
"""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


_a   :      Dict       =				logging.getLogger(__name__)

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

_a   :      Dict       =				{
    """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   :      List[str]       =				{
    """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   :      Optional[int]       =				sorted(arg_to_scheduler.keys())
_a   :      Tuple       =				"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""






class   _UpperCAmelCase					(       pl.LightningModule):
    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_  )
      _snake_case  :    str            = 0
      _snake_case  :    Tuple            = Path(self.hparams.output_dir  )
      _snake_case  :    Optional[int]            = self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
        _snake_case  :    List[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=snake_case_    ,     **snake_case_    ,     )
      else:
        _snake_case  :    PretrainedConfig            = config

      _snake_case  :    List[Any]            = ("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:
        _snake_case  :    Tuple            = AutoTokenizer.from_pretrained(
            self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path    ,     cache_dir=snake_case_    ,     )
      else:
        _snake_case  :    PreTrainedTokenizer            = tokenizer
      _snake_case  :    List[Any]            = MODEL_MODES[mode]
      if model is None:
        _snake_case  :    int            = 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:
        _snake_case  :    Optional[int]            = model
    def        lowerCamelCase__						(     self    ,     *snake_case_    ,     **snake_case_  ):
      _snake_case  :    List[Any]            = self.model_type.from_pretrained(*snake_case_    ,     **snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = arg_to_scheduler[self.hparams.lr_scheduler]
      _snake_case  :    Union[str, Any]            = get_schedule_func(
          self.opt    ,     num_warmup_steps=self.hparams.warmup_steps    ,     num_training_steps=self.total_steps()  )
      _snake_case  :    Union[str, Any]            = {"scheduler": scheduler, "interval": "step", "frequency": 1}
      return scheduler
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = self.model
      _snake_case  :    List[Any]            = ["bias", "LayerNorm.weight"]
      _snake_case  :    Optional[Any]            = [
          {
              "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  :    int            = Adafactor(
            snake_case_    ,     lr=self.hparams.learning_rate    ,     scale_parameter=snake_case_    ,     relative_step=snake_case_  )

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

      _snake_case  :    List[str]            = self.get_lr_scheduler()

      return [optimizer], [scheduler]
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_  ):
      return self.validation_step(snake_case_    ,     snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      return self.validation_end(snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    str            = max(1    ,     self.hparams.gpus  )  # TODO: consider num_tpu_cores
      _snake_case  :    List[str]            = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
      return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      if stage == "test":
        _snake_case  :    int            = len(self.test_dataloader().dataset  )
      else:
        _snake_case  :    Tuple            = self.get_dataloader("train"    ,     self.hparams.train_batch_size    ,     shuffle=snake_case_  )
        _snake_case  :    Union[str, Any]            = len(self.train_dataloader().dataset  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_ = False  ):
      raise NotImplementedError("You must implement this for your task"  )
    def        lowerCamelCase__						(     self  ):
      return self.train_loader
    def        lowerCamelCase__						(     self  ):
      return self.get_dataloader("dev"    ,     self.hparams.eval_batch_size    ,     shuffle=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      return self.get_dataloader("test"    ,     self.hparams.eval_batch_size    ,     shuffle=snake_case_  )
    def        lowerCamelCase__						(     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        lowerCamelCase__						(     self    ,     snake_case_  ):
      _snake_case  :    Optional[Any]            = self.output_dir.joinpath("best_tfmr"  )
      _snake_case  :    int            = self.step_count
      self.model.save_pretrained(snake_case_  )
      self.tokenizer.save_pretrained(snake_case_  )


    @staticmethod
    def        lowerCamelCase__						(     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   _UpperCAmelCase					(       pl.Callback):
    def        lowerCamelCase__						(     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   _UpperCAmelCase					(       pl.Callback):
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    Optional[int]            = trainer.lr_schedulers[0]["scheduler"]
      _snake_case  :    int            = {F'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()  )}
      pl_module.logger.log_metrics(snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_  ):
      rank_zero_info("***** Validation results *****"  )
      _snake_case  :    Union[str, Any]            = 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        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_  ):
      rank_zero_info("***** Test results *****"  )
      _snake_case  :    List[Any]            = trainer.callback_metrics
      # Log and save results to file
      _snake_case  :    Optional[int]            = 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 					a__ ( a :					Optional[Any]							,  a :					Tuple		):

  """simple docstring"""







  parser.add_argument(
      "--output_dir"							,  default=a							,  type=a							,  required=a							,  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=a							,  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=a		)
  parser.add_argument("--max_grad_norm"							,  dest="gradient_clip_val"							,  default=1.0							,  type=a							,  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=a							,  default=1							,  help="Number of updates steps to accumulate before performing a backward/update pass."							,  )
  parser.add_argument("--seed"							,  type=a							,  default=42							,  help="random seed for initialization"		)
  parser.add_argument(
      "--data_dir"							,  default=a							,  type=a							,  required=a							,  help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."							,  )



def 					a__ ( a :					BaseTransformer							,  a :					argparse.Namespace							,  a :					str=None							,  a :					Tuple=True							,  a :					Optional[Any]=None							,  a :					Dict=[]							,  a :					str=None							,  a :					int=None							,  **a :					Optional[int]							,  ):

  """simple docstring"""







  pl.seed_everything(args.seed		)

  # init model
  _snake_case  :    str            = Path(model.hparams.output_dir		)
  odir.mkdir(exist_ok=a		)

  # add custom checkpoints
  if checkpoint_callback is None:
    _snake_case  :    str            = 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(a		)
  if logging_callback is None:
    _snake_case  :    Optional[int]            = LoggingCallback()

  _snake_case  :    List[Any]            = {}

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

  if args.gpus > 1:
    _snake_case  :    Dict            = "auto"  # "ddp"
    _snake_case  :    str            = "ddp"

  _snake_case  :    Optional[int]            = args.accumulate_grad_batches
  _snake_case  :    Tuple            = None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
  _snake_case  :    Union[str, Any]            = "auto"

  _snake_case  :    Dict            = pl.Trainer.from_argparse_args(
      a							,  weights_summary=a							,  callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]							,  logger=a							,  **a							,  )

  if args.do_train:
    trainer.fit(a		)

  return trainer



"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import MobileNetVaImageProcessor
class SCREAMING_SNAKE_CASE       (      unittest.TestCase					):






	'''simple docstring'''







	def __init__(  self :Tuple       ,    __magic_name__ :str       ,    __magic_name__ :Optional[Any]=7       ,    __magic_name__ :int=3       ,    __magic_name__ :Union[str, Any]=18       ,    __magic_name__ :Union[str, Any]=30       ,    __magic_name__ :str=400       ,    __magic_name__ :str=True       ,    __magic_name__ :Any=None       ,    __magic_name__ :Tuple=True       ,    __magic_name__ :List[str]=None       ,    )			->   Optional[int]:

			'''simple docstring'''






			a__   		=     size if size is not None else {'''shortest_edge''': 20}
			a__   		=     crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
			a__   		=     parent
			a__   		=     batch_size
			a__   		=     num_channels
			a__   		=     image_size
			a__   		=     min_resolution
			a__   		=     max_resolution
			a__   		=     do_resize
			a__   		=     size
			a__   		=     do_center_crop
			a__   		=     crop_size






	def    _UpperCamelCase							(  self :Tuple			)			->   Any:

			'''simple docstring'''






			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_center_crop": self.do_center_crop,
			    "crop_size": self.crop_size,
			}

@require_torch
@require_vision
class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					,   unittest.TestCase					):






	'''simple docstring'''







	snake_case__							:		str         =		MobileNetVaImageProcessor if is_vision_available() else None


	def    _UpperCamelCase							(  self :Union[str, Any]			)			->   str:

			'''simple docstring'''






			a__   		=     MobileNetVaImageProcessingTester(self			)


	@property
	def    _UpperCamelCase							(  self :List[str]			)			->   Optional[Any]:

			'''simple docstring'''






			return self.image_processor_tester.prepare_image_processor_dict()


	def    _UpperCamelCase							(  self :Tuple			)			->   Tuple:

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)
			self.assertTrue(hasattr(__magic_name__       ,    '''do_resize'''			)			)
			self.assertTrue(hasattr(__magic_name__       ,    '''size'''			)			)
			self.assertTrue(hasattr(__magic_name__       ,    '''do_center_crop'''			)			)
			self.assertTrue(hasattr(__magic_name__       ,    '''center_crop'''			)			)


	def    _UpperCamelCase							(  self :List[Any]			)			->   int:

			'''simple docstring'''






			a__   		=     self.image_processing_class.from_dict(self.image_processor_dict			)
			self.assertEqual(image_processor.size       ,    {'''shortest_edge''': 20}			)
			self.assertEqual(image_processor.crop_size       ,    {'''height''': 18, '''width''': 18}			)

			a__   		=     self.image_processing_class.from_dict(self.image_processor_dict       ,    size=42       ,    crop_size=84			)
			self.assertEqual(image_processor.size       ,    {'''shortest_edge''': 42}			)
			self.assertEqual(image_processor.crop_size       ,    {'''height''': 84, '''width''': 84}			)


	def    _UpperCamelCase							(  self :Optional[int]			)			->   Any:

			'''simple docstring'''






			pass


	def    _UpperCamelCase							(  self :Dict			)			->   Tuple:

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)
			# create random PIL images
			a__   		=     prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__magic_name__			)
			for image in image_inputs:
					self.assertIsInstance(__magic_name__       ,    Image.Image			)

			# Test not batched input
			a__   		=     image_processing(image_inputs[0]       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )

			# Test batched
			a__   		=     image_processing(__magic_name__       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )


	def    _UpperCamelCase							(  self :Optional[Any]			)			->   Union[str, Any]:

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)
			# create random numpy tensors
			a__   		=     prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__magic_name__       ,    numpify=__magic_name__			)
			for image in image_inputs:
					self.assertIsInstance(__magic_name__       ,    np.ndarray			)

			# Test not batched input
			a__   		=     image_processing(image_inputs[0]       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )

			# Test batched
			a__   		=     image_processing(__magic_name__       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )






	def    _UpperCamelCase							(  self :List[Any]			)			->   str:

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)
			# create random PyTorch tensors
			a__   		=     prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__magic_name__       ,    torchify=__magic_name__			)
			for image in image_inputs:
					self.assertIsInstance(__magic_name__       ,    torch.Tensor			)

			# Test not batched input
			a__   		=     image_processing(image_inputs[0]       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )

			# Test batched
			a__   		=     image_processing(__magic_name__       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )




