"""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


lowerCAmelCase__    :    Optional[Any]			=      logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

lowerCAmelCase__    :    List[Any]			=      {
    '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__    :    List[Any]			=      {
    '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__    :    List[str]			=      sorted(arg_to_scheduler.keys())
lowerCAmelCase__    :    List[str]			=      '{' + ', '.join(arg_to_scheduler_choices) + '}'
class 							snake_case			(					pl.LightningModule							):




					"""simple docstring"""

					def __init__(							self  :      Optional[int]       ,lowerCamelCase__  :      argparse.Namespace       ,lowerCamelCase__  :      List[str]=None       ,lowerCamelCase__  :      Optional[int]="base"       ,lowerCamelCase__  :      str=None       ,lowerCamelCase__  :      Optional[int]=None       ,lowerCamelCase__  :      List[str]=None       ,**lowerCamelCase__  :      Optional[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__     )
									UpperCAmelCase__			 =				0
									UpperCAmelCase__			 =				Path(self.hparams.output_dir     )
									UpperCAmelCase__			 =				self.hparams.cache_dir if self.hparams.cache_dir else None
									if config is None:
													UpperCAmelCase__			 =				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:
													UpperCAmelCase__			 =				config

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







					def 						__lowerCAmelCase		(							self  :      Any       ,*lowerCamelCase__  :      Any       ,**lowerCamelCase__  :      str     ):
									UpperCAmelCase__			 =				self.model_type.from_pretrained(*lowerCamelCase__       ,**lowerCamelCase__     )







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







					def 						__lowerCAmelCase		(							self  :      Dict     ):
									UpperCAmelCase__			 =				self.model
									UpperCAmelCase__			 =				['bias', 'LayerNorm.weight']
									UpperCAmelCase__			 =				[
									    {
									        '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:
													UpperCAmelCase__			 =				Adafactor(
													    lowerCamelCase__       ,lr=self.hparams.learning_rate       ,scale_parameter=lowerCamelCase__       ,relative_step=lowerCamelCase__     )

									else:
													UpperCAmelCase__			 =				AdamW(
													    lowerCamelCase__       ,lr=self.hparams.learning_rate       ,eps=self.hparams.adam_epsilon     )
									UpperCAmelCase__			 =				optimizer

									UpperCAmelCase__			 =				self.get_lr_scheduler()

									return [optimizer], [scheduler]







					def 						__lowerCAmelCase		(							self  :      Union[str, Any]       ,lowerCamelCase__  :      Optional[int]       ,lowerCamelCase__  :      str     ):
									return self.validation_step(lowerCamelCase__       ,lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      Optional[Any]       ,lowerCamelCase__  :      Dict     ):
									return self.validation_end(lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      Optional[int]     ):
									UpperCAmelCase__			 =				max(1       ,self.hparams.gpus     )  # TODO: consider num_tpu_cores
									UpperCAmelCase__			 =				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  :      int       ,lowerCamelCase__  :      Tuple     ):
									if mode == "test":
													UpperCAmelCase__			 =				len(self.test_dataloader().dataset     )
									else:
													UpperCAmelCase__			 =				self.get_dataloader('train'       ,self.hparams.train_batch_size       ,shuffle=lowerCamelCase__     )
													UpperCAmelCase__			 =				len(self.train_dataloader().dataset     )







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







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







					def 						__lowerCAmelCase		(							self  :      List[Any]     ):
									return self.get_dataloader('dev'       ,self.hparams.eval_batch_size       ,shuffle=lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      int     ):
									return self.get_dataloader('test'       ,self.hparams.eval_batch_size       ,shuffle=lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      List[str]       ,lowerCamelCase__  :      Union[str, Any]     ):
									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 						__lowerCAmelCase		(							self  :      Dict       ,lowerCamelCase__  :      Dict[str, Any]     ):
									UpperCAmelCase__			 =				self.output_dir.joinpath('best_tfmr'     )
									UpperCAmelCase__			 =				self.step_count
									self.model.save_pretrained(lowerCamelCase__     )
									self.tokenizer.save_pretrained(lowerCamelCase__     )







					@staticmethod
					def 						__lowerCAmelCase		(							lowerCamelCase__  :      List[Any]       ,lowerCamelCase__  :      Any     ):
									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 							snake_case			(					pl.Callback							):




					"""simple docstring"""

					def 						__lowerCAmelCase		(							self  :      List[Any]       ,lowerCamelCase__  :      Dict       ,lowerCamelCase__  :      List[str]     ):
									UpperCAmelCase__			 =				trainer.lr_schedulers[0]['scheduler']
									UpperCAmelCase__			 =				{f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()     )}
									pl_module.logger.log_metrics(lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      List[Any]       ,lowerCamelCase__  :      pl.Trainer       ,lowerCamelCase__  :      pl.LightningModule     ):
									rank_zero_info('***** Validation results *****'     )
									UpperCAmelCase__			 =				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 						__lowerCAmelCase		(							self  :      List[Any]       ,lowerCamelCase__  :      pl.Trainer       ,lowerCamelCase__  :      pl.LightningModule     ):
									rank_zero_info('***** Test results *****'     )
									UpperCAmelCase__			 =				trainer.callback_metrics
									# Log and save results to file
									UpperCAmelCase__			 =				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     a_		(   lowerCamelCase   ,     lowerCamelCase			):
				#  To allow all pl args uncomment the following line
				#  parser = pl.Trainer.add_argparse_args(parser)
				parser.add_argument(
				    '--output_dir'   ,     default=lowerCamelCase   ,     type=lowerCamelCase   ,     required=lowerCamelCase   ,     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=lowerCamelCase   ,     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=lowerCamelCase			)
				parser.add_argument('--max_grad_norm'   ,     dest='gradient_clip_val'   ,     default=1.0   ,     type=lowerCamelCase   ,     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=lowerCamelCase   ,     default=1   ,     help='Number of updates steps to accumulate before performing a backward/update pass.'   ,     )
				parser.add_argument('--seed'   ,     type=lowerCamelCase   ,     default=4_2   ,     help='random seed for initialization'			)
				parser.add_argument(
				    '--data_dir'   ,     default=lowerCamelCase   ,     type=lowerCamelCase   ,     required=lowerCamelCase   ,     help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'   ,     )







def     a_		(   lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase=None   ,     lowerCamelCase=True   ,     lowerCamelCase=[]   ,     lowerCamelCase=None   ,     lowerCamelCase=None   ,     **lowerCamelCase   ,     ):
				pl.seed_everything(args.seed			)

				# init model
				UpperCAmelCase__			 =				Path(model.hparams.output_dir			)
				odir.mkdir(exist_ok=lowerCamelCase			)

				# add custom checkpoints
				if checkpoint_callback is None:
								UpperCAmelCase__			 =				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(lowerCamelCase			)
				if logging_callback is None:
								UpperCAmelCase__			 =				LoggingCallback()

				UpperCAmelCase__			 =				{}

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

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

				UpperCAmelCase__			 =				args.accumulate_grad_batches
				UpperCAmelCase__			 =				extra_train_kwargs.get('accelerator'   ,     lowerCamelCase			)
				UpperCAmelCase__			 =				extra_train_kwargs.get('profiler'   ,     lowerCamelCase			)

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

				if args.do_train:
								trainer.fit(lowerCamelCase			)

				return trainer




"""simple docstring"""






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__    :    List[Any]			=      logging.get_logger(__name__)

lowerCAmelCase__    :    Dict			=      {
    'apple/mobilevit-small': 'https://huggingface.co/apple/mobilevit-small/resolve/main/config.json',
    'apple/mobilevit-x-small': 'https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json',
    'apple/mobilevit-xx-small': 'https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json',
    'apple/deeplabv3-mobilevit-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-x-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-xx-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}
class 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					snake_case__							       =					"mobilevit"







					def __init__(							self  :      Union[str, Any]       ,lowerCamelCase__  :      Any=3       ,lowerCamelCase__  :      str=256       ,lowerCamelCase__  :      Optional[int]=2       ,lowerCamelCase__  :      Union[str, Any]=[144, 192, 240]       ,lowerCamelCase__  :      Optional[int]=[16, 32, 64, 96, 128, 160, 640]       ,lowerCamelCase__  :      Dict=4       ,lowerCamelCase__  :      int=2.0       ,lowerCamelCase__  :      Optional[Any]=4.0       ,lowerCamelCase__  :      Optional[int]="silu"       ,lowerCamelCase__  :      List[str]=3       ,lowerCamelCase__  :      int=32       ,lowerCamelCase__  :      int=0.1       ,lowerCamelCase__  :      List[str]=0.0       ,lowerCamelCase__  :      List[str]=0.1       ,lowerCamelCase__  :      Tuple=0.0_2       ,lowerCamelCase__  :      Union[str, Any]=1e-5       ,lowerCamelCase__  :      Tuple=True       ,lowerCamelCase__  :      Dict=256       ,lowerCamelCase__  :      Any=[6, 12, 18]       ,lowerCamelCase__  :      Optional[int]=0.1       ,lowerCamelCase__  :      List[str]=255       ,**lowerCamelCase__  :      List[Any]       ,):
									super().__init__(**lowerCamelCase__     )

									UpperCAmelCase__			 =				num_channels
									UpperCAmelCase__			 =				image_size
									UpperCAmelCase__			 =				patch_size
									UpperCAmelCase__			 =				hidden_sizes
									UpperCAmelCase__			 =				neck_hidden_sizes
									UpperCAmelCase__			 =				num_attention_heads
									UpperCAmelCase__			 =				mlp_ratio
									UpperCAmelCase__			 =				expand_ratio
									UpperCAmelCase__			 =				hidden_act
									UpperCAmelCase__			 =				conv_kernel_size
									UpperCAmelCase__			 =				output_stride
									UpperCAmelCase__			 =				hidden_dropout_prob
									UpperCAmelCase__			 =				attention_probs_dropout_prob
									UpperCAmelCase__			 =				classifier_dropout_prob
									UpperCAmelCase__			 =				initializer_range
									UpperCAmelCase__			 =				layer_norm_eps
									UpperCAmelCase__			 =				qkv_bias

									# decode head attributes for semantic segmentation
									UpperCAmelCase__			 =				aspp_out_channels
									UpperCAmelCase__			 =				atrous_rates
									UpperCAmelCase__			 =				aspp_dropout_prob
									UpperCAmelCase__			 =				semantic_loss_ignore_index






class 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					snake_case__							       =					version.parse("1.11"							)







					@property
					def 						__lowerCAmelCase		(							self  :      str     ):
									return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})]     )







					@property
					def 						__lowerCAmelCase		(							self  :      Dict     ):
									if self.task == "image-classification":
													return OrderedDict([('logits', {0: 'batch'})]     )
									else:
													return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]     )







					@property
					def 						__lowerCAmelCase		(							self  :      str     ):
									return 1e-4



