'''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   A_      (       SCREAMING_SNAKE_CASE_						)			->Optional[int]:
  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_						)			->Any:
  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    (		__a      ):


  """simple docstring"""

  def __init__(		self      :       List[str]   ,				lowercase_      :       Pipeline   ,				lowercase_      :       PipelineDataFormat      ):







    '''simple docstring'''


    lowercase_     =       nlp
    lowercase_     =       reader




  @staticmethod
  def 		lowerCamelCase__  (		lowercase_      :       ArgumentParser      ):







    '''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 		lowerCamelCase__  (		self      :       Any      ):







    '''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_      )


'''simple docstring'''


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						(DDPMParallelScheduler,)




  def 		lowerCamelCase__  (		self      :       Optional[Any]   ,				**lowercase_      :       str      ):







    '''simple docstring'''


    lowercase_     =       {
        """num_train_timesteps""": 1_000,
        """beta_start""": 0.0_0_0_1,
        """beta_end""": 0.0_2,
        """beta_schedule""": """linear""",
        """variance_type""": """fixed_small""",
        """clip_sample""": True,
    }

    config.update(**lowercase_      )
    return config




  def 		lowerCamelCase__  (		self      :       Optional[Any]      ):







    '''simple docstring'''


    for timesteps in [1, 5, 100, 1_000]:
      self.check_over_configs(num_train_timesteps=lowercase_      )




  def 		lowerCamelCase__  (		self      :       List[Any]      ):







    '''simple docstring'''


    for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]   ,				[0.0_0_2, 0.0_2, 0.2, 2]      ):
      self.check_over_configs(beta_start=lowercase_   ,				beta_end=lowercase_      )




  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


    for schedule in ["linear", "squaredcos_cap_v2"]:
      self.check_over_configs(beta_schedule=lowercase_      )




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    for variance in ["fixed_small", "fixed_large", "other"]:
      self.check_over_configs(variance_type=lowercase_      )




  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    for clip_sample in [True, False]:
      self.check_over_configs(clip_sample=lowercase_      )




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    self.check_over_configs(thresholding=lowercase_      )
    for threshold in [0.5, 1.0, 2.0]:
      for prediction_type in ["epsilon", "sample", "v_prediction"]:
        self.check_over_configs(
            thresholding=lowercase_   ,				prediction_type=lowercase_   ,				sample_max_value=lowercase_   ,				)




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    for prediction_type in ["epsilon", "sample", "v_prediction"]:
      self.check_over_configs(prediction_type=lowercase_      )




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    for t in [0, 500, 999]:
      self.check_over_forward(time_step=lowercase_      )




  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config()
    lowercase_     =       scheduler_class(**lowercase_      )

    assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 0.0      )      ) < 1e-5
    assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.0_0_9_7_9      )      ) < 1e-5
    assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.0_2      )      ) < 1e-5




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config()
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       len(lowercase_      )

    lowercase_     =       self.dummy_model()
    lowercase_     =       self.dummy_sample_deter
    lowercase_     =       self.dummy_sample_deter + 0.1
    lowercase_     =       self.dummy_sample_deter - 0.1

    lowercase_     =       samplea.shape[0]
    lowercase_     =       torch.stack([samplea, samplea, samplea]   ,				dim=0      )
    lowercase_     =       torch.arange(lowercase_      )[0:3, None].repeat(1   ,				lowercase_      )

    lowercase_     =       model(samples.flatten(0   ,				1      )   ,				timesteps.flatten(0   ,				1      )      )
    lowercase_     =       scheduler.batch_step_no_noise(lowercase_   ,				timesteps.flatten(0   ,				1      )   ,				samples.flatten(0   ,				1      )      )

    lowercase_     =       torch.sum(torch.abs(lowercase_      )      )
    lowercase_     =       torch.mean(torch.abs(lowercase_      )      )

    assert abs(result_sum.item() - 1_1_5_3.1_8_3_3      ) < 1e-2
    assert abs(result_mean.item() - 0.5_0_0_5      ) < 1e-3




  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config()
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       len(lowercase_      )

    lowercase_     =       self.dummy_model()
    lowercase_     =       self.dummy_sample_deter
    lowercase_     =       torch.manual_seed(0      )

    for t in reversed(range(lowercase_      )      ):
      # 1. predict noise residual
      lowercase_     =       model(lowercase_   ,				lowercase_      )

      # 2. predict previous mean of sample x_t-1
      lowercase_     =       scheduler.step(lowercase_   ,				lowercase_   ,				lowercase_   ,				generator=lowercase_      ).prev_sample

      lowercase_     =       pred_prev_sample

    lowercase_     =       torch.sum(torch.abs(lowercase_      )      )
    lowercase_     =       torch.mean(torch.abs(lowercase_      )      )

    assert abs(result_sum.item() - 2_5_8.9_6_0_6      ) < 1e-2
    assert abs(result_mean.item() - 0.3_3_7_2      ) < 1e-3




  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config(prediction_type="""v_prediction"""      )
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       len(lowercase_      )

    lowercase_     =       self.dummy_model()
    lowercase_     =       self.dummy_sample_deter
    lowercase_     =       torch.manual_seed(0      )

    for t in reversed(range(lowercase_      )      ):
      # 1. predict noise residual
      lowercase_     =       model(lowercase_   ,				lowercase_      )

      # 2. predict previous mean of sample x_t-1
      lowercase_     =       scheduler.step(lowercase_   ,				lowercase_   ,				lowercase_   ,				generator=lowercase_      ).prev_sample

      lowercase_     =       pred_prev_sample

    lowercase_     =       torch.sum(torch.abs(lowercase_      )      )
    lowercase_     =       torch.mean(torch.abs(lowercase_      )      )

    assert abs(result_sum.item() - 2_0_2.0_2_9_6      ) < 1e-2
    assert abs(result_mean.item() - 0.2_6_3_1      ) < 1e-3




  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config()
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       [100, 87, 50, 1, 0]

    scheduler.set_timesteps(timesteps=lowercase_      )

    lowercase_     =       scheduler.timesteps

    for i, timestep in enumerate(lowercase_      ):
      if i == len(lowercase_      ) - 1:
        lowercase_     =       -1
      else:
        lowercase_     =       timesteps[i + 1]

      lowercase_     =       scheduler.previous_timestep(lowercase_      )
      lowercase_     =       prev_t.item()

      self.assertEqual(lowercase_   ,				lowercase_      )




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config()
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       [100, 87, 50, 51, 0]

    with self.assertRaises(lowercase_   ,				msg="""`custom_timesteps` must be in descending order."""      ):
      scheduler.set_timesteps(timesteps=lowercase_      )




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config()
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       [100, 87, 50, 1, 0]
    lowercase_     =       len(lowercase_      )

    with self.assertRaises(lowercase_   ,				msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""      ):
      scheduler.set_timesteps(num_inference_steps=lowercase_   ,				timesteps=lowercase_      )





  def 		lowerCamelCase__  (		self      :       Optional[Any]      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config()
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       [scheduler.config.num_train_timesteps]

    with self.assertRaises(
        lowercase_   ,				msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""   ,				):
      scheduler.set_timesteps(timesteps=lowercase_      )

