




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


UpperCAmelCase_									=	logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()




def 					lowerCamelCase__       (						A__       :					Optional[int]	):





       '''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 					lowerCamelCase__       (						A__       :					List[str]  ,			A__       :					Any  ,			A__       :					Optional[int]	):





       '''simple docstring'''



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




def 					lowerCamelCase__       (						A__       :					Dict  ,			A__       :					Optional[int]  ,			A__       :					List[str]	):





       '''simple docstring'''



       __lowerCamelCase   =						[line.strip() for line in open(A__  ,			"""r"""	).readlines()]
       __lowerCamelCase   =						[]

       if args.gold_data_mode == "qa":
              __lowerCamelCase   =						pd.read_csv(A__  ,			sep="""\t"""  ,			header=A__	)
              for answer_list in data[1]:
                     __lowerCamelCase   =						ast.literal_eval(A__	)
                     answers.append(A__	)
       else:
              __lowerCamelCase   =						[line.strip() for line in open(A__  ,			"""r"""	).readlines()]
              __lowerCamelCase   =						[[reference] for reference in references]

       __lowerCamelCase   =						__lowerCamelCase   =						__lowerCamelCase   =						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__	)

       __lowerCamelCase   =						100.0 * em / total
       __lowerCamelCase   =						100.0 * fa / total

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




def 					lowerCamelCase__       (						A__       :					List[str]  ,			A__       :					Union[str, Any]  ,			A__       :					Optional[int]	):





       '''simple docstring'''



       __lowerCamelCase   =						args.k
       __lowerCamelCase   =						[line.strip() for line in open(A__  ,			"""r"""	).readlines()]
       __lowerCamelCase   =						[line.strip() for line in open(A__  ,			"""r"""	).readlines()]

       __lowerCamelCase   =						__lowerCamelCase   =						0
       for hypo, reference in zip(A__  ,			A__	):
              __lowerCamelCase   =						set(hypo.split("""\t"""	)[:k]	)
              __lowerCamelCase   =						set(reference.split("""\t"""	)	)
              total += 1
              em += len(hypo_provenance & ref_provenance	) / k

       __lowerCamelCase   =						100.0 * em / total
       logger.info(f'Precision@{k}: {em: .2f}'	)




def 					lowerCamelCase__       (						A__       :					int  ,			A__       :					Union[str, Any]  ,			A__       :					str	):





       '''simple docstring'''



       def strip_title(A__       :					int	):
              if title.startswith("""\""""	):
                     __lowerCamelCase   =						title[1:]
              if title.endswith("""\""""	):
                     __lowerCamelCase   =						title[:-1]
              return title

       __lowerCamelCase   =						rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
           A__  ,			return_tensors="""pt"""  ,			padding=A__  ,			truncation=A__  ,			)["""input_ids"""].to(args.device	)

       __lowerCamelCase   =						rag_model.rag.question_encoder(A__	)
       __lowerCamelCase   =						question_enc_outputs[0]

       __lowerCamelCase   =						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"""  ,			)
       __lowerCamelCase   =						rag_model.retriever.index.get_doc_dicts(result.doc_ids	)
       __lowerCamelCase   =						[]
       for docs in all_docs:
              __lowerCamelCase   =						[strip_title(A__	) for title in docs["""title"""]]
              provenance_strings.append("""\t""".join(A__	)	)
       return provenance_strings




def 					lowerCamelCase__       (						A__       :					Optional[int]  ,			A__       :					Dict  ,			A__       :					List[Any]	):





       '''simple docstring'''



       with torch.no_grad():
              __lowerCamelCase   =						rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                  A__  ,			return_tensors="""pt"""  ,			padding=A__  ,			truncation=A__	)

              __lowerCamelCase   =						inputs_dict.input_ids.to(args.device	)
              __lowerCamelCase   =						inputs_dict.attention_mask.to(args.device	)
              __lowerCamelCase   =						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]]  ,			)
              __lowerCamelCase   =						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 					lowerCamelCase__       (						):





       '''simple docstring'''



       __lowerCamelCase   =						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."""  ,			)
       __lowerCamelCase   =						parser.parse_args()
       __lowerCamelCase   =						torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""	)
       return args




def 					lowerCamelCase__       (						A__       :					List[Any]	):





       '''simple docstring'''



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

       __lowerCamelCase   =						(
           [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__	)

       __lowerCamelCase   =						get_scores if args.eval_mode == """e2e""" else get_precision_at_k
       __lowerCamelCase   =						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"""	):
                     __lowerCamelCase   =						RagRetriever.from_pretrained(A__  ,			**A__	)
                     __lowerCamelCase   =						model_class.from_pretrained(A__  ,			retriever=A__  ,			**A__	)
                     model.retriever.init_retrieval()
              else:
                     __lowerCamelCase   =						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:
                     __lowerCamelCase   =						[]
                     for line in tqdm(A__	):
                            questions.append(line.strip()	)
                            if len(A__	) == args.eval_batch_size:
                                   __lowerCamelCase   =						evaluate_batch_fn(A__  ,			A__  ,			A__	)
                                   preds_file.write("""\n""".join(A__	) + """\n"""	)
                                   preds_file.flush()
                                   __lowerCamelCase   =						[]
                     if len(A__	) > 0:
                            __lowerCamelCase   =						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__":
       UpperCAmelCase_									=	get_args()
       main(args)





'''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 	a_					( lowerCamelCase	:     int	, lowerCamelCase	:     Optional[Any]	, lowerCamelCase	:     Union[str, Any]=0				):
   # Format the message.
   if name is None:
      lowerCAmelCase     =			None
   else:
      lowerCAmelCase     =			'.' * max(0	, spaces - 2				) + '# {:' + str(50 - spaces				) + 's}'
      lowerCAmelCase     =			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 	a_					( lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     List[str]	, lowerCamelCase	:     List[Any]	, lowerCamelCase	:     Dict	, lowerCamelCase	:     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.
   lowerCAmelCase     =			param.size()
   if checkpoint_version == 1.0:
      # version 1.0 stores [num_heads * hidden_size * num_splits, :]
      lowerCAmelCase     =			(num_heads, hidden_size, num_splits) + input_shape[1:]
      lowerCAmelCase     =			param.view(*lowerCamelCase				)
      lowerCAmelCase     =			param.transpose(0	, 2				)
      lowerCAmelCase     =			param.transpose(1	, 2				).contiguous()
   elif checkpoint_version >= 2.0:
      # other versions store [num_heads * num_splits * hidden_size, :]
      lowerCAmelCase     =			(num_heads, num_splits, hidden_size) + input_shape[1:]
      lowerCAmelCase     =			param.view(*lowerCamelCase				)
      lowerCAmelCase     =			param.transpose(0	, 1				).contiguous()
   lowerCAmelCase     =			param.view(*lowerCamelCase				)
   return param





def 	a_					( lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     str				):
   # The converted output model.
   lowerCAmelCase     =			{}

   # old versions did not store training args
   lowerCAmelCase     =			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))

      lowerCAmelCase     =			ds_args.padded_vocab_size
      lowerCAmelCase     =			ds_args.max_position_embeddings
      lowerCAmelCase     =			ds_args.hidden_size
      lowerCAmelCase     =			ds_args.num_layers
      lowerCAmelCase     =			ds_args.num_attention_heads
      lowerCAmelCase     =			ds_args.ffn_hidden_size
      # pprint(config)

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

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

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

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

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

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

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

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

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

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

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

         # Insert a "dummy" tensor for masked_bias.
         lowerCAmelCase     =			torch.tensor(-1e4	, dtype=torch.floataa				)
         lowerCAmelCase     =			masked_bias

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

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

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

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

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

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

   # It should be done!
   return output_state_dict





def 	a_					( ):
   # Create the argument parser.
   lowerCAmelCase     =			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.'	, )
   lowerCAmelCase     =			parser.parse_args()

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

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

      # Spell out all parameters in case the defaults change.
      lowerCAmelCase     =			GPTaConfig(
          vocab_size=50257	, n_positions=1024	, n_embd=1024	, n_layer=24	, n_head=16	, n_inner=4096	, 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=50256	, eos_token_id=50256	, )
   else:
      lowerCAmelCase     =			GPTaConfig.from_json_file(args.config_file				)

   lowerCAmelCase     =			['GPT2LMHeadModel']

   # Convert.
   print('Converting'				)
   lowerCAmelCase     =			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:
      lowerCAmelCase     =			ds_args.tokenizer_type
      if tokenizer_type == "GPT2BPETokenizer":
         lowerCAmelCase     =			'gpt2'
      elif tokenizer_type == "PretrainedFromHF":
         lowerCAmelCase     =			ds_args.tokenizer_name_or_path
      else:
         raise ValueError(f'''Unrecognized tokenizer_type {tokenizer_type}'''				)
   else:
      lowerCAmelCase     =			'gpt2'

   lowerCAmelCase     =			AutoTokenizer.from_pretrained(lowerCamelCase				)
   lowerCAmelCase     =			type(lowerCamelCase				).__name__
   lowerCAmelCase     =			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.
   lowerCAmelCase     =			os.path.join(lowerCamelCase	, 'pytorch_model.bin'				)
   print(f'''Saving checkpoint to "{output_checkpoint_file}"'''				)
   torch.save(lowerCamelCase	, lowerCamelCase				)


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

if __name__ == "__main__":
       main()

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





