from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    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.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
    from fairscale.optim import OSS


__lowercase										= logging.get_logger(__name__)

__lowercase										= {
    '''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,
    '''constant''': get_constant_schedule,
    '''constant_w_warmup''': get_constant_schedule_with_warmup,
}


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase=None			,			__lowercase=None			,			*__lowercase			,			**__lowercase)	->     str:
        super().__init__(*__lowercase			,			**__lowercase)

        if config is None:
            assert isinstance(self.model			,			__lowercase), (
                "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
                f""" {self.model.__class__}"""
            )
            __UpperCamelCase					:List[str]        =		self.model.config
        else:
            __UpperCamelCase					:Optional[int]        =		config

        __UpperCamelCase					:str        =		data_args
        __UpperCamelCase					:List[Any]        =		self.config.tgt_vocab_size if isinstance(self.config			,			__lowercase) else self.config.vocab_size

        if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
            assert self.config.pad_token_id is not None, (
                "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
                " calculation or doing label smoothing."
            )

        if self.config.pad_token_id is None and self.config.eos_token_id is not None:
            logger.warning(
                f"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
                ''' padding..''')

        if self.args.label_smoothing == 0:
            __UpperCamelCase					:Any        =		torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)
        else:
            # dynamically import label_smoothed_nll_loss
            from utils import label_smoothed_nll_loss

            __UpperCamelCase					:Optional[Any]        =		label_smoothed_nll_loss







    def       UpperCamelCase__       (     self			,			__lowercase)	->     Optional[int]:
        if self.optimizer is None:
            __UpperCamelCase					:Any        =		['''bias''', '''LayerNorm.weight''']
            __UpperCamelCase					:List[str]        =		[
                {
                    '''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)],
                    '''weight_decay''': self.args.weight_decay,
                },
                {
                    '''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)],
                    '''weight_decay''': 0.0,
                },
            ]
            __UpperCamelCase					:int        =		Adafactor if self.args.adafactor else AdamW
            if self.args.adafactor:
                __UpperCamelCase					:Any        =		Adafactor
                __UpperCamelCase					:Dict        =		{'''scale_parameter''': False, '''relative_step''': False}
            else:
                __UpperCamelCase					:int        =		AdamW
                __UpperCamelCase					:Dict        =		{
                    '''betas''': (self.args.adam_betaa, self.args.adam_betaa),
                    '''eps''': self.args.adam_epsilon,
                }
            __UpperCamelCase					:int        =		self.args.learning_rate
            if self.sharded_ddp:
                __UpperCamelCase					:Any        =		OSS(
                    params=__lowercase			,			optim=__lowercase			,			**__lowercase			,			)
            else:
                __UpperCamelCase					:str        =		optimizer_cls(__lowercase			,			**__lowercase)

        if self.lr_scheduler is None:
            __UpperCamelCase					:List[str]        =		self._get_lr_scheduler(__lowercase)
        else:  # ignoring --lr_scheduler
            logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.''')







    def       UpperCamelCase__       (     self			,			__lowercase)	->     Tuple:
        __UpperCamelCase					:Dict        =		arg_to_scheduler[self.args.lr_scheduler]
        if self.args.lr_scheduler == "constant":
            __UpperCamelCase					:List[str]        =		schedule_func(self.optimizer)
        elif self.args.lr_scheduler == "constant_w_warmup":
            __UpperCamelCase					:List[Any]        =		schedule_func(self.optimizer			,			num_warmup_steps=self.args.warmup_steps)
        else:
            __UpperCamelCase					:str        =		schedule_func(
                self.optimizer			,			num_warmup_steps=self.args.warmup_steps			,			num_training_steps=__lowercase)
        return scheduler







    def       UpperCamelCase__       (     self)	->     Optional[torch.utils.data.Sampler]:
        if isinstance(self.train_dataset			,			torch.utils.data.IterableDataset):
            return None
        elif is_torch_tpu_available():
            return get_tpu_sampler(self.train_dataset)
        else:
            if self.args.sortish_sampler:
                self.train_dataset.make_sortish_sampler(
                    self.args.per_device_train_batch_size			,			distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)			,			)

            return (
                RandomSampler(self.train_dataset)
                if self.args.local_rank == -1
                else DistributedSampler(self.train_dataset)
            )







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     Any:
        if self.args.label_smoothing == 0:
            if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
                # force training to ignore pad token
                __UpperCamelCase					:str        =		model(**__lowercase			,			use_cache=__lowercase)[0]
                __UpperCamelCase					:Any        =		self.loss_fn(logits.view(-1			,			logits.shape[-1])			,			labels.view(-1))
            else:
                # compute usual loss via models
                __UpperCamelCase  ,	__UpperCamelCase					:Optional[int]        =		model(**__lowercase			,			labels=__lowercase			,			use_cache=__lowercase)[:2]
        else:
            # compute label smoothed loss
            __UpperCamelCase					:List[str]        =		model(**__lowercase			,			use_cache=__lowercase)[0]
            __UpperCamelCase					:Optional[int]        =		torch.nn.functional.log_softmax(__lowercase			,			dim=-1)
            __UpperCamelCase  ,	__UpperCamelCase					:List[str]        =		self.loss_fn(__lowercase			,			__lowercase			,			self.args.label_smoothing			,			ignore_index=self.config.pad_token_id)
        return loss, logits







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase)	->     int:
        __UpperCamelCase					:Optional[Any]        =		inputs.pop('''labels''')
        __UpperCamelCase  ,	__UpperCamelCase					:int        =		self._compute_loss(__lowercase			,			__lowercase			,			__lowercase)
        return loss







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase = None			,			)	->     Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
        __UpperCamelCase					:List[str]        =		self._prepare_inputs(__lowercase)

        __UpperCamelCase					:str        =		{
            '''max_length''': self.data_args.val_max_target_length
            if self.data_args is not None
            else self.config.max_length,
            '''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
        }

        if self.args.predict_with_generate and not self.args.prediction_loss_only:
            __UpperCamelCase					:Dict        =		self.model.generate(
                inputs['''input_ids''']			,			attention_mask=inputs['''attention_mask''']			,			**__lowercase			,			)
            # in case the batch is shorter than max length, the output should be padded
            if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                __UpperCamelCase					:Any        =		self._pad_tensors_to_max_len(__lowercase			,			gen_kwargs['''max_length'''])

        __UpperCamelCase					:str        =		inputs.pop('''labels''')
        with torch.no_grad():
            # compute loss on predict data
            __UpperCamelCase  ,	__UpperCamelCase					:Optional[Any]        =		self._compute_loss(__lowercase			,			__lowercase			,			__lowercase)

        __UpperCamelCase					:List[Any]        =		loss.mean().detach()
        if self.args.prediction_loss_only:
            return (loss, None, None)

        __UpperCamelCase					:str        =		generated_tokens if self.args.predict_with_generate else logits

        if labels.shape[-1] < gen_kwargs["max_length"]:
            __UpperCamelCase					:Any        =		self._pad_tensors_to_max_len(__lowercase			,			gen_kwargs['''max_length'''])

        return (loss, logits, labels)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase)	->     Tuple:
        # If PAD token is not defined at least EOS token has to be defined
        __UpperCamelCase					:List[Any]        =		self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

        if pad_token_id is None:
            raise ValueError(
                '''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
                f""" padded to `max_length`={max_length}""")

        __UpperCamelCase					:Optional[Any]        =		pad_token_id * torch.ones(
            (tensor.shape[0], max_length)			,			dtype=tensor.dtype			,			device=tensor.device)
        __UpperCamelCase					:List[Any]        =		tensor
        return padded_tensor






from typing import TYPE_CHECKING

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


__lowercase										= {
    '''configuration_layoutlmv3''': [
        '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LayoutLMv3Config''',
        '''LayoutLMv3OnnxConfig''',
    ],
    '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
    '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= ['''LayoutLMv3TokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= [
        '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''LayoutLMv3ForQuestionAnswering''',
        '''LayoutLMv3ForSequenceClassification''',
        '''LayoutLMv3ForTokenClassification''',
        '''LayoutLMv3Model''',
        '''LayoutLMv3PreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= [
        '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFLayoutLMv3ForQuestionAnswering''',
        '''TFLayoutLMv3ForSequenceClassification''',
        '''TFLayoutLMv3ForTokenClassification''',
        '''TFLayoutLMv3Model''',
        '''TFLayoutLMv3PreTrainedModel''',
    ]

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= ['''LayoutLMv3FeatureExtractor''']
    __lowercase										= ['''LayoutLMv3ImageProcessor''']


if TYPE_CHECKING:
    from .configuration_layoutlmva import (
        LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
        LayoutLMvaConfig,
        LayoutLMvaOnnxConfig,
    )
    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_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_layoutlmva import (
            LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMvaForQuestionAnswering,
            LayoutLMvaForSequenceClassification,
            LayoutLMvaForTokenClassification,
            LayoutLMvaModel,
            LayoutLMvaPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_layoutlmva import (
            TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFLayoutLMvaForQuestionAnswering,
            TFLayoutLMvaForSequenceClassification,
            TFLayoutLMvaForTokenClassification,
            TFLayoutLMvaModel,
            TFLayoutLMvaPreTrainedModel,
        )

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

else:
    import sys

    __lowercase										= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





