






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_			,			)					->					Dict:
														'''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_   )
														lowerCAmelCase_										=     0
														lowerCAmelCase_										=     Path(self.hparams.output_dir   )
														lowerCAmelCase_										=     self.hparams.cache_dir if self.hparams.cache_dir else None
														if config is None:
																					lowerCAmelCase_										=     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_										=     config

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

							def      _lowercase       (		self			,			*lowercase_			,			**lowercase_   )					->					Tuple:
														'''simple docstring'''

														lowerCAmelCase_										=     self.model_type.from_pretrained(*lowercase_			,			**lowercase_   )

							def      _lowercase       (		self   )					->					Tuple:
														'''simple docstring'''

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

							def      _lowercase       (		self   )					->					Optional[Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     self.model
														lowerCAmelCase_										=     ['bias', 'LayerNorm.weight']
														lowerCAmelCase_										=     [
														    {
														        '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:
																					lowerCAmelCase_										=     Adafactor(
																					    lowercase_			,			lr=self.hparams.learning_rate			,			scale_parameter=lowercase_			,			relative_step=lowercase_   )

														else:
																					lowerCAmelCase_										=     AdamW(
																					    lowercase_			,			lr=self.hparams.learning_rate			,			eps=self.hparams.adam_epsilon   )
														lowerCAmelCase_										=     optimizer

														lowerCAmelCase_										=     self.get_lr_scheduler()

														return [optimizer], [scheduler]

							def      _lowercase       (		self			,			lowercase_			,			lowercase_   )					->					List[Any]:
														'''simple docstring'''

														return self.validation_step(lowercase_			,			lowercase_   )

							def      _lowercase       (		self			,			lowercase_   )					->					List[str]:
														'''simple docstring'''

														return self.validation_end(lowercase_   )

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

														lowerCAmelCase_										=     max(1			,			self.hparams.gpus   )  # TODO: consider num_tpu_cores
														lowerCAmelCase_										=     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			,			lowercase_   )					->					Optional[int]:
														'''simple docstring'''

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

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_ = False   )					->					List[Any]:
														'''simple docstring'''

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

							def      _lowercase       (		self   )					->					Optional[Any]:
														'''simple docstring'''

														return self.train_loader

							def      _lowercase       (		self   )					->					List[str]:
														'''simple docstring'''

														return self.get_dataloader('dev'			,			self.hparams.eval_batch_size			,			shuffle=lowercase_   )

							def      _lowercase       (		self   )					->					str:
														'''simple docstring'''

														return self.get_dataloader('test'			,			self.hparams.eval_batch_size			,			shuffle=lowercase_   )

							def      _lowercase       (		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      _lowercase       (		self			,			lowercase_   )					->					None:
														'''simple docstring'''

														lowerCAmelCase_										=     self.output_dir.joinpath('best_tfmr'   )
														lowerCAmelCase_										=     self.step_count
														self.model.save_pretrained(lowercase_   )
														self.tokenizer.save_pretrained(lowercase_   )






							@staticmethod
							def      _lowercase       (		lowercase_			,			lowercase_   )					->					Optional[int]:
														'''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=3_2			,			type=lowercase_   )
														parser.add_argument('--eval_batch_size'			,			default=3_2			,			type=lowercase_   )
														parser.add_argument('--adafactor'			,			action='store_true'   )




class 							a_ (						pl.Callback		):


							'''simple docstring'''






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

														lowerCAmelCase_										=     trainer.lr_schedulers[0]['scheduler']
														lowerCAmelCase_										=     {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()   )}
														pl_module.logger.log_metrics(lowercase_   )

							def      _lowercase       (		self			,			lowercase_			,			lowercase_   )					->					Optional[int]:
														'''simple docstring'''

														rank_zero_info('***** Validation results *****'   )
														lowerCAmelCase_										=     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			,			lowercase_			,			lowercase_   )					->					str:
														'''simple docstring'''

														rank_zero_info('***** Test results *****'   )
														lowerCAmelCase_										=     trainer.callback_metrics
														# Log and save results to file
														lowerCAmelCase_										=     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					(						a_ ,						a_	)	->			None:
							#  To allow all pl args uncomment the following line
							#  parser = pl.Trainer.add_argparse_args(parser)
							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      lowerCamelCase					(						a_ ,						a_ ,						a_=None ,						a_=True ,						a_=[] ,						a_=None ,						a_=None ,						**a_ ,						)	->			List[str]:
							pl.seed_everything(args.seed	)

							# init model
							lowerCAmelCase_										=     Path(model.hparams.output_dir	)
							odir.mkdir(exist_ok=a_	)

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

							lowerCAmelCase_										=     {}

							# TODO: remove with PyTorch 1.6 since pl uses native amp
							if args.fpaa:
														lowerCAmelCase_										=     16
														lowerCAmelCase_										=     args.fpaa_opt_level

							if args.gpus > 1:
														lowerCAmelCase_										=     'ddp'

							lowerCAmelCase_										=     args.accumulate_grad_batches
							lowerCAmelCase_										=     extra_train_kwargs.get('accelerator' ,						a_	)
							lowerCAmelCase_										=     extra_train_kwargs.get('profiler' ,						a_	)

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

							if args.do_train:
														trainer.fit(a_	)

							return trainer











from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowerCamelCase_						     =  logging.get_logger(__name__)




class 							a_ (						a_		):


							'''simple docstring'''






							__a:	Optional[Any]     =    ['''pixel_values''']

							def __init__(		self			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = PILImageResampling.BICUBIC			,			lowercase_ = True			,			lowercase_ = 1 / 2_5_5			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = None			,			**lowercase_			,			)					->					None:
														'''simple docstring'''

														super().__init__(**lowercase_   )
														lowerCAmelCase_										=     crop_size if crop_size is not None else {'height': 2_5_6, 'width': 2_5_6}
														lowerCAmelCase_										=     get_size_dict(lowercase_			,			param_name='crop_size'   )
														lowerCAmelCase_										=     size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
														lowerCAmelCase_										=     get_size_dict(lowercase_   )

														lowerCAmelCase_										=     do_center_crop
														lowerCAmelCase_										=     crop_size
														lowerCAmelCase_										=     do_resize
														lowerCAmelCase_										=     size
														lowerCAmelCase_										=     resample
														lowerCAmelCase_										=     do_rescale
														lowerCAmelCase_										=     rescale_factor
														lowerCAmelCase_										=     do_normalize
														lowerCAmelCase_										=     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
														lowerCAmelCase_										=     image_std if image_std is not None else IMAGENET_DEFAULT_STD

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_ = None			,			lowercase_ = None			,			**lowercase_			,			)					->					np.ndarray:
														'''simple docstring'''

														lowerCAmelCase_										=     self.size if size is None else size
														lowerCAmelCase_										=     get_size_dict(lowercase_   )
														lowerCAmelCase_										=     get_size_dict(lowercase_			,			param_name='crop_size'   )

														lowerCAmelCase_						,   lowerCAmelCase_										=     get_image_size(lowercase_   )
														lowerCAmelCase_										=     min(lowercase_			,			lowercase_   )
														lowerCAmelCase_										=     (size['height'] / crop_size['height']) * min_dim
														lowerCAmelCase_										=     (size['width'] / crop_size['width']) * min_dim
														return center_crop(lowercase_			,			size=(cropped_height, cropped_width)			,			data_format=lowercase_			,			**lowercase_   )

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_ = PIL.Image.BICUBIC			,			lowercase_ = None			,			**lowercase_			,			)					->					np.ndarray:
														'''simple docstring'''

														lowerCAmelCase_										=     get_size_dict(lowercase_   )
														if "height" not in size or "width" not in size:
																					raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''   )
														return resize(
														    lowercase_			,			size=(size['height'], size['width'])			,			resample=lowercase_			,			data_format=lowercase_			,			**lowercase_   )

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_ = None			,			**lowercase_			,			)					->					Any:
														'''simple docstring'''

														return rescale(lowercase_			,			scale=lowercase_			,			data_format=lowercase_			,			**lowercase_   )

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ = None			,			**lowercase_			,			)					->					np.ndarray:
														'''simple docstring'''

														return normalize(lowercase_			,			mean=lowercase_			,			std=lowercase_			,			data_format=lowercase_			,			**lowercase_   )






							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = ChannelDimension.FIRST			,			**lowercase_			,			)					->					PIL.Image.Image:
														'''simple docstring'''

														lowerCAmelCase_										=     do_center_crop if do_center_crop is not None else self.do_center_crop
														lowerCAmelCase_										=     crop_size if crop_size is not None else self.crop_size
														lowerCAmelCase_										=     get_size_dict(lowercase_			,			param_name='crop_size'   )
														lowerCAmelCase_										=     do_resize if do_resize is not None else self.do_resize
														lowerCAmelCase_										=     size if size is not None else self.size
														lowerCAmelCase_										=     get_size_dict(lowercase_   )
														lowerCAmelCase_										=     resample if resample is not None else self.resample
														lowerCAmelCase_										=     do_rescale if do_rescale is not None else self.do_rescale
														lowerCAmelCase_										=     rescale_factor if rescale_factor is not None else self.rescale_factor
														lowerCAmelCase_										=     do_normalize if do_normalize is not None else self.do_normalize
														lowerCAmelCase_										=     image_mean if image_mean is not None else self.image_mean
														lowerCAmelCase_										=     image_std if image_std is not None else self.image_std

														lowerCAmelCase_										=     make_list_of_images(lowercase_   )

														if not valid_images(lowercase_   ):
																					raise ValueError(
																					    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																					    'torch.Tensor, tf.Tensor or jax.ndarray.'   )

														if do_center_crop and crop_size is None:
																					raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'   )

														if do_resize and size is None:
																					raise ValueError('Size must be specified if do_resize is True.'   )

														if do_rescale and rescale_factor is None:
																					raise ValueError('Rescale factor must be specified if do_rescale is True.'   )

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError('Image mean and image standard deviation must be specified if do_normalize is True.'   )

														# All transformations expect numpy arrays.
														lowerCAmelCase_										=     [to_numpy_array(lowercase_   ) for image in images]

														if do_center_crop:
																					lowerCAmelCase_										=     [self.center_crop(lowercase_			,			lowercase_			,			size=lowercase_   ) for image in images]

														if do_resize:
																					lowerCAmelCase_										=     [self.resize(image=lowercase_			,			size=lowercase_			,			resample=lowercase_   ) for image in images]

														if do_rescale:
																					lowerCAmelCase_										=     [self.rescale(image=lowercase_			,			scale=lowercase_   ) for image in images]

														if do_normalize:
																					lowerCAmelCase_										=     [self.normalize(image=lowercase_			,			mean=lowercase_			,			std=lowercase_   ) for image in images]

														lowerCAmelCase_										=     [to_channel_dimension_format(lowercase_			,			lowercase_   ) for image in images]

														lowerCAmelCase_										=     {'pixel_values': images}
														return BatchFeature(data=lowercase_			,			tensor_type=lowercase_   )



