


'''simple docstring'''




from typing import List

import numpy as np





def 	a_					( lowerCamelCase	:     dict				):
   lowerCAmelCase     =			{key: len(lowerCamelCase				) for key, value in gen_kwargs.items() if isinstance(lowerCamelCase	, lowerCamelCase				)}
   if len(set(lists_lengths.values()				)				) > 1:
      raise RuntimeError(
          (
              'Sharding is ambiguous for this dataset: '
              + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
              + '\n'.join(f'''\t- key {key} has length {length}''' for key, length in lists_lengths.items()				)
              + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
              + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
          )				)
   lowerCAmelCase     =			max(lists_lengths.values()	, default=0				)
   return max(1	, lowerCamelCase				)





def 	a_					( lowerCamelCase	:     int	, lowerCamelCase	:     int				):
   lowerCAmelCase     =			[]
   for group_idx in range(lowerCamelCase				):
      lowerCAmelCase     =			num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
      if num_shards_to_add == 0:
         break
      lowerCAmelCase     =			shards_indices_per_group[-1].stop if shards_indices_per_group else 0
      lowerCAmelCase     =			range(lowerCamelCase	, start + num_shards_to_add				)
      shards_indices_per_group.append(lowerCamelCase				)
   return shards_indices_per_group





def 	a_					( lowerCamelCase	:     dict	, lowerCamelCase	:     int				):
   lowerCAmelCase     =			_number_of_shards_in_gen_kwargs(lowerCamelCase				)
   if num_shards == 1:
      return [dict(lowerCamelCase				)]
   else:
      lowerCAmelCase     =			_distribute_shards(num_shards=lowerCamelCase	, max_num_jobs=lowerCamelCase				)
      return [
          {
              key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
              if isinstance(lowerCamelCase	, lowerCamelCase				)
              else value
              for key, value in gen_kwargs.items()
          }
          for group_idx in range(len(lowerCamelCase				)				)
      ]





def 	a_					( lowerCamelCase	:     List[dict]				):
   return {
       key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
       if isinstance(gen_kwargs_list[0][key]	, lowerCamelCase				)
       else gen_kwargs_list[0][key]
       for key in gen_kwargs_list[0]
   }





def 	a_					( lowerCamelCase	:     np.random.Generator	, lowerCamelCase	:     dict				):
   lowerCAmelCase     =			{len(lowerCamelCase				) for value in gen_kwargs.values() if isinstance(lowerCamelCase	, lowerCamelCase				)}
   lowerCAmelCase     =			{}
   for size in list_sizes:
      lowerCAmelCase     =			list(range(lowerCamelCase				)				)
      rng.shuffle(indices_per_size[size]				)
   # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
   lowerCAmelCase     =			dict(lowerCamelCase				)
   for key, value in shuffled_kwargs.items():
      if isinstance(lowerCamelCase	, lowerCamelCase				):
         lowerCAmelCase     =			[value[i] for i in indices_per_size[len(lowerCamelCase				)]]
   return shuffled_kwargs









'''simple docstring'''




# 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_					( lowerCamelCase	:     int	, lowerCamelCase	:     Optional[Any]	, lowerCamelCase	:     Union[str, Any]=0				):
   # Format the message.
   if name is None:
      lowerCAmelCase     =			None
   else:
      lowerCAmelCase     =			'.' * max(0	, spaces - 2				) + '# {:' + str(50 - spaces				) + 's}'
      lowerCAmelCase     =			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	:     Optional[int]	, lowerCamelCase	:     List[str]	, lowerCamelCase	:     List[Any]	, lowerCamelCase	:     Dict	, lowerCamelCase	:     Tuple				):
   # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
   # for compatibility with later versions of NVIDIA Megatron-LM.
   # The inverse operation is performed inside Megatron-LM to read checkpoints:
   # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
   # If param is the weight tensor of the self-attention block, the returned tensor
   # will have to be transposed one more time to be read by HuggingFace GPT2.
   lowerCAmelCase     =			param.size()
   if checkpoint_version == 1.0:
      # version 1.0 stores [num_heads * hidden_size * num_splits, :]
      lowerCAmelCase     =			(num_heads, hidden_size, num_splits) + input_shape[1:]
      lowerCAmelCase     =			param.view(*lowerCamelCase				)
      lowerCAmelCase     =			param.transpose(0	, 2				)
      lowerCAmelCase     =			param.transpose(1	, 2				).contiguous()
   elif checkpoint_version >= 2.0:
      # other versions store [num_heads * num_splits * hidden_size, :]
      lowerCAmelCase     =			(num_heads, num_splits, hidden_size) + input_shape[1:]
      lowerCAmelCase     =			param.view(*lowerCamelCase				)
      lowerCAmelCase     =			param.transpose(0	, 1				).contiguous()
   lowerCAmelCase     =			param.view(*lowerCamelCase				)
   return param





def 	a_					( lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     str				):
   # The converted output model.
   lowerCAmelCase     =			{}

   # old versions did not store training args
   lowerCAmelCase     =			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))

      lowerCAmelCase     =			ds_args.padded_vocab_size
      lowerCAmelCase     =			ds_args.max_position_embeddings
      lowerCAmelCase     =			ds_args.hidden_size
      lowerCAmelCase     =			ds_args.num_layers
      lowerCAmelCase     =			ds_args.num_attention_heads
      lowerCAmelCase     =			ds_args.ffn_hidden_size
      # pprint(config)

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

   # The model.
   lowerCAmelCase     =			input_state_dict['model']
   # The language model.
   lowerCAmelCase     =			model['language_model']
   # The embeddings.
   lowerCAmelCase     =			lm['embedding']

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

   # The position embeddings.
   lowerCAmelCase     =			embeddings['position_embeddings']['weight']
   # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
   lowerCAmelCase     =			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.
   lowerCAmelCase     =			pos_embeddings

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

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

   # The simple map of names for "automated" rules.
   lowerCAmelCase     =			{
       '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.
      lowerCAmelCase     =			layer_re.match(lowerCamelCase				)

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

      # The index of the layer.
      lowerCAmelCase     =			int(m.group(1				)				)
      # The name of the operation.
      lowerCAmelCase     =			m.group(2				)
      # Is it a weight or a bias?
      lowerCAmelCase     =			m.group(3				)

      # The name of the layer.
      lowerCAmelCase     =			f'''transformer.h.{layer_idx}'''

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

         # Insert a "dummy" tensor for masked_bias.
         lowerCAmelCase     =			torch.tensor(-1e4	, dtype=torch.floataa				)
         lowerCAmelCase     =			masked_bias

         lowerCAmelCase     =			fix_query_key_value_ordering(lowerCamelCase	, lowerCamelCase	, 3	, lowerCamelCase	, lowerCamelCase				)
         # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
         lowerCAmelCase     =			out_val.transpose(0	, 1				).contiguous()
         # Store.
         lowerCAmelCase     =			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":
         lowerCAmelCase     =			fix_query_key_value_ordering(lowerCamelCase	, lowerCamelCase	, 3	, lowerCamelCase	, lowerCamelCase				)
         # Store. No change of shape.
         lowerCAmelCase     =			out_val

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

      # Copy the bias.
      elif weight_or_bias == "bias":
         lowerCAmelCase     =			megatron_to_transformers[op_name]
         lowerCAmelCase     =			val

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

   # The final layernorm.
   lowerCAmelCase     =			transformer['final_layernorm.weight']
   lowerCAmelCase     =			transformer['final_layernorm.bias']

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

   # It should be done!
   return output_state_dict





def 	a_					( ):
   # Create the argument parser.
   lowerCAmelCase     =			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.'	, )
   lowerCAmelCase     =			parser.parse_args()

   # Extract the basename.
   lowerCAmelCase     =			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:
            lowerCAmelCase     =			torch.load(lowerCamelCase	, map_location='cpu'				)
   else:
      lowerCAmelCase     =			torch.load(args.path_to_checkpoint	, map_location='cpu'				)

   lowerCAmelCase     =			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:
            lowerCAmelCase     =			'gelu_fast'
         elif ds_args.openai_gelu:
            lowerCAmelCase     =			'gelu_new'
         else:
            lowerCAmelCase     =			'gelu'
      else:
         # in the very early days this used to be "gelu_new"
         lowerCAmelCase     =			'gelu_new'

      # Spell out all parameters in case the defaults change.
      lowerCAmelCase     =			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:
      lowerCAmelCase     =			GPTaConfig.from_json_file(args.config_file				)

   lowerCAmelCase     =			['GPT2LMHeadModel']

   # Convert.
   print('Converting'				)
   lowerCAmelCase     =			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:
      lowerCAmelCase     =			ds_args.tokenizer_type
      if tokenizer_type == "GPT2BPETokenizer":
         lowerCAmelCase     =			'gpt2'
      elif tokenizer_type == "PretrainedFromHF":
         lowerCAmelCase     =			ds_args.tokenizer_name_or_path
      else:
         raise ValueError(f'''Unrecognized tokenizer_type {tokenizer_type}'''				)
   else:
      lowerCAmelCase     =			'gpt2'

   lowerCAmelCase     =			AutoTokenizer.from_pretrained(lowerCamelCase				)
   lowerCAmelCase     =			type(lowerCamelCase				).__name__
   lowerCAmelCase     =			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.
   lowerCAmelCase     =			os.path.join(lowerCamelCase	, 'pytorch_model.bin'				)
   print(f'''Saving checkpoint to "{output_checkpoint_file}"'''				)
   torch.save(lowerCamelCase	, lowerCamelCase				)


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

if __name__ == "__main__":
       main()

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





