# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig







def     _a				(      lowercase__  :			Optional[int]     ,  lowercase__  :			Optional[int]     ,  lowercase__  :			Optional[int]=0 ):







     '''simple docstring'''


     if name is None:
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		None
     else:
          SCREAMING_SNAKE_CASE__				:				str			=		'.' * max(0     ,  spaces - 2 ) + '# {:' + str(50 - spaces ) + 's}'
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		fmt.format(lowercase__ )

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







def     _a				(      lowercase__  :			Optional[Any]     ,  lowercase__  :			List[Any]     ,  lowercase__  :			Optional[int]     ,  lowercase__  :			List[Any]     ,  lowercase__  :			List[Any] ):







     '''simple docstring'''


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







def     _a				(      lowercase__  :			Optional[Any]     ,  lowercase__  :			Dict     ,  lowercase__  :			List[str] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{}

     # old versions did not store training args
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		input_state_dict.get('args'     ,  lowercase__ )
     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))

          SCREAMING_SNAKE_CASE__				:				Tuple			=		ds_args.padded_vocab_size
          SCREAMING_SNAKE_CASE__				:				List[str]			=		ds_args.max_position_embeddings
          SCREAMING_SNAKE_CASE__				:				str			=		ds_args.hidden_size
          SCREAMING_SNAKE_CASE__				:				Tuple			=		ds_args.num_layers
          SCREAMING_SNAKE_CASE__				:				str			=		ds_args.num_attention_heads
          SCREAMING_SNAKE_CASE__				:				Tuple			=		ds_args.ffn_hidden_size
          # pprint(config)

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

     # The model.
     SCREAMING_SNAKE_CASE__				:				Tuple			=		input_state_dict['model']
     # The language model.
     SCREAMING_SNAKE_CASE__				:				Dict			=		model['language_model']
     # The embeddings.
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		lm['embedding']

     # The word embeddings.
     SCREAMING_SNAKE_CASE__				:				str			=		embeddings['word_embeddings']['weight']
     # Truncate the embedding table to vocab_size rows.
     SCREAMING_SNAKE_CASE__				:				List[str]			=		word_embeddings[: config.vocab_size, :]
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		word_embeddings

     # The position embeddings.
     SCREAMING_SNAKE_CASE__				:				List[str]			=		embeddings['position_embeddings']['weight']
     # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
     SCREAMING_SNAKE_CASE__				:				int			=		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.
     SCREAMING_SNAKE_CASE__				:				Dict			=		pos_embeddings

     # The transformer.
     SCREAMING_SNAKE_CASE__				:				Any			=		lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

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

     # The simple map of names for "automated" rules.
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{
         '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.
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		layer_re.match(lowercase__ )

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

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

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

          # For layernorm(s), simply store the layer norm.
          if op_name.endswith('layernorm' ):
               SCREAMING_SNAKE_CASE__				:				Dict			=		'ln_1' if op_name.startswith('input' ) else 'ln_2'
               SCREAMING_SNAKE_CASE__				:				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.
               SCREAMING_SNAKE_CASE__				:				Tuple			=		torch.tril(torch.ones((n_positions, n_positions)     ,  dtype=torch.floataa ) ).view(
                   1     ,  1     ,  lowercase__     ,  lowercase__ )
               SCREAMING_SNAKE_CASE__				:				Any			=		causal_mask

               # Insert a "dummy" tensor for masked_bias.
               SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.tensor(-1E4     ,  dtype=torch.floataa )
               SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		masked_bias

               SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		fix_query_key_value_ordering(lowercase__     ,  lowercase__     ,  3     ,  lowercase__     ,  lowercase__ )
               # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
               SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		out_val.transpose(0     ,  1 ).contiguous()
               # Store.
               SCREAMING_SNAKE_CASE__				:				List[str]			=		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":
               SCREAMING_SNAKE_CASE__				:				int			=		fix_query_key_value_ordering(lowercase__     ,  lowercase__     ,  3     ,  lowercase__     ,  lowercase__ )
               # Store. No change of shape.
               SCREAMING_SNAKE_CASE__				:				Optional[int]			=		out_val

          # Transpose the weights.
          elif weight_or_bias == "weight":
               SCREAMING_SNAKE_CASE__				:				List[str]			=		megatron_to_transformers[op_name]
               SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		val.transpose(0     ,  1 )

          # Copy the bias.
          elif weight_or_bias == "bias":
               SCREAMING_SNAKE_CASE__				:				Optional[int]			=		megatron_to_transformers[op_name]
               SCREAMING_SNAKE_CASE__				:				Tuple			=		val

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

     # The final layernorm.
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		transformer['final_layernorm.weight']
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		transformer['final_layernorm.bias']

     # For LM head, transformers' wants the matrix to weight embeddings.
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		word_embeddings

     # It should be done!
     return output_state_dict







def     _a				(      ):







     '''simple docstring'''


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

     # Extract the basename.
     SCREAMING_SNAKE_CASE__				:				str			=		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:
                    SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.load(lowercase__     ,  map_location='cpu' )
     else:
          SCREAMING_SNAKE_CASE__				:				Any			=		torch.load(args.path_to_checkpoint     ,  map_location='cpu' )

     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		input_state_dict.get('args'     ,  lowercase__ )

     # 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:
                    SCREAMING_SNAKE_CASE__				:				List[Any]			=		'gelu_fast'
               elif ds_args.openai_gelu:
                    SCREAMING_SNAKE_CASE__				:				Any			=		'gelu_new'
               else:
                    SCREAMING_SNAKE_CASE__				:				Dict			=		'gelu'
          else:
               # in the very early days this used to be "gelu_new"
               SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		'gelu_new'

          # Spell out all parameters in case the defaults change.
          SCREAMING_SNAKE_CASE__				:				int			=		GPTaConfig(
              vocab_size=5_02_57     ,  n_positions=10_24     ,  n_embd=10_24     ,  n_layer=24     ,  n_head=16     ,  n_inner=40_96     ,  activation_function=lowercase__     ,  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=lowercase__     ,  summary_activation=lowercase__     ,  summary_proj_to_labels=lowercase__     ,  summary_first_dropout=0.1     ,  scale_attn_weights=lowercase__     ,  use_cache=lowercase__     ,  bos_token_id=5_02_56     ,  eos_token_id=5_02_56     ,  )
     else:
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		GPTaConfig.from_json_file(args.config_file )

     SCREAMING_SNAKE_CASE__				:				Tuple			=		['GPT2LMHeadModel']

     # Convert.
     print('Converting' )
     SCREAMING_SNAKE_CASE__				:				Dict			=		convert_megatron_checkpoint(lowercase__     ,  lowercase__     ,  lowercase__ )

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

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

     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		AutoTokenizer.from_pretrained(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Tuple			=		type(lowercase__ ).__name__
     SCREAMING_SNAKE_CASE__				:				Tuple			=		tokenizer_class

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

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

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


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

if __name__ == "__main__":
      main()

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




from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
      import accelerate







def     _a				(      lowercase__  :			List[str] ):







     '''simple docstring'''


     if not is_accelerate_available():
          return method
     SCREAMING_SNAKE_CASE__				:				str			=		version.parse(accelerate.__version__ ).base_version
     if version.parse(lowercase__ ) < version.parse('0.17.0' ):
          return method

     def wrapper(self  :			Optional[int]     ,  *lowercase__  :			int     ,  **lowercase__  :			Tuple ):
          if hasattr(self     ,  '_hf_hook' ) and hasattr(self._hf_hook     ,  'pre_forward' ):
               self._hf_hook.pre_forward(self )
          return method(self     ,  *lowercase__     ,  **lowercase__ )

     return wrapper



