





'''simple docstring'''

from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__lowerCAmelCase							:	str			=200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
__lowerCAmelCase							:	Union[str, Any]			=50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
__lowerCAmelCase							:	Dict			=0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))





def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					str       ):
    A__				=			len([g for position, g in enumerate(_lowerCamelCase       ) if g == main_target[position]]       )
    return (item, float(_lowerCamelCase       ))





def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					str       ):
    A__				=			random.randint(0						,       len(_lowerCamelCase       ) - 1       )
    A__				=			parent_a[:random_slice] + parent_a[random_slice:]
    A__				=			parent_a[:random_slice] + parent_a[random_slice:]
    return (child_a, child_a)





def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					list[str]       ):
    A__				=			list(_lowerCamelCase       )
    if random.uniform(0						,       1       ) < MUTATION_PROBABILITY:
        A__				=			random.choice(_lowerCamelCase       )
    return "".join(_lowerCamelCase       )





def 						UpperCamelCase				(							_lowerCamelCase					:					tuple[str, float]						,       _lowerCamelCase					:					list[tuple[str, float]]						,       _lowerCamelCase					:					list[str]						,       ):
    A__				=			[]
    # Generate more children proportionally to the fitness score.
    A__				=			int(parent_a[1] * 1_00       ) + 1
    A__				=			10 if child_n >= 10 else child_n
    for _ in range(_lowerCamelCase       ):
        A__				=			population_score[random.randint(0						,       _lowerCamelCase       )][0]

        A__,	A__				=			crossover(parent_a[0]						,       _lowerCamelCase       )
        # Append new string to the population list.
        pop.append(mutate(_lowerCamelCase						,       _lowerCamelCase       )       )
        pop.append(mutate(_lowerCamelCase						,       _lowerCamelCase       )       )
    return pop





def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					list[str]						,       _lowerCamelCase					:					bool = True       ):

    # Verify if N_POPULATION is bigger than N_SELECTED
    if N_POPULATION < N_SELECTED:
        A__				=			F"{N_POPULATION} must be bigger than {N_SELECTED}"
        raise ValueError(_lowerCamelCase       )
    # Verify that the target contains no genes besides the ones inside genes variable.
    A__				=			sorted({c for c in target if c not in genes}       )
    if not_in_genes_list:
        A__				=			F"{not_in_genes_list} is not in genes list, evolution cannot converge"
        raise ValueError(_lowerCamelCase       )

    # Generate random starting population.
    A__				=			[]
    for _ in range(_lowerCamelCase       ):
        population.append("".join([random.choice(_lowerCamelCase       ) for i in range(len(_lowerCamelCase       )       )]       )       )

    # Just some logs to know what the algorithms is doing.
    A__,	A__				=			0, 0

    # This loop will end when we find a perfect match for our target.
    while True:
        generation += 1
        total_population += len(_lowerCamelCase       )

        # Random population created. Now it's time to evaluate.

        # Adding a bit of concurrency can make everything faster,
        #
        # import concurrent.futures
        # population_score: list[tuple[str, float]] = []
        # with concurrent.futures.ThreadPoolExecutor(
        #                                   max_workers=NUM_WORKERS) as executor:
        #     futures = {executor.submit(evaluate, item) for item in population}
        #     concurrent.futures.wait(futures)
        #     population_score = [item.result() for item in futures]
        #
        # but with a simple algorithm like this, it will probably be slower.
        # We just need to call evaluate for every item inside the population.
        A__				=			[evaluate(_lowerCamelCase						,       _lowerCamelCase       ) for item in population]

        # Check if there is a matching evolution.
        A__				=			sorted(_lowerCamelCase						,       key=lambda _lowerCamelCase       : x[1]						,       reverse=_lowerCamelCase       )
        if population_score[0][0] == target:
            return (generation, total_population, population_score[0][0])

        # Print the best result every 10 generation.
        # Just to know that the algorithm is working.
        if debug and generation % 10 == 0:
            print(
                F"\nGeneration: {generation}"
                F"\nTotal Population:{total_population}"
                F"\nBest score: {population_score[0][1]}"
                F"\nBest string: {population_score[0][0]}"       )

        # Flush the old population, keeping some of the best evolutions.
        # Keeping this avoid regression of evolution.
        A__				=			population[: int(N_POPULATION / 3       )]
        population.clear()
        population.extend(_lowerCamelCase       )
        # Normalize population score to be between 0 and 1.
        A__				=			[
            (item, score / len(_lowerCamelCase       )) for item, score in population_score
        ]

        # This is selection
        for i in range(_lowerCamelCase       ):
            population.extend(select(population_score[int(_lowerCamelCase       )]						,       _lowerCamelCase						,       _lowerCamelCase       )       )
            # Check if the population has already reached the maximum value and if so,
            # break the cycle.  If this check is disabled, the algorithm will take
            # forever to compute large strings, but will also calculate small strings in
            # a far fewer generations.
            if len(_lowerCamelCase       ) > N_POPULATION:
                break


if __name__ == "__main__":
  __lowerCAmelCase							:	Any			=(
      "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
  )
  __lowerCAmelCase							:	str			=list(
      " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
      "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
  )
  __lowerCAmelCase	,     __lowerCAmelCase	,     __lowerCAmelCase							:	Dict			=basic(target_str, genes_list)
  print(
      f"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
  )










'''simple docstring'''

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    UpperCAmelCase	(       unittest.TestCase				):
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							Tuple:
     A__				=			tempfile.mkdtemp()

     # fmt: off
     A__				=			["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
     A__				=			dict(zip(lowercase_						,       range(len(lowercase_							)							)							)							)
     A__				=			["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
     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(lowercase_							) + "\n"							)
     with open(self.merges_file						,       "w"						,       encoding="utf-8"							) as fp:
         fp.write("\n".join(lowercase_							)							)

     A__				=			{
         "do_resize": True,
         "size": 20,
         "do_center_crop": True,
         "crop_size": 18,
         "do_normalize": True,
         "image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
         "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
     }
     A__				=			os.path.join(self.tmpdirname						,       lowercase_							)
     with open(self.image_processor_file						,       "w"						,       encoding="utf-8"							) as fp:
         json.dump(lowercase_						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :Any						,       **lowercase_      :Union[str, Any]							)->							Tuple:
     return CLIPTokenizer.from_pretrained(self.tmpdirname						,       **lowercase_							)
 def      UpperCAmelCase_							(				self      :Any						,       **lowercase_      :Tuple							)->							Dict:
     return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,       **lowercase_							)
 def      UpperCAmelCase_							(				self      :Dict						,       **lowercase_      :Union[str, Any]							)->							Any:
     return CLIPImageProcessor.from_pretrained(self.tmpdirname						,       **lowercase_							)
 def      UpperCAmelCase_							(				self      :List[str]							)->							int:
     shutil.rmtree(self.tmpdirname							)
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Optional[int]:
     A__				=			[np.random.randint(2_55						,       size=(3, 30, 4_00)						,       dtype=np.uinta							)]

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

     return image_inputs
 def      UpperCAmelCase_							(				self      :int							)->							List[Any]:
     A__				=			self.get_tokenizer()
     A__				=			self.get_rust_tokenizer()
     A__				=			self.get_image_processor()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)
     processor_slow.save_pretrained(self.tmpdirname							)
     A__				=			CLIPProcessor.from_pretrained(self.tmpdirname						,       use_fast=lowercase_							)

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)
     processor_fast.save_pretrained(self.tmpdirname							)
     A__				=			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						,       lowercase_							)
     self.assertIsInstance(processor_fast.tokenizer						,       lowercase_							)

     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						,       lowercase_							)
     self.assertIsInstance(processor_fast.image_processor						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							Optional[int]:
     A__				=			CLIPProcessor(tokenizer=self.get_tokenizer()						,       image_processor=self.get_image_processor()							)
     processor.save_pretrained(self.tmpdirname							)

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

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

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

     self.assertEqual(processor.image_processor.to_json_string()						,       image_processor_add_kwargs.to_json_string()							)
     self.assertIsInstance(processor.image_processor						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Tuple:
     A__				=			self.get_image_processor()
     A__				=			self.get_tokenizer()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)

     A__				=			self.prepare_image_inputs()

     A__				=			image_processor(lowercase_						,       return_tensors="np"							)
     A__				=			processor(images=lowercase_						,       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      UpperCAmelCase_							(				self      :Optional[int]							)->							Dict:
     A__				=			self.get_image_processor()
     A__				=			self.get_tokenizer()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)

     A__				=			"lower newer"

     A__				=			processor(text=lowercase_							)

     A__				=			tokenizer(lowercase_							)

     for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]						,       encoded_processor[key]							)
 def      UpperCAmelCase_							(				self      :str							)->							Any:
     A__				=			self.get_image_processor()
     A__				=			self.get_tokenizer()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)

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

     A__				=			processor(text=lowercase_						,       images=lowercase_							)

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

     # test if it raises when no input is passed
     with pytest.raises(lowercase_							):
         processor()
 def      UpperCAmelCase_							(				self      :Tuple							)->							Tuple:
     A__				=			self.get_image_processor()
     A__				=			self.get_tokenizer()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)

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

     A__				=			processor.batch_decode(lowercase_							)
     A__				=			tokenizer.batch_decode(lowercase_							)

     self.assertListEqual(lowercase_						,       lowercase_							)



 def      UpperCAmelCase_							(				self      :List[Any]							)->							Dict:
     A__				=			self.get_image_processor()
     A__				=			self.get_tokenizer()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)

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

     A__				=			processor(text=lowercase_						,       images=lowercase_							)

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



