



'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


_A					:     Union[str, Any]      =logging.get_logger(__name__)

_A					:     str      ={
    '''flaubert/flaubert_small_cased''': '''https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json''',
    '''flaubert/flaubert_base_uncased''': '''https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json''',
    '''flaubert/flaubert_base_cased''': '''https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json''',
    '''flaubert/flaubert_large_cased''': '''https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json''',
}



class 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



					A_         =    """flaubert"""
					A_         =    {
					    """hidden_size""": """emb_dim""",
					    """num_attention_heads""": """n_heads""",
					    """num_hidden_layers""": """n_layers""",
					    """n_words""": """vocab_size""",  # For backward compatibility
					}




					def __init__(	self					:						List[str]  ,	UpperCamelCase_					:						Dict=False  ,	UpperCamelCase_					:						Any=0.0  ,	UpperCamelCase_					:						Tuple=3_0145  ,	UpperCamelCase_					:						Optional[int]=2048  ,	UpperCamelCase_					:						Dict=12  ,	UpperCamelCase_					:						Dict=16  ,	UpperCamelCase_					:						Optional[int]=0.1  ,	UpperCamelCase_					:						Optional[int]=0.1  ,	UpperCamelCase_					:						Union[str, Any]=True  ,	UpperCamelCase_					:						str=False  ,	UpperCamelCase_					:						List[str]=False  ,	UpperCamelCase_					:						Dict=False  ,	UpperCamelCase_					:						Dict=1  ,	UpperCamelCase_					:						Tuple=True  ,	UpperCamelCase_					:						int=512  ,	UpperCamelCase_					:						Any=2048**-0.5  ,	UpperCamelCase_					:						Dict=1E-12  ,	UpperCamelCase_					:						Dict=0.02  ,	UpperCamelCase_					:						str=0  ,	UpperCamelCase_					:						Tuple=1  ,	UpperCamelCase_					:						int=2  ,	UpperCamelCase_					:						Any=3  ,	UpperCamelCase_					:						List[str]=5  ,	UpperCamelCase_					:						Any=True  ,	UpperCamelCase_					:						Any="first"  ,	UpperCamelCase_					:						Any=True  ,	UpperCamelCase_					:						Dict=None  ,	UpperCamelCase_					:						Optional[int]=True  ,	UpperCamelCase_					:						Tuple=0.1  ,	UpperCamelCase_					:						List[str]=5  ,	UpperCamelCase_					:						Dict=5  ,	UpperCamelCase_					:						Union[str, Any]=0  ,	UpperCamelCase_					:						Optional[int]=0  ,	UpperCamelCase_					:						Union[str, Any]=2  ,	UpperCamelCase_					:						str=0  ,	**UpperCamelCase_					:						str  ,	)	->							Union[str, Any]:



						'''simple docstring'''





						_lowercase       :							Optional[int]  	=		pre_norm
						_lowercase       :							List[Any]  	=		layerdrop
						_lowercase       :							List[str]  	=		vocab_size
						_lowercase       :							Optional[Any]  	=		emb_dim
						_lowercase       :							str  	=		n_layers
						_lowercase       :							List[Any]  	=		n_heads
						_lowercase       :							Any  	=		dropout
						_lowercase       :							Tuple  	=		attention_dropout
						_lowercase       :							Tuple  	=		gelu_activation
						_lowercase       :							Union[str, Any]  	=		sinusoidal_embeddings
						_lowercase       :							Any  	=		causal
						_lowercase       :							int  	=		asm
						_lowercase       :							Dict  	=		n_langs
						_lowercase       :							int  	=		use_lang_emb
						_lowercase       :							Union[str, Any]  	=		layer_norm_eps
						_lowercase       :							Optional[int]  	=		bos_index
						_lowercase       :							Tuple  	=		eos_index
						_lowercase       :							Optional[Any]  	=		pad_index
						_lowercase       :							Any  	=		unk_index
						_lowercase       :							int  	=		mask_index
						_lowercase       :							List[str]  	=		is_encoder
						_lowercase       :							Union[str, Any]  	=		max_position_embeddings
						_lowercase       :							Union[str, Any]  	=		embed_init_std
						_lowercase       :							Optional[Any]  	=		init_std
						_lowercase       :							Tuple  	=		summary_type
						_lowercase       :							Optional[Any]  	=		summary_use_proj
						_lowercase       :							Union[str, Any]  	=		summary_activation
						_lowercase       :							Optional[Any]  	=		summary_proj_to_labels
						_lowercase       :							Tuple  	=		summary_first_dropout
						_lowercase       :							Optional[Any]  	=		start_n_top
						_lowercase       :							Union[str, Any]  	=		end_n_top
						_lowercase       :							int  	=		mask_token_id
						_lowercase       :							List[str]  	=		lang_id

						if "n_words" in kwargs:
							_lowercase       :							str  	=		kwargs['n_words']

						super().__init__(pad_token_id=UpperCamelCase_  ,	bos_token_id=UpperCamelCase_  ,	**UpperCamelCase_       )






class 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



					@property
					def     __UpperCAmelCase  (	self					:						str       )	->							Tuple:



						'''simple docstring'''





						if self.task == "multiple-choice":
							_lowercase       :							Dict  	=		{0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
							_lowercase       :							List[str]  	=		{0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]       )


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

# Load iris file
lowerCamelCase__        =						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
lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__        =						train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

lowerCamelCase__        =						KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
lowerCamelCase__        =						[[1, 2, 1, 4], [2, 3, 4, 5]]

lowerCamelCase__        =						knn.predict(X_new)

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