
'''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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


a_											=						logging.get_logger(__name__)

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Tuple     			=						b.T
    snake_case_ :    Any     			=						np.sum(np.square(__SCREAMING_SNAKE_CASE       ),		axis=1       )
    snake_case_ :    Union[str, Any]     			=						np.sum(np.square(__SCREAMING_SNAKE_CASE       ),		axis=0       )
    snake_case_ :    List[str]     			=						np.matmul(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[Any]     			=						aa[:, None] - 2 * ab + ba[None, :]
    return d

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    str     			=						x.reshape(-1,		3       )
    snake_case_ :    List[Any]     			=						squared_euclidean_distance(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    return np.argmin(__SCREAMING_SNAKE_CASE,		axis=1       )






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						["""pixel_values"""]





 def __init__(    self ,       lowercase_ = None ,       lowercase_ = True ,       lowercase_ = None ,       lowercase_ = PILImageResampling.BILINEAR ,       lowercase_ = True ,       lowercase_ = True ,       **lowercase_ ,       ):
     super().__init__(**lowercase_)
     snake_case_ :    List[Any]     			=						size if size is not None else {"height": 2_56, "width": 2_56}
     snake_case_ :    str     			=						get_size_dict(lowercase_)
     snake_case_ :    Optional[Any]     			=						np.array(lowercase_) if clusters is not None else None
     snake_case_ :    Optional[Any]     			=						do_resize
     snake_case_ :    Union[str, Any]     			=						size
     snake_case_ :    Optional[int]     			=						resample
     snake_case_ :    List[str]     			=						do_normalize
     snake_case_ :    Optional[Any]     			=						do_color_quantize





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ = PILImageResampling.BILINEAR ,       lowercase_ = None ,       **lowercase_ ,       ):
     snake_case_ :    List[str]     			=						get_size_dict(lowercase_)
     if "height" not in size or "width" not in size:
         raise ValueError(F'Size dictionary must contain both height and width keys. Got {size.keys()}')
     return resize(
         lowercase_ ,       size=(size["height"], size["width"]) ,       resample=lowercase_ ,       data_format=lowercase_ ,       **lowercase_)





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None ,       ):
     snake_case_ :    Union[str, Any]     			=						rescale(image=lowercase_ ,       scale=1 / 127.5 ,       data_format=lowercase_)
     snake_case_ :    List[Any]     			=						image - 1
     return image





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None ,       lowercase_ = None ,       lowercase_ = None ,       lowercase_ = None ,       lowercase_ = None ,       lowercase_ = None ,       lowercase_ = None ,       lowercase_ = ChannelDimension.FIRST ,       **lowercase_ ,       ):
     snake_case_ :    Optional[Any]     			=						do_resize if do_resize is not None else self.do_resize
     snake_case_ :    Tuple     			=						size if size is not None else self.size
     snake_case_ :    Tuple     			=						get_size_dict(lowercase_)
     snake_case_ :    Optional[int]     			=						resample if resample is not None else self.resample
     snake_case_ :    Optional[int]     			=						do_normalize if do_normalize is not None else self.do_normalize
     snake_case_ :    str     			=						do_color_quantize if do_color_quantize is not None else self.do_color_quantize
     snake_case_ :    Optional[Any]     			=						clusters if clusters is not None else self.clusters
     snake_case_ :    Tuple     			=						np.array(lowercase_)

     snake_case_ :    Tuple     			=						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_color_quantize and clusters is None:
         raise ValueError("Clusters must be specified if do_color_quantize is True.")

     # All transformations expect numpy arrays.
     snake_case_ :    Optional[int]     			=						[to_numpy_array(lowercase_) for image in images]

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

     if do_normalize:
         snake_case_ :    List[str]     			=						[self.normalize(image=lowercase_) for image in images]

     if do_color_quantize:
         snake_case_ :    Optional[Any]     			=						[to_channel_dimension_format(lowercase_ ,       ChannelDimension.LAST) for image in images]
         # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
         snake_case_ :    Optional[Any]     			=						np.array(lowercase_)
         snake_case_ :    Any     			=						color_quantize(lowercase_ ,       lowercase_).reshape(images.shape[:-1])

         # flatten to (batch_size, height*width)
         snake_case_ :    List[str]     			=						images.shape[0]
         snake_case_ :    Optional[int]     			=						images.reshape(lowercase_ ,       -1)

         # We need to convert back to a list of images to keep consistent behaviour across processors.
         snake_case_ :    Tuple     			=						list(lowercase_)
     else:
         snake_case_ :    List[str]     			=						[to_channel_dimension_format(lowercase_ ,       lowercase_) for image in images]

     snake_case_ :    List[Any]     			=						{"input_ids": images}
     return BatchFeature(data=lowercase_ ,       tensor_type=lowercase_)






'''simple docstring'''
from typing import TYPE_CHECKING

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


a_											=						{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_											=						[
      "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "BioGptForCausalLM",
      "BioGptForTokenClassification",
      "BioGptForSequenceClassification",
      "BioGptModel",
      "BioGptPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
  from .tokenization_biogpt import BioGptTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_biogpt import (
        BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptPreTrainedModel,
    )


else:
  import sys

  a_											=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




