






'''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      _UpperCAmelCase    (  snake_case_							,					unittest.TestCase		):




			"""simple docstring"""

			snake_case					       =		BartTokenizer
			snake_case					       =		BartTokenizerFast
			snake_case					       =		True
			snake_case					       =		filter_roberta_detectors

			def 		lowerCAmelCase				(	self				:			int	):





									'''simple docstring'''





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

									_A      =			os.path.join(self.tmpdirname   ,   VOCAB_FILES_NAMES["vocab_file"]	)
									_A      =			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 		lowerCAmelCase				(	self				:			List[Any]   ,   **__UpperCAmelCase				:			Tuple	):





									'''simple docstring'''





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

			def 		lowerCAmelCase				(	self				:			Optional[int]   ,   **__UpperCAmelCase				:			str	):





									'''simple docstring'''





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

			def 		lowerCAmelCase				(	self				:			Any   ,   __UpperCAmelCase				:			Optional[Any]	):





									'''simple docstring'''





									return "lower newer", "lower newer"

			@cached_property
			def 		lowerCAmelCase				(	self				:			Optional[Any]	):





									'''simple docstring'''





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

			@cached_property
			def 		lowerCAmelCase				(	self				:			List[Any]	):





									'''simple docstring'''





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

			@require_torch
			def 		lowerCAmelCase				(	self				:			Optional[Any]	):





									'''simple docstring'''





									_A      =			["A long paragraph for summarization.", "Another paragraph for summarization."]
									_A      =			[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_A      =			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	)
															_A      =			batch.input_ids.tolist()[0]
															self.assertListEqual(__UpperCAmelCase   ,   __UpperCAmelCase	)
															# Test that special tokens are reset

			@require_torch
			def 		lowerCAmelCase				(	self				:			int	):





									'''simple docstring'''





									_A      =			["A long paragraph for summarization.", "Another paragraph for summarization."]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_A      =			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 		lowerCAmelCase				(	self				:			Optional[int]	):





									'''simple docstring'''





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

			@require_torch
			def 		lowerCAmelCase				(	self				:			Optional[Any]	):





									'''simple docstring'''





									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_A      =			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 		lowerCAmelCase				(	self				:			Tuple	):





									'''simple docstring'''





									_A      =			["A long paragraph for summarization."]
									_A      =			[
									    "Summary of the text.",
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_A      =			tokenizer(__UpperCAmelCase   ,   return_tensors="pt"	)
															_A      =			tokenizer(text_target=__UpperCAmelCase   ,   return_tensors="pt"	)
															_A      =			inputs["input_ids"]
															_A      =			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 		lowerCAmelCase				(	self				:			List[str]	):





									'''simple docstring'''





									pass

			def 		lowerCAmelCase				(	self				:			Tuple	):





									'''simple docstring'''





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

																					_A      =			tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]	)
																					_A      =			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, 50264, 3823, 487, 21992, 3645, 4, 2]	)
																					self.assertSequenceEqual(tokens_r["input_ids"]   ,   [0, 250, 6, 50264, 3823, 487, 21992, 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       __lowercase		(    __lowercase ) ->		int:

      '''simple docstring'''


      _A      =			str(__lowercase ).strip()
      if not bin_string:
            raise ValueError("Empty string was passed to the function" )
      _A      =			bin_string[0] == "-"
      if is_negative:
            _A      =			bin_string[1:]
      if not all(char in "01" for char in bin_string ):
            raise ValueError("Non-binary value was passed to the function" )
      _A      =			0
      for char in bin_string:
            _A      =			2 * decimal_number + int(__lowercase )
      return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
    from doctest import testmod

    testmod()





