





import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 						lowerCamelCase       (__lowerCamelCase							,     unittest.TestCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   GPTaTokenizer
							UpperCAmelCase_      						=   GPTaTokenizerFast
							UpperCAmelCase_      						=   True
							UpperCAmelCase_      						=   {"add_prefix_space": True}
							UpperCAmelCase_      						=   False






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





														"""simple docstring"""
														super().setUp()

														# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
														SCREAMING_SNAKE_CASE__       :      int     =						[
														    "l",
														    "o",
														    "w",
														    "e",
														    "r",
														    "s",
														    "t",
														    "i",
														    "d",
														    "n",
														    "\u0120",
														    "\u0120l",
														    "\u0120n",
														    "\u0120lo",
														    "\u0120low",
														    "er",
														    "\u0120lowest",
														    "\u0120newer",
														    "\u0120wider",
														    "<unk>",
														    "<|endoftext|>",
														]
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						dict(zip(_UpperCAmelCase,			range(len(_UpperCAmelCase						)						)						)						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
														SCREAMING_SNAKE_CASE__       :      str     =						{"unk_token": "<unk>"}

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES["vocab_file"]						)
														SCREAMING_SNAKE_CASE__       :      str     =						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     :					int,			**_UpperCAmelCase     :					str						)				->			str:





														"""simple docstring"""
														kwargs.update(self.special_tokens_map						)
														return GPTaTokenizer.from_pretrained(self.tmpdirname,			**_UpperCAmelCase						)






							def  A_     ( self     :					str,			**_UpperCAmelCase     :					List[Any]						)				->			Optional[int]:





														"""simple docstring"""
														kwargs.update(self.special_tokens_map						)
														return GPTaTokenizerFast.from_pretrained(self.tmpdirname,			**_UpperCAmelCase						)






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						"lower newer"
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						"lower newer"
														return input_text, output_text






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						GPTaTokenizer(self.vocab_file,			self.merges_file,			**self.special_tokens_map						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						"lower newer"
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
														SCREAMING_SNAKE_CASE__       :      int     =						tokenizer.tokenize(_UpperCAmelCase,			add_prefix_space=_UpperCAmelCase						)
														self.assertListEqual(_UpperCAmelCase,			_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Tuple     =						tokens + [tokenizer.unk_token]
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase						),			_UpperCAmelCase						)






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





														"""simple docstring"""
														if not self.test_rust_tokenizer:
																					return

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.get_tokenizer()
														SCREAMING_SNAKE_CASE__       :      str     =						self.get_rust_tokenizer(add_prefix_space=_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						"lower newer"

														# Testing tokenization
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						tokenizer.tokenize(_UpperCAmelCase,			add_prefix_space=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						rust_tokenizer.tokenize(_UpperCAmelCase						)
														self.assertListEqual(_UpperCAmelCase,			_UpperCAmelCase						)

														# Testing conversion to ids without special tokens
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						tokenizer.encode(_UpperCAmelCase,			add_special_tokens=_UpperCAmelCase,			add_prefix_space=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Any     =						rust_tokenizer.encode(_UpperCAmelCase,			add_special_tokens=_UpperCAmelCase						)
														self.assertListEqual(_UpperCAmelCase,			_UpperCAmelCase						)

														# Testing conversion to ids with special tokens
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.get_rust_tokenizer(add_prefix_space=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						tokenizer.encode(_UpperCAmelCase,			add_prefix_space=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      int     =						rust_tokenizer.encode(_UpperCAmelCase						)
														self.assertListEqual(_UpperCAmelCase,			_UpperCAmelCase						)

														# Testing the unknown token
														SCREAMING_SNAKE_CASE__       :      Tuple     =						tokens + [rust_tokenizer.unk_token]
														SCREAMING_SNAKE_CASE__       :      List[str]     =						[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
														self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_UpperCAmelCase						),			_UpperCAmelCase						)






							def  A_     ( self     :					List[str],			*_UpperCAmelCase     :					Optional[Any],			**_UpperCAmelCase     :					Optional[int]						)				->			int:





														"""simple docstring"""
														# It's very difficult to mix/test pretokenization with byte-level
														# And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
														pass






							def  A_     ( self     :					int,			_UpperCAmelCase     :					str=1_5						)				->			List[str]:





														"""simple docstring"""
														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase,			**_UpperCAmelCase						)

																												# Simple input
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						"This is a simple input"
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						["This is a simple input 1", "This is a simple input 2"]
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						("This is a simple input", "This is a pair")
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[
																												    ("This is a simple input 1", "This is a simple input 2"),
																												    ("This is a simple pair 1", "This is a simple pair 2"),
																												]

																												# Simple input tests
																												self.assertRaises(_UpperCAmelCase,			tokenizer_r.encode,			_UpperCAmelCase,			max_length=_UpperCAmelCase,			padding="max_length"						)

																												# Simple input
																												self.assertRaises(_UpperCAmelCase,			tokenizer_r.encode_plus,			_UpperCAmelCase,			max_length=_UpperCAmelCase,			padding="max_length"						)

																												# Simple input
																												self.assertRaises(
																												    _UpperCAmelCase,			tokenizer_r.batch_encode_plus,			_UpperCAmelCase,			max_length=_UpperCAmelCase,			padding="max_length",			)

																												# Pair input
																												self.assertRaises(_UpperCAmelCase,			tokenizer_r.encode,			_UpperCAmelCase,			max_length=_UpperCAmelCase,			padding="max_length"						)

																												# Pair input
																												self.assertRaises(_UpperCAmelCase,			tokenizer_r.encode_plus,			_UpperCAmelCase,			max_length=_UpperCAmelCase,			padding="max_length"						)

																												# Pair input
																												self.assertRaises(
																												    _UpperCAmelCase,			tokenizer_r.batch_encode_plus,			_UpperCAmelCase,			max_length=_UpperCAmelCase,			padding="max_length",			)






							def  A_     ( self     :					Optional[Any]						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						GPTaTokenizer.from_pretrained(self.tmpdirname,			pad_token="<pad>"						)

														# Simple input
														SCREAMING_SNAKE_CASE__       :      Dict     =						"This is a simple input"
														SCREAMING_SNAKE_CASE__       :      str     =						["This is a simple input looooooooong", "This is a simple input"]
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						("This is a simple input", "This is a pair")
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[
														    ("This is a simple input loooooong", "This is a simple input"),
														    ("This is a simple pair loooooong", "This is a simple pair"),
														]

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						tokenizer.pad_token_id

														SCREAMING_SNAKE_CASE__       :      Tuple     =						tokenizer(_UpperCAmelCase,			padding="max_length",			max_length=3_0,			return_tensors="np"						)
														SCREAMING_SNAKE_CASE__       :      Any     =						tokenizer(_UpperCAmelCase,			padding=_UpperCAmelCase,			truncate=_UpperCAmelCase,			return_tensors="np"						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						tokenizer(*_UpperCAmelCase,			padding="max_length",			max_length=6_0,			return_tensors="np"						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						tokenizer(_UpperCAmelCase,			padding=_UpperCAmelCase,			truncate=_UpperCAmelCase,			return_tensors="np"						)

														# s
														# test single string max_length padding
														self.assertEqual(out_s["input_ids"].shape[-1],			3_0						)
														self.assertTrue(pad_token_id in out_s["input_ids"]						)
														self.assertTrue(0 in out_s["attention_mask"]						)

														# s2
														# test automatic padding
														self.assertEqual(out_sa["input_ids"].shape[-1],			3_3						)
														# long slice doesn't have padding
														self.assertFalse(pad_token_id in out_sa["input_ids"][0]						)
														self.assertFalse(0 in out_sa["attention_mask"][0]						)
														# short slice does have padding
														self.assertTrue(pad_token_id in out_sa["input_ids"][1]						)
														self.assertTrue(0 in out_sa["attention_mask"][1]						)

														# p
														# test single pair max_length padding
														self.assertEqual(out_p["input_ids"].shape[-1],			6_0						)
														self.assertTrue(pad_token_id in out_p["input_ids"]						)
														self.assertTrue(0 in out_p["attention_mask"]						)

														# p2
														# test automatic padding pair
														self.assertEqual(out_pa["input_ids"].shape[-1],			5_2						)
														# long slice pair doesn't have padding
														self.assertFalse(pad_token_id in out_pa["input_ids"][0]						)
														self.assertFalse(0 in out_pa["attention_mask"][0]						)
														# short slice pair does have padding
														self.assertTrue(pad_token_id in out_pa["input_ids"][1]						)
														self.assertTrue(0 in out_pa["attention_mask"][1]						)






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						"$$$"
														SCREAMING_SNAKE_CASE__       :      List[str]     =						GPTaTokenizer.from_pretrained(self.tmpdirname,			bos_token=_UpperCAmelCase,			add_bos_token=_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						"This is a simple input"
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						["This is a simple input 1", "This is a simple input 2"]

														SCREAMING_SNAKE_CASE__       :      Any     =						tokenizer.bos_token_id

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						tokenizer(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						tokenizer(_UpperCAmelCase						)

														self.assertEqual(out_s.input_ids[0],			_UpperCAmelCase						)
														self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids						)						)

														SCREAMING_SNAKE_CASE__       :      Dict     =						tokenizer.decode(out_s.input_ids						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						tokenizer.batch_decode(out_sa.input_ids						)

														self.assertEqual(decode_s.split()[0],			_UpperCAmelCase						)
														self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa						)						)






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





														"""simple docstring"""
														pass






							def  A_     ( self     :					Tuple						)				->			Tuple:





														"""simple docstring"""
														# TODO: change to self.get_tokenizers() when the fast version is implemented
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[self.get_tokenizer(do_lower_case=_UpperCAmelCase,			add_bos_token=_UpperCAmelCase						)]
														for tokenizer in tokenizers:
																					with self.subTest(F'''{tokenizer.__class__.__name__}'''						):
																												SCREAMING_SNAKE_CASE__       :      str     =						"Encode this."
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						"This one too please."
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						tokenizer.encode(_UpperCAmelCase,			add_special_tokens=_UpperCAmelCase						)
																												encoded_sequence += tokenizer.encode(_UpperCAmelCase,			add_special_tokens=_UpperCAmelCase						)
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						tokenizer.encode_plus(
																												    _UpperCAmelCase,			_UpperCAmelCase,			add_special_tokens=_UpperCAmelCase,			return_special_tokens_mask=_UpperCAmelCase,			)
																												SCREAMING_SNAKE_CASE__       :      Any     =						encoded_sequence_dict["input_ids"]
																												SCREAMING_SNAKE_CASE__       :      int     =						encoded_sequence_dict["special_tokens_mask"]
																												self.assertEqual(len(_UpperCAmelCase						),			len(_UpperCAmelCase						)						)

																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						[
																												    (x if not special_tokens_mask[i] else None) for i, x in enumerate(_UpperCAmelCase						)
																												]
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[x for x in filtered_sequence if x is not None]
																												self.assertEqual(_UpperCAmelCase,			_UpperCAmelCase						)


@require_tokenizers
class 						lowerCamelCase       (unittest.TestCase      ):







							"""simple docstring"""




							def  A_     ( self     :					Any						)				->			Tuple:





														"""simple docstring"""
														# More context:
														# https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
														# https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
														# https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						AutoTokenizer.from_pretrained("facebook/opt-350m",			from_slow=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Dict     =						"A photo of a cat"

														SCREAMING_SNAKE_CASE__       :      List[str]     =						tokenizer.encode(
														    _UpperCAmelCase,			)
														self.assertEqual(_UpperCAmelCase,			[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]						)
														tokenizer.save_pretrained("test_opt"						)

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						AutoTokenizer.from_pretrained("./test_opt"						)
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						tokenizer.encode(
														    _UpperCAmelCase,			)
														self.assertEqual(_UpperCAmelCase,			[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]						)






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						AutoTokenizer.from_pretrained("facebook/opt-350m",			use_slow=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						"A photo of a cat"

														SCREAMING_SNAKE_CASE__       :      Any     =						tokenizer.encode(
														    _UpperCAmelCase,			)
														# Same as above
														self.assertEqual(_UpperCAmelCase,			[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]						)






							@unittest.skip("This test is failing because of a bug in the fast tokenizer"						)
							def  A_     ( self     :					Optional[Any]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						AutoTokenizer.from_pretrained("facebook/opt-350m",			from_slow=_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						"bos"
														SCREAMING_SNAKE_CASE__       :      Any     =						tokenizer.get_vocab()["bos"]

														SCREAMING_SNAKE_CASE__       :      str     =						"A photo of a cat"
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						tokenizer.encode(
														    _UpperCAmelCase,			)
														# We changed the bos token
														self.assertEqual(_UpperCAmelCase,			[3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]						)
														tokenizer.save_pretrained("./tok"						)
														SCREAMING_SNAKE_CASE__       :      str     =						AutoTokenizer.from_pretrained("./tok"						)
														self.assertTrue(tokenizer.is_fast						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						tokenizer.encode(
														    _UpperCAmelCase,			)
														self.assertEqual(_UpperCAmelCase,			[3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]						)













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




def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  complex			,				SCREAMING_SNAKE_CASE__      :  str = "x"			,				SCREAMING_SNAKE_CASE__      :  float = 10**-10			,				SCREAMING_SNAKE_CASE__      :  int = 1			,				)	->    complex:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						symbols(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      str     =						lambdify(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      int     =						lambdify(SCREAMING_SNAKE_CASE__			,				diff(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )       )

							SCREAMING_SNAKE_CASE__       :      Any     =						starting_point

							while True:
														if diff_function(SCREAMING_SNAKE_CASE__       ) != 0:
																					SCREAMING_SNAKE_CASE__       :      Any     =						prev_guess - multiplicity * func(SCREAMING_SNAKE_CASE__       ) / diff_function(
																					    SCREAMING_SNAKE_CASE__       )
														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

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						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', 1_0, precision=0.005)}",
		)

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






