




"""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)
a      =							logging.getLogger()

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






class       SCREAMING_SNAKE_CASE__					(					_a					):


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


						def   __lowercase (							self				:			Any						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			str = "pytorch"					):
										lowerCAmelCase  =				self.get_auto_remove_tmp_dir()
										lowerCAmelCase  =				os.path.join(lowerCAmelCase						,						"""output"""					)
										lowerCAmelCase  =				os.path.join(lowerCAmelCase						,						"""data"""					)
										self._create_dummy_data(data_dir=lowerCAmelCase					)

										lowerCAmelCase  =				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"""					)

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

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


						@require_torch_gpu
						def   __lowercase (							self				:			List[Any]					):
										lowerCAmelCase  =				self._run_finetune(gpus=1					)
										self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]						,						0.2					)


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


						@require_torch_gpu
						@require_ray
						def   __lowercase (							self				:			Any					):
										lowerCAmelCase  =				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   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				self._run_finetune(gpus=1						,						distributed_retriever="""ray"""					)
										self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]						,						0.2					)











"""simple docstring"""







import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						@property
						def   __lowercase (							self				:			Any					):
										torch.manual_seed(0					)
										lowerCAmelCase  =				UNetaDModel(
										    block_out_channels=(32, 64)						,						layers_per_block=2						,						sample_size=32						,						in_channels=3						,						out_channels=3						,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,						)
										return model




						def   __lowercase (							self				:			Optional[int]					):
										lowerCAmelCase  =				self.dummy_uncond_unet
										lowerCAmelCase  =				KarrasVeScheduler()

										lowerCAmelCase  =				KarrasVePipeline(unet=lowerCAmelCase						,						scheduler=lowerCAmelCase					)
										pipe.to(lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(num_inference_steps=2						,						generator=lowerCAmelCase						,						output_type="""numpy"""					).images

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(num_inference_steps=2						,						generator=lowerCAmelCase						,						output_type="""numpy"""						,						return_dict=lowerCAmelCase					)[0]

										lowerCAmelCase  =				image[0, -3:, -3:, -1]
										lowerCAmelCase  =				image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCAmelCase  =				np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2






@slow
@require_torch
class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				"""google/ncsnpp-celebahq-256"""
										lowerCAmelCase  =				UNetaDModel.from_pretrained(lowerCAmelCase					)
										lowerCAmelCase  =				KarrasVeScheduler()

										lowerCAmelCase  =				KarrasVePipeline(unet=lowerCAmelCase						,						scheduler=lowerCAmelCase					)
										pipe.to(lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(num_inference_steps=20						,						generator=lowerCAmelCase						,						output_type="""numpy"""					).images

										lowerCAmelCase  =				image[0, -3:, -3:, -1]
										assert image.shape == (1, 256, 256, 3)
										lowerCAmelCase  =				np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2





