

"""simple docstring"""




import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/








"""simple docstring"""




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, 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_vision_available, logging


if is_vision_available():
      import PIL


__UpperCAmelCase	=			logging.get_logger(__name__)







class        __lowercase     (      __lowerCamelCase   ):
  snake_case_			    =    ["""pixel_values"""]

  def __init__(					self						:     Tuple   ,A						:     bool = True   ,A						:     Dict[str, int] = None   ,A						:     PILImageResampling = PIL.Image.NEAREST   ,A						:     bool = False   ,A						:     Dict[str, int] = None   ,A						:     Union[int, float] = 1 / 255   ,A						:     bool = False   ,A						:     bool = True   ,A						:     bool = True   ,A						:     Optional[Union[float, List[float]]] = None   ,A						:     Optional[Union[float, List[float]]] = None   ,A						:     bool = True   ,**A						:     List[str]   ,):






   '''simple docstring'''
   super().__init__(**A      )
   UpperCAmelCase__   :					Optional[Any]   				=   size if size is not None else {"""height""": 346, """width""": 346}
   UpperCAmelCase__   :					Union[str, Any]   				=   get_size_dict(A      )
   UpperCAmelCase__   :					Union[str, Any]   				=   crop_size if crop_size is not None else {"""height""": 289, """width""": 289}
   UpperCAmelCase__   :					Optional[Any]   				=   get_size_dict(A   ,param_name="""crop_size"""      )

   UpperCAmelCase__   :					Optional[int]   				=   do_resize
   UpperCAmelCase__   :					Optional[int]   				=   size
   UpperCAmelCase__   :					Optional[int]   				=   resample
   UpperCAmelCase__   :					List[str]   				=   do_center_crop
   UpperCAmelCase__   :					int   				=   crop_size
   UpperCAmelCase__   :					List[Any]   				=   do_rescale
   UpperCAmelCase__   :					List[str]   				=   rescale_factor
   UpperCAmelCase__   :					List[str]   				=   rescale_offset
   UpperCAmelCase__   :					Any   				=   do_normalize
   UpperCAmelCase__   :					Tuple   				=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   UpperCAmelCase__   :					int   				=   image_std if image_std is not None else IMAGENET_STANDARD_STD
   UpperCAmelCase__   :					Optional[int]   				=   include_top

  def 				__lowercase    (					self						:     Tuple   ,A						:     np.ndarray   ,A						:     Dict[str, int]   ,A						:     PILImageResampling = PIL.Image.NEAREST   ,A						:     Optional[Union[str, ChannelDimension]] = None   ,**A						:     Optional[int]   ,):






   '''simple docstring'''
   UpperCAmelCase__   :					List[Any]   				=   get_size_dict(A      )
   if "height" not in size or "width" not in size:
    raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"      )
   return resize(
       A   ,size=(size["""height"""], size["""width"""])   ,resample=A   ,data_format=A   ,**A      )

  def 				__lowercase    (					self						:     Union[str, Any]   ,A						:     np.ndarray   ,A						:     Dict[str, int]   ,A						:     Optional[Union[str, ChannelDimension]] = None   ,**A						:     Union[str, Any]   ,):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[Any]   				=   get_size_dict(A      )
   if "height" not in size or "width" not in size:
    raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"      )
   return center_crop(A   ,size=(size["""height"""], size["""width"""])   ,data_format=A   ,**A      )

  def 				__lowercase    (					self						:     List[Any]   ,A						:     np.ndarray   ,A						:     Union[int, float]   ,A						:     bool = True   ,A						:     Optional[Union[str, ChannelDimension]] = None   ,**A						:     Any   ,):






   '''simple docstring'''
   if offset:
    UpperCAmelCase__   :					Tuple   				=   (image - 1_2_7.5) * scale
    if data_format is not None:
     UpperCAmelCase__   :					Any   				=   to_channel_dimension_format(A   ,A      )
    UpperCAmelCase__   :					Union[str, Any]   				=   rescaled_image.astype(np.floataa      )
   else:
    UpperCAmelCase__   :					List[str]   				=   rescale(A   ,scale=A   ,data_format=A   ,**A      )
   return rescaled_image

  def 				__lowercase    (					self						:     Optional[Any]   ,A						:     np.ndarray   ,A						:     Union[float, List[float]]   ,A						:     Union[float, List[float]]   ,A						:     Optional[Union[str, ChannelDimension]] = None   ,**A						:     List[Any]   ,):






   '''simple docstring'''
   return normalize(A   ,mean=A   ,std=A   ,data_format=A   ,**A      )




  def 				__lowercase    (					self						:     Optional[int]   ,A						:     ImageInput   ,A						:     bool = None   ,A						:     Dict[str, int] = None   ,A						:     str=None   ,A						:     bool = None   ,A						:     Dict[str, int] = None   ,A						:     bool = None   ,A						:     float = None   ,A						:     bool = None   ,A						:     bool = None   ,A						:     Optional[Union[float, List[float]]] = None   ,A						:     Optional[Union[float, List[float]]] = None   ,A						:     bool = None   ,A						:     Optional[Union[str, TensorType]] = None   ,A						:     ChannelDimension = ChannelDimension.FIRST   ,**A						:     Optional[int]   ,):






   '''simple docstring'''
   UpperCAmelCase__   :					List[str]   				=   do_resize if do_resize is not None else self.do_resize
   UpperCAmelCase__   :					Optional[int]   				=   resample if resample is not None else self.resample
   UpperCAmelCase__   :					List[str]   				=   do_center_crop if do_center_crop is not None else self.do_center_crop
   UpperCAmelCase__   :					List[str]   				=   do_rescale if do_rescale is not None else self.do_rescale
   UpperCAmelCase__   :					Any   				=   rescale_factor if rescale_factor is not None else self.rescale_factor
   UpperCAmelCase__   :					str   				=   rescale_offset if rescale_offset is not None else self.rescale_offset
   UpperCAmelCase__   :					Union[str, Any]   				=   do_normalize if do_normalize is not None else self.do_normalize
   UpperCAmelCase__   :					int   				=   image_mean if image_mean is not None else self.image_mean
   UpperCAmelCase__   :					Any   				=   image_std if image_std is not None else self.image_std
   UpperCAmelCase__   :					List[Any]   				=   include_top if include_top is not None else self.include_top

   UpperCAmelCase__   :					List[Any]   				=   size if size is not None else self.size
   UpperCAmelCase__   :					int   				=   get_size_dict(A      )
   UpperCAmelCase__   :					Any   				=   crop_size if crop_size is not None else self.crop_size
   UpperCAmelCase__   :					str   				=   get_size_dict(A   ,param_name="""crop_size"""      )

   UpperCAmelCase__   :					Any   				=   make_list_of_images(A      )

   if not valid_images(A      ):
    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_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.
   UpperCAmelCase__   :					List[str]   				=   [to_numpy_array(A      ) for image in images]

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

   if do_center_crop:
    UpperCAmelCase__   :					Tuple   				=   [self.center_crop(image=A   ,size=A      ) for image in images]

   if do_rescale:
    UpperCAmelCase__   :					int   				=   [self.rescale(image=A   ,scale=A   ,offset=A      ) for image in images]

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

   if include_top:
    UpperCAmelCase__   :					Dict   				=   [self.normalize(image=A   ,mean=[0, 0, 0]   ,std=A      ) for image in images]

   UpperCAmelCase__   :					Optional[Any]   				=   [to_channel_dimension_format(A   ,A      ) for image in images]

   UpperCAmelCase__   :					Dict   				=   {"""pixel_values""": images}
   return BatchFeature(data=A   ,tensor_type=A      )





