
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__      (   lowercase__		,	unittest.TestCase	):
						UpperCamelCase_							: List[Any]	=		LEDTokenizer
						UpperCamelCase_							: Union[str, Any]	=		LEDTokenizerFast
						UpperCamelCase_							: Optional[int]	=		True

						def  A_				(			self				)					->      Any:


								'''simple docstring'''

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

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

						def  A_				(			self       ,							**a				)					->      List[str]:


								'''simple docstring'''

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

						def  A_				(			self       ,							**a				)					->      Any:


								'''simple docstring'''

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

						def  A_				(			self       ,							a				)					->      Union[str, Any]:


								'''simple docstring'''

								return "lower newer", "lower newer"

						@cached_property
						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

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

						@cached_property
						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

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

						@require_torch
						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								_UpperCamelCase						    =	[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]:
										_UpperCamelCase						    =	tokenizer(UpperCAmelCase__       ,							max_length=len(UpperCAmelCase__				)       ,							padding=UpperCAmelCase__       ,							return_tensors="""pt"""				)
										self.assertIsInstance(UpperCAmelCase__       ,							UpperCAmelCase__				)

										self.assertEqual((2, 9)       ,							batch.input_ids.shape				)
										self.assertEqual((2, 9)       ,							batch.attention_mask.shape				)
										_UpperCamelCase						    =	batch.input_ids.tolist()[0]
										self.assertListEqual(UpperCAmelCase__       ,							UpperCAmelCase__				)

						@require_torch
						def  A_				(			self				)					->      Any:


								'''simple docstring'''

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

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


								'''simple docstring'''

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

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


								'''simple docstring'''

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

						@require_torch
						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	['''A long paragraph for summarization.''']
								_UpperCamelCase						    =	[
								    '''Summary of the text.''',
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										_UpperCamelCase						    =	tokenizer(UpperCAmelCase__       ,							return_tensors="""pt"""				)
										_UpperCamelCase						    =	tokenizer(text_target=UpperCAmelCase__       ,							return_tensors="""pt"""				)
										_UpperCamelCase						    =	inputs['''input_ids''']
										_UpperCamelCase						    =	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				)					->      List[Any]:


								'''simple docstring'''

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

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

						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								pass







						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
												_UpperCamelCase						    =	self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__       ,							**UpperCAmelCase__				)
												_UpperCamelCase						    =	self.tokenizer_class.from_pretrained(UpperCAmelCase__       ,							**UpperCAmelCase__				)
												_UpperCamelCase						    =	'''A, <mask> AllenNLP sentence.'''
												_UpperCamelCase						    =	tokenizer_r.encode_plus(UpperCAmelCase__       ,							add_special_tokens=UpperCAmelCase__       ,							return_token_type_ids=UpperCAmelCase__				)
												_UpperCamelCase						    =	tokenizer_p.encode_plus(UpperCAmelCase__       ,							add_special_tokens=UpperCAmelCase__       ,							return_token_type_ids=UpperCAmelCase__				)
												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"""]				)       ,							)

												_UpperCamelCase						    =	tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]				)
												_UpperCamelCase						    =	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(
												    UpperCAmelCase__       ,							["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]				)
												self.assertSequenceEqual(
												    UpperCAmelCase__       ,							["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]				)







from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase = "x"						,  lowerCAmelCase = 1_0**-1_0						,  lowerCAmelCase = 1						,  )		->				complex:

		"""simple docstring"""






		_UpperCamelCase						    =	symbols(lowerCAmelCase )
		_UpperCamelCase						    =	lambdify(lowerCAmelCase						,  lowerCAmelCase )
		_UpperCamelCase						    =	lambdify(lowerCAmelCase						,  diff(lowerCAmelCase						,  lowerCAmelCase ) )

		_UpperCamelCase						    =	starting_point

		while True:
				if diff_function(lowerCAmelCase ) != 0:
						_UpperCamelCase						    =	prev_guess - multiplicity * func(lowerCAmelCase ) / diff_function(
						    lowerCAmelCase )
				else:
						raise ZeroDivisionError("""Could not find root""" ) from None

				# Precision is checked by comparing the difference of consecutive guesses
				if abs(next_guess - prev_guess ) < precision:
						return next_guess

				_UpperCamelCase						    =	next_guess


# 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
		# Find fourth Root of 5
		print(F"""The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}""")

		# Find value of e
		print(
		    "The root of log(y) - 1 = 0 is ",
		    F"""{newton_raphson("log(y) - 1", 2, variable="y")}""",
		)

		# Exponential Roots
		print(
		    "The root of exp(x) - 1 = 0 is",
		    F"""{newton_raphson("exp(x) - 1", 10, precision=0.0_05)}""",
		)

		# Find root of cos(x)
		print(F"""The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}""")



