



# 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						):





							'''simple docstring'''




							if name is None:
														_lowerCAmelCase  :							List[Any]				     =					None
							else:
														_lowerCAmelCase  :							List[Any]				     =					"." * max(0       ,				spaces - 2						) + "# {:" + str(50 - spaces						) + "s}"
														_lowerCAmelCase  :							Optional[Any]				     =					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						):





							'''simple docstring'''




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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Dict				     =					{}

							# old versions did not store training args
							_lowerCAmelCase  :							str				     =					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))

														_lowerCAmelCase  :							int				     =					ds_args.padded_vocab_size
														_lowerCAmelCase  :							int				     =					ds_args.max_position_embeddings
														_lowerCAmelCase  :							Optional[int]				     =					ds_args.hidden_size
														_lowerCAmelCase  :							Dict				     =					ds_args.num_layers
														_lowerCAmelCase  :							Dict				     =					ds_args.num_attention_heads
														_lowerCAmelCase  :							Union[str, Any]				     =					ds_args.ffn_hidden_size
														# pprint(config)

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

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

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

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

							# The transformer.
							_lowerCAmelCase  :							Optional[int]				     =					lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

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

							# The simple map of names for "automated" rules.
							_lowerCAmelCase  :							Dict				     =					{
							    "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.
														_lowerCAmelCase  :							str				     =					layer_re.match(_lowerCamelCase						)

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

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

														# The name of the layer.
														_lowerCAmelCase  :							Tuple				     =					F"transformer.h.{layer_idx}"

														# For layernorm(s), simply store the layer norm.
														if op_name.endswith("layernorm"						):
																					_lowerCAmelCase  :							List[Any]				     =					"ln_1" if op_name.startswith("input"						) else "ln_2"
																					_lowerCAmelCase  :							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":
																					# Insert a tensor of 1x1xDxD bias.
																					_lowerCAmelCase  :							int				     =					torch.tril(torch.ones((n_positions, n_positions)       ,				dtype=torch.floataa						)						).view(
																					    1       ,				1       ,				_lowerCamelCase       ,				_lowerCamelCase						)
																					_lowerCAmelCase  :							Tuple				     =					causal_mask

																					# Insert a "dummy" tensor for masked_bias.
																					_lowerCAmelCase  :							Union[str, Any]				     =					torch.tensor(-1e4       ,				dtype=torch.floataa						)
																					_lowerCAmelCase  :							Dict				     =					masked_bias

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

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

														# Copy the bias.
														elif weight_or_bias == "bias":
																					_lowerCAmelCase  :							Tuple				     =					megatron_to_transformers[op_name]
																					_lowerCAmelCase  :							Any				     =					val

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

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

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

							# It should be done!
							return output_state_dict






def        A       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							str				     =					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."       ,				)
							_lowerCAmelCase  :							Dict				     =					parser.parse_args()

							# Extract the basename.
							_lowerCAmelCase  :							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:
																												_lowerCAmelCase  :							Union[str, Any]				     =					torch.load(_lowerCamelCase       ,				map_location="cpu"						)
							else:
														_lowerCAmelCase  :							Dict				     =					torch.load(args.path_to_checkpoint       ,				map_location="cpu"						)

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

														# Spell out all parameters in case the defaults change.
														_lowerCAmelCase  :							Tuple				     =					GPTaConfig(
														    vocab_size=50_257       ,				n_positions=1_024       ,				n_embd=1_024       ,				n_layer=24       ,				n_head=16       ,				n_inner=4_096       ,				activation_function=_lowerCamelCase       ,				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=_lowerCamelCase       ,				summary_activation=_lowerCamelCase       ,				summary_proj_to_labels=_lowerCamelCase       ,				summary_first_dropout=0.1       ,				scale_attn_weights=_lowerCamelCase       ,				use_cache=_lowerCamelCase       ,				bos_token_id=50_256       ,				eos_token_id=50_256       ,				)
							else:
														_lowerCAmelCase  :							Tuple				     =					GPTaConfig.from_json_file(args.config_file						)

							_lowerCAmelCase  :							str				     =					["GPT2LMHeadModel"]

							# Convert.
							print("Converting"						)
							_lowerCAmelCase  :							Any				     =					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:
														_lowerCAmelCase  :							int				     =					ds_args.tokenizer_type
														if tokenizer_type == "GPT2BPETokenizer":
																					_lowerCAmelCase  :							Optional[Any]				     =					"gpt2"
														elif tokenizer_type == "PretrainedFromHF":
																					_lowerCAmelCase  :							str				     =					ds_args.tokenizer_name_or_path
														else:
																					raise ValueError(F"Unrecognized tokenizer_type {tokenizer_type}"						)
							else:
														_lowerCAmelCase  :							Union[str, Any]				     =					"gpt2"

							_lowerCAmelCase  :							Any				     =					AutoTokenizer.from_pretrained(_lowerCamelCase						)
							_lowerCAmelCase  :							List[Any]				     =					type(_lowerCamelCase						).__name__
							_lowerCAmelCase  :							Any				     =					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.
							_lowerCAmelCase  :							Optional[int]				     =					os.path.join(_lowerCamelCase       ,				"pytorch_model.bin"						)
							print(F"Saving checkpoint to \"{output_checkpoint_file}\""						)
							torch.save(_lowerCamelCase       ,				_lowerCamelCase						)


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

if __name__ == "__main__":
						main()

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





import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format="%(message)s")






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							return input_array.reshape((input_array.size, 1)						)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Any				     =					np.nan
							for i in range(_lowerCamelCase						):
														_lowerCAmelCase  :							Tuple				     =					features[:, labels == i]
														_lowerCAmelCase  :							Dict				     =					data.mean(1						)
														# Centralize the data of class i
														_lowerCAmelCase  :							Union[str, Any]				     =					data - column_reshape(_lowerCamelCase						)
														if i > 0:
																					# If covariance_sum is not None
																					covariance_sum += np.dot(_lowerCamelCase       ,				centered_data.T						)
														else:
																					# If covariance_sum is np.nan (i.e. first loop)
																					_lowerCAmelCase  :							int				     =					np.dot(_lowerCamelCase       ,				centered_data.T						)

							return covariance_sum / features.shape[1]






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[Any]				     =					features.mean(1						)
							_lowerCAmelCase  :							List[str]				     =					np.nan
							for i in range(_lowerCamelCase						):
														_lowerCAmelCase  :							str				     =					features[:, labels == i]
														_lowerCAmelCase  :							Optional[Any]				     =					data.shape[1]
														_lowerCAmelCase  :							Optional[Any]				     =					data.mean(1						)
														if i > 0:
																					# If covariance_sum is not None
																					covariance_sum += device_data * np.dot(
																					    column_reshape(_lowerCamelCase						) - column_reshape(_lowerCamelCase						)       ,				(column_reshape(_lowerCamelCase						) - column_reshape(_lowerCamelCase						)).T       ,				)
														else:
																					# If covariance_sum is np.nan (i.e. first loop)
																					_lowerCAmelCase  :							Optional[Any]				     =					device_data * np.dot(
																					    column_reshape(_lowerCamelCase						) - column_reshape(_lowerCamelCase						)       ,				(column_reshape(_lowerCamelCase						) - column_reshape(_lowerCamelCase						)).T       ,				)

							return covariance_sum / features.shape[1]






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							if features.any():
														_lowerCAmelCase  :							List[Any]				     =					features.mean(1						)
														# Center the dataset
														_lowerCAmelCase  :							List[Any]				     =					features - np.reshape(_lowerCamelCase       ,				(data_mean.size, 1)						)
														_lowerCAmelCase  :							Optional[Any]				     =					np.dot(_lowerCamelCase       ,				centered_data.T						) / features.shape[1]
														_lowerCAmelCase     ,					_lowerCAmelCase  :							List[Any]				     =					np.linalg.eigh(_lowerCamelCase						)
														# Take all the columns in the reverse order (-1), and then takes only the first
														_lowerCAmelCase  :							Union[str, Any]				     =					eigenvectors[:, ::-1][:, 0:dimensions]
														# Project the database on the new space
														_lowerCAmelCase  :							List[Any]				     =					np.dot(filtered_eigenvectors.T       ,				_lowerCamelCase						)
														logging.info("Principal Component Analysis computed"						)

														return projected_data
							else:
														logging.basicConfig(level=logging.ERROR       ,				format="%(message)s"       ,				force=_lowerCamelCase						)
														logging.error("Dataset empty"						)
														raise AssertionError






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							assert classes > dimensions

							# Check if features have been already loaded
							if features.any:
														_lowerCAmelCase     ,					_lowerCAmelCase  :							List[str]				     =					eigh(
														    covariance_between_classes(_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)       ,				covariance_within_classes(_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)       ,				)
														_lowerCAmelCase  :							List[str]				     =					eigenvectors[:, ::-1][:, :dimensions]
														_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase  :							Any				     =					np.linalg.svd(_lowerCamelCase						)
														_lowerCAmelCase  :							Optional[Any]				     =					svd_matrix[:, 0:dimensions]
														_lowerCAmelCase  :							str				     =					np.dot(filtered_svd_matrix.T       ,				_lowerCamelCase						)
														logging.info("Linear Discriminant Analysis computed"						)

														return projected_data
							else:
														logging.basicConfig(level=logging.ERROR       ,				format="%(message)s"       ,				force=_lowerCamelCase						)
														logging.error("Dataset empty"						)
														raise AssertionError






def        A       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[int]				     =					np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]						)
							_lowerCAmelCase  :							List[Any]				     =					np.array([0, 0, 0, 1, 1]						)
							_lowerCAmelCase  :							List[Any]				     =					2
							_lowerCAmelCase  :							Union[str, Any]				     =					2

							# Assert that the function raises an AssertionError if dimensions > classes
							with pytest.raises(_lowerCamelCase						) as error_info:
														_lowerCAmelCase  :							Union[str, Any]				     =					linear_discriminant_analysis(
														    _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)
														if isinstance(_lowerCamelCase       ,				np.ndarray						):
																					raise AssertionError(
																					    "Did not raise AssertionError for dimensions > classes"						)
														assert error_info.type is AssertionError






def        A       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[Any]				     =					np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]						)
							_lowerCAmelCase  :							List[str]				     =					2
							_lowerCAmelCase  :							List[Any]				     =					np.array([[6.92_82_03_23, 8.66_02_54_04, 10.39_23_04_85], [3.0, 3.0, 3.0]]						)

							with pytest.raises(_lowerCamelCase						) as error_info:
														_lowerCAmelCase  :							Tuple				     =					principal_component_analysis(_lowerCamelCase       ,				_lowerCamelCase						)
														if not np.allclose(_lowerCamelCase       ,				_lowerCamelCase						):
																					raise AssertionError
														assert error_info.type is AssertionError


if __name__ == "__main__":
						import doctest

						doctest.testmod()
