


"""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 					lowercase      (    A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







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


def 					lowercase      (    A_  ,       A_	)->				str:
  '''simple docstring'''







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







class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Tuple     =	["""pixel_values"""]






    def __init__( self    :   List[Any]    ,			__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    :   Optional[Any]    ,			):
      super().__init__(**__UpperCAmelCase)
      a						:		Dict											=      size if size is not None else {"height": 256, "width": 256}
      a						:		Tuple											=      get_size_dict(__UpperCAmelCase)
      a						:		Dict											=      np.array(__UpperCAmelCase) if clusters is not None else None
      a						:		Optional[Any]											=      do_resize
      a						:		Dict											=      size
      a						:		Any											=      resample
      a						:		int											=      do_normalize
      a						:		Tuple											=      do_color_quantize






    def      __snake_case							( self    :   Tuple    ,			__UpperCAmelCase    :   np.ndarray    ,			__UpperCAmelCase    :   Dict[str, int]    ,			__UpperCAmelCase    :   PILImageResampling = PILImageResampling.BILINEAR    ,			__UpperCAmelCase    :   Optional[Union[str, ChannelDimension]] = None    ,			**__UpperCAmelCase    :   int    ,			):
      a						:		List[Any]											=      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      __snake_case							( self    :   Any    ,			__UpperCAmelCase    :   np.ndarray    ,			__UpperCAmelCase    :   Optional[Union[str, ChannelDimension]] = None    ,			):
      a						:		str											=      rescale(image=__UpperCAmelCase    ,			scale=1 / 127.5    ,			data_format=__UpperCAmelCase)
      a						:		Optional[Any]											=      image - 1
      return image






    def      __snake_case							( 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    :   int    ,			):
      a						:		List[str]											=      do_resize if do_resize is not None else self.do_resize
      a						:		Dict											=      size if size is not None else self.size
      a						:		List[str]											=      get_size_dict(__UpperCAmelCase)
      a						:		List[str]											=      resample if resample is not None else self.resample
      a						:		str											=      do_normalize if do_normalize is not None else self.do_normalize
      a						:		Dict											=      do_color_quantize if do_color_quantize is not None else self.do_color_quantize
      a						:		Tuple											=      clusters if clusters is not None else self.clusters
      a						:		Dict											=      np.array(__UpperCAmelCase)

      a						:		List[Any]											=      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.
      a						:		int											=      [to_numpy_array(__UpperCAmelCase) for image in images]

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

      if do_normalize:
        a						:		int											=      [self.normalize(image=__UpperCAmelCase) for image in images]

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

        # flatten to (batch_size, height*width)
        a						:		Optional[Any]											=      images.shape[0]
        a						:		List[Any]											=      images.reshape(__UpperCAmelCase    ,			-1)

        # We need to convert back to a list of images to keep consistent behaviour across processors.
        a						:		Optional[int]											=      list(__UpperCAmelCase)
      else:
        a						:		Union[str, Any]											=      [to_channel_dimension_format(__UpperCAmelCase    ,			__UpperCAmelCase) for image in images]

      a						:		Union[str, Any]											=      {"input_ids": images}
      return BatchFeature(data=__UpperCAmelCase    ,			tensor_type=__UpperCAmelCase)










"""simple docstring"""







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
 from ...onnx.config import PatchingSpec
 from ...tokenization_utils_base import PreTrainedTokenizerBase


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Optional[Any]     =	"""longformer"""






    def __init__( self    :   List[Any]    ,			__UpperCAmelCase    :   Union[List[int], int] = 512    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   int = 1    ,			__UpperCAmelCase    :   int = 0    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   int = 30522    ,			__UpperCAmelCase    :   int = 768    ,			__UpperCAmelCase    :   int = 12    ,			__UpperCAmelCase    :   int = 12    ,			__UpperCAmelCase    :   int = 3072    ,			__UpperCAmelCase    :   str = "gelu"    ,			__UpperCAmelCase    :   float = 0.1    ,			__UpperCAmelCase    :   float = 0.1    ,			__UpperCAmelCase    :   int = 512    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   float = 0.02    ,			__UpperCAmelCase    :   float = 1e-12    ,			__UpperCAmelCase    :   bool = False    ,			**__UpperCAmelCase    :   int    ,			):
      super().__init__(pad_token_id=__UpperCAmelCase    ,			**__UpperCAmelCase)

      a						:		int											=      attention_window
      a						:		List[str]											=      sep_token_id
      a						:		List[Any]											=      bos_token_id
      a						:		int											=      eos_token_id
      a						:		Union[str, Any]											=      vocab_size
      a						:		Dict											=      hidden_size
      a						:		int											=      num_hidden_layers
      a						:		int											=      num_attention_heads
      a						:		Optional[int]											=      hidden_act
      a						:		Union[str, Any]											=      intermediate_size
      a						:		Tuple											=      hidden_dropout_prob
      a						:		Union[str, Any]											=      attention_probs_dropout_prob
      a						:		str											=      max_position_embeddings
      a						:		Dict											=      type_vocab_size
      a						:		Optional[Any]											=      initializer_range
      a						:		List[Any]											=      layer_norm_eps
      a						:		List[str]											=      onnx_export






class 						_A  (  _a  ):







    """simple docstring"""




    def __init__( self    :   Tuple    ,			__UpperCAmelCase    :   "PretrainedConfig"    ,			__UpperCAmelCase    :   str = "default"    ,			__UpperCAmelCase    :   "List[PatchingSpec]" = None):
      super().__init__(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      a						:		Optional[Any]											=      True






    @property
    def      __snake_case							( self    :   int):
      if self.task == "multiple-choice":
        a						:		List[Any]											=      {0: "batch", 1: "choice", 2: "sequence"}
      else:
        a						:		int											=      {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
              ("global_attention_mask", dynamic_axis),
          ])






    @property
    def      __snake_case							( self    :   Union[str, Any]):
      a						:		str											=      super().outputs
      if self.task == "default":
        a						:		Tuple											=      {0: "batch"}
      return outputs






    @property
    def      __snake_case							( self    :   Optional[int]):
      return 1e-4






    @property
    def      __snake_case							( self    :   str):
      # needs to be >= 14 to support tril operator
      return max(super().default_onnx_opset    ,			14)






    def      __snake_case							( self    :   List[str]    ,			__UpperCAmelCase    :   "PreTrainedTokenizerBase"    ,			__UpperCAmelCase    :   int = -1    ,			__UpperCAmelCase    :   int = -1    ,			__UpperCAmelCase    :   bool = False    ,			__UpperCAmelCase    :   Optional[TensorType] = None    ,			):
      a						:		str											=      super().generate_dummy_inputs(
          preprocessor=__UpperCAmelCase    ,			batch_size=__UpperCAmelCase    ,			seq_length=__UpperCAmelCase    ,			is_pair=__UpperCAmelCase    ,			framework=__UpperCAmelCase)
      import torch

      # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
      # makes the export fail randomly
      a						:		Union[str, Any]											=      torch.zeros_like(inputs["input_ids"])
      # make every second token global
      a						:		Dict											=      1

      return inputs






