



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

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
	from ...processing_utils import ProcessorMixin
	from ...utils import TensorType


_lowerCAmelCase	:   Optional[int]    =   logging.get_logger(__name__)

_lowerCAmelCase	:   Optional[int]    =   {
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	'layoutlmv3'
		def __init__(							self    ,a_=5_0265    ,a_=768    ,a_=12    ,a_=12    ,a_=3072    ,a_="gelu"    ,a_=0.1    ,a_=0.1    ,a_=512    ,a_=2    ,a_=0.02    ,a_=1e-5    ,a_=1    ,a_=0    ,a_=2    ,a_=1024    ,a_=128    ,a_=128    ,a_=True    ,a_=32    ,a_=128    ,a_=64    ,a_=256    ,a_=True    ,a_=True    ,a_=True    ,a_=224    ,a_=3    ,a_=16    ,a_=None    ,**a_    ,):







						"""simple docstring"""
						super().__init__(
						    vocab_size=a_    ,hidden_size=a_    ,num_hidden_layers=a_    ,num_attention_heads=a_    ,intermediate_size=a_    ,hidden_act=a_    ,hidden_dropout_prob=a_    ,attention_probs_dropout_prob=a_    ,max_position_embeddings=a_    ,type_vocab_size=a_    ,initializer_range=a_    ,layer_norm_eps=a_    ,pad_token_id=a_    ,bos_token_id=a_    ,eos_token_id=a_    ,**a_    ,)
						lowerCAmelCase__		  =  max_ad_position_embeddings
						lowerCAmelCase__		  =  coordinate_size
						lowerCAmelCase__		  =  shape_size
						lowerCAmelCase__		  =  has_relative_attention_bias
						lowerCAmelCase__		  =  rel_pos_bins
						lowerCAmelCase__		  =  max_rel_pos
						lowerCAmelCase__		  =  has_spatial_attention_bias
						lowerCAmelCase__		  =  rel_ad_pos_bins
						lowerCAmelCase__		  =  max_rel_ad_pos
						lowerCAmelCase__		  =  text_embed
						lowerCAmelCase__		  =  visual_embed
						lowerCAmelCase__		  =  input_size
						lowerCAmelCase__		  =  num_channels
						lowerCAmelCase__		  =  patch_size
						lowerCAmelCase__		  =  classifier_dropout



class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	version.parse('1.12' )
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# The order of inputs is different for question answering and sequence classification
						if self.task in ["question-answering", "sequence-classification"]:
										return OrderedDict(
										    [
										        ('input_ids', {0: 'batch', 1: 'sequence'}),
										        ('attention_mask', {0: 'batch', 1: 'sequence'}),
										        ('bbox', {0: 'batch', 1: 'sequence'}),
										        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
										    ]						)
						else:
										return OrderedDict(
										    [
										        ('input_ids', {0: 'batch', 1: 'sequence'}),
										        ('bbox', {0: 'batch', 1: 'sequence'}),
										        ('attention_mask', {0: 'batch', 1: 'sequence'}),
										        ('pixel_values', {0: 'batch', 1: 'num_channels'}),
										    ]						)
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return 1e-5
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return 12



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_ = -1    ,a_ = -1    ,a_ = False    ,a_ = None    ,a_ = 3    ,a_ = 40    ,a_ = 40    ,):







						"""simple docstring"""
						setattr(processor.image_processor    ,'apply_ocr'    ,a_						)

						# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
						lowerCAmelCase__		  =  compute_effective_axis_dimension(
						    a_    ,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
						lowerCAmelCase__		  =  processor.tokenizer.num_special_tokens_to_add(a_						)
						lowerCAmelCase__		  =  compute_effective_axis_dimension(
						    a_    ,fixed_dimension=OnnxConfig.default_fixed_sequence    ,num_token_to_add=a_						)
						# Generate dummy inputs according to compute batch and sequence
						lowerCAmelCase__		  =  [[' '.join([processor.tokenizer.unk_token]						) * seq_length]] * batch_size

						# Generate dummy bounding boxes
						lowerCAmelCase__		  =  [[[48, 84, 73, 128]]] * batch_size

						# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
						# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
						lowerCAmelCase__		  =  self._generate_dummy_images(a_    ,a_    ,a_    ,a_						)

						lowerCAmelCase__		  =  dict(
						    processor(
						        a_    ,text=a_    ,boxes=a_    ,return_tensors=a_    ,)						)

						return inputs





from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_lowerCAmelCase	:   int    =   datasets.load_iris()

_lowerCAmelCase	:   Tuple    =   np.array(data["data"])
_lowerCAmelCase	:   Any    =   np.array(data["target"])
_lowerCAmelCase	:   Union[str, Any]    =   data["target_names"]

_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase	:   Optional[Any]    =   train_test_split(X, y)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Tuple:



				"""simple docstring"""





				return np.linalg.norm(np.array(snake_case__					) - np.array(snake_case__					)					)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__=5					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  zip(snake_case__    ,			snake_case__					)
				# List of distances of all points from the point to be classified
				lowerCAmelCase__		  =  []
				for data_point in data:
								lowerCAmelCase__		  =  euclidean_distance(data_point[0]    ,			snake_case__					)
								distances.append((distance, data_point[1])					)
				# Choosing 'k' points with the least distances.
				lowerCAmelCase__		  =  [i[1] for i in sorted(snake_case__					)[:k]]
				# Most commonly occurring class among them
				# is the class into which the point is classified
				lowerCAmelCase__		  =  Counter(snake_case__					).most_common(1					)[0][0]
				return classes[result]


if __name__ == "__main__":
	print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
