






"""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 			UpperCamelCase_       (a_     ):





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: int						)						->			str:
				os.makedirs(_lowerCamelCase	,	exist_ok=_lowerCamelCase						)
				UpperCAmelCase_     : Optional[Any]			     =					{"source": "What is love ?", "target": "life"}
				UpperCAmelCase_     : List[Any]			     =					{"train": 12, "val": 2, "test": 2}
				for split in ["train", "test", "val"]:
							for field in ["source", "target"]:
										UpperCAmelCase_     : Union[str, Any]			     =					"\n".join([contents[field]] * n_lines[split]						)
										with open(os.path.join(_lowerCamelCase	,	f"""{split}.{field}"""						)	,	"w"						) as f:
													f.write(_lowerCamelCase						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Any	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: Union[str, Any] = "pytorch"						)						->			List[Any]:
				UpperCAmelCase_     : Tuple			     =					self.get_auto_remove_tmp_dir()
				UpperCAmelCase_     : str			     =					os.path.join(_lowerCamelCase	,	"output"						)
				UpperCAmelCase_     : List[str]			     =					os.path.join(_lowerCamelCase	,	"data"						)
				self._create_dummy_data(data_dir=_lowerCamelCase						)

				UpperCAmelCase_     : List[str]			     =					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"						)

				UpperCAmelCase_     : Optional[int]			     =					[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_     : List[str]			     =					json.load(_lowerCamelCase						)
				return result





	@require_torch_gpu
	def      _SCREAMING_SNAKE_CASE      (      self					: int						)						->			Dict:
				UpperCAmelCase_     : Optional[int]			     =					self._run_finetune(gpus=1						)
				self.assertGreaterEqual(result["test"][0]["test_avg_em"]	,	0.2						)





	@require_torch_multi_gpu
	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			str:
				UpperCAmelCase_     : Any			     =					self._run_finetune(gpus=2						)
				self.assertGreaterEqual(result["test"][0]["test_avg_em"]	,	0.2						)





	@require_torch_gpu
	@require_ray
	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]						)						->			Optional[int]:
				UpperCAmelCase_     : Tuple			     =					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      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			Tuple:
				UpperCAmelCase_     : Optional[int]			     =					self._run_finetune(gpus=1	,	distributed_retriever="ray"						)
				self.assertGreaterEqual(result["test"][0]["test_avg_em"]	,	0.2						)











"""simple docstring"""



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput

class        a     (   a_	):







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







    @torch.no_grad()
    def __call__(			self							,		_lowerCamelCase = 1							,		_lowerCamelCase = None							,		_lowerCamelCase = 5_0							,		_lowerCamelCase = "pil"							,		_lowerCamelCase = True							,		**_lowerCamelCase							,		):
      lowercase							    =       torch.randn(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,		generator=_lowerCamelCase							,		)
      lowercase							    =       image.to(self.device       )

      # set step values
      self.scheduler.set_timesteps(_lowerCamelCase       )

      for t in self.progress_bar(self.scheduler.timesteps       ):
        # 1. predict noise model_output
        lowercase							    =       self.unet(_lowerCamelCase							,		_lowerCamelCase       ).sample

        # 2. predict previous mean of image x_t-1 and add variance depending on eta
        # eta corresponds to η in paper and should be between [0, 1]
        # do x_t -> x_t-1
        lowercase							    =       self.scheduler.step(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ).prev_sample

      lowercase							    =       (image / 2 + 0.5).clamp(0							,		1       )
      lowercase							    =       image.cpu().permute(0							,		2							,		3							,		1       ).numpy()
      if output_type == "pil":
        lowercase							    =       self.numpy_to_pil(_lowerCamelCase       )

      if not return_dict:
        return (image,), "This is a local test"

      return ImagePipelineOutput(images=_lowerCamelCase       ), "This is a local test"


