

import argparse
import glob
import inspect
import os
import re
import unittest

from get_test_info import get_test_classes

from tests.test_pipeline_mixin import pipeline_test_mapping


_SCREAMING_SNAKE_CASE							 =			{}
for task, _ in pipeline_test_mapping.items():
      _SCREAMING_SNAKE_CASE							 =			{"""pt""": None, """tf""": None}


# DO **NOT** add item to this set (unless the reason is approved)
_SCREAMING_SNAKE_CASE							 =			{
    """tests/models/esm/test_modeling_esmfold.py""",  # The pipeline test mapping is added to `test_modeling_esm.py`
}





def  _snake_case		(snake_case_					:     int  )							->		Optional[Any]:






  if "ModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
    return "pt"
  elif "TFModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
    return "tf"
  elif "FlaxModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
    return "flax"
  else:
    return None





def  _snake_case		(snake_case_					:     int    ,       snake_case_					:     Optional[int]  )							->		Tuple:






  if PIPELINE_TEST_MAPPING[task].get(_UpperCAmelCase    ,       _UpperCAmelCase  ) is not None:
    return PIPELINE_TEST_MAPPING[task][framework]

  __UpperCAmelCase	:  Optional[Any]		=     pipeline_test_mapping[task]['test']
  __UpperCAmelCase	:  str		=     None

  if framework == "pt":
    __UpperCAmelCase	:  Dict		=     getattr(_UpperCAmelCase    ,       'model_mapping'    ,       _UpperCAmelCase  )
  elif framework == "tf":
    __UpperCAmelCase	:  Tuple		=     getattr(_UpperCAmelCase    ,       'tf_model_mapping'    ,       _UpperCAmelCase  )

  if mapping is not None:
    __UpperCAmelCase	:  Any		=     dict(mapping.items()  )

  # cache the results
  __UpperCAmelCase	:  str		=     mapping
  return mapping





def  _snake_case		(snake_case_					:     Tuple    ,       snake_case_					:     Optional[Any]  )							->		Dict:
  __UpperCAmelCase	:  List[str]		=     get_framework(_UpperCAmelCase  )
  if framework is None:
    return None
  __UpperCAmelCase	:  Union[str, Any]		=     get_mapping_for_task(_UpperCAmelCase    ,       _UpperCAmelCase  )
  if mapping is None:
    return None

  __UpperCAmelCase	:  str		=     list({model_class.config_class for model_class in test_class.all_model_classes}  )
  if len(_UpperCAmelCase  ) != 1:
    raise ValueError('There should be exactly one configuration class from `test_class.all_model_classes`.'  )

  # This could be a list/tuple of model classes, but it's rare.
  __UpperCAmelCase	:  Optional[int]		=     mapping.get(config_classes[0]    ,       _UpperCAmelCase  )
  if isinstance(_UpperCAmelCase    ,       (tuple, list)  ):
    __UpperCAmelCase	:  Tuple		=     sorted(_UpperCAmelCase    ,       key=lambda snake_case_  : x.__name__  )

  return model_class





def  _snake_case		(snake_case_					:     Any  )							->		str:
  __UpperCAmelCase	:  Dict		=     [(task, get_model_for_pipeline_test(_UpperCAmelCase    ,       _UpperCAmelCase  )) for task in pipeline_test_mapping]
  __UpperCAmelCase	:  Dict		=     sorted([(task, model) for task, model in mapping if model is not None]    ,       key=lambda snake_case_  : x[0]  )

  return dict(_UpperCAmelCase  )





def  _snake_case		(snake_case_					:     Optional[int]  )							->		str:
  __UpperCAmelCase	:  List[str]		=     get_framework(_UpperCAmelCase  )
  if framework == "pt":
    __UpperCAmelCase	:  List[Any]		=     'torch'
  __UpperCAmelCase	:  List[Any]		=     '{}'

  __UpperCAmelCase	:  Optional[int]		=     get_pipeline_model_mapping(_UpperCAmelCase  )
  if len(_UpperCAmelCase  ) == 0:
    return ""

  __UpperCAmelCase	:  List[str]		=     []
  for task, model_classes in mapping.items():
    if isinstance(_UpperCAmelCase    ,       (tuple, list)  ):
      # A list/tuple of model classes
      __UpperCAmelCase	:  Optional[Any]		=     '(' + ', '.join([x.__name__ for x in model_classes]  ) + ')'
    else:
      # A single model class
      __UpperCAmelCase	:  Optional[int]		=     model_classes.__name__
    texts.append(F"\"{task}\": {value}"  )
  __UpperCAmelCase	:  Optional[int]		=     '{' + ', '.join(_UpperCAmelCase  ) + '}'
  __UpperCAmelCase	:  int		=     F"pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}"

  return text





def  _snake_case		(snake_case_					:     int  )							->		List[Any]:
  __UpperCAmelCase	:  str		=     {'ModelTesterMixin', 'TFModelTesterMixin', 'FlaxModelTesterMixin'}
  if not issubclass(_UpperCAmelCase    ,       unittest.TestCase  ):
    return False
  return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]  )  ) > 0





def  _snake_case		(snake_case_					:     Optional[int]  )							->		Any:
  __UpperCAmelCase	:  Tuple		=     [x for x in get_test_classes(_UpperCAmelCase  ) if is_valid_test_class(_UpperCAmelCase  )]

  __UpperCAmelCase	:  Optional[int]		=     None
  for test_class in test_classes:
    # If a test class has defined `pipeline_model_mapping`, let's take it
    if getattr(_UpperCAmelCase    ,       'pipeline_model_mapping'    ,       _UpperCAmelCase  ) is not None:
      __UpperCAmelCase	:  Union[str, Any]		=     test_class
      break
       # Take the test class with the shortest name (just a heuristic)
  if target_test_class is None and len(_UpperCAmelCase  ) > 0:
    __UpperCAmelCase	:  List[Any]		=     sorted(_UpperCAmelCase    ,       key=lambda snake_case_  : (len(x.__name__  ), x.__name__)  )[0]

  return target_test_class





def  _snake_case		(snake_case_					:     Any    ,       snake_case_					:     int    ,       snake_case_					:     str  )							->		List[str]:
  __UpperCAmelCase	:  Tuple		=     start_idx
  for idx, line in enumerate(lines[start_idx:]  ):
    __UpperCAmelCase	:  List[str]		=     len(_UpperCAmelCase  ) - len(line.lstrip()  )
    if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
      __UpperCAmelCase	:  List[str]		=     start_idx + idx
    elif idx > 0 and indent <= indent_level:
      # Outside the definition block of `pipeline_model_mapping`
      break

  return end_idx





def  _snake_case		(snake_case_					:     Optional[Any]    ,       snake_case_					:     Tuple=False  )							->		int:






  if getattr(_UpperCAmelCase    ,       'pipeline_model_mapping'    ,       _UpperCAmelCase  ) is not None:
    if not overwrite:
      return "", -1

  __UpperCAmelCase	:  Optional[Any]		=     get_pipeline_model_mapping_string(_UpperCAmelCase  )
  if len(_UpperCAmelCase  ) == 0:
    return "", -1
  __UpperCAmelCase	:  int		=     line_to_add + '\n'

  # The code defined the class `test_class`
  __UpperCAmelCase,			__UpperCAmelCase	:  Optional[int]		=     inspect.getsourcelines(_UpperCAmelCase  )
  # `inspect` gives the code for an object, including decorator(s) if any.
  # We (only) need the exact line of the class definition.
  for idx, line in enumerate(_UpperCAmelCase  ):
    if line.lstrip().startswith('class '  ):
      __UpperCAmelCase	:  List[str]		=     class_lines[idx:]
      class_start_line_no += idx
      break
  __UpperCAmelCase	:  Tuple		=     class_start_line_no + len(_UpperCAmelCase  ) - 1

  # The index in `class_lines` that starts the definition of `all_model_classes`, `all_generative_model_classes` or
  # `pipeline_model_mapping`. This assumes they are defined in such order, and we take the start index of the last
  # block that appears in a `test_class`.
  __UpperCAmelCase	:  str		=     None
  # The indent level of the line at `class_lines[start_idx]` (if defined)
  __UpperCAmelCase	:  Optional[Any]		=     0
  # To record if `pipeline_model_mapping` is found in `test_class`.
  __UpperCAmelCase	:  List[str]		=     None
  for idx, line in enumerate(_UpperCAmelCase  ):
    if line.strip().startswith('all_model_classes = '  ):
      __UpperCAmelCase	:  List[Any]		=     len(_UpperCAmelCase  ) - len(line.lstrip()  )
      __UpperCAmelCase	:  Tuple		=     idx
    elif line.strip().startswith('all_generative_model_classes = '  ):
      __UpperCAmelCase	:  Dict		=     len(_UpperCAmelCase  ) - len(line.lstrip()  )
      __UpperCAmelCase	:  Union[str, Any]		=     idx
    elif line.strip().startswith('pipeline_model_mapping = '  ):
      __UpperCAmelCase	:  Any		=     len(_UpperCAmelCase  ) - len(line.lstrip()  )
      __UpperCAmelCase	:  int		=     idx
      __UpperCAmelCase	:  Union[str, Any]		=     line
      break

  if start_idx is None:
    return "", -1
  # Find the ending index (inclusive) of the above found block.
  __UpperCAmelCase	:  Optional[Any]		=     find_block_ending(_UpperCAmelCase    ,       _UpperCAmelCase    ,       _UpperCAmelCase  )

  # Extract `is_xxx_available()` from existing blocks: some models require specific libraries like `timm` and use
  # `is_timm_available()` instead of `is_torch_available()`.
  # Keep leading and trailing whitespaces
  __UpperCAmelCase	:  int		=     re.compile(R'\s(is_\S+?_available\(\))\s'  )
  for line in class_lines[start_idx : end_idx + 1]:
    __UpperCAmelCase	:  Dict		=     r.search(_UpperCAmelCase  )
    if backend_condition is not None:
      # replace the leading and trailing whitespaces to the space character " ".
      __UpperCAmelCase	:  Dict		=     ' ' + backend_condition[0][1:-1] + ' '
      __UpperCAmelCase	:  Any		=     r.sub(_UpperCAmelCase    ,       _UpperCAmelCase  )
      break

  if def_line is None:
    # `pipeline_model_mapping` is not defined. The target index is set to the ending index (inclusive) of
    # `all_model_classes` or `all_generative_model_classes`.
    __UpperCAmelCase	:  Optional[int]		=     end_idx
  else:
    # `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
    __UpperCAmelCase	:  Tuple		=     start_idx - 1
    # mark the lines of the currently existing `pipeline_model_mapping` to be removed.
    for idx in range(_UpperCAmelCase    ,       end_idx + 1  ):
      # These lines are going to be removed before writing to the test file.
      __UpperCAmelCase	:  int		=     None  # noqa

       # Make sure the test class is a subclass of `PipelineTesterMixin`.
  __UpperCAmelCase	:  Optional[Any]		=     [x.__name__ for x in test_class.__bases__]
  if "PipelineTesterMixin" not in parent_classes:
    # Put `PipelineTesterMixin` just before `unittest.TestCase`
    __UpperCAmelCase	:  Optional[Any]		=     [x for x in parent_classes if x != 'TestCase'] + ['PipelineTesterMixin']
    if "TestCase" in parent_classes:
      # Here we **assume** the original string is always with `unittest.TestCase`.
      _parent_classes.append('unittest.TestCase'  )
    __UpperCAmelCase	:  str		=     ', '.join(_parent_classes  )
    for idx, line in enumerate(_UpperCAmelCase  ):
      # Find the ending of the declaration of `test_class`
      if line.strip().endswith('):'  ):
        # mark the lines of the declaration of `test_class` to be removed
        for _idx in range(idx + 1  ):
          __UpperCAmelCase	:  int		=     None  # noqa
        break
            # Add the new, one-line, class declaration for `test_class`
    __UpperCAmelCase	:  Optional[int]		=     F"class {test_class.__name__}({parent_classes}):\n"

  # Add indentation
  __UpperCAmelCase	:  Dict		=     ' ' * indent_level + line_to_add
  # Insert `pipeline_model_mapping` to `class_lines`.
  # (The line at `target_idx` should be kept by definition!)
  __UpperCAmelCase	:  List[Any]		=     class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
  # Remove the lines that are marked to be removed
  __UpperCAmelCase	:  Tuple		=     [x for x in class_lines if x is not None]

  # Move from test class to module (in order to write to the test file)
  __UpperCAmelCase	:  str		=     inspect.getsourcelines(inspect.getmodule(_UpperCAmelCase  )  )[0]
  # Be careful with the 1-off between line numbers and array indices
  __UpperCAmelCase	:  Optional[int]		=     module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
  __UpperCAmelCase	:  Dict		=     ''.join(_UpperCAmelCase  )

  __UpperCAmelCase	:  List[Any]		=     inspect.getsourcefile(_UpperCAmelCase  )
  with open(_UpperCAmelCase    ,       'w'    ,       encoding='UTF-8'    ,       newline='\n'  ) as fp:
    fp.write(_UpperCAmelCase  )

  return line_to_add





def  _snake_case		(snake_case_					:     List[Any]    ,       snake_case_					:     List[Any]=False  )							->		Dict:
  __UpperCAmelCase	:  Tuple		=     find_test_class(_UpperCAmelCase  )
  if test_class:
    add_pipeline_model_mapping(_UpperCAmelCase    ,       overwrite=_UpperCAmelCase  )


if __name__ == "__main__":
      _SCREAMING_SNAKE_CASE							 =			argparse.ArgumentParser()
      parser.add_argument(
          '''--test_file''', type=str, help='''A path to the test file, starting with the repository\'s `tests` directory.'''
      )
      parser.add_argument(
          '''--all''',
          action='''store_true''',
          help='''If to check and modify all test files.''',
      )
      parser.add_argument(
          '''--overwrite''',
          action='''store_true''',
          help='''If to overwrite a test class if it has already defined `pipeline_model_mapping`.''',
      )
      _SCREAMING_SNAKE_CASE							 =			parser.parse_args()

      if not args.all and not args.test_file:
            raise ValueError('''Please specify either `test_file` or pass `--all` to check/modify all test files.''')
      elif args.all and args.test_file:
            raise ValueError('''Only one of `--test_file` and `--all` could be specified.''')

      _SCREAMING_SNAKE_CASE							 =			[]
      if args.test_file:
            _SCREAMING_SNAKE_CASE							 =			[args.test_file]
      else:
            _SCREAMING_SNAKE_CASE							 =			os.path.join('''tests''', '''models''', '''**''', '''test_modeling_*.py''')
            for test_file in glob.glob(pattern):
                  # `Flax` is not concerned at this moment
                  if not test_file.startswith('''test_modeling_flax_'''):
                        test_files.append(test_file)

      for test_file in test_files:
            if test_file in TEST_FILE_TO_IGNORE:
                  print(F'''[SKIPPED] {test_file} is skipped as it is in `TEST_FILE_TO_IGNORE` in the file {__file__}.''')
                  continue
            add_pipeline_model_mapping_to_test_file(test_file, overwrite=args.overwrite)












"""simple docstring"""


import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase=0.999						,  _UpperCAmelCase="cosine"						,  )     ->	str:

 '''simple docstring'''






 if alpha_transform_type == "cosine":

  def alpha_bar_fn(_UpperCAmelCase						):
   return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(_UpperCAmelCase						):
   return math.exp(t * -12.0						)

 else:
  raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"						)

 snake_case__     				=					[]
 for i in range(_UpperCAmelCase						):
  snake_case__     				=					i / num_diffusion_timesteps
  snake_case__     				=					(i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(_UpperCAmelCase						) / alpha_bar_fn(_UpperCAmelCase						)						,  _UpperCAmelCase						)						)
 return torch.tensor(_UpperCAmelCase						,  dtype=torch.floataa						)



class   lowercase_					(  __lowerCAmelCase					,__lowerCAmelCase				):
       snake_case__					:				Tuple									=  [e.name for e in KarrasDiffusionSchedulers]
       snake_case__					:				Any									=  2

       @register_to_config
       def __init__(      self ,       __SCREAMING_SNAKE_CASE = 1000 ,       __SCREAMING_SNAKE_CASE = 0.00085 ,       __SCREAMING_SNAKE_CASE = 0.012 ,       __SCREAMING_SNAKE_CASE = "linear" ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = "epsilon" ,       __SCREAMING_SNAKE_CASE = "linspace" ,       __SCREAMING_SNAKE_CASE = 0 ,       ):






        '''simple docstring'''

        if trained_betas is not None:
         snake_case__     				=					torch.tensor(__SCREAMING_SNAKE_CASE ,       dtype=torch.floataa  )
        elif beta_schedule == "linear":
         snake_case__     				=					torch.linspace(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       dtype=torch.floataa  )
        elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         snake_case__     				=					(
             torch.linspace(beta_start**0.5 ,       beta_end**0.5 ,       __SCREAMING_SNAKE_CASE ,       dtype=torch.floataa  ) ** 2
         )
        elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         snake_case__     				=					betas_for_alpha_bar(__SCREAMING_SNAKE_CASE  )
        else:
         raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"  )

        snake_case__     				=					1.0 - self.betas
        snake_case__     				=					torch.cumprod(self.alphas ,       dim=0  )

        #  set all values
        self.set_timesteps(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE=None  ):






        '''simple docstring'''

        if schedule_timesteps is None:
         snake_case__     				=					self.timesteps

        snake_case__     				=					(schedule_timesteps == timestep).nonzero()

        # The sigma index that is taken for the **very** first `step`
        # is always the second index (or the last index if there is only 1)
        # This way we can ensure we don't accidentally skip a sigma in
        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
        if len(self._index_counter  ) == 0:
         snake_case__     				=					1 if len(__SCREAMING_SNAKE_CASE  ) > 1 else 0
        else:
         snake_case__     				=					timestep.cpu().item() if torch.is_tensor(__SCREAMING_SNAKE_CASE  ) else timestep
         snake_case__     				=					self._index_counter[timestep_int]

        return indices[pos].item()

       @property
       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        if self.config.timestep_spacing in ["linspace", "trailing"]:
         return self.sigmas.max()

        return (self.sigmas.max() ** 2 + 1) ** 0.5

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        snake_case__     				=					self.index_for_timestep(__SCREAMING_SNAKE_CASE  )

        if self.state_in_first_order:
         snake_case__     				=					self.sigmas[step_index]
        else:
         snake_case__     				=					self.sigmas_interpol[step_index - 1]

        snake_case__     				=					sample / ((sigma**2 + 1) ** 0.5)
        return sample

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = None ,       ):






        '''simple docstring'''

        snake_case__     				=					num_inference_steps

        snake_case__     				=					num_train_timesteps or self.config.num_train_timesteps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
         snake_case__     				=					np.linspace(0 ,       num_train_timesteps - 1 ,       __SCREAMING_SNAKE_CASE ,       dtype=__SCREAMING_SNAKE_CASE  )[::-1].copy()
        elif self.config.timestep_spacing == "leading":
         snake_case__     				=					num_train_timesteps // self.num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
         snake_case__     				=					(np.arange(0 ,       __SCREAMING_SNAKE_CASE  ) * step_ratio).round()[::-1].copy().astype(__SCREAMING_SNAKE_CASE  )
         timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
         snake_case__     				=					num_train_timesteps / self.num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
         snake_case__     				=					(np.arange(__SCREAMING_SNAKE_CASE ,       0 ,       -step_ratio  )).round().copy().astype(__SCREAMING_SNAKE_CASE  )
         timesteps -= 1
        else:
         raise ValueError(
             F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."  )

        snake_case__     				=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
        snake_case__     				=					torch.from_numpy(np.log(__SCREAMING_SNAKE_CASE  )  ).to(__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					np.interp(__SCREAMING_SNAKE_CASE ,       np.arange(0 ,       len(__SCREAMING_SNAKE_CASE  )  ) ,       __SCREAMING_SNAKE_CASE  )
        snake_case__     				=					np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
        snake_case__     				=					torch.from_numpy(__SCREAMING_SNAKE_CASE  ).to(device=__SCREAMING_SNAKE_CASE  )

        # compute up and down sigmas
        snake_case__     				=					sigmas.roll(-1  )
        snake_case__     				=					0.0
        snake_case__     				=					(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
        snake_case__     				=					(sigmas_next**2 - sigmas_up**2) ** 0.5
        snake_case__     				=					0.0

        # compute interpolated sigmas
        snake_case__     				=					sigmas.log().lerp(sigmas_down.log() ,       0.5  ).exp()
        snake_case__     				=					0.0

        # set sigmas
        snake_case__     				=					torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2  ), sigmas[-1:]]  )
        snake_case__     				=					torch.cat(
            [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2  ), sigmas_interpol[-1:]]  )
        snake_case__     				=					torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2  ), sigmas_up[-1:]]  )
        snake_case__     				=					torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2  ), sigmas_down[-1:]]  )

        if str(__SCREAMING_SNAKE_CASE  ).startswith("mps"  ):
         # mps does not support float64
         snake_case__     				=					torch.from_numpy(__SCREAMING_SNAKE_CASE  ).to(__SCREAMING_SNAKE_CASE ,       dtype=torch.floataa  )
        else:
         snake_case__     				=					torch.from_numpy(__SCREAMING_SNAKE_CASE  ).to(__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					self.sigma_to_t(__SCREAMING_SNAKE_CASE  ).to(__SCREAMING_SNAKE_CASE ,       dtype=timesteps.dtype  )
        snake_case__     				=					torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None]) ,       dim=-1  ).flatten()

        snake_case__     				=					torch.cat([timesteps[:1], interleaved_timesteps]  )

        snake_case__     				=					None

        # for exp beta schedules, such as the one for `pipeline_shap_e.py`
        # we need an index counter
        snake_case__     				=					defaultdict(__SCREAMING_SNAKE_CASE  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					sigma.log()

        # get distribution
        snake_case__     				=					log_sigma - self.log_sigmas[:, None]

        # get sigmas range
        snake_case__     				=					dists.ge(0  ).cumsum(dim=0  ).argmax(dim=0  ).clamp(max=self.log_sigmas.shape[0] - 2  )
        snake_case__     				=					low_idx + 1

        snake_case__     				=					self.log_sigmas[low_idx]
        snake_case__     				=					self.log_sigmas[high_idx]

        # interpolate sigmas
        snake_case__     				=					(low - log_sigma) / (low - high)
        snake_case__     				=					w.clamp(0 ,       1  )

        # transform interpolation to time range
        snake_case__     				=					(1 - w) * low_idx + w * high_idx
        snake_case__     				=					t.view(sigma.shape  )
        return t

       @property
       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        return self.sample is None

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = True ,       ):






        '''simple docstring'''

        snake_case__     				=					self.index_for_timestep(__SCREAMING_SNAKE_CASE  )

        # advance index counter by 1
        snake_case__     				=					timestep.cpu().item() if torch.is_tensor(__SCREAMING_SNAKE_CASE  ) else timestep
        self._index_counter[timestep_int] += 1

        if self.state_in_first_order:
         snake_case__     				=					self.sigmas[step_index]
         snake_case__     				=					self.sigmas_interpol[step_index]
         snake_case__     				=					self.sigmas_up[step_index]
         snake_case__     				=					self.sigmas_down[step_index - 1]
        else:
         # 2nd order / KPDM2's method
         snake_case__     				=					self.sigmas[step_index - 1]
         snake_case__     				=					self.sigmas_interpol[step_index - 1]
         snake_case__     				=					self.sigmas_up[step_index - 1]
         snake_case__     				=					self.sigmas_down[step_index - 1]

        # currently only gamma=0 is supported. This usually works best anyways.
        # We can support gamma in the future but then need to scale the timestep before
        # passing it to the model which requires a change in API
        snake_case__     				=					0
        snake_case__     				=					sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

        snake_case__     				=					model_output.device
        snake_case__     				=					randn_tensor(model_output.shape ,       dtype=model_output.dtype ,       device=__SCREAMING_SNAKE_CASE ,       generator=__SCREAMING_SNAKE_CASE  )

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
         snake_case__     				=					sigma_hat if self.state_in_first_order else sigma_interpol
         snake_case__     				=					sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
         snake_case__     				=					sigma_hat if self.state_in_first_order else sigma_interpol
         snake_case__     				=					model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
             sample / (sigma_input**2 + 1)
         )
        elif self.config.prediction_type == "sample":
         raise NotImplementedError("prediction_type not implemented yet: sample"  )
        else:
         raise ValueError(
             F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"  )

        if self.state_in_first_order:
         # 2. Convert to an ODE derivative for 1st order
         snake_case__     				=					(sample - pred_original_sample) / sigma_hat
         # 3. delta timestep
         snake_case__     				=					sigma_interpol - sigma_hat

         # store for 2nd order step
         snake_case__     				=					sample
         snake_case__     				=					dt
         snake_case__     				=					sample + derivative * dt
        else:
         # DPM-Solver-2
         # 2. Convert to an ODE derivative for 2nd order
         snake_case__     				=					(sample - pred_original_sample) / sigma_interpol
         # 3. delta timestep
         snake_case__     				=					sigma_down - sigma_hat

         snake_case__     				=					self.sample
         snake_case__     				=					None

         snake_case__     				=					sample + derivative * dt
         snake_case__     				=					prev_sample + noise * sigma_up

        if not return_dict:
         return (prev_sample,)

        return SchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        snake_case__     				=					self.sigmas.to(device=original_samples.device ,       dtype=original_samples.dtype  )
        if original_samples.device.type == "mps" and torch.is_floating_point(__SCREAMING_SNAKE_CASE  ):
         # mps does not support float64
         snake_case__     				=					self.timesteps.to(original_samples.device ,       dtype=torch.floataa  )
         snake_case__     				=					timesteps.to(original_samples.device ,       dtype=torch.floataa  )
        else:
         snake_case__     				=					self.timesteps.to(original_samples.device  )
         snake_case__     				=					timesteps.to(original_samples.device  )

        snake_case__     				=					[self.index_for_timestep(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ) for t in timesteps]

        snake_case__     				=					sigmas[step_indices].flatten()
        while len(sigma.shape  ) < len(original_samples.shape  ):
         snake_case__     				=					sigma.unsqueeze(-1  )

        snake_case__     				=					original_samples + noise * sigma
        return noisy_samples

       def __len__(      self  ):






        '''simple docstring'''

        return self.config.num_train_timesteps

