






'''simple docstring'''







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

import flax
import jax
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 _lowercase			:
							a          = 42

							# setable values
							a          = 42
							a          = 42
							a          = None




							@classmethod
							def 	lowerCamelCase_       (							cls:       Tuple    ,							UpperCamelCase__:       CommonSchedulerState    ,							UpperCamelCase__:       jnp.ndarray    ,							UpperCamelCase__:       jnp.ndarray  ):
														return cls(common=UpperCamelCase__    ,							init_noise_sigma=UpperCamelCase__    ,							timesteps=UpperCamelCase__  )



@dataclass
class _lowercase			(       _lowercase	):
							a          = 42



class _lowercase			(       _lowercase       ,   _lowercase	):
							a          = [e.name for e in FlaxKarrasDiffusionSchedulers]

							a          = 42




							@property
							def 	lowerCamelCase_       (							self:       Tuple  ):
														return True




							@register_to_config
							def __init__(							self:       Tuple    ,							UpperCamelCase__:       int = 1_000    ,							UpperCamelCase__:       float = 0.0_001    ,							UpperCamelCase__:       float = 0.02    ,							UpperCamelCase__:       str = "linear"    ,							UpperCamelCase__:       Optional[jnp.ndarray] = None    ,							UpperCamelCase__:       str = "fixed_small"    ,							UpperCamelCase__:       bool = True    ,							UpperCamelCase__:       str = "epsilon"    ,							UpperCamelCase__:       jnp.dtype = jnp.floataa    ,							):
														lowerCamelCase__      :			Optional[Any] 						= dtype




							def 	lowerCamelCase_       (							self:       Any    ,							UpperCamelCase__:       Optional[CommonSchedulerState] = None  ):
														if common is None:
																					lowerCamelCase__      :			int 						= CommonSchedulerState.create(self  )

														# standard deviation of the initial noise distribution
														lowerCamelCase__      :			List[Any] 						= jnp.array(1.0    ,							dtype=self.dtype  )

														lowerCamelCase__      :			Tuple 						= jnp.arange(0    ,							self.config.num_train_timesteps  ).round()[::-1]

														return DDPMSchedulerState.create(
														    common=UpperCamelCase__    ,							init_noise_sigma=UpperCamelCase__    ,							timesteps=UpperCamelCase__    ,							)




							def 	lowerCamelCase_       (							self:       List[str]    ,							UpperCamelCase__:       DDPMSchedulerState    ,							UpperCamelCase__:       jnp.ndarray    ,							UpperCamelCase__:       Optional[int] = None  ):
														return sample




							def 	lowerCamelCase_       (							self:       List[str]    ,							UpperCamelCase__:       DDPMSchedulerState    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       Tuple = ()  ):
														lowerCamelCase__      :			int 						= 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
														lowerCamelCase__      :			Optional[int] 						= (jnp.arange(0    ,							UpperCamelCase__  ) * step_ratio).round()[::-1]

														return state.replace(
														    num_inference_steps=UpperCamelCase__    ,							timesteps=UpperCamelCase__    ,							)




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       DDPMSchedulerState    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       Any=None    ,							UpperCamelCase__:       Union[str, Any]=None  ):
														lowerCamelCase__      :			List[Any] 						= state.common.alphas_cumprod[t]
														lowerCamelCase__      :			Any 						= jnp.where(t > 0    ,							state.common.alphas_cumprod[t - 1]    ,							jnp.array(1.0    ,							dtype=self.dtype  )  )

														# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
														# and sample from it to get previous sample
														# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
														lowerCamelCase__      :			Tuple 						= (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

														if variance_type is None:
																					lowerCamelCase__      :			str 						= self.config.variance_type

														# hacks - were probably added for training stability
														if variance_type == "fixed_small":
																					lowerCamelCase__      :			str 						= jnp.clip(UpperCamelCase__    ,							a_min=1e-20  )
														# for rl-diffuser https://arxiv.org/abs/2205.09991
														elif variance_type == "fixed_small_log":
																					lowerCamelCase__      :			str 						= jnp.log(jnp.clip(UpperCamelCase__    ,							a_min=1e-20  )  )
														elif variance_type == "fixed_large":
																					lowerCamelCase__      :			Dict 						= state.common.betas[t]
														elif variance_type == "fixed_large_log":
																					# Glide max_log
																					lowerCamelCase__      :			Any 						= jnp.log(state.common.betas[t]  )
														elif variance_type == "learned":
																					return predicted_variance
														elif variance_type == "learned_range":
																					lowerCamelCase__      :			List[str] 						= variance
																					lowerCamelCase__      :			Union[str, Any] 						= state.common.betas[t]
																					lowerCamelCase__      :			List[Any] 						= (predicted_variance + 1) / 2
																					lowerCamelCase__      :			Tuple 						= frac * max_log + (1 - frac) * min_log

														return variance




							def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       DDPMSchedulerState    ,							UpperCamelCase__:       jnp.ndarray    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       jnp.ndarray    ,							UpperCamelCase__:       Optional[jax.random.KeyArray] = None    ,							UpperCamelCase__:       bool = True    ,							):
														lowerCamelCase__      :			Tuple 						= timestep

														if key is None:
																					lowerCamelCase__      :			List[Any] 						= jax.random.PRNGKey(0  )

														if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
																					lowerCamelCase__      :			Any 						= jnp.split(UpperCamelCase__    ,							sample.shape[1]    ,							axis=1  )
														else:
																					lowerCamelCase__      :			Optional[int] 						= None

														# 1. compute alphas, betas
														lowerCamelCase__      :			Any 						= state.common.alphas_cumprod[t]
														lowerCamelCase__      :			Any 						= jnp.where(t > 0    ,							state.common.alphas_cumprod[t - 1]    ,							jnp.array(1.0    ,							dtype=self.dtype  )  )
														lowerCamelCase__      :			Any 						= 1 - alpha_prod_t
														lowerCamelCase__      :			Union[str, Any] 						= 1 - alpha_prod_t_prev

														# 2. compute predicted original sample from predicted noise also called
														# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
														if self.config.prediction_type == "epsilon":
																					lowerCamelCase__      :			Tuple 						= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
														elif self.config.prediction_type == "sample":
																					lowerCamelCase__      :			Dict 						= model_output
														elif self.config.prediction_type == "v_prediction":
																					lowerCamelCase__      :			List[Any] 						= (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
														else:
																					raise ValueError(
																					    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
																					    """ for the FlaxDDPMScheduler."""  )

														# 3. Clip "predicted x_0"
														if self.config.clip_sample:
																					lowerCamelCase__      :			Dict 						= jnp.clip(UpperCamelCase__    ,							-1    ,							1  )

														# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
														# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
														lowerCamelCase__      :			Union[str, Any] 						= (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
														lowerCamelCase__      :			Tuple 						= state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

														# 5. Compute predicted previous sample µ_t
														# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
														lowerCamelCase__      :			Optional[int] 						= pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

														# 6. Add noise
														def random_variance():
																					lowerCamelCase__      :			List[str] 						= jax.random.split(UpperCamelCase__    ,							num=1  )
																					lowerCamelCase__      :			List[Any] 						= jax.random.normal(UpperCamelCase__    ,							shape=model_output.shape    ,							dtype=self.dtype  )
																					return (self._get_variance(UpperCamelCase__    ,							UpperCamelCase__    ,							predicted_variance=UpperCamelCase__  ) ** 0.5) * noise

														lowerCamelCase__      :			int 						= jnp.where(t > 0    ,							random_variance()    ,							jnp.zeros(model_output.shape    ,							dtype=self.dtype  )  )

														lowerCamelCase__      :			int 						= pred_prev_sample + variance

														if not return_dict:
																					return (pred_prev_sample, state)

														return FlaxDDPMSchedulerOutput(prev_sample=UpperCamelCase__    ,							state=UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       List[str]    ,							UpperCamelCase__:       DDPMSchedulerState    ,							UpperCamelCase__:       jnp.ndarray    ,							UpperCamelCase__:       jnp.ndarray    ,							UpperCamelCase__:       jnp.ndarray    ,							):
														return add_noise_common(state.common    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       List[str]    ,							UpperCamelCase__:       DDPMSchedulerState    ,							UpperCamelCase__:       jnp.ndarray    ,							UpperCamelCase__:       jnp.ndarray    ,							UpperCamelCase__:       jnp.ndarray    ,							):
														return get_velocity_common(state.common    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )




							def __len__(							self:       int  ):
														return self.config.num_train_timesteps









'''simple docstring'''







import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE

class _lowercase			(       _lowercase	):
							a          = """"""
							a          = (
							    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
							)
							a          = None  # compression type in fsspec. ex: "gzip"
							a          = None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz




							def __init__(							self:       str    ,							UpperCamelCase__:       str = ""    ,							UpperCamelCase__:       Optional[str] = None    ,							UpperCamelCase__:       Optional[dict] = None    ,							**UpperCamelCase__:       List[Any]  ):
														super().__init__(self    ,							**UpperCamelCase__  )
														# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
														lowerCamelCase__      :			List[Any] 						= fsspec.open(
														    UpperCamelCase__    ,							mode="""rb"""    ,							protocol=UpperCamelCase__    ,							compression=self.compression    ,							client_kwargs={
														        """requote_redirect_url""": False,  # see https://github.com/huggingface/datasets/pull/5459
														        """trust_env""": True,  # Enable reading proxy env variables.
														        **(target_options or {}).pop("""client_kwargs"""    ,							{}  ),  # To avoid issues if it was already passed.
														    }    ,							**(target_options or {})    ,							)
														lowerCamelCase__      :			str 						= os.path.basename(self.file.path.split("""::"""  )[0]  )
														lowerCamelCase__      :			Union[str, Any] 						= (
														    self.compressed_name[: self.compressed_name.rindex("""."""  )]
														    if """.""" in self.compressed_name
														    else self.compressed_name
														)
														lowerCamelCase__      :			Tuple 						= None




							@classmethod
							def 	lowerCamelCase_       (							cls:       Optional[int]    ,							UpperCamelCase__:       Optional[int]  ):
														# compressed file paths are always relative to the archive root
														return super()._strip_protocol(UpperCamelCase__  ).lstrip("""/"""  )




							def 	lowerCamelCase_       (							self:       Tuple  ):
														if self.dir_cache is None:
																					lowerCamelCase__      :			Dict 						= {**self.file.fs.info(self.file.path  ), """name""": self.uncompressed_name}
																					lowerCamelCase__      :			int 						= {f["""name"""]: f}




							def 	lowerCamelCase_       (							self:       List[str]    ,							UpperCamelCase__:       str  ):
														return self.file.open().read()




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       str = "rb"    ,							UpperCamelCase__:       Optional[int]=None    ,							UpperCamelCase__:       Tuple=True    ,							UpperCamelCase__:       Tuple=None    ,							**UpperCamelCase__:       Optional[Any]    ,							):
														lowerCamelCase__      :			Union[str, Any] 						= self._strip_protocol(UpperCamelCase__  )
														if mode != "rb":
																					raise ValueError(F'''Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\''''  )
														return self.file.open()



class _lowercase			(       _lowercase	):
							a          = """bz2"""
							a          = """bz2"""
							a          = """.bz2"""



class _lowercase			(       _lowercase	):
							a          = """gzip"""
							a          = """gzip"""
							a          = """.gz"""



class _lowercase			(       _lowercase	):
							a          = """lz4"""
							a          = """lz4"""
							a          = """.lz4"""



class _lowercase			(       _lowercase	):
							a          = """xz"""
							a          = """xz"""
							a          = """.xz"""



class _lowercase			(       _lowercase	):
							a          = """zstd"""
							a          = """zstd"""
							a          = """.zst"""




							def __init__(							self:       int    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       str = "rb"    ,							UpperCamelCase__:       Optional[str] = None    ,							UpperCamelCase__:       Optional[dict] = None    ,							UpperCamelCase__:       int = DEFAULT_BLOCK_SIZE    ,							**UpperCamelCase__:       Dict    ,							):
														super().__init__(
														    fo=UpperCamelCase__    ,							mode=UpperCamelCase__    ,							target_protocol=UpperCamelCase__    ,							target_options=UpperCamelCase__    ,							block_size=UpperCamelCase__    ,							**UpperCamelCase__    ,							)
														# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
														#
														# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
														#     out.close = close
														# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
														#
														# see https://github.com/intake/filesystem_spec/issues/725
														lowerCamelCase__      :			Tuple 						= self.file.__enter__

														class _lowercase			:




																					def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Any  ):
																												lowerCamelCase__      :			Optional[int] 						= file_




																					def __enter__(							self:       List[Any]  ):
																												self._file.__enter__()
																												return self




																					def __exit__(							self:       Any    ,							*UpperCamelCase__:       str    ,							**UpperCamelCase__:       Any  ):
																												self._file.__exit__(*UpperCamelCase__    ,							**UpperCamelCase__  )




																					def __iter__(							self:       Any  ):
																												return iter(self._file  )




																					def 	lowerCamelCase_       (							self:       List[Any]  ):
																												return next(self._file  )




																					def __getattr__(							self:       List[str]    ,							UpperCamelCase__:       Dict  ):
																												return getattr(self._file    ,							UpperCamelCase__  )

														def fixed_enter(*UpperCamelCase__:       Union[str, Any]    ,							**UpperCamelCase__:       List[str]  ):
																					return WrappedFile(_enter(*UpperCamelCase__    ,							**UpperCamelCase__  )  )

														lowerCamelCase__      :			Optional[Any] 						= fixed_enter

