


from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class   A				(  __snake_case  ):





      def __init__(		self						:					Optional[Any]					,       __lowerCamelCase						:					Optional[int]					,       __lowerCamelCase						:					Dict       )    ->   Dict:






         """simple docstring"""







         super().__init__()

         # make sure scheduler can always be converted to DDIM
         UpperCAmelCase							=     DDIMScheduler.from_config(scheduler.config       )

         self.register_modules(unet=__lowerCamelCase					,       scheduler=__lowerCamelCase       )





      @torch.no_grad()
      def __call__(		self						:					str					,       __lowerCamelCase						:					int = 1					,       __lowerCamelCase						:					Optional[Union[torch.Generator, List[torch.Generator]]] = None					,       __lowerCamelCase						:					float = 0.0					,       __lowerCamelCase						:					int = 5_0					,       __lowerCamelCase						:					Optional[bool] = None					,       __lowerCamelCase						:					Optional[str] = "pil"					,       __lowerCamelCase						:					bool = True					,       )    ->   Union[ImagePipelineOutput, Tuple]:






         """simple docstring"""







         if isinstance(self.unet.config.sample_size					,       __lowerCamelCase       ):
            UpperCAmelCase							=     (
                batch_size,
                self.unet.config.in_channels,
                self.unet.config.sample_size,
                self.unet.config.sample_size,
            )
         else:
            UpperCAmelCase							=     (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

         if isinstance(__lowerCamelCase					,       __lowerCamelCase       ) and len(__lowerCamelCase       ) != batch_size:
            raise ValueError(
                F"""You have passed a list of generators of length {len(__lowerCamelCase       )}, but requested an effective batch"""
                F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""       )

         UpperCAmelCase							=     randn_tensor(__lowerCamelCase					,       generator=__lowerCamelCase					,       device=self.device					,       dtype=self.unet.dtype       )

         # set step values
         self.scheduler.set_timesteps(__lowerCamelCase       )

         for t in self.progress_bar(self.scheduler.timesteps       ):
            # 1. predict noise model_output
            UpperCAmelCase							=     self.unet(__lowerCamelCase					,       __lowerCamelCase       ).sample

            # 2. predict previous mean of image x_t-1 and add variance depending on eta
            # eta corresponds to η in paper and should be between [0, 1]
            # do x_t -> x_t-1
            UpperCAmelCase							=     self.scheduler.step(
                __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       eta=__lowerCamelCase					,       use_clipped_model_output=__lowerCamelCase					,       generator=__lowerCamelCase       ).prev_sample

         UpperCAmelCase							=     (image / 2 + 0.5).clamp(0					,       1       )
         UpperCAmelCase							=     image.cpu().permute(0					,       2					,       3					,       1       ).numpy()
         if output_type == "pil":
            UpperCAmelCase							=     self.numpy_to_pil(__lowerCamelCase       )

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=__lowerCamelCase       )







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


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


set_seed(42)
__a     =			"""sshleifer/student_marian_en_ro_6_1"""
__a     =			"""sshleifer/tiny-mbart"""






@require_torch
class   __lowercase				(  __snake_case  ):





      def        _lowercase				(		self						:					Dict					,       __lowerCamelCase						:					List[Any]=False					,       __lowerCamelCase						:					str=None					,       __lowerCamelCase						:					Any=True					,       __lowerCamelCase						:					Union[str, Any]=True					,       __lowerCamelCase						:					str=True					,       __lowerCamelCase						:					List[str]=True					,       )    ->   List[Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.run_trainer(
             eval_steps=1					,       max_len=1_2					,       model_name=__lowerCamelCase					,       num_train_epochs=1					,       distributed=__lowerCamelCase					,       extra_args_str=__lowerCamelCase					,       predict_with_generate=__lowerCamelCase					,       do_train=__lowerCamelCase					,       do_eval=__lowerCamelCase					,       do_predict=__lowerCamelCase					,       )
         UpperCAmelCase							=     TrainerState.load_from_json(os.path.join(__lowerCamelCase					,       """trainer_state.json"""       )       ).log_history

         if not do_eval:
            return

         UpperCAmelCase							=     [log for log in logs if """eval_loss""" in log.keys()]

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

            UpperCAmelCase							=     eval_metrics[-1]
            assert isinstance(last_step_stats["""eval_bleu"""]					,       __lowerCamelCase       )
            assert not math.isnan(float(last_step_stats["""eval_loss"""]       )       ), "eval_loss must not be `nan`"





      @require_torch_non_multi_gpu
      def        _lowercase				(		self						:					Dict       )    ->   str:






         """simple docstring"""







         self.run_seqaseq_quick()





      @require_torch_multi_gpu
      def        _lowercase				(		self						:					Tuple       )    ->   Any:






         """simple docstring"""







         self.run_seqaseq_quick(distributed=__lowerCamelCase       )





      @require_torch_multi_gpu
      def        _lowercase				(		self						:					Optional[int]       )    ->   Optional[int]:






         """simple docstring"""







         self.run_seqaseq_quick(distributed=__lowerCamelCase       )





      @unittest.skip("""Requires an update of the env running those tests"""       )
      @require_torch_multi_gpu
      @require_fairscale
      def        _lowercase				(		self						:					Union[str, Any]       )    ->   Optional[int]:






         """simple docstring"""







         self.run_seqaseq_quick(distributed=__lowerCamelCase					,       extra_args_str="""--sharded_ddp simple"""       )





      @unittest.skip("""Requires an update of the env running those tests"""       )
      @require_torch_multi_gpu
      @require_fairscale
      def        _lowercase				(		self						:					List[Any]       )    ->   Union[str, Any]:






         """simple docstring"""







         self.run_seqaseq_quick(distributed=__lowerCamelCase					,       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        _lowercase				(		self						:					Dict       )    ->   Tuple:






         """simple docstring"""







         self.run_seqaseq_quick(distributed=__lowerCamelCase					,       extra_args_str="""--sharded_ddp zero_dp_2"""					,       predict_with_generate=__lowerCamelCase       )





      @unittest.skip("""Requires an update of the env running those tests"""       )
      @require_torch_multi_gpu
      @require_fairscale
      def        _lowercase				(		self						:					Optional[int]       )    ->   Dict:






         """simple docstring"""







         self.run_seqaseq_quick(
             distributed=__lowerCamelCase					,       extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""					,       predict_with_generate=__lowerCamelCase       )





      @require_apex
      @require_torch_gpu
      def        _lowercase				(		self						:					str       )    ->   Optional[Any]:






         """simple docstring"""







         self.run_seqaseq_quick(distributed=__lowerCamelCase					,       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=__lowerCamelCase					,       extra_args_str="""--fp16 --fp16_backend=apex"""       )





      @parameterized.expand(["""base""", """low""", """high""", """mixed"""]       )
      @require_torch_multi_gpu
      def        _lowercase				(		self						:					Union[str, Any]					,       __lowerCamelCase						:					Optional[Any]       )    ->   Optional[Any]:






         """simple docstring"""







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





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






         """simple docstring"""







         UpperCAmelCase							=     self.run_trainer(
             eval_steps=2					,       max_len=1_2_8					,       model_name=__lowerCamelCase					,       learning_rate=3e-4					,       num_train_epochs=1_0					,       distributed=__lowerCamelCase					,       )

         # Check metrics
         UpperCAmelCase							=     TrainerState.load_from_json(os.path.join(__lowerCamelCase					,       """trainer_state.json"""       )       ).log_history
         UpperCAmelCase							=     [log for log in logs if """eval_loss""" in log.keys()]
         UpperCAmelCase							=     eval_metrics[0]
         UpperCAmelCase							=     eval_metrics[-1]

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

         # test if do_predict saves generations and metrics
         UpperCAmelCase							=     os.listdir(__lowerCamelCase       )
         UpperCAmelCase							=     {os.path.basename(__lowerCamelCase       ) for p in contents}
         assert "generated_predictions.txt" in contents
         assert "predict_results.json" in contents





      @slow
      @require_bitsandbytes
      def        _lowercase				(		self						:					str       )    ->   int:






         """simple docstring"""







         from transformers.training_args import OptimizerNames

         def train_and_return_metrics(__lowerCamelCase						:					str       ) -> Tuple[int, float]:
            UpperCAmelCase							=     """--skip_memory_metrics 0"""

            UpperCAmelCase							=     self.run_trainer(
                max_len=1_2_8					,       model_name=__lowerCamelCase					,       learning_rate=3e-4					,       num_train_epochs=1					,       optim=__lowerCamelCase					,       distributed=__lowerCamelCase					,       extra_args_str=__lowerCamelCase					,       do_eval=__lowerCamelCase					,       do_predict=__lowerCamelCase					,       n_gpus_to_use=1					,       )

            # Check metrics
            UpperCAmelCase							=     TrainerState.load_from_json(Path(__lowerCamelCase					,       """trainer_state.json"""       )       ).log_history
            UpperCAmelCase							=     int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**2_0       )
            UpperCAmelCase							=     int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**2_0       )

            UpperCAmelCase							=     logs[0]["""train_loss"""]
            return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

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

         UpperCAmelCase							=     gpu_alloc_mem_orig - gpu_alloc_mem_bnb

         UpperCAmelCase							=     gpu_peak_mem_orig + gpu_alloc_mem_orig
         UpperCAmelCase							=     gpu_peak_mem_bnb + gpu_alloc_mem_bnb
         UpperCAmelCase							=     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							=     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(
             __lowerCamelCase					,       __lowerCamelCase					,       """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(
             __lowerCamelCase					,       __lowerCamelCase					,       """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(
             __lowerCamelCase					,       __lowerCamelCase					,       F"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"""       )





      def        _lowercase				(		self						:					Any					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					str					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					float = 3e-3					,       __lowerCamelCase						:					str = "adafactor"					,       __lowerCamelCase						:					bool = False					,       __lowerCamelCase						:					str = None					,       __lowerCamelCase						:					int = 0					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					int = None					,       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
         UpperCAmelCase							=     self.get_auto_remove_tmp_dir()
         UpperCAmelCase							=     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(__lowerCamelCase       )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(__lowerCamelCase       )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        """.split()

         UpperCAmelCase							=     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(__lowerCamelCase       )}
        """.split()

         UpperCAmelCase							=     """
            --do_predict
        """.split()

         UpperCAmelCase							=     []
         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							=     get_gpu_count()
            UpperCAmelCase							=     get_torch_dist_unique_port()
            UpperCAmelCase							=     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							=     [sys.executable] + distributed_args + args
            # keep for quick debug
            # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
            execute_subprocess_async(__lowerCamelCase					,       env=self.get_env()       )
         else:
            UpperCAmelCase							=     ["""run_translation.py"""] + args
            with patch.object(__lowerCamelCase					,       """argv"""					,       __lowerCamelCase       ):
               main()

         return output_dir



