




'''simple docstring'''
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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


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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 	SCREAMING_SNAKE_CASE(    __A					):
       snake_case_			:					torch.FloatTensor
       snake_case_			:					Optional[torch.FloatTensor]   = None





def     snake_case_  (       a__		:     Optional[int]   ,a__		:     List[str]=0.9_9_9   ,a__		:     Any="cosine"   ,):



    """simple docstring"""




    if alpha_transform_type == "cosine":

        def alpha_bar_fn(a__		:     List[Any]      ):
            return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2      ) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(a__		:     List[str]      ):
            return math.exp(t * -12.0      )

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

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






class 	SCREAMING_SNAKE_CASE(    __A       ,				__A					):
       snake_case_			:					Tuple   = [e.name for e in KarrasDiffusionSchedulers]
       snake_case_			:					int   = 1

       @register_to_config
       def __init__(   self ,		lowerCamelCase__ = 1000 ,		lowerCamelCase__ = 0.00_01 ,		lowerCamelCase__ = 0.02 ,		lowerCamelCase__ = "linear" ,		lowerCamelCase__ = None ,		lowerCamelCase__ = "epsilon" ,		lowerCamelCase__ = "linspace" ,		lowerCamelCase__ = 0 ,		)   -> List[Any]:

           """simple docstring"""



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

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

           __lowercase          =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
           __lowercase          =						np.concatenate([sigmas[::-1], [0.0]]					).astype(np.floataa					)
           __lowercase          =						torch.from_numpy(lowerCamelCase__					)

           # setable values
           __lowercase          =						None
           __lowercase          =						np.linspace(0 ,		num_train_timesteps - 1 ,		lowerCamelCase__ ,		dtype=lowerCamelCase__					)[::-1].copy()
           __lowercase          =						torch.from_numpy(lowerCamelCase__					)
           __lowercase          =						False

       @property
       def    snake_case__      (   self					)   -> Tuple:

           """simple docstring"""



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

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

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__					)   -> torch.FloatTensor:

           """simple docstring"""



           if isinstance(lowerCamelCase__ ,		torch.Tensor					):
               __lowercase          =						timestep.to(self.timesteps.device					)
           __lowercase          =						(self.timesteps == timestep).nonzero().item()
           __lowercase          =						self.sigmas[step_index]
           __lowercase          =						sample / ((sigma**2 + 1) ** 0.5)
           __lowercase          =						True
           return sample

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__ = None					)   -> Union[str, Any]:

           """simple docstring"""



           __lowercase          =						num_inference_steps

           # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
           if self.config.timestep_spacing == "linspace":
               __lowercase          =						np.linspace(0 ,		self.config.num_train_timesteps - 1 ,		lowerCamelCase__ ,		dtype=lowerCamelCase__					)[
                   ::-1
               ].copy()
           elif self.config.timestep_spacing == "leading":
               __lowercase          =						self.config.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
               __lowercase          =						(np.arange(0 ,		lowerCamelCase__					) * step_ratio).round()[::-1].copy().astype(lowerCamelCase__					)
               timesteps += self.config.steps_offset
           elif self.config.timestep_spacing == "trailing":
               __lowercase          =						self.config.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
               __lowercase          =						(np.arange(self.config.num_train_timesteps ,		0 ,		-step_ratio					)).round().copy().astype(lowerCamelCase__					)
               timesteps -= 1
           else:
               raise ValueError(
                   F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'					)

           __lowercase          =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
           __lowercase          =						np.interp(lowerCamelCase__ ,		np.arange(0 ,		len(lowerCamelCase__					)					) ,		lowerCamelCase__					)
           __lowercase          =						np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
           __lowercase          =						torch.from_numpy(lowerCamelCase__					).to(device=lowerCamelCase__					)
           if str(lowerCamelCase__					).startswith("""mps"""					):
               # mps does not support float64
               __lowercase          =						torch.from_numpy(lowerCamelCase__					).to(lowerCamelCase__ ,		dtype=torch.floataa					)
           else:
               __lowercase          =						torch.from_numpy(lowerCamelCase__					).to(device=lowerCamelCase__					)

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ = None ,		lowerCamelCase__ = True ,		)   -> Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

           """simple docstring"""



           if (
               isinstance(lowerCamelCase__ ,		lowerCamelCase__					)
               or isinstance(lowerCamelCase__ ,		torch.IntTensor					)
               or isinstance(lowerCamelCase__ ,		torch.LongTensor					)
           ):
               raise ValueError(
                   (
                       """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
                       """ `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"""
                       """ one of the `scheduler.timesteps` as a timestep."""
                   ) ,		)

           if not self.is_scale_input_called:
               logger.warning(
                   """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
                   """See `StableDiffusionPipeline` for a usage example."""					)

           if isinstance(lowerCamelCase__ ,		torch.Tensor					):
               __lowercase          =						timestep.to(self.timesteps.device					)

           __lowercase          =						(self.timesteps == timestep).nonzero().item()
           __lowercase          =						self.sigmas[step_index]

           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           if self.config.prediction_type == "epsilon":
               __lowercase          =						sample - sigma * model_output
           elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               __lowercase          =						model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`'					)

           __lowercase          =						self.sigmas[step_index]
           __lowercase          =						self.sigmas[step_index + 1]
           __lowercase          =						(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
           __lowercase          =						(sigma_to**2 - sigma_up**2) ** 0.5

           # 2. Convert to an ODE derivative
           __lowercase          =						(sample - pred_original_sample) / sigma

           __lowercase          =						sigma_down - sigma

           __lowercase          =						sample + derivative * dt

           __lowercase          =						model_output.device
           __lowercase          =						randn_tensor(model_output.shape ,		dtype=model_output.dtype ,		device=lowerCamelCase__ ,		generator=lowerCamelCase__					)

           __lowercase          =						prev_sample + noise * sigma_up

           if not return_dict:
               return (prev_sample,)

           return EulerAncestralDiscreteSchedulerOutput(
               prev_sample=lowerCamelCase__ ,		pred_original_sample=lowerCamelCase__					)

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		)   -> torch.FloatTensor:

           """simple docstring"""



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

           __lowercase          =						[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

           __lowercase          =						original_samples + noise * sigma
           return noisy_samples




       def __len__(   self					)   -> Optional[Any]:

           """simple docstring"""



           return self.config.num_train_timesteps










'''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


A							:				Any			     =	{}
for task, _ in pipeline_test_mapping.items():
  A							:				Optional[Any]			     =	{"""pt""": None, """tf""": None}


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



    """simple docstring"""




    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_  (       a__		:     Union[str, Any]   ,a__		:     str      ):



    """simple docstring"""




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

    __lowercase          =						pipeline_test_mapping[task]["""test"""]
    __lowercase          =						None

    if framework == "pt":
        __lowercase          =						getattr(a__   ,"""model_mapping"""   ,a__      )
    elif framework == "tf":
        __lowercase          =						getattr(a__   ,"""tf_model_mapping"""   ,a__      )

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

    # cache the results
    __lowercase          =						mapping
    return mapping
def     snake_case_  (       a__		:     Optional[int]   ,a__		:     Optional[Any]      ):



    """simple docstring"""




    __lowercase          =						get_framework(a__      )
    if framework is None:
        return None
    __lowercase          =						get_mapping_for_task(a__   ,a__      )
    if mapping is None:
        return None

    __lowercase          =						list({model_class.config_class for model_class in test_class.all_model_classes}      )
    if len(a__      ) != 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.
    __lowercase          =						mapping.get(config_classes[0]   ,a__      )
    if isinstance(a__   ,(tuple, list)      ):
        __lowercase          =						sorted(a__   ,key=lambda a__      : x.__name__      )

    return model_class
def     snake_case_  (       a__		:     Any      ):



    """simple docstring"""




    __lowercase          =						[(task, get_model_for_pipeline_test(a__   ,a__      )) for task in pipeline_test_mapping]
    __lowercase          =						sorted([(task, model) for task, model in mapping if model is not None]   ,key=lambda a__      : x[0]      )

    return dict(a__      )
def     snake_case_  (       a__		:     Optional[int]      ):



    """simple docstring"""




    __lowercase          =						get_framework(a__      )
    if framework == "pt":
        __lowercase          =						"""torch"""
    __lowercase          =						"""{}"""

    __lowercase          =						get_pipeline_model_mapping(a__      )
    if len(a__      ) == 0:
        return ""

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

    return text
def     snake_case_  (       a__		:     List[Any]      ):



    """simple docstring"""




    __lowercase          =						{"""ModelTesterMixin""", """TFModelTesterMixin""", """FlaxModelTesterMixin"""}
    if not issubclass(a__   ,unittest.TestCase      ):
        return False
    return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]      )      ) > 0
def     snake_case_  (       a__		:     List[Any]      ):



    """simple docstring"""




    __lowercase          =						[x for x in get_test_classes(a__      ) if is_valid_test_class(a__      )]

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

    return target_test_class
def     snake_case_  (       a__		:     Any   ,a__		:     Optional[int]   ,a__		:     int      ):



    """simple docstring"""




    __lowercase          =						start_idx
    for idx, line in enumerate(lines[start_idx:]      ):
        __lowercase          =						len(a__      ) - len(line.lstrip()      )
        if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
            __lowercase          =						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_  (       a__		:     int   ,a__		:     List[Any]=False      ):



    """simple docstring"""




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

    __lowercase          =						get_pipeline_model_mapping_string(a__      )
    if len(a__      ) == 0:
        return "", -1
    __lowercase          =						line_to_add + """\n"""

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

    if start_idx is None:
        return "", -1
    # Find the ending index (inclusive) of the above found block.
    __lowercase          =						find_block_ending(a__   ,a__   ,a__      )

    # 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
    __lowercase          =						re.compile(r"""\s(is_\S+?_available\(\))\s"""      )
    for line in class_lines[start_idx : end_idx + 1]:
        __lowercase          =						r.search(a__      )
        if backend_condition is not None:
            # replace the leading and trailing whitespaces to the space character " ".
            __lowercase          =						""" """ + backend_condition[0][1:-1] + """ """
            __lowercase          =						r.sub(a__   ,a__      )
            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`.
        __lowercase          =						end_idx
    else:
        # `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
        __lowercase          =						start_idx - 1
        # mark the lines of the currently existing `pipeline_model_mapping` to be removed.
        for idx in range(a__   ,end_idx + 1      ):
            # These lines are going to be removed before writing to the test file.
            __lowercase          =						None  # noqa

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

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

    __lowercase          =						inspect.getsourcefile(a__      )
    with open(a__   ,"""w"""   ,encoding="""UTF-8"""   ,newline="""\n"""      ) as fp:
        fp.write(a__      )

    return line_to_add
def     snake_case_  (       a__		:     List[str]   ,a__		:     str=False      ):



    """simple docstring"""




    __lowercase          =						find_test_class(a__      )
    if test_class:
        add_pipeline_model_mapping(a__   ,overwrite=a__      )


if __name__ == "__main__":
  A							:				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`.""",
  )
  A							:				Dict			     =	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.""")

  A							:				Union[str, Any]			     =	[]
  if args.test_file:
    A							:				int			     =	[args.test_file]
  else:
    A							:				Optional[int]			     =	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)




