

'''simple docstring'''

from typing import TYPE_CHECKING

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


lowercase__         =      {
    "configuration_mvp": ["MVP_PRETRAINED_CONFIG_ARCHIVE_MAP", "MvpConfig", "MvpOnnxConfig"],
    "tokenization_mvp": ["MvpTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__         =      ["MvpTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__         =      [
     "MVP_PRETRAINED_MODEL_ARCHIVE_LIST",
     "MvpForCausalLM",
     "MvpForConditionalGeneration",
     "MvpForQuestionAnswering",
     "MvpForSequenceClassification",
     "MvpModel",
     "MvpPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
 from .tokenization_mvp import MvpTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mvp_fast import MvpTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mvp import (
      MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
      MvpForCausalLM,
      MvpForConditionalGeneration,
      MvpForQuestionAnswering,
      MvpForSequenceClassification,
      MvpModel,
      MvpPreTrainedModel,
  )

else:
 import sys

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







'''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


lowercase__         =      logging.get_logger(__name__)




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[Any]						 =			b.T
 UpperCAmelCase	:     Optional[int]						 =			np.sum(np.square(UpperCAmelCase_     )  ,	axis=1     )
 UpperCAmelCase	:     List[Any]						 =			np.sum(np.square(UpperCAmelCase_     )  ,	axis=0     )
 UpperCAmelCase	:     List[str]						 =			np.matmul(UpperCAmelCase_  ,	UpperCAmelCase_     )
 UpperCAmelCase	:     Union[str, Any]						 =			aa[:, None] - 2 * ab + ba[None, :]
 return d




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     int						 =			x.reshape(-1  ,	3     )
 UpperCAmelCase	:     Optional[int]						 =			squared_euclidean_distance(UpperCAmelCase_  ,	UpperCAmelCase_     )
 return np.argmin(UpperCAmelCase_  ,	axis=1     )




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[Any]  		=  ["""pixel_values"""]


     def __init__(		self			:					List[Any]					,      lowercase_			:					Optional[Union[List[List[int]], np.ndarray]] = None					,      lowercase_			:					bool = True					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					bool = True					,      lowercase_			:					bool = True					,      **lowercase_			:					Optional[Any]					,      )						->     None:
      super().__init__(**lowercase_	)
      UpperCAmelCase	:     Any						 =			size if size is not None else {'height': 256, 'width': 256}
      UpperCAmelCase	:     List[Any]						 =			get_size_dict(lowercase_	)
      UpperCAmelCase	:     str						 =			np.array(lowercase_	) if clusters is not None else None
      UpperCAmelCase	:     Any						 =			do_resize
      UpperCAmelCase	:     List[Any]						 =			size
      UpperCAmelCase	:     Any						 =			resample
      UpperCAmelCase	:     Dict						 =			do_normalize
      UpperCAmelCase	:     List[Any]						 =			do_color_quantize


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					np.ndarray					,      lowercase_			:					Dict[str, int]					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					Any					,      )						->     np.ndarray:
      UpperCAmelCase	:     Dict						 =			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        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					np.ndarray					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      )						->     np.ndarray:
      UpperCAmelCase	:     int						 =			rescale(image=lowercase_					,      scale=1 / 127.5					,      data_format=lowercase_	)
      UpperCAmelCase	:     Dict						 =			image - 1
      return image




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

      UpperCAmelCase	:     int						 =			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.
      UpperCAmelCase	:     Dict						 =			[to_numpy_array(lowercase_	) for image in images]

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

      if do_normalize:
       UpperCAmelCase	:     Tuple						 =			[self.normalize(image=lowercase_	) for image in images]

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

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

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

      UpperCAmelCase	:     Any						 =			{'input_ids': images}
      return BatchFeature(data=lowercase_					,      tensor_type=lowercase_	)




