



# 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     UpperCamelCase						(	__lowercase						:			Any						,__lowercase						:			Union[str, Any]						,__lowercase						:			Dict=0     ):
		'''simple docstring'''




		if name is None:
				A_		:    List[Any]         =				None
		else:
				A_		:    Dict         =				"." * max(0						,spaces - 2     ) + "# {:" + str(50 - spaces     ) + "s}"
				A_		:    Dict         =				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						(	__lowercase						:			str						,__lowercase						:			Union[str, Any]						,__lowercase						:			Dict						,__lowercase						:			Dict						,__lowercase						:			Dict     ):
		'''simple docstring'''




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




def     UpperCamelCase						(	__lowercase						:			List[Any]						,__lowercase						:			Optional[Any]						,__lowercase						:			str     ):
		'''simple docstring'''




		A_		:    str         =				{}

		# old versions did not store training args
		A_		:    Tuple         =				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_		:    Any         =				ds_args.padded_vocab_size
				A_		:    List[Any]         =				ds_args.max_position_embeddings
				A_		:    Any         =				ds_args.hidden_size
				A_		:    Any         =				ds_args.num_layers
				A_		:    Any         =				ds_args.num_attention_heads
				A_		:    Optional[Any]         =				ds_args.ffn_hidden_size
				# pprint(config)

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

		# The model.
		A_		:    Any         =				input_state_dict["model"]
		# The language model.
		A_		:    List[str]         =				model["language_model"]
		# The embeddings.
		A_		:    List[Any]         =				lm["embedding"]

		# The word embeddings.
		A_		:    List[Any]         =				embeddings["word_embeddings"]["weight"]
		# Truncate the embedding table to vocab_size rows.
		A_		:    Any         =				word_embeddings[: config.vocab_size, :]
		A_		:    int         =				word_embeddings

		# The position embeddings.
		A_		:    str         =				embeddings["position_embeddings"]["weight"]
		# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
		A_		:    List[str]         =				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.
		A_		:    Optional[int]         =				pos_embeddings

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

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

		# The simple map of names for "automated" rules.
		A_		:    int         =				{
		    "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.
				A_		:    int         =				layer_re.match(_a     )

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

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

				# The name of the layer.
				A_		:    Optional[Any]         =				f'''transformer.h.{layer_idx}'''

				# For layernorm(s), simply store the layer norm.
				if op_name.endswith('layernorm'     ):
						A_		:    Optional[int]         =				"ln_1" if op_name.startswith('input'     ) else "ln_2"
						A_		:    int         =				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.
						A_		:    Union[str, Any]         =				torch.tril(torch.ones((n_positions, n_positions)						,dtype=torch.floataa     )     ).view(
						    1						,1						,_a						,_a     )
						A_		:    Dict         =				causal_mask

						# Insert a "dummy" tensor for masked_bias.
						A_		:    Optional[int]         =				torch.tensor(-1e4						,dtype=torch.floataa     )
						A_		:    List[str]         =				masked_bias

						A_		:    Union[str, Any]         =				fix_query_key_value_ordering(_a						,_a						,3						,_a						,_a     )
						# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
						A_		:    Optional[int]         =				out_val.transpose(0						,1     ).contiguous()
						# Store.
						A_		:    str         =				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":
						A_		:    str         =				fix_query_key_value_ordering(_a						,_a						,3						,_a						,_a     )
						# Store. No change of shape.
						A_		:    Any         =				out_val

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

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

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

		# The final layernorm.
		A_		:    Any         =				transformer["final_layernorm.weight"]
		A_		:    Optional[Any]         =				transformer["final_layernorm.bias"]

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

		# It should be done!
		return output_state_dict




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Tuple         =				argparse.ArgumentParser()
		parser.add_argument('--print-checkpoint-structure'						,action='store_true'     )
		parser.add_argument(
		    'path_to_checkpoint'						,type=_a						,help='Path to the checkpoint file (.zip archive or direct .pt file)'						,)
		parser.add_argument(
		    '--config_file'						,default=''						,type=_a						,help='An optional config json file describing the pre-trained model.'						,)
		A_		:    List[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_		:    str         =				torch.load(_a						,map_location='cpu'     )
		else:
				A_		:    List[str]         =				torch.load(args.path_to_checkpoint						,map_location='cpu'     )

		A_		:    Any         =				input_state_dict.get('args'						,_a     )

		# 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:
								A_		:    Any         =				"gelu_fast"
						elif ds_args.openai_gelu:
								A_		:    Any         =				"gelu_new"
						else:
								A_		:    List[Any]         =				"gelu"
				else:
						# in the very early days this used to be "gelu_new"
						A_		:    Any         =				"gelu_new"

				# Spell out all parameters in case the defaults change.
				A_		:    Tuple         =				GPTaConfig(
				    vocab_size=5_02_57						,n_positions=10_24						,n_embd=10_24						,n_layer=24						,n_head=16						,n_inner=40_96						,activation_function=_a						,resid_pdrop=0.1						,embd_pdrop=0.1						,attn_pdrop=0.1						,layer_norm_epsilon=1e-5						,initializer_range=0.02						,summary_type='cls_index'						,summary_use_proj=_a						,summary_activation=_a						,summary_proj_to_labels=_a						,summary_first_dropout=0.1						,scale_attn_weights=_a						,use_cache=_a						,bos_token_id=5_02_56						,eos_token_id=5_02_56						,)
		else:
				A_		:    Dict         =				GPTaConfig.from_json_file(args.config_file     )

		A_		:    Optional[Any]         =				["GPT2LMHeadModel"]

		# Convert.
		print('Converting'     )
		A_		:    List[str]         =				convert_megatron_checkpoint(_a						,_a						,_a     )

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

		# Add tokenizer class info to config
		# see https://github.com/huggingface/transformers/issues/13906)
		if ds_args is not None:
				A_		:    Optional[Any]         =				ds_args.tokenizer_type
				if tokenizer_type == "GPT2BPETokenizer":
						A_		:    List[Any]         =				"gpt2"
				elif tokenizer_type == "PretrainedFromHF":
						A_		:    List[Any]         =				ds_args.tokenizer_name_or_path
				else:
						raise ValueError(f'''Unrecognized tokenizer_type {tokenizer_type}'''     )
		else:
				A_		:    Optional[Any]         =				"gpt2"

		A_		:    Any         =				AutoTokenizer.from_pretrained(_a     )
		A_		:    int         =				type(_a     ).__name__
		A_		:    str         =				tokenizer_class

		# Store the config to file.
		print('Saving config'     )
		config.save_pretrained(_a     )

		# Save tokenizer based on args
		print(f'''Adding {tokenizer_class} tokenizer files'''     )
		tokenizer.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()

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







from ..utils import DummyObject, requires_backends





class UpperCAmelCase						( metaclass=__A  ):


				'''simple docstring'''






				lowerCamelCase_    = ['''onnx''']
				def __init__(						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(self		,     ['onnx'] )
				@classmethod
				def   lowerCAmelCase_       (						cls		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(cls		,     ['onnx'] )







				@classmethod
				def   lowerCAmelCase_       (						cls		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(cls		,     ['onnx'] )






