"""simple docstring"""
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        __snake_case	(					__lowerCAmelCase    ):



      def __init__(						self   ,      lowercase=None   ,      lowercase=None   ,      *lowercase   ,      **lowercase)     ->      Tuple:





        '''simple docstring'''





        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__}'
          )
          a__:					Any      							=     self.model.config
        else:
          a__:					Union[str, Any]      							=     config

        a__:					Optional[Any]      							=     data_args
        a__:					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:
          a__:					Tuple      							=     torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)
        else:
          # dynamically import label_smoothed_nll_loss
          from utils import label_smoothed_nll_loss

          a__:					List[Any]      							=     label_smoothed_nll_loss



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      Optional[int]:





        '''simple docstring'''





        if self.optimizer is None:
          a__:					str      							=     ['bias', 'LayerNorm.weight']
          a__:					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,
              },
          ]
          a__:					Dict      							=     Adafactor if self.args.adafactor else AdamW
          if self.args.adafactor:
            a__:					Optional[int]      							=     Adafactor
            a__:					Optional[Any]      							=     {'scale_parameter': False, 'relative_step': False}
          else:
            a__:					Optional[int]      							=     AdamW
            a__:					Optional[Any]      							=     {
                'betas': (self.args.adam_betaa, self.args.adam_betaa),
                'eps': self.args.adam_epsilon,
            }
          a__:					int      							=     self.args.learning_rate
          if self.sharded_ddp:
            a__:					Optional[int]      							=     OSS(
                params=lowercase   ,      optim=lowercase   ,      **lowercase   ,      )
          else:
            a__:					Optional[Any]      							=     optimizer_cls(lowercase   ,      **lowercase)

        if self.lr_scheduler is None:
          a__:					int      							=     self._get_lr_scheduler(lowercase)
        else:  # ignoring --lr_scheduler
          logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.')



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					int      							=     arg_to_scheduler[self.args.lr_scheduler]
        if self.args.lr_scheduler == "constant":
          a__:					List[str]      							=     schedule_func(self.optimizer)
        elif self.args.lr_scheduler == "constant_w_warmup":
          a__:					Dict      							=     schedule_func(self.optimizer   ,      num_warmup_steps=self.args.warmup_steps)
        else:
          a__:					Tuple      							=     schedule_func(
              self.optimizer   ,      num_warmup_steps=self.args.warmup_steps   ,      num_training_steps=lowercase)
        return scheduler



      def   lowerCamelCase_       (						self)     ->      Optional[torch.utils.data.Sampler]:





        '''simple docstring'''





        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   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase)     ->      Union[str, Any]:





        '''simple docstring'''





        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
            a__:					Dict      							=     model(**lowercase   ,      use_cache=lowercase)[0]
            a__:					Dict      							=     self.loss_fn(logits.view(-1   ,      logits.shape[-1])   ,      labels.view(-1))
          else:
            # compute usual loss via models
            a__							,   a__:					Optional[int]      							=     model(**lowercase   ,      labels=lowercase   ,      use_cache=lowercase)[:2]
        else:
          # compute label smoothed loss
          a__:					List[Any]      							=     model(**lowercase   ,      use_cache=lowercase)[0]
          a__:					List[Any]      							=     torch.nn.functional.log_softmax(lowercase   ,      dim=-1)
          a__							,   a__:					Any      							=     self.loss_fn(lowercase   ,      lowercase   ,      self.args.label_smoothing   ,      ignore_index=self.config.pad_token_id)
        return loss, logits



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





        '''simple docstring'''





        a__:					str      							=     inputs.pop('labels')
        a__							,   a__:					Optional[int]      							=     self._compute_loss(lowercase   ,      lowercase   ,      lowercase)
        return loss



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase = None   ,      )     ->      Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     self._prepare_inputs(lowercase)

        a__:					List[Any]      							=     {
            '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:
          a__:					Any      							=     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"]:
            a__:					Optional[Any]      							=     self._pad_tensors_to_max_len(lowercase   ,      gen_kwargs['max_length'])

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

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

        a__:					Union[str, Any]      							=     generated_tokens if self.args.predict_with_generate else logits

        if labels.shape[-1] < gen_kwargs["max_length"]:
          a__:					Optional[int]      							=     self._pad_tensors_to_max_len(lowercase   ,      gen_kwargs['max_length'])

        return (loss, logits, labels)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					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}')

        a__:					Tuple      							=     pad_token_id * torch.ones(
            (tensor.shape[0], max_length)   ,      dtype=tensor.dtype   ,      device=tensor.device)
        a__:					int      							=     tensor
        return padded_tensor



"""simple docstring"""
from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from ..image_utils import load_image


if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

lowercase__ 	=     logging.get_logger(__name__)


lowercase__ 	=     Dict[str, Any]
lowercase__ 	=     List[Prediction]




@add_end_docstrings(__lowerCAmelCase    )
class        __snake_case	(					__lowerCAmelCase    ):



      def __init__(						self   ,      *lowercase   ,      **lowercase)     ->      Dict:





        '''simple docstring'''





        super().__init__(*lowercase   ,      **lowercase)

        if self.framework == "tf":
          raise ValueError(f'The {self.__class__} is only available in PyTorch.')

        requires_backends(self   ,      'vision')
        self.check_model_type(
            dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()))



      def   lowerCamelCase_       (						self   ,      **lowercase)     ->      int:





        '''simple docstring'''





        a__:					Optional[Any]      							=     {}
        if "threshold" in kwargs:
          a__:					Dict      							=     kwargs['threshold']
        return {}, {}, postprocess_kwargs



      def __call__(						self   ,      *lowercase   ,      **lowercase)     ->      Union[Predictions, List[Prediction]]:





        '''simple docstring'''





        return super().__call__(*lowercase   ,      **lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      List[Any]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     load_image(lowercase)
        a__:					List[Any]      							=     torch.IntTensor([[image.height, image.width]])
        a__:					Any      							=     self.image_processor(images=[image]   ,      return_tensors='pt')
        if self.tokenizer is not None:
          a__:					Any      							=     self.tokenizer(text=inputs['words']   ,      boxes=inputs['boxes']   ,      return_tensors='pt')
        a__:					List[str]      							=     target_size
        return inputs



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      int:





        '''simple docstring'''





        a__:					Any      							=     model_inputs.pop('target_size')
        a__:					Union[str, Any]      							=     self.model(**lowercase)
        a__:					List[str]      							=     outputs.__class__({'target_size': target_size, **outputs})
        if self.tokenizer is not None:
          a__:					Union[str, Any]      							=     model_inputs['bbox']
        return model_outputs



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase=0.9)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					int      							=     model_outputs['target_size']
        if self.tokenizer is not None:
          # This is a LayoutLMForTokenClassification variant.
          # The OCR got the boxes and the model classified the words.
          a__							,   a__:					str      							=     target_size[0].tolist()

          def unnormalize(lowercase):
            return self._get_bounding_box(
                torch.Tensor(
                    [
                        (width * bbox[0] / 10_00),
                        (height * bbox[1] / 10_00),
                        (width * bbox[2] / 10_00),
                        (height * bbox[3] / 10_00),
                    ]))

          a__							,   a__:					Optional[Any]      							=     model_outputs['logits'].squeeze(0).softmax(dim=-1).max(dim=-1)
          a__:					str      							=     [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
          a__:					Union[str, Any]      							=     [unnormalize(lowercase) for bbox in model_outputs['bbox'].squeeze(0)]
          a__:					Dict      							=     ['score', 'label', 'box']
          a__:					Any      							=     [dict(zip(lowercase   ,      lowercase)) for vals in zip(scores.tolist()   ,      lowercase   ,      lowercase) if vals[0] > threshold]
        else:
          # This is a regular ForObjectDetectionModel
          a__:					List[str]      							=     self.image_processor.post_process_object_detection(lowercase   ,      lowercase   ,      lowercase)
          a__:					Tuple      							=     raw_annotations[0]
          a__:					List[str]      							=     raw_annotation['scores']
          a__:					int      							=     raw_annotation['labels']
          a__:					int      							=     raw_annotation['boxes']

          a__:					List[Any]      							=     scores.tolist()
          a__:					Any      							=     [self.model.config.idalabel[label.item()] for label in labels]
          a__:					Dict      							=     [self._get_bounding_box(lowercase) for box in boxes]

          # {"scores": [...], ...} --> [{"score":x, ...}, ...]
          a__:					Optional[Any]      							=     ['score', 'label', 'box']
          a__:					List[Any]      							=     [
              dict(zip(lowercase   ,      lowercase))
              for vals in zip(raw_annotation['scores']   ,      raw_annotation['labels']   ,      raw_annotation['boxes'])
          ]

        return annotation



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      Dict[str, int]:





        '''simple docstring'''





        if self.framework != "pt":
          raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.')
        a__							,   a__							,   a__							,   a__:					List[Any]      							=     box.int().tolist()
        a__:					Optional[int]      							=     {
            'xmin': xmin,
            'ymin': ymin,
            'xmax': xmax,
            'ymax': ymax,
        }
        return bbox


