




# 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(       UpperCamelCase__							:			List[Any]				,       UpperCamelCase__							:			Tuple				,       UpperCamelCase__							:			Union[str, Any]=0				)    -> Any:
							'''simple docstring'''


							if name is None:
														__lowercase      =    None
							else:
														__lowercase      =    '''.''' * max(0				,       spaces - 2				) + '''# {:''' + str(50 - spaces				) + '''s}'''
														__lowercase      =    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 		_A(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			Optional[int]				,       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			List[Any]				)    -> Optional[Any]:
							'''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(*UpperCamelCase__				)
														__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(*UpperCamelCase__				)
														__lowercase      =    param.transpose(0				,       1				).contiguous()
							__lowercase      =    param.view(*UpperCamelCase__				)
							return param







def 		_A(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			Optional[Any]				,       UpperCamelCase__							:			Union[str, Any]				)    -> Dict:
							'''simple docstring'''


							__lowercase      =    {}

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

														__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(UpperCamelCase__				)

														# 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				,       UpperCamelCase__				,       UpperCamelCase__				)
																					__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(UpperCamelCase__				,       UpperCamelCase__				,       3				,       UpperCamelCase__				,       UpperCamelCase__				)
																					# 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(UpperCamelCase__				,       UpperCamelCase__				,       3				,       UpperCamelCase__				,       UpperCamelCase__				)
																					# 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(       )    -> Union[str, Any]:
							'''simple docstring'''


							__lowercase      =    argparse.ArgumentParser()
							parser.add_argument('''--print-checkpoint-structure'''				,       action='''store_true'''				)
							parser.add_argument(
							    '''path_to_checkpoint'''				,       type=UpperCamelCase__				,       help='''Path to the checkpoint file (.zip archive or direct .pt file)'''				,       )
							parser.add_argument(
							    '''--config_file'''				,       default=''''''				,       type=UpperCamelCase__				,       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(UpperCamelCase__				,       map_location='''cpu'''				)
							else:
														__lowercase      =    torch.load(args.path_to_checkpoint				,       map_location='''cpu'''				)

							__lowercase      =    input_state_dict.get('''args'''				,       UpperCamelCase__				)

							# 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=5_0257				,       n_positions=1024				,       n_embd=1024				,       n_layer=24				,       n_head=16				,       n_inner=4096				,       activation_function=UpperCamelCase__				,       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=UpperCamelCase__				,       summary_activation=UpperCamelCase__				,       summary_proj_to_labels=UpperCamelCase__				,       summary_first_dropout=0.1				,       scale_attn_weights=UpperCamelCase__				,       use_cache=UpperCamelCase__				,       bos_token_id=5_0256				,       eos_token_id=5_0256				,       )
							else:
														__lowercase      =    GPTaConfig.from_json_file(args.config_file				)

							__lowercase      =    ['''GPT2LMHeadModel''']

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

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

							# 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(UpperCamelCase__				)
							__lowercase      =    type(UpperCamelCase__				).__name__
							__lowercase      =    tokenizer_class

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

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

							# Store the state_dict to file.
							__lowercase      =    os.path.join(UpperCamelCase__				,       '''pytorch_model.bin'''				)
							print(F'Saving checkpoint to "{output_checkpoint_file}"'				)
							torch.save(UpperCamelCase__				,       UpperCamelCase__				)


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

if __name__ == "__main__":
			main()

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












import itertools
import string
from collections.abc import Generator, Iterable







def 		_A(       UpperCamelCase__							:			Iterable[str]				,       UpperCamelCase__							:			int				)    -> Generator[tuple[str, ...], None, None]:
							'''simple docstring'''


							__lowercase      =    iter(UpperCamelCase__				)
							while True:
														__lowercase      =    tuple(itertools.islice(UpperCamelCase__				,       UpperCamelCase__				)				)
														if not chunk:
																					return
														yield chunk







def 		_A(       UpperCamelCase__							:			str				)    -> str:
							'''simple docstring'''


							__lowercase      =    ''''''.join([c.upper() for c in dirty if c in string.ascii_letters]				)
							__lowercase      =    ''''''

							if len(UpperCamelCase__				) < 2:
														return dirty

							for i in range(len(UpperCamelCase__				) - 1				):
														clean += dirty[i]

														if dirty[i] == dirty[i + 1]:
																					clean += "X"

							clean += dirty[-1]

							if len(UpperCamelCase__				) & 1:
														clean += "X"

							return clean







def 		_A(       UpperCamelCase__							:			str				)    -> list[str]:
							'''simple docstring'''


							__lowercase      =    '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
							# we're using a list instead of a '2d' array because it makes the math
							# for setting up the table and doing the actual encoding/decoding simpler
							__lowercase      =    []

							# copy key chars into the table if they are in `alphabet` ignoring duplicates
							for char in key.upper():
														if char not in table and char in alphabet:
																					table.append(UpperCamelCase__				)

    # fill the rest of the table in with the remaining alphabet chars
							for char in alphabet:
														if char not in table:
																					table.append(UpperCamelCase__				)

							return table







def 		_A(       UpperCamelCase__							:			str				,       UpperCamelCase__							:			str				)    -> str:
							'''simple docstring'''


							__lowercase      =    generate_table(UpperCamelCase__				)
							__lowercase      =    prepare_input(UpperCamelCase__				)
							__lowercase      =    ''''''

							# https://en.wikipedia.org/wiki/Playfair_cipher#Description
							for chara, chara in chunker(UpperCamelCase__				,       2				):
														__lowercase     ,     __lowercase      =    divmod(table.index(UpperCamelCase__				)				,       5				)
														__lowercase     ,     __lowercase      =    divmod(table.index(UpperCamelCase__				)				,       5				)

														if rowa == rowa:
																					ciphertext += table[rowa * 5 + (cola + 1) % 5]
																					ciphertext += table[rowa * 5 + (cola + 1) % 5]
														elif cola == cola:
																					ciphertext += table[((rowa + 1) % 5) * 5 + cola]
																					ciphertext += table[((rowa + 1) % 5) * 5 + cola]
														else:  # rectangle
																					ciphertext += table[rowa * 5 + cola]
																					ciphertext += table[rowa * 5 + cola]

							return ciphertext







def 		_A(       UpperCamelCase__							:			str				,       UpperCamelCase__							:			str				)    -> str:
							'''simple docstring'''


							__lowercase      =    generate_table(UpperCamelCase__				)
							__lowercase      =    ''''''

							# https://en.wikipedia.org/wiki/Playfair_cipher#Description
							for chara, chara in chunker(UpperCamelCase__				,       2				):
														__lowercase     ,     __lowercase      =    divmod(table.index(UpperCamelCase__				)				,       5				)
														__lowercase     ,     __lowercase      =    divmod(table.index(UpperCamelCase__				)				,       5				)

														if rowa == rowa:
																					plaintext += table[rowa * 5 + (cola - 1) % 5]
																					plaintext += table[rowa * 5 + (cola - 1) % 5]
														elif cola == cola:
																					plaintext += table[((rowa - 1) % 5) * 5 + cola]
																					plaintext += table[((rowa - 1) % 5) * 5 + cola]
														else:  # rectangle
																					plaintext += table[rowa * 5 + cola]
																					plaintext += table[rowa * 5 + cola]

							return plaintext






