

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 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	List[Any],lowercase_			:	List[str]=False,lowercase_			:	Any=None,lowercase_			:	Union[str, Any]=True,lowercase_			:	List[str]=True,lowercase_			:	Optional[Any]=True,lowercase_			:	Dict=True,)->   Optional[int]:





        '''simple docstring'''







        A__   				=       self.run_trainer(
            eval_steps=1,max_len=1_2,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_,)
        A__   				=       TrainerState.load_from_json(os.path.join(lowercase_,'trainer_state.json'		)		).log_history

        if not do_eval:
            return

        A__   				=       [log for log in logs if 'eval_loss' in log.keys()]

        A__   				=       eval_metrics[0]
        if predict_with_generate:
            assert "eval_bleu" in first_step_stats

            A__   				=       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 						snake_case__					(				self			:	int		)->   Any:





        '''simple docstring'''







        self.run_seqaseq_quick()


    @require_torch_multi_gpu
    def 						snake_case__					(				self			:	Dict		)->   Any:





        '''simple docstring'''







        self.run_seqaseq_quick(distributed=lowercase_		)


    @require_torch_multi_gpu
    def 						snake_case__					(				self			:	Union[str, Any]		)->   List[Any]:





        '''simple docstring'''







        self.run_seqaseq_quick(distributed=lowercase_		)


    @unittest.skip('Requires an update of the env running those tests'		)
    @require_torch_multi_gpu
    @require_fairscale
    def 						snake_case__					(				self			:	List[Any]		)->   Optional[Any]:





        '''simple docstring'''







        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 						snake_case__					(				self			:	Tuple		)->   int:





        '''simple docstring'''







        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 						snake_case__					(				self			:	List[Any]		)->   List[Any]:





        '''simple docstring'''







        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 						snake_case__					(				self			:	List[Any]		)->   Optional[Any]:





        '''simple docstring'''







        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 						snake_case__					(				self			:	Union[str, Any]		)->   List[str]:





        '''simple docstring'''







        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 						snake_case__					(				self			:	List[Any],lowercase_			:	List[Any]		)->   List[str]:





        '''simple docstring'''







        A__   				=       {
            # 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},
        }

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


    @slow
    def 						snake_case__					(				self			:	Tuple		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       self.run_trainer(
            eval_steps=2,max_len=1_2_8,model_name=lowercase_,learning_rate=3E-4,num_train_epochs=1_0,distributed=lowercase_,)

        # Check metrics
        A__   				=       TrainerState.load_from_json(os.path.join(lowercase_,'trainer_state.json'		)		).log_history
        A__   				=       [log for log in logs if 'eval_loss' in log.keys()]
        A__   				=       eval_metrics[0]
        A__   				=       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
        A__   				=       os.listdir(lowercase_		)
        A__   				=       {os.path.basename(lowercase_		) for p in contents}
        assert "generated_predictions.txt" in contents
        assert "predict_results.json" in contents


    @slow
    @require_bitsandbytes
    def 						snake_case__					(				self			:	List[str]		)->   Optional[int]:





        '''simple docstring'''







        from transformers.training_args import OptimizerNames

        def train_and_return_metrics(lowercase_			:	str		) -> Tuple[int, float]:
            A__   				=       '--skip_memory_metrics 0'

            A__   				=       self.run_trainer(
                max_len=1_2_8,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
            A__   				=       TrainerState.load_from_json(Path(lowercase_,'trainer_state.json'		)		).log_history
            A__   				=       int(logs[0]['train_mem_gpu_peaked_delta'] / 2**2_0		)
            A__   				=       int(logs[0]['train_mem_gpu_alloc_delta'] / 2**2_0		)

            A__   				=       logs[0]['train_loss']
            return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

        A__				,						A__				,						A__   				=       train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value		)
        A__				,						A__				,						A__   				=       train_and_return_metrics(OptimizerNames.ADAMW_BNB.value		)

        A__   				=       gpu_alloc_mem_orig - gpu_alloc_mem_bnb

        A__   				=       gpu_peak_mem_orig + gpu_alloc_mem_orig
        A__   				=       gpu_peak_mem_bnb + gpu_alloc_mem_bnb
        A__   				=       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
        A__   				=       1_2_0

        # 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 						snake_case__					(				self			:	Any,lowercase_			:	int,lowercase_			:	str,lowercase_			:	int,lowercase_			:	float = 3E-3,lowercase_			:	str = "adafactor",lowercase_			:	bool = False,lowercase_			:	str = None,lowercase_			:	int = 0,lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	int = None,)->   Optional[int]:





        '''simple docstring'''







        A__   				=       self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
        A__   				=       self.get_auto_remove_tmp_dir()
        A__   				=       F'\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(lowercase_		)}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(lowercase_		)}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        '.split()

        A__   				=       F'\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(lowercase_		)}\n        '.split()

        A__   				=       '\n            --do_predict\n        '.split()

        A__   				=       []
        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:
                A__   				=       get_gpu_count()
            A__   				=       get_torch_dist_unique_port()
            A__   				=       F'\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            '.split()
            A__   				=       [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:
            A__   				=       ['run_translation.py'] + args
            with patch.object(lowercase_,'argv',lowercase_		):
                main()

        return output_dir





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
     if not (is_transformers_available() and is_torch_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import (
         ImageTextPipelineOutput,
         UniDiffuserPipeline,
     )
else:
     from .modeling_text_decoder import UniDiffuserTextDecoder
     from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
     from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline

