


"""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)
lowercase_			     =  logging.getLogger()

lowercase_			     =  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		def 			__UpperCAmelCase       (  self							, _a      ):
				os.makedirs(_a							, exist_ok=_a      )
				__a           =			{'''source''': '''What is love ?''', '''target''': '''life'''}
				__a           =			{'''train''': 12, '''val''': 2, '''test''': 2}
				for split in ["train", "test", "val"]:
						for field in ["source", "target"]:
								__a           =			'''\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"      ):
				__a           =			self.get_auto_remove_tmp_dir()
				__a           =			os.path.join(_a							, '''output'''      )
				__a           =			os.path.join(_a							, '''data'''      )
				self._create_dummy_data(data_dir=_a      )

				__a           =			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'''      )

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

				__a           =			os.path.join(_a							, '''metrics.json'''      )
				with open(_a      ) as f:
						__a           =			json.load(_a      )
				return result

		@require_torch_gpu
		def 			__UpperCAmelCase       (  self      ):
				__a           =			self._run_finetune(gpus=1      )
				self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']							, 0.2      )

		@require_torch_multi_gpu
		def 			__UpperCAmelCase       (  self      ):
				__a           =			self._run_finetune(gpus=2      )
				self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']							, 0.2      )

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










"""simple docstring"""




import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation
class 					__lowerCAmelCase					(   unittest.TestCase       ):


		'''simple docstring'''



		def 			__UpperCAmelCase       (  self      ):
				__a           =			get_activation('''swish'''      )

				self.assertIsInstance(_a							, nn.SiLU      )

				self.assertEqual(act(torch.tensor(-100							, dtype=torch.floataa      )      ).item()							, 0      )
				self.assertNotEqual(act(torch.tensor(-1							, dtype=torch.floataa      )      ).item()							, 0      )
				self.assertEqual(act(torch.tensor(0							, dtype=torch.floataa      )      ).item()							, 0      )
				self.assertEqual(act(torch.tensor(20							, dtype=torch.floataa      )      ).item()							, 20      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			get_activation('''silu'''      )

				self.assertIsInstance(_a							, nn.SiLU      )

				self.assertEqual(act(torch.tensor(-100							, dtype=torch.floataa      )      ).item()							, 0      )
				self.assertNotEqual(act(torch.tensor(-1							, dtype=torch.floataa      )      ).item()							, 0      )
				self.assertEqual(act(torch.tensor(0							, dtype=torch.floataa      )      ).item()							, 0      )
				self.assertEqual(act(torch.tensor(20							, dtype=torch.floataa      )      ).item()							, 20      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			get_activation('''mish'''      )

				self.assertIsInstance(_a							, nn.Mish      )

				self.assertEqual(act(torch.tensor(-200							, dtype=torch.floataa      )      ).item()							, 0      )
				self.assertNotEqual(act(torch.tensor(-1							, dtype=torch.floataa      )      ).item()							, 0      )
				self.assertEqual(act(torch.tensor(0							, dtype=torch.floataa      )      ).item()							, 0      )
				self.assertEqual(act(torch.tensor(20							, dtype=torch.floataa      )      ).item()							, 20      )





		def 			__UpperCAmelCase       (  self      ):
				__a           =			get_activation('''gelu'''      )

				self.assertIsInstance(_a							, nn.GELU      )

				self.assertEqual(act(torch.tensor(-100							, dtype=torch.floataa      )      ).item()							, 0      )
				self.assertNotEqual(act(torch.tensor(-1							, dtype=torch.floataa      )      ).item()							, 0      )
				self.assertEqual(act(torch.tensor(0							, dtype=torch.floataa      )      ).item()							, 0      )
				self.assertEqual(act(torch.tensor(20							, dtype=torch.floataa      )      ).item()							, 20      )






