
"""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    _snake_case					(		snake_case__ :  Tuple						,   snake_case__ :  int						,   snake_case__ :  Optional[int]=0  ):
	# Format the message.
	if name is None:
		A     = None
	else:
		A     = '.' * max(0						,   spaces - 2  ) + '# {:' + str(50 - spaces  ) + 's}'
		A     = fmt.format(snake_case__  )

	# Print and recurse (if needed).
	if isinstance(snake_case__						,   snake_case__  ):
		if msg is not None:
			print(snake_case__  )
		for k in val.keys():
			recursive_print(snake_case__						,   val[k]						,   spaces + 2  )
	elif isinstance(snake_case__						,   torch.Tensor  ):
		print(snake_case__						,   ':'						,   val.size()  )
	else:
		print(snake_case__						,   ':'						,   snake_case__  )






def    _snake_case					(		snake_case__ :  str						,   snake_case__ :  Tuple						,   snake_case__ :  Tuple						,   snake_case__ :  Dict						,   snake_case__ :  Union[str, Any]  ):
	# 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 GPT2.
	A     = param.size()
	if checkpoint_version == 1.0:
		# version 1.0 stores [num_heads * hidden_size * num_splits, :]
		A     = (num_heads, hidden_size, num_splits) + input_shape[1:]
		A     = param.view(*snake_case__  )
		A     = param.transpose(0						,   2  )
		A     = param.transpose(1						,   2  ).contiguous()
	elif checkpoint_version >= 2.0:
		# other versions store [num_heads * num_splits * hidden_size, :]
		A     = (num_heads, num_splits, hidden_size) + input_shape[1:]
		A     = param.view(*snake_case__  )
		A     = param.transpose(0						,   1  ).contiguous()
	A     = param.view(*snake_case__  )
	return param






def    _snake_case					(		snake_case__ :  Dict						,   snake_case__ :  str						,   snake_case__ :  int  ):
	# The converted output model.
	A     = {}

	# old versions did not store training args
	A     = input_state_dict.get('args'						,   snake_case__  )
	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))

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

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

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

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

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

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

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

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

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

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

		# The name of the layer.
		A     = F'transformer.h.{layer_idx}'

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

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

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

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

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

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

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

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

	# It should be done!
	return output_state_dict






def    _snake_case					(		):
	# Create the argument parser.
	A     = argparse.ArgumentParser()
	parser.add_argument('--print-checkpoint-structure'						,   action='store_true'  )
	parser.add_argument(
	    'path_to_checkpoint'						,   type=snake_case__						,   help='Path to the checkpoint file (.zip archive or direct .pt file)'						,   )
	parser.add_argument(
	    '--config_file'						,   default=''						,   type=snake_case__						,   help='An optional config json file describing the pre-trained model.'						,   )
	A     = parser.parse_args()

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

	A     = input_state_dict.get('args'						,   snake_case__  )

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

		# Spell out all parameters in case the defaults change.
		A     = GPTaConfig(
		    vocab_size=5_0257						,   n_positions=1024						,   n_embd=1024						,   n_layer=24						,   n_head=16						,   n_inner=4096						,   activation_function=snake_case__						,   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=snake_case__						,   summary_activation=snake_case__						,   summary_proj_to_labels=snake_case__						,   summary_first_dropout=0.1						,   scale_attn_weights=snake_case__						,   use_cache=snake_case__						,   bos_token_id=5_0256						,   eos_token_id=5_0256						,   )
	else:
		A     = GPTaConfig.from_json_file(args.config_file  )

	A     = ['GPT2LMHeadModel']

	# Convert.
	print('Converting'  )
	A     = convert_megatron_checkpoint(snake_case__						,   snake_case__						,   snake_case__  )

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

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

	A     = AutoTokenizer.from_pretrained(snake_case__  )
	A     = type(snake_case__  ).__name__
	A     = tokenizer_class

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

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

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


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

if __name__ == "__main__":
		main()

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

"""simple docstring"""


import os

# Precomputes a list of the 100 first triangular numbers
_lowercase				 =					[int(0.5 * n * (n + 1)) for n in range(1, 1_01)]






def    _snake_case					(		):
	A     = os.path.dirname(os.path.realpath(snake_case__  )  )
	A     = os.path.join(snake_case__						,   'words.txt'  )

	A     = ''
	with open(snake_case__  ) as f:
		A     = f.readline()

	A     = [word.strip('"'  ) for word in words.strip('\r\n'  ).split(','  )]
	A     = [
	    word
	    for word in [sum(ord(snake_case__  ) - 64 for x in word  ) for word in words]
	    if word in TRIANGULAR_NUMBERS
	]
	return len(snake_case__  )


if __name__ == "__main__":
		print(solution())