

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


lowerCAmelCase__						=	logging.getLogger(__name__)

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

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

class    a__  (   pl.LightningModule     ):
			"""simple docstring"""







			def __init__(      self , lowercase , lowercase=None , lowercase="base" , lowercase=None , lowercase=None , lowercase=None , **lowercase , )					->  Optional[Any]:
							'''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(lowercase    )
							A__            =     0
							A__            =     Path(self.hparams.output_dir    )
							A__            =     self.hparams.cache_dir if self.hparams.cache_dir else None
							if config is None:
											A__            =     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:
											A__            =     config

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


			def        UpperCamelCase						(      self , *lowercase , **lowercase    )					->  List[str]:
							'''simple docstring'''






							A__            =     self.model_type.from_pretrained(*lowercase , **lowercase    )


			def        UpperCamelCase						(      self    )					->  Any:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  Any:
							'''simple docstring'''






							A__            =     self.model
							A__            =     ["bias", "LayerNorm.weight"]
							A__            =     [
							    {
							        "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:
											A__            =     Adafactor(
											    lowercase , lr=self.hparams.learning_rate , scale_parameter=lowercase , relative_step=lowercase    )

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

							A__            =     self.get_lr_scheduler()

							return [optimizer], [scheduler]


			def        UpperCamelCase						(      self , lowercase , lowercase    )					->  int:
							'''simple docstring'''






							return self.validation_step(lowercase , lowercase    )


			def        UpperCamelCase						(      self , lowercase    )					->  str:
							'''simple docstring'''






							return self.validation_end(lowercase    )


			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self , lowercase    )					->  List[Any]:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self , lowercase , lowercase , lowercase = False    )					->  Dict:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  Union[str, Any]:
							'''simple docstring'''






							return self.train_loader


			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  Union[str, Any]:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self , lowercase    )					->  Tuple:
							'''simple docstring'''






							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        UpperCamelCase						(      self , lowercase    )					->  None:
							'''simple docstring'''






							A__            =     self.output_dir.joinpath("best_tfmr"    )
							A__            =     self.step_count
							self.model.save_pretrained(lowercase    )
							self.tokenizer.save_pretrained(lowercase    )


			@staticmethod
			def        UpperCamelCase						(      lowercase , lowercase    )					->  Optional[Any]:
							'''simple docstring'''






							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="" , 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    a__  (   pl.Callback     ):
			"""simple docstring"""







			def        UpperCamelCase						(      self , lowercase , lowercase    )					->  Any:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self , lowercase , lowercase    )					->  Any:
							'''simple docstring'''






							rank_zero_info("***** Validation results *****"    )
							A__            =     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        UpperCamelCase						(      self , lowercase , lowercase    )					->  Tuple:
							'''simple docstring'''






							rank_zero_info("***** Test results *****"    )
							A__            =     trainer.callback_metrics
							# Log and save results to file
							A__            =     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_:							List[str]		,    SCREAMING_SNAKE_CASE_:							Dict )     -> None:




				'''simple docstring'''





				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_:							List[str]=None		,    SCREAMING_SNAKE_CASE_:							Any=True		,    SCREAMING_SNAKE_CASE_:							Any=[]		,    SCREAMING_SNAKE_CASE_:							int=None		,    SCREAMING_SNAKE_CASE_:							List[Any]=None		,    **SCREAMING_SNAKE_CASE_:							str		,    )     -> Union[str, Any]:




				'''simple docstring'''





				pl.seed_everything(args.seed )

				# init model
				A__            =     Path(model.hparams.output_dir )
				odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )

				# add custom checkpoints
				if checkpoint_callback is None:
								A__            =     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:
								A__            =     LoggingCallback()

				A__            =     {}

				# TODO: remove with PyTorch 1.6 since pl uses native amp
				if args.fpaa:
								A__            =     1_6
								A__            =     args.fpaa_opt_level

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

				A__            =     args.accumulate_grad_batches
				A__            =     extra_train_kwargs.get("accelerator"		,    SCREAMING_SNAKE_CASE_ )
				A__            =     extra_train_kwargs.get("profiler"		,    SCREAMING_SNAKE_CASE_ )

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

				if args.do_train:
								trainer.fit(SCREAMING_SNAKE_CASE_ )

				return trainer



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__						=	logging.get_logger(__name__)

lowerCAmelCase__						=	{
    """MBZUAI/swiftformer-xs""": """https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json""",
}

class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    'swiftformer'


			def __init__(      self , lowercase=3 , lowercase=[3, 3, 6, 4] , lowercase=[48, 56, 112, 220] , lowercase=4 , lowercase=[True, True, True, True] , lowercase="gelu" , lowercase=3 , lowercase=2 , lowercase=1 , lowercase=0.0 , lowercase=True , lowercase=1e-5 , lowercase=1e-5 , **lowercase , )					->  Optional[int]:
							'''simple docstring'''






							super().__init__(**lowercase    )
							A__            =     num_channels
							A__            =     depths
							A__            =     embed_dims
							A__            =     mlp_ratio
							A__            =     downsamples
							A__            =     hidden_act
							A__            =     down_patch_size
							A__            =     down_stride
							A__            =     down_pad
							A__            =     drop_path_rate
							A__            =     use_layer_scale
							A__            =     layer_scale_init_value
							A__            =     batch_norm_eps

class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    version.parse('1.11'     )


			@property
			def        UpperCamelCase						(      self    )					->  Mapping[str, Mapping[int, str]]:
							'''simple docstring'''






							return OrderedDict(
							    [
							        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
							    ]    )


			@property
			def        UpperCamelCase						(      self    )					->  float:
							'''simple docstring'''






							return 1e-4

