





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

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowerCamelCase_       =		logging.get_logger(__name__)

lowerCamelCase_       =		{
    "deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				str     						=  '''perceiver'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=2_56		,SCREAMING_SNAKE_CASE__=12_80		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=26		,SCREAMING_SNAKE_CASE__=8		,SCREAMING_SNAKE_CASE__=8		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__="kv"		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__="gelu"		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-12		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=2_62		,SCREAMING_SNAKE_CASE__=20_48		,SCREAMING_SNAKE_CASE__=56		,SCREAMING_SNAKE_CASE__=[3_68, 4_96]		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=19_20		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=[1, 16, 2_24, 2_24]		,**SCREAMING_SNAKE_CASE__		,)      ->				Tuple:





             """simple docstring"""






             super().__init__(**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  num_latents
             __SCREAMING_SNAKE_CASE       :int		    =  d_latents
             __SCREAMING_SNAKE_CASE       :Any		    =  d_model
             __SCREAMING_SNAKE_CASE       :Any		    =  num_blocks
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  num_self_attends_per_block
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  num_self_attention_heads
             __SCREAMING_SNAKE_CASE       :str		    =  num_cross_attention_heads
             __SCREAMING_SNAKE_CASE       :Any		    =  qk_channels
             __SCREAMING_SNAKE_CASE       :int		    =  v_channels
             __SCREAMING_SNAKE_CASE       :str		    =  cross_attention_shape_for_attention
             __SCREAMING_SNAKE_CASE       :Dict		    =  self_attention_widening_factor
             __SCREAMING_SNAKE_CASE       :Tuple		    =  cross_attention_widening_factor
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  hidden_act
             __SCREAMING_SNAKE_CASE       :int		    =  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  initializer_range
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  layer_norm_eps
             __SCREAMING_SNAKE_CASE       :int		    =  use_query_residual
             # masked language modeling attributes
             __SCREAMING_SNAKE_CASE       :Any		    =  vocab_size
             __SCREAMING_SNAKE_CASE       :Dict		    =  max_position_embeddings
             # image classification attributes
             __SCREAMING_SNAKE_CASE       :Tuple		    =  image_size
             # flow attributes
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  train_size
             # multimodal autoencoding attributes
             __SCREAMING_SNAKE_CASE       :Any		    =  num_frames
             __SCREAMING_SNAKE_CASE       :int		    =  audio_samples_per_frame
             __SCREAMING_SNAKE_CASE       :str		    =  samples_per_patch
             __SCREAMING_SNAKE_CASE       :Dict		    =  output_shape






class  _SCREAMING_SNAKE_CASE(       A  ):




       @property
       def    _UpperCamelCase					(						self							)      ->				Mapping[str, Mapping[int, str]]:





             """simple docstring"""






             if self.task == "multiple-choice":
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
             else:
                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  {0: '''batch''', 1: '''sequence'''}
             return OrderedDict(
                 [
                     ('''inputs''', dynamic_axis),
                     ('''attention_mask''', dynamic_axis),
                 ]							)




       @property
       def    _UpperCamelCase					(						self							)      ->				float:





             """simple docstring"""






             return 1E-4




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = 3		,SCREAMING_SNAKE_CASE__ = 40		,SCREAMING_SNAKE_CASE__ = 40		,)      ->				Mapping[str, Any]:





             """simple docstring"""






             if isinstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							):
                   # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
                   __SCREAMING_SNAKE_CASE       :str		    =  compute_effective_axis_dimension(
                       SCREAMING_SNAKE_CASE__		,fixed_dimension=OnnxConfig.default_fixed_batch		,num_token_to_add=0							)
                   # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  preprocessor.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE       :int		    =  compute_effective_axis_dimension(
                       SCREAMING_SNAKE_CASE__		,fixed_dimension=OnnxConfig.default_fixed_sequence		,num_token_to_add=SCREAMING_SNAKE_CASE__							)
                   # Generate dummy inputs according to compute batch and sequence
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  [''' '''.join(['''a''']							) * seq_length] * batch_size
                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  dict(preprocessor(SCREAMING_SNAKE_CASE__		,return_tensors=SCREAMING_SNAKE_CASE__							)							)
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  inputs.pop('''input_ids'''							)
                   return inputs
             elif isinstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							) and preprocessor.model_input_names[0] == "pixel_values":
                   # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  compute_effective_axis_dimension(SCREAMING_SNAKE_CASE__		,fixed_dimension=OnnxConfig.default_fixed_batch							)
                   __SCREAMING_SNAKE_CASE       :str		    =  self._generate_dummy_images(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  dict(preprocessor(images=SCREAMING_SNAKE_CASE__		,return_tensors=SCREAMING_SNAKE_CASE__							)							)
                   __SCREAMING_SNAKE_CASE       :str		    =  inputs.pop('''pixel_values'''							)
                   return inputs
             else:
                   raise ValueError(
                       '''Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'''							)






"""simple docstring"""
def 					__lowerCamelCase       (							a_					:		Union[str, Any]			,       a_					:		Optional[Any]							)      ->				Union[str, Any]:
      return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2



def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		Any=0							)      ->				Optional[Any]:
      return sorted(a_			,       key=lambda a_							: x[column]							)



def 					__lowerCamelCase       (							a_					:		Optional[Any]			,       a_					:		Optional[int]			,       a_					:		str=float('''inf'''							)							)      ->				str:

      for i in range(points_counts - 1							):
            for j in range(i + 1			,       a_							):
                  __SCREAMING_SNAKE_CASE       :Dict		    =  euclidean_distance_sqr(points[i]			,       points[j]							)
                  if current_dis < min_dis:
                        __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  current_dis
      return min_dis



def 					__lowerCamelCase       (							a_					:		List[Any]			,       a_					:		Any			,       a_					:		Optional[int]=float('''inf'''							)							)      ->				Optional[Any]:

      for i in range(min(6			,       points_counts - 1							)			,       a_							):
            for j in range(max(0			,       i - 6							)			,       a_							):
                  __SCREAMING_SNAKE_CASE       :Dict		    =  euclidean_distance_sqr(points[i]			,       points[j]							)
                  if current_dis < min_dis:
                        __SCREAMING_SNAKE_CASE       :int		    =  current_dis
      return min_dis



def 					__lowerCamelCase       (							a_					:		str			,       a_					:		List[Any]			,       a_					:		int							)      ->				Optional[int]:

      # base case
      if points_counts <= 3:
            return dis_between_closest_pair(a_			,       a_							)

      # recursion
      __SCREAMING_SNAKE_CASE       :int		    =  points_counts // 2
      __SCREAMING_SNAKE_CASE       :Dict		    =  closest_pair_of_points_sqr(
          a_			,       points_sorted_on_y[:mid]			,       a_							)
      __SCREAMING_SNAKE_CASE       :Any		    =  closest_pair_of_points_sqr(
          a_			,       points_sorted_on_y[mid:]			,       points_counts - mid							)
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  min(a_			,       a_							)
      __SCREAMING_SNAKE_CASE       :str		    =  []
      for point in points_sorted_on_x:
            if abs(point[0] - points_sorted_on_x[mid][0]							) < closest_pair_dis:
                  cross_strip.append(a_							)

      __SCREAMING_SNAKE_CASE       :Dict		    =  dis_between_closest_in_strip(
          a_			,       len(a_							)			,       a_							)
      return min(a_			,       a_							)



def 					__lowerCamelCase       (							a_					:		int			,       a_					:		Tuple							)      ->				List[Any]:
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  column_based_sort(a_			,       column=0							)
      __SCREAMING_SNAKE_CASE       :int		    =  column_based_sort(a_			,       column=1							)
      return (
          closest_pair_of_points_sqr(
              a_			,       a_			,       a_							)
      ) ** 0.5


if __name__ == "__main__":
  lowerCamelCase_       =		[(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
  print("Distance:", closest_pair_of_points(points, len(points)))