import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

__lowercase							 =					logging.getLogger()



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		argparse.ArgumentParser()
    parser.add_argument('''-f'''	)
    __UpperCamelCase     :Any			  =		parser.parse_args()
    return args.f



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, Any]			  =		{}
    __UpperCamelCase     :str			  =		os.path.join(SCREAMING_SNAKE_CASE  ,			'''all_results.json'''	)
    if os.path.exists(SCREAMING_SNAKE_CASE	):
        with open(SCREAMING_SNAKE_CASE  ,			'''r'''	) as f:
            __UpperCamelCase     :Optional[Any]			  =		json.load(SCREAMING_SNAKE_CASE	)
    else:
        raise ValueError(f"""can't find {path}"""	)
    return results



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :Optional[Any]			  =		torch.cuda.is_available() and torch_device == '''cuda'''
    return is_using_cuda and is_apex_available()


__lowercase							 =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    @classmethod
    def 			UpperCamelCase__   (	cls)       ->	int:
        # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
        __UpperCamelCase     :List[Any]			  =		tempfile.mkdtemp()
        __UpperCamelCase     :List[Any]			  =		os.path.join(cls.tmpdir , '''default_config.yml''')
        write_basic_config(save_location=cls.configPath)
        __UpperCamelCase     :int			  =		['''accelerate''', '''launch''', '''--config_file''', cls.configPath]







    @classmethod
    def 			UpperCamelCase__   (	cls)       ->	Union[str, Any]:
        shutil.rmtree(cls.tmpdir)







    @mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''})
    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :List[str]			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :Any			  =		f"""
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        if is_cuda_and_apex_available():
            testargs.append('''--fp16''')

        run_command(self._launch_args + testargs)
        __UpperCamelCase     :Optional[Any]			  =		get_results(__lowercase)
        self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75)
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''epoch_0''')))
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''glue_no_trainer''')))







    @mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''})
    def 			UpperCamelCase__   (	self)       ->	int:
        __UpperCamelCase     :Any			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :List[str]			  =		f"""
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        if torch.cuda.device_count() > 1:
            # Skipping because there are not enough batches to train the model + would need a drop_last to work.
            return

        run_command(self._launch_args + testargs)
        __UpperCamelCase     :Dict			  =		get_results(__lowercase)
        self.assertLess(result['''perplexity'''] , 100)
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''epoch_0''')))
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''clm_no_trainer''')))







    @mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''})
    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :List[Any]			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :List[Any]			  =		f"""
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs)
        __UpperCamelCase     :Union[str, Any]			  =		get_results(__lowercase)
        self.assertLess(result['''perplexity'''] , 42)
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''epoch_0''')))
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''mlm_no_trainer''')))







    @mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''})
    def 			UpperCamelCase__   (	self)       ->	Tuple:
        # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
        __UpperCamelCase     :Union[str, Any]			  =		7 if get_gpu_count() > 1 else 2

        __UpperCamelCase     :List[Any]			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :List[Any]			  =		f"""
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs)
        __UpperCamelCase     :Optional[Any]			  =		get_results(__lowercase)
        self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75)
        self.assertLess(result['''train_loss'''] , 0.5)
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''epoch_0''')))
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''ner_no_trainer''')))







    @unittest.skip(reason='''Fix me @muellerzr''')
    @mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''})
    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :Union[str, Any]			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :int			  =		f"""
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs)
        __UpperCamelCase     :Any			  =		get_results(__lowercase)
        # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
        self.assertGreaterEqual(result['''eval_f1'''] , 28)
        self.assertGreaterEqual(result['''eval_exact'''] , 28)
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''epoch_0''')))
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''qa_no_trainer''')))







    @mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''})
    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :int			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :int			  =		f"""
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs)
        __UpperCamelCase     :Dict			  =		get_results(__lowercase)
        self.assertGreaterEqual(result['''eval_accuracy'''] , 0.8)
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''swag_no_trainer''')))







    @slow
    @mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''})
    def 			UpperCamelCase__   (	self)       ->	str:
        __UpperCamelCase     :str			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :Dict			  =		f"""
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs)
        __UpperCamelCase     :Tuple			  =		get_results(__lowercase)
        self.assertGreaterEqual(result['''eval_rouge1'''] , 10)
        self.assertGreaterEqual(result['''eval_rouge2'''] , 2)
        self.assertGreaterEqual(result['''eval_rougeL'''] , 7)
        self.assertGreaterEqual(result['''eval_rougeLsum'''] , 7)
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''epoch_0''')))
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''summarization_no_trainer''')))







    @slow
    @mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''})
    def 			UpperCamelCase__   (	self)       ->	str:
        __UpperCamelCase     :Optional[int]			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :Dict			  =		f"""
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs)
        __UpperCamelCase     :Optional[int]			  =		get_results(__lowercase)
        self.assertGreaterEqual(result['''eval_bleu'''] , 30)
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''epoch_0''')))
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''translation_no_trainer''')))







    @slow
    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :Tuple			  =		logging.StreamHandler(sys.stdout)
        logger.addHandler(__lowercase)

        __UpperCamelCase     :int			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :Any			  =		f"""
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        """.split()

        run_command(self._launch_args + testargs)
        __UpperCamelCase     :List[str]			  =		get_results(__lowercase)
        self.assertGreaterEqual(result['''eval_overall_accuracy'''] , 0.10)







    @mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''})
    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        __UpperCamelCase     :Union[str, Any]			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :Optional[Any]			  =		f"""
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        """.split()

        if is_cuda_and_apex_available():
            testargs.append('''--fp16''')

        run_command(self._launch_args + testargs)
        __UpperCamelCase     :Optional[Any]			  =		get_results(__lowercase)
        # The base model scores a 25%
        self.assertGreaterEqual(result['''eval_accuracy'''] , 0.6)
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''step_1''')))
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''image_classification_no_trainer''')))






from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__lowercase							 =					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.
__lowercase							 =					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.
__lowercase							 =					0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Any			  =		len([g for position, g in enumerate(SCREAMING_SNAKE_CASE	) if g == main_target[position]]	)
    return (item, float(SCREAMING_SNAKE_CASE	))



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Optional[Any]			  =		random.randint(0  ,			len(SCREAMING_SNAKE_CASE	) - 1	)
    __UpperCamelCase     :Tuple			  =		parent_a[:random_slice] + parent_a[random_slice:]
    __UpperCamelCase     :Union[str, Any]			  =		parent_a[:random_slice] + parent_a[random_slice:]
    return (child_a, child_a)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :int			  =		list(SCREAMING_SNAKE_CASE	)
    if random.uniform(0  ,			1	) < MUTATION_PROBABILITY:
        __UpperCamelCase     :str			  =		random.choice(SCREAMING_SNAKE_CASE	)
    return "".join(SCREAMING_SNAKE_CASE	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			):
    '''simple docstring'''







    __UpperCamelCase     :int			  =		[]
    # Generate more children proportionally to the fitness score.
    __UpperCamelCase     :int			  =		int(parent_a[1] * 100	) + 1
    __UpperCamelCase     :List[str]			  =		10 if child_n >= 10 else child_n
    for _ in range(SCREAMING_SNAKE_CASE	):
        __UpperCamelCase     :Optional[Any]			  =		population_score[random.randint(0  ,			SCREAMING_SNAKE_CASE	)][0]

        __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		crossover(parent_a[0]  ,			SCREAMING_SNAKE_CASE	)
        # Append new string to the population list.
        pop.append(mutate(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)	)
        pop.append(mutate(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)	)
    return pop



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = True	):
    '''simple docstring'''







    if N_POPULATION < N_SELECTED:
        __UpperCamelCase     :List[Any]			  =		f"""{N_POPULATION} must be bigger than {N_SELECTED}"""
        raise ValueError(SCREAMING_SNAKE_CASE	)
    # Verify that the target contains no genes besides the ones inside genes variable.
    __UpperCamelCase     :List[str]			  =		sorted({c for c in target if c not in genes}	)
    if not_in_genes_list:
        __UpperCamelCase     :Optional[int]			  =		f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
        raise ValueError(SCREAMING_SNAKE_CASE	)

    # Generate random starting population.
    __UpperCamelCase     :int			  =		[]
    for _ in range(SCREAMING_SNAKE_CASE	):
        population.append(''''''.join([random.choice(SCREAMING_SNAKE_CASE	) for i in range(len(SCREAMING_SNAKE_CASE	)	)]	)	)

    # Just some logs to know what the algorithms is doing.
    __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		0, 0

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

        # 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.
        __UpperCamelCase     :Tuple			  =		[evaluate(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	) for item in population]

        # Check if there is a matching evolution.
        __UpperCamelCase     :Tuple			  =		sorted(SCREAMING_SNAKE_CASE  ,			key=lambda SCREAMING_SNAKE_CASE	: x[1]  ,			reverse=SCREAMING_SNAKE_CASE	)
        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.
        __UpperCamelCase     :str			  =		population[: int(N_POPULATION / 3	)]
        population.clear()
        population.extend(SCREAMING_SNAKE_CASE	)
        # Normalize population score to be between 0 and 1.
        __UpperCamelCase     :Union[str, Any]			  =		[
            (item, score / len(SCREAMING_SNAKE_CASE	)) for item, score in population_score
        ]

        # This is selection
        for i in range(SCREAMING_SNAKE_CASE	):
            population.extend(select(population_score[int(SCREAMING_SNAKE_CASE	)]  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)	)
            # 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(SCREAMING_SNAKE_CASE	) > N_POPULATION:
                break


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





