




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


__magic_name__     :     List[Any]  							=					{}
for task, _ in pipeline_test_mapping.items():
      __magic_name__     :     Union[str, Any]  							=					{"""pt""": None, """tf""": None}


# DO **NOT** add item to this set (unless the reason is approved)
__magic_name__     :     List[Any]  							=					{
    """tests/models/esm/test_modeling_esmfold.py""",  # The pipeline test mapping is added to `test_modeling_esm.py`
}
def 		a_       (   __lowerCAmelCase							):

  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 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase							):
  # Use the cached results
  if PIPELINE_TEST_MAPPING[task].get(__lowerCAmelCase      ,   __lowerCAmelCase							) is not None:
    return PIPELINE_TEST_MAPPING[task][framework]

  lowerCAmelCase__								=			pipeline_test_mapping[task]['''test''']
  lowerCAmelCase__								=			None

  if framework == "pt":
    lowerCAmelCase__								=			getattr(__lowerCAmelCase      ,   '''model_mapping'''      ,   __lowerCAmelCase							)
  elif framework == "tf":
    lowerCAmelCase__								=			getattr(__lowerCAmelCase      ,   '''tf_model_mapping'''      ,   __lowerCAmelCase							)

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

  # cache the results
  lowerCAmelCase__								=			mapping
  return mapping
def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase							):
  lowerCAmelCase__								=			get_framework(__lowerCAmelCase							)
  if framework is None:
    return None
  lowerCAmelCase__								=			get_mapping_for_task(__lowerCAmelCase      ,   __lowerCAmelCase							)
  if mapping is None:
    return None

  lowerCAmelCase__								=			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.
  lowerCAmelCase__								=			mapping.get(config_classes[0]      ,   __lowerCAmelCase							)
  if isinstance(__lowerCAmelCase      ,   (tuple, list)							):
    lowerCAmelCase__								=			sorted(__lowerCAmelCase      ,   key=lambda __lowerCAmelCase							: x.__name__							)

  return model_class
def 		a_       (   __lowerCAmelCase							):
  lowerCAmelCase__								=			[(task, get_model_for_pipeline_test(__lowerCAmelCase      ,   __lowerCAmelCase							)) for task in pipeline_test_mapping]
  lowerCAmelCase__								=			sorted([(task, model) for task, model in mapping if model is not None]      ,   key=lambda __lowerCAmelCase							: x[0]							)

  return dict(__lowerCAmelCase							)
def 		a_       (   __lowerCAmelCase							):
  lowerCAmelCase__								=			get_framework(__lowerCAmelCase							)
  if framework == "pt":
    lowerCAmelCase__								=			'''torch'''
  lowerCAmelCase__								=			'''{}'''

  lowerCAmelCase__								=			get_pipeline_model_mapping(__lowerCAmelCase							)
  if len(__lowerCAmelCase							) == 0:
    return ""

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

  return text
def 		a_       (   __lowerCAmelCase							):
  lowerCAmelCase__								=			{'''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 		a_       (   __lowerCAmelCase							):
  lowerCAmelCase__								=			[x for x in get_test_classes(__lowerCAmelCase							) if is_valid_test_class(__lowerCAmelCase							)]

  lowerCAmelCase__								=			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:
      lowerCAmelCase__								=			test_class
      break
    # Take the test class with the shortest name (just a heuristic)
  if target_test_class is None and len(__lowerCAmelCase							) > 0:
    lowerCAmelCase__								=			sorted(__lowerCAmelCase      ,   key=lambda __lowerCAmelCase							: (len(x.__name__							), x.__name__)							)[0]

  return target_test_class
def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase      ,   __lowerCAmelCase							):
  lowerCAmelCase__								=			start_idx
  for idx, line in enumerate(lines[start_idx:]							):
    lowerCAmelCase__								=			len(__lowerCAmelCase							) - len(line.lstrip()							)
    if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
      lowerCAmelCase__								=			start_idx + idx
    elif idx > 0 and indent <= indent_level:
      # Outside the definition block of `pipeline_model_mapping`
      break

  return end_idx
def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase=False							):
  if getattr(__lowerCAmelCase      ,   '''pipeline_model_mapping'''      ,   __lowerCAmelCase							) is not None:
    if not overwrite:
      return "", -1

  lowerCAmelCase__								=			get_pipeline_model_mapping_string(__lowerCAmelCase							)
  if len(__lowerCAmelCase							) == 0:
    return "", -1
  lowerCAmelCase__								=			line_to_add + '''\n'''

  # The code defined the class `test_class`
  lowerCAmelCase__							,			lowerCAmelCase__								=			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 '''							):
      lowerCAmelCase__								=			class_lines[idx:]
      class_start_line_no += idx
      break
  lowerCAmelCase__								=			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`.
  lowerCAmelCase__								=			None
  # The indent level of the line at `class_lines[start_idx]` (if defined)
  lowerCAmelCase__								=			0
  # To record if `pipeline_model_mapping` is found in `test_class`.
  lowerCAmelCase__								=			None
  for idx, line in enumerate(__lowerCAmelCase							):
    if line.strip().startswith('''all_model_classes = '''							):
      lowerCAmelCase__								=			len(__lowerCAmelCase							) - len(line.lstrip()							)
      lowerCAmelCase__								=			idx
    elif line.strip().startswith('''all_generative_model_classes = '''							):
      lowerCAmelCase__								=			len(__lowerCAmelCase							) - len(line.lstrip()							)
      lowerCAmelCase__								=			idx
    elif line.strip().startswith('''pipeline_model_mapping = '''							):
      lowerCAmelCase__								=			len(__lowerCAmelCase							) - len(line.lstrip()							)
      lowerCAmelCase__								=			idx
      lowerCAmelCase__								=			line
      break

  if start_idx is None:
    return "", -1
  # Find the ending index (inclusive) of the above found block.
  lowerCAmelCase__								=			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
  lowerCAmelCase__								=			re.compile(r'''\s(is_\S+?_available\(\))\s'''							)
  for line in class_lines[start_idx : end_idx + 1]:
    lowerCAmelCase__								=			r.search(__lowerCAmelCase							)
    if backend_condition is not None:
      # replace the leading and trailing whitespaces to the space character " ".
      lowerCAmelCase__								=			''' ''' + backend_condition[0][1:-1] + ''' '''
      lowerCAmelCase__								=			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`.
    lowerCAmelCase__								=			end_idx
  else:
    # `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
    lowerCAmelCase__								=			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.
      lowerCAmelCase__								=			None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
  lowerCAmelCase__								=			[x.__name__ for x in test_class.__bases__]
  if "PipelineTesterMixin" not in parent_classes:
    # Put `PipelineTesterMixin` just before `unittest.TestCase`
    lowerCAmelCase__								=			[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'''							)
    lowerCAmelCase__								=			''', '''.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							):
          lowerCAmelCase__								=			None  # noqa
        break
        # Add the new, one-line, class declaration for `test_class`
    lowerCAmelCase__								=			F"""class {test_class.__name__}({parent_classes}):\n"""

  # Add indentation
  lowerCAmelCase__								=			''' ''' * indent_level + line_to_add
  # Insert `pipeline_model_mapping` to `class_lines`.
  # (The line at `target_idx` should be kept by definition!)
  lowerCAmelCase__								=			class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
  # Remove the lines that are marked to be removed
  lowerCAmelCase__								=			[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)
  lowerCAmelCase__								=			inspect.getsourcelines(inspect.getmodule(__lowerCAmelCase							)							)[0]
  # Be careful with the 1-off between line numbers and array indices
  lowerCAmelCase__								=			module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
  lowerCAmelCase__								=			''''''.join(__lowerCAmelCase							)

  lowerCAmelCase__								=			inspect.getsourcefile(__lowerCAmelCase							)
  with open(__lowerCAmelCase      ,   '''w'''      ,   encoding='''UTF-8'''      ,   newline='''\n'''							) as fp:
    fp.write(__lowerCAmelCase							)

  return line_to_add
def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase=False							):
  lowerCAmelCase__								=			find_test_class(__lowerCAmelCase							)
  if test_class:
    add_pipeline_model_mapping(__lowerCAmelCase      ,   overwrite=__lowerCAmelCase							)


if __name__ == "__main__":
      __magic_name__     :     List[Any]  							=					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`.""",
      )
      __magic_name__     :     Optional[Any]  							=					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.""")

      __magic_name__     :     Union[str, Any]  							=					[]
      if args.test_file:
            __magic_name__     :     Union[str, Any]  							=					[args.test_file]
      else:
            __magic_name__     :     Tuple  							=					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)







from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)





@flax.struct.dataclass
class 						SCREAMING_SNAKE_CASE__		:
     lowercase_   :			CommonSchedulerState

     # setable values
     lowercase_   :			jnp.ndarray
     lowercase_   :			jnp.ndarray
     lowercase_   :			jnp.ndarray
     lowercase_   :			Optional[int]   =   None

     # running values
     lowercase_   :			Optional[jnp.ndarray]   =   None
     @classmethod
     def 					A__			(	cls  :		List[str]					,	__lowerCamelCase  :		CommonSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray	):



       """simple docstring"""
       return cls(common=__lowerCamelCase					,	init_noise_sigma=__lowerCamelCase					,	timesteps=__lowerCamelCase					,	sigmas=__lowerCamelCase	)





@dataclass
class 						SCREAMING_SNAKE_CASE__		(_a ):
     lowercase_   :			LMSDiscreteSchedulerState





class 						SCREAMING_SNAKE_CASE__		(_a						,				_a ):
     lowercase_   :			int   =   [e.name for e in FlaxKarrasDiffusionSchedulers]

     lowercase_   :			jnp.dtype
     @property
     def 					A__			(	self  :		Optional[Any]	):



       """simple docstring"""
       return True
     @register_to_config
     def __init__(	self  :		int					,	__lowerCamelCase  :		int = 10_00					,	__lowerCamelCase  :		float = 0.0001					,	__lowerCamelCase  :		float = 0.02					,	__lowerCamelCase  :		str = "linear"					,	__lowerCamelCase  :		Optional[jnp.ndarray] = None					,	__lowerCamelCase  :		str = "epsilon"					,	__lowerCamelCase  :		jnp.dtype = jnp.floataa					,	):



       """simple docstring"""
       lowerCAmelCase__								=			dtype
     def 					A__			(	self  :		List[str]					,	__lowerCamelCase  :		Optional[CommonSchedulerState] = None	):



       """simple docstring"""
       if common is None:
         lowerCAmelCase__								=			CommonSchedulerState.create(self	)

       lowerCAmelCase__								=			jnp.arange(0					,	self.config.num_train_timesteps	).round()[::-1]
       lowerCAmelCase__								=			((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

       # standard deviation of the initial noise distribution
       lowerCAmelCase__								=			sigmas.max()

       return LMSDiscreteSchedulerState.create(
           common=__lowerCamelCase					,	init_noise_sigma=__lowerCamelCase					,	timesteps=__lowerCamelCase					,	sigmas=__lowerCamelCase					,	)
     def 					A__			(	self  :		str					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		int	):



       """simple docstring"""
       ((lowerCAmelCase__)							,			)								=			jnp.where(state.timesteps == timestep					,	size=1	)
       lowerCAmelCase__								=			step_index[0]

       lowerCAmelCase__								=			state.sigmas[step_index]
       lowerCAmelCase__								=			sample / ((sigma**2 + 1) ** 0.5)
       return sample
     def 					A__			(	self  :		List[Any]					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		Optional[int]					,	__lowerCamelCase  :		str					,	__lowerCamelCase  :		Any	):



       """simple docstring"""

       def lms_derivative(__lowerCamelCase  :		int	):
         lowerCAmelCase__								=			1.0
         for k in range(__lowerCamelCase	):
           if current_order == k:
             continue
           prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
         return prod

       lowerCAmelCase__								=			integrate.quad(__lowerCamelCase					,	state.sigmas[t]					,	state.sigmas[t + 1]					,	epsrel=1e-4	)[0]

       return integrated_coeff
     def 					A__			(	self  :		str					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		int					,	__lowerCamelCase  :		Tuple = ()	):



       """simple docstring"""
       lowerCAmelCase__								=			jnp.linspace(self.config.num_train_timesteps - 1					,	0					,	__lowerCamelCase					,	dtype=self.dtype	)

       lowerCAmelCase__								=			jnp.floor(__lowerCamelCase	).astype(jnp.intaa	)
       lowerCAmelCase__								=			jnp.ceil(__lowerCamelCase	).astype(jnp.intaa	)

       lowerCAmelCase__								=			jnp.mod(__lowerCamelCase					,	1.0	)

       lowerCAmelCase__								=			((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
       lowerCAmelCase__								=			(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
       lowerCAmelCase__								=			jnp.concatenate([sigmas, jnp.array([0.0]					,	dtype=self.dtype	)]	)

       lowerCAmelCase__								=			timesteps.astype(jnp.intaa	)

       # initial running values
       lowerCAmelCase__								=			jnp.zeros((0,) + shape					,	dtype=self.dtype	)

       return state.replace(
           timesteps=__lowerCamelCase					,	sigmas=__lowerCamelCase					,	num_inference_steps=__lowerCamelCase					,	derivatives=__lowerCamelCase					,	)
     def 					A__			(	self  :		Union[str, Any]					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		int					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		int = 4					,	__lowerCamelCase  :		bool = True					,	):



       """simple docstring"""
       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'''	)

       lowerCAmelCase__								=			state.sigmas[timestep]

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
         lowerCAmelCase__								=			sample - sigma * model_output
       elif self.config.prediction_type == "v_prediction":
         # * c_out + input * c_skip
         lowerCAmelCase__								=			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
       else:
         raise ValueError(
             F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""	)

       # 2. Convert to an ODE derivative
       lowerCAmelCase__								=			(sample - pred_original_sample) / sigma
       lowerCAmelCase__								=			state.replace(derivatives=jnp.append(state.derivatives					,	__lowerCamelCase	)	)
       if len(state.derivatives	) > order:
         lowerCAmelCase__								=			state.replace(derivatives=jnp.delete(state.derivatives					,	0	)	)

       # 3. Compute linear multistep coefficients
       lowerCAmelCase__								=			min(timestep + 1					,	__lowerCamelCase	)
       lowerCAmelCase__								=			[self.get_lms_coefficient(__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase	) for curr_order in range(__lowerCamelCase	)]

       # 4. Compute previous sample based on the derivatives path
       lowerCAmelCase__								=			sample + sum(
           coeff * derivative for coeff, derivative in zip(__lowerCamelCase					,	reversed(state.derivatives	)	)	)

       if not return_dict:
         return (prev_sample, state)

       return FlaxLMSSchedulerOutput(prev_sample=__lowerCamelCase					,	state=__lowerCamelCase	)
     def 					A__			(	self  :		Dict					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray					,	):



       """simple docstring"""
       lowerCAmelCase__								=			state.sigmas[timesteps].flatten()
       lowerCAmelCase__								=			broadcast_to_shape_from_left(__lowerCamelCase					,	noise.shape	)

       lowerCAmelCase__								=			original_samples + noise * sigma

       return noisy_samples



     def __len__(	self  :		str	):



       """simple docstring"""
       return self.config.num_train_timesteps

