





# 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_bert/convert_megatron_bert_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 MegatronBertConfig





def     lowerCamelCase__  (  UpperCamelCase__      :							Union[str, Any]							,     UpperCamelCase__      :							Union[str, Any]							,     UpperCamelCase__      :							Tuple=0    )      ->			List[Any]:



					'''simple docstring'''




					if name is None:
										_snake_case						=					None
					else:
										_snake_case						=					'.' * max(0							,     spaces - 2    ) + '# {:' + str(50 - spaces    ) + 's}'
										_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     lowerCamelCase__  (  UpperCamelCase__      :							str							,     UpperCamelCase__      :							Any							,     UpperCamelCase__      :							Tuple							,     UpperCamelCase__      :							Optional[int]							,     UpperCamelCase__      :							Optional[Any]    )      ->			Optional[int]:



					'''simple docstring'''




					_snake_case						=					param.size()
					if checkpoint_version == 1.0:
										# version 1.0 stores [num_heads * hidden_size * num_splits, :]
										_snake_case						=					(num_heads, hidden_size, num_splits) + input_shape[1:]
										_snake_case						=					param.view(*UpperCamelCase__    )
										_snake_case						=					param.transpose(0							,     2    )
										_snake_case						=					param.transpose(1							,     2    ).contiguous()
					elif checkpoint_version >= 2.0:
										# other versions store [num_heads * num_splits * hidden_size, :]
										_snake_case						=					(num_heads, num_splits, hidden_size) + input_shape[1:]
										_snake_case						=					param.view(*UpperCamelCase__    )
										_snake_case						=					param.transpose(0							,     1    ).contiguous()
					_snake_case						=					param.view(*UpperCamelCase__    )
					return param





def     lowerCamelCase__  (  UpperCamelCase__      :							Any							,     UpperCamelCase__      :							Tuple							,     UpperCamelCase__      :							Dict    )      ->			List[Any]:



					'''simple docstring'''




					_snake_case						=					{}

					# old versions did not store training args
					_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))

										_snake_case						=					ds_args.tokenizer_type
										_snake_case						=					ds_args.padded_vocab_size
										_snake_case						=					ds_args.max_position_embeddings
										_snake_case						=					ds_args.hidden_size
										_snake_case						=					ds_args.num_layers
										_snake_case						=					ds_args.num_attention_heads
										_snake_case						=					ds_args.ffn_hidden_size if 'ffn_hidden_size' in ds_args else 4 * ds_args.hidden_size
										# pprint(config)

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

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

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

					# The position embeddings.
					_snake_case						=					embeddings['position_embeddings']['weight']
					assert pos_embeddings.size(0    ) == config.max_position_embeddings and pos_embeddings.size(1    ) == config.hidden_size
					# Store the position embeddings.
					_snake_case						=					pos_embeddings

					# The token-type embeddings.
					_snake_case						=					embeddings['tokentype_embeddings']['weight']
					# Store the position embeddings.
					_snake_case						=					tokentype_embeddings

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

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

					# The simple map of names for "automated" rules.
					_snake_case						=					{
					    'attention.dense': '.attention.output.dense.',
					    'self_attention.dense': '.attention.output.dense.',
					    'mlp.dense_h_to_4h': '.intermediate.dense.',
					    'mlp.dense_4h_to_h': '.output.dense.',
					}

					# Keep track of the attention/query/value tensor.
					_snake_case						=					None

					# Extract the layers.
					for key, val in transformer.items():
										# Match the name.
										_snake_case						=					layer_re.match(UpperCamelCase__    )

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

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

										# The name of the layer.
										_snake_case						=					F'''bert.encoder.layer.{layer_idx}'''

										# For layernorm(s), simply store the layer norm.
										if op_name.endswith('layernorm'    ):
															_snake_case						=					'attention.ln' if op_name.startswith('input'    ) else 'ln'
															_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":
															# Make sure the QKV pointer is nil.
															assert attention_qkv_weight is None, ""

															_snake_case						=					fix_query_key_value_ordering(UpperCamelCase__							,     UpperCamelCase__							,     3							,     UpperCamelCase__							,     UpperCamelCase__    )
															# Store the tensor as we need the bias as well to interleave QKV and biases.
															_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":
															# Make sure we read the weight tensor.
															assert attention_qkv_weight is not None, ""

															# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
															_snake_case						=					attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
															_snake_case						=					attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
															_snake_case						=					attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

															_snake_case						=					fix_query_key_value_ordering(UpperCamelCase__							,     UpperCamelCase__							,     3							,     UpperCamelCase__							,     UpperCamelCase__    )
															# Split the bias.
															_snake_case						=					out_val[0 * config.hidden_size : 1 * config.hidden_size]
															_snake_case						=					out_val[1 * config.hidden_size : 2 * config.hidden_size]
															_snake_case						=					out_val[2 * config.hidden_size : 3 * config.hidden_size]

															# Store.
															_snake_case						=					q
															_snake_case						=					q_bias
															_snake_case						=					k
															_snake_case						=					k_bias
															_snake_case						=					v
															_snake_case						=					v_bias

															# Clear the stored tensor.
															_snake_case						=					None

										# Copy weights and biases as is.
										elif weight_or_bias in ["weight", "bias"]:
															_snake_case						=					megatron_to_transformers[op_name]
															_snake_case						=					val

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

					# The pooler.
					_snake_case						=					lm['pooler']

					# Store the matrix and the bias.
					_snake_case						=					pooler['dense.weight']
					_snake_case						=					pooler['dense.bias']

					# The LM head from Megatron (for RACE).
					_snake_case						=					model['lm_head']

					# The transform matrix.
					_snake_case						=					lm_head['dense.weight']
					_snake_case						=					lm_head['dense.bias']

					# The transform LN.
					_snake_case						=					lm_head['layernorm.weight']
					_snake_case						=					lm_head['layernorm.bias']

					# For the decoder, we replicate the weights.
					_snake_case						=					word_embeddings
					_snake_case						=					lm_head['bias']

					# The classifier from Megatron (for MLNI).
					_snake_case						=					model['binary_head']

					# Store the classifier.
					_snake_case						=					binary_head['weight']
					_snake_case						=					binary_head['bias']

					# It should be done!
					return output_state_dict





def     lowerCamelCase__  (  )      ->			Optional[Any]:



					'''simple docstring'''




					_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 ZIP file containing the checkpoint'    )
					parser.add_argument(
					    '--config_file'							,     default=''							,     type=UpperCamelCase__							,     help='An optional config json file describing the pre-trained model.'							,     )
					_snake_case						=					parser.parse_args()

					# Extract the basename.
					_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:
																				_snake_case						=					torch.load(UpperCamelCase__							,     map_location='cpu'    )
					else:
										_snake_case						=					torch.load(args.path_to_checkpoint							,     map_location='cpu'    )

					if args.config_file == "":
										# Default config of megatron-bert 345m
										_snake_case						=					MegatronBertConfig()

										# different megatron-bert-*-345m models have different vocab sizes, so override the default
										# config (which is for megatron-bert-cased-345m) with the actual vocab dimension
										_snake_case						=					input_state_dict['model']['lm_head']['bias'].numel()
					else:
										_snake_case						=					MegatronBertConfig.from_json_file(args.config_file    )

					# Convert.
					print('Converting'    )
					_snake_case						=					convert_megatron_checkpoint(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__    )

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

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

					# Store the state_dict to file.
					_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()

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










# Copyright 2021 The HuggingFace Team. 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.

import json
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import yaml

from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION


UpperCAmelCase_   				=  os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
UpperCAmelCase_   				=  os.path.join(hf_cache_home, """accelerate""")
UpperCAmelCase_   				=  os.path.join(cache_dir, """default_config.yaml""")
UpperCAmelCase_   				=  os.path.join(cache_dir, """default_config.yaml""")

# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
					UpperCAmelCase_   				=  default_yaml_config_file
else:
					UpperCAmelCase_   				=  default_json_config_file





def     lowerCamelCase__  (  UpperCamelCase__      :							List[str]    )      ->			str:



					'''simple docstring'''




					if config_file is not None:
										if not os.path.isfile(UpperCamelCase__    ):
															raise FileNotFoundError(
															    F'''The passed configuration file `{config_file}` does not exist. '''
															    'Please pass an existing file to `accelerate launch`, or use the the default one '
															    'created through `accelerate config` and run `accelerate launch` '
															    'without the `--config_file` argument.'    )
					else:
										_snake_case						=					default_config_file
					with open(UpperCamelCase__							,     'r'							,     encoding='utf-8'    ) as f:
										if config_file.endswith('.json'    ):
															if (
															    json.load(UpperCamelCase__    ).get('compute_environment'							,     ComputeEnvironment.LOCAL_MACHINE    )
															    == ComputeEnvironment.LOCAL_MACHINE
															):
																				_snake_case						=					ClusterConfig
															else:
																				_snake_case						=					SageMakerConfig
															return config_class.from_json_file(json_file=UpperCamelCase__    )
										else:
															if (
															    yaml.safe_load(UpperCamelCase__    ).get('compute_environment'							,     ComputeEnvironment.LOCAL_MACHINE    )
															    == ComputeEnvironment.LOCAL_MACHINE
															):
																				_snake_case						=					ClusterConfig
															else:
																				_snake_case						=					SageMakerConfig
															return config_class.from_yaml_file(yaml_file=UpperCamelCase__    )
@dataclass
class       UpperCamelCase_		:
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						42




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										_snake_case						=					self.__dict__
										# For serialization, it's best to convert Enums to strings (or their underlying value type).
										for key, value in result.items():
															if isinstance(lowerCAmelCase_					,    lowerCAmelCase_	):
																				_snake_case						=					value.value
															if isinstance(lowerCAmelCase_					,    lowerCAmelCase_	) and not bool(lowerCAmelCase_	):
																				_snake_case						=					None
										_snake_case						=					{k: v for k, v in result.items() if v is not None}
										return result




					@classmethod
					def 						lowerCAmelCase      (		cls					,    lowerCAmelCase_=None	)		->				List[Any]:
										_snake_case						=					default_json_config_file if json_file is None else json_file
										with open(lowerCAmelCase_					,    'r'					,    encoding='utf-8'	) as f:
															_snake_case						=					json.load(lowerCAmelCase_	)
										if "compute_environment" not in config_dict:
															_snake_case						=					ComputeEnvironment.LOCAL_MACHINE
										if "mixed_precision" not in config_dict:
															_snake_case						=					'fp16' if ('fp16' in config_dict and config_dict['fp16']) else None
										if "fp16" in config_dict:  # Convert the config to the new format.
															del config_dict["fp16"]
										if "dynamo_backend" in config_dict:  # Convert the config to the new format.
															_snake_case						=					config_dict.pop('dynamo_backend'	)
															_snake_case						=					{} if dynamo_backend == 'NO' else {'dynamo_backend': dynamo_backend}
										if "use_cpu" not in config_dict:
															_snake_case						=					False
										return cls(**lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				List[str]:
										with open(lowerCAmelCase_					,    'w'					,    encoding='utf-8'	) as f:
															_snake_case						=					json.dumps(self.to_dict()					,    indent=2					,    sort_keys=lowerCAmelCase_	) + '\n'
															f.write(lowerCAmelCase_	)




					@classmethod
					def 						lowerCAmelCase      (		cls					,    lowerCAmelCase_=None	)		->				int:
										_snake_case						=					default_yaml_config_file if yaml_file is None else yaml_file
										with open(lowerCAmelCase_					,    'r'					,    encoding='utf-8'	) as f:
															_snake_case						=					yaml.safe_load(lowerCAmelCase_	)
										if "compute_environment" not in config_dict:
															_snake_case						=					ComputeEnvironment.LOCAL_MACHINE

										if "mixed_precision" not in config_dict:
															_snake_case						=					'fp16' if ('fp16' in config_dict and config_dict['fp16']) else None
										if isinstance(config_dict['mixed_precision']					,    lowerCAmelCase_	) and not config_dict["mixed_precision"]:
															_snake_case						=					'no'
										if "fp16" in config_dict:  # Convert the config to the new format.
															del config_dict["fp16"]
										if "dynamo_backend" in config_dict:  # Convert the config to the new format.
															_snake_case						=					config_dict.pop('dynamo_backend'	)
															_snake_case						=					{} if dynamo_backend == 'NO' else {'dynamo_backend': dynamo_backend}
										if "use_cpu" not in config_dict:
															_snake_case						=					False
										return cls(**lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				List[str]:
										with open(lowerCAmelCase_					,    'w'					,    encoding='utf-8'	) as f:
															yaml.safe_dump(self.to_dict()					,    lowerCAmelCase_	)






					def 						lowerCAmelCase      (		self	)		->				Tuple:
										if isinstance(self.compute_environment					,    lowerCAmelCase_	):
															_snake_case						=					ComputeEnvironment(self.compute_environment	)
										if isinstance(self.distributed_type					,    lowerCAmelCase_	):
															if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
																				_snake_case						=					SageMakerDistributedType(self.distributed_type	)
															else:
																				_snake_case						=					DistributedType(self.distributed_type	)
										if self.dynamo_config is None:
															_snake_case						=					{}





@dataclass
class       UpperCamelCase_		(	_lowerCamelCase       ):
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						0
					lowerCAmelCase_				=						1
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						"static"
					lowerCAmelCase_				=						False
					lowerCAmelCase_				=						"main"

					# args for deepspeed_plugin
					lowerCAmelCase_				=						None
					# args for fsdp
					lowerCAmelCase_				=						None
					# args for megatron_lm
					lowerCAmelCase_				=						None
					# args for ipex
					lowerCAmelCase_				=						None
					# args for TPU
					lowerCAmelCase_				=						False

					# args for TPU pods
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						False
					lowerCAmelCase_				=						False
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None

					# args for dynamo
					lowerCAmelCase_				=						None




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										if self.deepspeed_config is None:
															_snake_case						=					{}
										if self.fsdp_config is None:
															_snake_case						=					{}
										if self.megatron_lm_config is None:
															_snake_case						=					{}
										if self.ipex_config is None:
															_snake_case						=					{}
										return super().__post_init__()





@dataclass
class       UpperCamelCase_		(	_lowerCamelCase       ):
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						"us-east-1"
					lowerCAmelCase_				=						1
					lowerCAmelCase_				=						"all"
					lowerCAmelCase_				=						f"accelerate-sagemaker-{num_machines}"
					lowerCAmelCase_				=						SAGEMAKER_PYTORCH_VERSION
					lowerCAmelCase_				=						SAGEMAKER_TRANSFORMERS_VERSION
					lowerCAmelCase_				=						SAGEMAKER_PYTHON_VERSION
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None



