


import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class        _lowerCAmelCase    (			__lowerCAmelCase							,    unittest.TestCase							):


  '''simple docstring'''
  a_							:       List[Any]      					=RobertaTokenizer
  a_							:       str      					=RobertaTokenizerFast
  a_							:       List[str]      					=True
  a_							:       Dict      					={"""cls_token""": """<s>"""}







  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    super().setUp()

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

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







  def UpperCamelCase_ (      self      :					int      ,				**UpperCamelCase      :					Optional[Any]							):


    '''simple docstring'''





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







  def UpperCamelCase_ (      self      :					int      ,				**UpperCamelCase      :					Tuple							):


    '''simple docstring'''





    kwargs.update(self.special_tokens_map							)
    return RobertaTokenizerFast.from_pretrained(self.tmpdirname      ,				**_UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Any      ,				UpperCamelCase      :					str							):


    '''simple docstring'''





    _snake_case   :				Tuple														=	"""lower newer"""
    _snake_case   :				Dict														=	"""lower newer"""
    return input_text, output_text







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


    '''simple docstring'''





    _snake_case   :				Optional[int]														=	self.tokenizer_class(self.vocab_file      ,				self.merges_file      ,				**self.special_tokens_map							)
    _snake_case   :				List[Any]														=	"""lower newer"""
    _snake_case   :				str														=	["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
    _snake_case   :				Any														=	tokenizer.tokenize(_UpperCamelCase							)  # , add_prefix_space=True)
    self.assertListEqual(_UpperCamelCase      ,				_UpperCamelCase							)

    _snake_case   :				Optional[Any]														=	tokens + [tokenizer.unk_token]
    _snake_case   :				Dict														=	[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase							)      ,				_UpperCamelCase							)







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


    '''simple docstring'''





    _snake_case   :				Optional[int]														=	self.get_tokenizer()

    self.assertListEqual(tokenizer.encode('Hello world!'      ,				add_special_tokens=_UpperCamelCase							)      ,				[0, 3_14_14, 2_32, 3_28, 2]							)
    self.assertListEqual(
        tokenizer.encode('Hello world! cécé herlolip 418'      ,				add_special_tokens=_UpperCamelCase							)      ,				[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]      ,				)







  @slow
  def UpperCamelCase_ (      self      :					List[Any]							):


    '''simple docstring'''





    _snake_case   :				int														=	self.tokenizer_class.from_pretrained('roberta-base'							)

    _snake_case   :				Optional[Any]														=	tokenizer.encode('sequence builders'      ,				add_special_tokens=_UpperCamelCase							)
    _snake_case   :				Optional[int]														=	tokenizer.encode('multi-sequence build'      ,				add_special_tokens=_UpperCamelCase							)

    _snake_case   :				Dict														=	tokenizer.encode(
        'sequence builders'      ,				add_special_tokens=_UpperCamelCase      ,				add_prefix_space=_UpperCamelCase							)
    _snake_case   :				Union[str, Any]														=	tokenizer.encode(
        'sequence builders'      ,				'multi-sequence build'      ,				add_special_tokens=_UpperCamelCase      ,				add_prefix_space=_UpperCamelCase							)

    _snake_case   :				List[str]														=	tokenizer.build_inputs_with_special_tokens(_UpperCamelCase							)
    _snake_case   :				Dict														=	tokenizer.build_inputs_with_special_tokens(_UpperCamelCase      ,				_UpperCamelCase							)

    assert encoded_sentence == encoded_text_from_decode
    assert encoded_pair == encoded_pair_from_decode







  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    _snake_case   :				str														=	self.get_tokenizer()

    _snake_case   :				List[Any]														=	"""Encode this sequence."""
    _snake_case   :				Any														=	tokenizer.byte_encoder[""" """.encode('utf-8'							)[0]]

    # Testing encoder arguments
    _snake_case   :				Any														=	tokenizer.encode(_UpperCamelCase      ,				add_special_tokens=_UpperCamelCase      ,				add_prefix_space=_UpperCamelCase							)
    _snake_case   :				int														=	tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
    self.assertNotEqual(_UpperCamelCase      ,				_UpperCamelCase							)

    _snake_case   :				List[str]														=	tokenizer.encode(_UpperCamelCase      ,				add_special_tokens=_UpperCamelCase      ,				add_prefix_space=_UpperCamelCase							)
    _snake_case   :				int														=	tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
    self.assertEqual(_UpperCamelCase      ,				_UpperCamelCase							)

    tokenizer.add_special_tokens({'bos_token': '<s>'}							)
    _snake_case   :				Optional[Any]														=	tokenizer.encode(_UpperCamelCase      ,				add_special_tokens=_UpperCamelCase							)
    _snake_case   :				Any														=	tokenizer.convert_ids_to_tokens(encoded[1]							)[0]
    self.assertNotEqual(_UpperCamelCase      ,				_UpperCamelCase							)

    # Testing spaces after special tokens
    _snake_case   :				Optional[Any]														=	"""<mask>"""
    tokenizer.add_special_tokens(
        {'mask_token': AddedToken(_UpperCamelCase      ,				lstrip=_UpperCamelCase      ,				rstrip=_UpperCamelCase							)}							)  # mask token has a left space
    _snake_case   :				int														=	tokenizer.convert_tokens_to_ids(_UpperCamelCase							)

    _snake_case   :				str														=	"""Encode <mask> sequence"""
    _snake_case   :				Union[str, Any]														=	"""Encode <mask>sequence"""

    _snake_case   :				str														=	tokenizer.encode(_UpperCamelCase							)
    _snake_case   :				Optional[int]														=	encoded.index(_UpperCamelCase							)
    _snake_case   :				Optional[Any]														=	tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
    self.assertEqual(_UpperCamelCase      ,				_UpperCamelCase							)

    _snake_case   :				Any														=	tokenizer.encode(_UpperCamelCase							)
    _snake_case   :				Any														=	encoded.index(_UpperCamelCase							)
    _snake_case   :				Dict														=	tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
    self.assertNotEqual(_UpperCamelCase      ,				_UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    pass







  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
      with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
        _snake_case   :				Union[str, Any]														=	self.rust_tokenizer_class.from_pretrained(_UpperCamelCase      ,				**_UpperCamelCase							)
        _snake_case   :				Dict														=	self.tokenizer_class.from_pretrained(_UpperCamelCase      ,				**_UpperCamelCase							)
        _snake_case   :				str														=	"""A, <mask> AllenNLP sentence."""
        _snake_case   :				int														=	tokenizer_r.encode_plus(_UpperCamelCase      ,				add_special_tokens=_UpperCamelCase      ,				return_token_type_ids=_UpperCamelCase							)
        _snake_case   :				Union[str, Any]														=	tokenizer_p.encode_plus(_UpperCamelCase      ,				add_special_tokens=_UpperCamelCase      ,				return_token_type_ids=_UpperCamelCase							)

        # 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']							)      ,				)

        _snake_case   :				Any														=	tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']							)
        _snake_case   :				Optional[Any]														=	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_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>']							)







  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





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

      _snake_case   :				List[str]														=	json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()							)
      _snake_case   :				Optional[int]														=	json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()							)

      self.assertEqual(pre_tokenizer_state['add_prefix_space']      ,				_UpperCamelCase							)

      self.assertEqual(post_processor_state['add_prefix_space']      ,				_UpperCamelCase							)
      self.assertEqual(post_processor_state['trim_offsets']      ,				_UpperCamelCase							)







  def UpperCamelCase_ (      self      :					str							):


    '''simple docstring'''





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

        _snake_case   :				int														=	self.rust_tokenizer_class.from_pretrained(
            _UpperCamelCase      ,				use_fast=_UpperCamelCase      ,				add_prefix_space=_UpperCamelCase      ,				trim_offsets=_UpperCamelCase							)
        _snake_case   :				List[Any]														=	tokenizer_r(_UpperCamelCase      ,				return_offsets_mapping=_UpperCamelCase      ,				add_special_tokens=_UpperCamelCase							)
        self.assertEqual(encoding.offset_mapping[0]      ,				(0, len(_UpperCamelCase							))							)
        self.assertEqual(
            encoding.offset_mapping[1]      ,				(len(_UpperCamelCase							) + 1, len(_UpperCamelCase							) + 1 + len(_UpperCamelCase							))      ,				)

        _snake_case   :				Optional[Any]														=	self.rust_tokenizer_class.from_pretrained(
            _UpperCamelCase      ,				use_fast=_UpperCamelCase      ,				add_prefix_space=_UpperCamelCase      ,				trim_offsets=_UpperCamelCase							)
        _snake_case   :				Optional[Any]														=	tokenizer_r(_UpperCamelCase      ,				return_offsets_mapping=_UpperCamelCase      ,				add_special_tokens=_UpperCamelCase							)
        self.assertEqual(encoding.offset_mapping[0]      ,				(0, len(_UpperCamelCase							))							)
        self.assertEqual(
            encoding.offset_mapping[1]      ,				(len(_UpperCamelCase							) + 1, len(_UpperCamelCase							) + 1 + len(_UpperCamelCase							))      ,				)

        _snake_case   :				Optional[Any]														=	self.rust_tokenizer_class.from_pretrained(
            _UpperCamelCase      ,				use_fast=_UpperCamelCase      ,				add_prefix_space=_UpperCamelCase      ,				trim_offsets=_UpperCamelCase							)
        _snake_case   :				Optional[int]														=	tokenizer_r(_UpperCamelCase      ,				return_offsets_mapping=_UpperCamelCase      ,				add_special_tokens=_UpperCamelCase							)
        self.assertEqual(encoding.offset_mapping[0]      ,				(0, len(_UpperCamelCase							))							)
        self.assertEqual(
            encoding.offset_mapping[1]      ,				(len(_UpperCamelCase							), len(_UpperCamelCase							) + 1 + len(_UpperCamelCase							))      ,				)

        _snake_case   :				Union[str, Any]														=	self.rust_tokenizer_class.from_pretrained(
            _UpperCamelCase      ,				use_fast=_UpperCamelCase      ,				add_prefix_space=_UpperCamelCase      ,				trim_offsets=_UpperCamelCase							)
        _snake_case   :				Optional[int]														=	tokenizer_r(_UpperCamelCase      ,				return_offsets_mapping=_UpperCamelCase      ,				add_special_tokens=_UpperCamelCase							)
        self.assertEqual(encoding.offset_mapping[0]      ,				(0, len(_UpperCamelCase							))							)
        self.assertEqual(
            encoding.offset_mapping[1]      ,				(len(_UpperCamelCase							), len(_UpperCamelCase							) + 1 + len(_UpperCamelCase							))      ,				)

        _snake_case   :				Union[str, Any]														=	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)),
        # )

        _snake_case   :				Union[str, Any]														=	self.rust_tokenizer_class.from_pretrained(
            _UpperCamelCase      ,				use_fast=_UpperCamelCase      ,				add_prefix_space=_UpperCamelCase      ,				trim_offsets=_UpperCamelCase							)
        _snake_case   :				List[str]														=	tokenizer_r(_UpperCamelCase      ,				return_offsets_mapping=_UpperCamelCase      ,				add_special_tokens=_UpperCamelCase							)
        self.assertEqual(encoding.offset_mapping[0]      ,				(1, 1 + len(_UpperCamelCase							))							)
        self.assertEqual(
            encoding.offset_mapping[1]      ,				(1 + len(_UpperCamelCase							) + 1, 1 + len(_UpperCamelCase							) + 1 + len(_UpperCamelCase							))      ,				)

        _snake_case   :				Union[str, Any]														=	self.rust_tokenizer_class.from_pretrained(
            _UpperCamelCase      ,				use_fast=_UpperCamelCase      ,				add_prefix_space=_UpperCamelCase      ,				trim_offsets=_UpperCamelCase							)
        _snake_case   :				int														=	tokenizer_r(_UpperCamelCase      ,				return_offsets_mapping=_UpperCamelCase      ,				add_special_tokens=_UpperCamelCase							)
        self.assertEqual(encoding.offset_mapping[0]      ,				(0, 1 + len(_UpperCamelCase							))							)
        self.assertEqual(
            encoding.offset_mapping[1]      ,				(1 + len(_UpperCamelCase							), 1 + len(_UpperCamelCase							) + 1 + len(_UpperCamelCase							))      ,				)

        _snake_case   :				Dict														=	self.rust_tokenizer_class.from_pretrained(
            _UpperCamelCase      ,				use_fast=_UpperCamelCase      ,				add_prefix_space=_UpperCamelCase      ,				trim_offsets=_UpperCamelCase							)
        _snake_case   :				Tuple														=	tokenizer_r(_UpperCamelCase      ,				return_offsets_mapping=_UpperCamelCase      ,				add_special_tokens=_UpperCamelCase							)
        self.assertEqual(encoding.offset_mapping[0]      ,				(0, 1 + len(_UpperCamelCase							))							)
        self.assertEqual(
            encoding.offset_mapping[1]      ,				(1 + len(_UpperCamelCase							), 1 + len(_UpperCamelCase							) + 1 + len(_UpperCamelCase							))      ,				)











import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def     lowerCamelCase_	(      lowerCAmelCase:	Tuple    ,							lowerCAmelCase:	bool = True    ,							lowerCAmelCase:	float = math.inf    ,							lowerCAmelCase:	float = -math.inf    ,							lowerCAmelCase:	float = math.inf    ,							lowerCAmelCase:	float = -math.inf    ,							lowerCAmelCase:	bool = False    ,							lowerCAmelCase:	float = 1_00    ,							lowerCAmelCase:	float = 0.0_1    ,							lowerCAmelCase:	float = 1    ,							)->		Any:
  _snake_case   :				int														=	False
  _snake_case   :				Any														=	search_prob
  _snake_case   :				Tuple														=	start_temperate
  _snake_case   :				Any														=	[]
  _snake_case   :				List[str]														=	0
  _snake_case   :				Optional[Any]														=	None

  while not search_end:
    _snake_case   :				List[Any]														=	current_state.score()
    if best_state is None or current_score > best_state.score():
      _snake_case   :				Dict														=	current_state
    scores.append(lowerCAmelCase				)
    iterations += 1
    _snake_case   :				Optional[int]														=	None
    _snake_case   :				Union[str, Any]														=	current_state.get_neighbors()
    while (
        next_state is None and neighbors
    ):  # till we do not find a neighbor that we can move to
      _snake_case   :				Dict														=	random.randint(0    ,							len(lowerCAmelCase				) - 1				)  # picking a random neighbor
      _snake_case   :				int														=	neighbors.pop(lowerCAmelCase				)
      _snake_case   :				Union[str, Any]														=	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:
        _snake_case   :				Union[str, Any]														=	change * -1  # in case we are finding minimum
      if change > 0:  # improves the solution
        _snake_case   :				Union[str, Any]														=	picked_neighbor
      else:
        _snake_case   :				Optional[Any]														=	(math.e) ** (
            change / current_temp
        )  # probability generation function
        if random.random() < probability:  # random number within probability
          _snake_case   :				int														=	picked_neighbor
    _snake_case   :				List[Any]														=	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
      _snake_case   :				List[str]														=	True
    else:
      _snake_case   :				Union[str, Any]														=	next_state

  if visualization:
    from matplotlib import pyplot as plt

    plt.plot(range(lowerCAmelCase				)    ,							lowerCAmelCase				)
    plt.xlabel('Iterations'				)
    plt.ylabel('Function values'				)
    plt.show()
  return best_state


if __name__ == "__main__":


      def     lowerCamelCase_	(      lowerCAmelCase:	Any    ,							lowerCAmelCase:	List[Any]				)->		List[Any]:
        return (x**2) + (y**2)

      # starting the problem with initial coordinates (12, 47)
      lowerCAmelCase_						   =    SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
      lowerCAmelCase_						   =    simulated_annealing(
          prob, find_max=False, max_x=100, 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)
      lowerCAmelCase_						   =    SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
      lowerCAmelCase_						   =    simulated_annealing(
          prob, find_max=True, max_x=100, 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     lowerCamelCase_	(      lowerCAmelCase:	Any    ,							lowerCAmelCase:	Dict				)->		Dict:
        return (3 * x**2) - (6 * y)

      lowerCAmelCase_						   =    SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      lowerCAmelCase_						   =    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()}"""
      )

      lowerCAmelCase_						   =    SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      lowerCAmelCase_						   =    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()}"""
      )




