





"""simple docstring"""


from hashlib import mda, shaaaa

A_     :      Optional[int]								=(shaaaa, mda)







class 	__a   :



     def __init__(       self					,     a__ = 8   ):
            _lowerCamelCase    	=					0b0
            _lowerCamelCase    	=					size



     def 						snake_case_			(       self					,     a__   ):
            _lowerCamelCase    	=					self.hash_(UpperCAmelCase_   )
            self.bitarray |= h



     def 						snake_case_			(       self					,     a__   ):
            _lowerCamelCase    	=					self.hash_(UpperCAmelCase_   )
            return (h & self.bitarray) == h



     def __contains__(       self					,     a__   ):
            return self.exists(UpperCAmelCase_   )



     def 						snake_case_			(       self					,     a__   ):
            _lowerCamelCase    	=					bin(UpperCAmelCase_   )[2:]
            return res.zfill(self.size   )



     @property
     def 						snake_case_			(       self   ):
            return self.format_bin(self.bitarray   )



     def 						snake_case_			(       self					,     a__   ):
            _lowerCamelCase    	=					0b0
            for func in HASH_FUNCTIONS:
                   _lowerCamelCase    	=					(
                       int.from_bytes(func(value.encode()   ).digest()					,     'little'   ) % self.size
                   )
                   res |= 2**position
            return res



     def 						snake_case_			(       self					,     a__   ):
            return self.format_bin(self.hash_(UpperCAmelCase_   )   )



     @property
     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					bin(self.bitarray   ).count('1'   )
            return (n_ones / self.size) ** len(UpperCAmelCase_   )













# 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    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase=0 )  ->     Optional[Any]:
				# Format the message.
				if name is None:
								lowerCamelCase		       =None
				else:
								lowerCamelCase		       =""".""" * max(0	,					spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}"""
								lowerCamelCase		       =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 )  ->     Tuple:
				# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
				# for compatibility with later versions of NVIDIA Megatron-LM.
				# The inverse operation is performed inside Megatron-LM to read checkpoints:
				# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
				# If param is the weight tensor of the self-attention block, the returned tensor
				# will have to be transposed one more time to be read by HuggingFace BERT.
				lowerCamelCase		       =param.size()
				if checkpoint_version == 1.0:
								# version 1.0 stores [num_heads * hidden_size * num_splits, :]
								lowerCamelCase		       =(num_heads, hidden_size, num_splits) + input_shape[1:]
								lowerCamelCase		       =param.view(*_UpperCAmelCase )
								lowerCamelCase		       =param.transpose(0	,					2 )
								lowerCamelCase		       =param.transpose(1	,					2 ).contiguous()
				elif checkpoint_version >= 2.0:
								# other versions store [num_heads * num_splits * hidden_size, :]
								lowerCamelCase		       =(num_heads, num_splits, hidden_size) + input_shape[1:]
								lowerCamelCase		       =param.view(*_UpperCAmelCase )
								lowerCamelCase		       =param.transpose(0	,					1 ).contiguous()
				lowerCamelCase		       =param.view(*_UpperCAmelCase )
				return param





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )  ->     List[Any]:
				# The converted output model.
				lowerCamelCase		       ={}

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

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

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

				# The model.
				lowerCamelCase		       =input_state_dict["""model"""]
				# The language model.
				lowerCamelCase		       =model["""language_model"""]
				# The embeddings.
				lowerCamelCase		       =lm["""embedding"""]

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

				# The position embeddings.
				lowerCamelCase		       =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.
				lowerCamelCase		       =pos_embeddings

				# The token-type embeddings.
				lowerCamelCase		       =embeddings["""tokentype_embeddings"""]["""weight"""]
				# Store the position embeddings.
				lowerCamelCase		       =tokentype_embeddings

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

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

				# The simple map of names for "automated" rules.
				lowerCamelCase		       ={
				    """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.
				lowerCamelCase		       =None

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

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

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

								# The name of the layer.
								lowerCamelCase		       =F"""bert.encoder.layer.{layer_idx}"""

								# For layernorm(s), simply store the layer norm.
								if op_name.endswith("""layernorm""" ):
												lowerCamelCase		       ="""attention.ln""" if op_name.startswith("""input""" ) else """ln"""
												lowerCamelCase		       =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, ""

												lowerCamelCase		       =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.
												lowerCamelCase		       =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.
												lowerCamelCase		       =attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
												lowerCamelCase		       =attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
												lowerCamelCase		       =attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

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

												# Clear the stored tensor.
												lowerCamelCase		       =None

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

    # The final layernorm.
				lowerCamelCase		       =transformer["""final_layernorm.weight"""]
				lowerCamelCase		       =transformer["""final_layernorm.bias"""]

				# The pooler.
				lowerCamelCase		       =lm["""pooler"""]

				# Store the matrix and the bias.
				lowerCamelCase		       =pooler["""dense.weight"""]
				lowerCamelCase		       =pooler["""dense.bias"""]

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

				# The transform matrix.
				lowerCamelCase		       =lm_head["""dense.weight"""]
				lowerCamelCase		       =lm_head["""dense.bias"""]

				# The transform LN.
				lowerCamelCase		       =lm_head["""layernorm.weight"""]
				lowerCamelCase		       =lm_head["""layernorm.bias"""]

				# For the decoder, we replicate the weights.
				lowerCamelCase		       =word_embeddings
				lowerCamelCase		       =lm_head["""bias"""]

				# The classifier from Megatron (for MLNI).
				lowerCamelCase		       =model["""binary_head"""]

				# Store the classifier.
				lowerCamelCase		       =binary_head["""weight"""]
				lowerCamelCase		       =binary_head["""bias"""]

				# It should be done!
				return output_state_dict





def    _lowercase    (  )  ->     str:
				# Create the argument parser.
				lowerCamelCase		       =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."""	,					)
				lowerCamelCase		       =parser.parse_args()

				# Extract the basename.
				lowerCamelCase		       =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		       =torch.load(_UpperCAmelCase	,					map_location="""cpu""" )
				else:
								lowerCamelCase		       =torch.load(args.path_to_checkpoint	,					map_location="""cpu""" )

				if args.config_file == "":
								# Default config of megatron-bert 345m
								lowerCamelCase		       =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
								lowerCamelCase		       =input_state_dict["""model"""]["""lm_head"""]["""bias"""].numel()
				else:
								lowerCamelCase		       =MegatronBertConfig.from_json_file(args.config_file )

				# Convert.
				print("""Converting""" )
				lowerCamelCase		       =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.
				lowerCamelCase		       =os.path.join(_UpperCAmelCase	,					"""pytorch_model.bin""" )
				print(F"""Saving checkpoint to \"{output_checkpoint_file}\"""" )
				torch.save(_UpperCAmelCase	,					_UpperCAmelCase )


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

if __name__ == "__main__":
							main()

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





