





'''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 SCREAMING_SNAKE_CASE_ (					__lowerCAmelCase							,       unittest.TestCase   ):





					"""simple docstring"""


					A__				 =    LEDTokenizer
					A__				 =    LEDTokenizerFast
					A__				 =    True





					def 					__magic_name__ ( self	):
										super().setUp()
										lowerCamelCase__       =       [
										    '''l''',
										    '''o''',
										    '''w''',
										    '''e''',
										    '''r''',
										    '''s''',
										    '''t''',
										    '''i''',
										    '''d''',
										    '''n''',
										    '''\u0120''',
										    '''\u0120l''',
										    '''\u0120n''',
										    '''\u0120lo''',
										    '''\u0120low''',
										    '''er''',
										    '''\u0120lowest''',
										    '''\u0120newer''',
										    '''\u0120wider''',
										    '''<unk>''',
										]
										lowerCamelCase__       =       dict(zip(lowerCamelCase__	,  range(len(lowerCamelCase__	)	)	)	)
										lowerCamelCase__       =       ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
										lowerCamelCase__       =       {'''unk_token''': '''<unk>'''}

										lowerCamelCase__       =       os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["vocab_file"]	)
										lowerCamelCase__       =       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 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										kwargs.update(self.special_tokens_map	)
										return self.tokenizer_class.from_pretrained(self.tmpdirname	,  **lowerCamelCase__	)





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										kwargs.update(self.special_tokens_map	)
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname	,  **lowerCamelCase__	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):
										return "lower newer", "lower newer"





					@cached_property
					def 					__magic_name__ ( self	):
										return LEDTokenizer.from_pretrained("allenai/led-base-16384"	)





					@cached_property
					def 					__magic_name__ ( self	):
										return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"	)





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

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





					@require_torch
					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															lowerCamelCase__       =       tokenizer(lowerCamelCase__	,  padding=lowerCamelCase__	,  return_tensors="pt"	)
															self.assertIn("input_ids"	,  lowerCamelCase__	)
															self.assertIn("attention_mask"	,  lowerCamelCase__	)
															self.assertNotIn("labels"	,  lowerCamelCase__	)
															self.assertNotIn("decoder_attention_mask"	,  lowerCamelCase__	)





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





					@require_torch
					def 					__magic_name__ ( self	):






										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															lowerCamelCase__       =       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, 5122)	)





					@require_torch
					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       ['''A long paragraph for summarization.''']
										lowerCamelCase__       =       [
										    '''Summary of the text.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															lowerCamelCase__       =       tokenizer(lowerCamelCase__	,  return_tensors="pt"	)
															lowerCamelCase__       =       tokenizer(text_target=lowerCamelCase__	,  return_tensors="pt"	)
															lowerCamelCase__       =       inputs['''input_ids''']
															lowerCamelCase__       =       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 					__magic_name__ ( self	):






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

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





					def 					__magic_name__ ( self	):
										pass






					def 					__magic_name__ ( self	):






										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"	):
																				lowerCamelCase__       =       self.rust_tokenizer_class.from_pretrained(lowerCamelCase__	,  **lowerCamelCase__	)
																				lowerCamelCase__       =       self.tokenizer_class.from_pretrained(lowerCamelCase__	,  **lowerCamelCase__	)
																				lowerCamelCase__       =       '''A, <mask> AllenNLP sentence.'''
																				lowerCamelCase__       =       tokenizer_r.encode_plus(lowerCamelCase__	,  add_special_tokens=lowerCamelCase__	,  return_token_type_ids=lowerCamelCase__	)
																				lowerCamelCase__       =       tokenizer_p.encode_plus(lowerCamelCase__	,  add_special_tokens=lowerCamelCase__	,  return_token_type_ids=lowerCamelCase__	)
																				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__       =       tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]	)
																				lowerCamelCase__       =       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(
																				    lowerCamelCase__	,  ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)
																				self.assertSequenceEqual(
																				    lowerCamelCase__	,  ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)








def    __UpperCamelCase      (    a) ->float:
					return 10 - x * x





def    __UpperCamelCase      (    a,     a) ->float:
					# Bolzano theory in order to find if there is a root between a and b
					if equation(a) * equation(a) >= 0:
										raise ValueError("Wrong space!")

					lowerCamelCase__       =       a
					while (b - a) >= 0.0_1:
										# Find middle point
										lowerCamelCase__       =       (a + b) / 2
										# Check if middle point is root
										if equation(a) == 0.0:
															break
										# Decide the side to repeat the steps
										if equation(a) * equation(a) < 0:
															lowerCamelCase__       =       c
										else:
															lowerCamelCase__       =       c
					return c


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						print(bisection(-2, 5))
						print(bisection(0, 6))

