code: 
  
  
  
  
  
  '''simple docstring'''
  
  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
  
  
  lowercase_			       =				{}
  for task, _ in pipeline_test_mapping.items():
        lowercase_			       =				{"""pt""": None, """tf""": None}
  
  
  # DO **NOT** add item to this set (unless the reason is approved)
  lowercase_			       =				{
      """tests/models/esm/test_modeling_esmfold.py""",  # The pipeline test mapping is added to `test_modeling_esm.py`
  }
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Any					)		->int:
  
      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 lowerCamelCase		(  __lowerCamelCase				:					Tuple      , __lowerCamelCase				:					List[str]					)		->Union[str, Any]:
      # Use the cached results
      if PIPELINE_TEST_MAPPING[task].get(__lowerCamelCase      , __lowerCamelCase					) is not None:
          return PIPELINE_TEST_MAPPING[task][framework]
  
      _SCREAMING_SNAKE_CASE             =    pipeline_test_mapping[task]["""test"""]
      _SCREAMING_SNAKE_CASE             =    None
  
      if framework == "pt":
          _SCREAMING_SNAKE_CASE             =    getattr(__lowerCamelCase      , """model_mapping"""      , __lowerCamelCase					)
      elif framework == "tf":
          _SCREAMING_SNAKE_CASE             =    getattr(__lowerCamelCase      , """tf_model_mapping"""      , __lowerCamelCase					)
  
      if mapping is not None:
          _SCREAMING_SNAKE_CASE             =    dict(mapping.items()					)
  
      # cache the results
      _SCREAMING_SNAKE_CASE             =    mapping
      return mapping
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					List[str]      , __lowerCamelCase				:					Optional[Any]					)		->Optional[Any]:
      _SCREAMING_SNAKE_CASE             =    get_framework(__lowerCamelCase					)
      if framework is None:
          return None
      _SCREAMING_SNAKE_CASE             =    get_mapping_for_task(__lowerCamelCase      , __lowerCamelCase					)
      if mapping is None:
          return None
  
      _SCREAMING_SNAKE_CASE             =    list({model_class.config_class for model_class in test_class.all_model_classes}					)
      if len(__lowerCamelCase					) != 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.
      _SCREAMING_SNAKE_CASE             =    mapping.get(config_classes[0]      , __lowerCamelCase					)
      if isinstance(__lowerCamelCase      , (tuple, list)					):
          _SCREAMING_SNAKE_CASE             =    sorted(__lowerCamelCase      , key=lambda __lowerCamelCase					: x.__name__					)
  
      return model_class
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int					)		->Tuple:
      _SCREAMING_SNAKE_CASE             =    [(task, get_model_for_pipeline_test(__lowerCamelCase      , __lowerCamelCase					)) for task in pipeline_test_mapping]
      _SCREAMING_SNAKE_CASE             =    sorted([(task, model) for task, model in mapping if model is not None]      , key=lambda __lowerCamelCase					: x[0]					)
  
      return dict(__lowerCamelCase					)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Any					)		->str:
      _SCREAMING_SNAKE_CASE             =    get_framework(__lowerCamelCase					)
      if framework == "pt":
          _SCREAMING_SNAKE_CASE             =    """torch"""
      _SCREAMING_SNAKE_CASE             =    """{}"""
  
      _SCREAMING_SNAKE_CASE             =    get_pipeline_model_mapping(__lowerCamelCase					)
      if len(__lowerCamelCase					) == 0:
          return ""
  
      _SCREAMING_SNAKE_CASE             =    []
      for task, model_classes in mapping.items():
          if isinstance(__lowerCamelCase      , (tuple, list)					):
              # A list/tuple of model classes
              _SCREAMING_SNAKE_CASE             =    """(""" + """, """.join([x.__name__ for x in model_classes]					) + """)"""
          else:
              # A single model class
              _SCREAMING_SNAKE_CASE             =    model_classes.__name__
          texts.append(F'"{task}": {value}'					)
      _SCREAMING_SNAKE_CASE             =    """{""" + """, """.join(__lowerCamelCase					) + """}"""
      _SCREAMING_SNAKE_CASE             =    F'pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}'
  
      return text
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Optional[Any]					)		->List[str]:
      _SCREAMING_SNAKE_CASE             =    {"""ModelTesterMixin""", """TFModelTesterMixin""", """FlaxModelTesterMixin"""}
      if not issubclass(__lowerCamelCase      , unittest.TestCase					):
          return False
      return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]					)					) > 0
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int					)		->Optional[int]:
      _SCREAMING_SNAKE_CASE             =    [x for x in get_test_classes(__lowerCamelCase					) if is_valid_test_class(__lowerCamelCase					)]
  
      _SCREAMING_SNAKE_CASE             =    None
      for test_class in test_classes:
          # If a test class has defined `pipeline_model_mapping`, let's take it
          if getattr(__lowerCamelCase      , """pipeline_model_mapping"""      , __lowerCamelCase					) is not None:
              _SCREAMING_SNAKE_CASE             =    test_class
              break
      # Take the test class with the shortest name (just a heuristic)
      if target_test_class is None and len(__lowerCamelCase					) > 0:
          _SCREAMING_SNAKE_CASE             =    sorted(__lowerCamelCase      , key=lambda __lowerCamelCase					: (len(x.__name__					), x.__name__)					)[0]
  
      return target_test_class
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Union[str, Any]      , __lowerCamelCase				:					Dict      , __lowerCamelCase				:					Optional[int]					)		->List[Any]:
      _SCREAMING_SNAKE_CASE             =    start_idx
      for idx, line in enumerate(lines[start_idx:]					):
          _SCREAMING_SNAKE_CASE             =    len(__lowerCamelCase					) - len(line.lstrip()					)
          if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
              _SCREAMING_SNAKE_CASE             =    start_idx + idx
          elif idx > 0 and indent <= indent_level:
              # Outside the definition block of `pipeline_model_mapping`
              break
  
      return end_idx
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Any      , __lowerCamelCase				:					int=False					)		->int:
      if getattr(__lowerCamelCase      , """pipeline_model_mapping"""      , __lowerCamelCase					) is not None:
          if not overwrite:
              return "", -1
  
      _SCREAMING_SNAKE_CASE             =    get_pipeline_model_mapping_string(__lowerCamelCase					)
      if len(__lowerCamelCase					) == 0:
          return "", -1
      _SCREAMING_SNAKE_CASE             =    line_to_add + """\n"""
  
      # The code defined the class `test_class`
      _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    inspect.getsourcelines(__lowerCamelCase					)
      # `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(__lowerCamelCase					):
          if line.lstrip().startswith("""class """					):
              _SCREAMING_SNAKE_CASE             =    class_lines[idx:]
              class_start_line_no += idx
              break
      _SCREAMING_SNAKE_CASE             =    class_start_line_no + len(__lowerCamelCase					) - 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`.
      _SCREAMING_SNAKE_CASE             =    None
      # The indent level of the line at `class_lines[start_idx]` (if defined)
      _SCREAMING_SNAKE_CASE             =    0
      # To record if `pipeline_model_mapping` is found in `test_class`.
      _SCREAMING_SNAKE_CASE             =    None
      for idx, line in enumerate(__lowerCamelCase					):
          if line.strip().startswith("""all_model_classes = """					):
              _SCREAMING_SNAKE_CASE             =    len(__lowerCamelCase					) - len(line.lstrip()					)
              _SCREAMING_SNAKE_CASE             =    idx
          elif line.strip().startswith("""all_generative_model_classes = """					):
              _SCREAMING_SNAKE_CASE             =    len(__lowerCamelCase					) - len(line.lstrip()					)
              _SCREAMING_SNAKE_CASE             =    idx
          elif line.strip().startswith("""pipeline_model_mapping = """					):
              _SCREAMING_SNAKE_CASE             =    len(__lowerCamelCase					) - len(line.lstrip()					)
              _SCREAMING_SNAKE_CASE             =    idx
              _SCREAMING_SNAKE_CASE             =    line
              break
  
      if start_idx is None:
          return "", -1
      # Find the ending index (inclusive) of the above found block.
      _SCREAMING_SNAKE_CASE             =    find_block_ending(__lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase					)
  
      # 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
      _SCREAMING_SNAKE_CASE             =    re.compile(R"""\s(is_\S+?_available\(\))\s"""					)
      for line in class_lines[start_idx : end_idx + 1]:
          _SCREAMING_SNAKE_CASE             =    r.search(__lowerCamelCase					)
          if backend_condition is not None:
              # replace the leading and trailing whitespaces to the space character " ".
              _SCREAMING_SNAKE_CASE             =    """ """ + backend_condition[0][1:-1] + """ """
              _SCREAMING_SNAKE_CASE             =    r.sub(__lowerCamelCase      , __lowerCamelCase					)
              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`.
          _SCREAMING_SNAKE_CASE             =    end_idx
      else:
          # `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
          _SCREAMING_SNAKE_CASE             =    start_idx - 1
          # mark the lines of the currently existing `pipeline_model_mapping` to be removed.
          for idx in range(__lowerCamelCase      , end_idx + 1					):
              # These lines are going to be removed before writing to the test file.
              _SCREAMING_SNAKE_CASE             =    None  # noqa
  
      # Make sure the test class is a subclass of `PipelineTesterMixin`.
      _SCREAMING_SNAKE_CASE             =    [x.__name__ for x in test_class.__bases__]
      if "PipelineTesterMixin" not in parent_classes:
          # Put `PipelineTesterMixin` just before `unittest.TestCase`
          _SCREAMING_SNAKE_CASE             =    [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"""					)
          _SCREAMING_SNAKE_CASE             =    """, """.join(_parent_classes					)
          for idx, line in enumerate(__lowerCamelCase					):
              # 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					):
                      _SCREAMING_SNAKE_CASE             =    None  # noqa
                  break
          # Add the new, one-line, class declaration for `test_class`
          _SCREAMING_SNAKE_CASE             =    F'class {test_class.__name__}({parent_classes}):\n'
  
      # Add indentation
      _SCREAMING_SNAKE_CASE             =    """ """ * indent_level + line_to_add
      # Insert `pipeline_model_mapping` to `class_lines`.
      # (The line at `target_idx` should be kept by definition!)
      _SCREAMING_SNAKE_CASE             =    class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
      # Remove the lines that are marked to be removed
      _SCREAMING_SNAKE_CASE             =    [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)
      _SCREAMING_SNAKE_CASE             =    inspect.getsourcelines(inspect.getmodule(__lowerCamelCase					)					)[0]
      # Be careful with the 1-off between line numbers and array indices
      _SCREAMING_SNAKE_CASE             =    module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
      _SCREAMING_SNAKE_CASE             =    """""".join(__lowerCamelCase					)
  
      _SCREAMING_SNAKE_CASE             =    inspect.getsourcefile(__lowerCamelCase					)
      with open(__lowerCamelCase      , """w"""      , encoding="""UTF-8"""      , newline="""\n"""					) as fp:
          fp.write(__lowerCamelCase					)
  
      return line_to_add
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Optional[int]      , __lowerCamelCase				:					str=False					)		->str:
      _SCREAMING_SNAKE_CASE             =    find_test_class(__lowerCamelCase					)
      if test_class:
          add_pipeline_model_mapping(__lowerCamelCase      , overwrite=__lowerCamelCase					)
  
  
  if __name__ == "__main__":
        lowercase_			       =				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`.""",
        )
        lowercase_			       =				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.""")
  
        lowercase_			       =				[]
        if args.test_file:
              lowercase_			       =				[args.test_file]
        else:
              lowercase_			       =				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)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  # DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
  # and https://github.com/hojonathanho/diffusion
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import (
      CommonSchedulerState,
      FlaxKarrasDiffusionSchedulers,
      FlaxSchedulerMixin,
      FlaxSchedulerOutput,
      add_noise_common,
      get_velocity_common,
  )
  
  
  @flax.struct.dataclass
  class       a_  :
  
      '''simple docstring'''
      UpperCamelCase									=				42
      UpperCamelCase									=				42
  
      # setable values
      UpperCamelCase									=				42
      UpperCamelCase									=				42
      UpperCamelCase									=				None
  
  
      @classmethod
      def     snake_case_(	cls				,	A				,	A				,	A				,	A				,	)	->     List[Any]:
          return cls(
              common=A				,	final_alpha_cumprod=A				,	init_noise_sigma=A				,	timesteps=A				,	)
  
  
  
  
  @dataclass
  class       a_  (       snake_case_ ):
  
      '''simple docstring'''
      UpperCamelCase									=				42
  
  
  
  
  class       a_  (       snake_case_   ,					snake_case_ ):
  
      '''simple docstring'''
      UpperCamelCase									=				[e.name for e in FlaxKarrasDiffusionSchedulers]
  
      UpperCamelCase									=				42
  
  
      @property
      def     snake_case_(	self )	->     List[Any]:
          return True
  
  
      @register_to_config
      def __init__(	self				,	A = 1000				,	A = 0.0001				,	A = 0.02				,	A = "linear"				,	A = None				,	A = True				,	A = 0				,	A = "epsilon"				,	A = jnp.floataa				,	)	->     int:
          _SCREAMING_SNAKE_CASE             =    dtype
  
  
      def     snake_case_(	self				,	A = None )	->     DDIMSchedulerState:
          if common is None:
              _SCREAMING_SNAKE_CASE             =    CommonSchedulerState.create(self )
  
          # At every step in ddim, we are looking into the previous alphas_cumprod
          # For the final step, there is no previous alphas_cumprod because we are already at 0
          # `set_alpha_to_one` decides whether we set this parameter simply to one or
          # whether we use the final alpha of the "non-previous" one.
          _SCREAMING_SNAKE_CASE             =    (
              jnp.array(1.0				,	dtype=self.dtype ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
          )
  
          # standard deviation of the initial noise distribution
          _SCREAMING_SNAKE_CASE             =    jnp.array(1.0				,	dtype=self.dtype )
  
          _SCREAMING_SNAKE_CASE             =    jnp.arange(0				,	self.config.num_train_timesteps ).round()[::-1]
  
          return DDIMSchedulerState.create(
              common=A				,	final_alpha_cumprod=A				,	init_noise_sigma=A				,	timesteps=A				,	)
  
  
      def     snake_case_(	self				,	A				,	A				,	A = None )	->     jnp.ndarray:
          return sample
  
  
      def     snake_case_(	self				,	A				,	A				,	A = () )	->     DDIMSchedulerState:
          _SCREAMING_SNAKE_CASE             =    self.config.num_train_timesteps // num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # rounding to avoid issues when num_inference_step is power of 3
          _SCREAMING_SNAKE_CASE             =    (jnp.arange(0				,	A ) * step_ratio).round()[::-1] + self.config.steps_offset
  
          return state.replace(
              num_inference_steps=A				,	timesteps=A				,	)
  
  
      def     snake_case_(	self				,	A				,	A				,	A )	->     int:
          _SCREAMING_SNAKE_CASE             =    state.common.alphas_cumprod[timestep]
          _SCREAMING_SNAKE_CASE             =    jnp.where(
              prev_timestep >= 0				,	state.common.alphas_cumprod[prev_timestep]				,	state.final_alpha_cumprod )
          _SCREAMING_SNAKE_CASE             =    1 - alpha_prod_t
          _SCREAMING_SNAKE_CASE             =    1 - alpha_prod_t_prev
  
          _SCREAMING_SNAKE_CASE             =    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
          return variance
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A = 0.0				,	A = True				,	)	->     Union[FlaxDDIMSchedulerOutput, Tuple]:
          if state.num_inference_steps is None:
              raise ValueError(
                  """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler""" )
  
          # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
          # Ideally, read DDIM paper in-detail understanding
  
          # Notation (<variable name> -> <name in paper>
          # - pred_noise_t -> e_theta(x_t, t)
          # - pred_original_sample -> f_theta(x_t, t) or x_0
          # - std_dev_t -> sigma_t
          # - eta -> η
          # - pred_sample_direction -> "direction pointing to x_t"
          # - pred_prev_sample -> "x_t-1"
  
          # 1. get previous step value (=t-1)
          _SCREAMING_SNAKE_CASE             =    timestep - self.config.num_train_timesteps // state.num_inference_steps
  
          _SCREAMING_SNAKE_CASE             =    state.common.alphas_cumprod
          _SCREAMING_SNAKE_CASE             =    state.final_alpha_cumprod
  
          # 2. compute alphas, betas
          _SCREAMING_SNAKE_CASE             =    alphas_cumprod[timestep]
          _SCREAMING_SNAKE_CASE             =    jnp.where(prev_timestep >= 0				,	alphas_cumprod[prev_timestep]				,	A )
  
          _SCREAMING_SNAKE_CASE             =    1 - alpha_prod_t
  
          # 3. compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          if self.config.prediction_type == "epsilon":
              _SCREAMING_SNAKE_CASE             =    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
              _SCREAMING_SNAKE_CASE             =    model_output
          elif self.config.prediction_type == "sample":
              _SCREAMING_SNAKE_CASE             =    model_output
              _SCREAMING_SNAKE_CASE             =    (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
          elif self.config.prediction_type == "v_prediction":
              _SCREAMING_SNAKE_CASE             =    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
              _SCREAMING_SNAKE_CASE             =    (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
          else:
              raise ValueError(
                  f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
                  """ `v_prediction`""" )
  
          # 4. compute variance: "sigma_t(η)" -> see formula (16)
          # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
          _SCREAMING_SNAKE_CASE             =    self._get_variance(A				,	A				,	A )
          _SCREAMING_SNAKE_CASE             =    eta * variance ** 0.5
  
          # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          _SCREAMING_SNAKE_CASE             =    (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon
  
          # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          _SCREAMING_SNAKE_CASE             =    alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
  
          if not return_dict:
              return (prev_sample, state)
  
          return FlaxDDIMSchedulerOutput(prev_sample=A				,	state=A )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	)	->     jnp.ndarray:
          return add_noise_common(state.common				,	A				,	A				,	A )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	)	->     jnp.ndarray:
          return get_velocity_common(state.common				,	A				,	A				,	A )
  
  
  
  
  
      def __len__(	self )	->     Any:
          return self.config.num_train_timesteps
  
  
  
  