import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


__lowercase							 =					os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'{bindir}/../../examples/pytorch/translation'):
    from run_translation import main  # noqa


set_seed(42)
__lowercase							 =					'''sshleifer/student_marian_en_ro_6_1'''
__lowercase							 =					'''sshleifer/tiny-mbart'''


@require_torch
class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    def 			UpperCamelCase__   (	self , __lowercase=False , __lowercase=None , __lowercase=True , __lowercase=True , __lowercase=True , __lowercase=True , )       ->	Optional[int]:
        __UpperCamelCase     :int			  =		self.run_trainer(
            eval_steps=1 , max_len=12 , model_name=__lowercase , num_train_epochs=1 , distributed=__lowercase , extra_args_str=__lowercase , predict_with_generate=__lowercase , do_train=__lowercase , do_eval=__lowercase , do_predict=__lowercase , )
        __UpperCamelCase     :int			  =		TrainerState.load_from_json(os.path.join(__lowercase , '''trainer_state.json''')).log_history

        if not do_eval:
            return

        __UpperCamelCase     :Union[str, Any]			  =		[log for log in logs if '''eval_loss''' in log.keys()]

        __UpperCamelCase     :Optional[int]			  =		eval_metrics[0]
        if predict_with_generate:
            assert "eval_bleu" in first_step_stats

            __UpperCamelCase     :Optional[Any]			  =		eval_metrics[-1]
            assert isinstance(last_step_stats['''eval_bleu'''] , __lowercase)
            assert not math.isnan(float(last_step_stats['''eval_loss'''])), "eval_loss must not be `nan`"







    @require_torch_non_multi_gpu
    def 			UpperCamelCase__   (	self)       ->	str:
        self.run_seqaseq_quick()







    @require_torch_multi_gpu
    def 			UpperCamelCase__   (	self)       ->	Any:
        self.run_seqaseq_quick(distributed=__lowercase)







    @require_torch_multi_gpu
    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        self.run_seqaseq_quick(distributed=__lowercase)







    @unittest.skip('''Requires an update of the env running those tests''')
    @require_torch_multi_gpu
    @require_fairscale
    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        self.run_seqaseq_quick(distributed=__lowercase , extra_args_str='''--sharded_ddp simple''')







    @unittest.skip('''Requires an update of the env running those tests''')
    @require_torch_multi_gpu
    @require_fairscale
    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        self.run_seqaseq_quick(distributed=__lowercase , extra_args_str='''--sharded_ddp simple --fp16''')







    @unittest.skip('''Requires an update of the env running those tests''')
    @require_torch_multi_gpu
    @require_fairscale
    def 			UpperCamelCase__   (	self)       ->	Any:
        self.run_seqaseq_quick(distributed=__lowercase , extra_args_str='''--sharded_ddp zero_dp_2''' , predict_with_generate=__lowercase)







    @unittest.skip('''Requires an update of the env running those tests''')
    @require_torch_multi_gpu
    @require_fairscale
    def 			UpperCamelCase__   (	self)       ->	Dict:
        self.run_seqaseq_quick(
            distributed=__lowercase , extra_args_str='''--sharded_ddp zero_dp_2 --fp16''' , predict_with_generate=__lowercase)







    @require_apex
    @require_torch_gpu
    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        # XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
        # program and it breaks other tests that run from the same pytest worker, therefore until this is
        # sorted out it must be run only in an external program, that is distributed=True in this
        # test and only under one or more gpus - if we want cpu will need to make a special test
        #
        # specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
        # 2nd main() call it botches the future eval.
        #
        self.run_seqaseq_quick(distributed=__lowercase , extra_args_str='''--fp16 --fp16_backend=apex''')
        # test 2nd time - was getting eval_loss': nan'
        # to reproduce the problem set distributed=False
        self.run_seqaseq_quick(distributed=__lowercase , extra_args_str='''--fp16 --fp16_backend=apex''')







    @parameterized.expand(['''base''', '''low''', '''high''', '''mixed'''])
    @require_torch_multi_gpu
    def 			UpperCamelCase__   (	self , __lowercase)       ->	str:
        # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
        __UpperCamelCase     :List[str]			  =		{
            # test with the default log_level - should be info and thus log info once
            '''base''': {'''extra_args_str''': '''''', '''n_matches''': 1},
            # test with low log_level and log_level_replica - should be noisy on all processes
            # now the info string should appear twice on 2 processes
            '''low''': {'''extra_args_str''': '''--log_level debug --log_level_replica debug''', '''n_matches''': 2},
            # test with high log_level and low log_level_replica
            # now the info string should appear once only on the replica
            '''high''': {'''extra_args_str''': '''--log_level error --log_level_replica debug''', '''n_matches''': 1},
            # test with high log_level and log_level_replica - should be quiet on all processes
            '''mixed''': {'''extra_args_str''': '''--log_level error --log_level_replica error''', '''n_matches''': 0},
        }

        __UpperCamelCase     :Union[str, Any]			  =		experiments[experiment_id]
        __UpperCamelCase     :List[Any]			  =		{'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
        __UpperCamelCase     :Any			  =		'''Running training'''
        with CaptureStderr() as cl:
            self.run_seqaseq_quick(**__lowercase , extra_args_str=data['''extra_args_str'''])
        __UpperCamelCase     :str			  =		len(re.findall(__lowercase , cl.err))
        self.assertEqual(__lowercase , data['''n_matches'''])







    @slow
    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :Optional[Any]			  =		self.run_trainer(
            eval_steps=2 , max_len=128 , model_name=__lowercase , learning_rate=3E-4 , num_train_epochs=10 , distributed=__lowercase , )

        # Check metrics
        __UpperCamelCase     :int			  =		TrainerState.load_from_json(os.path.join(__lowercase , '''trainer_state.json''')).log_history
        __UpperCamelCase     :Dict			  =		[log for log in logs if '''eval_loss''' in log.keys()]
        __UpperCamelCase     :Optional[Any]			  =		eval_metrics[0]
        __UpperCamelCase     :List[Any]			  =		eval_metrics[-1]

        assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
        assert isinstance(last_step_stats['''eval_bleu'''] , __lowercase)

        # test if do_predict saves generations and metrics
        __UpperCamelCase     :Any			  =		os.listdir(__lowercase)
        __UpperCamelCase     :List[Any]			  =		{os.path.basename(__lowercase) for p in contents}
        assert "generated_predictions.txt" in contents
        assert "predict_results.json" in contents







    @slow
    @require_bitsandbytes
    def 			UpperCamelCase__   (	self)       ->	List[str]:
        from transformers.training_args import OptimizerNames

        def train_and_return_metrics(__lowercase) -> Tuple[int, float]:
            __UpperCamelCase     :Optional[Any]			  =		'''--skip_memory_metrics 0'''

            __UpperCamelCase     :List[str]			  =		self.run_trainer(
                max_len=128 , model_name=__lowercase , learning_rate=3E-4 , num_train_epochs=1 , optim=__lowercase , distributed=__lowercase , extra_args_str=__lowercase , do_eval=__lowercase , do_predict=__lowercase , n_gpus_to_use=1 , )

            # Check metrics
            __UpperCamelCase     :int			  =		TrainerState.load_from_json(Path(__lowercase , '''trainer_state.json''')).log_history
            __UpperCamelCase     :List[str]			  =		int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20)
            __UpperCamelCase     :Union[str, Any]			  =		int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20)

            __UpperCamelCase     :List[str]			  =		logs[0]['''train_loss''']
            return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Optional[Any]			  =		train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value)
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		train_and_return_metrics(OptimizerNames.ADAMW_BNB.value)

        __UpperCamelCase     :List[Any]			  =		gpu_alloc_mem_orig - gpu_alloc_mem_bnb

        __UpperCamelCase     :List[Any]			  =		gpu_peak_mem_orig + gpu_alloc_mem_orig
        __UpperCamelCase     :int			  =		gpu_peak_mem_bnb + gpu_alloc_mem_bnb
        __UpperCamelCase     :Optional[int]			  =		gpu_total_mem_orig - gpu_total_mem_bnb

        # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
        # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
        # in 2 bytes and the diff in optim memory usage is derived as so:
        #
        # - normal 25*8=~200MB (8 bytes per param)
        # - bnb    25*2= ~50MB (2 bytes per param)
        #
        # Thus we should expect ~150MB total memory saved.
        #
        # Peak memory should be the same - the total should be different by about that same margin
        #
        # After leaving a small margin to accommodate for differences between gpus let's check
        # that we have at least 120MB in savings
        __UpperCamelCase     :str			  =		120

        # uncomment the following if this test starts failing - requires py38 for a new print feature
        # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
        # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
        # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
        # print(f"{gpu_alloc_mem_diff=}MB")
        # print(f"{gpu_peak_mem_diff=}MB")
        # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
        # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

        self.assertGreater(
            __lowercase , __lowercase , '''should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'''
            f""" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"""
            f""" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB""" , )

        self.assertGreater(
            __lowercase , __lowercase , '''should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'''
            f""" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"""
            f""" gpu_total_mem_bnb={gpu_total_mem_bnb}MB""" , )

        self.assertEqual(
            __lowercase , __lowercase , f"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}""")







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase , __lowercase = 3E-3 , __lowercase = "adafactor" , __lowercase = False , __lowercase = None , __lowercase = 0 , __lowercase = True , __lowercase = True , __lowercase = True , __lowercase = True , __lowercase = None , )       ->	Union[str, Any]:
        __UpperCamelCase     :Any			  =		self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
        __UpperCamelCase     :List[Any]			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :Optional[Any]			  =		f"""
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(__lowercase)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(__lowercase)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        """.split()

        __UpperCamelCase     :Dict			  =		f"""
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(__lowercase)}
        """.split()

        __UpperCamelCase     :Tuple			  =		'''
            --do_predict
        '''.split()

        __UpperCamelCase     :Optional[int]			  =		[]
        if do_train:
            args += args_train

        if do_eval:
            args += args_eval

        if do_predict:
            args += args_predict

        if predict_with_generate:
            args += "--predict_with_generate".split()

        if do_train:
            if optim == "adafactor":
                args += "--adafactor".split()
            else:
                args += f"""--optim {optim}""".split()

        if extra_args_str is not None:
            args += extra_args_str.split()

        if distributed:
            if n_gpus_to_use is None:
                __UpperCamelCase     :Optional[Any]			  =		get_gpu_count()
            __UpperCamelCase     :List[Any]			  =		get_torch_dist_unique_port()
            __UpperCamelCase     :Any			  =		f"""
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            """.split()
            __UpperCamelCase     :str			  =		[sys.executable] + distributed_args + args
            # keep for quick debug
            # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
            execute_subprocess_async(__lowercase , env=self.get_env())
        else:
            __UpperCamelCase     :int			  =		['''run_translation.py'''] + args
            with patch.object(__lowercase , '''argv''' , __lowercase):
                main()

        return output_dir






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}'
    )





