
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

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


if is_torch_available():
      import torch

if is_vision_available():
      import PIL


lowercase__          =logging.get_logger(__name__)




def        __UpperCamelCase					(    lowerCAmelCase__							:    np.ndarray  ,       lowerCAmelCase__							:    Union[int, Iterable[int]]  ,       lowerCAmelCase__							:    bool  ,       lowerCAmelCase__							:    int  ):
 def constraint_to_multiple_of(lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    int=0  ,       lowerCAmelCase__							:    Union[str, Any]=None  ):
  __a			:  Optional[int]		    =  round(val / multiple  ) * multiple

  if max_val is not None and x > max_val:
   __a			:  str		    =  math.floor(val / multiple  ) * multiple

  if x < min_val:
   __a			:  str		    =  math.ceil(val / multiple  ) * multiple

  return x

 __a			:  Any		    =  (output_size, output_size) if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ) else output_size

 __a			:  List[Any]		    =  get_image_size(lowerCAmelCase__  )
 __a			:  str		    =  output_size

 # determine new height and width
 __a			:  str		    =  output_height / input_height
 __a			:  Dict		    =  output_width / input_width

 if keep_aspect_ratio:
  # scale as little as possible
  if abs(1 - scale_width  ) < abs(1 - scale_height  ):
   # fit width
   __a			:  List[str]		    =  scale_width
  else:
   # fit height
   __a			:  Dict		    =  scale_height

 __a			:  Optional[int]		    =  constraint_to_multiple_of(scale_height * input_height  ,       multiple=lowerCAmelCase__  )
 __a			:  List[Any]		    =  constraint_to_multiple_of(scale_width * input_width  ,       multiple=lowerCAmelCase__  )

 return (new_height, new_width)



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							str     	=				["pixel_values"]
   def __init__(self			: int ,    snake_case_			: bool = True ,    snake_case_			: Dict[str, int] = None ,    snake_case_			: PILImageResampling = PILImageResampling.BILINEAR ,    snake_case_			: bool = False ,    snake_case_			: int = 1 ,    snake_case_			: bool = True ,    snake_case_			: Union[int, float] = 1 / 2_5_5 ,    snake_case_			: bool = True ,    snake_case_			: Optional[Union[float, List[float]]] = None ,    snake_case_			: Optional[Union[float, List[float]]] = None ,    **snake_case_			: Tuple ,    ):
    super().__init__(**snake_case_       )
    __a			:  str		    =  size if size is not None else {'''height''': 3_8_4, '''width''': 3_8_4}
    __a			:  Dict		    =  get_size_dict(snake_case_       )
    __a			:  Optional[int]		    =  do_resize
    __a			:  Dict		    =  size
    __a			:  Union[str, Any]		    =  keep_aspect_ratio
    __a			:  Optional[Any]		    =  ensure_multiple_of
    __a			:  Union[str, Any]		    =  resample
    __a			:  List[Any]		    =  do_rescale
    __a			:  Optional[Any]		    =  rescale_factor
    __a			:  List[str]		    =  do_normalize
    __a			:  Tuple		    =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __a			:  Optional[int]		    =  image_std if image_std is not None else IMAGENET_STANDARD_STD
   def 				lowerCAmelCase      (self			: Optional[int] ,    snake_case_			: np.ndarray ,    snake_case_			: Dict[str, int] ,    snake_case_			: bool = False ,    snake_case_			: int = 1 ,    snake_case_			: PILImageResampling = PILImageResampling.BICUBIC ,    snake_case_			: Optional[Union[str, ChannelDimension]] = None ,    **snake_case_			: int ,    ):
    __a			:  Union[str, Any]		    =  get_size_dict(snake_case_       )
    if "height" not in size or "width" not in size:
     raise ValueError(f"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"       )
    __a			:  Optional[Any]		    =  get_resize_output_image_size(
        snake_case_ ,    output_size=(size['''height'''], size['''width''']) ,    keep_aspect_ratio=snake_case_ ,    multiple=snake_case_ ,    )
    return resize(snake_case_ ,    size=snake_case_ ,    resample=snake_case_ ,    data_format=snake_case_ ,    **snake_case_       )
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: np.ndarray ,    snake_case_			: Union[int, float] ,    snake_case_			: Optional[Union[str, ChannelDimension]] = None ,    **snake_case_			: List[Any] ,    ):
    return rescale(snake_case_ ,    scale=snake_case_ ,    data_format=snake_case_ ,    **snake_case_       )
   def 				lowerCAmelCase      (self			: Tuple ,    snake_case_			: np.ndarray ,    snake_case_			: Union[float, List[float]] ,    snake_case_			: Union[float, List[float]] ,    snake_case_			: Optional[Union[str, ChannelDimension]] = None ,    **snake_case_			: Optional[int] ,    ):
    return normalize(snake_case_ ,    mean=snake_case_ ,    std=snake_case_ ,    data_format=snake_case_ ,    **snake_case_       )
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: ImageInput ,    snake_case_			: bool = None ,    snake_case_			: int = None ,    snake_case_			: bool = None ,    snake_case_			: int = None ,    snake_case_			: PILImageResampling = None ,    snake_case_			: bool = None ,    snake_case_			: float = None ,    snake_case_			: bool = None ,    snake_case_			: Optional[Union[float, List[float]]] = None ,    snake_case_			: Optional[Union[float, List[float]]] = None ,    snake_case_			: Optional[Union[str, TensorType]] = None ,    snake_case_			: ChannelDimension = ChannelDimension.FIRST ,    **snake_case_			: int ,    ):
    __a			:  Optional[Any]		    =  do_resize if do_resize is not None else self.do_resize
    __a			:  Optional[Any]		    =  size if size is not None else self.size
    __a			:  Union[str, Any]		    =  get_size_dict(snake_case_       )
    __a			:  Union[str, Any]		    =  keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
    __a			:  Optional[int]		    =  ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
    __a			:  Any		    =  resample if resample is not None else self.resample
    __a			:  Optional[Any]		    =  do_rescale if do_rescale is not None else self.do_rescale
    __a			:  Dict		    =  rescale_factor if rescale_factor is not None else self.rescale_factor
    __a			:  Optional[int]		    =  do_normalize if do_normalize is not None else self.do_normalize
    __a			:  Any		    =  image_mean if image_mean is not None else self.image_mean
    __a			:  str		    =  image_std if image_std is not None else self.image_std

    __a			:  List[str]		    =  make_list_of_images(snake_case_       )

    if not valid_images(snake_case_       ):
     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 or resample is None:
     raise ValueError('''Size and resample must be specified if do_resize 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.
    __a			:  Union[str, Any]		    =  [to_numpy_array(snake_case_       ) for image in images]

    if do_resize:
     __a			:  str		    =  [self.resize(image=snake_case_ ,    size=snake_case_ ,    resample=snake_case_       ) for image in images]

    if do_rescale:
     __a			:  Optional[int]		    =  [self.rescale(image=snake_case_ ,    scale=snake_case_       ) for image in images]

    if do_normalize:
     __a			:  int		    =  [self.normalize(image=snake_case_ ,    mean=snake_case_ ,    std=snake_case_       ) for image in images]

    __a			:  Optional[Any]		    =  [to_channel_dimension_format(snake_case_ ,    snake_case_       ) for image in images]

    __a			:  Union[str, Any]		    =  {'''pixel_values''': images}
    return BatchFeature(data=snake_case_ ,    tensor_type=snake_case_       )







   def 				lowerCAmelCase      (self			: str ,    snake_case_			: Dict ,    snake_case_			: List[Tuple] = None       ):
    __a			:  List[str]		    =  outputs.logits

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

     if is_torch_tensor(snake_case_       ):
      __a			:  Tuple		    =  target_sizes.numpy()

     __a			:  Union[str, Any]		    =  []

     for idx in range(len(snake_case_       )       ):
      __a			:  List[Any]		    =  torch.nn.functional.interpolate(
          logits[idx].unsqueeze(dim=0       ) ,    size=target_sizes[idx] ,    mode='''bilinear''' ,    align_corners=snake_case_       )
      __a			:  int		    =  resized_logits[0].argmax(dim=0       )
      semantic_segmentation.append(snake_case_       )
    else:
     __a			:  Any		    =  logits.argmax(dim=1       )
     __a			:  Optional[int]		    =  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

    return semantic_segmentation


from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
