





"""simple docstring"""





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 		SCREAMING_SNAKE_CASE_							(    pl.LightningModule   ):
				"""simple docstring"""




				def __init__(     self  :str						,    __lowercase  :argparse.Namespace						,    __lowercase  :Optional[int]=None						,    __lowercase  :str="base"						,    __lowercase  :Optional[int]=None						,    __lowercase  :List[str]=None						,    __lowercase  :Tuple=None						,    **__lowercase  :Optional[int]						,    ):
											super().__init__()
											# TODO: move to self.save_hyperparameters()
											# self.save_hyperparameters()
											# can also expand arguments into trainer signature for easier reading

											self.save_hyperparameters(__lowercase			)
											__lowerCamelCase      :      List[Any]         =0
											__lowerCamelCase      :      List[str]         =Path(self.hparams.output_dir			)
											__lowerCamelCase      :      Optional[int]         =self.hparams.cache_dir if self.hparams.cache_dir else None
											if config is None:
																		__lowerCamelCase      :      str         =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=__lowercase						,    **__lowercase						,    )
											else:
																		__lowerCamelCase      :      PretrainedConfig         =config

											__lowerCamelCase      :      Union[str, Any]         =('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
											for p in extra_model_params:
																		if getattr(self.hparams						,    __lowercase						,    __lowercase			):
																									assert hasattr(self.config						,    __lowercase			), f'model config doesn\'t have a `{p}` attribute'
																									setattr(self.config						,    __lowercase						,    getattr(self.hparams						,    __lowercase			)			)

											if tokenizer is None:
																		__lowerCamelCase      :      List[str]         =AutoTokenizer.from_pretrained(
																		    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path						,    cache_dir=__lowercase						,    )
											else:
																		__lowerCamelCase      :      PreTrainedTokenizer         =tokenizer
											__lowerCamelCase      :      str         =MODEL_MODES[mode]
											if model is None:
																		__lowerCamelCase      :      List[Any]         =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=__lowercase						,    )
											else:
																		__lowerCamelCase      :      Union[str, Any]         =model




				def 					__lowercase  (     self  :List[str]						,    *__lowercase  :Optional[int]						,    **__lowercase  :Optional[Any]			):
											__lowerCamelCase      :      str         =self.model_type.from_pretrained(*__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :Union[str, Any]			):
											__lowerCamelCase      :      List[Any]         =arg_to_scheduler[self.hparams.lr_scheduler]
											__lowerCamelCase      :      str         =get_schedule_func(
											    self.opt						,    num_warmup_steps=self.hparams.warmup_steps						,    num_training_steps=self.total_steps()			)
											__lowerCamelCase      :      Union[str, Any]         ={'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
											return scheduler




				def 					__lowercase  (     self  :Any			):
											__lowerCamelCase      :      List[str]         =self.model
											__lowerCamelCase      :      int         =['''bias''', '''LayerNorm.weight''']
											__lowerCamelCase      :      Optional[Any]         =[
											    {
											        '''params''': [
											            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay			)
											        ],  # check this named paramters
											        '''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      :      Optional[int]         =Adafactor(
																		    __lowercase						,    lr=self.hparams.learning_rate						,    scale_parameter=__lowercase						,    relative_step=__lowercase			)

											else:
																		__lowerCamelCase      :      int         =AdamW(
																		    __lowercase						,    lr=self.hparams.learning_rate						,    eps=self.hparams.adam_epsilon			)
											__lowerCamelCase      :      int         =optimizer

											__lowerCamelCase      :      str         =self.get_lr_scheduler()

											return [optimizer], [scheduler]




				def 					__lowercase  (     self  :List[Any]						,    __lowercase  :Any						,    __lowercase  :Optional[int]			):
											return self.validation_step(__lowercase						,    __lowercase			)




				def 					__lowercase  (     self  :Optional[Any]						,    __lowercase  :str			):
											return self.validation_end(__lowercase			)




				def 					__lowercase  (     self  :Union[str, Any]			):
											__lowerCamelCase      :      str         =max(1						,    self.hparams.gpus			)  # TODO: consider num_tpu_cores
											__lowerCamelCase      :      Optional[int]         =self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
											return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :Dict			):
											if stage == "test":
																		__lowerCamelCase      :      List[Any]         =len(self.test_dataloader().dataset			)
											else:
																		__lowerCamelCase      :      Optional[int]         =self.get_dataloader('''train'''						,    self.hparams.train_batch_size						,    shuffle=__lowercase			)
																		__lowerCamelCase      :      str         =len(self.train_dataloader().dataset			)




				def 					__lowercase  (     self  :int						,    __lowercase  :str						,    __lowercase  :int						,    __lowercase  :bool = False			):
											raise NotImplementedError('''You must implement this for your task'''			)




				def 					__lowercase  (     self  :Tuple			):
											return self.train_loader




				def 					__lowercase  (     self  :Tuple			):
											return self.get_dataloader('''dev'''						,    self.hparams.eval_batch_size						,    shuffle=__lowercase			)




				def 					__lowercase  (     self  :List[Any]			):
											return self.get_dataloader('''test'''						,    self.hparams.eval_batch_size						,    shuffle=__lowercase			)




				def 					__lowercase  (     self  :List[str]						,    __lowercase  :Union[str, Any]			):
											return os.path.join(
											    self.hparams.data_dir						,    '''cached_{}_{}_{}'''.format(
											        __lowercase						,    list(filter(__lowercase						,    self.hparams.model_name_or_path.split('''/'''			)			)			).pop()						,    str(self.hparams.max_seq_length			)						,    )						,    )




				@pl.utilities.rank_zero_only
				def 					__lowercase  (     self  :int						,    __lowercase  :Dict[str, Any]			):
											__lowerCamelCase      :      int         =self.output_dir.joinpath('''best_tfmr'''			)
											__lowerCamelCase      :      List[str]         =self.step_count
											self.model.save_pretrained(__lowercase			)
											self.tokenizer.save_pretrained(__lowercase			)




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

class 		SCREAMING_SNAKE_CASE_							(    pl.Callback   ):
				"""simple docstring"""




				def 					__lowercase  (     self  :Dict						,    __lowercase  :Union[str, Any]						,    __lowercase  :Union[str, Any]			):
											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 		SCREAMING_SNAKE_CASE_							(    pl.Callback   ):
				"""simple docstring"""




				def 					__lowercase  (     self  :Any						,    __lowercase  :int						,    __lowercase  :List[Any]			):
											# print(pl_module.model.rag)
											for name, param in pl_module.model.rag.named_parameters():
																		if param.grad is None:
																									print(__lowercase			)

class 		SCREAMING_SNAKE_CASE_							(    pl.Callback   ):
				"""simple docstring"""




				def 					__lowercase  (     self  :Any						,    __lowercase  :Any						,    __lowercase  :Optional[int]			):
											__lowerCamelCase      :      Union[str, Any]         =trainer.lr_schedulers[0]['''scheduler''']
											__lowerCamelCase      :      List[str]         ={f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()			)}
											pl_module.logger.log_metrics(__lowercase			)




				def 					__lowercase  (     self  :List[Any]						,    __lowercase  :pl.Trainer						,    __lowercase  :pl.LightningModule			):
											rank_zero_info('''***** Validation results *****'''			)
											__lowerCamelCase      :      int         =trainer.callback_metrics
											# Log results
											for key in sorted(__lowercase			):
																		if key not in ["log", "progress_bar"]:
																									rank_zero_info('''{} = {}\n'''.format(__lowercase						,    str(metrics[key]			)			)			)




				def 					__lowercase  (     self  :Optional[int]						,    __lowercase  :pl.Trainer						,    __lowercase  :pl.LightningModule			):
											rank_zero_info('''***** Test results *****'''			)
											__lowerCamelCase      :      Dict         =trainer.callback_metrics
											# Log and save results to file
											__lowerCamelCase      :      str         =os.path.join(pl_module.hparams.output_dir						,    '''test_results.txt'''			)
											with open(__lowercase						,    '''w'''			) as writer:
																		for key in sorted(__lowercase			):
																									if key not in ["log", "progress_bar"]:
																																rank_zero_info('''{} = {}\n'''.format(__lowercase						,    str(metrics[key]			)			)			)
																																writer.write('''{} = {}\n'''.format(__lowercase						,    str(metrics[key]			)			)			)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Dict    ,						SCREAMING_SNAKE_CASE       :							Tuple   ):


							'''simple docstring'''







							parser.add_argument(
							    '''--output_dir'''    ,						default=str(Path(SCREAMING_SNAKE_CASE   ).parent / '''test_run''' / '''model_checkpoints'''   )    ,						type=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=str(Path(SCREAMING_SNAKE_CASE   ).parent / '''test_run''' / '''dummy-train-data'''   )    ,						type=SCREAMING_SNAKE_CASE    ,						help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''    ,						)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							BaseTransformer    ,						SCREAMING_SNAKE_CASE       :							argparse.Namespace    ,						SCREAMING_SNAKE_CASE       :							Any=None    ,						SCREAMING_SNAKE_CASE       :							List[str]=True    ,						SCREAMING_SNAKE_CASE       :							int=[]    ,						SCREAMING_SNAKE_CASE       :							Tuple=None    ,						SCREAMING_SNAKE_CASE       :							str=None    ,						**SCREAMING_SNAKE_CASE       :							Union[str, Any]    ,						):


							'''simple docstring'''







							pl.seed_everything(args.seed   )

							# init model
							__lowerCamelCase      :      str         =Path(model.hparams.output_dir   )
							odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE   )

							# add custom checkpoints
							if checkpoint_callback is None:
														__lowerCamelCase      :      Optional[Any]         =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:
														__lowerCamelCase      :      int         =LoggingCallback()

							__lowerCamelCase      :      Any         ={}

							if args.fpaa:
														__lowerCamelCase      :      Any         =16

							if args.gpus > 1:
														__lowerCamelCase      :      Any         ='''auto'''
														__lowerCamelCase      :      List[Any]         ='''ddp'''

							__lowerCamelCase      :      int         =args.accumulate_grad_batches
							__lowerCamelCase      :      List[Any]         =None
							__lowerCamelCase      :      int         ='''auto'''

							__lowerCamelCase      :      Optional[int]         =pl.Trainer.from_argparse_args(
							    SCREAMING_SNAKE_CASE    ,						weights_summary=SCREAMING_SNAKE_CASE    ,						callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]    ,						logger=SCREAMING_SNAKE_CASE    ,						val_check_interval=1    ,						num_sanity_val_steps=2    ,						**SCREAMING_SNAKE_CASE    ,						)

							if args.do_train:
														trainer.fit(SCREAMING_SNAKE_CASE   )

							else:
														print('''RAG modeling tests with new set functions successfuly executed!'''   )
							return trainer











"""simple docstring"""





import builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


_UpperCamelCase							=      False
try:
							_UpperCamelCase							=      _is_package_available('google.colab')
except ModuleNotFoundError:
							pass
@input.register
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def __init__(     self  :Union[str, Any]						,    __lowercase  :str = None						,    __lowercase  :list = []			):
											__lowerCamelCase      :      Any         =0
											__lowerCamelCase      :      List[str]         =choices
											__lowerCamelCase      :      int         =prompt
											if sys.platform == "win32":
																		__lowerCamelCase      :      Dict         ='''*'''
											else:
																		__lowerCamelCase      :      Union[str, Any]         ='''➔ '''




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :Any						,    __lowercase  :str = ""			):
											if sys.platform != "win32":
																		writeColor(self.choices[index]						,    32						,    __lowercase			)
											else:
																		forceWrite(self.choices[index]						,    __lowercase			)




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :int			):
											if index == self.position:
																		forceWrite(f' {self.arrow_char} '			)
																		self.write_choice(__lowercase			)
											else:
																		forceWrite(f'    {self.choices[index]}'			)
											reset_cursor()




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :Direction						,    __lowercase  :int = 1			):
											__lowerCamelCase      :      List[str]         =self.position
											if direction == Direction.DOWN:
																		if self.position + 1 >= len(self.choices			):
																									return
																		self.position += num_spaces
											else:
																		if self.position - 1 < 0:
																									return
																		self.position -= num_spaces
											clear_line()
											self.print_choice(__lowercase			)
											move_cursor(__lowercase						,    direction.name			)
											self.print_choice(self.position			)




				@input.mark(KEYMAP['''up''']			)
				def 					__lowercase  (     self  :Union[str, Any]			):
											self.move_direction(Direction.UP			)




				@input.mark(KEYMAP['''down''']			)
				def 					__lowercase  (     self  :Union[str, Any]			):
											self.move_direction(Direction.DOWN			)




				@input.mark(KEYMAP['''newline''']			)
				def 					__lowercase  (     self  :Any			):
											move_cursor(len(self.choices			) - self.position						,    '''DOWN'''			)
											return self.position




				@input.mark(KEYMAP['''interrupt''']			)
				def 					__lowercase  (     self  :Any			):
											move_cursor(len(self.choices			) - self.position						,    '''DOWN'''			)
											raise KeyboardInterrupt




				@input.mark_multiple(*[KEYMAP[str(__lowercase			)] for number in range(10			)]			)
				def 					__lowercase  (     self  :Any			):
											__lowerCamelCase      :      Tuple         =int(chr(self.current_selection			)			)
											__lowerCamelCase      :      Dict         =index - self.position
											if index == self.position:
																		return
											if index < len(self.choices			):
																		if self.position > index:
																									self.move_direction(Direction.UP						,    -movement			)
																		elif self.position < index:
																									self.move_direction(Direction.DOWN						,    __lowercase			)
																		else:
																									return
											else:
																		return




				def 					__lowercase  (     self  :Optional[int]						,    __lowercase  :int = 0			):
											if self.prompt:
																		linebreak()
																		forceWrite(self.prompt						,    '''\n'''			)
																		if in_colab:
																									forceWrite('''Please input a choice index (starting from 0), and press enter'''						,    '''\n'''			)
																		else:
																									forceWrite('''Please select a choice using the arrow or number keys, and selecting with enter'''						,    '''\n'''			)
											__lowerCamelCase      :      Union[str, Any]         =default_choice
											for i in range(len(self.choices			)			):
																		self.print_choice(__lowercase			)
																		forceWrite('''\n'''			)
											move_cursor(len(self.choices			) - self.position						,    '''UP'''			)
											with cursor.hide():
																		while True:
																									if in_colab:
																																try:
																																							__lowerCamelCase      :      Union[str, Any]         =int(builtins.input()			)
																																except ValueError:
																																							__lowerCamelCase      :      Optional[Any]         =default_choice
																									else:
																																__lowerCamelCase      :      Dict         =self.handle_input()
																									if choice is not None:
																																reset_cursor()
																																for _ in range(len(self.choices			) + 1			):
																																							move_cursor(1						,    '''UP'''			)
																																							clear_line()
																																self.write_choice(__lowercase						,    '''\n'''			)
																																return choice




