


'''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 lowerCamelCase__     (		__lowerCamelCase			:				Tuple			,       __lowerCamelCase			:				str			,       __lowerCamelCase			:				List[Any]=0				):







   '''simple docstring'''




   if name is None:
      _UpperCAmelCase  :  Tuple				       =None
   else:
      _UpperCAmelCase  :  Optional[int]				       ='.' * max(0			,       spaces - 2				) + '# {:' + str(5_0 - spaces				) + 's}'
      _UpperCAmelCase  :  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 lowerCamelCase__     (		__lowerCamelCase			:				Optional[Any]			,       __lowerCamelCase			:				int			,       __lowerCamelCase			:				str			,       __lowerCamelCase			:				List[str]			,       __lowerCamelCase			:				Optional[Any]				):







   '''simple docstring'''




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





def lowerCamelCase__     (		__lowerCamelCase			:				List[str]			,       __lowerCamelCase			:				List[str]			,       __lowerCamelCase			:				int				):







   '''simple docstring'''




   _UpperCAmelCase  :  Optional[Any]				       ={}

   # old versions did not store training args
   _UpperCAmelCase  :  Optional[int]				       =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))

      _UpperCAmelCase  :  Tuple				       =ds_args.padded_vocab_size
      _UpperCAmelCase  :  Optional[int]				       =ds_args.max_position_embeddings
      _UpperCAmelCase  :  List[str]				       =ds_args.hidden_size
      _UpperCAmelCase  :  Dict				       =ds_args.num_layers
      _UpperCAmelCase  :  Tuple				       =ds_args.num_attention_heads
      _UpperCAmelCase  :  Optional[Any]				       =ds_args.ffn_hidden_size
      # pprint(config)

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

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

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

   # The position embeddings.
   _UpperCAmelCase  :  Optional[Any]				       =embeddings['position_embeddings']['weight']
   # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
   _UpperCAmelCase  :  Dict				       =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  :  Tuple				       =pos_embeddings

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

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

   # The simple map of names for "automated" rules.
   _UpperCAmelCase  :  Dict				       ={
       '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  :  Tuple				       =layer_re.match(__lowerCamelCase				)

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

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

      # The name of the layer.
      _UpperCAmelCase  :  int				       =f"transformer.h.{layer_idx}"

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

         # Insert a "dummy" tensor for masked_bias.
         _UpperCAmelCase  :  Union[str, Any]				       =torch.tensor(-1e4			,       dtype=torch.floataa				)
         _UpperCAmelCase  :  int				       =masked_bias

         _UpperCAmelCase  :  Optional[int]				       =fix_query_key_value_ordering(__lowerCamelCase			,       __lowerCamelCase			,       3			,       __lowerCamelCase			,       __lowerCamelCase				)
         # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
         _UpperCAmelCase  :  Optional[Any]				       =out_val.transpose(0			,       1				).contiguous()
         # Store.
         _UpperCAmelCase  :  Dict				       =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  :  Optional[int]				       =fix_query_key_value_ordering(__lowerCamelCase			,       __lowerCamelCase			,       3			,       __lowerCamelCase			,       __lowerCamelCase				)
         # Store. No change of shape.
         _UpperCAmelCase  :  Dict				       =out_val

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

      # Copy the bias.
      elif weight_or_bias == "bias":
         _UpperCAmelCase  :  Optional[int]				       =megatron_to_transformers[op_name]
         _UpperCAmelCase  :  List[Any]				       =val

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

   # The final layernorm.
   _UpperCAmelCase  :  List[Any]				       =transformer['final_layernorm.weight']
   _UpperCAmelCase  :  Optional[int]				       =transformer['final_layernorm.bias']

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

   # It should be done!
   return output_state_dict





def lowerCamelCase__     (		):







   '''simple docstring'''




   _UpperCAmelCase  :  List[Any]				       =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.'			,       )
   _UpperCAmelCase  :  Union[str, Any]				       =parser.parse_args()

   # Extract the basename.
   _UpperCAmelCase  :  Any				       =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  :  Optional[int]				       =torch.load(__lowerCamelCase			,       map_location='cpu'				)
   else:
      _UpperCAmelCase  :  Tuple				       =torch.load(args.path_to_checkpoint			,       map_location='cpu'				)

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

      # Spell out all parameters in case the defaults change.
      _UpperCAmelCase  :  Dict				       =GPTaConfig(
          vocab_size=5_0_2_5_7			,       n_positions=1_0_2_4			,       n_embd=1_0_2_4			,       n_layer=2_4			,       n_head=1_6			,       n_inner=4_0_9_6			,       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=5_0_2_5_6			,       eos_token_id=5_0_2_5_6			,       )
   else:
      _UpperCAmelCase  :  Optional[Any]				       =GPTaConfig.from_json_file(args.config_file				)

   _UpperCAmelCase  :  str				       =['GPT2LMHeadModel']

   # Convert.
   print('Converting'				)
   _UpperCAmelCase  :  Tuple				       =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:
      _UpperCAmelCase  :  Optional[int]				       =ds_args.tokenizer_type
      if tokenizer_type == "GPT2BPETokenizer":
         _UpperCAmelCase  :  Tuple				       ='gpt2'
      elif tokenizer_type == "PretrainedFromHF":
         _UpperCAmelCase  :  Optional[int]				       =ds_args.tokenizer_name_or_path
      else:
         raise ValueError(f"Unrecognized tokenizer_type {tokenizer_type}"				)
   else:
      _UpperCAmelCase  :  Any				       ='gpt2'

   _UpperCAmelCase  :  List[str]				       =AutoTokenizer.from_pretrained(__lowerCamelCase				)
   _UpperCAmelCase  :  Dict				       =type(__lowerCamelCase				).__name__
   _UpperCAmelCase  :  Optional[int]				       =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.
   _UpperCAmelCase  :  Any				       =os.path.join(__lowerCamelCase			,       'pytorch_model.bin'				)
   print(f"Saving checkpoint to \"{output_checkpoint_file}\""				)
   torch.save(__lowerCamelCase			,       __lowerCamelCase				)


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

if __name__ == "__main__":
 main()

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





'''simple docstring'''






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class    __magic_name__				(						lowerCAmelCase							):
       UpperCAmelCase		 =["image_processor", "tokenizer"]
       UpperCAmelCase		 ="CLIPImageProcessor"
       UpperCAmelCase		 =("CLIPTokenizer", "CLIPTokenizerFast")






       def __init__(     self    ,				snake_case=None    ,				snake_case=None    ,				**snake_case)		->    Dict:




          '''simple docstring'''
          _UpperCAmelCase  :  Union[str, Any]				       =None
          if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'    ,				snake_case    ,				)
             _UpperCAmelCase  :  str				       =kwargs.pop('feature_extractor')

          _UpperCAmelCase  :  List[str]				       =image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.')
          if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.')

          super().__init__(snake_case    ,				snake_case)






       def __call__(     self    ,				snake_case=None    ,				snake_case=None    ,				snake_case=None    ,				**snake_case)		->    Optional[Any]:




          '''simple docstring'''

          if text is None and images is None:
             raise ValueError('You have to specify either text or images. Both cannot be none.')

          if text is not None:
             _UpperCAmelCase  :  List[Any]				       =self.tokenizer(snake_case    ,				return_tensors=snake_case    ,				**snake_case)

          if images is not None:
             _UpperCAmelCase  :  List[str]				       =self.image_processor(snake_case    ,				return_tensors=snake_case    ,				**snake_case)

          if text is not None and images is not None:
             _UpperCAmelCase  :  Any				       =image_features.pixel_values
             return encoding
          elif text is not None:
             return encoding
          else:
             return BatchEncoding(data=dict(**snake_case)    ,				tensor_type=snake_case)






       def      lowerCAmelCase						(     self    ,				*snake_case    ,				**snake_case)		->    int:




          '''simple docstring'''
          return self.tokenizer.batch_decode(*snake_case    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				*snake_case    ,				**snake_case)		->    Optional[int]:




          '''simple docstring'''
          return self.tokenizer.decode(*snake_case    ,				**snake_case)






       @property
       def      lowerCAmelCase						(     self)		->    List[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  List[Any]				       =self.tokenizer.model_input_names
          _UpperCAmelCase  :  Any				       =self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))






       @property
       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          warnings.warn(
              '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,				snake_case    ,				)
          return self.image_processor_class






       @property
       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          warnings.warn(
              '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,				snake_case    ,				)
          return self.image_processor

