

from typing import List, Union

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


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_tf_available():
  import tensorflow as tf

  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
  from ..tf_utils import stable_softmax

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowercase_   							=  logging.get_logger(__name__)





@add_end_docstrings(SCREAMING_SNAKE_CASE				)
class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):




  def __init__(							self				,  *lowerCAmelCase				,  **lowerCAmelCase   )    ->					int:
        '''simple docstring'''






        super().__init__(*lowerCAmelCase				,  **lowerCAmelCase   )
        requires_backends(self				,  'vision'   )
        self.check_model_type(
            TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
            if self.framework == 'tf'
            else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING   )




  def     A__	(							self				,  lowerCAmelCase=None   )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			={}
        if top_k is not None:
              _lowercase      			=top_k
        return {}, {}, postprocess_params




  def __call__(							self				,  lowerCAmelCase				,  **lowerCAmelCase   )    ->					Dict:
        '''simple docstring'''






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




  def     A__	(							self				,  lowerCAmelCase   )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			=load_image(lowerCAmelCase   )
        _lowercase      			=self.image_processor(images=lowerCAmelCase				,  return_tensors=self.framework   )
        return model_inputs




  def     A__	(							self				,  lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=self.model(**lowerCAmelCase   )
        return model_outputs




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase=5   )    ->					Any:
        '''simple docstring'''






        if top_k > self.model.config.num_labels:
              _lowercase      			=self.model.config.num_labels

        if self.framework == "pt":
              _lowercase      			=model_outputs.logits.softmax(-1   )[0]
              _lowercase			,			_lowercase      			=probs.topk(lowerCAmelCase   )
        elif self.framework == "tf":
              _lowercase      			=stable_softmax(model_outputs.logits				,  axis=-1   )[0]
              _lowercase      			=tf.math.top_k(lowerCAmelCase				,  k=lowerCAmelCase   )
              _lowercase			,			_lowercase      			=topk.values.numpy(), topk.indices.numpy()
        else:
              raise ValueError(F'''Unsupported framework: {self.framework}'''   )

        _lowercase      			=scores.tolist()
        _lowercase      			=ids.tolist()
        return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCAmelCase				,  lowerCAmelCase   )]





# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')





class 					__lowerCAmelCase	:




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase = True				,  lowerCAmelCase = False   )    ->					str:
        '''simple docstring'''






        _lowercase      			=scheduler
        _lowercase      			=optimizers if isinstance(lowerCAmelCase				,  (list, tuple)   ) else [optimizers]
        _lowercase      			=split_batches
        _lowercase      			=step_with_optimizer
        _lowercase      			=GradientState()




  def     A__	(							self				,  *lowerCAmelCase				,  **lowerCAmelCase   )    ->					List[Any]:
        '''simple docstring'''






        if not self.step_with_optimizer:
              # No link between scheduler and optimizer -> just step
              self.scheduler.step(*lowerCAmelCase				,  **lowerCAmelCase   )
              return

        # Otherwise, first make sure the optimizer was stepped.
        if not self.gradient_state.sync_gradients:
              if self.gradient_state.adjust_scheduler:
                    self.scheduler._step_count += 1
              return

        for opt in self.optimizers:
              if opt.step_was_skipped:
                    return
        if self.split_batches:
              # Split batches -> the training dataloader batch size is not changed so one step per training step
              self.scheduler.step(*lowerCAmelCase				,  **lowerCAmelCase   )
        else:
              # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
              # num_processes steps per training step
              _lowercase      			=AcceleratorState().num_processes
              for _ in range(lowerCAmelCase   ):
                    # Special case when using OneCycle and `drop_last` was not used
                    if hasattr(self.scheduler				,  'total_steps'   ):
                          if self.scheduler._step_count <= self.scheduler.total_steps:
                                self.scheduler.step(*lowerCAmelCase				,  **lowerCAmelCase   )
                    else:
                          self.scheduler.step(*lowerCAmelCase				,  **lowerCAmelCase   )




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






        return self.scheduler.get_last_lr()




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






        return self.scheduler.state_dict()




  def     A__	(							self				,  lowerCAmelCase   )    ->					str:
        '''simple docstring'''






        self.scheduler.load_state_dict(lowerCAmelCase   )




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






        return self.scheduler.get_lr()




  def     A__	(							self				,  *lowerCAmelCase				,  **lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






        return self.scheduler.print_lr(*lowerCAmelCase				,  **lowerCAmelCase   )


