






'''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 		UpperCamelCase__     :




		'''simple docstring'''





		_UpperCamelCase						=       42

		# setable values
		_UpperCamelCase						=       42
		_UpperCamelCase						=       42
		_UpperCamelCase						=       None






		@classmethod
		def       UpperCamelCase_       (   cls ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								return cls(common=_lowerCAmelCase ,init_noise_sigma=_lowerCAmelCase ,timesteps=_lowerCAmelCase							)




@dataclass
class 		UpperCamelCase__     (a      ):




		'''simple docstring'''





		_UpperCamelCase						=       42




class 		UpperCamelCase__     (a ,a      ):




		'''simple docstring'''





		_UpperCamelCase						=       [e.name for e in FlaxKarrasDiffusionSchedulers]

		_UpperCamelCase						=       42






		@property
		def       UpperCamelCase_       (   self							):
								return True






		@register_to_config
		def __init__(   self ,_lowerCAmelCase = 10_00 ,_lowerCAmelCase = 0.0001 ,_lowerCAmelCase = 0.02 ,_lowerCAmelCase = "linear" ,_lowerCAmelCase = None ,_lowerCAmelCase = "fixed_small" ,_lowerCAmelCase = True ,_lowerCAmelCase = "epsilon" ,_lowerCAmelCase = jnp.floataa ,):
								lowerCamelCase__								=	dtype






		def       UpperCamelCase_       (   self ,_lowerCAmelCase = None							):
								if common is None:
														lowerCamelCase__								=	CommonSchedulerState.create(self							)

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

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

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






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase = None							):
								return sample






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase = ()							):
								lowerCamelCase__								=	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__								=	(jnp.arange(0 ,_lowerCAmelCase							) * step_ratio).round()[::-1]

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






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ,_lowerCAmelCase=None							):
								lowerCamelCase__								=	state.common.alphas_cumprod[t]
								lowerCamelCase__								=	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__								=	(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

								return variance






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase = None ,_lowerCAmelCase = True ,):
								lowerCamelCase__								=	timestep

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

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

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

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

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

								lowerCamelCase__								=	pred_prev_sample + variance

								if not return_dict:
														return (pred_prev_sample, state)

								return FlaxDDPMSchedulerOutput(prev_sample=_lowerCAmelCase ,state=_lowerCAmelCase							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,):
								return add_noise_common(state.common ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,):
								return get_velocity_common(state.common ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							)






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


"""simple docstring"""



import os
from collections import deque

import torch
from torch.utils.data import Dataset
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_=""   ,      snake_case_="train"							):







										'''simple docstring'''


										assert os.path.isdir(snake_case_							)

										A__       :     Dict								=			[]
										A__       :     Optional[Any]								=			os.listdir(snake_case_							)
										for story_filename in story_filenames_list:
																	if "summary" in story_filename:
																								continue
																	A__       :     Any								=			os.path.join(snake_case_   ,      snake_case_							)
																	if not os.path.isfile(snake_case_							):
																								continue
																	self.documents.append(snake_case_							)





			def __len__(			self							):







										'''simple docstring'''


										return len(self.documents							)





			def __getitem__(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     List[str]								=			self.documents[idx]
										A__       :     List[Any]								=			document_path.split("""/"""							)[-1]
										with open(snake_case_   ,      encoding="""utf-8"""							) as source:
																	A__       :     Dict								=			source.read()
																	A__				,							A__       :     Dict								=			process_story(snake_case_							)
										return document_name, story_lines, summary_lines





def  _A(		lowerCAmelCase							):
							A__       :     Optional[int]								=			list(filter(lambda lowerCAmelCase							: len(lowerCAmelCase							) != 0			,  [line.strip() for line in raw_story.split("""\n"""							)]							)							)

							# for some unknown reason some lines miss a period, add it
							A__       :     List[str]								=			[_add_missing_period(lowerCAmelCase							) for line in nonempty_lines]

							# gather article lines
							A__       :     Union[str, Any]								=			[]
							A__       :     Optional[Any]								=			deque(lowerCAmelCase							)
							while True:
														try:
																					A__       :     List[str]								=			lines.popleft()
																					if element.startswith("""@highlight"""							):
																												break
																					story_lines.append(lowerCAmelCase							)
														except IndexError:
																					# if "@highlight" is absent from the file we pop
																					# all elements until there is None, raising an exception.
																					return story_lines, []

    # gather summary lines
							A__       :     List[str]								=			list(filter(lambda lowerCAmelCase							: not t.startswith("""@highlight"""							)			,  lowerCAmelCase							)							)

							return story_lines, summary_lines

def  _A(		lowerCAmelCase							):
							A__       :     Tuple								=			[""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
							if line.startswith("""@highlight"""							):
														return line
							if line[-1] in END_TOKENS:
														return line
							return line + "."

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							if len(lowerCAmelCase							) > block_size:
														return sequence[:block_size]
							else:
														sequence.extend([pad_token_id] * (block_size - len(lowerCAmelCase							))							)
														return sequence

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Any								=			torch.ones_like(lowerCAmelCase							)
							A__       :     Any								=			sequence == pad_token_id
							A__       :     Any								=			0
							return mask

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Dict								=			[tokenizer.encode(lowerCAmelCase							) for line in story_lines]
							A__       :     Any								=			[token for sentence in story_lines_token_ids for token in sentence]
							A__       :     List[str]								=			[tokenizer.encode(lowerCAmelCase							) for line in summary_lines]
							A__       :     int								=			[token for sentence in summary_lines_token_ids for token in sentence]

							return story_token_ids, summary_token_ids



def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     str								=			[]
							for sequence in batch:
														A__       :     List[Any]								=			-1
														A__       :     Dict								=			[]
														for s in sequence:
																					if s == separator_token_id:
																												sentence_num += 1
																					embeddings.append(sentence_num % 2							)
														batch_embeddings.append(lowerCAmelCase							)
							return torch.tensor(lowerCAmelCase							)





