

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE  :						int         =    logging.get_logger(__name__)






class  UpperCAmelCase__	( lowercase__ ):






  """simple docstring"""


  a     =      ["pixel_values"]






  def __init__(							self		:	int			,     __lowerCamelCase		:	bool = True			,     __lowerCamelCase		:	Optional[Dict[str, int]] = None			,     __lowerCamelCase		:	PILImageResampling = PILImageResampling.BILINEAR			,     __lowerCamelCase		:	bool = True			,     __lowerCamelCase		:	Dict[str, int] = None			,     __lowerCamelCase		:	bool = True			,     __lowerCamelCase		:	Union[int, float] = 1 / 255			,     __lowerCamelCase		:	bool = True			,     __lowerCamelCase		:	Optional[Union[float, List[float]]] = None			,     __lowerCamelCase		:	Optional[Union[float, List[float]]] = None			,     **__lowerCamelCase		:	List[str]			,     )	->							None:
       super().__init__(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__     :   List[str]					=					size if size is not None else {"shortest_edge": 256}
       SCREAMING_SNAKE_CASE__     :   Any					=					get_size_dict(__lowerCamelCase			,     default_to_square=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__     :   List[Any]					=					crop_size if crop_size is not None else {"height": 224, "width": 224}
       SCREAMING_SNAKE_CASE__     :   Dict					=					get_size_dict(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__     :   List[str]					=					do_resize
       SCREAMING_SNAKE_CASE__     :   List[str]					=					size
       SCREAMING_SNAKE_CASE__     :   List[Any]					=					resample
       SCREAMING_SNAKE_CASE__     :   int					=					do_center_crop
       SCREAMING_SNAKE_CASE__     :   Optional[Any]					=					crop_size
       SCREAMING_SNAKE_CASE__     :   Any					=					do_rescale
       SCREAMING_SNAKE_CASE__     :   Any					=					rescale_factor
       SCREAMING_SNAKE_CASE__     :   int					=					do_normalize
       SCREAMING_SNAKE_CASE__     :   str					=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       SCREAMING_SNAKE_CASE__     :   int					=					image_std if image_std is not None else IMAGENET_STANDARD_STD






  def    lowercase_	(							self		:	Tuple			,     __lowerCamelCase		:	np.ndarray			,     __lowerCamelCase		:	Dict[str, int]			,     __lowerCamelCase		:	PILImageResampling = PILImageResampling.BICUBIC			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = None			,     **__lowerCamelCase		:	Optional[int]			,     )	->							np.ndarray:
       SCREAMING_SNAKE_CASE__     :   List[Any]					=					get_size_dict(__lowerCamelCase			,     default_to_square=__lowerCamelCase      )
       if "shortest_edge" not in size:
            raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''      )
       SCREAMING_SNAKE_CASE__     :   Optional[int]					=					get_resize_output_image_size(__lowerCamelCase			,     size=size['''shortest_edge''']			,     default_to_square=__lowerCamelCase      )
       return resize(__lowerCamelCase			,     size=__lowerCamelCase			,     resample=__lowerCamelCase			,     data_format=__lowerCamelCase			,     **__lowerCamelCase      )






  def    lowercase_	(							self		:	List[Any]			,     __lowerCamelCase		:	np.ndarray			,     __lowerCamelCase		:	Dict[str, int]			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = None			,     **__lowerCamelCase		:	List[Any]			,     )	->							np.ndarray:
       SCREAMING_SNAKE_CASE__     :   Tuple					=					get_size_dict(__lowerCamelCase      )
       return center_crop(__lowerCamelCase			,     size=(size['''height'''], size['''width'''])			,     data_format=__lowerCamelCase			,     **__lowerCamelCase      )






  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	np.ndarray			,     __lowerCamelCase		:	float			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = None			,     **__lowerCamelCase		:	Dict      )	->							np.ndarray:
       return rescale(__lowerCamelCase			,     scale=__lowerCamelCase			,     data_format=__lowerCamelCase			,     **__lowerCamelCase      )






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	np.ndarray			,     __lowerCamelCase		:	Union[float, List[float]]			,     __lowerCamelCase		:	Union[float, List[float]]			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = None			,     **__lowerCamelCase		:	Union[str, Any]			,     )	->							np.ndarray:
       return normalize(__lowerCamelCase			,     mean=__lowerCamelCase			,     std=__lowerCamelCase			,     data_format=__lowerCamelCase			,     **__lowerCamelCase      )







  def    lowercase_	(							self		:	Tuple			,     __lowerCamelCase		:	ImageInput			,     __lowerCamelCase		:	Optional[bool] = None			,     __lowerCamelCase		:	Dict[str, int] = None			,     __lowerCamelCase		:	PILImageResampling = None			,     __lowerCamelCase		:	bool = None			,     __lowerCamelCase		:	Dict[str, int] = None			,     __lowerCamelCase		:	Optional[bool] = None			,     __lowerCamelCase		:	Optional[float] = None			,     __lowerCamelCase		:	Optional[bool] = None			,     __lowerCamelCase		:	Optional[Union[float, List[float]]] = None			,     __lowerCamelCase		:	Optional[Union[float, List[float]]] = None			,     __lowerCamelCase		:	Optional[Union[str, TensorType]] = None			,     __lowerCamelCase		:	Union[str, ChannelDimension] = ChannelDimension.FIRST			,     **__lowerCamelCase		:	Any			,     )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__     :   Optional[Any]					=					do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__     :   Union[str, Any]					=					size if size is not None else self.size
       SCREAMING_SNAKE_CASE__     :   Dict					=					get_size_dict(__lowerCamelCase			,     default_to_square=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__     :   str					=					resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE__     :   List[str]					=					do_center_crop if do_center_crop is not None else self.do_center_crop
       SCREAMING_SNAKE_CASE__     :   Optional[int]					=					crop_size if crop_size is not None else self.crop_size
       SCREAMING_SNAKE_CASE__     :   Dict					=					get_size_dict(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__     :   List[str]					=					do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE__     :   int					=					rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE__     :   Dict					=					do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE__     :   Optional[int]					=					image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE__     :   Tuple					=					image_std if image_std is not None else self.image_std

       SCREAMING_SNAKE_CASE__     :   Union[str, Any]					=					make_list_of_images(__lowerCamelCase      )

       if not valid_images(__lowerCamelCase      ):
            raise ValueError(
                '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                '''torch.Tensor, tf.Tensor or jax.ndarray.'''      )

       if do_resize and size is None:
            raise ValueError('''Size must be specified if do_resize is True.'''      )

       if do_center_crop and crop_size is None:
            raise ValueError('''Crop size must be specified if do_center_crop is True.'''      )

       if do_rescale and rescale_factor is None:
            raise ValueError('''Rescale factor must be specified if do_rescale is True.'''      )

       if do_normalize and (image_mean is None or image_std is None):
            raise ValueError('''Image mean and std must be specified if do_normalize is True.'''      )

       # All transformations expect numpy arrays.
       SCREAMING_SNAKE_CASE__     :   List[str]					=					[to_numpy_array(__lowerCamelCase      ) for image in images]

       if do_resize:
            SCREAMING_SNAKE_CASE__     :   Tuple					=					[self.resize(image=__lowerCamelCase			,     size=__lowerCamelCase			,     resample=__lowerCamelCase      ) for image in images]

       if do_center_crop:
            SCREAMING_SNAKE_CASE__     :   List[Any]					=					[self.center_crop(image=__lowerCamelCase			,     size=__lowerCamelCase      ) for image in images]

       if do_rescale:
            SCREAMING_SNAKE_CASE__     :   List[str]					=					[self.rescale(image=__lowerCamelCase			,     scale=__lowerCamelCase      ) for image in images]

       if do_normalize:
            SCREAMING_SNAKE_CASE__     :   Dict					=					[self.normalize(image=__lowerCamelCase			,     mean=__lowerCamelCase			,     std=__lowerCamelCase      ) for image in images]

       SCREAMING_SNAKE_CASE__     :   Dict					=					[to_channel_dimension_format(__lowerCamelCase			,     __lowerCamelCase      ) for image in images]

       SCREAMING_SNAKE_CASE__     :   Optional[int]					=					{"pixel_values": images}
       return BatchFeature(data=__lowerCamelCase			,     tensor_type=__lowerCamelCase      )





import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm
       import torch_xla.debug.metrics as met






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  def __init__(							self		:	Tuple			,     *__lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Any=None			,     __lowerCamelCase		:	Optional[int]=None			,     **__lowerCamelCase		:	Optional[int]      )	->							List[str]:
       super().__init__(*__lowerCamelCase			,     **__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					eval_examples
       SCREAMING_SNAKE_CASE__					=					post_process_function






  def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	Dict=None			,     __lowerCamelCase		:	int=None			,     __lowerCamelCase		:	str=None			,     __lowerCamelCase		:	str = "eval"      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					self.eval_dataset if eval_dataset is None else eval_dataset
       SCREAMING_SNAKE_CASE__					=					self.get_eval_dataloader(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					self.eval_examples if eval_examples is None else eval_examples

       # Temporarily disable metric computation, we will do it in the loop here.
       SCREAMING_SNAKE_CASE__					=					self.compute_metrics
       SCREAMING_SNAKE_CASE__					=					None
       SCREAMING_SNAKE_CASE__					=					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       SCREAMING_SNAKE_CASE__					=					time.time()
       try:
            SCREAMING_SNAKE_CASE__					=					eval_loop(
                __lowerCamelCase			,     description='''Evaluation'''			,     prediction_loss_only=True if compute_metrics is None else None			,     ignore_keys=__lowerCamelCase			,     metric_key_prefix=__lowerCamelCase			,     )
       finally:
            SCREAMING_SNAKE_CASE__					=					compute_metrics
       SCREAMING_SNAKE_CASE__					=					self.args.eval_batch_size * self.args.world_size
       if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
            start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
       output.metrics.update(
           speed_metrics(
               __lowerCamelCase			,     __lowerCamelCase			,     num_samples=output.num_samples			,     num_steps=math.ceil(output.num_samples / total_batch_size      )			,     )      )
       if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
            # Only the main node write the results by default
            SCREAMING_SNAKE_CASE__					=					self.post_process_function(__lowerCamelCase			,     __lowerCamelCase			,     output.predictions      )
            SCREAMING_SNAKE_CASE__					=					self.compute_metrics(__lowerCamelCase      )

            # Prefix all keys with metric_key_prefix + '_'
            for key in list(metrics.keys()      ):
                 if not key.startswith(f'''{metric_key_prefix}_'''      ):
                      SCREAMING_SNAKE_CASE__					=					metrics.pop(__lowerCamelCase      )
            metrics.update(output.metrics      )
       else:
            SCREAMING_SNAKE_CASE__					=					output.metrics

       if self.args.should_log:
            # Only the main node log the results by default
            self.log(__lowerCamelCase      )

       if self.args.tpu_metrics_debug or self.args.debug:
            # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
            xm.master_print(met.metrics_report()      )

       SCREAMING_SNAKE_CASE__					=					self.callback_handler.on_evaluate(self.args			,     self.state			,     self.control			,     __lowerCamelCase      )
       return metrics







  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	str=None			,     __lowerCamelCase		:	str = "test"      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					self.get_test_dataloader(__lowerCamelCase      )

       # Temporarily disable metric computation, we will do it in the loop here.
       SCREAMING_SNAKE_CASE__					=					self.compute_metrics
       SCREAMING_SNAKE_CASE__					=					None
       SCREAMING_SNAKE_CASE__					=					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       SCREAMING_SNAKE_CASE__					=					time.time()
       try:
            SCREAMING_SNAKE_CASE__					=					eval_loop(
                __lowerCamelCase			,     description='''Prediction'''			,     prediction_loss_only=True if compute_metrics is None else None			,     ignore_keys=__lowerCamelCase			,     metric_key_prefix=__lowerCamelCase			,     )
       finally:
            SCREAMING_SNAKE_CASE__					=					compute_metrics
       SCREAMING_SNAKE_CASE__					=					self.args.eval_batch_size * self.args.world_size
       if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
            start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
       output.metrics.update(
           speed_metrics(
               __lowerCamelCase			,     __lowerCamelCase			,     num_samples=output.num_samples			,     num_steps=math.ceil(output.num_samples / total_batch_size      )			,     )      )

       if self.post_process_function is None or self.compute_metrics is None:
            return output

       SCREAMING_SNAKE_CASE__					=					self.post_process_function(__lowerCamelCase			,     __lowerCamelCase			,     output.predictions			,     '''predict'''      )
       SCREAMING_SNAKE_CASE__					=					self.compute_metrics(__lowerCamelCase      )

       # Prefix all keys with metric_key_prefix + '_'
       for key in list(metrics.keys()      ):
            if not key.startswith(f'''{metric_key_prefix}_'''      ):
                 SCREAMING_SNAKE_CASE__					=					metrics.pop(__lowerCamelCase      )
       metrics.update(output.metrics      )
       return PredictionOutput(predictions=predictions.predictions			,     label_ids=predictions.label_ids			,     metrics=__lowerCamelCase      )


