# 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  snake_case						(__lowercase ,      __lowercase ,      __lowercase=0						)	->	List[str]:

						'''simple docstring'''



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

						# Print and recurse (if needed).
						if isinstance(__lowercase ,      __lowercase						):
												if msg is not None:
																		print(__lowercase						)
												for k in val.keys():
																		recursive_print(__lowercase ,      val[k] ,      spaces + 2						)
						elif isinstance(__lowercase ,      torch.Tensor						):
												print(__lowercase ,      ":" ,      val.size()						)
						else:
												print(__lowercase ,      ":" ,      __lowercase						)
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase ,      __lowercase ,      __lowercase						)	->	Union[str, Any]:

						'''simple docstring'''



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

						'''simple docstring'''



						_snake_case						:					List[Any]    =   {}

						# old versions did not store training args
						_snake_case						:					Any    =   input_state_dict.get("args" ,      __lowercase						)
						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))

												_snake_case						:					str    =   ds_args.padded_vocab_size
												_snake_case						:					List[Any]    =   ds_args.max_position_embeddings
												_snake_case						:					Union[str, Any]    =   ds_args.hidden_size
												_snake_case						:					Union[str, Any]    =   ds_args.num_layers
												_snake_case						:					List[Any]    =   ds_args.num_attention_heads
												_snake_case						:					Any    =   ds_args.ffn_hidden_size
												# pprint(config)

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

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

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

						# The position embeddings.
						_snake_case						:					List[Any]    =   embeddings["position_embeddings"]["weight"]
						# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
						_snake_case						:					Dict    =   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.
						_snake_case						:					Any    =   pos_embeddings

						# The transformer.
						_snake_case						:					Union[str, Any]    =   lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

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

						# The simple map of names for "automated" rules.
						_snake_case						:					Union[str, Any]    =   {
						    "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.
												_snake_case						:					Optional[Any]    =   layer_re.match(__lowercase						)

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

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

												# The name of the layer.
												_snake_case						:					List[Any]    =   F"""transformer.h.{layer_idx}"""

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

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

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

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

												# Copy the bias.
												elif weight_or_bias == "bias":
																		_snake_case						:					Optional[int]    =   megatron_to_transformers[op_name]
																		_snake_case						:					Tuple    =   val

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

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

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

						# It should be done!
						return output_state_dict
def  snake_case						()	->	Tuple:

						'''simple docstring'''



						_snake_case						:					List[str]    =   argparse.ArgumentParser()
						parser.add_argument("--print-checkpoint-structure" ,      action="store_true"						)
						parser.add_argument(
						    "path_to_checkpoint" ,      type=__lowercase ,      help="Path to the checkpoint file (.zip archive or direct .pt file)" ,      )
						parser.add_argument(
						    "--config_file" ,      default="" ,      type=__lowercase ,      help="An optional config json file describing the pre-trained model." ,      )
						_snake_case						:					Optional[Any]    =   parser.parse_args()

						# Extract the basename.
						_snake_case						:					List[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:
																								_snake_case						:					Dict    =   torch.load(__lowercase ,      map_location="cpu"						)
						else:
												_snake_case						:					List[str]    =   torch.load(args.path_to_checkpoint ,      map_location="cpu"						)

						_snake_case						:					Tuple    =   input_state_dict.get("args" ,      __lowercase						)

						# 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:
																								_snake_case						:					Union[str, Any]    =   "gelu_fast"
																		elif ds_args.openai_gelu:
																								_snake_case						:					Dict    =   "gelu_new"
																		else:
																								_snake_case						:					Optional[Any]    =   "gelu"
												else:
																		# in the very early days this used to be "gelu_new"
																		_snake_case						:					str    =   "gelu_new"

												# Spell out all parameters in case the defaults change.
												_snake_case						:					str    =   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=__lowercase ,      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=__lowercase ,      summary_activation=__lowercase ,      summary_proj_to_labels=__lowercase ,      summary_first_dropout=0.1 ,      scale_attn_weights=__lowercase ,      use_cache=__lowercase ,      bos_token_id=50_256 ,      eos_token_id=50_256 ,      )
						else:
												_snake_case						:					Tuple    =   GPTaConfig.from_json_file(args.config_file						)

						_snake_case						:					str    =   ["GPT2LMHeadModel"]

						# Convert.
						print("Converting"						)
						_snake_case						:					Union[str, Any]    =   convert_megatron_checkpoint(__lowercase ,      __lowercase ,      __lowercase						)

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

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

						_snake_case						:					Union[str, Any]    =   AutoTokenizer.from_pretrained(__lowercase						)
						_snake_case						:					Dict    =   type(__lowercase						).__name__
						_snake_case						:					Optional[int]    =   tokenizer_class

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

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

						# Store the state_dict to file.
						_snake_case						:					Union[str, Any]    =   os.path.join(__lowercase ,      "pytorch_model.bin"						)
						print(F"""Saving checkpoint to \"{output_checkpoint_file}\""""						)
						torch.save(__lowercase ,      __lowercase						)


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

if __name__ == "__main__":
				main()

####################################################################################################
import os

import pytest
from attr import dataclass


__SCREAMING_SNAKE_CASE						:   int         =			'us-east-1'  # defaults region



@dataclass
class        lowercase_   :
							_lowerCamelCase						=		42
							_lowerCamelCase						=		'arn:aws:iam::558105141721:role/sagemaker_execution_role'
							_lowerCamelCase						=		{
							    'task_name': 'mnli',
							    'per_device_train_batch_size': 16,
							    'per_device_eval_batch_size': 16,
							    'do_train': True,
							    'do_eval': True,
							    'do_predict': True,
							    'output_dir': '/opt/ml/model',
							    'overwrite_output_dir': True,
							    'max_steps': 500,
							    'save_steps': 5_500,
							}
							_lowerCamelCase						=		{**hyperparameters, 'max_steps': 1_000}



							@property
							def 	UpperCamelCase	(						self    ):
													if self.framework == "pytorch":
																			return [
																			    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
																			    {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
																			    {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
																			]
													else:
																			return [
																			    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
																			    {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
																			    {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
																			]



							@property
							def 	UpperCamelCase	(						self    ):
													return f"""{self.framework}-transfromers-test"""



							@property
							def 	UpperCamelCase	(						self    ):
													return f"""./tests/sagemaker/scripts/{self.framework}"""



							@property
							def 	UpperCamelCase	(						self    ):
													if self.framework == "pytorch":
																			return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
													else:
																			return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"




@pytest.fixture(scope="class"						)
def  snake_case						(__lowercase						)	->	str:

						'''simple docstring'''



						_snake_case						:					List[str]    =   SageMakerTestEnvironment(framework=request.cls.framework						)