



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  UpperCAmelCase__    (    lowercase__   ) ->      Optional[int]:
      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(lowercase__   ):
                  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  UpperCAmelCase__    (    lowercase__   ) ->      Optional[int]:
      __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=lowercase__						,       output_path=args.output						,       input_path=args.input						,       column=args.column if args.column else nlp.default_input_names						,       overwrite=args.overwrite						,       )
      return RunCommand(lowercase__						,       lowercase__   )







class 			_lowerCAmelCase   ( _UpperCAmelCase			):






   """simple docstring"""





   def __init__(				self	:       Dict			,     lowercase	:       Pipeline			,     lowercase	:       PipelineDataFormat   )						->					Optional[Any]:
         """simple docstring"""







         __lowercase					=				nlp
         __lowercase					=				reader





   @staticmethod
   def 		snake_case__			(				lowercase	:       ArgumentParser   )						->					Union[str, Any]:
         """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=lowercase			,     help="""Path to the file to use for inference"""   )
         run_parser.add_argument("""--output"""			,     type=lowercase			,     help="""Path to the file that will be used post to write results."""   )
         run_parser.add_argument("""--model"""			,     type=lowercase			,     help="""Name or path to the model to instantiate."""   )
         run_parser.add_argument("""--config"""			,     type=lowercase			,     help="""Name or path to the model's config to instantiate."""   )
         run_parser.add_argument(
             """--tokenizer"""			,     type=lowercase			,     help="""Name of the tokenizer to use. (default: same as the model name)"""   )
         run_parser.add_argument(
             """--column"""			,     type=lowercase			,     help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""			,     )
         run_parser.add_argument(
             """--format"""			,     type=lowercase			,     default="""infer"""			,     choices=PipelineDataFormat.SUPPORTED_FORMATS			,     help="""Input format to read from"""			,     )
         run_parser.add_argument(
             """--device"""			,     type=lowercase			,     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=lowercase   )







   def 		snake_case__			(				self	:       str   )						->					Optional[int]:
         """simple docstring"""







         __lowercase	,			__lowercase					=				self._nlp, []

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

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










import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class 			_lowerCAmelCase   ( _UpperCAmelCase    ,					_UpperCAmelCase    ,					unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  int      =			IFImgaImgSuperResolutionPipeline
			lowercase__      :  Optional[int]      =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
			lowercase__      :  Optional[int]      =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""}			)
			lowercase__      :  Tuple      =			PipelineTesterMixin.required_optional_params - {"""latents"""}





			def 		snake_case__			(				self	:       Tuple   )						->					Any:
									"""simple docstring"""







									return self._get_superresolution_dummy_components()





			def 		snake_case__			(				self	:       List[str]			,     lowercase	:       Optional[int]			,     lowercase	:       Optional[Any]=0   )						->					Union[str, Any]:
									"""simple docstring"""







									if str(lowercase   ).startswith("""mps"""   ):
															__lowercase					=				torch.manual_seed(lowercase   )
									else:
															__lowercase					=				torch.Generator(device=lowercase   ).manual_seed(lowercase   )

									__lowercase					=				floats_tensor((1, 3, 32, 32)			,     rng=random.Random(lowercase   )   ).to(lowercase   )
									__lowercase					=				floats_tensor((1, 3, 16, 16)			,     rng=random.Random(lowercase   )   ).to(lowercase   )

									__lowercase					=				{
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """image""": image,
									    """original_image""": original_image,
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """output_type""": """numpy""",
									}

									return inputs





			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()			,     reason="""XFormers attention is only available with CUDA and `xformers` installed"""			,     )
			def 		snake_case__			(				self	:       Optional[Any]   )						->					Tuple:
									"""simple docstring"""







									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )





			def 		snake_case__			(				self	:       List[Any]   )						->					Union[str, Any]:
									"""simple docstring"""







									self._test_save_load_optional_components()





			@unittest.skipIf(torch_device != """cuda"""			,     reason="""float16 requires CUDA"""   )
			def 		snake_case__			(				self	:       List[Any]   )						->					Dict:
									"""simple docstring"""







									super().test_save_load_floataa(expected_max_diff=1E-1   )





			def 		snake_case__			(				self	:       Dict   )						->					int:
									"""simple docstring"""







									self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )





			def 		snake_case__			(				self	:       Optional[Any]   )						->					int:
									"""simple docstring"""







									self._test_save_load_local()







			def 		snake_case__			(				self	:       Optional[int]   )						->					Optional[int]:
									"""simple docstring"""







									self._test_inference_batch_single_identical(
									    expected_max_diff=1E-2			,     )





