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


lowercase_  							=			logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

lowercase_  							=			{
    "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
lowercase_  							=			{
    "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
}
lowercase_  							=			sorted(arg_to_scheduler.keys())
lowercase_  							=			"{" + ", ".join(arg_to_scheduler_choices) + "}"







class      SCREAMING_SNAKE_CASE__					(  pl.LightningModule    ):

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

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

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

           if tokenizer is None:
               __snake_case       :				Dict 			=    AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path			,			cache_dir=_lowerCAmelCase			,			)
           else:
               __snake_case       :				PreTrainedTokenizer 			=    tokenizer
           __snake_case       :				List[Any] 			=    MODEL_MODES[mode]
           if model is None:
               __snake_case       :				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=_lowerCAmelCase			,			)
           else:
               __snake_case       :				Optional[Any] 			=    model

       def 			snake_case__    (							self					:     List[Any]			,			*_lowerCAmelCase					:     Tuple			,			**_lowerCAmelCase					:     str			):
           __snake_case       :				Optional[Any] 			=    self.model_type.from_pretrained(*_lowerCAmelCase			,			**_lowerCAmelCase			)

       def 			snake_case__    (							self					:     int			):
           __snake_case       :				List[str] 			=    arg_to_scheduler[self.hparams.lr_scheduler]
           __snake_case       :				Optional[int] 			=    get_schedule_func(
               self.opt			,			num_warmup_steps=self.hparams.warmup_steps			,			num_training_steps=self.total_steps()			)
           __snake_case       :				Tuple 			=    {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
           return scheduler

       def 			snake_case__    (							self					:     List[Any]			):
           __snake_case       :				Any 			=    self.model
           __snake_case       :				str 			=    ["""bias""", """LayerNorm.weight"""]
           __snake_case       :				Optional[int] 			=    [
               {
                   """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:
               __snake_case       :				str 			=    Adafactor(
                   _lowerCAmelCase			,			lr=self.hparams.learning_rate			,			scale_parameter=_lowerCAmelCase			,			relative_step=_lowerCAmelCase			)

           else:
               __snake_case       :				List[Any] 			=    AdamW(
                   _lowerCAmelCase			,			lr=self.hparams.learning_rate			,			eps=self.hparams.adam_epsilon			)
           __snake_case       :				Optional[int] 			=    optimizer

           __snake_case       :				int 			=    self.get_lr_scheduler()

           return [optimizer], [scheduler]

       def 			snake_case__    (							self					:     Optional[Any]			,			_lowerCAmelCase					:     Union[str, Any]			,			_lowerCAmelCase					:     Union[str, Any]			):
           return self.validation_step(_lowerCAmelCase			,			_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Optional[Any]			,			_lowerCAmelCase					:     List[Any]			):
           return self.validation_end(_lowerCAmelCase			)

       def 			snake_case__    (							self					:     int			):
           __snake_case       :				str 			=    max(1			,			self.hparams.gpus			)  # TODO: consider num_tpu_cores
           __snake_case       :				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 			snake_case__    (							self					:     Optional[int]			,			_lowerCAmelCase					:     Optional[int]			):
           if stage == "test":
               __snake_case       :				str 			=    len(self.test_dataloader().dataset			)
           else:
               __snake_case       :				str 			=    self.get_dataloader("""train"""			,			self.hparams.train_batch_size			,			shuffle=_lowerCAmelCase			)
               __snake_case       :				Optional[Any] 			=    len(self.train_dataloader().dataset			)

       def 			snake_case__    (							self					:     List[Any]			,			_lowerCAmelCase					:     str			,			_lowerCAmelCase					:     int			,			_lowerCAmelCase					:     bool = False			):
           raise NotImplementedError("""You must implement this for your task"""			)

       def 			snake_case__    (							self					:     List[Any]			):
           return self.train_loader

       def 			snake_case__    (							self					:     Union[str, Any]			):
           return self.get_dataloader("""dev"""			,			self.hparams.eval_batch_size			,			shuffle=_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Union[str, Any]			):
           return self.get_dataloader("""test"""			,			self.hparams.eval_batch_size			,			shuffle=_lowerCAmelCase			)

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

       @pl.utilities.rank_zero_only
       def 			snake_case__    (							self					:     Union[str, Any]			,			_lowerCAmelCase					:     Dict[str, Any]			):
           __snake_case       :				Any 			=    self.output_dir.joinpath("""best_tfmr"""			)
           __snake_case       :				Optional[int] 			=    self.step_count
           self.model.save_pretrained(_lowerCAmelCase			)
           self.tokenizer.save_pretrained(_lowerCAmelCase			)





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







class      SCREAMING_SNAKE_CASE__					(  pl.Callback    ):

       def 			snake_case__    (							self					:     Optional[Any]			,			_lowerCAmelCase					:     Dict			,			_lowerCAmelCase					:     int			):
           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    ):

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

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





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







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Tuple		,							__SCREAMING_SNAKE_CASE       :					Tuple   ):







    '''simple docstring'''
    #  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=4_2		,							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 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					BaseTransformer		,							__SCREAMING_SNAKE_CASE       :					argparse.Namespace		,							__SCREAMING_SNAKE_CASE       :					Union[str, Any]=None		,							__SCREAMING_SNAKE_CASE       :					str=True		,							__SCREAMING_SNAKE_CASE       :					List[Any]=None		,							__SCREAMING_SNAKE_CASE       :					List[Any]=[]		,							__SCREAMING_SNAKE_CASE       :					List[Any]=None		,							__SCREAMING_SNAKE_CASE       :					Optional[int]=None		,							**__SCREAMING_SNAKE_CASE       :					List[str]		,							):







    '''simple docstring'''
    pl.seed_everything(args.seed   )

    # init model
    __snake_case       :				Union[str, Any] 			=    Path(model.hparams.output_dir   )
    odir.mkdir(exist_ok=__SCREAMING_SNAKE_CASE   )

    # add custom checkpoints
    if checkpoint_callback is None:
        __snake_case       :				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:
        __snake_case       :				List[Any] 			=    LoggingCallback()

    __snake_case       :				str 			=    {}

    # TODO: remove with PyTorch 1.6 since pl uses native amp
    if args.fpaa:
        __snake_case       :				Dict 			=    1_6
        # train_params["amp_level"] = args.fp16_opt_level

    if args.gpus > 1:
        __snake_case       :				Dict 			=    """auto"""  # "ddp"
        __snake_case       :				Union[str, Any] 			=    """ddp"""

    __snake_case       :				List[str] 			=    args.accumulate_grad_batches
    __snake_case       :				Any 			=    None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
    __snake_case       :				Tuple 			=    """auto"""

    __snake_case       :				List[str] 			=    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 warnings

from .generation import FlaxGenerationMixin







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       # warning at import time
       warnings.warn(
           "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
           "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."   ,							__UpperCamelCase   ,							)



