

'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


a       :      Any  	=			logging.get_logger(__name__)

a       :      Union[str, Any]  	=			{
    '''squeezebert/squeezebert-uncased''': (
        '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'''
    ),
    '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json''',
    '''squeezebert/squeezebert-mnli-headless''': (
        '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'''
    ),
}







class   UpperCamelCase__   (       snake_case_       ):


  """simple docstring"""

  SCREAMING_SNAKE_CASE__  :     str       =							SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  SCREAMING_SNAKE_CASE__  :     int       =							"""squeezebert"""







  def __init__(			self   ,      snake_case=3_0_5_2_2   ,      snake_case=7_6_8   ,      snake_case=1_2   ,      snake_case=1_2   ,      snake_case=3_0_7_2   ,      snake_case="gelu"   ,      snake_case=0.1   ,      snake_case=0.1   ,      snake_case=5_1_2   ,      snake_case=2   ,      snake_case=0.02   ,      snake_case=1e-12   ,      snake_case=0   ,      snake_case=7_6_8   ,      snake_case=4   ,      snake_case=4   ,      snake_case=4   ,      snake_case=1   ,      snake_case=4   ,      snake_case=4   ,      **snake_case   ,      ):


       '''simple docstring'''

       super().__init__(pad_token_id=snake_case   ,      **snake_case      )

       UpperCAmelCase		:							Any        =       vocab_size
       UpperCAmelCase		:							Tuple        =       hidden_size
       UpperCAmelCase		:							str        =       num_hidden_layers
       UpperCAmelCase		:							Optional[int]        =       num_attention_heads
       UpperCAmelCase		:							List[Any]        =       hidden_act
       UpperCAmelCase		:							Union[str, Any]        =       intermediate_size
       UpperCAmelCase		:							int        =       hidden_dropout_prob
       UpperCAmelCase		:							List[Any]        =       attention_probs_dropout_prob
       UpperCAmelCase		:							Any        =       max_position_embeddings
       UpperCAmelCase		:							List[Any]        =       type_vocab_size
       UpperCAmelCase		:							Any        =       initializer_range
       UpperCAmelCase		:							Optional[Any]        =       layer_norm_eps
       UpperCAmelCase		:							Any        =       embedding_size
       UpperCAmelCase		:							Optional[Any]        =       q_groups
       UpperCAmelCase		:							List[str]        =       k_groups
       UpperCAmelCase		:							List[Any]        =       v_groups
       UpperCAmelCase		:							str        =       post_attention_groups
       UpperCAmelCase		:							Optional[int]        =       intermediate_groups
       UpperCAmelCase		:							Optional[int]        =       output_groups







class   UpperCamelCase__   (       snake_case_       ):


  """simple docstring"""

  @property
  def 				A_	(			self      ):


       '''simple docstring'''

       if self.task == "multiple-choice":
            UpperCAmelCase		:							Union[str, Any]        =       {0: "batch", 1: "choice", 2: "sequence"}
       else:
            UpperCAmelCase		:							Union[str, Any]        =       {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
               ("token_type_ids", dynamic_axis),
           ]      )



'''simple docstring'''

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
a       :      List[Any]  	=			load_iris()
iris.keys()


print(F'Target names: \n {iris.target_names} ')
print(F'\n Features: \n {iris.feature_names}')

# Train set e Test set
a	,						a	,						a	,						a       :      List[Any]  	=			train_test_split(
    iris["data"], iris["target"], random_state=4
)

# KNN

a       :      Tuple  	=			KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
a       :      Optional[int]  	=			[[1, 2, 1, 4], [2, 3, 4, 5]]

a       :      Tuple  	=			knn.predict(X_new)

print(
    F'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    F' {iris["target_names"][prediction]}'
)
