
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	:      Any  					=					logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

A	:      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
A	:      Union[str, 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
}
A	:      Union[str, Any]  					=					sorted(arg_to_scheduler.keys())
A	:      Tuple  					=					"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""







class      __A(					pl.LightningModule     ):




				def __init__(	self						,	_snake_case						,	_snake_case=None						,	_snake_case="base"						,	_snake_case=None						,	_snake_case=None						,	_snake_case=None						,	**_snake_case						,	)       -> str:


								'''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(__lowerCamelCase							)
								__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=__lowerCamelCase						,	**__lowerCamelCase						,	)
								else:
												__a     				=       config

								__a     				=       ('''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:
												__a     				=       AutoTokenizer.from_pretrained(
												    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path						,	cache_dir=__lowerCamelCase						,	)
								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=__lowerCamelCase						,	)
								else:
												__a     				=       model




				def 							SCREAMING_SNAKE_CASE_						(	self						,	*_snake_case						,	**_snake_case							)       -> Optional[int]:


								'''simple docstring'''




								__a     				=       self.model_type.from_pretrained(*__lowerCamelCase						,	**__lowerCamelCase							)




				def 							SCREAMING_SNAKE_CASE_						(	self							)       -> int:


								'''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 							SCREAMING_SNAKE_CASE_						(	self							)       -> str:


								'''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							)
								        ],  # check this named paramters
								        '''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(
												    __lowerCamelCase						,	lr=self.hparams.learning_rate						,	scale_parameter=__lowerCamelCase						,	relative_step=__lowerCamelCase							)

								else:
												__a     				=       AdamW(
												    __lowerCamelCase						,	lr=self.hparams.learning_rate						,	eps=self.hparams.adam_epsilon							)
								__a     				=       optimizer

								__a     				=       self.get_lr_scheduler()

								return [optimizer], [scheduler]




				def 							SCREAMING_SNAKE_CASE_						(	self						,	_snake_case						,	_snake_case							)       -> Any:


								'''simple docstring'''




								return self.validation_step(__lowerCamelCase						,	__lowerCamelCase							)




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


								'''simple docstring'''




								return self.validation_end(__lowerCamelCase							)




				def 							SCREAMING_SNAKE_CASE_						(	self							)       -> Tuple:


								'''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 							SCREAMING_SNAKE_CASE_						(	self						,	_snake_case							)       -> Union[str, Any]:


								'''simple docstring'''




								if stage == "test":
												__a     				=       len(self.test_dataloader().dataset							)
								else:
												__a     				=       self.get_dataloader('''train'''						,	self.hparams.train_batch_size						,	shuffle=__lowerCamelCase							)
												__a     				=       len(self.train_dataloader().dataset							)




				def 							SCREAMING_SNAKE_CASE_						(	self						,	_snake_case						,	_snake_case						,	_snake_case = False							)       -> str:


								'''simple docstring'''




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




				def 							SCREAMING_SNAKE_CASE_						(	self							)       -> Optional[Any]:


								'''simple docstring'''




								return self.train_loader




				def 							SCREAMING_SNAKE_CASE_						(	self							)       -> str:


								'''simple docstring'''




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




				def 							SCREAMING_SNAKE_CASE_						(	self							)       -> List[str]:


								'''simple docstring'''




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




				def 							SCREAMING_SNAKE_CASE_						(	self						,	_snake_case							)       -> List[str]:


								'''simple docstring'''




								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 							SCREAMING_SNAKE_CASE_						(	self						,	_snake_case							)       -> List[Any]:


								'''simple docstring'''




								__a     				=       self.output_dir.joinpath('''best_tfmr'''							)
								__a     				=       self.step_count
								self.model.save_pretrained(__lowerCamelCase							)
								self.tokenizer.save_pretrained(__lowerCamelCase							)




				@staticmethod
				def 							SCREAMING_SNAKE_CASE_						(	_snake_case						,	_snake_case							)       -> List[str]:


								'''simple docstring'''




								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=str(Path(__lowerCamelCase							).parent / '''test_run''' / '''cache'''							)						,	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      __A(					pl.Callback     ):




				def 							SCREAMING_SNAKE_CASE_						(	self						,	_snake_case						,	_snake_case							)       -> Optional[int]:


								'''simple docstring'''




								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      __A(					pl.Callback     ):




				def 							SCREAMING_SNAKE_CASE_						(	self						,	_snake_case						,	_snake_case							)       -> Optional[int]:


								'''simple docstring'''




								for name, param in pl_module.model.rag.named_parameters():
												if param.grad is None:
																print(__lowerCamelCase							)







class      __A(					pl.Callback     ):




				def 							SCREAMING_SNAKE_CASE_						(	self						,	_snake_case						,	_snake_case							)       -> List[str]:


								'''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(__lowerCamelCase							)




				def 							SCREAMING_SNAKE_CASE_						(	self						,	_snake_case						,	_snake_case							)       -> List[Any]:


								'''simple docstring'''




								rank_zero_info('''***** Validation results *****'''							)
								__a     				=       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 							SCREAMING_SNAKE_CASE_						(	self						,	_snake_case						,	_snake_case							)       -> Any:


								'''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(__lowerCamelCase						,	'''w'''							) as writer:
												for key in sorted(__lowerCamelCase							):
																if key not in ["log", "progress_bar"]:
																				rank_zero_info('''{} = {}\n'''.format(__lowerCamelCase						,	str(metrics[key]							)							)							)
																				writer.write('''{} = {}\n'''.format(__lowerCamelCase						,	str(metrics[key]							)							)							)



def        __lowerCAmelCase     (     a__		,       a__    )			->    Union[str, Any]:
				#  To allow all pl args uncomment the following line
				#  parser = pl.Trainer.add_argparse_args(parser)
				parser.add_argument(
				    '''--output_dir'''		,       default=str(Path(__lowerCAmelCase    ).parent / '''test_run''' / '''model_checkpoints'''    )		,       type=__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=42		,       help='''random seed for initialization'''    )
				parser.add_argument(
				    '''--data_dir'''		,       default=str(Path(__lowerCAmelCase    ).parent / '''test_run''' / '''dummy-train-data'''    )		,       type=__lowerCAmelCase		,       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__		,       )			->    Optional[int]:
				pl.seed_everything(args.seed    )

				# init model
				__a     				=       Path(model.hparams.output_dir    )
				odir.mkdir(exist_ok=__lowerCAmelCase    )

				# 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(__lowerCAmelCase    )
				if logging_callback is None:
								__a     				=       LoggingCallback()

				__a     				=       {}

				if args.fpaa:
								__a     				=       16

				if args.gpus > 1:
								__a     				=       '''auto'''
								__a     				=       '''ddp'''

				__a     				=       args.accumulate_grad_batches
				__a     				=       None
				__a     				=       '''auto'''

				__a     				=       pl.Trainer.from_argparse_args(
				    __lowerCAmelCase		,       weights_summary=__lowerCAmelCase		,       callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]		,       logger=__lowerCAmelCase		,       val_check_interval=1		,       num_sanity_val_steps=2		,       **__lowerCAmelCase		,       )

				if args.do_train:
								trainer.fit(__lowerCAmelCase    )

				else:
								print('''RAG modeling tests with new set functions successfuly executed!'''    )
				return trainer





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__magic_name__  :  Any					=       {
    """configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMv2Config"""],
    """processing_layoutlmv2""": ["""LayoutLMv2Processor"""],
    """tokenization_layoutlmv2""": ["""LayoutLMv2Tokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  List[str]					=       ["""LayoutLMv2TokenizerFast"""]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  Any					=       ["""LayoutLMv2FeatureExtractor"""]
      __magic_name__  :  Any					=       ["""LayoutLMv2ImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  Any					=       [
          """LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """LayoutLMv2ForQuestionAnswering""",
          """LayoutLMv2ForSequenceClassification""",
          """LayoutLMv2ForTokenClassification""",
          """LayoutLMv2Layer""",
          """LayoutLMv2Model""",
          """LayoutLMv2PreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
      from .processing_layoutlmva import LayoutLMvaProcessor
      from .tokenization_layoutlmva import LayoutLMvaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_layoutlmva import (
                LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                LayoutLMvaForQuestionAnswering,
                LayoutLMvaForSequenceClassification,
                LayoutLMvaForTokenClassification,
                LayoutLMvaLayer,
                LayoutLMvaModel,
                LayoutLMvaPreTrainedModel,
            )
else:
      import sys

      __magic_name__  :  Union[str, Any]					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

