


"""simple docstring"""
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 	lowercase__				(					A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					LEDTokenizer
   __UpperCAmelCase							       =					LEDTokenizerFast
   __UpperCAmelCase							       =					True





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

      _lowerCamelCase	:					Tuple   =		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(SCREAMING_SNAKE_CASE) + """\n""")
      with open(self.merges_file      ,   """w"""      ,   encoding="""utf-8""") as fp:
         fp.write("""\n""".join(SCREAMING_SNAKE_CASE))





   def 		UpperCamelCase_  (							self      ,   **SCREAMING_SNAKE_CASE)  ->  List[Any]:
      kwargs.update(self.special_tokens_map)
      return self.tokenizer_class.from_pretrained(self.tmpdirname      ,   **SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   **SCREAMING_SNAKE_CASE)  ->  Optional[Any]:
      kwargs.update(self.special_tokens_map)
      return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,   **SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Optional[Any]:
      return "lower newer", "lower newer"





   @cached_property
   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      return LEDTokenizer.from_pretrained("""allenai/led-base-16384""")





   @cached_property
   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384""")





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

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

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





   @require_torch
   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					List[Any]   =		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         _lowerCamelCase	:					Optional[Any]   =		tokenizer(SCREAMING_SNAKE_CASE      ,   padding=SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""")
         self.assertIn("""input_ids"""      ,   SCREAMING_SNAKE_CASE)
         self.assertIn("""attention_mask"""      ,   SCREAMING_SNAKE_CASE)
         self.assertNotIn("""labels"""      ,   SCREAMING_SNAKE_CASE)
         self.assertNotIn("""decoder_attention_mask"""      ,   SCREAMING_SNAKE_CASE)





   @require_torch
   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      _lowerCamelCase	:					Dict   =		[
          """Summary of the text.""",
          """Another summary.""",
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         _lowerCamelCase	:					int   =		tokenizer(text_target=SCREAMING_SNAKE_CASE      ,   max_length=32      ,   padding="""max_length"""      ,   return_tensors="""pt""")
         self.assertEqual(32      ,   targets["""input_ids"""].shape[1])





   @require_torch
   def 		UpperCamelCase_  (							self)  ->  int:
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         _lowerCamelCase	:					Union[str, Any]   =		tokenizer(
             ["""I am a small frog""" * 1024, """I am a small frog"""]      ,   padding=SCREAMING_SNAKE_CASE      ,   truncation=SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""")
         self.assertIsInstance(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)
         self.assertEqual(batch.input_ids.shape      ,   (2, 5122))





   @require_torch
   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Tuple   =		["""A long paragraph for summarization."""]
      _lowerCamelCase	:					Optional[int]   =		[
          """Summary of the text.""",
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         _lowerCamelCase	:					Optional[int]   =		tokenizer(SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""")
         _lowerCamelCase	:					Optional[int]   =		tokenizer(text_target=SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""")
         _lowerCamelCase	:					Optional[Any]   =		inputs["""input_ids"""]
         _lowerCamelCase	:					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 		UpperCamelCase_  (							self)  ->  List[Any]:
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         _lowerCamelCase	:					List[Any]   =		["""Summary of the text.""", """Another summary."""]
         _lowerCamelCase	:					Tuple   =		[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

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





   def 		UpperCamelCase_  (							self)  ->  str:
      pass





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

            _lowerCamelCase	:					Optional[Any]   =		tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""])
            _lowerCamelCase	:					Any   =		tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""])
            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(
                SCREAMING_SNAKE_CASE      ,   ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""])
            self.assertSequenceEqual(
                SCREAMING_SNAKE_CASE      ,   ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""])





"""simple docstring"""
from collections import defaultdict

def 				_snake_case      (   __snake_case							:      str						, __snake_case							:      str   ):




   """simple docstring"""







   _lowerCamelCase	:					Tuple   =		first_str.lower().strip()
   _lowerCamelCase	:					int   =		second_str.lower().strip()

   # Remove whitespace
   _lowerCamelCase	:					Any   =		first_str.replace(""" """						, """"""   )
   _lowerCamelCase	:					List[str]   =		second_str.replace(""" """						, """"""   )

   # Strings of different lengths are not anagrams
   if len(__snake_case   ) != len(__snake_case   ):
      return False

   # Default values for count should be 0
   _lowerCamelCase	:					defaultdict[str, int]   =		defaultdict(__snake_case   )

   # For each character in input strings,
   # increment count in the corresponding
   for i in range(len(__snake_case   )   ):
      count[first_str[i]] += 1
      count[second_str[i]] -= 1

   return all(_count == 0 for _count in count.values()   )


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

 testmod()
 UpperCAmelCase				=						input("""Enter the first string """).strip()
 UpperCAmelCase				=						input("""Enter the second string """).strip()

 UpperCAmelCase				=						check_anagrams(input_a, input_b)
 print(f'''{input_a} and {input_b} are {"" if status else "not "}anagrams.''')

