





"""simple docstring"""






from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


__UpperCAmelCase						=			logging.get_logger(__name__)  # pylint: disable=invalid-name






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



			'''simple docstring'''
			if not path:
						return "pipe"

			for ext in PipelineDataFormat.SUPPORTED_FORMATS:
						if path.endswith(lowerCAmelCase__			):
									return ext

			raise Exception(
			    F"Unable to determine file format from file extension {path}. "
			    F"Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"			)






def      lowercase__ (					lowerCAmelCase__   :			Optional[Any]			)    ->			Union[str, Any]:



			'''simple docstring'''
			a__  :	List[Any]		 =			pipeline(
			    task=args.task							,			model=args.model if args.model else None							,			config=args.config							,			tokenizer=args.tokenizer							,			device=args.device							,			)
			a__  :	Union[str, Any]		 =			try_infer_format_from_ext(args.input			) if args.format == "infer" else args.format
			a__  :	Any		 =			PipelineDataFormat.from_str(
			    format=lowerCAmelCase__							,			output_path=args.output							,			input_path=args.input							,			column=args.column if args.column else nlp.default_input_names							,			overwrite=args.overwrite							,			)
			return RunCommand(lowerCAmelCase__							,			lowerCAmelCase__			)





class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				def __init__(		self    :			int	, a_    :			Pipeline	, a_    :			PipelineDataFormat    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	List[Any]		 =			nlp
							a__  :	str		 =			reader
				@staticmethod
				def       UpperCAmelCase      (		a_    :			ArgumentParser    )	->	Optional[int]:



							'''simple docstring'''



							a__  :	List[str]		 =			parser.add_parser("run"	, help="Run a pipeline through the CLI"    )
							run_parser.add_argument("--task"	, choices=get_supported_tasks()	, help="Task to run"    )
							run_parser.add_argument("--input"	, type=a_	, help="Path to the file to use for inference"    )
							run_parser.add_argument("--output"	, type=a_	, help="Path to the file that will be used post to write results."    )
							run_parser.add_argument("--model"	, type=a_	, help="Name or path to the model to instantiate."    )
							run_parser.add_argument("--config"	, type=a_	, help="Name or path to the model's config to instantiate."    )
							run_parser.add_argument(
							    "--tokenizer"	, type=a_	, help="Name of the tokenizer to use. (default: same as the model name)"    )
							run_parser.add_argument(
							    "--column"	, type=a_	, help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)"	, )
							run_parser.add_argument(
							    "--format"	, type=a_	, default="infer"	, choices=PipelineDataFormat.SUPPORTED_FORMATS	, help="Input format to read from"	, )
							run_parser.add_argument(
							    "--device"	, type=a_	, default=-1	, help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"	, )
							run_parser.add_argument("--overwrite"	, action="store_true"	, help="Allow overwriting the output file."    )
							run_parser.set_defaults(func=a_    )






				def       UpperCAmelCase      (		self    :			int    )	->	int:



							'''simple docstring'''



							a__ ,	a__  :	Tuple		 =			self._nlp, []

							for entry in self._reader:
										a__  :	str		 =			nlp(**a_    ) if self._reader.is_multi_columns else nlp(a_    )
										if isinstance(a_	, a_    ):
													outputs.append(a_    )
										else:
													outputs += output

        # Saving data
							if self._nlp.binary_output:
										a__  :	Tuple		 =			self._reader.save_binary(a_    )
										logger.warning(F"Current pipeline requires output to be in binary format, saving at {binary_path}"    )
							else:
										self._reader.save(a_    )






"""simple docstring"""






import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


__UpperCAmelCase						=			False



class 					__UpperCAmelCase							(						unittest.TestCase  ):
				def       UpperCAmelCase      (		self    :			Tuple	, a_    :			Optional[Any]=32    )	->	Union[str, Any]:



							'''simple docstring'''



							set_seed(0    )
							a__  :	Any		 =			UNetaDModel(sample_size=a_	, in_channels=3	, out_channels=3    )
							a__  :	Union[str, Any]		 =			torch.optim.SGD(model.parameters()	, lr=0.0001    )
							return model, optimizer






				@slow
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Any:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
							a__  :	Dict		 =			DDPMScheduler(
							    num_train_timesteps=10_00	, beta_start=0.0001	, beta_end=0.02	, beta_schedule="linear"	, clip_sample=a_	, )
							a__  :	Optional[Any]		 =			DDIMScheduler(
							    num_train_timesteps=10_00	, beta_start=0.0001	, beta_end=0.02	, beta_schedule="linear"	, clip_sample=a_	, )

							assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

							# shared batches for DDPM and DDIM
							set_seed(0    )
							a__  :	List[str]		 =			[torch.randn((4, 3, 32, 32)    ).clip(-1	, 1    ).to(a_    ) for _ in range(4    )]
							a__  :	List[str]		 =			[torch.randn((4, 3, 32, 32)    ).to(a_    ) for _ in range(4    )]
							a__  :	Optional[Any]		 =			[torch.randint(0	, 10_00	, (4,)    ).long().to(a_    ) for _ in range(4    )]

							# train with a DDPM scheduler
							a__ ,	a__  :	Union[str, Any]		 =			self.get_model_optimizer(resolution=32    )
							model.train().to(a_    )
							for i in range(4    ):
										optimizer.zero_grad()
										a__  :	Optional[int]		 =			ddpm_scheduler.add_noise(clean_images[i]	, noise[i]	, timesteps[i]    )
										a__  :	List[str]		 =			model(a_	, timesteps[i]    ).sample
										a__  :	Optional[int]		 =			torch.nn.functional.mse_loss(a_	, noise[i]    )
										loss.backward()
										optimizer.step()
							del model, optimizer

							# recreate the model and optimizer, and retry with DDIM
							a__ ,	a__  :	Optional[Any]		 =			self.get_model_optimizer(resolution=32    )
							model.train().to(a_    )
							for i in range(4    ):
										optimizer.zero_grad()
										a__  :	List[str]		 =			ddim_scheduler.add_noise(clean_images[i]	, noise[i]	, timesteps[i]    )
										a__  :	Optional[int]		 =			model(a_	, timesteps[i]    ).sample
										a__  :	Union[str, Any]		 =			torch.nn.functional.mse_loss(a_	, noise[i]    )
										loss.backward()
										optimizer.step()
							del model, optimizer

							self.assertTrue(torch.allclose(a_	, a_	, atol=1E-5    )    )
							self.assertTrue(torch.allclose(a_	, a_	, atol=1E-5    )    )