


'''simple docstring'''







import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class  lowerCAmelCase_						(       __snake_case		,    unittest.TestCase      ):





		"""simple docstring"""



		a_       :Union[str, Any]												=LongformerTokenizer
		a_       :Optional[Any]												=True
		a_       :Any												=LongformerTokenizerFast
		a_       :List[str]												=True

		def __a						(					self     :							int						):





							'''simple docstring'''

							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							__a       =   [
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "\u0120",
							    "\u0120l",
							    "\u0120n",
							    "\u0120lo",
							    "\u0120low",
							    "er",
							    "\u0120lowest",
							    "\u0120newer",
							    "\u0120wider",
							    "<unk>",
							]
							__a       =   dict(zip(A_				,							range(len(A_						)						)						)						)
							__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(A_						) + """\n"""						)
							with open(self.merges_file				,							"""w"""				,							encoding="""utf-8"""						) as fp:
												fp.write("""\n""".join(A_						)						)

		def __a						(					self     :							Union[str, Any]				,							**SCREAMING_SNAKE_CASE__     :							List[str]						):





							'''simple docstring'''

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

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





							'''simple docstring'''

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

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





							'''simple docstring'''

							__a       =   "lower newer"
							__a       =   "lower newer"
							return input_text, output_text

		def __a						(					self     :							List[str]						):





							'''simple docstring'''

							__a       =   self.tokenizer_class(self.vocab_file				,							self.merges_file				,							**self.special_tokens_map						)
							__a       =   "lower newer"
							__a       =   ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
							__a       =   tokenizer.tokenize(A_						)  # , add_prefix_space=True)
							self.assertListEqual(A_				,							A_						)

							__a       =   tokens + [tokenizer.unk_token]
							__a       =   [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_						)				,							A_						)

		def __a						(					self     :							Any						):





							'''simple docstring'''

							__a       =   self.get_tokenizer()

							self.assertListEqual(tokenizer.encode("""Hello world!"""				,							add_special_tokens=A_						)				,							[0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]						)
							self.assertListEqual(
							    tokenizer.encode("""Hello world! cécé herlolip 418"""				,							add_special_tokens=A_						)				,							[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]				,							)

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





							'''simple docstring'''

							__a       =   self.tokenizer_class.from_pretrained("""allenai/longformer-base-4096"""						)

							__a       =   tokenizer.encode("""sequence builders"""				,							add_special_tokens=A_						)
							__a       =   tokenizer.encode("""multi-sequence build"""				,							add_special_tokens=A_						)

							__a       =   tokenizer.encode(
							    """sequence builders"""				,							add_special_tokens=A_				,							add_prefix_space=A_						)
							__a       =   tokenizer.encode(
							    """sequence builders"""				,							"""multi-sequence build"""				,							add_special_tokens=A_				,							add_prefix_space=A_						)

							__a       =   tokenizer.build_inputs_with_special_tokens(A_						)
							__a       =   tokenizer.build_inputs_with_special_tokens(A_				,							A_						)

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode

		def __a						(					self     :							Dict						):





							'''simple docstring'''

							__a       =   self.get_tokenizer()

							__a       =   "Encode this sequence."
							__a       =   tokenizer.byte_encoder[" ".encode("""utf-8"""						)[0]]

							# Testing encoder arguments
							__a       =   tokenizer.encode(A_				,							add_special_tokens=A_				,							add_prefix_space=A_						)
							__a       =   tokenizer.convert_ids_to_tokens(encoded[0]						)[0]
							self.assertNotEqual(A_				,							A_						)

							__a       =   tokenizer.encode(A_				,							add_special_tokens=A_				,							add_prefix_space=A_						)
							__a       =   tokenizer.convert_ids_to_tokens(encoded[0]						)[0]
							self.assertEqual(A_				,							A_						)

							tokenizer.add_special_tokens({"""bos_token""": """<s>"""}						)
							__a       =   tokenizer.encode(A_				,							add_special_tokens=A_						)
							__a       =   tokenizer.convert_ids_to_tokens(encoded[1]						)[0]
							self.assertNotEqual(A_				,							A_						)

							# Testing spaces after special tokens
							__a       =   "<mask>"
							tokenizer.add_special_tokens(
							    {"""mask_token""": AddedToken(A_				,							lstrip=A_				,							rstrip=A_						)}						)  # mask token has a left space
							__a       =   tokenizer.convert_tokens_to_ids(A_						)

							__a       =   "Encode <mask> sequence"
							__a       =   "Encode <mask>sequence"

							__a       =   tokenizer.encode(A_						)
							__a       =   encoded.index(A_						)
							__a       =   tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]						)[0]
							self.assertEqual(A_				,							A_						)

							__a       =   tokenizer.encode(A_						)
							__a       =   encoded.index(A_						)
							__a       =   tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]						)[0]
							self.assertNotEqual(A_				,							A_						)

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





							'''simple docstring'''

							pass

		def __a						(					self     :							Optional[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(A_				,							**A_						)
																	__a       =   self.tokenizer_class.from_pretrained(A_				,							**A_						)
																	__a       =   "A, <mask> AllenNLP sentence."
																	__a       =   tokenizer_r.encode_plus(A_				,							add_special_tokens=A_				,							return_token_type_ids=A_						)
																	__a       =   tokenizer_p.encode_plus(A_				,							add_special_tokens=A_				,							return_token_type_ids=A_						)

																	# token_type_ids should put 0 everywhere
																	self.assertEqual(sum(tokens_r["""token_type_ids"""]						)				,							sum(tokens_p["""token_type_ids"""]						)						)

																	# attention_mask should put 1 everywhere, so sum over length should be 1
																	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"""]						)

																	# Rust correctly handles the space before the mask while python doesnt
																	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(
																	    A_				,							["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]						)
																	self.assertSequenceEqual(
																	    A_				,							["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]						)

		def __a						(					self     :							Tuple						):





							'''simple docstring'''

							for trim_offsets, add_prefix_space in itertools.product([True, False]				,							repeat=2						):
												__a       =   self.rust_tokenizer_class.from_pretrained(
												    self.tmpdirname				,							use_fast=A_				,							add_prefix_space=A_				,							trim_offsets=A_						)

												__a       =   json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()						)
												__a       =   json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()						)

												self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]				,							A_						)

												self.assertEqual(post_processor_state["""add_prefix_space"""]				,							A_						)
												self.assertEqual(post_processor_state["""trim_offsets"""]				,							A_						)


		def __a						(					self     :							List[str]						):





							'''simple docstring'''

							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
																	__a       =   "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
																	__a       =   f'''{text_of_1_token} {text_of_1_token}'''

																	__a       =   self.rust_tokenizer_class.from_pretrained(
																	    A_				,							use_fast=A_				,							add_prefix_space=A_				,							trim_offsets=A_						)
																	__a       =   tokenizer_r(A_				,							return_offsets_mapping=A_				,							add_special_tokens=A_						)
																	self.assertEqual(encoding.offset_mapping[0]				,							(0, len(A_						))						)
																	self.assertEqual(
																	    encoding.offset_mapping[1]				,							(len(A_						) + 1, len(A_						) + 1 + len(A_						))				,							)

																	__a       =   self.rust_tokenizer_class.from_pretrained(
																	    A_				,							use_fast=A_				,							add_prefix_space=A_				,							trim_offsets=A_						)
																	__a       =   tokenizer_r(A_				,							return_offsets_mapping=A_				,							add_special_tokens=A_						)
																	self.assertEqual(encoding.offset_mapping[0]				,							(0, len(A_						))						)
																	self.assertEqual(
																	    encoding.offset_mapping[1]				,							(len(A_						) + 1, len(A_						) + 1 + len(A_						))				,							)

																	__a       =   self.rust_tokenizer_class.from_pretrained(
																	    A_				,							use_fast=A_				,							add_prefix_space=A_				,							trim_offsets=A_						)
																	__a       =   tokenizer_r(A_				,							return_offsets_mapping=A_				,							add_special_tokens=A_						)
																	self.assertEqual(encoding.offset_mapping[0]				,							(0, len(A_						))						)
																	self.assertEqual(
																	    encoding.offset_mapping[1]				,							(len(A_						), len(A_						) + 1 + len(A_						))				,							)

																	__a       =   self.rust_tokenizer_class.from_pretrained(
																	    A_				,							use_fast=A_				,							add_prefix_space=A_				,							trim_offsets=A_						)
																	__a       =   tokenizer_r(A_				,							return_offsets_mapping=A_				,							add_special_tokens=A_						)
																	self.assertEqual(encoding.offset_mapping[0]				,							(0, len(A_						))						)
																	self.assertEqual(
																	    encoding.offset_mapping[1]				,							(len(A_						), len(A_						) + 1 + len(A_						))				,							)

																	__a       =   f''' {text}'''

																	# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																	#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																	# )
																	# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																	# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																	# self.assertEqual(
																	#     encoding.offset_mapping[1],
																	#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																	# )

																	__a       =   self.rust_tokenizer_class.from_pretrained(
																	    A_				,							use_fast=A_				,							add_prefix_space=A_				,							trim_offsets=A_						)
																	__a       =   tokenizer_r(A_				,							return_offsets_mapping=A_				,							add_special_tokens=A_						)
																	self.assertEqual(encoding.offset_mapping[0]				,							(1, 1 + len(A_						))						)
																	self.assertEqual(
																	    encoding.offset_mapping[1]				,							(1 + len(A_						) + 1, 1 + len(A_						) + 1 + len(A_						))				,							)

																	__a       =   self.rust_tokenizer_class.from_pretrained(
																	    A_				,							use_fast=A_				,							add_prefix_space=A_				,							trim_offsets=A_						)
																	__a       =   tokenizer_r(A_				,							return_offsets_mapping=A_				,							add_special_tokens=A_						)
																	self.assertEqual(encoding.offset_mapping[0]				,							(0, 1 + len(A_						))						)
																	self.assertEqual(
																	    encoding.offset_mapping[1]				,							(1 + len(A_						), 1 + len(A_						) + 1 + len(A_						))				,							)

																	__a       =   self.rust_tokenizer_class.from_pretrained(
																	    A_				,							use_fast=A_				,							add_prefix_space=A_				,							trim_offsets=A_						)
																	__a       =   tokenizer_r(A_				,							return_offsets_mapping=A_				,							add_special_tokens=A_						)
																	self.assertEqual(encoding.offset_mapping[0]				,							(0, 1 + len(A_						))						)
																	self.assertEqual(
																	    encoding.offset_mapping[1]				,							(1 + len(A_						), 1 + len(A_						) + 1 + len(A_						))				,							)










'''simple docstring'''







import math
import random
from typing import Any

from .hill_climbing import SearchProblem




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE = True				,  __SCREAMING_SNAKE_CASE = math.inf				,  __SCREAMING_SNAKE_CASE = -math.inf				,  __SCREAMING_SNAKE_CASE = math.inf				,  __SCREAMING_SNAKE_CASE = -math.inf				,  __SCREAMING_SNAKE_CASE = False				,  __SCREAMING_SNAKE_CASE = 100				,  __SCREAMING_SNAKE_CASE = 0.01				,  __SCREAMING_SNAKE_CASE = 1				,  )				->					Any:
     """simple docstring"""




     __a       =   False
     __a       =   search_prob
     __a       =   start_temperate
     __a       =   []
     __a       =   0
     __a       =   None

     while not search_end:
          __a       =   current_state.score()
          if best_state is None or current_score > best_state.score():
               __a       =   current_state
          scores.append(__SCREAMING_SNAKE_CASE		)
          iterations += 1
          __a       =   None
          __a       =   current_state.get_neighbors()
          while (
              next_state is None and neighbors
          ):  # till we do not find a neighbor that we can move to
               __a       =   random.randint(0				,  len(__SCREAMING_SNAKE_CASE		) - 1		)  # picking a random neighbor
               __a       =   neighbors.pop(__SCREAMING_SNAKE_CASE		)
               __a       =   picked_neighbor.score() - current_score

               if (
                   picked_neighbor.x > max_x
                   or picked_neighbor.x < min_x
                   or picked_neighbor.y > max_y
                   or picked_neighbor.y < min_y
               ):
                    continue  # neighbor outside our bounds

               if not find_max:
                    __a       =   change * -1  # in case we are finding minimum
               if change > 0:  # improves the solution
                    __a       =   picked_neighbor
               else:
                    __a       =   (math.e) ** (
                        change / current_temp
                    )  # probability generation function
                    if random.random() < probability:  # random number within probability
                         __a       =   picked_neighbor
          __a       =   current_temp - (current_temp * rate_of_decrease)

          if current_temp < threshold_temp or next_state is None:
               # temperature below threshold, or could not find a suitable neighbor
               __a       =   True
          else:
               __a       =   next_state

     if visualization:
          from matplotlib import pyplot as plt

          plt.plot(range(__SCREAMING_SNAKE_CASE		)				,  __SCREAMING_SNAKE_CASE		)
          plt.xlabel("""Iterations"""		)
          plt.ylabel("""Function values"""		)
          plt.show()
     return best_state


if __name__ == "__main__":




 def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					int:
      """simple docstring"""




      return (x**2) + (y**2)

 # starting the problem with initial coordinates (12, 47)
 SCREAMING_SNAKE_CASE_      =			SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 SCREAMING_SNAKE_CASE_      =			simulated_annealing(
     prob, find_max=False, max_x=1_00, min_x=5, max_y=50, min_y=-5, visualization=True
 )
 print(
     'The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
     f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
 )

 # starting the problem with initial coordinates (12, 47)
 SCREAMING_SNAKE_CASE_      =			SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 SCREAMING_SNAKE_CASE_      =			simulated_annealing(
     prob, find_max=True, max_x=1_00, min_x=5, max_y=50, min_y=-5, visualization=True
 )
 print(
     'The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
     f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
 )




 def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					int:
      """simple docstring"""




      return (3 * x**2) - (6 * y)

 SCREAMING_SNAKE_CASE_      =			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 SCREAMING_SNAKE_CASE_      =			simulated_annealing(prob, find_max=False, visualization=True)
 print(
     'The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
     f"""{local_min.score()}"""
 )

 SCREAMING_SNAKE_CASE_      =			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 SCREAMING_SNAKE_CASE_      =			simulated_annealing(prob, find_max=True, visualization=True)



 print(
     'The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
     f"""{local_min.score()}"""
 )






