
'''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  __snake_case     (    _UpperCAmelCase					:       Dict,      _UpperCAmelCase					:       Dict,      _UpperCAmelCase					:       Union[str, Any]=0):
   # Format the message.
   if name is None:
      UpperCamelCase       =  None
   else:
      UpperCamelCase       =  '''.''' * max(0,      spaces - 2) + '''# {:''' + str(50 - spaces) + '''s}'''
      UpperCamelCase       =  fmt.format(_UpperCAmelCase)

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



def  __snake_case     (    _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       Optional[Any],      _UpperCAmelCase					:       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.
   UpperCamelCase       =  param.size()
   if checkpoint_version == 1.0:
      # version 1.0 stores [num_heads * hidden_size * num_splits, :]
      UpperCamelCase       =  (num_heads, hidden_size, num_splits) + input_shape[1:]
      UpperCamelCase       =  param.view(*_UpperCAmelCase)
      UpperCamelCase       =  param.transpose(0,      2)
      UpperCamelCase       =  param.transpose(1,      2).contiguous()
   elif checkpoint_version >= 2.0:
      # other versions store [num_heads * num_splits * hidden_size, :]
      UpperCamelCase       =  (num_heads, num_splits, hidden_size) + input_shape[1:]
      UpperCamelCase       =  param.view(*_UpperCAmelCase)
      UpperCamelCase       =  param.transpose(0,      1).contiguous()
   UpperCamelCase       =  param.view(*_UpperCAmelCase)
   return param



def  __snake_case     (    _UpperCAmelCase					:       Union[str, Any],      _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       Optional[int]):
   # The converted output model.
   UpperCamelCase       =  {}

   # old versions did not store training args
   UpperCamelCase       =  input_state_dict.get('''args''',      _UpperCAmelCase)
   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       =  ds_args.padded_vocab_size
      UpperCamelCase       =  ds_args.max_position_embeddings
      UpperCamelCase       =  ds_args.hidden_size
      UpperCamelCase       =  ds_args.num_layers
      UpperCamelCase       =  ds_args.num_attention_heads
      UpperCamelCase       =  ds_args.ffn_hidden_size
      # pprint(config)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

   # It should be done!
   return output_state_dict



def  __snake_case     (    ):
   # Create the argument parser.
   UpperCamelCase       =  argparse.ArgumentParser()
   parser.add_argument('''--print-checkpoint-structure''',      action='''store_true''')
   parser.add_argument(
       '''path_to_checkpoint''',      type=_UpperCAmelCase,      help='''Path to the checkpoint file (.zip archive or direct .pt file)''',      )
   parser.add_argument(
       '''--config_file''',      default='''''',      type=_UpperCAmelCase,      help='''An optional config json file describing the pre-trained model.''',      )
   UpperCamelCase       =  parser.parse_args()

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

   UpperCamelCase       =  input_state_dict.get('''args''',      _UpperCAmelCase)

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

      # Spell out all parameters in case the defaults change.
      UpperCamelCase       =  GPTaConfig(
          vocab_size=5_0257,      n_positions=1024,      n_embd=1024,      n_layer=24,      n_head=16,      n_inner=4096,      activation_function=_UpperCAmelCase,      resid_pdrop=0.1,      embd_pdrop=0.1,      attn_pdrop=0.1,      layer_norm_epsilon=1E-5,      initializer_range=0.0_2,      summary_type='''cls_index''',      summary_use_proj=_UpperCAmelCase,      summary_activation=_UpperCAmelCase,      summary_proj_to_labels=_UpperCAmelCase,      summary_first_dropout=0.1,      scale_attn_weights=_UpperCAmelCase,      use_cache=_UpperCAmelCase,      bos_token_id=5_0256,      eos_token_id=5_0256,      )
   else:
      UpperCamelCase       =  GPTaConfig.from_json_file(args.config_file)

   UpperCamelCase       =  ['''GPT2LMHeadModel''']

   # Convert.
   print('''Converting''')
   UpperCamelCase       =  convert_megatron_checkpoint(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase)

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

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

   UpperCamelCase       =  AutoTokenizer.from_pretrained(_UpperCAmelCase)
   UpperCamelCase       =  type(_UpperCAmelCase).__name__
   UpperCamelCase       =  tokenizer_class

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

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

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


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

if __name__ == "__main__":
   main()

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







'''simple docstring'''


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_pegasus import PegasusTokenizer
else:
   snake_case_ :  Dict     =					None


snake_case_ :  int     =					logging.get_logger(__name__)


snake_case_ :  int     =					'▁'

snake_case_ :  Optional[int]     =					{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

snake_case_ :  List[Any]     =					{
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

snake_case_ :  Tuple     =					{
    'google/pegasus-xsum': 512,
}


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							VOCAB_FILES_NAMES
   _snake_case		   =							PRETRAINED_VOCAB_FILES_MAP
   _snake_case		   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _snake_case		   =							PegasusTokenizer
   _snake_case		   =							['''input_ids''', '''attention_mask''']







   def __init__(			self ,   lowerCamelCase__=None ,   lowerCamelCase__=None ,   lowerCamelCase__="<pad>" ,   lowerCamelCase__="</s>" ,   lowerCamelCase__="<unk>" ,   lowerCamelCase__="<mask_2>" ,   lowerCamelCase__="<mask_1>" ,   lowerCamelCase__=None ,   lowerCamelCase__=1_0_3 ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  offset

      if additional_special_tokens is not None:
         if not isinstance(lowerCamelCase__ ,   lowerCamelCase__   ):
            raise TypeError(
                f'additional_special_tokens should be of type {type(lowerCamelCase__   )}, but is'
                f' {type(lowerCamelCase__   )}'   )

         UpperCamelCase       =  (
             ([mask_token_sent] + additional_special_tokens)
             if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
             else additional_special_tokens
         )
         # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
         additional_special_tokens_extended += [
             f'<unk_{i}>' for i in range(len(lowerCamelCase__   ) ,   self.offset - 1   )
         ]

         if len(set(lowerCamelCase__   )   ) != len(lowerCamelCase__   ):
            raise ValueError(
                '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
                f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'   )
         UpperCamelCase       =  additional_special_tokens_extended
      else:
         UpperCamelCase       =  [mask_token_sent] if mask_token_sent is not None else []
         additional_special_tokens += [f'<unk_{i}>' for i in range(2 ,   self.offset   )]

      super().__init__(
          lowerCamelCase__ ,   tokenizer_file=lowerCamelCase__ ,   pad_token=lowerCamelCase__ ,   eos_token=lowerCamelCase__ ,   unk_token=lowerCamelCase__ ,   mask_token=lowerCamelCase__ ,   mask_token_sent=lowerCamelCase__ ,   offset=lowerCamelCase__ ,   additional_special_tokens=lowerCamelCase__ ,   **lowerCamelCase__ ,   )
      UpperCamelCase       =  vocab_file
      UpperCamelCase       =  False if not self.vocab_file else True







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  set(self.all_special_ids   )  # call it once instead of inside list comp
      all_special_ids.remove(self.unk_token_id   )  # <unk> is only sometimes special

      if all_special_ids != set(range(len(self.additional_special_tokens   ) + 3   )   ):
         raise ValueError(
             '''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
             f' {len(self.additional_special_tokens   )} additional_special_tokens, but got {all_special_ids}'   )

      return [1 if x in all_special_ids else 0 for x in seq]







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None ,   lowerCamelCase__ = False   ):

      '''simple docstring'''







      if already_has_special_tokens:
         return self._special_token_mask(lowerCamelCase__   )
      elif token_ids_a is None:
         return self._special_token_mask(lowerCamelCase__   ) + [1]
      else:
         return self._special_token_mask(token_ids_a + token_ids_a   ) + [1]







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__=None   ):

      '''simple docstring'''







      if token_ids_a is None:
         return token_ids_a + [self.eos_token_id]
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return token_ids_a + token_ids_a + [self.eos_token_id]







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      if not self.can_save_slow_tokenizer:
         raise ValueError(
             '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
             '''tokenizer.'''   )

      if not os.path.isdir(lowerCamelCase__   ):
         logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
         return
      UpperCamelCase       =  os.path.join(
          lowerCamelCase__ ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

      if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowerCamelCase__   ):
         copyfile(self.vocab_file ,   lowerCamelCase__   )

      return (out_vocab_file,)





