

'''simple docstring'''

from typing import Dict, List, Optional, Tuple, 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, is_torch_available, is_torch_tensor, logging


if is_torch_available():
       import torch


lowerCAmelCase_ : Dict						  =      logging.get_logger(__name__)





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=['pixel_values']


   def __init__(   self				:	Tuple   ,		__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				:	Optional[int]   ,		)      ->					None:
         super().__init__(**__lowerCAmelCase)
         lowercase_     =      size if size is not None else {"shortest_edge": 256}
         lowercase_     =      get_size_dict(__lowerCAmelCase   ,		default_to_square=__lowerCAmelCase)
         lowercase_     =      crop_size if crop_size is not None else {"height": 224, "width": 224}
         lowercase_     =      get_size_dict(__lowerCAmelCase   ,		param_name="crop_size")
         lowercase_     =      do_resize
         lowercase_     =      size
         lowercase_     =      resample
         lowercase_     =      do_center_crop
         lowercase_     =      crop_size
         lowercase_     =      do_rescale
         lowercase_     =      rescale_factor
         lowercase_     =      do_normalize
         lowercase_     =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         lowercase_     =      image_std if image_std is not None else IMAGENET_STANDARD_STD


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	Dict[str, int]   ,		__lowerCAmelCase				:	PILImageResampling = PILImageResampling.BICUBIC   ,		__lowerCAmelCase				:	Optional[Union[str, ChannelDimension]] = None   ,		**__lowerCAmelCase				:	Optional[int]   ,		)      ->					np.ndarray:
         lowercase_     =      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()}')
         lowercase_     =      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 __UpperCAmelCase       (   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	Dict[str, int]   ,		__lowerCAmelCase				:	Optional[Union[str, ChannelDimension]] = None   ,		**__lowerCAmelCase				:	Union[str, Any]   ,		)      ->					np.ndarray:
         lowercase_     =      get_size_dict(__lowerCAmelCase)
         if "height" not in size or "width" not in size:
               raise ValueError(F'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}')
         return center_crop(__lowerCAmelCase   ,		size=(size["height"], size["width"])   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)


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


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


   def __UpperCAmelCase       (   self				:	Any   ,		__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				:	Tuple   ,		)      ->					List[Any]:
         lowercase_     =      do_resize if do_resize is not None else self.do_resize
         lowercase_     =      size if size is not None else self.size
         lowercase_     =      get_size_dict(__lowerCAmelCase   ,		default_to_square=__lowerCAmelCase)
         lowercase_     =      resample if resample is not None else self.resample
         lowercase_     =      do_center_crop if do_center_crop is not None else self.do_center_crop
         lowercase_     =      crop_size if crop_size is not None else self.crop_size
         lowercase_     =      get_size_dict(__lowerCAmelCase   ,		param_name="crop_size")
         lowercase_     =      do_rescale if do_rescale is not None else self.do_rescale
         lowercase_     =      rescale_factor if rescale_factor is not None else self.rescale_factor
         lowercase_     =      do_normalize if do_normalize is not None else self.do_normalize
         lowercase_     =      image_mean if image_mean is not None else self.image_mean
         lowercase_     =      image_std if image_std is not None else self.image_std

         lowercase_     =      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.
         lowercase_     =      [to_numpy_array(__lowerCAmelCase) for image in images]

         if do_resize:
               lowercase_     =      [self.resize(image=__lowerCAmelCase   ,		size=__lowerCAmelCase   ,		resample=__lowerCAmelCase) for image in images]

         if do_center_crop:
               lowercase_     =      [self.center_crop(image=__lowerCAmelCase   ,		size=__lowerCAmelCase) for image in images]

         if do_rescale:
               lowercase_     =      [self.rescale(image=__lowerCAmelCase   ,		scale=__lowerCAmelCase) for image in images]

         if do_normalize:
               lowercase_     =      [self.normalize(image=__lowerCAmelCase   ,		mean=__lowerCAmelCase   ,		std=__lowerCAmelCase) for image in images]

         lowercase_     =      [to_channel_dimension_format(__lowerCAmelCase   ,		__lowerCAmelCase) for image in images]

         lowercase_     =      {"pixel_values": images}
         return BatchFeature(data=__lowerCAmelCase   ,		tensor_type=__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	List[Any]   ,		__lowerCAmelCase				:	List[Tuple] = None)      ->					Any:
         lowercase_     =      outputs.logits

         # Resize logits and compute semantic segmentation maps
         if target_sizes is not None:
               if len(__lowerCAmelCase) != len(__lowerCAmelCase):
                     raise ValueError(
                         "Make sure that you pass in as many target sizes as the batch dimension of the logits")

               if is_torch_tensor(__lowerCAmelCase):
                     lowercase_     =      target_sizes.numpy()

               lowercase_     =      []

               for idx in range(len(__lowerCAmelCase)):
                     lowercase_     =      torch.nn.functional.interpolate(
                         logits[idx].unsqueeze(dim=0)   ,		size=target_sizes[idx]   ,		mode="bilinear"   ,		align_corners=__lowerCAmelCase)
                     lowercase_     =      resized_logits[0].argmax(dim=0)
                     semantic_segmentation.append(__lowerCAmelCase)
         else:
               lowercase_     =      logits.argmax(dim=1)
               lowercase_     =      [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

         return semantic_segmentation







'''simple docstring'''

import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


lowerCAmelCase_ : Dict						  =      logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__ )
class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=field(default=__lowerCamelCase ,				metadata={'help': 'Whether to use SortishSampler or not.'} )
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'} )
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': (
               'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
               'to the `max_length` value of the model configuration.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': (
               'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
               'to the `num_beams` value of the model configuration.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
       } ,				)


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Optional[int]:
         lowercase_     =      super().to_dict()
         for k, v in d.items():
               if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
                     lowercase_     =      v.to_dict()
         return d






