

"""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   lowercase_	(    _lowercase       :		int							,      _lowercase       :		str							,      _lowercase       :		Tuple=0	):




					'''simple docstring'''






					if name is None:
										UpperCAmelCase			:   Tuple      =				None
					else:
										UpperCAmelCase			:   List[Any]      =				"." * max(0							,      spaces - 2	) + "# {:" + str(50 - spaces	) + "s}"
										UpperCAmelCase			:   Any      =				fmt.format(__A	)

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



def   lowercase_	(    _lowercase       :		str							,      _lowercase       :		int							,      _lowercase       :		Optional[Any]							,      _lowercase       :		Optional[Any]							,      _lowercase       :		Union[str, Any]	):




					'''simple docstring'''






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



def   lowercase_	(    _lowercase       :		List[str]							,      _lowercase       :		Optional[Any]							,      _lowercase       :		int	):




					'''simple docstring'''






					UpperCAmelCase			:   List[str]      =				{}

					# old versions did not store training args
					UpperCAmelCase			:   Optional[Any]      =				input_state_dict.get("args"							,      __A	)
					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))

										UpperCAmelCase			:   Any      =				ds_args.padded_vocab_size
										UpperCAmelCase			:   Optional[int]      =				ds_args.max_position_embeddings
										UpperCAmelCase			:   Any      =				ds_args.hidden_size
										UpperCAmelCase			:   int      =				ds_args.num_layers
										UpperCAmelCase			:   Tuple      =				ds_args.num_attention_heads
										UpperCAmelCase			:   List[str]      =				ds_args.ffn_hidden_size
										# pprint(config)

					# The number of heads.
					UpperCAmelCase			:   Optional[int]      =				config.n_head
					# The hidden_size per head.
					UpperCAmelCase			:   List[Any]      =				config.n_embd // config.n_head
					# Megatron-LM checkpoint version
					if "checkpoint_version" in input_state_dict.keys():
										UpperCAmelCase			:   Union[str, Any]      =				input_state_dict["checkpoint_version"]
					else:
										UpperCAmelCase			:   Any      =				0.0

					# The model.
					UpperCAmelCase			:   int      =				input_state_dict["model"]
					# The language model.
					UpperCAmelCase			:   List[str]      =				model["language_model"]
					# The embeddings.
					UpperCAmelCase			:   int      =				lm["embedding"]

					# The word embeddings.
					UpperCAmelCase			:   List[Any]      =				embeddings["word_embeddings"]["weight"]
					# Truncate the embedding table to vocab_size rows.
					UpperCAmelCase			:   Tuple      =				word_embeddings[: config.vocab_size, :]
					UpperCAmelCase			:   Dict      =				word_embeddings

					# The position embeddings.
					UpperCAmelCase			:   str      =				embeddings["position_embeddings"]["weight"]
					# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
					UpperCAmelCase			:   str      =				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.
					UpperCAmelCase			:   Any      =				pos_embeddings

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

					# The regex to extract layer names.
					UpperCAmelCase			:   Optional[int]      =				re.compile(R"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"	)

					# The simple map of names for "automated" rules.
					UpperCAmelCase			:   Union[str, Any]      =				{
					    "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.
										UpperCAmelCase			:   Dict      =				layer_re.match(__A	)

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

										# The index of the layer.
										UpperCAmelCase			:   List[Any]      =				int(m.group(1	)	)
										# The name of the operation.
										UpperCAmelCase			:   Optional[int]      =				m.group(2	)
										# Is it a weight or a bias?
										UpperCAmelCase			:   int      =				m.group(3	)

										# The name of the layer.
										UpperCAmelCase			:   int      =				F"""transformer.h.{layer_idx}"""

										# For layernorm(s), simply store the layer norm.
										if op_name.endswith("layernorm"	):
															UpperCAmelCase			:   Tuple      =				"ln_1" if op_name.startswith("input"	) else "ln_2"
															UpperCAmelCase			:   int      =				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.
															UpperCAmelCase			:   Optional[int]      =				torch.tril(torch.ones((n_positions, n_positions)							,      dtype=torch.floataa	)	).view(
															    1							,      1							,      __A							,      __A	)
															UpperCAmelCase			:   int      =				causal_mask

															# Insert a "dummy" tensor for masked_bias.
															UpperCAmelCase			:   str      =				torch.tensor(-1E4							,      dtype=torch.floataa	)
															UpperCAmelCase			:   Tuple      =				masked_bias

															UpperCAmelCase			:   str      =				fix_query_key_value_ordering(__A							,      __A							,      3							,      __A							,      __A	)
															# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
															UpperCAmelCase			:   Union[str, Any]      =				out_val.transpose(0							,      1	).contiguous()
															# Store.
															UpperCAmelCase			:   List[Any]      =				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":
															UpperCAmelCase			:   Dict      =				fix_query_key_value_ordering(__A							,      __A							,      3							,      __A							,      __A	)
															# Store. No change of shape.
															UpperCAmelCase			:   int      =				out_val

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

										# Copy the bias.
										elif weight_or_bias == "bias":
															UpperCAmelCase			:   int      =				megatron_to_transformers[op_name]
															UpperCAmelCase			:   str      =				val

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

					# The final layernorm.
					UpperCAmelCase			:   List[Any]      =				transformer["final_layernorm.weight"]
					UpperCAmelCase			:   Tuple      =				transformer["final_layernorm.bias"]

					# For LM head, transformers' wants the matrix to weight embeddings.
					UpperCAmelCase			:   List[str]      =				word_embeddings

					# It should be done!
					return output_state_dict



def   lowercase_	(    ):




					'''simple docstring'''






					UpperCAmelCase			:   Any      =				argparse.ArgumentParser()
					parser.add_argument("--print-checkpoint-structure"							,      action="store_true"	)
					parser.add_argument(
					    "path_to_checkpoint"							,      type=__A							,      help="Path to the checkpoint file (.zip archive or direct .pt file)"							,      )
					parser.add_argument(
					    "--config_file"							,      default=""							,      type=__A							,      help="An optional config json file describing the pre-trained model."							,      )
					UpperCAmelCase			:   List[str]      =				parser.parse_args()

					# Extract the basename.
					UpperCAmelCase			:   List[Any]      =				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:
																				UpperCAmelCase			:   Dict      =				torch.load(__A							,      map_location="cpu"	)
					else:
										UpperCAmelCase			:   Union[str, Any]      =				torch.load(args.path_to_checkpoint							,      map_location="cpu"	)

					UpperCAmelCase			:   Dict      =				input_state_dict.get("args"							,      __A	)

					# 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:
																				UpperCAmelCase			:   Optional[Any]      =				"gelu_fast"
															elif ds_args.openai_gelu:
																				UpperCAmelCase			:   int      =				"gelu_new"
															else:
																				UpperCAmelCase			:   int      =				"gelu"
										else:
															# in the very early days this used to be "gelu_new"
															UpperCAmelCase			:   List[Any]      =				"gelu_new"

										# Spell out all parameters in case the defaults change.
										UpperCAmelCase			:   int      =				GPTaConfig(
										    vocab_size=5_02_57							,      n_positions=10_24							,      n_embd=10_24							,      n_layer=24							,      n_head=16							,      n_inner=40_96							,      activation_function=__A							,      resid_pdrop=0.1							,      embd_pdrop=0.1							,      attn_pdrop=0.1							,      layer_norm_epsilon=1E-5							,      initializer_range=0.0_2							,      summary_type="cls_index"							,      summary_use_proj=__A							,      summary_activation=__A							,      summary_proj_to_labels=__A							,      summary_first_dropout=0.1							,      scale_attn_weights=__A							,      use_cache=__A							,      bos_token_id=5_02_56							,      eos_token_id=5_02_56							,      )
					else:
										UpperCAmelCase			:   Tuple      =				GPTaConfig.from_json_file(args.config_file	)

					UpperCAmelCase			:   str      =				["GPT2LMHeadModel"]

					# Convert.
					print("Converting"	)
					UpperCAmelCase			:   Dict      =				convert_megatron_checkpoint(__A							,      __A							,      __A	)

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

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

					UpperCAmelCase			:   str      =				AutoTokenizer.from_pretrained(__A	)
					UpperCAmelCase			:   Tuple      =				type(__A	).__name__
					UpperCAmelCase			:   Optional[int]      =				tokenizer_class

					# Store the config to file.
					print("Saving config"	)
					config.save_pretrained(__A	)

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

					# Store the state_dict to file.
					UpperCAmelCase			:   List[str]      =				os.path.join(__A							,      "pytorch_model.bin"	)
					print(F"""Saving checkpoint to \"{output_checkpoint_file}\""""	)
					torch.save(__A							,      __A	)


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

if __name__ == "__main__":
						main()

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





"""simple docstring"""






from __future__ import annotations

from collections import Counter
from random import random







class       snake_case__  :







			def __init__(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				{}







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				{}







			def 			__lowerCAmelCase				(			self			: Optional[int]			,  lowercase			: str			,  lowercase			: str			,  lowercase			: float  ):





								'''simple docstring'''



								if nodea not in self.connections:
													self.add_node(lowercase  )
								if nodea not in self.connections:
													self.add_node(lowercase  )
								UpperCAmelCase			:   int      =				probability







			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''simple docstring'''



								return list(self.connections  )







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				0
								UpperCAmelCase			:   Union[str, Any]      =				random()

								for dest in self.connections[node]:
													current_probability += self.connections[node][dest]
													if current_probability > random_value:
																		return dest
								return ""



def   lowercase_	(    _lowercase       :		str							,      _lowercase       :		list[tuple[str, str, float]]							,      _lowercase       :		int	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				MarkovChainGraphUndirectedUnweighted()

					for nodea, nodea, probability in transitions:
										graph.add_transition_probability(_lowercase							,      _lowercase							,      _lowercase	)

					UpperCAmelCase			:   List[Any]      =				Counter(graph.get_nodes()	)
					UpperCAmelCase			:   List[Any]      =				start

					for _ in range(_lowercase	):
										UpperCAmelCase			:   Optional[Any]      =				graph.transition(_lowercase	)
										visited[node] += 1

					return visited


if __name__ == "__main__":
	import doctest

	doctest.testmod()


