





'''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_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_   (	SCREAMING_SNAKE_CASE__   :      Union[str, Any],      SCREAMING_SNAKE_CASE__   :      Dict,      SCREAMING_SNAKE_CASE__   :      Tuple=0 )			->						Any:
						# Format the message.
						if name is None:
												UpperCAmelCase_      :   Tuple      =	None
						else:
												UpperCAmelCase_      :   Dict      =	'''.''' * max(0,      spaces - 2 ) + '''# {:''' + str(50 - spaces ) + '''s}'''
												UpperCAmelCase_      :   List[str]      =	fmt.format(SCREAMING_SNAKE_CASE__ )

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





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Any,      SCREAMING_SNAKE_CASE__   :      Any,      SCREAMING_SNAKE_CASE__   :      Tuple,      SCREAMING_SNAKE_CASE__   :      Any,      SCREAMING_SNAKE_CASE__   :      Union[str, Any] )			->						List[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 BERT.
						UpperCAmelCase_      :   Optional[Any]      =	param.size()
						if checkpoint_version == 1.0:
												# version 1.0 stores [num_heads * hidden_size * num_splits, :]
												UpperCAmelCase_      :   Tuple      =	(num_heads, hidden_size, num_splits) + input_shape[1:]
												UpperCAmelCase_      :   Any      =	param.view(*SCREAMING_SNAKE_CASE__ )
												UpperCAmelCase_      :   Optional[int]      =	param.transpose(0,      2 )
												UpperCAmelCase_      :   str      =	param.transpose(1,      2 ).contiguous()
						elif checkpoint_version >= 2.0:
												# other versions store [num_heads * num_splits * hidden_size, :]
												UpperCAmelCase_      :   str      =	(num_heads, num_splits, hidden_size) + input_shape[1:]
												UpperCAmelCase_      :   Any      =	param.view(*SCREAMING_SNAKE_CASE__ )
												UpperCAmelCase_      :   Any      =	param.transpose(0,      1 ).contiguous()
						UpperCAmelCase_      :   Optional[int]      =	param.view(*SCREAMING_SNAKE_CASE__ )
						return param





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      int,      SCREAMING_SNAKE_CASE__   :      int,      SCREAMING_SNAKE_CASE__   :      Dict )			->						Tuple:
						# The converted output model.
						UpperCAmelCase_      :   List[Any]      =	{}

						# old versions did not store training args
						UpperCAmelCase_      :   Optional[Any]      =	input_state_dict.get('''args''',      SCREAMING_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))

												UpperCAmelCase_      :   List[str]      =	ds_args.tokenizer_type
												UpperCAmelCase_      :   Any      =	ds_args.padded_vocab_size
												UpperCAmelCase_      :   List[str]      =	ds_args.max_position_embeddings
												UpperCAmelCase_      :   int      =	ds_args.hidden_size
												UpperCAmelCase_      :   Optional[Any]      =	ds_args.num_layers
												UpperCAmelCase_      :   Union[str, Any]      =	ds_args.num_attention_heads
												UpperCAmelCase_      :   Optional[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.
						UpperCAmelCase_      :   Tuple      =	config.num_attention_heads
						# The hidden_size per head.
						UpperCAmelCase_      :   Dict      =	config.hidden_size // heads
						# Megatron-LM checkpoint version
						if "checkpoint_version" in input_state_dict.keys():
												UpperCAmelCase_      :   Optional[Any]      =	input_state_dict['''checkpoint_version''']
						else:
												UpperCAmelCase_      :   Dict      =	0.0

						# The model.
						UpperCAmelCase_      :   str      =	input_state_dict['''model''']
						# The language model.
						UpperCAmelCase_      :   str      =	model['''language_model''']
						# The embeddings.
						UpperCAmelCase_      :   Any      =	lm['''embedding''']

						# The word embeddings.
						UpperCAmelCase_      :   Dict      =	embeddings['''word_embeddings''']['''weight''']
						# Truncate the embedding table to vocab_size rows.
						UpperCAmelCase_      :   List[str]      =	word_embeddings[: config.vocab_size, :]
						# Store the word embeddings.
						UpperCAmelCase_      :   Union[str, Any]      =	word_embeddings

						# The position embeddings.
						UpperCAmelCase_      :   Any      =	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.
						UpperCAmelCase_      :   List[Any]      =	pos_embeddings

						# The token-type embeddings.
						UpperCAmelCase_      :   Tuple      =	embeddings['''tokentype_embeddings''']['''weight''']
						# Store the position embeddings.
						UpperCAmelCase_      :   Union[str, Any]      =	tokentype_embeddings

						# The transformer.
						UpperCAmelCase_      :   Optional[Any]      =	lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

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

						# The simple map of names for "automated" rules.
						UpperCAmelCase_      :   List[str]      =	{
						    '''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.
						UpperCAmelCase_      :   str      =	None

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

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

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

												# The name of the layer.
												UpperCAmelCase_      :   Any      =	F"""bert.encoder.layer.{layer_idx}"""

												# For layernorm(s), simply store the layer norm.
												if op_name.endswith('''layernorm''' ):
																		UpperCAmelCase_      :   Dict      =	'''attention.ln''' if op_name.startswith('''input''' ) else '''ln'''
																		UpperCAmelCase_      :   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, ""

																		UpperCAmelCase_      :   List[str]      =	fix_query_key_value_ordering(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      3,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
																		# Store the tensor as we need the bias as well to interleave QKV and biases.
																		UpperCAmelCase_      :   Union[str, 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":
																		# 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.
																		UpperCAmelCase_      :   Tuple      =	attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
																		UpperCAmelCase_      :   List[str]      =	attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
																		UpperCAmelCase_      :   Optional[int]      =	attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

																		UpperCAmelCase_      :   str      =	fix_query_key_value_ordering(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      3,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
																		# Split the bias.
																		UpperCAmelCase_      :   Dict      =	out_val[0 * config.hidden_size : 1 * config.hidden_size]
																		UpperCAmelCase_      :   str      =	out_val[1 * config.hidden_size : 2 * config.hidden_size]
																		UpperCAmelCase_      :   Optional[Any]      =	out_val[2 * config.hidden_size : 3 * config.hidden_size]

																		# Store.
																		UpperCAmelCase_      :   Any      =	q
																		UpperCAmelCase_      :   Optional[int]      =	q_bias
																		UpperCAmelCase_      :   int      =	k
																		UpperCAmelCase_      :   Dict      =	k_bias
																		UpperCAmelCase_      :   Any      =	v
																		UpperCAmelCase_      :   Optional[Any]      =	v_bias

																		# Clear the stored tensor.
																		UpperCAmelCase_      :   Tuple      =	None

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

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

						# The pooler.
						UpperCAmelCase_      :   int      =	lm['''pooler''']

						# Store the matrix and the bias.
						UpperCAmelCase_      :   Any      =	pooler['''dense.weight''']
						UpperCAmelCase_      :   List[str]      =	pooler['''dense.bias''']

						# The LM head from Megatron (for RACE).
						UpperCAmelCase_      :   Optional[int]      =	model['''lm_head''']

						# The transform matrix.
						UpperCAmelCase_      :   List[str]      =	lm_head['''dense.weight''']
						UpperCAmelCase_      :   Any      =	lm_head['''dense.bias''']

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

						# For the decoder, we replicate the weights.
						UpperCAmelCase_      :   List[Any]      =	word_embeddings
						UpperCAmelCase_      :   List[str]      =	lm_head['''bias''']

						# The classifier from Megatron (for MLNI).
						UpperCAmelCase_      :   str      =	model['''binary_head''']

						# Store the classifier.
						UpperCAmelCase_      :   Optional[int]      =	binary_head['''weight''']
						UpperCAmelCase_      :   Tuple      =	binary_head['''bias''']

						# It should be done!
						return output_state_dict





def 							lowerCamelCase_   (	)			->						int:
						# Create the argument parser.
						UpperCAmelCase_      :   List[Any]      =	argparse.ArgumentParser()
						parser.add_argument('''--print-checkpoint-structure''',      action='''store_true''' )
						parser.add_argument('''path_to_checkpoint''',      type=SCREAMING_SNAKE_CASE__,      help='''Path to the ZIP file containing the checkpoint''' )
						parser.add_argument(
						    '''--config_file''',      default='''''',      type=SCREAMING_SNAKE_CASE__,      help='''An optional config json file describing the pre-trained model.''',      )
						UpperCAmelCase_      :   Any      =	parser.parse_args()

						# Extract the basename.
						UpperCAmelCase_      :   Optional[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_      :   List[Any]      =	torch.load(SCREAMING_SNAKE_CASE__,      map_location='''cpu''' )
						else:
												UpperCAmelCase_      :   List[Any]      =	torch.load(args.path_to_checkpoint,      map_location='''cpu''' )

						if args.config_file == "":
												# Default config of megatron-bert 345m
												UpperCAmelCase_      :   str      =	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
												UpperCAmelCase_      :   Any      =	input_state_dict['''model''']['''lm_head''']['''bias'''].numel()
						else:
												UpperCAmelCase_      :   Union[str, Any]      =	MegatronBertConfig.from_json_file(args.config_file )

						# Convert.
						print('''Converting''' )
						UpperCAmelCase_      :   Optional[Any]      =	convert_megatron_checkpoint(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )

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

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

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


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

if __name__ == "__main__":
					main()

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










import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


A__       =			logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

A__       =			{
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
A__       =			{
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
A__       =			sorted(arg_to_scheduler.keys())
A__       =			"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""





class    __lowerCAmelCase					(	pl.LightningModule			):




				def __init__(				self		,							_snake_case		,							_snake_case=None		,							_snake_case="base"		,							_snake_case=None		,							_snake_case=None		,							_snake_case=None		,							**_snake_case		,							):



									"""simple docstring"""





									super().__init__()
									# TODO: move to self.save_hyperparameters()
									# self.save_hyperparameters()
									# can also expand arguments into trainer signature for easier reading

									self.save_hyperparameters(_snake_case						)
									_lowerCAmelCase         =       0
									_lowerCAmelCase         =       Path(self.hparams.output_dir						)
									_lowerCAmelCase         =       self.hparams.cache_dir if self.hparams.cache_dir else None
									if config is None:
														_lowerCAmelCase         =       AutoConfig.from_pretrained(
														    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path		,							**({"""num_labels""": num_labels} if num_labels is not None else {})		,							cache_dir=_snake_case		,							**_snake_case		,							)
									else:
														_lowerCAmelCase         =       config

									_lowerCAmelCase         =       ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
									for p in extra_model_params:
														if getattr(self.hparams		,							_snake_case		,							_snake_case						):
																			assert hasattr(self.config		,							_snake_case						), F'model config doesn\'t have a `{p}` attribute'
																			setattr(self.config		,							_snake_case		,							getattr(self.hparams		,							_snake_case						)						)

									if tokenizer is None:
														_lowerCAmelCase         =       AutoTokenizer.from_pretrained(
														    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,							cache_dir=_snake_case		,							)
									else:
														_lowerCAmelCase         =       tokenizer
									_lowerCAmelCase         =       MODEL_MODES[mode]
									if model is None:
														_lowerCAmelCase         =       self.model_type.from_pretrained(
														    self.hparams.model_name_or_path		,							from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path						)		,							config=self.config		,							cache_dir=_snake_case		,							)
									else:
														_lowerCAmelCase         =       model




				def 				snake_case  (				self		,							*_snake_case		,							**_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.model_type.from_pretrained(*_snake_case		,							**_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       arg_to_scheduler[self.hparams.lr_scheduler]
									_lowerCAmelCase         =       get_schedule_func(
									    self.opt		,							num_warmup_steps=self.hparams.warmup_steps		,							num_training_steps=self.total_steps()						)
									_lowerCAmelCase         =       {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
									return scheduler




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.model
									_lowerCAmelCase         =       ["""bias""", """LayerNorm.weight"""]
									_lowerCAmelCase         =       [
									    {
									        """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay						)],
									        """weight_decay""": self.hparams.weight_decay,
									    },
									    {
									        """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay						)],
									        """weight_decay""": 0.0,
									    },
									]
									if self.hparams.adafactor:
														_lowerCAmelCase         =       Adafactor(
														    _snake_case		,							lr=self.hparams.learning_rate		,							scale_parameter=_snake_case		,							relative_step=_snake_case						)

									else:
														_lowerCAmelCase         =       AdamW(
														    _snake_case		,							lr=self.hparams.learning_rate		,							eps=self.hparams.adam_epsilon						)
									_lowerCAmelCase         =       optimizer

									_lowerCAmelCase         =       self.get_lr_scheduler()

									return [optimizer], [scheduler]




				def 				snake_case  (				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									return self.validation_step(_snake_case		,							_snake_case						)




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									return self.validation_end(_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       max(1		,							self.hparams.gpus						)  # TODO: consider num_tpu_cores
									_lowerCAmelCase         =       self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
									return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									if mode == "test":
														_lowerCAmelCase         =       len(self.test_dataloader().dataset						)
									else:
														_lowerCAmelCase         =       self.get_dataloader("""train"""		,							self.hparams.train_batch_size		,							shuffle=_snake_case						)
														_lowerCAmelCase         =       len(self.train_dataloader().dataset						)




				def 				snake_case  (				self		,							_snake_case		,							_snake_case		,							_snake_case = False						):



									"""simple docstring"""





									raise NotImplementedError("""You must implement this for your task"""						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									return self.train_loader




				def 				snake_case  (				self						):



									"""simple docstring"""





									return self.get_dataloader("""dev"""		,							self.hparams.eval_batch_size		,							shuffle=_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									return self.get_dataloader("""test"""		,							self.hparams.eval_batch_size		,							shuffle=_snake_case						)




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									return os.path.join(
									    self.hparams.data_dir		,							"""cached_{}_{}_{}""".format(
									        _snake_case		,							list(filter(_snake_case		,							self.hparams.model_name_or_path.split("""/"""						)						)						).pop()		,							str(self.hparams.max_seq_length						)		,							)		,							)




				@pl.utilities.rank_zero_only
				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.output_dir.joinpath("""best_tfmr"""						)
									_lowerCAmelCase         =       self.step_count
									self.model.save_pretrained(_snake_case						)
									self.tokenizer.save_pretrained(_snake_case						)




				@staticmethod
				def 				snake_case  (				_snake_case		,							_snake_case						):



									"""simple docstring"""





									parser.add_argument(
									    """--model_name_or_path"""		,							default=_snake_case		,							type=_snake_case		,							required=_snake_case		,							help="""Path to pretrained model or model identifier from huggingface.co/models"""		,							)
									parser.add_argument(
									    """--config_name"""		,							default=""""""		,							type=_snake_case		,							help="""Pretrained config name or path if not the same as model_name"""						)
									parser.add_argument(
									    """--tokenizer_name"""		,							default=_snake_case		,							type=_snake_case		,							help="""Pretrained tokenizer name or path if not the same as model_name"""		,							)
									parser.add_argument(
									    """--cache_dir"""		,							default=""""""		,							type=_snake_case		,							help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""		,							)
									parser.add_argument(
									    """--encoder_layerdrop"""		,							type=_snake_case		,							help="""Encoder layer dropout probability (Optional). Goes into model.config"""		,							)
									parser.add_argument(
									    """--decoder_layerdrop"""		,							type=_snake_case		,							help="""Decoder layer dropout probability (Optional). Goes into model.config"""		,							)
									parser.add_argument(
									    """--dropout"""		,							type=_snake_case		,							help="""Dropout probability (Optional). Goes into model.config"""		,							)
									parser.add_argument(
									    """--attention_dropout"""		,							type=_snake_case		,							help="""Attention dropout probability (Optional). Goes into model.config"""		,							)
									parser.add_argument("""--learning_rate"""		,							default=5e-5		,							type=_snake_case		,							help="""The initial learning rate for Adam."""						)
									parser.add_argument(
									    """--lr_scheduler"""		,							default="""linear"""		,							choices=_snake_case		,							metavar=_snake_case		,							type=_snake_case		,							help="""Learning rate scheduler"""		,							)
									parser.add_argument("""--weight_decay"""		,							default=0.0		,							type=_snake_case		,							help="""Weight decay if we apply some."""						)
									parser.add_argument("""--adam_epsilon"""		,							default=1e-8		,							type=_snake_case		,							help="""Epsilon for Adam optimizer."""						)
									parser.add_argument("""--warmup_steps"""		,							default=0		,							type=_snake_case		,							help="""Linear warmup over warmup_steps."""						)
									parser.add_argument("""--num_workers"""		,							default=4		,							type=_snake_case		,							help="""kwarg passed to DataLoader"""						)
									parser.add_argument("""--num_train_epochs"""		,							dest="""max_epochs"""		,							default=3		,							type=_snake_case						)
									parser.add_argument("""--train_batch_size"""		,							default=32		,							type=_snake_case						)
									parser.add_argument("""--eval_batch_size"""		,							default=32		,							type=_snake_case						)
									parser.add_argument("""--adafactor"""		,							action="""store_true"""						)





class    __lowerCAmelCase					(	pl.Callback			):




				def 				snake_case  (				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       trainer.lr_schedulers[0]["""scheduler"""]
									_lowerCAmelCase         =       {F'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()						)}
									pl_module.logger.log_metrics(_snake_case						)




				def 				snake_case  (				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									rank_zero_info("""***** Validation results *****"""						)
									_lowerCAmelCase         =       trainer.callback_metrics
									# Log results
									for key in sorted(_snake_case						):
														if key not in ["log", "progress_bar"]:
																			rank_zero_info("""{} = {}\n""".format(_snake_case		,							str(metrics[key]						)						)						)




				def 				snake_case  (				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									rank_zero_info("""***** Test results *****"""						)
									_lowerCAmelCase         =       trainer.callback_metrics
									# Log and save results to file
									_lowerCAmelCase         =       os.path.join(pl_module.hparams.output_dir		,							"""test_results.txt"""						)
									with open(_snake_case		,							"""w"""						) as writer:
														for key in sorted(_snake_case						):
																			if key not in ["log", "progress_bar"]:
																								rank_zero_info("""{} = {}\n""".format(_snake_case		,							str(metrics[key]						)						)						)
																								writer.write("""{} = {}\n""".format(_snake_case		,							str(metrics[key]						)						)						)


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					parser.add_argument(
					    """--output_dir"""      ,		default=snake_case      ,		type=snake_case      ,		required=snake_case      ,		help="""The output directory where the model predictions and checkpoints will be written."""      ,		)
					parser.add_argument(
					    """--fp16"""      ,		action="""store_true"""      ,		help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""      ,		)

					parser.add_argument(
					    """--fp16_opt_level"""      ,		type=snake_case      ,		default="""O2"""      ,		help=(
					        """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
					        """See details at https://nvidia.github.io/apex/amp.html"""
					    )      ,		)
					parser.add_argument("""--n_tpu_cores"""      ,		dest="""tpu_cores"""      ,		type=snake_case     )
					parser.add_argument("""--max_grad_norm"""      ,		dest="""gradient_clip_val"""      ,		default=1.0      ,		type=snake_case      ,		help="""Max gradient norm"""     )
					parser.add_argument("""--do_train"""      ,		action="""store_true"""      ,		help="""Whether to run training."""     )
					parser.add_argument("""--do_predict"""      ,		action="""store_true"""      ,		help="""Whether to run predictions on the test set."""     )
					parser.add_argument(
					    """--gradient_accumulation_steps"""      ,		dest="""accumulate_grad_batches"""      ,		type=snake_case      ,		default=1      ,		help="""Number of updates steps to accumulate before performing a backward/update pass."""      ,		)
					parser.add_argument("""--seed"""      ,		type=snake_case      ,		default=42      ,		help="""random seed for initialization"""     )
					parser.add_argument(
					    """--data_dir"""      ,		default=snake_case      ,		type=snake_case      ,		required=snake_case      ,		help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""      ,		)


def 						_UpperCAmelCase							( snake_case      ,		snake_case      ,		snake_case=None      ,		snake_case=True      ,		snake_case=[]      ,		snake_case=None      ,		snake_case=None      ,		**snake_case      ,		):





					"""simple docstring"""

					pl.seed_everything(args.seed     )

					# init model
					_lowerCAmelCase         =       Path(model.hparams.output_dir     )
					odir.mkdir(exist_ok=snake_case     )

					# add custom checkpoints
					if checkpoint_callback is None:
										_lowerCAmelCase         =       pl.callbacks.ModelCheckpoint(
										    filepath=args.output_dir      ,		prefix="""checkpoint"""      ,		monitor="""val_loss"""      ,		mode="""min"""      ,		save_top_k=1     )
					if early_stopping_callback:
										extra_callbacks.append(snake_case     )
					if logging_callback is None:
										_lowerCAmelCase         =       LoggingCallback()

					_lowerCAmelCase         =       {}

					# TODO: remove with PyTorch 1.6 since pl uses native amp
					if args.fpaa:
										_lowerCAmelCase         =       16
										_lowerCAmelCase         =       args.fpaa_opt_level

					if args.gpus > 1:
										_lowerCAmelCase         =       """ddp"""

					_lowerCAmelCase         =       args.accumulate_grad_batches
					_lowerCAmelCase         =       extra_train_kwargs.get("""accelerator"""      ,		snake_case     )
					_lowerCAmelCase         =       extra_train_kwargs.get("""profiler"""      ,		snake_case     )

					_lowerCAmelCase         =       pl.Trainer.from_argparse_args(
					    snake_case      ,		weights_summary=snake_case      ,		callbacks=[logging_callback] + extra_callbacks      ,		logger=snake_case      ,		checkpoint_callback=snake_case      ,		**snake_case      ,		)

					if args.do_train:
										trainer.fit(snake_case     )

					return trainer



