
"""simple docstring"""







# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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 							__magic_name__     :
					_SCREAMING_SNAKE_CASE       :					Any     							=   42

					# setable values
					_SCREAMING_SNAKE_CASE       :					int     							=   42
					_SCREAMING_SNAKE_CASE       :					Any     							=   42
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   None





					@classmethod
					def 							lowerCAmelCase      (				cls   :							Tuple							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							str							,						snake_case_   :							List[Any]    ):
												return cls(common=snake_case_							,						init_noise_sigma=snake_case_							,						timesteps=snake_case_    )







@dataclass
class 							__magic_name__     (		__UpperCAmelCase   ):
					_SCREAMING_SNAKE_CASE       :					str     							=   42







class 							__magic_name__     (		__UpperCAmelCase      ,		__UpperCAmelCase   ):
					_SCREAMING_SNAKE_CASE       :					Optional[Any]     							=   [e.name for e in FlaxKarrasDiffusionSchedulers]

					_SCREAMING_SNAKE_CASE       :					Union[str, Any]     							=   42





					@property
					def 							lowerCAmelCase      (				self   :							Optional[Any]    ):
												return True





					@register_to_config
					def __init__(				self   :							Optional[Any]							,						snake_case_   :							Dict = 1000							,						snake_case_   :							int = 0.0001							,						snake_case_   :							Optional[Any] = 0.02							,						snake_case_   :							Optional[int] = "linear"							,						snake_case_   :							Optional[int] = None							,						snake_case_   :							Optional[int] = "fixed_small"							,						snake_case_   :							Union[str, Any] = True							,						snake_case_   :							Optional[int] = "epsilon"							,						snake_case_   :							List[Any] = jnp.floataa							,						):
												__snake_case     			=       dtype





					def 							lowerCAmelCase      (				self   :							List[Any]							,						snake_case_   :							int = None    ):

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

												# standard deviation of the initial noise distribution
												__snake_case     			=       jnp.array(1.0							,						dtype=self.dtype    )

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

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





					def 							lowerCAmelCase      (				self   :							List[str]							,						snake_case_   :							Any							,						snake_case_   :							List[Any]							,						snake_case_   :							Union[str, Any] = None    ):
												return sample





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							Dict							,						snake_case_   :							Optional[int]							,						snake_case_   :							Dict = ()    ):
												__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
												__snake_case     			=       (jnp.arange(0							,						snake_case_    ) * step_ratio).round()[::-1]

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





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							Tuple							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Union[str, Any]=None							,						snake_case_   :							List[str]=None    ):
												__snake_case     			=       state.common.alphas_cumprod[t]
												__snake_case     			=       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
												__snake_case     			=       (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

												if variance_type is None:
																			__snake_case     			=       self.config.variance_type

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

												return variance





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							Any							,						snake_case_   :							List[str]							,						snake_case_   :							int							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Union[str, Any] = None							,						snake_case_   :							Optional[Any] = True							,						):
												__snake_case     			=       timestep

												if key is None:
																			__snake_case     			=       jax.random.PRNGKey(0    )

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

												# 1. compute alphas, betas
												__snake_case     			=       state.common.alphas_cumprod[t]
												__snake_case     			=       jnp.where(t > 0							,						state.common.alphas_cumprod[t - 1]							,						jnp.array(1.0							,						dtype=self.dtype    )    )
												__snake_case     			=       1 - alpha_prod_t
												__snake_case     			=       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":
																			__snake_case     			=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
												elif self.config.prediction_type == "sample":
																			__snake_case     			=       model_output
												elif self.config.prediction_type == "v_prediction":
																			__snake_case     			=       (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:
																			__snake_case     			=       jnp.clip(snake_case_							,						-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
												__snake_case     			=       (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
												__snake_case     			=       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
												__snake_case     			=       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

												# 6. Add noise
												def random_variance():
																			__snake_case     			=       jax.random.split(snake_case_							,						num=1    )
																			__snake_case     			=       jax.random.normal(snake_case_							,						shape=model_output.shape							,						dtype=self.dtype    )
																			return (self._get_variance(snake_case_							,						snake_case_							,						predicted_variance=snake_case_    ) ** 0.5) * noise

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

												__snake_case     			=       pred_prev_sample + variance

												if not return_dict:
																			return (pred_prev_sample, state)

												return FlaxDDPMSchedulerOutput(prev_sample=snake_case_							,						state=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							int							,						snake_case_   :							Any							,						snake_case_   :							List[str]							,						snake_case_   :							List[Any]							,						):
												return add_noise_common(state.common							,						snake_case_							,						snake_case_							,						snake_case_    )





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							Any							,						snake_case_   :							List[Any]							,						snake_case_   :							Tuple							,						snake_case_   :							Union[str, Any]							,						):
												return get_velocity_common(state.common							,						snake_case_							,						snake_case_							,						snake_case_    )





					def __len__(				self   :							Union[str, Any]    ):
												return self.config.num_train_timesteps


"""simple docstring"""







import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							__snake_case     			=       []
							for line in lines:
														__snake_case     			=       re.sub(r"#.*"							,    ""							,    SCREAMING_SNAKE_CASE )  # remove comments
														if line:
																					filtered_lines.append(SCREAMING_SNAKE_CASE )
							__snake_case     			=       "\n".join(SCREAMING_SNAKE_CASE )

							# Make a hash from all this code
							__snake_case     			=       full_str.encode("utf-8" )
							return shaaaa(SCREAMING_SNAKE_CASE ).hexdigest()


# get importable module names and hash for caching
_SCREAMING_SNAKE_CASE	       =      {
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
_SCREAMING_SNAKE_CASE	       =      {
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_SCREAMING_SNAKE_CASE	       =      {"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
_SCREAMING_SNAKE_CASE	       =      {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
		_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")



_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")
