_     ,  summary_first_dropout=0.1     ,  scale_attn_weights=lowerCAmelCase_     ,  use_cache=lowerCAmelCase_     ,  bos_token_id=50256     ,  eos_token_id=50256     ,  )
    else:
        UpperCamelCase       :List[str]								=   GPTaConfig.from_json_file(args.config_file       )

    UpperCamelCase       :Optional[int]								=   ["""GPT2LMHeadModel"""]

    # Convert.
    print('''Converting'''       )
    UpperCamelCase       :Union[str, Any]								=   convert_megatron_checkpoint(lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_       )

    # Print the structure of converted state dict.
    if args.print_checkpoint_structure:
        recursive_print(lowerCAmelCase_     ,  lowerCAmelCase_       )

    # Add tokenizer class info to config
    # see https://github.com/huggingface/transformers/issues/13906)
    if ds_args is not None:
        UpperCamelCase       :str								=   ds_args.tokenizer_type
        if tokenizer_type == "GPT2BPETokenizer":
            UpperCamelCase       :str								=   """gpt2"""
        elif tokenizer_type == "PretrainedFromHF":
            UpperCamelCase       :str								=   ds_args.tokenizer_name_or_path
        else:
            raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}'''       )
    else:
        UpperCamelCase       :Dict								=   """gpt2"""

    UpperCamelCase       :Optional[Any]								=   AutoTokenizer.from_pretrained(lowerCAmelCase_       )
    UpperCamelCase       :Dict								=   type(lowerCAmelCase_       ).__name__
    UpperCamelCase       :List[str]								=   tokenizer_class

    # Store the config to file.
    print('''Saving config'''       )
    config.save_pretrained(lowerCAmelCase_       )

    # Save tokenizer based on args
    print(F'''Adding {tokenizer_class} tokenizer files'''       )
    tokenizer.save_pretrained(lowerCAmelCase_       )

    # Store the state_dict to file.
    UpperCamelCase       :List[str]								=   os.path.join(lowerCAmelCase_     ,  '''pytorch_model.bin'''       )
    print(F'''Saving checkpoint to "{output_checkpoint_file}"'''       )
    torch.save(lowerCAmelCase_     ,  lowerCAmelCase_       )


####################################################################################################

if __name__ == "__main__":
 main()

####################################################################################################










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import _LazyModule


A_							: int						 =	{"""tokenization_bertweet""": ["""BertweetTokenizer"""]}


if TYPE_CHECKING:
   from .tokenization_bertweet import BertweetTokenizer

else:
   import sys

   A_							: int						 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



