





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     UpperCamelCase__					(     _SCREAMING_SNAKE_CASE				, unittest.TestCase				):






		'''simple docstring'''






		__a       :       str       =	LEDTokenizer
		__a       :       List[Any]       =	LEDTokenizerFast
		__a       :       Dict       =	True






		def 						snake_case__				(							self  )       -> List[Any]:






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

									lowercase_	:   List[Any]  				=     os.path.join(self.tmpdirname,   VOCAB_FILES_NAMES["""vocab_file"""]  )
									lowercase_	:   Tuple  				=     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 						snake_case__				(							self,   **snake_case__  )       -> Any:






									"""simple docstring"""
									kwargs.update(self.special_tokens_map  )
									return self.tokenizer_class.from_pretrained(self.tmpdirname,   **lowerCAmelCase_  )






		def 						snake_case__				(							self,   **snake_case__  )       -> Optional[int]:






									"""simple docstring"""
									kwargs.update(self.special_tokens_map  )
									return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,   **lowerCAmelCase_  )






		def 						snake_case__				(							self,   snake_case__  )       -> str:






									"""simple docstring"""
									return "lower newer", "lower newer"






		@cached_property
		def 						snake_case__				(							self  )       -> List[str]:






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






		@cached_property
		def 						snake_case__				(							self  )       -> int:






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






		@require_torch
		def 						snake_case__				(							self  )       -> Optional[Any]:






									"""simple docstring"""
									lowercase_	:   Optional[int]  				=     ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									lowercase_	:   Any  				=     [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																lowercase_	:   Optional[int]  				=     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  )
																lowercase_	:   int  				=     batch.input_ids.tolist()[0]
																self.assertListEqual(lowerCAmelCase_,   lowerCAmelCase_  )






		@require_torch
		def 						snake_case__				(							self  )       -> int:






									"""simple docstring"""
									lowercase_	:   Union[str, Any]  				=     ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																lowercase_	:   Tuple  				=     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 						snake_case__				(							self  )       -> Optional[Any]:






									"""simple docstring"""
									lowercase_	:   Tuple  				=     [
									    """Summary of the text.""",
									    """Another summary.""",
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																lowercase_	:   Optional[int]  				=     tokenizer(text_target=lowerCAmelCase_,   max_length=32,   padding="""max_length""",   return_tensors="""pt"""  )
																self.assertEqual(32,   targets["""input_ids"""].shape[1]  )






		@require_torch
		def 						snake_case__				(							self  )       -> str:






									"""simple docstring"""
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																lowercase_	:   Optional[Any]  				=     tokenizer(
																    ["""I am a small frog""" * 10_24, """I am a small frog"""],   padding=lowerCAmelCase_,   truncation=lowerCAmelCase_,   return_tensors="""pt"""  )
																self.assertIsInstance(lowerCAmelCase_,   lowerCAmelCase_  )
																self.assertEqual(batch.input_ids.shape,   (2, 51_22)  )






		@require_torch
		def 						snake_case__				(							self  )       -> List[Any]:






									"""simple docstring"""
									lowercase_	:   Dict  				=     ["""A long paragraph for summarization."""]
									lowercase_	:   Any  				=     [
									    """Summary of the text.""",
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																lowercase_	:   int  				=     tokenizer(lowerCAmelCase_,   return_tensors="""pt"""  )
																lowercase_	:   Optional[int]  				=     tokenizer(text_target=lowerCAmelCase_,   return_tensors="""pt"""  )
																lowercase_	:   str  				=     inputs["""input_ids"""]
																lowercase_	:   Tuple  				=     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  )       -> Tuple:






									"""simple docstring"""
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																lowercase_	:   str  				=     ["""Summary of the text.""", """Another summary."""]
																lowercase_	:   Dict  				=     [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

																lowercase_	:   Optional[Any]  				=     tokenizer(lowerCAmelCase_,   padding=lowerCAmelCase_  )
																lowercase_	:   Any  				=     [[0] * len(lowerCAmelCase_  ) for x in encoded_output["""input_ids"""]]
																lowercase_	:   Tuple  				=     tokenizer.pad(lowerCAmelCase_  )
																self.assertSequenceEqual(outputs["""global_attention_mask"""],   lowerCAmelCase_  )






		def 						snake_case__				(							self  )       -> Tuple:






									"""simple docstring"""
									pass






		def 						snake_case__				(							self  )       -> str:






									"""simple docstring"""
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
																							lowercase_	:   Any  				=     self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_,   **lowerCAmelCase_  )
																							lowercase_	:   Optional[Any]  				=     self.tokenizer_class.from_pretrained(lowerCAmelCase_,   **lowerCAmelCase_  )
																							lowercase_	:   Optional[Any]  				=     """A, <mask> AllenNLP sentence."""
																							lowercase_	:   List[Any]  				=     tokenizer_r.encode_plus(lowerCAmelCase_,   add_special_tokens=lowerCAmelCase_,   return_token_type_ids=lowerCAmelCase_  )
																							lowercase_	:   Any  				=     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"""]  ),   )

																							lowercase_	:   List[Any]  				=     tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]  )
																							lowercase_	:   Union[str, Any]  				=     tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]  )
																							self.assertSequenceEqual(tokens_p["""input_ids"""],   [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]  )
																							self.assertSequenceEqual(tokens_r["""input_ids"""],   [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 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

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ = 10**-10       ):
	A_		:    Tuple						     = a
	while True:
		A_		:    List[str]						     = Decimal(snake_case__       ) - (
		    Decimal(eval(snake_case__       )       ) / Decimal(eval(str(diff(snake_case__       )       )       )       )  # noqa: S307
		)
		# This number dictates the accuracy of the answer
		if abs(eval(snake_case__       )       ) < precision:  # noqa: S307
			return float(snake_case__       )


# Let's Execute
if __name__ == "__main__":
							# Find root of trigonometric function
							# Find value of pi
							print(F'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')
							# Find root of polynomial
							print(F'The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}')
							# Find Square Root of 5
							print(F'The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}')
							# Exponential Roots
							print(F'The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}')

