





"""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        lowercase__     (       lowercase_		,lowercase_		,lowercase_=0      )       ->		List[str]:




      """simple docstring"""

      if name is None:
            _UpperCamelCase   :						List[Any]    =							None
      else:
            _UpperCamelCase   :						Any    =							""".""" * max(0		,spaces - 2      ) + """# {:""" + str(50 - spaces      ) + """s}"""
            _UpperCamelCase   :						int    =							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        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_      )       ->		int:




      """simple docstring"""

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




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_      )       ->		Any:




      """simple docstring"""

      _UpperCamelCase   :						Union[str, Any]    =							{}

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

            _UpperCamelCase   :						Optional[int]    =							ds_args.padded_vocab_size
            _UpperCamelCase   :						List[str]    =							ds_args.max_position_embeddings
            _UpperCamelCase   :						Union[str, Any]    =							ds_args.hidden_size
            _UpperCamelCase   :						Union[str, Any]    =							ds_args.num_layers
            _UpperCamelCase   :						List[Any]    =							ds_args.num_attention_heads
            _UpperCamelCase   :						Any    =							ds_args.ffn_hidden_size
            # pprint(config)

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

      # The model.
      _UpperCamelCase   :						Tuple    =							input_state_dict["""model"""]
      # The language model.
      _UpperCamelCase   :						List[str]    =							model["""language_model"""]
      # The embeddings.
      _UpperCamelCase   :						Dict    =							lm["""embedding"""]

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

      # The position embeddings.
      _UpperCamelCase   :						Tuple    =							embeddings["""position_embeddings"""]["""weight"""]
      # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
      _UpperCamelCase   :						List[str]    =							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   :						Optional[Any]    =							pos_embeddings

      # The transformer.
      _UpperCamelCase   :						List[str]    =							lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]

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

      # The simple map of names for "automated" rules.
      _UpperCamelCase   :						Optional[int]    =							{
          """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   :						int    =							layer_re.match(lowercase_      )

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

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

            # The name of the layer.
            _UpperCamelCase   :						List[str]    =							F'''transformer.h.{layer_idx}'''

            # For layernorm(s), simply store the layer norm.
            if op_name.endswith("layernorm"      ):
                  _UpperCamelCase   :						Optional[Any]    =							"""ln_1""" if op_name.startswith("input"      ) else """ln_2"""
                  _UpperCamelCase   :						str    =							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   :						List[str]    =							torch.tril(torch.ones((n_positions, n_positions)		,dtype=torch.floataa      )      ).view(
                      1		,1		,lowercase_		,lowercase_      )
                  _UpperCamelCase   :						Any    =							causal_mask

                  # Insert a "dummy" tensor for masked_bias.
                  _UpperCamelCase   :						Dict    =							torch.tensor(-1e4		,dtype=torch.floataa      )
                  _UpperCamelCase   :						Tuple    =							masked_bias

                  _UpperCamelCase   :						List[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.
                  _UpperCamelCase   :						Optional[int]    =							out_val.transpose(0		,1      ).contiguous()
                  # Store.
                  _UpperCamelCase   :						int    =							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   :						List[str]    =							fix_query_key_value_ordering(lowercase_		,lowercase_		,3		,lowercase_		,lowercase_      )
                  # Store. No change of shape.
                  _UpperCamelCase   :						Dict    =							out_val

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

            # Copy the bias.
            elif weight_or_bias == "bias":
                  _UpperCamelCase   :						List[str]    =							megatron_to_transformers[op_name]
                  _UpperCamelCase   :						Dict    =							val

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

      # The final layernorm.
      _UpperCamelCase   :						Union[str, Any]    =							transformer["""final_layernorm.weight"""]
      _UpperCamelCase   :						List[Any]    =							transformer["""final_layernorm.bias"""]

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

      # It should be done!
      return output_state_dict




def        lowercase__     (       )       ->		Optional[Any]:




      """simple docstring"""

      _UpperCamelCase   :						List[str]    =							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."		,)
      _UpperCamelCase   :						Optional[int]    =							parser.parse_args()

      # Extract the basename.
      _UpperCamelCase   :						Union[str, 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   :						Union[str, Any]    =							torch.load(lowercase_		,map_location="cpu"      )
      else:
            _UpperCamelCase   :						int    =							torch.load(args.path_to_checkpoint		,map_location="cpu"      )

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

            # Spell out all parameters in case the defaults change.
            _UpperCamelCase   :						Tuple    =							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:
            _UpperCamelCase   :						List[str]    =							GPTaConfig.from_json_file(args.config_file      )

      _UpperCamelCase   :						Optional[Any]    =							["""GPT2LMHeadModel"""]

      # Convert.
      print("Converting"      )
      _UpperCamelCase   :						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:
            _UpperCamelCase   :						Dict    =							ds_args.tokenizer_type
            if tokenizer_type == "GPT2BPETokenizer":
                  _UpperCamelCase   :						List[Any]    =							"""gpt2"""
            elif tokenizer_type == "PretrainedFromHF":
                  _UpperCamelCase   :						str    =							ds_args.tokenizer_name_or_path
            else:
                  raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}'''      )
      else:
            _UpperCamelCase   :						Union[str, Any]    =							"""gpt2"""

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


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

if __name__ == "__main__":
  main()

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


lowerCamelCase__     :		List[str]        = """
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

lowerCamelCase__     :		List[Any]        = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
lowerCamelCase__     :		int        = {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}






