
# 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 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_=0		)		->		int:
					'''simple docstring'''


					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(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_		)		->		List[str]:
					'''simple docstring'''


					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(*UpperCamelCase_		)
										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(*UpperCamelCase_		)
										SCREAMING_SNAKE_CASE__													= param.transpose(0	,	1		).contiguous()
					SCREAMING_SNAKE_CASE__													= param.view(*UpperCamelCase_		)
					return param






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		Union[str, Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= {}

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

										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(UpperCamelCase_		)

										# 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	,	UpperCamelCase_	,	UpperCamelCase_		)
															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(UpperCamelCase_	,	UpperCamelCase_	,	3	,	UpperCamelCase_	,	UpperCamelCase_		)
															# 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(UpperCamelCase_	,	UpperCamelCase_	,	3	,	UpperCamelCase_	,	UpperCamelCase_		)
															# 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 							_lowercase							(     )		->		Dict:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= argparse.ArgumentParser()
					parser.add_argument('--print-checkpoint-structure'	,	action='store_true'		)
					parser.add_argument(
					    'path_to_checkpoint'	,	type=UpperCamelCase_	,	help='Path to the checkpoint file (.zip archive or direct .pt file)'	,	)
					parser.add_argument(
					    '--config_file'	,	default=''	,	type=UpperCamelCase_	,	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(UpperCamelCase_	,	map_location='cpu'		)
					else:
										SCREAMING_SNAKE_CASE__													= torch.load(args.path_to_checkpoint	,	map_location='cpu'		)

					SCREAMING_SNAKE_CASE__													= input_state_dict.get('args'	,	UpperCamelCase_		)

					# 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=UpperCamelCase_	,	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=UpperCamelCase_	,	summary_activation=UpperCamelCase_	,	summary_proj_to_labels=UpperCamelCase_	,	summary_first_dropout=0.1	,	scale_attn_weights=UpperCamelCase_	,	use_cache=UpperCamelCase_	,	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(UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)

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

					# 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(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= type(UpperCamelCase_		).__name__
					SCREAMING_SNAKE_CASE__													= tokenizer_class

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

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

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


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

if __name__ == "__main__":
				main()

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







def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		str:
					'''simple docstring'''


					if not isinstance(UpperCamelCase_	,	UpperCamelCase_		):
										raise ValueError('iterations must be defined as integers'		)
					if not isinstance(UpperCamelCase_	,	UpperCamelCase_		) or not number >= 1:
										raise ValueError(
										    'starting number must be\n                         and integer and be more than 0'		)
					if not iterations >= 1:
										raise ValueError('Iterations must be done more than 0 times to play FizzBuzz'		)

					SCREAMING_SNAKE_CASE__													= ''
					while number <= iterations:
										if number % 3 == 0:
															out += "Fizz"
										if number % 5 == 0:
															out += "Buzz"
										if 0 not in (number % 3, number % 5):
															out += str(UpperCamelCase_		)

										# print(out)
										number += 1
										out += " "
					return out


if __name__ == "__main__":
				import doctest

				doctest.testmod()





