
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


UpperCAmelCase     				=			logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

UpperCAmelCase     				=			{
    '''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
UpperCAmelCase     				=			{
    '''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
}
UpperCAmelCase     				=			sorted(arg_to_scheduler.keys())
UpperCAmelCase     				=			'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''







class 	A_       (      pl.LightningModule       ):




	'''simple docstring'''


	def __init__(	self		,   snake_case		,   snake_case=None		,   snake_case="base"		,   snake_case=None		,   snake_case=None		,   snake_case=None		,   **snake_case		,   ):
		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	)
		lowercase					  =   0
		lowercase					  =   Path(self.hparams.output_dir	)
		lowercase					  =   self.hparams.cache_dir if self.hparams.cache_dir else None
		if config is None:
			lowercase					  =   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:
			lowercase					  =   config

		lowercase					  =   ('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:
			lowercase					  =   AutoTokenizer.from_pretrained(
			    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,   cache_dir=snake_case		,   )
		else:
			lowercase					  =   tokenizer
		lowercase					  =   MODEL_MODES[mode]
		if model is None:
			lowercase					  =   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:
			lowercase					  =   model





	def      SCREAMING_SNAKE_CASE__						(	self		,   *snake_case		,   **snake_case	):
		lowercase					  =   self.model_type.from_pretrained(*snake_case		,   **snake_case	)





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





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.model
		lowercase					  =   ['bias', 'LayerNorm.weight']
		lowercase					  =   [
		    {
		        '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:
			lowercase					  =   Adafactor(
			    snake_case		,   lr=self.hparams.learning_rate		,   scale_parameter=snake_case		,   relative_step=snake_case	)

		else:
			lowercase					  =   AdamW(
			    snake_case		,   lr=self.hparams.learning_rate		,   eps=self.hparams.adam_epsilon	)
		lowercase					  =   optimizer

		lowercase					  =   self.get_lr_scheduler()

		return [optimizer], [scheduler]





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case	):
		return self.validation_step(snake_case		,   snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		return self.validation_end(snake_case	)





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





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		if stage == "test":
			lowercase					  =   len(self.test_dataloader().dataset	)
		else:
			lowercase					  =   self.get_dataloader('train'		,   self.hparams.train_batch_size		,   shuffle=snake_case	)
			lowercase					  =   len(self.train_dataloader().dataset	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case = False	):
		raise NotImplementedError('You must implement this for your task'	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		return self.train_loader





	def      SCREAMING_SNAKE_CASE__						(	self	):
		return self.get_dataloader('dev'		,   self.hparams.eval_batch_size		,   shuffle=snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		return self.get_dataloader('test'		,   self.hparams.eval_batch_size		,   shuffle=snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		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      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		lowercase					  =   self.output_dir.joinpath('best_tfmr'	)
		lowercase					  =   self.step_count
		self.model.save_pretrained(snake_case	)
		self.tokenizer.save_pretrained(snake_case	)





	@staticmethod
	def      SCREAMING_SNAKE_CASE__						(	snake_case		,   snake_case	):
		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 	A_       (      pl.Callback       ):




	'''simple docstring'''


	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case	):
		if (
		    trainer.is_global_zero and trainer.global_rank == 0
		):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
			pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.







class 	A_       (      pl.Callback       ):




	'''simple docstring'''


	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case	):
		lowercase					  =   trainer.lr_schedulers[0]['scheduler']
		lowercase					  =   {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
		pl_module.logger.log_metrics(snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case	):
		rank_zero_info('***** Validation results *****'	)
		lowercase					  =   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      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case	):
		rank_zero_info('***** Test results *****'	)
		lowercase					  =   trainer.callback_metrics
		# Log and save results to file
		lowercase					  =   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_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	#  To allow all pl args uncomment the following line
	#  parser = pl.Trainer.add_argparse_args(parser)
	parser.add_argument(
	    '--output_dir'			,							default=__SCREAMING_SNAKE_CASE			,							type=__SCREAMING_SNAKE_CASE			,							required=__SCREAMING_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=__SCREAMING_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=__SCREAMING_SNAKE_CASE							)
	parser.add_argument('--max_grad_norm'			,							dest='gradient_clip_val'			,							default=1.0			,							type=__SCREAMING_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=__SCREAMING_SNAKE_CASE			,							default=1			,							help='Number of updates steps to accumulate before performing a backward/update pass.'			,							)
	parser.add_argument('--seed'			,							type=__SCREAMING_SNAKE_CASE			,							default=42			,							help='random seed for initialization'							)
	parser.add_argument(
	    '--data_dir'			,							default=__SCREAMING_SNAKE_CASE			,							type=__SCREAMING_SNAKE_CASE			,							required=__SCREAMING_SNAKE_CASE			,							help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'			,							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE=None			,							__SCREAMING_SNAKE_CASE=True			,							__SCREAMING_SNAKE_CASE=None			,							__SCREAMING_SNAKE_CASE=[]			,							__SCREAMING_SNAKE_CASE=None			,							__SCREAMING_SNAKE_CASE=None			,							**__SCREAMING_SNAKE_CASE			,							):
	pl.seed_everything(args.seed							)

	# init model
	lowercase					  =   Path(model.hparams.output_dir							)
	odir.mkdir(exist_ok=__SCREAMING_SNAKE_CASE							)

	# add custom checkpoints
	if checkpoint_callback is None:
		lowercase					  =   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(__SCREAMING_SNAKE_CASE							)
	if logging_callback is None:
		lowercase					  =   LoggingCallback()

	lowercase					  =   {}

	# TODO: remove with PyTorch 1.6 since pl uses native amp
	if args.fpaa:
		lowercase					  =   16
		# train_params["amp_level"] = args.fp16_opt_level

	if args.gpus > 1:
		lowercase					  =   'auto'  # "ddp"
		lowercase					  =   'ddp'

	lowercase					  =   args.accumulate_grad_batches
	lowercase					  =   None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
	lowercase					  =   'auto'

	lowercase					  =   pl.Trainer.from_argparse_args(
	    __SCREAMING_SNAKE_CASE			,							weights_summary=__SCREAMING_SNAKE_CASE			,							callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]			,							logger=__SCREAMING_SNAKE_CASE			,							**__SCREAMING_SNAKE_CASE			,							)

	if args.do_train:
		trainer.fit(__SCREAMING_SNAKE_CASE							)

	return trainer



import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   copy.deepcopy(__SCREAMING_SNAKE_CASE							)
	lowercase					  =   sorted(__SCREAMING_SNAKE_CASE							)
	lowercase					  =   [bisect.bisect_right(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							) for y in x]
	return quantized






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE=0							):
	lowercase					  =   np.arange(start=3			,							stop=__SCREAMING_SNAKE_CASE			,							step=4							).tolist() if k > 0 else [10]
	lowercase					  =   _quantize(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							)
	# count number of elements per group
	lowercase					  =   np.unique(__SCREAMING_SNAKE_CASE			,							return_counts=__SCREAMING_SNAKE_CASE							)[1]
	lowercase					  =   [0] + bins + [np.inf]
	logger.info('Using {} as bins for aspect lengths quantization'.format(__SCREAMING_SNAKE_CASE							)							)
	logger.info('Count of instances per bin: {}'.format(__SCREAMING_SNAKE_CASE							)							)
	return groups







class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


	def __init__(	self		,   snake_case		,   snake_case		,   snake_case	):
		if not isinstance(snake_case		,   snake_case	):
			raise ValueError(
			    'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(snake_case	)	)
		lowercase					  =   sampler
		lowercase					  =   group_ids
		lowercase					  =   batch_size





	def __iter__(	self	):
		lowercase					  =   defaultdict(snake_case	)
		lowercase					  =   defaultdict(snake_case	)

		lowercase					  =   0
		for idx in self.sampler:
			lowercase					  =   self.group_ids[idx]
			buffer_per_group[group_id].append(snake_case	)
			samples_per_group[group_id].append(snake_case	)
			if len(buffer_per_group[group_id]	) == self.batch_size:
				yield buffer_per_group[group_id]  # TODO
				num_batches += 1
				del buffer_per_group[group_id]
			assert len(buffer_per_group[group_id]	) < self.batch_size

		# now we have run out of elements that satisfy
		# the group criteria, let's return the remaining
		# elements so that the size of the sampler is
		# deterministic
		lowercase					  =   len(self	)
		lowercase					  =   expected_num_batches - num_batches
		if num_remaining > 0:
			# for the remaining batches, group the batches by similar lengths
			lowercase					  =   []
			for group_id, idxs in sorted(buffer_per_group.items()		,   key=lambda snake_case	: x[0]	):
				batch_idx.extend(snake_case	)
				if len(snake_case	) >= self.batch_size:
					yield batch_idx[: self.batch_size]
					lowercase					  =   batch_idx[self.batch_size :]
					num_remaining -= 1
			if len(snake_case	) > 0:
				yield batch_idx
				num_remaining -= 1
		assert num_remaining == 0





	def __len__(	self	):
		return (len(self.sampler	) + self.batch_size - 1) // self.batch_size

