




"""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__    :							Union[str, Any]            =					logging.get_logger(__name__)




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


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




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    x.reshape(-1  ,						3       )
					__SCREAMING_SNAKE_CASE						       =    squared_euclidean_distance(lowerCAmelCase_  ,						lowerCAmelCase_       )
					return np.argmin(lowerCAmelCase_  ,						axis=1       )




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  List[str]    =      ["pixel_values"]




	def __init__(			self      :    List[str]      ,      UpperCAmelCase__      :    Optional[Union[List[List[int]], np.ndarray]] = None      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    Dict[str, int] = None      ,      UpperCAmelCase__      :    PILImageResampling = PILImageResampling.BILINEAR      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    bool = True      ,      **UpperCAmelCase__      :    List[Any]      ,      )	->     None:
						super().__init__(**UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    size if size is not None else {"height": 2_5_6, "width": 2_5_6}
						__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    np.array(UpperCAmelCase__       ) if clusters is not None else None
						__SCREAMING_SNAKE_CASE						       =    do_resize
						__SCREAMING_SNAKE_CASE						       =    size
						__SCREAMING_SNAKE_CASE						       =    resample
						__SCREAMING_SNAKE_CASE						       =    do_normalize
						__SCREAMING_SNAKE_CASE						       =    do_color_quantize




	def   UpperCAmelCase_				(			self      :    Tuple      ,      UpperCAmelCase__      :    np.ndarray      ,      UpperCAmelCase__      :    Dict[str, int]      ,      UpperCAmelCase__      :    PILImageResampling = PILImageResampling.BILINEAR      ,      UpperCAmelCase__      :    Optional[Union[str, ChannelDimension]] = None      ,      **UpperCAmelCase__      :    str      ,      )	->     np.ndarray:
						__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__       )
						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(
						    UpperCAmelCase__      ,      size=(size["height"], size["width"])      ,      resample=UpperCAmelCase__      ,      data_format=UpperCAmelCase__      ,      **UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    np.ndarray      ,      UpperCAmelCase__      :    Optional[Union[str, ChannelDimension]] = None      ,      )	->     np.ndarray:
						__SCREAMING_SNAKE_CASE						       =    rescale(image=UpperCAmelCase__      ,      scale=1 / 127.5      ,      data_format=UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    image - 1
						return image




	def   UpperCAmelCase_				(			self      :    Dict      ,      UpperCAmelCase__      :    ImageInput      ,      UpperCAmelCase__      :    bool = None      ,      UpperCAmelCase__      :    Dict[str, int] = None      ,      UpperCAmelCase__      :    PILImageResampling = None      ,      UpperCAmelCase__      :    bool = None      ,      UpperCAmelCase__      :    Optional[bool] = None      ,      UpperCAmelCase__      :    Optional[Union[List[List[int]], np.ndarray]] = None      ,      UpperCAmelCase__      :    Optional[Union[str, TensorType]] = None      ,      UpperCAmelCase__      :    Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST      ,      **UpperCAmelCase__      :    Optional[Any]      ,      )	->     PIL.Image.Image:
						__SCREAMING_SNAKE_CASE						       =    do_resize if do_resize is not None else self.do_resize
						__SCREAMING_SNAKE_CASE						       =    size if size is not None else self.size
						__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    resample if resample is not None else self.resample
						__SCREAMING_SNAKE_CASE						       =    do_normalize if do_normalize is not None else self.do_normalize
						__SCREAMING_SNAKE_CASE						       =    do_color_quantize if do_color_quantize is not None else self.do_color_quantize
						__SCREAMING_SNAKE_CASE						       =    clusters if clusters is not None else self.clusters
						__SCREAMING_SNAKE_CASE						       =    np.array(UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    make_list_of_images(UpperCAmelCase__       )

						if not valid_images(UpperCAmelCase__       ):
											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.
						__SCREAMING_SNAKE_CASE						       =    [to_numpy_array(UpperCAmelCase__       ) for image in images]

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

						if do_normalize:
											__SCREAMING_SNAKE_CASE						       =    [self.normalize(image=UpperCAmelCase__       ) for image in images]

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

											# flatten to (batch_size, height*width)
											__SCREAMING_SNAKE_CASE						       =    images.shape[0]
											__SCREAMING_SNAKE_CASE						       =    images.reshape(UpperCAmelCase__      ,      -1       )

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

						__SCREAMING_SNAKE_CASE						       =    {"input_ids": images}
						return BatchFeature(data=UpperCAmelCase__      ,      tensor_type=UpperCAmelCase__       )




"""simple docstring"""







from typing import TYPE_CHECKING

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


a__    :							List[str]            =					{
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							Union[str, Any]            =					[
		    '''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__    :							Optional[int]            =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
