
import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowercase_		=							logging.getLogger(__name__)







def 			lowerCAmelCase  (     )  ->List[Any]:






					"""simple docstring"""






					__magic_name__ :							List[Any]		       =							argparse.ArgumentParser(description='''PyTorch Transformer Language Model'''	)
					parser.add_argument('''--model_name''',     type=UpperCAmelCase,     default='''transfo-xl-wt103''',     help='''pretrained model name'''	)
					parser.add_argument(
					    '''--split''',     type=UpperCAmelCase,     default='''test''',     choices=['''all''', '''valid''', '''test'''],     help='''which split to evaluate'''	)
					parser.add_argument('''--batch_size''',     type=UpperCAmelCase,     default=10,     help='''batch size'''	)
					parser.add_argument('''--tgt_len''',     type=UpperCAmelCase,     default=128,     help='''number of tokens to predict'''	)
					parser.add_argument('''--ext_len''',     type=UpperCAmelCase,     default=0,     help='''length of the extended context'''	)
					parser.add_argument('''--mem_len''',     type=UpperCAmelCase,     default=1600,     help='''length of the retained previous heads'''	)
					parser.add_argument('''--clamp_len''',     type=UpperCAmelCase,     default=1000,     help='''max positional embedding index'''	)
					parser.add_argument('''--no_cuda''',     action='''store_true''',     help='''Do not use CUDA even though CUA is available'''	)
					parser.add_argument('''--work_dir''',     type=UpperCAmelCase,     required=UpperCAmelCase,     help='''path to the work_dir'''	)
					parser.add_argument('''--no_log''',     action='''store_true''',     help='''do not log the eval result'''	)
					parser.add_argument('''--same_length''',     action='''store_true''',     help='''set same length attention with masking'''	)
					parser.add_argument('''--server_ip''',     type=UpperCAmelCase,     default='''''',     help='''Can be used for distant debugging.'''	)
					parser.add_argument('''--server_port''',     type=UpperCAmelCase,     default='''''',     help='''Can be used for distant debugging.'''	)
					__magic_name__ :							Tuple		       =							parser.parse_args()
					assert args.ext_len >= 0, "extended context length must be non-negative"

					if args.server_ip and args.server_port:
										# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
										import ptvsd

										print('''Waiting for debugger attach'''	)
										ptvsd.enable_attach(address=(args.server_ip, args.server_port),     redirect_output=UpperCAmelCase	)
										ptvsd.wait_for_attach()

					__magic_name__ :							Tuple		       =							torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''	)
					logger.info('''device: {}'''.format(UpperCAmelCase	)	)

					# Load a pre-processed dataset
					# You can also build the corpus yourself using TransfoXLCorpus methods
					# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
					# and tokenizing the dataset
					# The pre-processed corpus is a convertion (using the conversion script )
					__magic_name__ :							Optional[Any]		       =							TransfoXLCorpus.from_pretrained(args.model_name	)

					__magic_name__ :							Any		       =							corpus.get_iterator('''valid''',     args.batch_size,     args.tgt_len,     device=UpperCAmelCase,     ext_len=args.ext_len	)
					__magic_name__ :							List[Any]		       =							corpus.get_iterator('''test''',     args.batch_size,     args.tgt_len,     device=UpperCAmelCase,     ext_len=args.ext_len	)

					# Load a pre-trained model
					__magic_name__ :							Union[str, Any]		       =							TransfoXLLMHeadModel.from_pretrained(args.model_name	)
					model.to(UpperCAmelCase	)

					logger.info(
					    '''Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'''.format(
					        args.batch_size,     args.tgt_len,     args.ext_len,     args.mem_len,     args.clamp_len	)	)

					model.reset_memory_length(args.mem_len	)
					if args.clamp_len > 0:
										__magic_name__ :							int		       =							args.clamp_len
					if args.same_length:
										__magic_name__ :							Optional[Any]		       =							True

					###############################################################################
					# Evaluation code
					###############################################################################
					def evaluate(UpperCAmelCase	):
										# Turn on evaluation mode which disables dropout.
										model.eval()
										__magic_name__ :							Optional[int]		       =							0, 0.0
										__magic_name__ :							int		       =							time.time()
										with torch.no_grad():
															__magic_name__ :							Tuple		       =							None
															for idx, (data, target, seq_len) in enumerate(UpperCAmelCase	):
																				__magic_name__ :							Dict		       =							model(UpperCAmelCase,     lm_labels=UpperCAmelCase,     mems=UpperCAmelCase	)
																				__magic_name__ :							Optional[int]		       =							ret
																				__magic_name__ :							Any		       =							loss.mean()
																				total_loss += seq_len * loss.item()
																				total_len += seq_len
															__magic_name__ :							int		       =							time.time() - start_time
										logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(UpperCAmelCase,     1000 * total_time / (idx + 1)	)	)
										return total_loss / total_len

					# Run on test data.
					if args.split == "all":
										__magic_name__ :							int		       =							evaluate(UpperCAmelCase	)
										__magic_name__ :							Dict		       =							evaluate(UpperCAmelCase	)
					elif args.split == "valid":
										__magic_name__ :							Optional[int]		       =							evaluate(UpperCAmelCase	)
										__magic_name__ :							int		       =							None
					elif args.split == "test":
										__magic_name__ :							Any		       =							evaluate(UpperCAmelCase	)
										__magic_name__ :							int		       =							None

					def format_log(UpperCAmelCase,     UpperCAmelCase	):
										__magic_name__ :							List[str]		       =							'''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(UpperCAmelCase,     UpperCAmelCase,     math.exp(UpperCAmelCase	)	)
										return log_str

					__magic_name__ :							str		       =							''''''
					if valid_loss is not None:
										log_str += format_log(UpperCAmelCase,     '''valid'''	)
					if test_loss is not None:
										log_str += format_log(UpperCAmelCase,     '''test'''	)

					logger.info('''=''' * 100	)
					logger.info(UpperCAmelCase	)
					logger.info('''=''' * 100	)


if __name__ == "__main__":
				main()









# 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_bert/convert_megatron_bert_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 MegatronBertConfig







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase=0	)  ->Union[str, Any]:






					"""simple docstring"""






					if name is None:
										__magic_name__ :							Tuple		       =							None
					else:
										__magic_name__ :							Any		       =							'''.''' * max(0,     spaces - 2	) + '''# {:''' + str(50 - spaces	) + '''s}'''
										__magic_name__ :							int		       =							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	)  ->Any:






					"""simple docstring"""






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







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase	)  ->Dict:






					"""simple docstring"""






					__magic_name__ :							Any		       =							{}

					# old versions did not store training args
					__magic_name__ :							Optional[int]		       =							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))

										__magic_name__ :							Dict		       =							ds_args.tokenizer_type
										__magic_name__ :							Tuple		       =							ds_args.padded_vocab_size
										__magic_name__ :							Optional[int]		       =							ds_args.max_position_embeddings
										__magic_name__ :							List[Any]		       =							ds_args.hidden_size
										__magic_name__ :							Dict		       =							ds_args.num_layers
										__magic_name__ :							Optional[int]		       =							ds_args.num_attention_heads
										__magic_name__ :							Any		       =							ds_args.ffn_hidden_size if '''ffn_hidden_size''' in ds_args else 4 * ds_args.hidden_size
										# pprint(config)

					# The number of heads.
					__magic_name__ :							Union[str, Any]		       =							config.num_attention_heads
					# The hidden_size per head.
					__magic_name__ :							Union[str, Any]		       =							config.hidden_size // heads
					# Megatron-LM checkpoint version
					if "checkpoint_version" in input_state_dict.keys():
										__magic_name__ :							Optional[Any]		       =							input_state_dict['''checkpoint_version''']
					else:
										__magic_name__ :							Tuple		       =							0.0

					# The model.
					__magic_name__ :							Any		       =							input_state_dict['''model''']
					# The language model.
					__magic_name__ :							Union[str, Any]		       =							model['''language_model''']
					# The embeddings.
					__magic_name__ :							List[Any]		       =							lm['''embedding''']

					# The word embeddings.
					__magic_name__ :							Tuple		       =							embeddings['''word_embeddings''']['''weight''']
					# Truncate the embedding table to vocab_size rows.
					__magic_name__ :							int		       =							word_embeddings[: config.vocab_size, :]
					# Store the word embeddings.
					__magic_name__ :							List[str]		       =							word_embeddings

					# The position embeddings.
					__magic_name__ :							str		       =							embeddings['''position_embeddings''']['''weight''']
					assert pos_embeddings.size(0	) == config.max_position_embeddings and pos_embeddings.size(1	) == config.hidden_size
					# Store the position embeddings.
					__magic_name__ :							Optional[Any]		       =							pos_embeddings

					# The token-type embeddings.
					__magic_name__ :							int		       =							embeddings['''tokentype_embeddings''']['''weight''']
					# Store the position embeddings.
					__magic_name__ :							Tuple		       =							tokentype_embeddings

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

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

					# The simple map of names for "automated" rules.
					__magic_name__ :							Union[str, Any]		       =							{
					    '''attention.dense''': '''.attention.output.dense.''',
					    '''self_attention.dense''': '''.attention.output.dense.''',
					    '''mlp.dense_h_to_4h''': '''.intermediate.dense.''',
					    '''mlp.dense_4h_to_h''': '''.output.dense.''',
					}

					# Keep track of the attention/query/value tensor.
					__magic_name__ :							Optional[Any]		       =							None

					# Extract the layers.
					for key, val in transformer.items():
										# Match the name.
										__magic_name__ :							int		       =							layer_re.match(UpperCAmelCase	)

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

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

										# The name of the layer.
										__magic_name__ :							List[Any]		       =							F'''bert.encoder.layer.{layer_idx}'''

										# For layernorm(s), simply store the layer norm.
										if op_name.endswith('''layernorm'''	):
															__magic_name__ :							Optional[int]		       =							'''attention.ln''' if op_name.startswith('''input'''	) else '''ln'''
															__magic_name__ :							List[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":
															# Make sure the QKV pointer is nil.
															assert attention_qkv_weight is None, ""

															__magic_name__ :							Any		       =							fix_query_key_value_ordering(UpperCAmelCase,     UpperCAmelCase,     3,     UpperCAmelCase,     UpperCAmelCase	)
															# Store the tensor as we need the bias as well to interleave QKV and biases.
															__magic_name__ :							str		       =							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":
															# Make sure we read the weight tensor.
															assert attention_qkv_weight is not None, ""

															# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
															__magic_name__ :							List[str]		       =							attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
															__magic_name__ :							str		       =							attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
															__magic_name__ :							Optional[Any]		       =							attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

															__magic_name__ :							str		       =							fix_query_key_value_ordering(UpperCAmelCase,     UpperCAmelCase,     3,     UpperCAmelCase,     UpperCAmelCase	)
															# Split the bias.
															__magic_name__ :							Optional[int]		       =							out_val[0 * config.hidden_size : 1 * config.hidden_size]
															__magic_name__ :							str		       =							out_val[1 * config.hidden_size : 2 * config.hidden_size]
															__magic_name__ :							List[str]		       =							out_val[2 * config.hidden_size : 3 * config.hidden_size]

															# Store.
															__magic_name__ :							str		       =							q
															__magic_name__ :							List[Any]		       =							q_bias
															__magic_name__ :							List[str]		       =							k
															__magic_name__ :							Optional[Any]		       =							k_bias
															__magic_name__ :							Union[str, Any]		       =							v
															__magic_name__ :							str		       =							v_bias

															# Clear the stored tensor.
															__magic_name__ :							List[str]		       =							None

										# Copy weights and biases as is.
										elif weight_or_bias in ["weight", "bias"]:
															__magic_name__ :							Dict		       =							megatron_to_transformers[op_name]
															__magic_name__ :							Optional[Any]		       =							val

    # The final layernorm.
					__magic_name__ :							Union[str, Any]		       =							transformer['''final_layernorm.weight''']
					__magic_name__ :							Union[str, Any]		       =							transformer['''final_layernorm.bias''']

					# The pooler.
					__magic_name__ :							List[Any]		       =							lm['''pooler''']

					# Store the matrix and the bias.
					__magic_name__ :							Tuple		       =							pooler['''dense.weight''']
					__magic_name__ :							str		       =							pooler['''dense.bias''']

					# The LM head from Megatron (for RACE).
					__magic_name__ :							Any		       =							model['''lm_head''']

					# The transform matrix.
					__magic_name__ :							Optional[int]		       =							lm_head['''dense.weight''']
					__magic_name__ :							int		       =							lm_head['''dense.bias''']

					# The transform LN.
					__magic_name__ :							str		       =							lm_head['''layernorm.weight''']
					__magic_name__ :							Any		       =							lm_head['''layernorm.bias''']

					# For the decoder, we replicate the weights.
					__magic_name__ :							int		       =							word_embeddings
					__magic_name__ :							Tuple		       =							lm_head['''bias''']

					# The classifier from Megatron (for MLNI).
					__magic_name__ :							Optional[Any]		       =							model['''binary_head''']

					# Store the classifier.
					__magic_name__ :							Union[str, Any]		       =							binary_head['''weight''']
					__magic_name__ :							List[str]		       =							binary_head['''bias''']

					# It should be done!
					return output_state_dict







def 			lowerCAmelCase  (     )  ->Optional[int]:






					"""simple docstring"""






					__magic_name__ :							Union[str, Any]		       =							argparse.ArgumentParser()
					parser.add_argument('''--print-checkpoint-structure''',     action='''store_true'''	)
					parser.add_argument('''path_to_checkpoint''',     type=UpperCAmelCase,     help='''Path to the ZIP file containing the checkpoint'''	)
					parser.add_argument(
					    '''--config_file''',     default='''''',     type=UpperCAmelCase,     help='''An optional config json file describing the pre-trained model.''',     )
					__magic_name__ :							Dict		       =							parser.parse_args()

					# Extract the basename.
					__magic_name__ :							Tuple		       =							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:
																				__magic_name__ :							Tuple		       =							torch.load(UpperCAmelCase,     map_location='''cpu'''	)
					else:
										__magic_name__ :							Optional[int]		       =							torch.load(args.path_to_checkpoint,     map_location='''cpu'''	)

					if args.config_file == "":
										# Default config of megatron-bert 345m
										__magic_name__ :							Tuple		       =							MegatronBertConfig()

										# different megatron-bert-*-345m models have different vocab sizes, so override the default
										# config (which is for megatron-bert-cased-345m) with the actual vocab dimension
										__magic_name__ :							Optional[int]		       =							input_state_dict['''model''']['''lm_head''']['''bias'''].numel()
					else:
										__magic_name__ :							Union[str, Any]		       =							MegatronBertConfig.from_json_file(args.config_file	)

					# Convert.
					print('''Converting'''	)
					__magic_name__ :							List[Any]		       =							convert_megatron_checkpoint(UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase	)

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

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

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


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

if __name__ == "__main__":
				main()

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



