

# 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(	__A   ,       __A   ,       __A=0			):
  # Format the message.
  if name is None:
    UpperCAmelCase					      =  None
  else:
    UpperCAmelCase					      =  "." * max(0   ,       spaces - 2			) + "# {:" + str(50 - spaces			) + "s}"
    UpperCAmelCase					      =  fmt.format(__A			)

  # Print and recurse (if needed).
  if isinstance(__A   ,       __A			):
    if msg is not None:
      print(__A			)
    for k in val.keys():
      recursive_print(__A   ,       val[k]   ,       spaces + 2			)
  elif isinstance(__A   ,       torch.Tensor			):
    print(__A   ,       ":"   ,       val.size()			)
  else:
    print(__A   ,       ":"   ,       __A			)
def      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A   ,       __A			):
  # 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(*__A			)
    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(*__A			)
    UpperCAmelCase					      =  param.transpose(0   ,       1			).contiguous()
  UpperCAmelCase					      =  param.view(*__A			)
  return param
def      _lowerCAmelCase(	__A   ,       __A   ,       __A			):
  # The converted output model.
  UpperCAmelCase					      =  {}

  # old versions did not store training args
  UpperCAmelCase					      =  input_state_dict.get("args"   ,       __A			)
  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(__A			)

    # 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   ,       __A   ,       __A			)
      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(__A   ,       __A   ,       3   ,       __A   ,       __A			)
      # 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(__A   ,       __A   ,       3   ,       __A   ,       __A			)
      # 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      _lowerCAmelCase(	):
  # Create the argument parser.
  UpperCAmelCase					      =  argparse.ArgumentParser()
  parser.add_argument("--print-checkpoint-structure"   ,       action="store_true"			)
  parser.add_argument(
      "path_to_checkpoint"   ,       type=__A   ,       help="Path to the checkpoint file (.zip archive or direct .pt file)"   ,       )
  parser.add_argument(
      "--config_file"   ,       default=""   ,       type=__A   ,       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(__A   ,       map_location="cpu"			)
  else:
    UpperCAmelCase					      =  torch.load(args.path_to_checkpoint   ,       map_location="cpu"			)

  UpperCAmelCase					      =  input_state_dict.get("args"   ,       __A			)

  # 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=50257   ,       n_positions=1024   ,       n_embd=1024   ,       n_layer=24   ,       n_head=16   ,       n_inner=4096   ,       activation_function=__A   ,       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=__A   ,       summary_activation=__A   ,       summary_proj_to_labels=__A   ,       summary_first_dropout=0.1   ,       scale_attn_weights=__A   ,       use_cache=__A   ,       bos_token_id=50256   ,       eos_token_id=50256   ,       )
  else:
    UpperCAmelCase					      =  GPTaConfig.from_json_file(args.config_file			)

  UpperCAmelCase					      =  ["GPT2LMHeadModel"]

  # Convert.
  print("Converting"			)
  UpperCAmelCase					      =  convert_megatron_checkpoint(__A   ,       __A   ,       __A			)

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

  # 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(__A			)
  UpperCAmelCase					      =  type(__A			).__name__
  UpperCAmelCase					      =  tokenizer_class

  # Store the config to file.
  print("Saving config"			)
  config.save_pretrained(__A			)

  # Save tokenizer based on args
  print(F"Adding {tokenizer_class} tokenizer files"			)
  tokenizer.save_pretrained(__A			)

  # Store the state_dict to file.
  UpperCAmelCase					      =  os.path.join(__A   ,       "pytorch_model.bin"			)
  print(F"Saving checkpoint to \"{output_checkpoint_file}\""			)
  torch.save(__A   ,       __A			)


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

if __name__ == "__main__":
      main()

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






import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowerCAmelCase__       = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
      from nltk import word_tokenize


lowerCAmelCase__       = "\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

lowerCAmelCase__       = "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

lowerCAmelCase__       = "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,						_KWARGS_DESCRIPTION    )
class 						__magic_name__    (       datasets.Metric    ):


 def _UpperCamelCase	(						self		: int    )    ->      str:
   return datasets.MetricInfo(
       description=_DESCRIPTION			,						citation=_CITATION			,						inputs_description=_KWARGS_DESCRIPTION			,						features=datasets.Features(
           {
               "predictions": datasets.Value("string"			,						id="sequence"    ),
               "references": datasets.Value("string"			,						id="sequence"    ),
           }    )			,						codebase_urls=["https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"]			,						reference_urls=[
           "https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score",
           "https://en.wikipedia.org/wiki/METEOR",
       ]			,						)


 def _UpperCamelCase	(						self		: Dict			,						lowerCAmelCase__		: List[Any]    )    ->      Dict:
   import nltk

   nltk.download("wordnet"    )
   if NLTK_VERSION >= version.Version("3.6.5"    ):
     nltk.download("punkt"    )
   if NLTK_VERSION >= version.Version("3.6.6"    ):
     nltk.download("omw-1.4"    )


 def _UpperCamelCase	(						self		: Union[str, Any]			,						lowerCAmelCase__		: int			,						lowerCAmelCase__		: Dict			,						lowerCAmelCase__		: Any=0.9			,						lowerCAmelCase__		: Tuple=3			,						lowerCAmelCase__		: Optional[int]=0.5    )    ->      Any:
   if NLTK_VERSION >= version.Version("3.6.5"    ):
     UpperCAmelCase					      =  [
         meteor_score.single_meteor_score(
             word_tokenize(lowerCAmelCase__    )			,						word_tokenize(lowerCAmelCase__    )			,						alpha=lowerCAmelCase__			,						beta=lowerCAmelCase__			,						gamma=lowerCAmelCase__    )
         for ref, pred in zip(lowerCAmelCase__			,						lowerCAmelCase__    )
     ]
   else:
     UpperCAmelCase					      =  [
         meteor_score.single_meteor_score(lowerCAmelCase__			,						lowerCAmelCase__			,						alpha=lowerCAmelCase__			,						beta=lowerCAmelCase__			,						gamma=lowerCAmelCase__    )
         for ref, pred in zip(lowerCAmelCase__			,						lowerCAmelCase__    )
     ]

   return {"meteor": np.mean(lowerCAmelCase__    )}



