




# 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 SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_=0						)       ->	Any:



       """simple docstring"""

       if name is None:
              A__							=   None
       else:
              A__							=   '''.''' * max(0			,    spaces - 2						) + '''# {:''' + str(50 - spaces						) + '''s}'''
              A__							=   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 SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_			,    lowercase_						)       ->	Dict:



       """simple docstring"""

       A__							=   param.size()
       if checkpoint_version == 1.0:
              # version 1.0 stores [num_heads * hidden_size * num_splits, :]
              A__							=   (num_heads, hidden_size, num_splits) + input_shape[1:]
              A__							=   param.view(*lowercase_						)
              A__							=   param.transpose(0			,    2						)
              A__							=   param.transpose(1			,    2						).contiguous()
       elif checkpoint_version >= 2.0:
              # other versions store [num_heads * num_splits * hidden_size, :]
              A__							=   (num_heads, num_splits, hidden_size) + input_shape[1:]
              A__							=   param.view(*lowercase_						)
              A__							=   param.transpose(0			,    1						).contiguous()
       A__							=   param.view(*lowercase_						)
       return param







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	List[Any]:



       """simple docstring"""

       A__							=   {}

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

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

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

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

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

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

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

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

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

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

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

              # The name of the layer.
              A__							=   f"""transformer.h.{layer_idx}"""

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

                     # Insert a "dummy" tensor for masked_bias.
                     A__							=   torch.tensor(-1E4			,    dtype=torch.floataa						)
                     A__							=   masked_bias

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

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

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

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

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

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

       # It should be done!
       return output_state_dict







def SCREAMING_SNAKE_CASE    (	)       ->	Optional[Any]:



       """simple docstring"""

       A__							=   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.'''			,    )
       A__							=   parser.parse_args()

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

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

              # Spell out all parameters in case the defaults change.
              A__							=   GPTaConfig(
                  vocab_size=50_257			,    n_positions=1_024			,    n_embd=1_024			,    n_layer=24			,    n_head=16			,    n_inner=4_096			,    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=50_256			,    eos_token_id=50_256			,    )
       else:
              A__							=   GPTaConfig.from_json_file(args.config_file						)

       A__							=   ['''GPT2LMHeadModel''']

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

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


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

if __name__ == "__main__":
  main()

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








def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	str:



       """simple docstring"""

       return " ".join(
           ''''''.join(word[::-1]						) if len(lowercase_						) > 4 else word for word in sentence.split()						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(reverse_long_words("""Hey wollef sroirraw"""))


