





'''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)










'''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



