



'''simple docstring'''



# 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:
														__lowercase       	=None
							else:
														__lowercase       	='.' * max(0  ,   spaces - 2					) + '# {:' + str(50 - spaces					) + 's}'
														__lowercase       	=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"""



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



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	={}

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

														__lowercase       	=ds_args.padded_vocab_size
														__lowercase       	=ds_args.max_position_embeddings
														__lowercase       	=ds_args.hidden_size
														__lowercase       	=ds_args.num_layers
														__lowercase       	=ds_args.num_attention_heads
														__lowercase       	=ds_args.ffn_hidden_size
														# pprint(config)

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

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

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

							# The position embeddings.
							__lowercase       	=embeddings['position_embeddings']['weight']
							# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
							__lowercase       	=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.
							__lowercase       	=pos_embeddings

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

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

							# The simple map of names for "automated" rules.
							__lowercase       	={
							    '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.
														__lowercase       	=layer_re.match(_lowerCAmelCase					)

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

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

														# The name of the layer.
														__lowercase       	=f"""transformer.h.{layer_idx}"""

														# For layernorm(s), simply store the layer norm.
														if op_name.endswith('layernorm'					):
																					__lowercase       	='ln_1' if op_name.startswith('input'					) else 'ln_2'
																					__lowercase       	=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.
																					__lowercase       	=torch.tril(torch.ones((n_positions, n_positions)  ,   dtype=torch.floataa					)					).view(
																					    1  ,   1  ,   _lowerCAmelCase  ,   _lowerCAmelCase					)
																					__lowercase       	=causal_mask

																					# Insert a "dummy" tensor for masked_bias.
																					__lowercase       	=torch.tensor(-1e4  ,   dtype=torch.floataa					)
																					__lowercase       	=masked_bias

																					__lowercase       	=fix_query_key_value_ordering(_lowerCAmelCase  ,   _lowerCAmelCase  ,   3  ,   _lowerCAmelCase  ,   _lowerCAmelCase					)
																					# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
																					__lowercase       	=out_val.transpose(0  ,   1					).contiguous()
																					# Store.
																					__lowercase       	=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":
																					__lowercase       	=fix_query_key_value_ordering(_lowerCAmelCase  ,   _lowerCAmelCase  ,   3  ,   _lowerCAmelCase  ,   _lowerCAmelCase					)
																					# Store. No change of shape.
																					__lowercase       	=out_val

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

														# Copy the bias.
														elif weight_or_bias == "bias":
																					__lowercase       	=megatron_to_transformers[op_name]
																					__lowercase       	=val

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

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

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

							# It should be done!
							return output_state_dict



def   _A       (   ):



							"""simple docstring"""



							__lowercase       	=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.'  ,   )
							__lowercase       	=parser.parse_args()

							# Extract the basename.
							__lowercase       	=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:
																												__lowercase       	=torch.load(_lowerCAmelCase  ,   map_location='cpu'					)
							else:
														__lowercase       	=torch.load(args.path_to_checkpoint  ,   map_location='cpu'					)

							__lowercase       	=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:
																												__lowercase       	='gelu_fast'
																					elif ds_args.openai_gelu:
																												__lowercase       	='gelu_new'
																					else:
																												__lowercase       	='gelu'
														else:
																					# in the very early days this used to be "gelu_new"
																					__lowercase       	='gelu_new'

														# Spell out all parameters in case the defaults change.
														__lowercase       	=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:
														__lowercase       	=GPTaConfig.from_json_file(args.config_file					)

							__lowercase       	=['GPT2LMHeadModel']

							# Convert.
							print('Converting'					)
							__lowercase       	=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:
														__lowercase       	=ds_args.tokenizer_type
														if tokenizer_type == "GPT2BPETokenizer":
																					__lowercase       	='gpt2'
														elif tokenizer_type == "PretrainedFromHF":
																					__lowercase       	=ds_args.tokenizer_name_or_path
														else:
																					raise ValueError(f"""Unrecognized tokenizer_type {tokenizer_type}"""					)
							else:
														__lowercase       	='gpt2'

							__lowercase       	=AutoTokenizer.from_pretrained(_lowerCAmelCase					)
							__lowercase       	=type(_lowerCAmelCase					).__name__
							__lowercase       	=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.
							__lowercase       	=os.path.join(_lowerCAmelCase  ,   'pytorch_model.bin'					)
							print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""					)
							torch.save(_lowerCAmelCase  ,   _lowerCAmelCase					)


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

if __name__ == "__main__":
		main()

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








'''simple docstring'''



import string



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=''
							for i in sequence:
														__lowercase       	=ord(_lowerCAmelCase					)
														if 65 <= extract <= 90:
																					output += chr(155 - extract					)
														elif 97 <= extract <= 122:
																					output += chr(219 - extract					)
														else:
																					output += i
							return output



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=string.ascii_letters
							__lowercase       	=string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
							return "".join(
							    letters_reversed[letters.index(_lowerCAmelCase					)] if c in letters else c for c in sequence					)



def   _A       (   ):



							"""simple docstring"""



							from timeit import timeit

							print('Running performance benchmarks...'					)
							__lowercase       	='from string import printable ; from __main__ import atbash, atbash_slow'
							print(f"""> atbash_slow(): {timeit('atbash_slow(printable)'  ,   setup=_lowerCAmelCase					)} seconds"""					)
							print(f""">      atbash(): {timeit('atbash(printable)'  ,   setup=_lowerCAmelCase					)} seconds"""					)


if __name__ == "__main__":
		for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
				print(f"{example} encrypted in atbash: {atbash(example)}")
		benchmark()



