


'''simple docstring'''


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowerCamelCase__					=   os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
lowerCamelCase__					=   '    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'






class 		lowerCAmelCase__	(	unittest.TestCase		):




			def 							lowerCAmelCase__			(						self     :   Any       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					tempfile.mkdtemp()
					os.makedirs(os.path.join(self.diffusers_dir		,     "schedulers/"       )       )
					_UpperCAmelCase						:				Optional[int]	    =					self.diffusers_dir
					shutil.copy(
					    os.path.join(lowerCamelCase__		,     "src/diffusers/schedulers/scheduling_ddpm.py"       )		,     os.path.join(self.diffusers_dir		,     "schedulers/scheduling_ddpm.py"       )		,     )




			def 							lowerCAmelCase__			(						self     :   int       )      ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					"src/diffusers"
					shutil.rmtree(self.diffusers_dir       )




			def 							lowerCAmelCase__			(						self     :   Union[str, Any]		,     lowerCamelCase__     :   List[Any]		,     lowerCamelCase__     :   Any		,     lowerCamelCase__     :   Optional[int]		,     lowerCamelCase__     :   Optional[Any]=None       )      ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
					if overwrite_result is not None:
							_UpperCAmelCase						:				List[Any]	    =					comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
					_UpperCAmelCase						:				str	    =					black.Mode(target_versions={black.TargetVersion.PYaa}		,     line_length=1_19       )
					_UpperCAmelCase						:				int	    =					black.format_str(lowerCamelCase__		,     mode=lowerCamelCase__       )
					_UpperCAmelCase						:				Tuple	    =					os.path.join(self.diffusers_dir		,     "new_code.py"       )
					with open(lowerCamelCase__		,     "w"		,     newline="\n"       ) as f:
							f.write(lowerCamelCase__       )
					if overwrite_result is None:
							self.assertTrue(len(check_copies.is_copy_consistent(lowerCamelCase__       )       ) == 0       )
					else:
							check_copies.is_copy_consistent(f.name		,     overwrite=lowerCamelCase__       )
							with open(lowerCamelCase__		,     "r"       ) as f:
									self.assertTrue(f.read()		,     lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   int       )      ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput"       )
					self.assertEqual(lowerCamelCase__		,     lowerCamelCase__       )






			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->Dict:



					'''simple docstring'''







					self.check_copy_consistency(
					    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"		,     "DDPMSchedulerOutput"		,     REFERENCE_CODE + "\n"		,     )

					# With no empty line at the end
					self.check_copy_consistency(
					    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"		,     "DDPMSchedulerOutput"		,     lowerCamelCase__		,     )

					# Copy consistency with rename
					self.check_copy_consistency(
					    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"		,     "TestSchedulerOutput"		,     re.sub("DDPM"		,     "Test"		,     lowerCamelCase__       )		,     )

					# Copy consistency with a really long name
					_UpperCAmelCase						:				int	    =					"TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
					self.check_copy_consistency(
					    F"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"""		,     F"""{long_class_name}SchedulerOutput"""		,     re.sub("Bert"		,     lowerCamelCase__		,     lowerCamelCase__       )		,     )

					# Copy consistency with overwrite
					self.check_copy_consistency(
					    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"		,     "TestSchedulerOutput"		,     lowerCamelCase__		,     overwrite_result=re.sub("DDPM"		,     "Test"		,     lowerCamelCase__       )		,     )







'''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)
lowerCamelCase__					=   logging.getLogger()

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






class 		lowerCAmelCase__	(	UpperCAmelCase__		):




			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   Optional[int]       )      ->Tuple:



					'''simple docstring'''







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




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   int		,     lowerCamelCase__     :   str = "pytorch"       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					self.get_auto_remove_tmp_dir()
					_UpperCAmelCase						:				int	    =					os.path.join(lowerCamelCase__		,     "output"       )
					_UpperCAmelCase						:				Tuple	    =					os.path.join(lowerCamelCase__		,     "data"       )
					self._create_dummy_data(data_dir=lowerCamelCase__       )

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

					_UpperCAmelCase						:				Optional[int]	    =					os.path.join(lowerCamelCase__		,     "metrics.json"       )
					with open(lowerCamelCase__       ) as f:
							_UpperCAmelCase						:				Dict	    =					json.load(lowerCamelCase__       )
					return result




			@require_torch_gpu
			def 							lowerCAmelCase__			(						self     :   Dict       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					self._run_finetune(gpus=1       )
					self.assertGreaterEqual(result["test"][0]["test_avg_em"]		,     0.2       )




			@require_torch_multi_gpu
			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					self._run_finetune(gpus=2       )
					self.assertGreaterEqual(result["test"][0]["test_avg_em"]		,     0.2       )




			@require_torch_gpu
			@require_ray
			def 							lowerCAmelCase__			(						self     :   int       )      ->str:



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					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 							lowerCAmelCase__			(						self     :   int       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase						:				str	    =					self._run_finetune(gpus=1		,     distributed_retriever="ray"       )
					self.assertGreaterEqual(result["test"][0]["test_avg_em"]		,     0.2       )



