"""simple docstring"""





import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig





def     a_					(				_lowerCAmelCase  :			Any       ,			_lowerCAmelCase  :			Dict       ,			_lowerCAmelCase  :			List[str]=0					):





       '''simple docstring'''

       if name is None:
              lowercase__      :		Optional[int]          =     None
       else:
              lowercase__      :		Tuple          =     '.' * max(0       ,			spaces - 2					) + '# {:' + str(50 - spaces					) + 's}'
              lowercase__      :		Optional[int]          =     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_					(				_lowerCAmelCase  :			Union[str, Any]       ,			_lowerCAmelCase  :			Dict       ,			_lowerCAmelCase  :			List[Any]       ,			_lowerCAmelCase  :			Tuple					):





       '''simple docstring'''

       lowercase__      :		int          =     param.size()
       # other versions store [num_heads * num_splits * hidden_size, :]
       lowercase__      :		Dict          =     (num_heads, num_splits, hidden_size) + input_shape[1:]
       lowercase__      :		Optional[int]          =     param.view(*_lowerCAmelCase					)
       lowercase__      :		Union[str, Any]          =     param.transpose(0       ,			1					).contiguous()
       lowercase__      :		List[str]          =     param.view(*_lowerCAmelCase					)
       return param





def     a_					(				_lowerCAmelCase  :			int       ,			_lowerCAmelCase  :			Tuple					):





       '''simple docstring'''

       lowercase__      :		int          =     config.n_positions
       lowercase__      :		int          =     config.n_layer
       lowercase__      :		Optional[Any]          =     config.vocab_size
       lowercase__      :		Tuple          =     config.n_head
       lowercase__      :		Optional[Any]          =     config.n_embd // config.n_head

       lowercase__      :		str          =     sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
       lowercase__      :		Union[str, Any]          =     {
           'transformer.wte.weight': word_embeddings,
           'transformer.wpe.weight': sd_megatron['model.language_model.embedding.position_embeddings.weight'],
           'transformer.ln_f.weight': sd_megatron['model.language_model.encoder.final_layernorm.weight'],
           'transformer.ln_f.bias': sd_megatron['model.language_model.encoder.final_layernorm.bias'],
       }

       lowercase__      :		str          =     'model.language_model.encoder.layers.'
       for i in range(_lowerCAmelCase					):
              lowercase__      :		Dict          =     torch.tril(torch.ones((n_positions, n_positions)       ,			dtype=torch.bool					)					)
              lowercase__      :		int          =     causal_mask.view(1       ,			1       ,			_lowerCAmelCase       ,			_lowerCAmelCase					)
              lowercase__      :		Any          =     causal_mask
              lowercase__      :		Optional[Any]          =     torch.tensor(-1E4       ,			dtype=torch.bfloataa					)

              lowercase__      :		Union[str, Any]          =     sd_megatron[f"""{pf}{i}.input_layernorm.weight"""]
              lowercase__      :		Any          =     sd_megatron[f"""{pf}{i}.input_layernorm.bias"""]

              lowercase__      :		int          =     sd_megatron[f"""{pf}{i}.self_attention.query_key_value.weight"""]
              lowercase__      :		Union[str, Any]          =     fix_query_key_value_ordering(_lowerCAmelCase       ,			3       ,			_lowerCAmelCase       ,			_lowerCAmelCase					)
              lowercase__      :		str          =     vala.transpose(0       ,			1					).contiguous()

              lowercase__      :		Dict          =     sd_megatron[f"""{pf}{i}.self_attention.query_key_value.bias"""]
              lowercase__      :		str          =     fix_query_key_value_ordering(_lowerCAmelCase       ,			3       ,			_lowerCAmelCase       ,			_lowerCAmelCase					)
              lowercase__      :		Dict          =     vala

              lowercase__      :		Union[str, Any]          =     sd_megatron[f"""{pf}{i}.self_attention.dense.weight"""].transpose(
                  0       ,			1					)
              lowercase__      :		Any          =     sd_megatron[f"""{pf}{i}.self_attention.dense.bias"""]
              lowercase__      :		Union[str, Any]          =     sd_megatron[f"""{pf}{i}.post_attention_layernorm.weight"""]
              lowercase__      :		int          =     sd_megatron[f"""{pf}{i}.post_attention_layernorm.bias"""]
              lowercase__      :		List[Any]          =     sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.weight"""].transpose(0       ,			1					)
              lowercase__      :		str          =     sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.bias"""]
              lowercase__      :		Optional[Any]          =     sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.weight"""].transpose(
                  0       ,			1					)
              lowercase__      :		List[Any]          =     sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.bias"""]

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

       return sd_hf





def     a_					(				_lowerCAmelCase  :			int       ,			_lowerCAmelCase  :			int					):





       '''simple docstring'''

       lowercase__      :		Tuple          =     6_4000
       lowercase__      :		Dict          =     config_megatron['encoder_seq_length']
       lowercase__      :		List[str]          =     config_megatron['hidden_size']
       lowercase__      :		Dict          =     config_megatron['num_layers']
       lowercase__      :		Dict          =     config_megatron['num_attention_heads']
       lowercase__      :		Any          =     config_megatron['ffn_hidden_size']
       lowercase__      :		Optional[Any]          =     'gelu'
       lowercase__      :		str          =     0.1
       lowercase__      :		Dict          =     0.1
       lowercase__      :		Any          =     0.1
       lowercase__      :		List[Any]          =     config_megatron['layernorm_epsilon']  # 1e-5
       lowercase__      :		Optional[Any]          =     config_megatron['init_method_std']  # 0.02
       lowercase__      :		Any          =     config_megatron['apply_query_key_layer_scaling']  # True
       lowercase__      :		str          =     True
       lowercase__      :		Any          =     True

       # This identifies the 6.7B (7B) model which uses a different tokenizer
       if config_megatron["hidden_size"] == 4096:
              lowercase__      :		Optional[int]          =     1  # <|endoftext|>
              lowercase__      :		Any          =     1  # <|endoftext|>
              lowercase__      :		Any          =     0  # <unk>
       else:
              lowercase__      :		str          =     2  # <s>
              lowercase__      :		List[str]          =     3  # <|endoftext|>
              lowercase__      :		int          =     0  # <pad>

       return config_hf





def     a_					(				_lowerCAmelCase  :			Optional[int]					):





       '''simple docstring'''

       print(_lowerCAmelCase					)

       lowercase__      :		Tuple          =     args.checkpoint_path
       lowercase__      :		Union[str, Any]          =     args.save_path
       if isfile(_lowerCAmelCase					):
              raise FileNotFoundError(f"""ERROR! could not find file {checkpoint_path}"""					)

       # Load the model.
       lowercase__      :		List[str]          =     torch.load(_lowerCAmelCase       ,			map_location='cpu'					)

       # Load the config.
       lowercase__      :		str          =     checkpoint['hyper_parameters']['cfg']
       lowercase__      :		Optional[int]          =     GPTaConfig()
       lowercase__      :		int          =     copy_config(config_hf=_lowerCAmelCase       ,			config_megatron=_lowerCAmelCase					)
       lowercase__      :		Tuple          =     ['GPT2LMHeadModel']

       lowercase__      :		Union[str, Any]          =     checkpoint['state_dict']

       # Convert.
       print('Converting'					)
       lowercase__      :		str          =     convert_megatron_checkpoint(_lowerCAmelCase       ,			_lowerCAmelCase					)

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

       lowercase__      :		Optional[Any]          =     'GPTSw3Tokenizer'

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

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


if __name__ == "__main__":
       _UpperCamelCase       :						Tuple     =   argparse.ArgumentParser()
       parser.add_argument(
           "--checkpoint_path",
           type=str,
           required=True,
           help="e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000",
       )
       parser.add_argument("--save_path", type=str, required=True, help="e.g. /home/user/gpt-sw3/hf")
       parser.add_argument("--print-checkpoint-structure", action="store_true")
       _UpperCamelCase       :						Dict     =   parser.parse_args()
       main(_args)


"""simple docstring"""





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCamelCase       :						int     =   {
    "configuration_retribert": ["RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RetriBertConfig"],
    "tokenization_retribert": ["RetriBertTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase       :						Tuple     =   ["RetriBertTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase       :						int     =   [
           "RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "RetriBertModel",
           "RetriBertPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
       from .tokenization_retribert import RetriBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_retribert_fast import RetriBertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_retribert import (
                  RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RetriBertModel,
                  RetriBertPreTrainedModel,
              )

else:
       import sys

       _UpperCamelCase       :						Tuple     =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

