






"""simple docstring"""







import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
_UpperCamelCase					      =  logging.getLogger()

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







class  lowerCamelCase__ (	snake_case			):






    def     _UpperCamelCase							(			self     ,A ):
          os.makedirs(A     ,exist_ok=A )
          UpperCAmelCase          =	{"""source""": """What is love ?""", """target""": """life"""}
          UpperCAmelCase          =	{"""train""": 12, """val""": 2, """test""": 2}
          for split in ["train", "test", "val"]:
                for field in ["source", "target"]:
                      UpperCAmelCase          =	"""\n""".join([contents[field]] * n_lines[split] )
                      with open(os.path.join(A     ,F'''{split}.{field}''' )     ,"""w""" ) as f:
                            f.write(A )






    def     _UpperCamelCase							(			self     ,A     ,A = "pytorch" ):
          UpperCAmelCase          =	self.get_auto_remove_tmp_dir()
          UpperCAmelCase          =	os.path.join(A     ,"""output""" )
          UpperCAmelCase          =	os.path.join(A     ,"""data""" )
          self._create_dummy_data(data_dir=A )

          UpperCAmelCase          =	F'''
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            '''.split()

          if gpus > 0:
                testargs.append(F'''--gpus={gpus}''' )
                if is_apex_available():
                      testargs.append("""--fp16""" )
          else:
                testargs.append("""--gpus=0""" )
                testargs.append("""--distributed_backend=ddp_cpu""" )
                testargs.append("""--num_processes=2""" )

          UpperCAmelCase          =	[sys.executable, str(Path(finetune_rag.__file__ ).resolve() )] + testargs
          execute_subprocess_async(A     ,env=self.get_env() )

          UpperCAmelCase          =	os.path.join(A     ,"""metrics.json""" )
          with open(A ) as f:
                UpperCAmelCase          =	json.load(A )
          return result






    @require_torch_gpu
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self._run_finetune(gpus=1 )
          self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]     ,0.2 )






    @require_torch_multi_gpu
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self._run_finetune(gpus=2 )
          self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]     ,0.2 )






    @require_torch_gpu
    @require_ray
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self._run_finetune(gpus=1     ,distributed_retriever="""ray""" )
          self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]     ,0.2 )






    @require_torch_multi_gpu
    @require_ray
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self._run_finetune(gpus=1     ,distributed_retriever="""ray""" )
          self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]     ,0.2 )











"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      42
    SCREAMING_SNAKE_CASE		=      42






    def __init__(			self     ,A     ,A ):
          super().__init__()
          self.register_modules(unet=A     ,scheduler=A )






    @torch.no_grad()
    def __call__(			self     ,A = 1     ,A = 2_000     ,A = None     ,A = "pil"     ,A = True     ,**A     ,):
          UpperCAmelCase          =	self.unet.config.sample_size
          UpperCAmelCase          =	(batch_size, 3, img_size, img_size)

          UpperCAmelCase          =	self.unet

          UpperCAmelCase          =	randn_tensor(A     ,generator=A ) * self.scheduler.init_noise_sigma
          UpperCAmelCase          =	sample.to(self.device )

          self.scheduler.set_timesteps(A )
          self.scheduler.set_sigmas(A )

          for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
                UpperCAmelCase          =	self.scheduler.sigmas[i] * torch.ones(shape[0]     ,device=self.device )

                # correction step
                for _ in range(self.scheduler.config.correct_steps ):
                      UpperCAmelCase          =	self.unet(A     ,A ).sample
                      UpperCAmelCase          =	self.scheduler.step_correct(A     ,A     ,generator=A ).prev_sample

                # prediction step
                UpperCAmelCase          =	model(A     ,A ).sample
                UpperCAmelCase          =	self.scheduler.step_pred(A     ,A     ,A     ,generator=A )

                UpperCAmelCase      , UpperCAmelCase          =	output.prev_sample, output.prev_sample_mean

          UpperCAmelCase          =	sample_mean.clamp(0     ,1 )
          UpperCAmelCase          =	sample.cpu().permute(0     ,2     ,3     ,1 ).numpy()
          if output_type == "pil":
                UpperCAmelCase          =	self.numpy_to_pil(A )

          if not return_dict:
                return (sample,)

          return ImagePipelineOutput(images=A )



