



"""simple docstring"""

import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


lowercase__			   =  "scheduler_config.json"




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					1
						_lowerCAmelCase							=					2
						_lowerCAmelCase							=					3
						_lowerCAmelCase							=					4
						_lowerCAmelCase							=					5







@dataclass
class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					4_2







class SCREAMING_SNAKE_CASE__					:
						_lowerCAmelCase							=					SCHEDULER_CONFIG_NAME
						_lowerCAmelCase							=					["dtype"]
						_lowerCAmelCase							=					[]
						_lowerCAmelCase							=					True



						@classmethod
						def     lowerCAmelCase__(cls						,     _lowercase = None						,     _lowercase = None						,     _lowercase=False						,     **_lowercase						,     ):





													'''simple docstring'''

													__a		:		Optional[Any]									=       cls.load_config(
													    pretrained_model_name_or_path=_lowercase						,     subfolder=_lowercase						,     return_unused_kwargs=_lowercase						,     **_lowercase						,     )
													__a		:		List[Any]									=       cls.from_config(_lowercase						,     return_unused_kwargs=_lowercase						,     **_lowercase							)

													if hasattr(_lowercase						,     """create_state"""							) and getattr(_lowercase						,     """has_state"""						,     _lowercase							):
																				__a		:		str									=       scheduler.create_state()

													if return_unused_kwargs:
																				return scheduler, state, unused_kwargs

													return scheduler, state



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = False						,     **_lowercase							):





													'''simple docstring'''

													self.save_config(save_directory=_lowercase						,     push_to_hub=_lowercase						,     **_lowercase							)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self._get_compatibles()





						@classmethod
						def     lowerCAmelCase__(cls							):





													'''simple docstring'''

													__a		:		Any									=       list(set([cls.__name__] + cls._compatibles							)							)
													__a		:		str									=       importlib.import_module(__name__.split("""."""							)[0]							)
													__a		:		Union[str, Any]									=       [
													    getattr(_lowercase						,     _lowercase							) for c in compatible_classes_str if hasattr(_lowercase						,     _lowercase							)
													]
													return compatible_classes







def       __magic_name__    (   _lowerCamelCase				: jnp.ndarray  ,       _lowerCamelCase				: Tuple[int]					):
							assert len(_lowerCamelCase					) >= x.ndim
							return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_lowerCamelCase					) - x.ndim)					)  ,       _lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: int=0.9_99  ,       _lowerCamelCase				: int=jnp.floataa					):

							def alpha_bar(_lowerCamelCase				: List[Any]					):
														return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

							__a		:		str									=       []
							for i in range(_lowerCamelCase					):
														__a		:		List[str]									=       i / num_diffusion_timesteps
														__a		:		List[Any]									=       (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar(_lowerCamelCase					) / alpha_bar(_lowerCamelCase					)  ,       _lowerCamelCase					)					)
							return jnp.array(_lowerCamelCase  ,       dtype=_lowerCamelCase					)




@flax.struct.dataclass
class SCREAMING_SNAKE_CASE__					:
						_lowerCAmelCase							=					4_2
						_lowerCAmelCase							=					4_2
						_lowerCAmelCase							=					4_2



						@classmethod
						def     lowerCAmelCase__(cls						,     _lowercase							):





													'''simple docstring'''

													__a		:		int									=       scheduler.config

													if config.trained_betas is not None:
																				__a		:		Tuple									=       jnp.asarray(config.trained_betas						,     dtype=scheduler.dtype							)
													elif config.beta_schedule == "linear":
																				__a		:		List[str]									=       jnp.linspace(config.beta_start						,     config.beta_end						,     config.num_train_timesteps						,     dtype=scheduler.dtype							)
													elif config.beta_schedule == "scaled_linear":
																				# this schedule is very specific to the latent diffusion model.
																				__a		:		str									=       (
																				    jnp.linspace(
																				        config.beta_start**0.5						,     config.beta_end**0.5						,     config.num_train_timesteps						,     dtype=scheduler.dtype							)
																				    ** 2
																				)
													elif config.beta_schedule == "squaredcos_cap_v2":
																				# Glide cosine schedule
																				__a		:		Tuple									=       betas_for_alpha_bar(config.num_train_timesteps						,     dtype=scheduler.dtype							)
													else:
																				raise NotImplementedError(
																				    F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'''							)

													__a		:		List[Any]									=       1.0 - betas

													__a		:		Union[str, Any]									=       jnp.cumprod(_lowercase						,     axis=0							)

													return cls(
													    alphas=_lowercase						,     betas=_lowercase						,     alphas_cumprod=_lowercase						,     )







def       __magic_name__    (   _lowerCamelCase				: CommonSchedulerState  ,       _lowerCamelCase				: jnp.ndarray  ,       _lowerCamelCase				: jnp.ndarray  ,       _lowerCamelCase				: jnp.ndarray					):
							__a		:		List[str]									=       state.alphas_cumprod

							__a		:		Dict									=       alphas_cumprod[timesteps] ** 0.5
							__a		:		Any									=       sqrt_alpha_prod.flatten()
							__a		:		Union[str, Any]									=       broadcast_to_shape_from_left(_lowerCamelCase  ,       original_samples.shape					)

							__a		:		Tuple									=       (1 - alphas_cumprod[timesteps]) ** 0.5
							__a		:		int									=       sqrt_one_minus_alpha_prod.flatten()
							__a		:		int									=       broadcast_to_shape_from_left(_lowerCamelCase  ,       original_samples.shape					)

							return sqrt_alpha_prod, sqrt_one_minus_alpha_prod





def       __magic_name__    (   _lowerCamelCase				: CommonSchedulerState  ,       _lowerCamelCase				: jnp.ndarray  ,       _lowerCamelCase				: jnp.ndarray  ,       _lowerCamelCase				: jnp.ndarray					):
							__a		:		Union[str, Any]									=       get_sqrt_alpha_prod(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
							__a		:		Tuple									=       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples







def       __magic_name__    (   _lowerCamelCase				: CommonSchedulerState  ,       _lowerCamelCase				: jnp.ndarray  ,       _lowerCamelCase				: jnp.ndarray  ,       _lowerCamelCase				: jnp.ndarray					):
							__a		:		Union[str, Any]									=       get_sqrt_alpha_prod(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
							__a		:		int									=       sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
							return velocity






"""simple docstring"""

from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters





def       __magic_name__    (   ):
							__a		:		Dict									=       {
							    """repo_name""": ["""test_repo1""", """test_repo2""", """test_repo3"""],
							    """path""": ["""test_1.py""", """test_2.py""", """unit_test.py"""],
							    """content""": ["""a """ * 2_0, """a """ * 3_0, """b """ * 7],
							}
							__a		:		Optional[Any]									=       Dataset.from_dict(_lowerCamelCase					)
							return dataset







class SCREAMING_SNAKE_CASE__					(		__snake_case      ):



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[int]									=       get_dataset()
													__a		:		List[Any]									=       make_duplicate_clusters(_lowercase						,     0.85							)
													self.assertEqual(len(duplicate_clusters[0]							)						,     2							)





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Tuple									=       get_dataset()
													__a							,     __a		:		Optional[Any]									=       deduplicate_dataset(_lowercase							)
													self.assertEqual(len(_lowercase							)						,     2							)
													print(_lowercase							)
													self.assertEqual(duplicate_clusters[0][0]["""copies"""]						,     2							)
													self.assertEqual(duplicate_clusters[0][0]["""is_extreme"""]						,     _lowercase							)

