




# 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    ,						lowerCAmelCase    ,						lowerCAmelCase=0 )->	List[str]:
		# Format the message.
		if name is None:
				SCREAMING_SNAKE_CASE_					      =							None
		else:
				SCREAMING_SNAKE_CASE_					      =							'.' * max(0    ,						spaces - 2 ) + '# {:' + str(50 - spaces ) + 's}'
				SCREAMING_SNAKE_CASE_					      =							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    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	List[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 GPT2.
		SCREAMING_SNAKE_CASE_					      =							param.size()
		if checkpoint_version == 1.0:
				# version 1.0 stores [num_heads * hidden_size * num_splits, :]
				SCREAMING_SNAKE_CASE_					      =							(num_heads, hidden_size, num_splits) + input_shape[1:]
				SCREAMING_SNAKE_CASE_					      =							param.view(*lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							param.transpose(0    ,						2 )
				SCREAMING_SNAKE_CASE_					      =							param.transpose(1    ,						2 ).contiguous()
		elif checkpoint_version >= 2.0:
				# other versions store [num_heads * num_splits * hidden_size, :]
				SCREAMING_SNAKE_CASE_					      =							(num_heads, num_splits, hidden_size) + input_shape[1:]
				SCREAMING_SNAKE_CASE_					      =							param.view(*lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							param.transpose(0    ,						1 ).contiguous()
		SCREAMING_SNAKE_CASE_					      =							param.view(*lowerCAmelCase )
		return param


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	Dict:
		# The converted output model.
		SCREAMING_SNAKE_CASE_					      =							{}

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

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

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

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

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

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

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

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

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

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

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

				# The name of the layer.
				SCREAMING_SNAKE_CASE_					      =							F'''transformer.h.{layer_idx}'''

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

						# Insert a "dummy" tensor for masked_bias.
						SCREAMING_SNAKE_CASE_					      =							torch.tensor(-1E4    ,						dtype=torch.floataa )
						SCREAMING_SNAKE_CASE_					      =							masked_bias

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

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

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

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

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

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

		# It should be done!
		return output_state_dict


def 					_a							(					)->	Any:
		# Create the argument parser.
		SCREAMING_SNAKE_CASE_					      =							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.'    ,						)
		SCREAMING_SNAKE_CASE_					      =							parser.parse_args()

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

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

				# Spell out all parameters in case the defaults change.
				SCREAMING_SNAKE_CASE_					      =							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=0.0_2    ,						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:
				SCREAMING_SNAKE_CASE_					      =							GPTaConfig.from_json_file(args.config_file )

		SCREAMING_SNAKE_CASE_					      =							['GPT2LMHeadModel']

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

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


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

if __name__ == "__main__":
						main()

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE:     Optional[int]									= {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Tuple									= ['''RoFormerTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Any									= [
						    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''RoFormerForCausalLM''',
						    '''RoFormerForMaskedLM''',
						    '''RoFormerForMultipleChoice''',
						    '''RoFormerForQuestionAnswering''',
						    '''RoFormerForSequenceClassification''',
						    '''RoFormerForTokenClassification''',
						    '''RoFormerLayer''',
						    '''RoFormerModel''',
						    '''RoFormerPreTrainedModel''',
						    '''load_tf_weights_in_roformer''',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Dict									= [
						    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFRoFormerForCausalLM''',
						    '''TFRoFormerForMaskedLM''',
						    '''TFRoFormerForMultipleChoice''',
						    '''TFRoFormerForQuestionAnswering''',
						    '''TFRoFormerForSequenceClassification''',
						    '''TFRoFormerForTokenClassification''',
						    '''TFRoFormerLayer''',
						    '''TFRoFormerModel''',
						    '''TFRoFormerPreTrainedModel''',
						]


try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     str									= [
						    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''FlaxRoFormerForMaskedLM''',
						    '''FlaxRoFormerForMultipleChoice''',
						    '''FlaxRoFormerForQuestionAnswering''',
						    '''FlaxRoFormerForSequenceClassification''',
						    '''FlaxRoFormerForTokenClassification''',
						    '''FlaxRoFormerModel''',
						    '''FlaxRoFormerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
						from .tokenization_roformer import RoFormerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_roformer_fast import RoFormerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_roformer import (
												    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RoFormerForCausalLM,
												    RoFormerForMaskedLM,
												    RoFormerForMultipleChoice,
												    RoFormerForQuestionAnswering,
												    RoFormerForSequenceClassification,
												    RoFormerForTokenClassification,
												    RoFormerLayer,
												    RoFormerModel,
												    RoFormerPreTrainedModel,
												    load_tf_weights_in_roformer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_roformer import (
												    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRoFormerForCausalLM,
												    TFRoFormerForMaskedLM,
												    TFRoFormerForMultipleChoice,
												    TFRoFormerForQuestionAnswering,
												    TFRoFormerForSequenceClassification,
												    TFRoFormerForTokenClassification,
												    TFRoFormerLayer,
												    TFRoFormerModel,
												    TFRoFormerPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_roformer import (
												    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlaxRoFormerForMaskedLM,
												    FlaxRoFormerForMultipleChoice,
												    FlaxRoFormerForQuestionAnswering,
												    FlaxRoFormerForSequenceClassification,
												    FlaxRoFormerForTokenClassification,
												    FlaxRoFormerModel,
												    FlaxRoFormerPreTrainedModel,
												)


else:
						import sys

						SCREAMING_SNAKE_CASE:     Any									= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)