


'''simple docstring'''

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 					snake_case   (   lowercase      ,						unittest.TestCase      ):





  """simple docstring"""




  _lowerCamelCase		   =  BartTokenizer
  _lowerCamelCase		   =  BartTokenizerFast
  _lowerCamelCase		   =  True
  _lowerCamelCase		   =  filter_roberta_detectors






  def   snake_case		(   self					):







   """simple docstring"""
   super().setUp()
   lowerCamelCase_								=  [
       "l",
       "o",
       "w",
       "e",
       "r",
       "s",
       "t",
       "i",
       "d",
       "n",
       "\u0120",
       "\u0120l",
       "\u0120n",
       "\u0120lo",
       "\u0120low",
       "er",
       "\u0120lowest",
       "\u0120newer",
       "\u0120wider",
       "<unk>",
   ]
   lowerCamelCase_								=  dict(zip(UpperCamelCase      ,  range(len(UpperCamelCase					)					)					)					)
   lowerCamelCase_								=  ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
   lowerCamelCase_								=  {"unk_token": "<unk>"}

   lowerCamelCase_								=  os.path.join(self.tmpdirname      ,  VOCAB_FILES_NAMES["vocab_file"]					)
   lowerCamelCase_								=  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(UpperCamelCase					) + "\n"					)
   with open(self.merges_file      ,  "w"      ,  encoding="utf-8"					) as fp:
    fp.write("\n".join(UpperCamelCase					)					)






  def   snake_case		(   self      ,  **UpperCamelCase					):







   """simple docstring"""
   kwargs.update(self.special_tokens_map					)
   return self.tokenizer_class.from_pretrained(self.tmpdirname      ,  **UpperCamelCase					)






  def   snake_case		(   self      ,  **UpperCamelCase					):







   """simple docstring"""
   kwargs.update(self.special_tokens_map					)
   return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,  **UpperCamelCase					)






  def   snake_case		(   self      ,  UpperCamelCase					):







   """simple docstring"""
   return "lower newer", "lower newer"






  @cached_property
  def   snake_case		(   self					):







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






  @cached_property
  def   snake_case		(   self					):







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






  @require_torch
  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  ["A long paragraph for summarization.", "Another paragraph for summarization."]
   lowerCamelCase_								=  [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
    lowerCamelCase_								=  tokenizer(UpperCamelCase      ,  max_length=len(UpperCamelCase					)      ,  padding=UpperCamelCase      ,  return_tensors="pt"					)
    self.assertIsInstance(UpperCamelCase      ,  UpperCamelCase					)

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






  @require_torch
  def   snake_case		(   self					):







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






  @require_torch
  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  [
       "Summary of the text.",
       "Another summary.",
   ]
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
    lowerCamelCase_								=  tokenizer(text_target=UpperCamelCase      ,  max_length=32      ,  padding="max_length"      ,  return_tensors="pt"					)
    self.assertEqual(32      ,  targets["input_ids"].shape[1]					)






  @require_torch
  def   snake_case		(   self					):







   """simple docstring"""
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
    lowerCamelCase_								=  tokenizer(
        ["I am a small frog" * 1024, "I am a small frog"]      ,  padding=UpperCamelCase      ,  truncation=UpperCamelCase      ,  return_tensors="pt"					)
    self.assertIsInstance(UpperCamelCase      ,  UpperCamelCase					)
    self.assertEqual(batch.input_ids.shape      ,  (2, 1024)					)






  @require_torch
  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  ["A long paragraph for summarization."]
   lowerCamelCase_								=  [
       "Summary of the text.",
   ]
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
    lowerCamelCase_								=  tokenizer(UpperCamelCase      ,  return_tensors="pt"					)
    lowerCamelCase_								=  tokenizer(text_target=UpperCamelCase      ,  return_tensors="pt"					)
    lowerCamelCase_								=  inputs["input_ids"]
    lowerCamelCase_								=  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   snake_case		(   self					):







   """simple docstring"""
   pass







  def   snake_case		(   self					):







   """simple docstring"""
   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
    with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
     lowerCamelCase_								=  self.rust_tokenizer_class.from_pretrained(UpperCamelCase      ,  **UpperCamelCase					)
     lowerCamelCase_								=  self.tokenizer_class.from_pretrained(UpperCamelCase      ,  **UpperCamelCase					)
     lowerCamelCase_								=  "A, <mask> AllenNLP sentence."
     lowerCamelCase_								=  tokenizer_r.encode_plus(UpperCamelCase      ,  add_special_tokens=UpperCamelCase      ,  return_token_type_ids=UpperCamelCase					)
     lowerCamelCase_								=  tokenizer_p.encode_plus(UpperCamelCase      ,  add_special_tokens=UpperCamelCase      ,  return_token_type_ids=UpperCamelCase					)

     # 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_								=  tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]					)
     lowerCamelCase_								=  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, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]					)
     self.assertSequenceEqual(tokens_r["input_ids"]      ,  [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]					)

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







'''simple docstring'''

def __snake_case (				UpperCAmelCase_						:						float		):
 return -num if num < 0 else num





def __snake_case (				UpperCAmelCase_						:						list[int]		):
 if len(UpperCAmelCase_		) == 0:
  raise ValueError("abs_min() arg is an empty sequence"		)
 lowerCamelCase_								=  x[0]
 for i in x:
  if abs_val(UpperCAmelCase_		) < abs_val(UpperCAmelCase_		):
   lowerCamelCase_								=  i
 return j





def __snake_case (				UpperCAmelCase_						:						list[int]		):
 if len(UpperCAmelCase_		) == 0:
  raise ValueError("abs_max() arg is an empty sequence"		)
 lowerCamelCase_								=  x[0]
 for i in x:
  if abs(UpperCAmelCase_		) > abs(UpperCAmelCase_		):
   lowerCamelCase_								=  i
 return j





def __snake_case (				UpperCAmelCase_						:						list[int]		):
 if len(UpperCAmelCase_		) == 0:
  raise ValueError("abs_max_sort() arg is an empty sequence"		)
 return sorted(UpperCAmelCase_ ,    key=UpperCAmelCase_		)[-1]





def __snake_case (				):
 assert abs_val(0		) == 0
 assert abs_val(34		) == 34
 assert abs_val(-100000000000		) == 100000000000

 lowerCamelCase_								=  [-3, -1, 2, -11]
 assert abs_max(UpperCAmelCase_		) == -11
 assert abs_max_sort(UpperCAmelCase_		) == -11
 assert abs_min(UpperCAmelCase_		) == -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     test_abs_val()
     print(abs_val(-34))  # --> 34



