



"""simple docstring"""


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  A_     :



						"""simple docstring"""
						__UpperCamelCase													=				42

						# setable values
						__UpperCamelCase													=				42
						__UpperCamelCase													=				42
						__UpperCamelCase													=				42
						__UpperCamelCase													=				None

						# running values
						__UpperCamelCase													=				None






						@classmethod
						def       UpperCAmelCase__	(   cls					:List[Any]  ,    lowercase_					:CommonSchedulerState  ,    lowercase_					:jnp.ndarray  ,    lowercase_					:jnp.ndarray  ,    lowercase_					:jnp.ndarray   )							->     Union[str, Any]:
												return cls(common=lowercase_  ,    init_noise_sigma=lowercase_  ,    timesteps=lowercase_  ,    sigmas=lowercase_   )







@dataclass
class  A_     (	_UpperCAmelCase							):



						"""simple docstring"""
						__UpperCamelCase													=				42







class  A_     (	_UpperCAmelCase      ,  _UpperCAmelCase							):



						"""simple docstring"""
						__UpperCamelCase													=				[e.name for e in FlaxKarrasDiffusionSchedulers]

						__UpperCamelCase													=				42






						@property
						def       UpperCAmelCase__	(   self					:int   )							->     Dict:
												return True






						@register_to_config
						def __init__(   self					:int  ,    lowercase_					:int = 10_00  ,    lowercase_					:float = 0.0001  ,    lowercase_					:float = 0.02  ,    lowercase_					:str = "linear"  ,    lowercase_					:Optional[jnp.ndarray] = None  ,    lowercase_					:str = "epsilon"  ,    lowercase_					:jnp.dtype = jnp.floataa  ,    )							->     int:
												UpperCAmelCase			 =					dtype






						def       UpperCAmelCase__	(   self					:Tuple  ,    lowercase_					:Optional[CommonSchedulerState] = None   )							->     LMSDiscreteSchedulerState:







												if common is None:
																		UpperCAmelCase			 =					CommonSchedulerState.create(self   )

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

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

												return LMSDiscreteSchedulerState.create(
												    common=lowercase_  ,    init_noise_sigma=lowercase_  ,    timesteps=lowercase_  ,    sigmas=lowercase_  ,    )






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:LMSDiscreteSchedulerState  ,    lowercase_					:jnp.ndarray  ,    lowercase_					:int   )							->     jnp.ndarray:
												((UpperCAmelCase		) ,       		)			 =					jnp.where(state.timesteps == timestep  ,    size=1   )
												UpperCAmelCase			 =					step_index[0]

												UpperCAmelCase			 =					state.sigmas[step_index]
												UpperCAmelCase			 =					sample / ((sigma**2 + 1) ** 0.5)
												return sample






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:LMSDiscreteSchedulerState  ,    lowercase_					:Optional[Any]  ,    lowercase_					:List[str]  ,    lowercase_					:Union[str, Any]   )							->     List[str]:







												def lms_derivative(lowercase_					:List[Any]   ):
																		UpperCAmelCase			 =					1.0
																		for k in range(lowercase_   ):
																								if current_order == k:
																														continue
																								prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
																		return prod

												UpperCAmelCase			 =					integrate.quad(lowercase_  ,    state.sigmas[t]  ,    state.sigmas[t + 1]  ,    epsrel=1E-4   )[0]

												return integrated_coeff






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:LMSDiscreteSchedulerState  ,    lowercase_					:int  ,    lowercase_					:Tuple = ()   )							->     LMSDiscreteSchedulerState:
												UpperCAmelCase			 =					jnp.linspace(self.config.num_train_timesteps - 1  ,    0  ,    lowercase_  ,    dtype=self.dtype   )

												UpperCAmelCase			 =					jnp.floor(lowercase_   ).astype(jnp.intaa   )
												UpperCAmelCase			 =					jnp.ceil(lowercase_   ).astype(jnp.intaa   )

												UpperCAmelCase			 =					jnp.mod(lowercase_  ,    1.0   )

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

												UpperCAmelCase			 =					timesteps.astype(jnp.intaa   )

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

												return state.replace(
												    timesteps=lowercase_  ,    sigmas=lowercase_  ,    num_inference_steps=lowercase_  ,    derivatives=lowercase_  ,    )






						def       UpperCAmelCase__	(   self					:Any  ,    lowercase_					:LMSDiscreteSchedulerState  ,    lowercase_					:jnp.ndarray  ,    lowercase_					:int  ,    lowercase_					:jnp.ndarray  ,    lowercase_					:int = 4  ,    lowercase_					:bool = True  ,    )							->     Union[FlaxLMSSchedulerOutput, 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'   )

												UpperCAmelCase			 =					state.sigmas[timestep]

												# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
												if self.config.prediction_type == "epsilon":
																		UpperCAmelCase			 =					sample - sigma * model_output
												elif self.config.prediction_type == "v_prediction":
																		# * c_out + input * c_skip
																		UpperCAmelCase			 =					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
												UpperCAmelCase			 =					(sample - pred_original_sample) / sigma
												UpperCAmelCase			 =					state.replace(derivatives=jnp.append(state.derivatives  ,    lowercase_   )   )
												if len(state.derivatives   ) > order:
																		UpperCAmelCase			 =					state.replace(derivatives=jnp.delete(state.derivatives  ,    0   )   )

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

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

												if not return_dict:
																		return (prev_sample, state)

												return FlaxLMSSchedulerOutput(prev_sample=lowercase_  ,    state=lowercase_   )






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:LMSDiscreteSchedulerState  ,    lowercase_					:jnp.ndarray  ,    lowercase_					:jnp.ndarray  ,    lowercase_					:jnp.ndarray  ,    )							->     jnp.ndarray:
												UpperCAmelCase			 =					state.sigmas[timesteps].flatten()
												UpperCAmelCase			 =					broadcast_to_shape_from_left(lowercase_  ,    noise.shape   )

												UpperCAmelCase			 =					original_samples + noise * sigma

												return noisy_samples






						def __len__(   self					:Dict   )							->     Any:
												return self.config.num_train_timesteps








import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      str )				-> str:





    '''simple docstring'''






    A__        =  min(re.search('^ +'       ,     SCREAMING_SNAKE_CASE__ ).end() if t.startswith(' ' ) else 0 for t in string.splitlines() )
    return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(SCREAMING_SNAKE_CASE__ )] )


lowercase_									=    "\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

lowercase_									=    "\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


lowercase_									=    "\\n# Begin of markdown\n\nSome cool dataset card\n"


class 					A		(				unittest.TestCase					):







    """simple docstring"""





    def 		snake_case__	( self					:			List[Any]						)->       str:





        '''simple docstring'''







        with tempfile.TemporaryDirectory() as tmp_dir:
            A__        =  Path(lowercase_						) / 'README.md'
            with open(lowercase_,'w+'						) as readme_file:
                readme_file.write(lowercase_						)
            A__        =  DatasetMetadata.from_readme(lowercase_						)
            self.assertDictEqual(lowercase_,{'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}						)

            with open(lowercase_,'w+'						) as readme_file:
                readme_file.write(lowercase_						)
            A__        =  DatasetMetadata.from_readme(lowercase_						)
            self.assertDictEqual(lowercase_,{}						)

            with open(lowercase_,'w+'						) as readme_file:
                readme_file.write(lowercase_						)
            A__        =  DatasetMetadata.from_readme(lowercase_						)
            self.assertEqual(lowercase_,{}						)


    def 		snake_case__	( self					:			Tuple						)->       List[Any]:





        '''simple docstring'''







        A__        =  _dedent(
            '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            '						)
        assert DatasetMetadata.from_yaml_string(lowercase_						)

        A__        =  _dedent(
            '\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            '						)
        with self.assertRaises(lowercase_						):
            DatasetMetadata.from_yaml_string(lowercase_						)

        A__        =  _dedent(
            '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            '						)
        assert DatasetMetadata.from_yaml_string(lowercase_						)

