

import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
	from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
	__snake_case							:     Dict 						=	None

if is_ray_available():
	import ray  # noqa: E402 # isort:skip
	from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
	__snake_case							:     Any 						=	None
	__snake_case							:     Dict 						=	None
	__snake_case							:     List[Any] 						=	None


def 			_UpperCAmelCase							(		a__):

			'''simple docstring'''



			if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
						a_		:		List[str]				    =				unittest.skip("""test requires Datasets, Faiss, psutil""")(_lowerCamelCase)
			return test_case



@require_distributed_retrieval
class    A__(_a    ):
						"""simple docstring"""






						def 	UpperCamelCase__			(  self  )      ->				int:
									a_		:		Optional[Any]				    =				tempfile.mkdtemp()
									a_		:		List[str]				    =				8

									# DPR tok
									a_		:		List[str]				    =				[
									    """[UNK]""",
									    """[CLS]""",
									    """[SEP]""",
									    """[PAD]""",
									    """[MASK]""",
									    """want""",
									    """##want""",
									    """##ed""",
									    """wa""",
									    """un""",
									    """runn""",
									    """##ing""",
									    """,""",
									    """low""",
									    """lowest""",
									]
									a_		:		List[Any]				    =				os.path.join(self.tmpdirname       ,     """dpr_tokenizer"""  )
									os.makedirs(snake_case_       ,     exist_ok=snake_case_  )
									a_		:		Dict				    =				os.path.join(snake_case_       ,     DPR_VOCAB_FILES_NAMES["""vocab_file"""]  )
									with open(self.vocab_file       ,     """w"""       ,     encoding="""utf-8"""  ) as vocab_writer:
												vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]  )  )

									# BART tok
									a_		:		List[str]				    =				[
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """\u0120""",
									    """\u0120l""",
									    """\u0120n""",
									    """\u0120lo""",
									    """\u0120low""",
									    """er""",
									    """\u0120lowest""",
									    """\u0120newer""",
									    """\u0120wider""",
									    """<unk>""",
									]
									a_		:		Dict				    =				dict(zip(snake_case_       ,     range(len(snake_case_  )  )  )  )
									a_		:		List[str]				    =				["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
									a_		:		Any				    =				{"""unk_token""": """<unk>"""}

									a_		:		Any				    =				os.path.join(self.tmpdirname       ,     """bart_tokenizer"""  )
									os.makedirs(snake_case_       ,     exist_ok=snake_case_  )
									a_		:		Union[str, Any]				    =				os.path.join(snake_case_       ,     BART_VOCAB_FILES_NAMES["""vocab_file"""]  )
									a_		:		Optional[int]				    =				os.path.join(snake_case_       ,     BART_VOCAB_FILES_NAMES["""merges_file"""]  )
									with open(self.vocab_file       ,     """w"""       ,     encoding="""utf-8"""  ) as fp:
												fp.write(json.dumps(snake_case_  ) + """\n"""  )
									with open(self.merges_file       ,     """w"""       ,     encoding="""utf-8"""  ) as fp:
												fp.write("""\n""".join(snake_case_  )  )





						def 	UpperCamelCase__			(  self  )      ->				List[Any]:
									return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,     """dpr_tokenizer"""  )  )





						def 	UpperCamelCase__			(  self  )      ->				str:
									return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,     """bart_tokenizer"""  )  )





						def 	UpperCamelCase__			(  self  )      ->				List[str]:
									shutil.rmtree(self.tmpdirname  )





						def 	UpperCamelCase__			(  self  )      ->				Union[str, Any]:
									a_		:		List[str]				    =				Dataset.from_dict(
									    {
									        """id""": ["""0""", """1"""],
									        """text""": ["""foo""", """bar"""],
									        """title""": ["""Foo""", """Bar"""],
									        """embeddings""": [np.ones(self.retrieval_vector_size  ), 2 * np.ones(self.retrieval_vector_size  )],
									    }  )
									dataset.add_faiss_index("""embeddings"""       ,     string_factory="""Flat"""       ,     metric_type=faiss.METRIC_INNER_PRODUCT  )
									return dataset





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase=12_345  )      ->				List[Any]:
									a_		:		Dict				    =				self.get_dummy_dataset()
									a_		:		str				    =				RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size       ,     question_encoder=DPRConfig().to_dict()       ,     generator=BartConfig().to_dict()       ,     )
									with patch("""transformers.models.rag.retrieval_rag.load_dataset"""  ) as mock_load_dataset:
												a_		:		int				    =				dataset
												a_		:		List[Any]				    =				RagPyTorchDistributedRetriever(
												    snake_case_       ,     question_encoder_tokenizer=self.get_dpr_tokenizer()       ,     generator_tokenizer=self.get_bart_tokenizer()       ,     )
												if init_retrieval:
															retriever.init_retrieval(snake_case_  )
									return retriever





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				str:
									# Have to run in local mode because sys.path modifications at top of
									# file are not propogated to remote workers.
									# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
									ray.init(local_mode=snake_case_  )
									a_		:		List[Any]				    =				RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size       ,     question_encoder=DPRConfig().to_dict()       ,     generator=BartConfig().to_dict()       ,     )
									a_		:		Union[str, Any]				    =				ray.remote(snake_case_  )
									a_		:		Union[str, Any]				    =				[remote_cls.remote() for _ in range(1  )]
									with patch("""transformers.models.rag.retrieval_rag.load_dataset"""  ) as mock_load_dataset:
												a_		:		List[Any]				    =				self.get_dummy_dataset()
												a_		:		Union[str, Any]				    =				RagRayDistributedRetriever(
												    snake_case_       ,     question_encoder_tokenizer=self.get_dpr_tokenizer()       ,     generator_tokenizer=self.get_bart_tokenizer()       ,     retrieval_workers=snake_case_       ,     )
												if init_retrieval:
															retriever.init_retrieval()
									return retriever





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase=12_345  )      ->				Optional[int]:
									a_		:		str				    =				self.get_dummy_dataset()
									a_		:		Optional[int]				    =				RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size       ,     question_encoder=DPRConfig().to_dict()       ,     generator=BartConfig().to_dict()       ,     index_name="""custom"""       ,     )
									if from_disk:
												a_		:		int				    =				os.path.join(self.tmpdirname       ,     """dataset"""  )
												a_		:		Union[str, Any]				    =				os.path.join(self.tmpdirname       ,     """index.faiss"""  )
												dataset.get_index("""embeddings"""  ).save(os.path.join(self.tmpdirname       ,     """index.faiss"""  )  )
												dataset.drop_index("""embeddings"""  )
												dataset.save_to_disk(os.path.join(self.tmpdirname       ,     """dataset"""  )  )
												del dataset
												a_		:		Tuple				    =				RagPyTorchDistributedRetriever(
												    snake_case_       ,     question_encoder_tokenizer=self.get_dpr_tokenizer()       ,     generator_tokenizer=self.get_bart_tokenizer()       ,     )
									else:
												a_		:		Optional[int]				    =				RagPyTorchDistributedRetriever(
												    snake_case_       ,     question_encoder_tokenizer=self.get_dpr_tokenizer()       ,     generator_tokenizer=self.get_bart_tokenizer()       ,     index=CustomHFIndex(config.retrieval_vector_size       ,     snake_case_  )       ,     )
									if init_retrieval:
												retriever.init_retrieval(snake_case_  )
									return retriever





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase  )      ->				Tuple:
									# Have to run in local mode because sys.path modifications at top of
									# file are not propogated to remote workers.
									# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
									ray.init(local_mode=snake_case_  )
									a_		:		Optional[Any]				    =				self.get_dummy_dataset()
									a_		:		str				    =				RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size       ,     question_encoder=DPRConfig().to_dict()       ,     generator=BartConfig().to_dict()       ,     index_name="""custom"""       ,     )
									a_		:		List[Any]				    =				ray.remote(snake_case_  )
									a_		:		Union[str, Any]				    =				[remote_cls.remote() for _ in range(1  )]
									if from_disk:
												a_		:		str				    =				os.path.join(self.tmpdirname       ,     """dataset"""  )
												a_		:		Tuple				    =				os.path.join(self.tmpdirname       ,     """index.faiss"""  )
												dataset.get_index("""embeddings"""  ).save(os.path.join(self.tmpdirname       ,     """index.faiss"""  )  )
												dataset.drop_index("""embeddings"""  )
												dataset.save_to_disk(os.path.join(self.tmpdirname       ,     """dataset"""  )  )
												del dataset
												a_		:		int				    =				RagRayDistributedRetriever(
												    snake_case_       ,     question_encoder_tokenizer=self.get_dpr_tokenizer()       ,     generator_tokenizer=self.get_bart_tokenizer()       ,     retrieval_workers=snake_case_       ,     index=CustomHFIndex.load_from_disk(
												        vector_size=config.retrieval_vector_size       ,     dataset_path=config.passages_path       ,     index_path=config.index_path       ,     )       ,     )
									else:
												a_		:		int				    =				RagRayDistributedRetriever(
												    snake_case_       ,     question_encoder_tokenizer=self.get_dpr_tokenizer()       ,     generator_tokenizer=self.get_bart_tokenizer()       ,     retrieval_workers=snake_case_       ,     index=CustomHFIndex(config.retrieval_vector_size       ,     snake_case_  )       ,     )
									if init_retrieval:
												retriever.init_retrieval()
									return retriever





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase  )      ->				Dict:
									a_		:		Optional[Any]				    =				retriever.retrieve(snake_case_       ,     n_docs=snake_case_  )
									self.assertEqual(retrieved_doc_embeds.shape       ,     (2, n_docs, self.retrieval_vector_size)  )
									self.assertEqual(len(snake_case_  )       ,     2  )
									self.assertEqual(sorted(doc_dicts[0]  )       ,     ["""embeddings""", """id""", """text""", """title"""]  )
									self.assertEqual(len(doc_dicts[0]["""id"""]  )       ,     snake_case_  )
									self.assertEqual(doc_dicts[0]["""id"""][0]       ,     """1"""  )  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]["""id"""][0]       ,     """0"""  )  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist()       ,     [[1], [0]]  )





						def 	UpperCamelCase__			(  self  )      ->				int:
									a_		:		Any				    =				1
									a_		:		Tuple				    =				np.array(
									    [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]       ,     dtype=np.floataa  )

									self.distributed_retriever_check(
									    self.get_dummy_pytorch_distributed_retriever(init_retrieval=snake_case_  )       ,     snake_case_       ,     snake_case_  )





						def 	UpperCamelCase__			(  self  )      ->				List[str]:
									a_		:		Tuple				    =				1
									a_		:		Optional[Any]				    =				np.array(
									    [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]       ,     dtype=np.floataa  )

									self.distributed_retriever_check(
									    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=snake_case_       ,     from_disk=snake_case_  )       ,     snake_case_       ,     snake_case_       ,     )





						def 	UpperCamelCase__			(  self  )      ->				Dict:
									a_		:		Optional[int]				    =				1
									a_		:		Optional[int]				    =				np.array(
									    [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]       ,     dtype=np.floataa  )

									self.distributed_retriever_check(
									    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=snake_case_       ,     from_disk=snake_case_  )       ,     snake_case_       ,     snake_case_       ,     )





						@require_ray
						def 	UpperCamelCase__			(  self  )      ->				Any:
									a_		:		Tuple				    =				1
									a_		:		Any				    =				np.array(
									    [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]       ,     dtype=np.floataa  )

									self.distributed_retriever_check(
									    self.get_dummy_ray_distributed_retriever(init_retrieval=snake_case_  )       ,     snake_case_       ,     snake_case_  )
									ray.shutdown()





						@require_ray
						def 	UpperCamelCase__			(  self  )      ->				Dict:
									a_		:		Union[str, Any]				    =				1
									a_		:		Dict				    =				np.array(
									    [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]       ,     dtype=np.floataa  )
									with self.assertRaises(snake_case_  ):
												self.distributed_retriever_check(
												    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=snake_case_       ,     from_disk=snake_case_  )       ,     snake_case_       ,     snake_case_       ,     )
									ray.shutdown()





						@require_ray
						def 	UpperCamelCase__			(  self  )      ->				Optional[int]:
									a_		:		Any				    =				1
									a_		:		Optional[Any]				    =				np.array(
									    [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]       ,     dtype=np.floataa  )

									self.distributed_retriever_check(
									    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=snake_case_       ,     from_disk=snake_case_  )       ,     snake_case_       ,     snake_case_  )
									ray.shutdown()





# 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 			_UpperCAmelCase							(		a__       ,					a__       ,					a__=0):

			'''simple docstring'''



			if name is None:
						a_		:		str				    =				None
			else:
						a_		:		List[str]				    =				""".""" * max(0       ,					spaces - 2) + """# {:""" + str(5_0 - spaces) + """s}"""
						a_		:		Optional[Any]				    =				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 			_UpperCAmelCase							(		a__       ,					a__       ,					a__       ,					a__       ,					a__):

			'''simple docstring'''



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


def 			_UpperCAmelCase							(		a__       ,					a__       ,					a__):

			'''simple docstring'''



			a_		:		Dict				    =				{}

			# old versions did not store training args
			a_		:		Union[str, Any]				    =				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))

						a_		:		Optional[Any]				    =				ds_args.tokenizer_type
						a_		:		str				    =				ds_args.padded_vocab_size
						a_		:		Optional[Any]				    =				ds_args.max_position_embeddings
						a_		:		Optional[Any]				    =				ds_args.hidden_size
						a_		:		Optional[Any]				    =				ds_args.num_layers
						a_		:		Optional[int]				    =				ds_args.num_attention_heads
						a_		:		List[Any]				    =				ds_args.ffn_hidden_size if """ffn_hidden_size""" in ds_args else 4 * ds_args.hidden_size
						# pprint(config)

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

			# The model.
			a_		:		Optional[Any]				    =				input_state_dict["""model"""]
			# The language model.
			a_		:		str				    =				model["""language_model"""]
			# The embeddings.
			a_		:		Dict				    =				lm["""embedding"""]

			# The word embeddings.
			a_		:		str				    =				embeddings["""word_embeddings"""]["""weight"""]
			# Truncate the embedding table to vocab_size rows.
			a_		:		Dict				    =				word_embeddings[: config.vocab_size, :]
			# Store the word embeddings.
			a_		:		str				    =				word_embeddings

			# The position embeddings.
			a_		:		Tuple				    =				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.
			a_		:		Optional[Any]				    =				pos_embeddings

			# The token-type embeddings.
			a_		:		Tuple				    =				embeddings["""tokentype_embeddings"""]["""weight"""]
			# Store the position embeddings.
			a_		:		List[Any]				    =				tokentype_embeddings

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

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

			# The simple map of names for "automated" rules.
			a_		:		List[str]				    =				{
			    """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.
			a_		:		Optional[Any]				    =				None

			# Extract the layers.
			for key, val in transformer.items():
						# Match the name.
						a_		:		Optional[Any]				    =				layer_re.match(a__)

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

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

						# The name of the layer.
						a_		:		Optional[Any]				    =				f'''bert.encoder.layer.{layer_idx}'''

						# For layernorm(s), simply store the layer norm.
						if op_name.endswith("""layernorm"""):
									a_		:		Any				    =				"""attention.ln""" if op_name.startswith("""input""") else """ln"""
									a_		:		Union[str, Any]				    =				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, ""

									a_		:		int				    =				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.
									a_		:		Any				    =				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.
									a_		:		Tuple				    =				attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
									a_		:		Tuple				    =				attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
									a_		:		Optional[Any]				    =				attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

									a_		:		Optional[Any]				    =				fix_query_key_value_ordering(a__       ,					a__       ,					3       ,					a__       ,					a__)
									# Split the bias.
									a_		:		Optional[int]				    =				out_val[0 * config.hidden_size : 1 * config.hidden_size]
									a_		:		int				    =				out_val[1 * config.hidden_size : 2 * config.hidden_size]
									a_		:		Tuple				    =				out_val[2 * config.hidden_size : 3 * config.hidden_size]

									# Store.
									a_		:		List[str]				    =				q
									a_		:		Any				    =				q_bias
									a_		:		int				    =				k
									a_		:		Optional[int]				    =				k_bias
									a_		:		Optional[Any]				    =				v
									a_		:		Dict				    =				v_bias

									# Clear the stored tensor.
									a_		:		Union[str, Any]				    =				None

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

    # The final layernorm.
			a_		:		int				    =				transformer["""final_layernorm.weight"""]
			a_		:		List[Any]				    =				transformer["""final_layernorm.bias"""]

			# The pooler.
			a_		:		List[str]				    =				lm["""pooler"""]

			# Store the matrix and the bias.
			a_		:		List[str]				    =				pooler["""dense.weight"""]
			a_		:		List[Any]				    =				pooler["""dense.bias"""]

			# The LM head from Megatron (for RACE).
			a_		:		Union[str, Any]				    =				model["""lm_head"""]

			# The transform matrix.
			a_		:		int				    =				lm_head["""dense.weight"""]
			a_		:		Union[str, Any]				    =				lm_head["""dense.bias"""]

			# The transform LN.
			a_		:		Any				    =				lm_head["""layernorm.weight"""]
			a_		:		List[str]				    =				lm_head["""layernorm.bias"""]

			# For the decoder, we replicate the weights.
			a_		:		Optional[Any]				    =				word_embeddings
			a_		:		int				    =				lm_head["""bias"""]

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

			# Store the classifier.
			a_		:		int				    =				binary_head["""weight"""]
			a_		:		str				    =				binary_head["""bias"""]

			# It should be done!
			return output_state_dict


def 			_UpperCAmelCase							(		):

			'''simple docstring'''



			a_		:		List[Any]				    =				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."""       ,					)
			a_		:		Union[str, Any]				    =				parser.parse_args()

			# Extract the basename.
			a_		:		Any				    =				os.path.dirname(args.path_to_checkpoint)

			# Load the model.
			# the .zip is very optional, let's keep it for backward compatibility
			print(f'''Extracting PyTorch state dictionary from "{args.path_to_checkpoint}"''')
			if args.path_to_checkpoint.endswith(""".zip"""):
						with zipfile.ZipFile(args.path_to_checkpoint       ,					"""r""") as checkpoint:
									with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""") as pytorch_dict:
												a_		:		int				    =				torch.load(a__       ,					map_location="""cpu""")
			else:
						a_		:		int				    =				torch.load(args.path_to_checkpoint       ,					map_location="""cpu""")

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

			# Convert.
			print("""Converting""")
			a_		:		int				    =				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.
			a_		:		Dict				    =				os.path.join(a__       ,					"""pytorch_model.bin""")
			print(f'''Saving checkpoint to "{output_checkpoint_file}"''')
			torch.save(a__       ,					a__)


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

if __name__ == "__main__":
	main()

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


