


"""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 _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase=0 ):

			'''simple docstring'''




			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 _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__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 _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__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.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
						# pprint(config)

			# The number of heads.
			__lowerCAmelCase							       =     config.n_head
			# The hidden_size per head.
			__lowerCAmelCase							       =     config.n_embd // config.n_head
			# 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, :]
			__lowerCAmelCase							       =     word_embeddings

			# The position embeddings.
			__lowerCAmelCase							       =     embeddings["position_embeddings"]["weight"]
			# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
			__lowerCAmelCase							       =     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.
			__lowerCAmelCase							       =     pos_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": ".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.
						__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"transformer.h.{layer_idx}"

						# For layernorm(s), simply store the layer norm.
						if op_name.endswith("layernorm" ):
									__lowerCAmelCase							       =     "ln_1" if op_name.startswith("input" ) else "ln_2"
									__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":
									# Insert a tensor of 1x1xDxD bias.
									__lowerCAmelCase							       =     torch.tril(torch.ones((n_positions, n_positions)       ,					dtype=torch.floataa ) ).view(
									    1       ,					1       ,					_UpperCamelCase       ,					_UpperCamelCase )
									__lowerCAmelCase							       =     causal_mask

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

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

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

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

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

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

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

			# It should be done!
			return output_state_dict


def _lowerCamelCase      (						):

			'''simple docstring'''




			__lowerCAmelCase							       =     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."       ,					)
			__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" )

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

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

			__lowerCAmelCase							       =     ["GPT2LMHeadModel"]

			# 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 )

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

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


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

if __name__ == "__main__":
	main()

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








"""simple docstring"""


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     [False] * len(_UpperCamelCase )
			__lowerCAmelCase							       =     []
			queue.append(_UpperCamelCase )
			__lowerCAmelCase							       =     True

			while queue:
						__lowerCAmelCase							       =     queue.pop(0 )
						for ind in range(len(graph[u] ) ):
									if visited[ind] is False and graph[u][ind] > 0:
												queue.append(_UpperCamelCase )
												__lowerCAmelCase							       =     True
												__lowerCAmelCase							       =     u

			return visited[t]


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     [-1] * (len(_UpperCamelCase ))
			__lowerCAmelCase							       =     0
			while bfs(_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):
						__lowerCAmelCase							       =     float("Inf" )
						__lowerCAmelCase							       =     sink

						while s != source:
									# Find the minimum value in select path
									__lowerCAmelCase							       =     min(_UpperCamelCase       ,					graph[parent[s]][s] )
									__lowerCAmelCase							       =     parent[s]

						max_flow += path_flow
						__lowerCAmelCase							       =     sink

						while v != source:
									__lowerCAmelCase							       =     parent[v]
									graph[u][v] -= path_flow
									graph[v][u] += path_flow
									__lowerCAmelCase							       =     parent[v]
			return max_flow


A	:  Union[str, Any]            =     [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

A       ,       A	:  Any            =     0, 5


print(ford_fulkerson(graph, source, sink))




