




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       _a (		SCREAMING_SNAKE_CASE_				:							Tuple	):
					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 (		SCREAMING_SNAKE_CASE_				:							Dict , SCREAMING_SNAKE_CASE_				:							str , SCREAMING_SNAKE_CASE_				:							Any	):
					return max(metric_fn(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	) for gt in ground_truths	)







def       _a (		SCREAMING_SNAKE_CASE_				:							Optional[Any] , SCREAMING_SNAKE_CASE_				:							Optional[int] , SCREAMING_SNAKE_CASE_				:							Union[str, Any]	):
					__lowerCAmelCase							  =			[line.strip() for line in open(SCREAMING_SNAKE_CASE_ , "r"	).readlines()]
					__lowerCAmelCase							  =			[]

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

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

					__lowerCAmelCase							  =			1_00.0 * em / total
					__lowerCAmelCase							  =			1_00.0 * fa / total

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







def       _a (		SCREAMING_SNAKE_CASE_				:							str , SCREAMING_SNAKE_CASE_				:							Union[str, Any] , SCREAMING_SNAKE_CASE_				:							Any	):
					__lowerCAmelCase							  =			args.k
					__lowerCAmelCase							  =			[line.strip() for line in open(SCREAMING_SNAKE_CASE_ , "r"	).readlines()]
					__lowerCAmelCase							  =			[line.strip() for line in open(SCREAMING_SNAKE_CASE_ , "r"	).readlines()]

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

					__lowerCAmelCase							  =			1_00.0 * em / total
					logger.info(F"""Precision@{k}: {em: .2f}"""	)







def       _a (		SCREAMING_SNAKE_CASE_				:							Any , SCREAMING_SNAKE_CASE_				:							Union[str, Any] , SCREAMING_SNAKE_CASE_				:							Dict	):
					def strip_title(SCREAMING_SNAKE_CASE_				:							Union[str, Any]	):
										if title.startswith("\""	):
															__lowerCAmelCase							  =			title[1:]
										if title.endswith("\""	):
															__lowerCAmelCase							  =			title[:-1]
										return title

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

					__lowerCAmelCase							  =			rag_model.rag.question_encoder(SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			question_enc_outputs[0]

					__lowerCAmelCase							  =			rag_model.retriever(
					    SCREAMING_SNAKE_CASE_ , 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(SCREAMING_SNAKE_CASE_	) for title in docs["title"]]
										provenance_strings.append("\t".join(SCREAMING_SNAKE_CASE_	)	)
					return provenance_strings







def       _a (		SCREAMING_SNAKE_CASE_				:							Optional[int] , SCREAMING_SNAKE_CASE_				:							List[str] , SCREAMING_SNAKE_CASE_				:							Union[str, Any]	):
					with torch.no_grad():
										__lowerCAmelCase							  =			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
										    SCREAMING_SNAKE_CASE_ , return_tensors="pt" , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_	)

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

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

										return answers







def       _a (		):
					__lowerCAmelCase							  =			argparse.ArgumentParser()
					parser.add_argument(
					    "--model_type" , choices=["rag_sequence", "rag_token", "bart"] , type=SCREAMING_SNAKE_CASE_ , 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=SCREAMING_SNAKE_CASE_ , choices=["exact", "compressed", "legacy"] , type=SCREAMING_SNAKE_CASE_ , help="RAG model retriever type" , )
					parser.add_argument(
					    "--index_path" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , help="Path to the retrieval index" , )
					parser.add_argument("--n_docs" , default=5 , type=SCREAMING_SNAKE_CASE_ , help="Number of retrieved docs"	)
					parser.add_argument(
					    "--model_name_or_path" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="Path to pretrained checkpoints or model identifier from huggingface.co/models" , )
					parser.add_argument(
					    "--eval_mode" , choices=["e2e", "retrieval"] , default="e2e" , type=SCREAMING_SNAKE_CASE_ , help=(
					        "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
					        " precision@k."
					    ) , )
					parser.add_argument("--k" , default=1 , type=SCREAMING_SNAKE_CASE_ , help="k for the precision@k calculation"	)
					parser.add_argument(
					    "--evaluation_set" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="Path to a file containing evaluation samples" , )
					parser.add_argument(
					    "--gold_data_path" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="Path to a tab-separated file with gold samples" , )
					parser.add_argument(
					    "--gold_data_mode" , default="qa" , type=SCREAMING_SNAKE_CASE_ , 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=SCREAMING_SNAKE_CASE_ , 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=SCREAMING_SNAKE_CASE_ , 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=SCREAMING_SNAKE_CASE_ , help="Number of beams to be used when generating answers" , )
					parser.add_argument("--min_length" , default=1 , type=SCREAMING_SNAKE_CASE_ , help="Min length of the generated answers"	)
					parser.add_argument("--max_length" , default=50 , type=SCREAMING_SNAKE_CASE_ , 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       _a (		SCREAMING_SNAKE_CASE_				:							Optional[int]	):
					__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" , SCREAMING_SNAKE_CASE_	)

					__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(SCREAMING_SNAKE_CASE_ , args.predictions_path , args.gold_data_path	)
															continue

										logger.info("***** Running evaluation for {} *****".format(SCREAMING_SNAKE_CASE_	)	)
										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(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_	)
															__lowerCAmelCase							  =			model_class.from_pretrained(SCREAMING_SNAKE_CASE_ , retriever=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_	)
															model.retriever.init_retrieval()
										else:
															__lowerCAmelCase							  =			model_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_	)
										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(SCREAMING_SNAKE_CASE_	):
																				questions.append(line.strip()	)
																				if len(SCREAMING_SNAKE_CASE_	) == args.eval_batch_size:
																									__lowerCAmelCase							  =			evaluate_batch_fn(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
																									preds_file.write("\n".join(SCREAMING_SNAKE_CASE_	) + "\n"	)
																									preds_file.flush()
																									__lowerCAmelCase							  =			[]
															if len(SCREAMING_SNAKE_CASE_	) > 0:
																				__lowerCAmelCase							  =			evaluate_batch_fn(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
																				preds_file.write("\n".join(SCREAMING_SNAKE_CASE_	)	)
																				preds_file.flush()

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


if __name__ == "__main__":
					UpperCamelCase__            =     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 (		SCREAMING_SNAKE_CASE_				:							Any , SCREAMING_SNAKE_CASE_				:							List[str] , SCREAMING_SNAKE_CASE_				:							List[str]=0	):
					# Format the message.
					if name is None:
										__lowerCAmelCase							  =			None
					else:
										__lowerCAmelCase							  =			"." * max(0 , spaces - 2	) + "# {:" + str(50 - spaces	) + "s}"
										__lowerCAmelCase							  =			fmt.format(SCREAMING_SNAKE_CASE_	)

					# Print and recurse (if needed).
					if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	):
										if msg is not None:
															print(SCREAMING_SNAKE_CASE_	)
										for k in val.keys():
															recursive_print(SCREAMING_SNAKE_CASE_ , val[k] , spaces + 2	)
					elif isinstance(SCREAMING_SNAKE_CASE_ , torch.Tensor	):
										print(SCREAMING_SNAKE_CASE_ , ":" , val.size()	)
					else:
										print(SCREAMING_SNAKE_CASE_ , ":" , SCREAMING_SNAKE_CASE_	)







def       _a (		SCREAMING_SNAKE_CASE_				:							Optional[int] , SCREAMING_SNAKE_CASE_				:							Dict , SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							str , SCREAMING_SNAKE_CASE_				:							Any	):
					# 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 BERT.
					__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(*SCREAMING_SNAKE_CASE_	)
										__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(*SCREAMING_SNAKE_CASE_	)
										__lowerCAmelCase							  =			param.transpose(0 , 1	).contiguous()
					__lowerCAmelCase							  =			param.view(*SCREAMING_SNAKE_CASE_	)
					return param







def       _a (		SCREAMING_SNAKE_CASE_				:							Any , SCREAMING_SNAKE_CASE_				:							Any , SCREAMING_SNAKE_CASE_				:							Optional[Any]	):
					# The converted output model.
					__lowerCAmelCase							  =			{}

					# old versions did not store training args
					__lowerCAmelCase							  =			input_state_dict.get("args" , SCREAMING_SNAKE_CASE_	)
					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.tokenizer_type
										__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 if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
										# pprint(config)

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

					# The position embeddings.
					__lowerCAmelCase							  =			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.
					__lowerCAmelCase							  =			pos_embeddings

					# The token-type embeddings.
					__lowerCAmelCase							  =			embeddings["tokentype_embeddings"]["weight"]
					# Store the position embeddings.
					__lowerCAmelCase							  =			tokentype_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": ".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.
					__lowerCAmelCase							  =			None

					# Extract the layers.
					for key, val in transformer.items():
										# Match the name.
										__lowerCAmelCase							  =			layer_re.match(SCREAMING_SNAKE_CASE_	)

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

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

															__lowerCAmelCase							  =			fix_query_key_value_ordering(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , 3 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
															# Store the tensor as we need the bias as well to interleave QKV and biases.
															__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":
															# 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.
															__lowerCAmelCase							  =			attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
															__lowerCAmelCase							  =			attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
															__lowerCAmelCase							  =			attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

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

															# Clear the stored tensor.
															__lowerCAmelCase							  =			None

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

    # The final layernorm.
					__lowerCAmelCase							  =			transformer["final_layernorm.weight"]
					__lowerCAmelCase							  =			transformer["final_layernorm.bias"]

					# The pooler.
					__lowerCAmelCase							  =			lm["pooler"]

					# Store the matrix and the bias.
					__lowerCAmelCase							  =			pooler["dense.weight"]
					__lowerCAmelCase							  =			pooler["dense.bias"]

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

					# The transform matrix.
					__lowerCAmelCase							  =			lm_head["dense.weight"]
					__lowerCAmelCase							  =			lm_head["dense.bias"]

					# The transform LN.
					__lowerCAmelCase							  =			lm_head["layernorm.weight"]
					__lowerCAmelCase							  =			lm_head["layernorm.bias"]

					# For the decoder, we replicate the weights.
					__lowerCAmelCase							  =			word_embeddings
					__lowerCAmelCase							  =			lm_head["bias"]

					# The classifier from Megatron (for MLNI).
					__lowerCAmelCase							  =			model["binary_head"]

					# Store the classifier.
					__lowerCAmelCase							  =			binary_head["weight"]
					__lowerCAmelCase							  =			binary_head["bias"]

					# 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=SCREAMING_SNAKE_CASE_ , help="Path to the ZIP file containing the checkpoint"	)
					parser.add_argument(
					    "--config_file" , default="" , type=SCREAMING_SNAKE_CASE_ , 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(SCREAMING_SNAKE_CASE_ , map_location="cpu"	)
					else:
										__lowerCAmelCase							  =			torch.load(args.path_to_checkpoint , map_location="cpu"	)

					if args.config_file == "":
										# Default config of megatron-bert 345m
										__lowerCAmelCase							  =			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
										__lowerCAmelCase							  =			input_state_dict["model"]["lm_head"]["bias"].numel()
					else:
										__lowerCAmelCase							  =			MegatronBertConfig.from_json_file(args.config_file	)

					# Convert.
					print("Converting"	)
					__lowerCAmelCase							  =			convert_megatron_checkpoint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)

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

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

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


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

if __name__ == "__main__":
					main()

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




