





import unittest

from transformers import ErnieMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    ErnieMForInformationExtraction,
							    ErnieMForMultipleChoice,
							    ErnieMForQuestionAnswering,
							    ErnieMForSequenceClassification,
							    ErnieMForTokenClassification,
							    ErnieMModel,
							)
							from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST

class 					__A   :


				def __init__(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_=13      ,       UpperCAmelCase_=7      ,       UpperCAmelCase_=True      ,       UpperCAmelCase_=True      ,       UpperCAmelCase_=True      ,       UpperCAmelCase_=99      ,       UpperCAmelCase_=32      ,       UpperCAmelCase_=5      ,       UpperCAmelCase_=4      ,       UpperCAmelCase_=37      ,       UpperCAmelCase_="gelu"      ,       UpperCAmelCase_=0.1      ,       UpperCAmelCase_=0.1      ,       UpperCAmelCase_=512      ,       UpperCAmelCase_=16      ,       UpperCAmelCase_=2      ,       UpperCAmelCase_=0.0_2      ,       UpperCAmelCase_=3      ,       UpperCAmelCase_=4      ,       UpperCAmelCase_=None      ,       ):
								lowerCamelCase		       =parent
								lowerCamelCase		       =batch_size
								lowerCamelCase		       =seq_length
								lowerCamelCase		       =is_training
								lowerCamelCase		       =use_input_mask
								lowerCamelCase		       =use_labels
								lowerCamelCase		       =vocab_size
								lowerCamelCase		       =hidden_size
								lowerCamelCase		       =num_hidden_layers
								lowerCamelCase		       =num_attention_heads
								lowerCamelCase		       =intermediate_size
								lowerCamelCase		       =hidden_act
								lowerCamelCase		       =hidden_dropout_prob
								lowerCamelCase		       =attention_probs_dropout_prob
								lowerCamelCase		       =max_position_embeddings
								lowerCamelCase		       =type_vocab_size
								lowerCamelCase		       =type_sequence_label_size
								lowerCamelCase		       =initializer_range
								lowerCamelCase		       =num_labels
								lowerCamelCase		       =num_choices
								lowerCamelCase		       =scope


				def _snake_case				(						self							):
								lowerCamelCase		       =ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size							)

								lowerCamelCase		       =None
								if self.use_input_mask:
												lowerCamelCase		       =random_attention_mask([self.batch_size, self.seq_length]							)

								lowerCamelCase		       =None
								lowerCamelCase		       =None
								lowerCamelCase		       =None
								if self.use_labels:
												lowerCamelCase		       =ids_tensor([self.batch_size]      ,       self.type_sequence_label_size							)
												lowerCamelCase		       =ids_tensor([self.batch_size, self.seq_length]      ,       self.num_labels							)
												lowerCamelCase		       =ids_tensor([self.batch_size]      ,       self.num_choices							)

								lowerCamelCase		       =self.get_config()

								return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


				def _snake_case				(						self							):
								lowerCamelCase		       =ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size							)

								lowerCamelCase		       =None
								if self.use_input_mask:
												lowerCamelCase		       =random_attention_mask([self.batch_size, self.seq_length]							)
								lowerCamelCase		       =self.get_config()

								return config, input_ids, input_mask


				def _snake_case				(						self							):
								return ErnieMConfig(
								    vocab_size=self.vocab_size      ,       hidden_size=self.hidden_size      ,       num_hidden_layers=self.num_hidden_layers      ,       num_attention_heads=self.num_attention_heads      ,       intermediate_size=self.intermediate_size      ,       hidden_act=self.hidden_act      ,       hidden_dropout_prob=self.hidden_dropout_prob      ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,       max_position_embeddings=self.max_position_embeddings      ,       type_vocab_size=self.type_vocab_size      ,       initializer_range=self.initializer_range      ,       )


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								lowerCamelCase		       =ErnieMModel(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase		       =model(UpperCAmelCase_      ,       return_dict=UpperCAmelCase_							)
								self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.seq_length, self.hidden_size)							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								lowerCamelCase		       =ErnieMForQuestionAnswering(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase		       =model(
								    UpperCAmelCase_      ,       attention_mask=UpperCAmelCase_      ,       start_positions=UpperCAmelCase_      ,       end_positions=UpperCAmelCase_      ,       )
								self.parent.assertEqual(result.start_logits.shape      ,       (self.batch_size, self.seq_length)							)
								self.parent.assertEqual(result.end_logits.shape      ,       (self.batch_size, self.seq_length)							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								lowerCamelCase		       =ErnieMForInformationExtraction(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase		       =torch.ones_like(UpperCAmelCase_      ,       dtype=torch.floataa							)
								lowerCamelCase		       =model(
								    UpperCAmelCase_      ,       attention_mask=UpperCAmelCase_      ,       start_positions=UpperCAmelCase_      ,       end_positions=UpperCAmelCase_      ,       )
								self.parent.assertEqual(result.start_logits.shape      ,       (self.batch_size, self.seq_length)							)
								self.parent.assertEqual(result.end_logits.shape      ,       (self.batch_size, self.seq_length)							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								lowerCamelCase		       =self.num_labels
								lowerCamelCase		       =ErnieMForSequenceClassification(UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase		       =model(UpperCAmelCase_      ,       attention_mask=UpperCAmelCase_      ,       labels=UpperCAmelCase_							)
								self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels)							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								lowerCamelCase		       =self.num_labels
								lowerCamelCase		       =ErnieMForTokenClassification(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								input_ids.to(UpperCAmelCase_							)
								input_mask.to(UpperCAmelCase_							)
								token_labels.to(UpperCAmelCase_							)

								lowerCamelCase		       =model(UpperCAmelCase_      ,       attention_mask=UpperCAmelCase_      ,       labels=UpperCAmelCase_							)

								self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.seq_length, self.num_labels)							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								lowerCamelCase		       =self.num_choices
								lowerCamelCase		       =ErnieMForMultipleChoice(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase		       =input_ids.unsqueeze(1							).expand(-1      ,       self.num_choices      ,       -1							).contiguous()
								lowerCamelCase		       =input_mask.unsqueeze(1							).expand(-1      ,       self.num_choices      ,       -1							).contiguous()
								lowerCamelCase		       =model(
								    UpperCAmelCase_      ,       attention_mask=UpperCAmelCase_      ,       labels=UpperCAmelCase_      ,       )
								self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_choices)							)


				def _snake_case				(						self							):
								lowerCamelCase		       =self.prepare_config_and_inputs()
								(
								    (
								    lowerCamelCase
								)       ,     (
								    lowerCamelCase
								)       ,     (
								    lowerCamelCase
								)       ,     (
								    lowerCamelCase
								)       ,     (
								    lowerCamelCase
								)       ,     (
								    lowerCamelCase
								)       ,     
								)		       =config_and_inputs
								lowerCamelCase		       ={"""input_ids""": input_ids, """attention_mask""": input_mask}
								return config, inputs_dict




@require_torch
class 					__A   (  a			,    a			,    unittest.TestCase			):
				__A            = (
				    (
				        ErnieMModel,
				        ErnieMForMultipleChoice,
				        ErnieMForQuestionAnswering,
				        ErnieMForSequenceClassification,
				        ErnieMForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				__A            = ()
				__A            = (
				    {
				        """feature-extraction""": ErnieMModel,
				        """question-answering""": ErnieMForQuestionAnswering,
				        """text-classification""": ErnieMForSequenceClassification,
				        """token-classification""": ErnieMForTokenClassification,
				        """zero-shot""": ErnieMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				__A            = False


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								if pipeline_test_casse_name == "QAPipelineTests":
												return True

								return False


				def _snake_case				(						self							):
								lowerCamelCase		       =ErnieMModelTester(self							)
								lowerCamelCase		       =ConfigTester(self      ,       config_class=UpperCAmelCase_      ,       hidden_size=37							)


				def _snake_case				(						self							):
								self.config_tester.run_common_tests()


				def _snake_case				(						self							):
								lowerCamelCase		       =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCAmelCase_							)


				def _snake_case				(						self							):
								lowerCamelCase		       =self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
												lowerCamelCase		       =type
												self.model_tester.create_and_check_model(*UpperCAmelCase_							)


				def _snake_case				(						self							):
								lowerCamelCase		       =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase_							)


				def _snake_case				(						self							):
								lowerCamelCase		       =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_							)


				def _snake_case				(						self							):
								lowerCamelCase		       =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_							)


				def _snake_case				(						self							):
								lowerCamelCase		       =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_information_extraction(*UpperCAmelCase_							)


				def _snake_case				(						self							):
								lowerCamelCase		       =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_							)


				@slow
				def _snake_case				(						self							):
								for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowerCamelCase		       =ErnieMModel.from_pretrained(UpperCAmelCase_							)
												self.assertIsNotNone(UpperCAmelCase_							)




@require_torch
class 					__A   (  unittest.TestCase			):


				@slow
				def _snake_case				(						self							):
								lowerCamelCase		       =ErnieMModel.from_pretrained("""susnato/ernie-m-base_pytorch"""							)
								model.eval()
								lowerCamelCase		       =torch.tensor([[0, 1, 2, 3, 4, 5]]							)
								lowerCamelCase		       =model(UpperCAmelCase_							)[0]

								# TODO Replace vocab size
								lowerCamelCase		       =768

								lowerCamelCase		       =torch.Size((1, 6, hidden_size)							)
								self.assertEqual(output.shape      ,       UpperCAmelCase_							)

								lowerCamelCase		       =torch.tensor(
								    [[[-0.0_0_1_2, 0.1_2_4_5, -0.0_2_1_4], [-0.0_7_4_2, 0.0_2_4_4, -0.0_7_7_1], [-0.0_3_3_3, 0.1_1_6_4, -0.1_5_5_4]]]							)

								self.assertTrue(torch.allclose(output[:, :3, :3]      ,       UpperCAmelCase_      ,       atol=1E-3							)							)












# 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    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase=0 )  ->     Optional[Any]:
				# Format the message.
				if name is None:
								lowerCamelCase		       =None
				else:
								lowerCamelCase		       =""".""" * max(0	,					spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}"""
								lowerCamelCase		       =fmt.format(_UpperCAmelCase )

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





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )  ->     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 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(*_UpperCAmelCase )
								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(*_UpperCAmelCase )
								lowerCamelCase		       =param.transpose(0	,					1 ).contiguous()
				lowerCamelCase		       =param.view(*_UpperCAmelCase )
				return param





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )  ->     List[Any]:
				# The converted output model.
				lowerCamelCase		       ={}

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

								# 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(_UpperCAmelCase	,					_UpperCAmelCase	,					3	,					_UpperCAmelCase	,					_UpperCAmelCase )
												# 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(_UpperCAmelCase	,					_UpperCAmelCase	,					3	,					_UpperCAmelCase	,					_UpperCAmelCase )
												# 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    _lowercase    (  )  ->     str:
				# Create the argument parser.
				lowerCamelCase		       =argparse.ArgumentParser()
				parser.add_argument("""--print-checkpoint-structure"""	,					action="""store_true""" )
				parser.add_argument("""path_to_checkpoint"""	,					type=_UpperCAmelCase	,					help="""Path to the ZIP file containing the checkpoint""" )
				parser.add_argument(
				    """--config_file"""	,					default=""""""	,					type=_UpperCAmelCase	,					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(_UpperCAmelCase	,					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(_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )

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

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

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


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

if __name__ == "__main__":
							main()

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





