
"""simple docstring"""






import pickle

import numpy as np
from matplotlib import pyplot as plt



class     A__     :




	'''simple docstring'''





	def __init__(  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	List[str] ,    _SCREAMING_SNAKE_CASE:	Dict ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	List[str] ,    _SCREAMING_SNAKE_CASE:	Dict=0.2 ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=0.2)       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						Union[str, Any]				= bp_numa
				__lowerCAmelCase   :						Tuple				= bp_numa
				__lowerCAmelCase   :						Optional[Any]				= bp_numa
				__lowerCAmelCase   :						str				= conva_get[:2]
				__lowerCAmelCase   :						Union[str, Any]				= conva_get[2]
				__lowerCAmelCase   :						int				= size_pa
				__lowerCAmelCase   :						Optional[Any]				= rate_w
				__lowerCAmelCase   :						Any				= rate_t
				__lowerCAmelCase   :						Optional[Any]				= [
				    np.mat(-1 * np.random.rand(self.conva[0] ,    self.conva[0]) + 0.5)
				    for i in range(self.conva[1])
				]
				__lowerCAmelCase   :						Optional[Any]				= np.mat(-1 * np.random.rand(self.num_bpa ,    self.num_bpa) + 0.5)
				__lowerCAmelCase   :						List[Any]				= np.mat(-1 * np.random.rand(self.num_bpa ,    self.num_bpa) + 0.5)
				__lowerCAmelCase   :						Tuple				= -2 * np.random.rand(self.conva[1]) + 1
				__lowerCAmelCase   :						Tuple				= -2 * np.random.rand(self.num_bpa) + 1
				__lowerCAmelCase   :						str				= -2 * np.random.rand(self.num_bpa) + 1

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any] ,    _SCREAMING_SNAKE_CASE:	Dict)       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= {
				    "num_bp1": self.num_bpa,
				    "num_bp2": self.num_bpa,
				    "num_bp3": self.num_bpa,
				    "conv1": self.conva,
				    "step_conv1": self.step_conva,
				    "size_pooling1": self.size_poolinga,
				    "rate_weight": self.rate_weight,
				    "rate_thre": self.rate_thre,
				    "w_conv1": self.w_conva,
				    "wkj": self.wkj,
				    "vji": self.vji,
				    "thre_conv1": self.thre_conva,
				    "thre_bp2": self.thre_bpa,
				    "thre_bp3": self.thre_bpa,
				}
				with open(_SCREAMING_SNAKE_CASE ,    "wb") as f:
							pickle.dump(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

				print(F"""Model saved： {save_path}""")

	@classmethod
	def 							_SCREAMING_SNAKE_CASE     (  cls:	int ,    _SCREAMING_SNAKE_CASE:	Any)       -> Tuple:

				"""simple docstring"""

				with open(_SCREAMING_SNAKE_CASE ,    "rb") as f:
							__lowerCAmelCase   :						Any				= pickle.load(_SCREAMING_SNAKE_CASE)  # noqa: S301

				__lowerCAmelCase   :						Optional[int]				= model_dic.get("conv1")
				conv_get.append(model_dic.get("step_conv1"))
				__lowerCAmelCase   :						Any				= model_dic.get("size_pooling1")
				__lowerCAmelCase   :						List[Any]				= model_dic.get("num_bp1")
				__lowerCAmelCase   :						List[Any]				= model_dic.get("num_bp2")
				__lowerCAmelCase   :						Optional[int]				= model_dic.get("num_bp3")
				__lowerCAmelCase   :						Any				= model_dic.get("rate_weight")
				__lowerCAmelCase   :						int				= model_dic.get("rate_thre")
				# create model instance
				__lowerCAmelCase   :						Any				= CNN(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
				# modify model parameter
				__lowerCAmelCase   :						Tuple				= model_dic.get("w_conv1")
				__lowerCAmelCase   :						Union[str, Any]				= model_dic.get("wkj")
				__lowerCAmelCase   :						Union[str, Any]				= model_dic.get("vji")
				__lowerCAmelCase   :						Any				= model_dic.get("thre_conv1")
				__lowerCAmelCase   :						List[Any]				= model_dic.get("thre_bp2")
				__lowerCAmelCase   :						Union[str, Any]				= model_dic.get("thre_bp3")
				return conv_ins

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	List[str])       -> Any:

				"""simple docstring"""

				return 1 / (1 + np.exp(-1 * x))

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple ,    _SCREAMING_SNAKE_CASE:	Dict)       -> Optional[int]:

				"""simple docstring"""

				return round(_SCREAMING_SNAKE_CASE ,    3)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Any ,    _SCREAMING_SNAKE_CASE:	Any ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	List[str] ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	Any)       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= convs[0]
				__lowerCAmelCase   :						int				= convs[1]
				__lowerCAmelCase   :						Union[str, Any]				= np.shape(_SCREAMING_SNAKE_CASE)[0]
				# get the data slice of original image data, data_focus
				__lowerCAmelCase   :						List[str]				= []
				for i_focus in range(0 ,    size_data - size_conv + 1 ,    _SCREAMING_SNAKE_CASE):
							for j_focus in range(0 ,    size_data - size_conv + 1 ,    _SCREAMING_SNAKE_CASE):
										__lowerCAmelCase   :						Dict				= data[
										    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
										]
										data_focus.append(_SCREAMING_SNAKE_CASE)
        # calculate the feature map of every single kernel, and saved as list of matrix
				__lowerCAmelCase   :						List[str]				= []
				__lowerCAmelCase   :						Any				= int((size_data - size_conv) / conv_step + 1)
				for i_map in range(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						Optional[int]				= []
							for i_focus in range(len(_SCREAMING_SNAKE_CASE)):
										__lowerCAmelCase   :						Optional[int]				= (
										    np.sum(np.multiply(data_focus[i_focus] ,    w_convs[i_map]))
										    - thre_convs[i_map]
										)
										featuremap.append(self.sig(_SCREAMING_SNAKE_CASE))
							__lowerCAmelCase   :						Dict				= np.asmatrix(_SCREAMING_SNAKE_CASE).reshape(
							    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
							data_featuremap.append(_SCREAMING_SNAKE_CASE)

				# expanding the data slice to One dimenssion
				__lowerCAmelCase   :						Optional[Any]				= []
				for each_focus in data_focus:
							focusa_list.extend(self.Expand_Mat(_SCREAMING_SNAKE_CASE))
				__lowerCAmelCase   :						List[Any]				= np.asarray(_SCREAMING_SNAKE_CASE)
				return focus_list, data_featuremap

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any] ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	Tuple ,    _SCREAMING_SNAKE_CASE:	Tuple="average_pool")       -> Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= len(featuremaps[0])
				__lowerCAmelCase   :						List[str]				= int(size_map / size_pooling)
				__lowerCAmelCase   :						List[Any]				= []
				for i_map in range(len(_SCREAMING_SNAKE_CASE)):
							__lowerCAmelCase   :						List[str]				= featuremaps[i_map]
							__lowerCAmelCase   :						List[Any]				= []
							for i_focus in range(0 ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
										for j_focus in range(0 ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
													__lowerCAmelCase   :						Tuple				= feature_map[
													    i_focus : i_focus + size_pooling,
													    j_focus : j_focus + size_pooling,
													]
													if pooling_type == "average_pool":
																# average pooling
																map_pooled.append(np.average(_SCREAMING_SNAKE_CASE))
													elif pooling_type == "max_pooling":
																# max pooling
																map_pooled.append(np.max(_SCREAMING_SNAKE_CASE))
							__lowerCAmelCase   :						Tuple				= np.asmatrix(_SCREAMING_SNAKE_CASE).reshape(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
							featuremap_pooled.append(_SCREAMING_SNAKE_CASE)
				return featuremap_pooled

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Optional[int])       -> Dict:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= []
				for i in range(len(_SCREAMING_SNAKE_CASE)):
							__lowerCAmelCase   :						List[str]				= np.shape(data[i])
							__lowerCAmelCase   :						List[str]				= data[i].reshape(1 ,    shapes[0] * shapes[1])
							__lowerCAmelCase   :						Optional[Any]				= data_listed.getA().tolist()[0]
							data_expanded.extend(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Tuple				= np.asarray(_SCREAMING_SNAKE_CASE)
				return data_expanded

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any] ,    _SCREAMING_SNAKE_CASE:	Any)       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= np.asarray(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Optional[Any]				= np.shape(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						List[Any]				= data_mat.reshape(1 ,    shapes[0] * shapes[1])
				return data_expanded

	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	str)       -> List[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= []
				__lowerCAmelCase   :						Optional[int]				= 0
				for i_map in range(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						Optional[int]				= np.ones((size_map, size_map))
							for i in range(0 ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
										for j in range(0 ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
													__lowerCAmelCase   :						List[str]				= pd_pool[
													    i_pool
													]
													__lowerCAmelCase   :						Tuple				= i_pool + 1
							__lowerCAmelCase   :						str				= np.multiply(
							    _SCREAMING_SNAKE_CASE ,    np.multiply(out_map[i_map] ,    (1 - out_map[i_map])))
							pd_all.append(_SCREAMING_SNAKE_CASE)
				return pd_all

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	Any ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	Tuple ,    _SCREAMING_SNAKE_CASE:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	Any=bool)       -> Any:

				"""simple docstring"""

				print("----------------------Start Training-------------------------")
				print((" - - Shape: Train_Data  ", np.shape(_SCREAMING_SNAKE_CASE)))
				print((" - - Shape: Teach_Data  ", np.shape(_SCREAMING_SNAKE_CASE)))
				__lowerCAmelCase   :						Dict				= 0
				__lowerCAmelCase   :						int				= []
				__lowerCAmelCase   :						Union[str, Any]				= 1_0000
				while rp < n_repeat and mse >= error_accuracy:
							__lowerCAmelCase   :						Tuple				= 0
							print(F"""-------------Learning Time {rp}--------------""")
							for p in range(len(_SCREAMING_SNAKE_CASE)):
										# print('------------Learning Image: %d--------------'%p)
										__lowerCAmelCase   :						Optional[Any]				= np.asmatrix(datas_train[p])
										__lowerCAmelCase   :						Dict				= np.asarray(datas_teach[p])
										__lowerCAmelCase   :						int				= self.convolute(
										    _SCREAMING_SNAKE_CASE ,    self.conva ,    self.w_conva ,    self.thre_conva ,    conv_step=self.step_conva ,    )
										__lowerCAmelCase   :						Optional[Any]				= self.pooling(_SCREAMING_SNAKE_CASE ,    self.size_poolinga)
										__lowerCAmelCase   :						List[Any]				= np.shape(_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase   :						Union[str, Any]				= self._expand(_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase   :						List[Any]				= data_bp_input

										__lowerCAmelCase   :						List[str]				= np.dot(_SCREAMING_SNAKE_CASE ,    self.vji.T) - self.thre_bpa
										__lowerCAmelCase   :						int				= self.sig(_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase   :						List[str]				= np.dot(_SCREAMING_SNAKE_CASE ,    self.wkj.T) - self.thre_bpa
										__lowerCAmelCase   :						Union[str, Any]				= self.sig(_SCREAMING_SNAKE_CASE)

										# --------------Model Leaning ------------------------
										# calculate error and gradient---------------
										__lowerCAmelCase   :						Dict				= np.multiply(
										    (data_teach - bp_outa) ,    np.multiply(_SCREAMING_SNAKE_CASE ,    (1 - bp_outa)))
										__lowerCAmelCase   :						List[Any]				= np.multiply(
										    np.dot(_SCREAMING_SNAKE_CASE ,    self.wkj) ,    np.multiply(_SCREAMING_SNAKE_CASE ,    (1 - bp_outa)))
										__lowerCAmelCase   :						Optional[int]				= np.dot(_SCREAMING_SNAKE_CASE ,    self.vji)

										__lowerCAmelCase   :						Dict				= pd_i_all / (self.size_poolinga * self.size_poolinga)
										__lowerCAmelCase   :						Union[str, Any]				= pd_conva_pooled.T.getA().tolist()
										__lowerCAmelCase   :						Tuple				= self._calculate_gradient_from_pool(
										    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    shape_featuremapa[0] ,    shape_featuremapa[1] ,    self.size_poolinga ,    )
										# weight and threshold learning process---------
										# convolution layer
										for k_conv in range(self.conva[1]):
													__lowerCAmelCase   :						List[Any]				= self._expand_mat(pd_conva_all[k_conv])
													__lowerCAmelCase   :						List[str]				= self.rate_weight * np.dot(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

													__lowerCAmelCase   :						Optional[int]				= self.w_conva[k_conv] + delta_w.reshape(
													    (self.conva[0], self.conva[0]))

													__lowerCAmelCase   :						List[str]				= (
													    self.thre_conva[k_conv]
													    - np.sum(pd_conva_all[k_conv]) * self.rate_thre
													)
										# all connected layer
										__lowerCAmelCase   :						Any				= self.wkj + pd_k_all.T * bp_outa * self.rate_weight
										__lowerCAmelCase   :						Any				= self.vji + pd_j_all.T * bp_outa * self.rate_weight
										__lowerCAmelCase   :						List[str]				= self.thre_bpa - pd_k_all * self.rate_thre
										__lowerCAmelCase   :						Tuple				= self.thre_bpa - pd_j_all * self.rate_thre
										# calculate the sum error of all single image
										__lowerCAmelCase   :						int				= np.sum(abs(data_teach - bp_outa))
										error_count += errors
										# print('   ----Teach      ',data_teach)
										# print('   ----BP_output  ',bp_out3)
							__lowerCAmelCase   :						Union[str, Any]				= rp + 1
							__lowerCAmelCase   :						Any				= error_count / patterns
							all_mse.append(_SCREAMING_SNAKE_CASE)

				def draw_error():
							__lowerCAmelCase   :						Optional[int]				= [error_accuracy for i in range(int(n_repeat * 1.2))]
							plt.plot(_SCREAMING_SNAKE_CASE ,    "+-")
							plt.plot(_SCREAMING_SNAKE_CASE ,    "r--")
							plt.xlabel("Learning Times")
							plt.ylabel("All_mse")
							plt.grid(_SCREAMING_SNAKE_CASE ,    alpha=0.5)
							plt.show()

				print("------------------Training Complished---------------------")
				print((" - - Training epoch: ", rp, F"""     - - Mse: {mse:.6f}"""))
				if draw_e:
							draw_error()
				return mse

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	List[str])       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= []
				print("-------------------Start Testing-------------------------")
				print((" - - Shape: Test_Data  ", np.shape(_SCREAMING_SNAKE_CASE)))
				for p in range(len(_SCREAMING_SNAKE_CASE)):
							__lowerCAmelCase   :						Optional[Any]				= np.asmatrix(datas_test[p])
							__lowerCAmelCase   :						List[str]				= self.convolute(
							    _SCREAMING_SNAKE_CASE ,    self.conva ,    self.w_conva ,    self.thre_conva ,    conv_step=self.step_conva ,    )
							__lowerCAmelCase   :						int				= self.pooling(_SCREAMING_SNAKE_CASE ,    self.size_poolinga)
							__lowerCAmelCase   :						Optional[Any]				= self._expand(_SCREAMING_SNAKE_CASE)

							__lowerCAmelCase   :						int				= data_bp_input
							__lowerCAmelCase   :						Optional[Any]				= bp_outa * self.vji.T - self.thre_bpa
							__lowerCAmelCase   :						Any				= self.sig(_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase   :						Tuple				= bp_outa * self.wkj.T - self.thre_bpa
							__lowerCAmelCase   :						Optional[Any]				= self.sig(_SCREAMING_SNAKE_CASE)
							produce_out.extend(bp_outa.getA().tolist())
				__lowerCAmelCase   :						Optional[Any]				= [list(map(self.do_round ,    _SCREAMING_SNAKE_CASE)) for each in produce_out]
				return np.asarray(_SCREAMING_SNAKE_CASE)







	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Union[str, Any])       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= np.asmatrix(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						List[Any]				= self.convolute(
				    _SCREAMING_SNAKE_CASE ,    self.conva ,    self.w_conva ,    self.thre_conva ,    conv_step=self.step_conva ,    )
				__lowerCAmelCase   :						Optional[int]				= self.pooling(_SCREAMING_SNAKE_CASE ,    self.size_poolinga)

				return data_conveda, data_pooleda


if __name__ == "__main__":
	pass




"""simple docstring"""






import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case							:     Any   =					get_tests_dir('fixtures/spiece.model')



@require_sentencepiece
@require_tokenizers
class     A__     (						__SCREAMING_SNAKE_CASE						,	unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			AlbertTokenizer
	SCREAMING_SNAKE_CASE					   =			AlbertTokenizerFast
	SCREAMING_SNAKE_CASE					   =			True
	SCREAMING_SNAKE_CASE					   =			True
	SCREAMING_SNAKE_CASE					   =			True

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> Optional[int]:

				"""simple docstring"""

				super().setUp()

				# We have a SentencePiece fixture for testing
				__lowerCAmelCase   :						List[Any]				= AlbertTokenizer(_SCREAMING_SNAKE_CASE)
				tokenizer.save_pretrained(self.tmpdirname)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	Optional[int])       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= "this is a test"
				__lowerCAmelCase   :						Any				= "this is a test"
				return input_text, output_text

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> List[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= "<pad>"
				__lowerCAmelCase   :						List[str]				= 0

				self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE) ,    _SCREAMING_SNAKE_CASE)
				self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE) ,    _SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> List[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= list(self.get_tokenizer().get_vocab().keys())

				self.assertEqual(vocab_keys[0] ,    "<pad>")
				self.assertEqual(vocab_keys[1] ,    "<unk>")
				self.assertEqual(vocab_keys[-1] ,    "▁eloquent")
				self.assertEqual(len(_SCREAMING_SNAKE_CASE) ,    3_0000)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> str:

				"""simple docstring"""

				self.assertEqual(self.get_tokenizer().vocab_size ,    3_0000)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> List[Any]:

				"""simple docstring"""

				if not self.test_rust_tokenizer:
							return

				__lowerCAmelCase   :						List[str]				= self.get_tokenizer()
				__lowerCAmelCase   :						Dict				= self.get_rust_tokenizer()

				__lowerCAmelCase   :						Optional[int]				= "I was born in 92000, and this is falsé."

				__lowerCAmelCase   :						Tuple				= tokenizer.tokenize(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						List[str]				= rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE)
				self.assertListEqual(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						List[Any]				= tokenizer.encode(_SCREAMING_SNAKE_CASE ,    add_special_tokens=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Any				= rust_tokenizer.encode(_SCREAMING_SNAKE_CASE ,    add_special_tokens=_SCREAMING_SNAKE_CASE)
				self.assertListEqual(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						List[str]				= self.get_rust_tokenizer()
				__lowerCAmelCase   :						str				= tokenizer.encode(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Dict				= rust_tokenizer.encode(_SCREAMING_SNAKE_CASE)
				self.assertListEqual(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= AlbertTokenizer(_SCREAMING_SNAKE_CASE ,    keep_accents=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						List[str]				= tokenizer.tokenize("This is a test")
				self.assertListEqual(_SCREAMING_SNAKE_CASE ,    ["▁this", "▁is", "▁a", "▁test"])

				self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE) ,    [48, 25, 21, 1289])

				__lowerCAmelCase   :						Union[str, Any]				= tokenizer.tokenize("I was born in 92000, and this is falsé.")
				self.assertListEqual(
				    _SCREAMING_SNAKE_CASE ,    ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."])
				__lowerCAmelCase   :						Dict				= tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE)
				self.assertListEqual(_SCREAMING_SNAKE_CASE ,    [31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9])

				__lowerCAmelCase   :						Any				= tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE)
				self.assertListEqual(
				    _SCREAMING_SNAKE_CASE ,    ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."] ,    )

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= AlbertTokenizer(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Dict				= tokenizer.encode("sequence builders")
				__lowerCAmelCase   :						List[Any]				= tokenizer.encode("multi-sequence build")

				__lowerCAmelCase   :						Union[str, Any]				= tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						List[str]				= tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

				assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
				assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
				    tokenizer.sep_token_id
				]







	@slow
	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= {"attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "input_ids": [[2, 2_1970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 1_2051, 18, 17, 7103, 2153, 673, 8, 3515, 1_8684, 8, 4461, 6, 1927, 297, 8, 1_2060, 2607, 18, 13, 5, 4461, 15, 1_0538, 38, 8, 135, 15, 822, 58, 15, 993, 1_0363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 1_0641, 6, 29, 84, 2512, 2430, 782, 1_8684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 1_1712, 15, 7103, 2153, 673, 17, 2_4883, 9990, 9, 3], [2, 1_1502, 25, 1006, 20, 782, 8, 1_1809, 855, 1732, 1_9393, 1_8667, 37, 367, 2_1018, 69, 1854, 34, 1_1860, 1_9124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2231, 886, 2385, 1_7659, 84, 14, 1_6792, 1952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
				# fmt: on

				self.tokenizer_integration_test_util(
				    expected_encoding=_SCREAMING_SNAKE_CASE ,    model_name="albert-base-v2" ,    revision="6b6560eaf5ff2e250b00c50f380c5389a9c2d82e" ,    )