





'''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  (       A__		,					unittest.TestCase						):







		'''simple docstring'''







		__lowerCAmelCase 				=    BartTokenizer
		__lowerCAmelCase 				=    BartTokenizerFast
		__lowerCAmelCase 				=    True
		__lowerCAmelCase 				=    filter_roberta_detectors







		def        A     (self						:      Any      ):
			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(_lowerCAmelCase      ,      range(len(_lowerCAmelCase      )      )      )      )
			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(_lowerCAmelCase      ) + """\n"""      )
			with open(self.merges_file      ,      """w"""      ,      encoding="""utf-8"""      ) as fp:
				fp.write("""\n""".join(_lowerCAmelCase      )      )







		def        A     (self						:      Union[str, Any]      ,      **_lowerCAmelCase						:      Any      ):
			kwargs.update(self.special_tokens_map      )
			return self.tokenizer_class.from_pretrained(self.tmpdirname      ,      **_lowerCAmelCase      )







		def        A     (self						:      Tuple      ,      **_lowerCAmelCase						:      Optional[int]      ):
			kwargs.update(self.special_tokens_map      )
			return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,      **_lowerCAmelCase      )







		def        A     (self						:      Union[str, Any]      ,      _lowerCAmelCase						:      Any      ):
			return "lower newer", "lower newer"







		@cached_property
		def        A     (self						:      Union[str, Any]      ):
			return BartTokenizer.from_pretrained("""facebook/bart-large"""      )







		@cached_property
		def        A     (self						:      List[Any]      ):
			return BartTokenizerFast.from_pretrained("""facebook/bart-large"""      )







		@require_torch
		def        A     (self						:      Optional[Any]      ):
			A							  =      ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
			A							  =      [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

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

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







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







		@require_torch
		def        A     (self						:      List[Any]      ):
			A							  =      [
			    """Summary of the text.""",
			    """Another summary.""",
			]
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
				A							  =      tokenizer(text_target=_lowerCAmelCase      ,      max_length=32      ,      padding="""max_length"""      ,      return_tensors="""pt"""      )
				self.assertEqual(32      ,      targets["""input_ids"""].shape[1]      )







		@require_torch
		def        A     (self						:      int      ):
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
				A							  =      tokenizer(
				    ["""I am a small frog""" * 1024, """I am a small frog"""]      ,      padding=_lowerCAmelCase      ,      truncation=_lowerCAmelCase      ,      return_tensors="""pt"""      )
				self.assertIsInstance(_lowerCAmelCase      ,      _lowerCAmelCase      )
				self.assertEqual(batch.input_ids.shape      ,      (2, 1024)      )







		@require_torch
		def        A     (self						:      str      ):
			A							  =      ["""A long paragraph for summarization."""]
			A							  =      [
			    """Summary of the text.""",
			]
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
				A							  =      tokenizer(_lowerCAmelCase      ,      return_tensors="""pt"""      )
				A							  =      tokenizer(text_target=_lowerCAmelCase      ,      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        A     (self						:      Optional[Any]      ):
			pass







		def        A     (self						:      Tuple      ):
			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(_lowerCAmelCase      ,      **_lowerCAmelCase      )
					A							  =      self.tokenizer_class.from_pretrained(_lowerCAmelCase      ,      **_lowerCAmelCase      )
					A							  =      """A, <mask> AllenNLP sentence."""
					A							  =      tokenizer_r.encode_plus(_lowerCAmelCase      ,      add_special_tokens=_lowerCAmelCase      ,      return_token_type_ids=_lowerCAmelCase      )
					A							  =      tokenizer_p.encode_plus(_lowerCAmelCase      ,      add_special_tokens=_lowerCAmelCase      ,      return_token_type_ids=_lowerCAmelCase      )

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








'''simple docstring'''


from __future__ import annotations



class 							__UpperCAmelCase  :







		'''simple docstring'''







		def __init__(self						:      Any      ,      _lowerCAmelCase						:      int      ):
			A							  =      value
			A							  =      None
			A							  =      None






class 							__UpperCAmelCase  :







		'''simple docstring'''







		__lowerCAmelCase 				=    42







		def __init__(self						:      List[str]      ):
			A							  =      0







		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      Node | None      ,      _lowerCAmelCase						:      int      ):
			if node is None:
				return

			if path_sum == self.target:
				self.paths += 1

			if node.left:
				self.depth_first_search(node.left      ,      path_sum + node.left.value      )
			if node.right:
				self.depth_first_search(node.right      ,      path_sum + node.right.value      )







		def        A     (self						:      Optional[Any]      ,      _lowerCAmelCase						:      Node | None      ,      _lowerCAmelCase						:      int | None = None      ):
			if node is None:
				return 0
			if target is not None:
				A							  =      target

			self.depth_first_search(_lowerCAmelCase      ,      node.value      )
			self.path_sum(node.left      )
			self.path_sum(node.right      )

			return self.paths


if __name__ == "__main__":
				import doctest

				doctest.testmod()

