



"""simple docstring"""






from argparse import ArgumentParser

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


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




def 			snake_case		(		lowerCAmelCase_	)						->				Any:
  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 			snake_case		(		lowerCAmelCase_	)						->				List[str]:
  _snake_case   	=      pipeline(
      task=args.task  ,						model=args.model if args.model else None  ,						config=args.config  ,						tokenizer=args.tokenizer  ,						device=args.device  ,						)
  _snake_case   	=      try_infer_format_from_ext(args.input	) if args.format == '''infer''' else args.format
  _snake_case   	=      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    (	__SCREAMING_SNAKE_CASE  ):



   def __init__(       self    :	Optional[Any]							,       __lowerCamelCase    :	Pipeline							,       __lowerCamelCase    :	PipelineDataFormat				):



     """simple docstring"""
     _snake_case   	=      nlp
     _snake_case   	=      reader



   @staticmethod
   def    __UpperCAmelCase    (       __lowerCamelCase    :	ArgumentParser				):



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





   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self._nlp, []

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

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












import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
 import jax
 import jax.numpy as jnp
 from flax.jax_utils import replicate
 from flax.training.common_utils import shard


@slow
@require_flax
class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        super().tearDown()
        gc.collect()



     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        A_   ,    A_       :					Optional[Any]        =      FlaxStableDiffusionPipeline.from_pretrained(
            "stabilityai/stable-diffusion-2"      ,   revision="bf16"      ,   dtype=jnp.bfloataa      ,   )

        A_       :					List[Any]        =      "A painting of a squirrel eating a burger"
        A_       :					int        =      jax.device_count()
        A_       :					Union[str, Any]        =      num_samples * [prompt]
        A_       :					Dict        =      sd_pipe.prepare_inputs(snake_case	)

        A_       :					List[Any]        =      replicate(snake_case	)
        A_       :					str        =      shard(snake_case	)

        A_       :					Any        =      jax.random.PRNGKey(0	)
        A_       :					Dict        =      jax.random.split(snake_case      ,   jax.device_count()	)

        A_       :					int        =      sd_pipe(snake_case      ,   snake_case      ,   snake_case      ,   num_inference_steps=25      ,   jit=snake_case	)[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        A_       :					List[str]        =      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
        A_       :					Union[str, Any]        =      images[0, 253:256, 253:256, -1]

        A_       :					Dict        =      jnp.asarray(jax.device_get(image_slice.flatten()	)	)
        A_       :					List[str]        =      jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]	)
        print(f"output_slice: {output_slice}"	)
        assert jnp.abs(output_slice - expected_slice	).max() < 1e-2





     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					str        =      "stabilityai/stable-diffusion-2"
        A_   ,    A_       :					Dict        =      FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case      ,   subfolder="scheduler"	)
        A_   ,    A_       :					Any        =      FlaxStableDiffusionPipeline.from_pretrained(
            snake_case      ,   scheduler=snake_case      ,   revision="bf16"      ,   dtype=jnp.bfloataa      ,   )
        A_       :					Any        =      scheduler_params

        A_       :					Optional[Any]        =      "A painting of a squirrel eating a burger"
        A_       :					Dict        =      jax.device_count()
        A_       :					List[str]        =      num_samples * [prompt]
        A_       :					int        =      sd_pipe.prepare_inputs(snake_case	)

        A_       :					Optional[Any]        =      replicate(snake_case	)
        A_       :					Dict        =      shard(snake_case	)

        A_       :					Any        =      jax.random.PRNGKey(0	)
        A_       :					Union[str, Any]        =      jax.random.split(snake_case      ,   jax.device_count()	)

        A_       :					Union[str, Any]        =      sd_pipe(snake_case      ,   snake_case      ,   snake_case      ,   num_inference_steps=25      ,   jit=snake_case	)[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        A_       :					List[Any]        =      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
        A_       :					Union[str, Any]        =      images[0, 253:256, 253:256, -1]

        A_       :					Tuple        =      jnp.asarray(jax.device_get(image_slice.flatten()	)	)
        A_       :					str        =      jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]	)
        print(f"output_slice: {output_slice}"	)
        assert jnp.abs(output_slice - expected_slice	).max() < 1e-2
