

import math
import random
from typing import Any

from .hill_climbing import SearchProblem







def 						lowerCamelCase__      ( __A    :Dict	,__A    :bool = True	,__A    :float = math.inf	,__A    :float = -math.inf	,__A    :float = math.inf	,__A    :float = -math.inf	,__A    :bool = False	,__A    :float = 1_0_0	,__A    :float = 0.01	,__A    :float = 1	,):


       """simple docstring"""




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

       while not search_end:
              __snake_case       	=							current_state.score()
              if best_state is None or current_score > best_state.score():
                     __snake_case       	=							current_state
              scores.append(__A		)
              iterations += 1
              __snake_case       	=							None
              __snake_case       	=							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       	=							random.randint(0	,len(__A		) - 1		)  # picking a random neighbor
                     __snake_case       	=							neighbors.pop(__A		)
                     __snake_case       	=							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       	=							change * -1  # in case we are finding minimum
                     if change > 0:  # improves the solution
                            __snake_case       	=							picked_neighbor
                     else:
                            __snake_case       	=							(math.e) ** (
                                change / current_temp
                            )  # probability generation function
                            if random.random() < probability:  # random number within probability
                                   __snake_case       	=							picked_neighbor
              __snake_case       	=							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       	=							True
              else:
                     __snake_case       	=							next_state

       if visualization:
              from matplotlib import pyplot as plt

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


if __name__ == "__main__":







    def 						lowerCamelCase__      ( __A    :Optional[Any]	,__A    :Optional[Any]		):


           """simple docstring"""




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

    # starting the problem with initial coordinates (12, 47)
    UpperCamelCase__		 =     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    UpperCamelCase__		 =     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)
    UpperCamelCase__		 =     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    UpperCamelCase__		 =     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__      ( __A    :int	,__A    :Any		):


           """simple docstring"""




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

    UpperCamelCase__		 =     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
    UpperCamelCase__		 =     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()}'
    )

    UpperCamelCase__		 =     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
    UpperCamelCase__		 =     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()}'
    )




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import CLIPImageProcessor, CLIPProcessor

@require_vision
class     __snake_case    (				unittest.TestCase  ):
       """simple docstring"""







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






              """simple docstring"""


              __snake_case       	=							tempfile.mkdtemp()

              # fmt: off
              __snake_case       	=							["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
              # fmt: on
              __snake_case       	=							dict(zip(_UpperCamelCase      , range(len(_UpperCamelCase   )   )   )   )
              __snake_case       	=							["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
              __snake_case       	=							{"""unk_token""": """<unk>"""}

              __snake_case       	=							os.path.join(self.tmpdirname      , VOCAB_FILES_NAMES["""vocab_file"""]   )
              __snake_case       	=							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   )   )

              __snake_case       	=							{
                  """do_resize""": True,
                  """size""": 20,
                  """do_center_crop""": True,
                  """crop_size""": 18,
                  """do_normalize""": True,
                  """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
                  """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
              }
              __snake_case       	=							os.path.join(self.tmpdirname      , _UpperCamelCase   )
              with open(self.image_processor_file      , """w"""      , encoding="""utf-8"""   ) as fp:
                     json.dump(_UpperCamelCase      , _UpperCamelCase   )


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






              """simple docstring"""


              return CLIPTokenizer.from_pretrained(self.tmpdirname      , **_UpperCamelCase   )


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






              """simple docstring"""


              return CLIPTokenizerFast.from_pretrained(self.tmpdirname      , **_UpperCamelCase   )


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






              """simple docstring"""


              return CLIPImageProcessor.from_pretrained(self.tmpdirname      , **_UpperCamelCase   )


       def 			a		(		self   )      ->							int:






              """simple docstring"""


              shutil.rmtree(self.tmpdirname   )


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






              """simple docstring"""


              __snake_case       	=							[np.random.randint(2_55      , size=(3, 30, 4_00)      , dtype=np.uinta   )]

              __snake_case       	=							[Image.fromarray(np.moveaxis(_UpperCamelCase      , 0      , -1   )   ) for x in image_inputs]

              return image_inputs


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






              """simple docstring"""


              __snake_case       	=							self.get_tokenizer()
              __snake_case       	=							self.get_rust_tokenizer()
              __snake_case       	=							self.get_image_processor()

              __snake_case       	=							CLIPProcessor(tokenizer=_UpperCamelCase      , image_processor=_UpperCamelCase   )
              processor_slow.save_pretrained(self.tmpdirname   )
              __snake_case       	=							CLIPProcessor.from_pretrained(self.tmpdirname      , use_fast=_UpperCamelCase   )

              __snake_case       	=							CLIPProcessor(tokenizer=_UpperCamelCase      , image_processor=_UpperCamelCase   )
              processor_fast.save_pretrained(self.tmpdirname   )
              __snake_case       	=							CLIPProcessor.from_pretrained(self.tmpdirname   )

              self.assertEqual(processor_slow.tokenizer.get_vocab()      , tokenizer_slow.get_vocab()   )
              self.assertEqual(processor_fast.tokenizer.get_vocab()      , tokenizer_fast.get_vocab()   )
              self.assertEqual(tokenizer_slow.get_vocab()      , tokenizer_fast.get_vocab()   )
              self.assertIsInstance(processor_slow.tokenizer      , _UpperCamelCase   )
              self.assertIsInstance(processor_fast.tokenizer      , _UpperCamelCase   )

              self.assertEqual(processor_slow.image_processor.to_json_string()      , image_processor.to_json_string()   )
              self.assertEqual(processor_fast.image_processor.to_json_string()      , image_processor.to_json_string()   )
              self.assertIsInstance(processor_slow.image_processor      , _UpperCamelCase   )
              self.assertIsInstance(processor_fast.image_processor      , _UpperCamelCase   )


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






              """simple docstring"""


              __snake_case       	=							CLIPProcessor(tokenizer=self.get_tokenizer()      , image_processor=self.get_image_processor()   )
              processor.save_pretrained(self.tmpdirname   )

              __snake_case       	=							self.get_tokenizer(bos_token="""(BOS)"""      , eos_token="""(EOS)"""   )
              __snake_case       	=							self.get_image_processor(do_normalize=_UpperCamelCase      , padding_value=1.0   )

              __snake_case       	=							CLIPProcessor.from_pretrained(
                  self.tmpdirname      , bos_token="""(BOS)"""      , eos_token="""(EOS)"""      , do_normalize=_UpperCamelCase      , padding_value=1.0   )

              self.assertEqual(processor.tokenizer.get_vocab()      , tokenizer_add_kwargs.get_vocab()   )
              self.assertIsInstance(processor.tokenizer      , _UpperCamelCase   )

              self.assertEqual(processor.image_processor.to_json_string()      , image_processor_add_kwargs.to_json_string()   )
              self.assertIsInstance(processor.image_processor      , _UpperCamelCase   )


       def 			a		(		self   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							self.get_image_processor()
              __snake_case       	=							self.get_tokenizer()

              __snake_case       	=							CLIPProcessor(tokenizer=_UpperCamelCase      , image_processor=_UpperCamelCase   )

              __snake_case       	=							self.prepare_image_inputs()

              __snake_case       	=							image_processor(_UpperCamelCase      , return_tensors="""np"""   )
              __snake_case       	=							processor(images=_UpperCamelCase      , return_tensors="""np"""   )

              for key in input_image_proc.keys():
                     self.assertAlmostEqual(input_image_proc[key].sum()      , input_processor[key].sum()      , delta=1E-2   )


       def 			a		(		self   )      ->							str:






              """simple docstring"""


              __snake_case       	=							self.get_image_processor()
              __snake_case       	=							self.get_tokenizer()

              __snake_case       	=							CLIPProcessor(tokenizer=_UpperCamelCase      , image_processor=_UpperCamelCase   )

              __snake_case       	=							"""lower newer"""

              __snake_case       	=							processor(text=_UpperCamelCase   )

              __snake_case       	=							tokenizer(_UpperCamelCase   )

              for key in encoded_tok.keys():
                     self.assertListEqual(encoded_tok[key]      , encoded_processor[key]   )


       def 			a		(		self   )      ->							str:






              """simple docstring"""


              __snake_case       	=							self.get_image_processor()
              __snake_case       	=							self.get_tokenizer()

              __snake_case       	=							CLIPProcessor(tokenizer=_UpperCamelCase      , image_processor=_UpperCamelCase   )

              __snake_case       	=							"""lower newer"""
              __snake_case       	=							self.prepare_image_inputs()

              __snake_case       	=							processor(text=_UpperCamelCase      , images=_UpperCamelCase   )

              self.assertListEqual(list(inputs.keys()   )      , ["""input_ids""", """attention_mask""", """pixel_values"""]   )

              # test if it raises when no input is passed
              with pytest.raises(_UpperCamelCase   ):
                     processor()


       def 			a		(		self   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							self.get_image_processor()
              __snake_case       	=							self.get_tokenizer()

              __snake_case       	=							CLIPProcessor(tokenizer=_UpperCamelCase      , image_processor=_UpperCamelCase   )

              __snake_case       	=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

              __snake_case       	=							processor.batch_decode(_UpperCamelCase   )
              __snake_case       	=							tokenizer.batch_decode(_UpperCamelCase   )

              self.assertListEqual(_UpperCamelCase      , _UpperCamelCase   )






       def 			a		(		self   )      ->							int:






              """simple docstring"""


              __snake_case       	=							self.get_image_processor()
              __snake_case       	=							self.get_tokenizer()

              __snake_case       	=							CLIPProcessor(tokenizer=_UpperCamelCase      , image_processor=_UpperCamelCase   )

              __snake_case       	=							"""lower newer"""
              __snake_case       	=							self.prepare_image_inputs()

              __snake_case       	=							processor(text=_UpperCamelCase      , images=_UpperCamelCase   )

              self.assertListEqual(list(inputs.keys()   )      , processor.model_input_names   )

