


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 : Optional[int]		=				logging.getLogger()

a : Optional[int]		=				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					def A      (				self ,       snake_case_						)   ->			str:







								'''simple docstring'''

								os.makedirs(snake_case_ ,       exist_ok=snake_case_						)
								__lowercase 					=   {'''source''': '''What is love ?''', '''target''': '''life'''}
								__lowercase 					=   {'''train''': 1_2, '''val''': 2, '''test''': 2}
								for split in ["train", "test", "val"]:
											for field in ["source", "target"]:
														__lowercase 					=   '''\n'''.join([contents[field]] * n_lines[split]						)
														with open(os.path.join(snake_case_ ,       F'{split}.{field}'						) ,       '''w'''						) as f:
																	f.write(snake_case_						)






					def A      (				self ,       snake_case_ ,       snake_case_ = "pytorch"						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   self.get_auto_remove_tmp_dir()
								__lowercase 					=   os.path.join(snake_case_ ,       '''output'''						)
								__lowercase 					=   os.path.join(snake_case_ ,       '''data'''						)
								self._create_dummy_data(data_dir=snake_case_						)

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

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

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






					@require_torch_gpu
					def A      (				self						)   ->			Union[str, Any]:







								'''simple docstring'''

								__lowercase 					=   self._run_finetune(gpus=1						)
								self.assertGreaterEqual(result['''test'''][0]['''test_avg_em'''] ,       0.2						)






					@require_torch_multi_gpu
					def A      (				self						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   self._run_finetune(gpus=2						)
								self.assertGreaterEqual(result['''test'''][0]['''test_avg_em'''] ,       0.2						)






					@require_torch_gpu
					@require_ray
					def A      (				self						)   ->			Optional[Any]:







								'''simple docstring'''

								__lowercase 					=   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 A      (				self						)   ->			str:







								'''simple docstring'''

								__lowercase 					=   self._run_finetune(gpus=1 ,       distributed_retriever='''ray'''						)
								self.assertGreaterEqual(result['''test'''][0]['''test_avg_em'''] ,       0.2						)






# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    '''pipelines_utils''',
    '''0.22.0''',
    '''Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.''',
    standard_warn=False,
    stacklevel=3,
)


