




import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


snake_case_					:					Dict       						=			logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()


def   A		(__A  :       List[Any]			)  -> Optional[Any]:







     """simple docstring"""


     if "token" in model_name_or_path:
          return "rag_token"
     if "sequence" in model_name_or_path:
          return "rag_sequence"
     if "bart" in model_name_or_path:
          return "bart"
     return None


def   A		(__A  :       Optional[Any]  ,							__A  :       Any  ,							__A  :       Union[str, Any]			)  -> Dict:







     """simple docstring"""


     return max(metric_fn(__A  ,							__A			) for gt in ground_truths			)


def   A		(__A  :       List[str]  ,							__A  :       Any  ,							__A  :       List[str]			)  -> Union[str, Any]:







     """simple docstring"""


     UpperCAmelCase_		=				[line.strip() for line in open(__A  ,							'''r'''			).readlines()]
     UpperCAmelCase_		=				[]

     if args.gold_data_mode == "qa":
          UpperCAmelCase_		=				pd.read_csv(__A  ,							sep='''\t'''  ,							header=__A			)
          for answer_list in data[1]:
               UpperCAmelCase_		=				ast.literal_eval(__A			)
               answers.append(__A			)
     else:
          UpperCAmelCase_		=				[line.strip() for line in open(__A  ,							'''r'''			).readlines()]
          UpperCAmelCase_		=				[[reference] for reference in references]

     UpperCAmelCase_		=				UpperCAmelCase_		=				UpperCAmelCase_		=				0
     for prediction, ground_truths in zip(__A  ,							__A			):
          total += 1
          em += metric_max_over_ground_truths(__A  ,							__A  ,							__A			)
          fa += metric_max_over_ground_truths(__A  ,							__A  ,							__A			)

     UpperCAmelCase_		=				100.0 * em / total
     UpperCAmelCase_		=				100.0 * fa / total

     logger.info(F"""F1: {fa:.2f}"""			)
     logger.info(F"""EM: {em:.2f}"""			)


def   A		(__A  :       Any  ,							__A  :       Tuple  ,							__A  :       Tuple			)  -> int:







     """simple docstring"""


     UpperCAmelCase_		=				args.k
     UpperCAmelCase_		=				[line.strip() for line in open(__A  ,							'''r'''			).readlines()]
     UpperCAmelCase_		=				[line.strip() for line in open(__A  ,							'''r'''			).readlines()]

     UpperCAmelCase_		=				UpperCAmelCase_		=				0
     for hypo, reference in zip(__A  ,							__A			):
          UpperCAmelCase_		=				set(hypo.split('''\t'''			)[:k]			)
          UpperCAmelCase_		=				set(reference.split('''\t'''			)			)
          total += 1
          em += len(hypo_provenance & ref_provenance			) / k

     UpperCAmelCase_		=				100.0 * em / total
     logger.info(F"""Precision@{k}: {em: .2f}"""			)


def   A		(__A  :       List[str]  ,							__A  :       str  ,							__A  :       int			)  -> Optional[int]:







     """simple docstring"""


     def strip_title(__A  :       str			):
          if title.startswith('''"'''			):
               UpperCAmelCase_		=				title[1:]
          if title.endswith('''"'''			):
               UpperCAmelCase_		=				title[:-1]
          return title

     UpperCAmelCase_		=				rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
         __A  ,							return_tensors='''pt'''  ,							padding=__A  ,							truncation=__A  ,							)['''input_ids'''].to(args.device			)

     UpperCAmelCase_		=				rag_model.rag.question_encoder(__A			)
     UpperCAmelCase_		=				question_enc_outputs[0]

     UpperCAmelCase_		=				rag_model.retriever(
         __A  ,							question_enc_pool_output.cpu().detach().to(torch.floataa			).numpy()  ,							prefix=rag_model.rag.generator.config.prefix  ,							n_docs=rag_model.config.n_docs  ,							return_tensors='''pt'''  ,							)
     UpperCAmelCase_		=				rag_model.retriever.index.get_doc_dicts(result.doc_ids			)
     UpperCAmelCase_		=				[]
     for docs in all_docs:
          UpperCAmelCase_		=				[strip_title(__A			) for title in docs['''title''']]
          provenance_strings.append('''\t'''.join(__A			)			)
     return provenance_strings


def   A		(__A  :       Any  ,							__A  :       List[Any]  ,							__A  :       Optional[Any]			)  -> Optional[int]:







     """simple docstring"""


     with torch.no_grad():
          UpperCAmelCase_		=				rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
              __A  ,							return_tensors='''pt'''  ,							padding=__A  ,							truncation=__A			)

          UpperCAmelCase_		=				inputs_dict.input_ids.to(args.device			)
          UpperCAmelCase_		=				inputs_dict.attention_mask.to(args.device			)
          UpperCAmelCase_		=				rag_model.generate(  # rag_model overwrites generate
              __A  ,							attention_mask=__A  ,							num_beams=args.num_beams  ,							min_length=args.min_length  ,							max_length=args.max_length  ,							early_stopping=__A  ,							num_return_sequences=1  ,							bad_words_ids=[[0, 0]]  ,							)
          UpperCAmelCase_		=				rag_model.retriever.generator_tokenizer.batch_decode(__A  ,							skip_special_tokens=__A			)

          if args.print_predictions:
               for q, a in zip(__A  ,							__A			):
                    logger.info('''Q: {} - A: {}'''.format(__A  ,							__A			)			)

          return answers


def   A		()  -> str:







     """simple docstring"""


     UpperCAmelCase_		=				argparse.ArgumentParser()
     parser.add_argument(
         '''--model_type'''  ,							choices=['''rag_sequence''', '''rag_token''', '''bart''']  ,							type=__A  ,							help=(
             '''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
             ''' model_name_or_path'''
         )  ,							)
     parser.add_argument(
         '''--index_name'''  ,							default=__A  ,							choices=['''exact''', '''compressed''', '''legacy''']  ,							type=__A  ,							help='''RAG model retriever type'''  ,							)
     parser.add_argument(
         '''--index_path'''  ,							default=__A  ,							type=__A  ,							help='''Path to the retrieval index'''  ,							)
     parser.add_argument('''--n_docs'''  ,							default=5  ,							type=__A  ,							help='''Number of retrieved docs'''			)
     parser.add_argument(
         '''--model_name_or_path'''  ,							default=__A  ,							type=__A  ,							required=__A  ,							help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''  ,							)
     parser.add_argument(
         '''--eval_mode'''  ,							choices=['''e2e''', '''retrieval''']  ,							default='''e2e'''  ,							type=__A  ,							help=(
             '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
             ''' precision@k.'''
         )  ,							)
     parser.add_argument('''--k'''  ,							default=1  ,							type=__A  ,							help='''k for the precision@k calculation'''			)
     parser.add_argument(
         '''--evaluation_set'''  ,							default=__A  ,							type=__A  ,							required=__A  ,							help='''Path to a file containing evaluation samples'''  ,							)
     parser.add_argument(
         '''--gold_data_path'''  ,							default=__A  ,							type=__A  ,							required=__A  ,							help='''Path to a tab-separated file with gold samples'''  ,							)
     parser.add_argument(
         '''--gold_data_mode'''  ,							default='''qa'''  ,							type=__A  ,							choices=['''qa''', '''ans''']  ,							help=(
             '''Format of the gold data file'''
             '''qa - a single line in the following format: question [tab] answer_list'''
             '''ans - a single line of the gold file contains the expected answer string'''
         )  ,							)
     parser.add_argument(
         '''--predictions_path'''  ,							type=__A  ,							default='''predictions.txt'''  ,							help='''Name of the predictions file, to be stored in the checkpoints directory'''  ,							)
     parser.add_argument(
         '''--eval_all_checkpoints'''  ,							action='''store_true'''  ,							help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'''  ,							)
     parser.add_argument(
         '''--eval_batch_size'''  ,							default=8  ,							type=__A  ,							help='''Batch size per GPU/CPU for evaluation.'''  ,							)
     parser.add_argument(
         '''--recalculate'''  ,							help='''Recalculate predictions even if the prediction file exists'''  ,							action='''store_true'''  ,							)
     parser.add_argument(
         '''--num_beams'''  ,							default=4  ,							type=__A  ,							help='''Number of beams to be used when generating answers'''  ,							)
     parser.add_argument('''--min_length'''  ,							default=1  ,							type=__A  ,							help='''Min length of the generated answers'''			)
     parser.add_argument('''--max_length'''  ,							default=50  ,							type=__A  ,							help='''Max length of the generated answers'''			)

     parser.add_argument(
         '''--print_predictions'''  ,							action='''store_true'''  ,							help='''If True, prints predictions while evaluating.'''  ,							)
     parser.add_argument(
         '''--print_docs'''  ,							action='''store_true'''  ,							help='''If True, prints docs retried while generating.'''  ,							)
     UpperCAmelCase_		=				parser.parse_args()
     UpperCAmelCase_		=				torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''			)
     return args


def   A		(__A  :       str			)  -> Tuple:







     """simple docstring"""


     UpperCAmelCase_		=				{}
     if args.model_type is None:
          UpperCAmelCase_		=				infer_model_type(args.model_name_or_path			)
          assert args.model_type is not None
     if args.model_type.startswith('''rag'''			):
          UpperCAmelCase_		=				RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
          UpperCAmelCase_		=				args.n_docs
          if args.index_name is not None:
               UpperCAmelCase_		=				args.index_name
          if args.index_path is not None:
               UpperCAmelCase_		=				args.index_path
     else:
          UpperCAmelCase_		=				BartForConditionalGeneration

     UpperCAmelCase_		=				(
         [f.path for f in os.scandir(args.model_name_or_path			) if f.is_dir()]
         if args.eval_all_checkpoints
         else [args.model_name_or_path]
     )

     logger.info('''Evaluate the following checkpoints: %s'''  ,							__A			)

     UpperCAmelCase_		=				get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
     UpperCAmelCase_		=				evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval

     for checkpoint in checkpoints:
          if os.path.exists(args.predictions_path			) and (not args.recalculate):
               logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path			)			)
               score_fn(__A  ,							args.predictions_path  ,							args.gold_data_path			)
               continue

          logger.info('''***** Running evaluation for {} *****'''.format(__A			)			)
          logger.info('''  Batch size = %d'''  ,							args.eval_batch_size			)
          logger.info('''  Predictions will be stored under {}'''.format(args.predictions_path			)			)

          if args.model_type.startswith('''rag'''			):
               UpperCAmelCase_		=				RagRetriever.from_pretrained(__A  ,							**__A			)
               UpperCAmelCase_		=				model_class.from_pretrained(__A  ,							retriever=__A  ,							**__A			)
               model.retriever.init_retrieval()
          else:
               UpperCAmelCase_		=				model_class.from_pretrained(__A  ,							**__A			)
          model.to(args.device			)

          with open(args.evaluation_set  ,							'''r'''			) as eval_file, open(args.predictions_path  ,							'''w'''			) as preds_file:
               UpperCAmelCase_		=				[]
               for line in tqdm(__A			):
                    questions.append(line.strip()			)
                    if len(__A			) == args.eval_batch_size:
                         UpperCAmelCase_		=				evaluate_batch_fn(__A  ,							__A  ,							__A			)
                         preds_file.write('''\n'''.join(__A			) + '''\n'''			)
                         preds_file.flush()
                         UpperCAmelCase_		=				[]
               if len(__A			) > 0:
                    UpperCAmelCase_		=				evaluate_batch_fn(__A  ,							__A  ,							__A			)
                    preds_file.write('''\n'''.join(__A			)			)
                    preds_file.flush()

               score_fn(__A  ,							args.predictions_path  ,							args.gold_data_path			)


if __name__ == "__main__":
    snake_case_					:					Tuple       						=			get_args()
    main(args)












# 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_bert/convert_megatron_bert_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 MegatronBertConfig


def   A		(__A  :       List[Any]  ,							__A  :       str  ,							__A  :       Optional[int]=0			)  -> Dict:







     """simple docstring"""


     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   A		(__A  :       Any  ,							__A  :       str  ,							__A  :       Tuple  ,							__A  :       Optional[int]  ,							__A  :       Any			)  -> Union[str, Any]:







     """simple docstring"""


     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   A		(__A  :       Optional[Any]  ,							__A  :       Tuple  ,							__A  :       Optional[int]			)  -> Union[str, Any]:







     """simple docstring"""


     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.tokenizer_type
          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 if '''ffn_hidden_size''' in ds_args else 4 * ds_args.hidden_size
          # pprint(config)

     # The number of heads.
     UpperCAmelCase_		=				config.num_attention_heads
     # The hidden_size per head.
     UpperCAmelCase_		=				config.hidden_size // heads
     # 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, :]
     # Store the word embeddings.
     UpperCAmelCase_		=				word_embeddings

     # The position embeddings.
     UpperCAmelCase_		=				embeddings['''position_embeddings''']['''weight''']
     assert pos_embeddings.size(0			) == config.max_position_embeddings and pos_embeddings.size(1			) == config.hidden_size
     # Store the position embeddings.
     UpperCAmelCase_		=				pos_embeddings

     # The token-type embeddings.
     UpperCAmelCase_		=				embeddings['''tokentype_embeddings''']['''weight''']
     # Store the position embeddings.
     UpperCAmelCase_		=				tokentype_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''': '''.attention.output.dense.''',
         '''self_attention.dense''': '''.attention.output.dense.''',
         '''mlp.dense_h_to_4h''': '''.intermediate.dense.''',
         '''mlp.dense_4h_to_h''': '''.output.dense.''',
     }

     # Keep track of the attention/query/value tensor.
     UpperCAmelCase_		=				None

     # 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"""bert.encoder.layer.{layer_idx}"""

          # For layernorm(s), simply store the layer norm.
          if op_name.endswith('''layernorm'''			):
               UpperCAmelCase_		=				'''attention.ln''' if op_name.startswith('''input'''			) else '''ln'''
               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":
               # Make sure the QKV pointer is nil.
               assert attention_qkv_weight is None, ""

               UpperCAmelCase_		=				fix_query_key_value_ordering(__A  ,							__A  ,							3  ,							__A  ,							__A			)
               # Store the tensor as we need the bias as well to interleave QKV and biases.
               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":
               # Make sure we read the weight tensor.
               assert attention_qkv_weight is not None, ""

               # Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
               UpperCAmelCase_		=				attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
               UpperCAmelCase_		=				attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
               UpperCAmelCase_		=				attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

               UpperCAmelCase_		=				fix_query_key_value_ordering(__A  ,							__A  ,							3  ,							__A  ,							__A			)
               # Split the bias.
               UpperCAmelCase_		=				out_val[0 * config.hidden_size : 1 * config.hidden_size]
               UpperCAmelCase_		=				out_val[1 * config.hidden_size : 2 * config.hidden_size]
               UpperCAmelCase_		=				out_val[2 * config.hidden_size : 3 * config.hidden_size]

               # Store.
               UpperCAmelCase_		=				q
               UpperCAmelCase_		=				q_bias
               UpperCAmelCase_		=				k
               UpperCAmelCase_		=				k_bias
               UpperCAmelCase_		=				v
               UpperCAmelCase_		=				v_bias

               # Clear the stored tensor.
               UpperCAmelCase_		=				None

          # Copy weights and biases as is.
          elif weight_or_bias in ["weight", "bias"]:
               UpperCAmelCase_		=				megatron_to_transformers[op_name]
               UpperCAmelCase_		=				val

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

     # The pooler.
     UpperCAmelCase_		=				lm['''pooler''']

     # Store the matrix and the bias.
     UpperCAmelCase_		=				pooler['''dense.weight''']
     UpperCAmelCase_		=				pooler['''dense.bias''']

     # The LM head from Megatron (for RACE).
     UpperCAmelCase_		=				model['''lm_head''']

     # The transform matrix.
     UpperCAmelCase_		=				lm_head['''dense.weight''']
     UpperCAmelCase_		=				lm_head['''dense.bias''']

     # The transform LN.
     UpperCAmelCase_		=				lm_head['''layernorm.weight''']
     UpperCAmelCase_		=				lm_head['''layernorm.bias''']

     # For the decoder, we replicate the weights.
     UpperCAmelCase_		=				word_embeddings
     UpperCAmelCase_		=				lm_head['''bias''']

     # The classifier from Megatron (for MLNI).
     UpperCAmelCase_		=				model['''binary_head''']

     # Store the classifier.
     UpperCAmelCase_		=				binary_head['''weight''']
     UpperCAmelCase_		=				binary_head['''bias''']

     # It should be done!
     return output_state_dict


def   A		()  -> Dict:







     """simple docstring"""


     UpperCAmelCase_		=				argparse.ArgumentParser()
     parser.add_argument('''--print-checkpoint-structure'''  ,							action='''store_true'''			)
     parser.add_argument('''path_to_checkpoint'''  ,							type=__A  ,							help='''Path to the ZIP file containing the checkpoint'''			)
     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'''			)

     if args.config_file == "":
          # Default config of megatron-bert 345m
          UpperCAmelCase_		=				MegatronBertConfig()

          # different megatron-bert-*-345m models have different vocab sizes, so override the default
          # config (which is for megatron-bert-cased-345m) with the actual vocab dimension
          UpperCAmelCase_		=				input_state_dict['''model''']['''lm_head''']['''bias'''].numel()
     else:
          UpperCAmelCase_		=				MegatronBertConfig.from_json_file(args.config_file			)

     # 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			)

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

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






