





import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led 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







@require_tokenizers
class 	__lowerCAmelCase      (       A_		,unittest.TestCase	):
   _UpperCamelCase			:    List[Any]  		=					LEDTokenizer
   _UpperCamelCase			:    Tuple  		=					LEDTokenizerFast
   _UpperCamelCase			:    Any  		=					True





   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





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

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





   def 						_snake_case			(			self       ,      **snake_case   )					->	List[Any]:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      **snake_case   )					->	Optional[Any]:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case   )					->	Optional[Any]:
      """simple docstring"""





      return "lower newer", "lower newer"





   @cached_property
   def 						_snake_case			(			self   )					->	Optional[Any]:
      """simple docstring"""





      return LEDTokenizer.from_pretrained("allenai/led-base-16384"   )





   @cached_property
   def 						_snake_case			(			self   )					->	Optional[int]:
      """simple docstring"""





      return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"   )





   @require_torch
   def 						_snake_case			(			self   )					->	Optional[Any]:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
      a__						:    List[str]   		=	[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         a__						:    List[str]   		=	tokenizer(snake_case       ,      max_length=len(snake_case   )       ,      padding=snake_case       ,      return_tensors="pt"   )
         self.assertIsInstance(snake_case       ,      snake_case   )

         self.assertEqual((2, 9)       ,      batch.input_ids.shape   )
         self.assertEqual((2, 9)       ,      batch.attention_mask.shape   )
         a__						:    List[str]   		=	batch.input_ids.tolist()[0]
         self.assertListEqual(snake_case       ,      snake_case   )





   @require_torch
   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      a__						:    List[Any]   		=	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         a__						:    Optional[Any]   		=	tokenizer(snake_case       ,      padding=snake_case       ,      return_tensors="pt"   )
         self.assertIn("input_ids"       ,      snake_case   )
         self.assertIn("attention_mask"       ,      snake_case   )
         self.assertNotIn("labels"       ,      snake_case   )
         self.assertNotIn("decoder_attention_mask"       ,      snake_case   )





   @require_torch
   def 						_snake_case			(			self   )					->	Union[str, Any]:
      """simple docstring"""





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





   @require_torch
   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         a__						:    Union[str, Any]   		=	tokenizer(
             ["I am a small frog" * 1_024, "I am a small frog"]       ,      padding=snake_case       ,      truncation=snake_case       ,      return_tensors="pt"   )
         self.assertIsInstance(snake_case       ,      snake_case   )
         self.assertEqual(batch.input_ids.shape       ,      (2, 5_122)   )





   @require_torch
   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      a__						:    Tuple   		=	["""A long paragraph for summarization."""]
      a__						:    Optional[int]   		=	[
          """Summary of the text.""",
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         a__						:    Optional[int]   		=	tokenizer(snake_case       ,      return_tensors="pt"   )
         a__						:    Optional[int]   		=	tokenizer(text_target=snake_case       ,      return_tensors="pt"   )
         a__						:    Optional[Any]   		=	inputs["""input_ids"""]
         a__						:    Any   		=	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()   )





   @require_torch
   def 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         a__						:    List[Any]   		=	["""Summary of the text.""", """Another summary."""]
         a__						:    Tuple   		=	[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

         a__						:    Optional[int]   		=	tokenizer(snake_case       ,      padding=snake_case   )
         a__						:    Tuple   		=	[[0] * len(snake_case   ) for x in encoded_output["""input_ids"""]]
         a__						:    List[str]   		=	tokenizer.pad(snake_case   )
         self.assertSequenceEqual(outputs["global_attention_mask"]       ,      snake_case   )





   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      pass





   def 						_snake_case			(			self   )					->	Optional[Any]:
      """simple docstring"""





      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
            a__						:    Union[str, Any]   		=	self.rust_tokenizer_class.from_pretrained(snake_case       ,      **snake_case   )
            a__						:    int   		=	self.tokenizer_class.from_pretrained(snake_case       ,      **snake_case   )
            a__						:    Optional[int]   		=	"""A, <mask> AllenNLP sentence."""
            a__						:    Optional[Any]   		=	tokenizer_r.encode_plus(snake_case       ,      add_special_tokens=snake_case       ,      return_token_type_ids=snake_case   )
            a__						:    int   		=	tokenizer_p.encode_plus(snake_case       ,      add_special_tokens=snake_case       ,      return_token_type_ids=snake_case   )
            self.assertEqual(sum(tokens_r["token_type_ids"]   )       ,      sum(tokens_p["token_type_ids"]   )   )
            self.assertEqual(
                sum(tokens_r["attention_mask"]   ) / len(tokens_r["attention_mask"]   )       ,      sum(tokens_p["attention_mask"]   ) / len(tokens_p["attention_mask"]   )       ,      )

            a__						:    Optional[Any]   		=	tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]   )
            a__						:    Any   		=	tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]   )
            self.assertSequenceEqual(tokens_p["input_ids"]       ,      [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]   )
            self.assertSequenceEqual(tokens_r["input_ids"]       ,      [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]   )

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





"""simple docstring"""
def 				_snake_case      (   __snake_case							:      str						, __snake_case							:      str   ):




   """simple docstring"""







   _lowerCamelCase	:					str   =		len(__snake_case   )
   _lowerCamelCase	:					Union[str, Any]   =		len(__snake_case   )
   _lowerCamelCase	:					int   =		[[False for _ in range(m + 1   )] for _ in range(n + 1   )]
   _lowerCamelCase	:					Union[str, Any]   =		True
   for i in range(__snake_case   ):
      for j in range(m + 1   ):
         if dp[i][j]:
            if j < m and a[i].upper() == b[j]:
               _lowerCamelCase	:					Tuple   =		True
            if a[i].islower():
               _lowerCamelCase	:					Tuple   =		True
   return dp[n][m]


if __name__ == "__main__":
 import doctest

 doctest.testmod()

