

import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors





@require_tokenizers
class A__     (			__lowerCAmelCase	,		unittest.TestCase			):



 """simple docstring"""







 _lowercase  :       Union[str, Any]  			=    BartTokenizer
 _lowercase  :       Union[str, Any]  			=    BartTokenizerFast
 _lowercase  :       List[Any]  			=    True
 _lowercase  :       Dict  			=    filter_roberta_detectors

 def  __magic_name__							(		self			:						Union[str, Any]							):






      '''simple docstring'''




      super().setUp()
      _lowerCAmelCase :       Optional[Any]	   =     [
          'l',
          'o',
          'w',
          'e',
          'r',
          's',
          't',
          'i',
          'd',
          'n',
          '\u0120',
          '\u0120l',
          '\u0120n',
          '\u0120lo',
          '\u0120low',
          'er',
          '\u0120lowest',
          '\u0120newer',
          '\u0120wider',
          '<unk>',
      ]
      _lowerCAmelCase :       Dict	   =     dict(zip(A_  ,    range(len(A_							)							)							)							)
      _lowerCAmelCase :       Optional[Any]	   =     ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
      _lowerCAmelCase :       int	   =     {'unk_token': '<unk>'}

      _lowerCAmelCase :       str	   =     os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES["vocab_file"]							)
      _lowerCAmelCase :       Dict	   =     os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES["merges_file"]							)
      with open(self.vocab_file  ,    "w"  ,    encoding="utf-8"							) as fp:
           fp.write(json.dumps(A_							) + "\n"							)
      with open(self.merges_file  ,    "w"  ,    encoding="utf-8"							) as fp:
           fp.write("\n".join(A_							)							)

 def  __magic_name__							(		self			:						Any  ,    **A_			:						str							):






      '''simple docstring'''




      kwargs.update(self.special_tokens_map							)
      return self.tokenizer_class.from_pretrained(self.tmpdirname  ,    **A_							)

 def  __magic_name__							(		self			:						List[str]  ,    **A_			:						List[str]							):






      '''simple docstring'''




      kwargs.update(self.special_tokens_map							)
      return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,    **A_							)

 def  __magic_name__							(		self			:						Any  ,    A_			:						Tuple							):






      '''simple docstring'''




      return "lower newer", "lower newer"

 @cached_property
 def  __magic_name__							(		self			:						Dict							):






      '''simple docstring'''




      return BartTokenizer.from_pretrained("facebook/bart-large"							)

 @cached_property
 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




      return BartTokenizerFast.from_pretrained("facebook/bart-large"							)

 @require_torch
 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




      _lowerCAmelCase :       Any	   =     ['A long paragraph for summarization.', 'Another paragraph for summarization.']
      _lowerCAmelCase :       Union[str, Any]	   =     [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           _lowerCAmelCase :       Optional[int]	   =     tokenizer(A_  ,    max_length=len(A_							)  ,    padding=A_  ,    return_tensors="pt"							)
           self.assertIsInstance(A_  ,    A_							)

           self.assertEqual((2, 9)  ,    batch.input_ids.shape							)
           self.assertEqual((2, 9)  ,    batch.attention_mask.shape							)
           _lowerCAmelCase :       Tuple	   =     batch.input_ids.tolist()[0]
           self.assertListEqual(A_  ,    A_							)
           # Test that special tokens are reset

 @require_torch
 def  __magic_name__							(		self			:						Tuple							):






      '''simple docstring'''




      _lowerCAmelCase :       Dict	   =     ['A long paragraph for summarization.', 'Another paragraph for summarization.']
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           _lowerCAmelCase :       str	   =     tokenizer(A_  ,    padding=A_  ,    return_tensors="pt"							)
           # check if input_ids are returned and no labels
           self.assertIn("input_ids"  ,    A_							)
           self.assertIn("attention_mask"  ,    A_							)
           self.assertNotIn("labels"  ,    A_							)
           self.assertNotIn("decoder_attention_mask"  ,    A_							)

 @require_torch
 def  __magic_name__							(		self			:						Any							):






      '''simple docstring'''




      _lowerCAmelCase :       int	   =     [
          'Summary of the text.',
          'Another summary.',
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           _lowerCAmelCase :       int	   =     tokenizer(text_target=A_  ,    max_length=3_2  ,    padding="max_length"  ,    return_tensors="pt"							)
           self.assertEqual(3_2  ,    targets["input_ids"].shape[1]							)

 @require_torch
 def  __magic_name__							(		self			:						List[Any]							):






      '''simple docstring'''




      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           _lowerCAmelCase :       Tuple	   =     tokenizer(
               ["I am a small frog" * 1_0_2_4, "I am a small frog"]  ,    padding=A_  ,    truncation=A_  ,    return_tensors="pt"							)
           self.assertIsInstance(A_  ,    A_							)
           self.assertEqual(batch.input_ids.shape  ,    (2, 1_0_2_4)							)

 @require_torch
 def  __magic_name__							(		self			:						str							):






      '''simple docstring'''




      _lowerCAmelCase :       str	   =     ['A long paragraph for summarization.']
      _lowerCAmelCase :       Union[str, Any]	   =     [
          'Summary of the text.',
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           _lowerCAmelCase :       List[str]	   =     tokenizer(A_  ,    return_tensors="pt"							)
           _lowerCAmelCase :       Optional[int]	   =     tokenizer(text_target=A_  ,    return_tensors="pt"							)
           _lowerCAmelCase :       List[str]	   =     inputs['input_ids']
           _lowerCAmelCase :       Optional[int]	   =     targets['input_ids']
           self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()							)
           self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()							)
           self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()							)
           self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()							)

 def  __magic_name__							(		self			:						List[str]							):






      '''simple docstring'''




      pass


 def  __magic_name__							(		self			:						str							):






      '''simple docstring'''




      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
                _lowerCAmelCase :       int	   =     self.rust_tokenizer_class.from_pretrained(A_  ,    **A_							)
                _lowerCAmelCase :       str	   =     self.tokenizer_class.from_pretrained(A_  ,    **A_							)
                _lowerCAmelCase :       Union[str, Any]	   =     'A, <mask> AllenNLP sentence.'
                _lowerCAmelCase :       int	   =     tokenizer_r.encode_plus(A_  ,    add_special_tokens=A_  ,    return_token_type_ids=A_							)
                _lowerCAmelCase :       Any	   =     tokenizer_p.encode_plus(A_  ,    add_special_tokens=A_  ,    return_token_type_ids=A_							)

                # token_type_ids should put 0 everywhere
                self.assertEqual(sum(tokens_r["token_type_ids"]							)  ,    sum(tokens_p["token_type_ids"]							)							)

                # attention_mask should put 1 everywhere, so sum over length should be 1
                self.assertEqual(
                    sum(tokens_r["attention_mask"]							) / len(tokens_r["attention_mask"]							)  ,    sum(tokens_p["attention_mask"]							) / len(tokens_p["attention_mask"]							)  ,    )

                _lowerCAmelCase :       Any	   =     tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]							)
                _lowerCAmelCase :       int	   =     tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]							)

                # Rust correctly handles the space before the mask while python doesnt
                self.assertSequenceEqual(tokens_p["input_ids"]  ,    [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]							)
                self.assertSequenceEqual(tokens_r["input_ids"]  ,    [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]							)

                self.assertSequenceEqual(
                    A_  ,    ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]							)
                self.assertSequenceEqual(
                    A_  ,    ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]							)







"""simple docstring"""
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
lowercase__   			=      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
lowercase__			,   lowercase__			,   lowercase__			,   lowercase__   			=      train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

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

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

lowercase__   			=      knn.predict(X_new)

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


