


'''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  lowerCAmelCase_						(       snake_case__		,    unittest.TestCase      ):





  """simple docstring"""



  a_       :Union[str, Any]												=LEDTokenizer
  a_       :List[Any]												=LEDTokenizerFast
  a_       :Union[str, Any]												=True

  def __a						(					self     :							Dict						):





       '''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(SCREAMING_SNAKE_CASE__				,							range(len(SCREAMING_SNAKE_CASE__						)						)						)						)
       __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(SCREAMING_SNAKE_CASE__						) + """\n"""						)
       with open(self.merges_file				,							"""w"""				,							encoding="""utf-8"""						) as fp:
            fp.write("""\n""".join(SCREAMING_SNAKE_CASE__						)						)

  def __a						(					self     :							Optional[Any]				,							**SCREAMING_SNAKE_CASE__     :							Optional[Any]						):





       '''simple docstring'''

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

  def __a						(					self     :							int				,							**SCREAMING_SNAKE_CASE__     :							Optional[int]						):





       '''simple docstring'''

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

  def __a						(					self     :							str				,							SCREAMING_SNAKE_CASE__     :							int						):





       '''simple docstring'''

       return "lower newer", "lower newer"

  @cached_property
  def __a						(					self     :							Dict						):





       '''simple docstring'''

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

  @cached_property
  def __a						(					self     :							Union[str, Any]						):





       '''simple docstring'''

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

  @require_torch
  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       __a       =   [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            __a       =   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						)
            __a       =   batch.input_ids.tolist()[0]
            self.assertListEqual(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)

  @require_torch
  def __a						(					self     :							Dict						):





       '''simple docstring'''

       __a       =   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            __a       =   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 __a						(					self     :							Union[str, Any]						):





       '''simple docstring'''

       __a       =   [
           """Summary of the text.""",
           """Another summary.""",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            __a       =   tokenizer(text_target=SCREAMING_SNAKE_CASE__				,							max_length=3_2				,							padding="""max_length"""				,							return_tensors="""pt"""						)
            self.assertEqual(3_2				,							targets["""input_ids"""].shape[1]						)

  @require_torch
  def __a						(					self     :							Any						):





       '''simple docstring'''

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            __a       =   tokenizer(
                ["""I am a small frog""" * 1_0_2_4, """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, 5_1_2_2)						)

  @require_torch
  def __a						(					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(SCREAMING_SNAKE_CASE__				,							return_tensors="""pt"""						)
            __a       =   tokenizer(text_target=SCREAMING_SNAKE_CASE__				,							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()						)

  @require_torch
  def __a						(					self     :							Dict						):





       '''simple docstring'''

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

            __a       =   tokenizer(SCREAMING_SNAKE_CASE__				,							padding=SCREAMING_SNAKE_CASE__						)
            __a       =   [[0] * len(SCREAMING_SNAKE_CASE__						) for x in encoded_output["""input_ids"""]]
            __a       =   tokenizer.pad(SCREAMING_SNAKE_CASE__						)
            self.assertSequenceEqual(outputs["""global_attention_mask"""]				,							SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       pass


  def __a						(					self     :							Any						):





       '''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(SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)
                 __a       =   self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)
                 __a       =   """A, <mask> AllenNLP sentence."""
                 __a       =   tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE__				,							add_special_tokens=SCREAMING_SNAKE_CASE__				,							return_token_type_ids=SCREAMING_SNAKE_CASE__						)
                 __a       =   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"""]						)				,							)

                 __a       =   tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]						)
                 __a       =   tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]						)
                 self.assertSequenceEqual(tokens_p["""input_ids"""]				,							[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]						)
                 self.assertSequenceEqual(tokens_r["""input_ids"""]				,							[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 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 math import pi, sqrt




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					float:
     """simple docstring"""




     if num <= 0:
          raise ValueError("""math domain error"""		)
     if num > 171.5:
          raise OverflowError("""math range error"""		)
     elif num - int(__SCREAMING_SNAKE_CASE		) not in (0, 0.5):
          raise NotImplementedError("""num must be an integer or a half-integer"""		)
     elif num == 0.5:
          return sqrt(__SCREAMING_SNAKE_CASE		)
     else:
          return 1.0 if num == 1 else (num - 1) * gamma(num - 1		)




def        __lowercase						(      )				->					None:
     """simple docstring"""




     assert gamma(0.5		) == sqrt(__SCREAMING_SNAKE_CASE		)
     assert gamma(1		) == 1.0
     assert gamma(2		) == 1.0


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

 testmod()
 SCREAMING_SNAKE_CASE_      =			1.0
 while num:
  SCREAMING_SNAKE_CASE_      =			float(input('Gamma of: '))
  print(f"""gamma({num}) = {gamma(num)}""")
  print('\nEnter 0 to exit...')






