


from argparse import ArgumentParser

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


lowerCAmelCase			=				logging.get_logger(__name__)  # pylint: disable=invalid-name
def      _a					( SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		if not path:
				return "pipe"

		for ext in PipelineDataFormat.SUPPORTED_FORMATS:
				if path.endswith(SCREAMING_SNAKE_CASE	):
						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      _a					( SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= pipeline(
		    task=args.task		,							model=args.model if args.model else None		,							config=args.config		,							tokenizer=args.tokenizer		,							device=args.device		,							)
		lowercase__				= try_infer_format_from_ext(args.input	) if args.format == '''infer''' else args.format
		lowercase__				= PipelineDataFormat.from_str(
		    format=SCREAMING_SNAKE_CASE		,							output_path=args.output		,							input_path=args.input		,							column=args.column if args.column else nlp.default_input_names		,							overwrite=args.overwrite		,							)
		return RunCommand(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)


class 			_a				(     UpperCamelCase__     ):

							def __init__(				self:      Dict       ,		UpperCamelCase_:      Pipeline       ,		UpperCamelCase_:      PipelineDataFormat				)     ->  Tuple:





									"""simple docstring"""







									lowercase__				= nlp
									lowercase__				= reader

							@staticmethod
							def  lowerCamelCase_					(				UpperCamelCase_:      ArgumentParser				)     ->  Dict:





									"""simple docstring"""







									lowercase__				= 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=UpperCamelCase_       ,		help='''Path to the file to use for inference'''				)
									run_parser.add_argument('''--output'''       ,		type=UpperCamelCase_       ,		help='''Path to the file that will be used post to write results.'''				)
									run_parser.add_argument('''--model'''       ,		type=UpperCamelCase_       ,		help='''Name or path to the model to instantiate.'''				)
									run_parser.add_argument('''--config'''       ,		type=UpperCamelCase_       ,		help='''Name or path to the model\'s config to instantiate.'''				)
									run_parser.add_argument(
									    '''--tokenizer'''       ,		type=UpperCamelCase_       ,		help='''Name of the tokenizer to use. (default: same as the model name)'''				)
									run_parser.add_argument(
									    '''--column'''       ,		type=UpperCamelCase_       ,		help='''Name of the column to use as input. (For multi columns input as QA use column1,columns2)'''       ,		)
									run_parser.add_argument(
									    '''--format'''       ,		type=UpperCamelCase_       ,		default='''infer'''       ,		choices=PipelineDataFormat.SUPPORTED_FORMATS       ,		help='''Input format to read from'''       ,		)
									run_parser.add_argument(
									    '''--device'''       ,		type=UpperCamelCase_       ,		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=UpperCamelCase_				)


							def  lowerCamelCase_					(				self:      int				)     ->  Optional[int]:





									"""simple docstring"""







									lowercase__	,						lowercase__				= self._nlp, []

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

        # Saving data
									if self._nlp.binary_output:
											lowercase__				= self._reader.save_binary(UpperCamelCase_				)
											logger.warning(f'Current pipeline requires output to be in binary format, saving at {binary_path}'				)
									else:
											self._reader.save(UpperCamelCase_				)









import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     @property
     def        lowerCAmelCase__		(    self							):
         torch.manual_seed(0							)
         _A     =							UNetaDModel(
             block_out_channels=(32, 64)				,     layers_per_block=2				,     sample_size=32				,     in_channels=3				,     out_channels=3				,     down_block_types=('DownBlock2D', 'AttnDownBlock2D')				,     up_block_types=('AttnUpBlock2D', 'UpBlock2D')				,     )
         return model




     def        lowerCAmelCase__		(    self							):
         _A     =							self.dummy_uncond_unet
         _A     =							KarrasVeScheduler()

         _A     =							KarrasVePipeline(unet=snake_case_				,     scheduler=snake_case_							)
         pipe.to(snake_case_							)
         pipe.set_progress_bar_config(disable=snake_case_							)

         _A     =							torch.manual_seed(0							)
         _A     =							pipe(num_inference_steps=2				,     generator=snake_case_				,     output_type='numpy'							).images

         _A     =							torch.manual_seed(0							)
         _A     =							pipe(num_inference_steps=2				,     generator=snake_case_				,     output_type='numpy'				,     return_dict=snake_case_							)[0]

         _A     =							image[0, -3:, -3:, -1]
         _A     =							image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         _A     =							np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2







@slow
@require_torch
class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     def        lowerCAmelCase__		(    self							):
         _A     =							'google/ncsnpp-celebahq-256'
         _A     =							UNetaDModel.from_pretrained(snake_case_							)
         _A     =							KarrasVeScheduler()

         _A     =							KarrasVePipeline(unet=snake_case_				,     scheduler=snake_case_							)
         pipe.to(snake_case_							)
         pipe.set_progress_bar_config(disable=snake_case_							)

         _A     =							torch.manual_seed(0							)
         _A     =							pipe(num_inference_steps=20				,     generator=snake_case_				,     output_type='numpy'							).images

         _A     =							image[0, -3:, -3:, -1]
         assert image.shape == (1, 256, 256, 3)
         _A     =							np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2





