



"""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__       (		__lowerCamelCase    :   int							,		__lowerCamelCase    :   Any							,		__lowerCamelCase    :   Union[str, Any]=0     ):
			"""simple docstring"""
			# Format the message.
			if name is None:
						lowerCamelCase__		:		Tuple		     =None
			else:
						lowerCamelCase__		:		List[str]		     ='''.''' * max(0							,		spaces - 2     ) + '''# {:''' + str(50 - spaces     ) + '''s}'''
						lowerCamelCase__		:		List[Any]		     =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 					snake_case__       (		__lowerCamelCase    :   List[Any]							,		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   str							,		__lowerCamelCase    :   List[Any]							,		__lowerCamelCase    :   Optional[int]     ):
			"""simple docstring"""
			# 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.
			lowerCamelCase__		:		Optional[Any]		     =param.size()
			if checkpoint_version == 1.0:
						# version 1.0 stores [num_heads * hidden_size * num_splits, :]
						lowerCamelCase__		:		Dict		     =(num_heads, hidden_size, num_splits) + input_shape[1:]
						lowerCamelCase__		:		str		     =param.view(*__lowerCamelCase     )
						lowerCamelCase__		:		Optional[Any]		     =param.transpose(0							,		2     )
						lowerCamelCase__		:		Any		     =param.transpose(1							,		2     ).contiguous()
			elif checkpoint_version >= 2.0:
						# other versions store [num_heads * num_splits * hidden_size, :]
						lowerCamelCase__		:		Any		     =(num_heads, num_splits, hidden_size) + input_shape[1:]
						lowerCamelCase__		:		Optional[int]		     =param.view(*__lowerCamelCase     )
						lowerCamelCase__		:		Optional[int]		     =param.transpose(0							,		1     ).contiguous()
			lowerCamelCase__		:		Union[str, Any]		     =param.view(*__lowerCamelCase     )
			return param






def 					snake_case__       (		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   List[Any]							,		__lowerCamelCase    :   Tuple     ):
			"""simple docstring"""
			# The converted output model.
			lowerCamelCase__		:		str		     ={}

			# old versions did not store training args
			lowerCamelCase__		:		str		     =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))

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

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

			# The model.
			lowerCamelCase__		:		List[str]		     =input_state_dict['''model''']
			# The language model.
			lowerCamelCase__		:		Optional[int]		     =model['''language_model''']
			# The embeddings.
			lowerCamelCase__		:		Optional[int]		     =lm['''embedding''']

			# The word embeddings.
			lowerCamelCase__		:		Optional[int]		     =embeddings['''word_embeddings''']['''weight''']
			# Truncate the embedding table to vocab_size rows.
			lowerCamelCase__		:		Optional[Any]		     =word_embeddings[: config.vocab_size, :]
			lowerCamelCase__		:		Tuple		     =word_embeddings

			# The position embeddings.
			lowerCamelCase__		:		List[Any]		     =embeddings['''position_embeddings''']['''weight''']
			# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
			lowerCamelCase__		:		Tuple		     =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__		:		int		     =pos_embeddings

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

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

			# The simple map of names for "automated" rules.
			lowerCamelCase__		:		int		     ={
			    '''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__		:		Union[str, Any]		     =layer_re.match(__lowerCamelCase     )

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

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

						# The name of the layer.
						lowerCamelCase__		:		List[Any]		     =f'''transformer.h.{layer_idx}'''

						# For layernorm(s), simply store the layer norm.
						if op_name.endswith('''layernorm'''     ):
									lowerCamelCase__		:		Optional[int]		     ='''ln_1''' if op_name.startswith('''input'''     ) else '''ln_2'''
									lowerCamelCase__		:		Union[str, Any]		     =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__		:		Optional[Any]		     =torch.tril(torch.ones((n_positions, n_positions)							,		dtype=torch.floataa     )     ).view(
									    1							,		1							,		__lowerCamelCase							,		__lowerCamelCase     )
									lowerCamelCase__		:		Optional[int]		     =causal_mask

									# Insert a "dummy" tensor for masked_bias.
									lowerCamelCase__		:		Optional[int]		     =torch.tensor(-1e4							,		dtype=torch.floataa     )
									lowerCamelCase__		:		Optional[int]		     =masked_bias

									lowerCamelCase__		:		int		     =fix_query_key_value_ordering(__lowerCamelCase							,		__lowerCamelCase							,		3							,		__lowerCamelCase							,		__lowerCamelCase     )
									# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
									lowerCamelCase__		:		Optional[Any]		     =out_val.transpose(0							,		1     ).contiguous()
									# Store.
									lowerCamelCase__		:		int		     =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__		:		Optional[int]		     =fix_query_key_value_ordering(__lowerCamelCase							,		__lowerCamelCase							,		3							,		__lowerCamelCase							,		__lowerCamelCase     )
									# Store. No change of shape.
									lowerCamelCase__		:		Dict		     =out_val

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

						# Copy the bias.
						elif weight_or_bias == "bias":
									lowerCamelCase__		:		Optional[Any]		     =megatron_to_transformers[op_name]
									lowerCamelCase__		:		str		     =val

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

			# The final layernorm.
			lowerCamelCase__		:		Optional[Any]		     =transformer['''final_layernorm.weight''']
			lowerCamelCase__		:		Optional[Any]		     =transformer['''final_layernorm.bias''']

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

			# It should be done!
			return output_state_dict






def 					snake_case__       (		):
			"""simple docstring"""
			# Create the argument parser.
			lowerCamelCase__		:		Optional[Any]		     =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.'''							,		)
			lowerCamelCase__		:		Optional[Any]		     =parser.parse_args()

			# Extract the basename.
			lowerCamelCase__		:		List[str]		     =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__		:		int		     =torch.load(__lowerCamelCase							,		map_location='''cpu'''     )
			else:
						lowerCamelCase__		:		Optional[int]		     =torch.load(args.path_to_checkpoint							,		map_location='''cpu'''     )

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

						# Spell out all parameters in case the defaults change.
						lowerCamelCase__		:		Union[str, Any]		     =GPTaConfig(
						    vocab_size=50257							,		n_positions=1024							,		n_embd=1024							,		n_layer=24							,		n_head=16							,		n_inner=4096							,		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=50256							,		eos_token_id=50256							,		)
			else:
						lowerCamelCase__		:		int		     =GPTaConfig.from_json_file(args.config_file     )

			lowerCamelCase__		:		int		     =['''GPT2LMHeadModel''']

			# Convert.
			print('''Converting'''     )
			lowerCamelCase__		:		Tuple		     =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:
						lowerCamelCase__		:		int		     =ds_args.tokenizer_type
						if tokenizer_type == "GPT2BPETokenizer":
									lowerCamelCase__		:		Union[str, Any]		     ='''gpt2'''
						elif tokenizer_type == "PretrainedFromHF":
									lowerCamelCase__		:		Union[str, Any]		     =ds_args.tokenizer_name_or_path
						else:
									raise ValueError(f'''Unrecognized tokenizer_type {tokenizer_type}'''     )
			else:
						lowerCamelCase__		:		List[Any]		     ='''gpt2'''

			lowerCamelCase__		:		List[Any]		     =AutoTokenizer.from_pretrained(__lowerCamelCase     )
			lowerCamelCase__		:		List[str]		     =type(__lowerCamelCase     ).__name__
			lowerCamelCase__		:		List[str]		     =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.
			lowerCamelCase__		:		Optional[int]		     =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"""






def 					snake_case__       (		__lowerCamelCase    :   int     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]		     =[0] * len(__lowerCamelCase     )
			lowerCamelCase__		:		List[Any]		     =[]
			lowerCamelCase__		:		List[Any]		     =[1] * len(__lowerCamelCase     )

			for values in graph.values():
						for i in values:
									indegree[i] += 1

			for i in range(len(__lowerCamelCase     )     ):
						if indegree[i] == 0:
									queue.append(__lowerCamelCase     )

			while queue:
						lowerCamelCase__		:		Tuple		     =queue.pop(0     )
						for x in graph[vertex]:
									indegree[x] -= 1

									if long_dist[vertex] + 1 > long_dist[x]:
												lowerCamelCase__		:		Optional[Any]		     =long_dist[vertex] + 1

									if indegree[x] == 0:
												queue.append(__lowerCamelCase     )

			print(max(__lowerCamelCase     )     )


# Adjacency list of Graph
_lowercase			:			Optional[Any]										=						{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}

longest_distance(graph)




