
"""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


__magic_name__		       =				logging.get_logger(__name__)


class    _lowerCAmelCase						(						lowerCamelCase__						):
       lowercase_     :     List[Any]       =							['pixel_values']



       def __init__(      self ,						a_ = True ,						a_ = None ,						a_ = PIL.Image.NEAREST ,						a_ = False ,						a_ = None ,						a_ = 1 / 255 ,						a_ = False ,						a_ = True ,						a_ = True ,						a_ = None ,						a_ = None ,						a_ = True ,						**a_ ,						)							->							None:
        super().__init__(**lowercase_   )
        _UpperCAmelCase =						size if size is not None else {"height": 346, "width": 346}
        _UpperCAmelCase =						get_size_dict(lowercase_   )
        _UpperCAmelCase =						crop_size if crop_size is not None else {"height": 289, "width": 289}
        _UpperCAmelCase =						get_size_dict(lowercase_ ,						param_name="crop_size"   )

        _UpperCAmelCase =						do_resize
        _UpperCAmelCase =						size
        _UpperCAmelCase =						resample
        _UpperCAmelCase =						do_center_crop
        _UpperCAmelCase =						crop_size
        _UpperCAmelCase =						do_rescale
        _UpperCAmelCase =						rescale_factor
        _UpperCAmelCase =						rescale_offset
        _UpperCAmelCase =						do_normalize
        _UpperCAmelCase =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        _UpperCAmelCase =						image_std if image_std is not None else IMAGENET_STANDARD_STD
        _UpperCAmelCase =						include_top



       def       _a   (      self ,						a_ ,						a_ ,						a_ = PIL.Image.NEAREST ,						a_ = None ,						**a_ ,						)							->							np.ndarray:
        _UpperCAmelCase =						get_size_dict(lowercase_   )
        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(
            lowercase_ ,						size=(size["height"], size["width"]) ,						resample=lowercase_ ,						data_format=lowercase_ ,						**lowercase_   )



       def       _a   (      self ,						a_ ,						a_ ,						a_ = None ,						**a_ ,						)							->							np.ndarray:
        _UpperCAmelCase =						get_size_dict(lowercase_   )
        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(lowercase_ ,						size=(size["height"], size["width"]) ,						data_format=lowercase_ ,						**lowercase_   )



       def       _a   (      self ,						a_ ,						a_ ,						a_ = True ,						a_ = None ,						**a_ ,						)							->							Union[str, Any]:






        if offset:
         _UpperCAmelCase =						(image - 127.5) * scale
         if data_format is not None:
          _UpperCAmelCase =						to_channel_dimension_format(lowercase_ ,						lowercase_   )
         _UpperCAmelCase =						rescaled_image.astype(np.floataa   )
        else:
         _UpperCAmelCase =						rescale(lowercase_ ,						scale=lowercase_ ,						data_format=lowercase_ ,						**lowercase_   )
        return rescaled_image



       def       _a   (      self ,						a_ ,						a_ ,						a_ ,						a_ = None ,						**a_ ,						)							->							np.ndarray:
        return normalize(lowercase_ ,						mean=lowercase_ ,						std=lowercase_ ,						data_format=lowercase_ ,						**lowercase_   )



       def       _a   (      self ,						a_ ,						a_ = None ,						a_ = None ,						a_=None ,						a_ = None ,						a_ = None ,						a_ = None ,						a_ = None ,						a_ = None ,						a_ = None ,						a_ = None ,						a_ = None ,						a_ = None ,						a_ = None ,						a_ = ChannelDimension.FIRST ,						**a_ ,						)							->							PIL.Image.Image:
        _UpperCAmelCase =						do_resize if do_resize is not None else self.do_resize
        _UpperCAmelCase =						resample if resample is not None else self.resample
        _UpperCAmelCase =						do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCAmelCase =						do_rescale if do_rescale is not None else self.do_rescale
        _UpperCAmelCase =						rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCAmelCase =						rescale_offset if rescale_offset is not None else self.rescale_offset
        _UpperCAmelCase =						do_normalize if do_normalize is not None else self.do_normalize
        _UpperCAmelCase =						image_mean if image_mean is not None else self.image_mean
        _UpperCAmelCase =						image_std if image_std is not None else self.image_std
        _UpperCAmelCase =						include_top if include_top is not None else self.include_top

        _UpperCAmelCase =						size if size is not None else self.size
        _UpperCAmelCase =						get_size_dict(lowercase_   )
        _UpperCAmelCase =						crop_size if crop_size is not None else self.crop_size
        _UpperCAmelCase =						get_size_dict(lowercase_ ,						param_name="crop_size"   )

        _UpperCAmelCase =						make_list_of_images(lowercase_   )

        if not valid_images(lowercase_   ):
         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 =						[to_numpy_array(lowercase_   ) for image in images]

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

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

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

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

        if include_top:
         _UpperCAmelCase =						[self.normalize(image=lowercase_ ,						mean=[0, 0, 0] ,						std=lowercase_   ) for image in images]

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

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






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


__magic_name__		       =				{}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__magic_name__		       =				['''CpmTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__magic_name__		       =				['''CpmTokenizerFast''']


if TYPE_CHECKING:
			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_cpm import CpmTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_cpm_fast import CpmTokenizerFast

else:
			import sys

			__magic_name__		       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




