



# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left

@flax.struct.dataclass
class 		UpperCAmelCase__      :




			'''simple docstring'''






			UpperCamelCase      						=	None
			UpperCamelCase      						=	None
			UpperCamelCase      						=	None




			@classmethod
			def 			snake_case__					(       cls							:			Tuple			):
					'''simple docstring'''





					return cls()

@dataclass
class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	42
			UpperCamelCase      						=	42
			UpperCamelCase      						=	None

class 		UpperCAmelCase__      (      __UpperCamelCase						,__UpperCamelCase       ):




			'''simple docstring'''






			@property
			def 			snake_case__					(       self							:			Dict			):
					'''simple docstring'''





					return True




			@register_to_config
			def __init__(       self							:			Any	,	a_							:			Dict = 20_00	,	a_							:			List[Any] = 0.1_5	,	a_							:			Optional[Any] = 0.0_1	,	a_							:			int = 1_3_4_8.0	,	a_							:			Tuple = 1e-5	,	a_							:			Any = 1	,	):
					'''simple docstring'''





					pass




			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[Any]													=     ScoreSdeVeSchedulerState.create()
					return self.set_sigmas(
					    __A	,	self.config.num_train_timesteps	,	self.config.sigma_min	,	self.config.sigma_max	,	self.config.sampling_eps	,	)




			def 			snake_case__					(       self							:			List[Any]	,	a_							:			List[Any]	,	a_							:			List[Any]	,	a_							:			Dict = ()	,	a_							:			int = None			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     sampling_eps if sampling_eps is not None else self.config.sampling_eps

					__UpperCAmelCase   :					str													=     jnp.linspace(1	,	__A	,	__A			)
					return state.replace(timesteps=__A			)




			def 			snake_case__					(       self							:			Optional[int]	,	a_							:			Union[str, Any]	,	a_							:			Any	,	a_							:			Tuple = None	,	a_							:			List[Any] = None	,	a_							:			Any = None	,	):
					'''simple docstring'''





					__UpperCAmelCase   :					List[Any]													=     sigma_min if sigma_min is not None else self.config.sigma_min
					__UpperCAmelCase   :					int													=     sigma_max if sigma_max is not None else self.config.sigma_max
					__UpperCAmelCase   :					int													=     sampling_eps if sampling_eps is not None else self.config.sampling_eps
					if state.timesteps is None:
							__UpperCAmelCase   :					Optional[Any]													=     self.set_timesteps(__A	,	__A	,	__A			)

					__UpperCAmelCase   :					Dict													=     jnp.exp(jnp.linspace(jnp.log(__A			)	,	jnp.log(__A			)	,	__A			)			)
					__UpperCAmelCase   :					List[Any]													=     jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]			)

					return state.replace(discrete_sigmas=__A	,	sigmas=__A			)




			def 			snake_case__					(       self							:			List[Any]	,	a_							:			Tuple	,	a_							:			int	,	a_							:			Any			):
					'''simple docstring'''





					return jnp.where(timesteps == 0	,	jnp.zeros_like(__A			)	,	state.discrete_sigmas[timesteps - 1]			)




			def 			snake_case__					(       self							:			Any	,	a_							:			Any	,	a_							:			str	,	a_							:			int	,	a_							:			str	,	a_							:			Any	,	a_							:			Any = True	,	):
					'''simple docstring'''





					if state.timesteps is None:
							raise ValueError(
							    '''`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''			)

					__UpperCAmelCase   :					Optional[int]													=     timestep * jnp.ones(
					    sample.shape[0]	,	)
					__UpperCAmelCase   :					Optional[int]													=     (timestep * (len(state.timesteps			) - 1)).long()

					__UpperCAmelCase   :					List[str]													=     state.discrete_sigmas[timesteps]
					__UpperCAmelCase   :					str													=     self.get_adjacent_sigma(__A	,	__A	,	__A			)
					__UpperCAmelCase   :					Union[str, Any]													=     jnp.zeros_like(__A			)
					__UpperCAmelCase   :					int													=     (sigma**2 - adjacent_sigma**2) ** 0.5

					# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
					# also equation 47 shows the analog from SDE models to ancestral sampling methods
					__UpperCAmelCase   :					List[Any]													=     diffusion.flatten()
					__UpperCAmelCase   :					Dict													=     broadcast_to_shape_from_left(__A	,	sample.shape			)
					__UpperCAmelCase   :					Optional[Any]													=     drift - diffusion**2 * model_output

					#  equation 6: sample noise for the diffusion term of
					__UpperCAmelCase   :					Optional[int]													=     random.split(__A	,	num=1			)
					__UpperCAmelCase   :					Dict													=     random.normal(key=__A	,	shape=sample.shape			)
					__UpperCAmelCase   :					Tuple													=     sample - drift  # subtract because `dt` is a small negative timestep
					# TODO is the variable diffusion the correct scaling term for the noise?
					__UpperCAmelCase   :					List[Any]													=     prev_sample_mean + diffusion * noise  # add impact of diffusion field g

					if not return_dict:
							return (prev_sample, prev_sample_mean, state)

					return FlaxSdeVeOutput(prev_sample=__A	,	prev_sample_mean=__A	,	state=__A			)




			def 			snake_case__					(       self							:			str	,	a_							:			Any	,	a_							:			Any	,	a_							:			Dict	,	a_							:			List[Any]	,	a_							:			Optional[int] = True	,	):
					'''simple docstring'''





					if state.timesteps is None:
							raise ValueError(
							    '''`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''			)

					# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
					# sample noise for correction
					__UpperCAmelCase   :					List[Any]													=     random.split(__A	,	num=1			)
					__UpperCAmelCase   :					Union[str, Any]													=     random.normal(key=__A	,	shape=sample.shape			)

					# compute step size from the model_output, the noise, and the snr
					__UpperCAmelCase   :					Any													=     jnp.linalg.norm(__A			)
					__UpperCAmelCase   :					Tuple													=     jnp.linalg.norm(__A			)
					__UpperCAmelCase   :					List[Any]													=     (self.config.snr * noise_norm / grad_norm) ** 2 * 2
					__UpperCAmelCase   :					Optional[Any]													=     step_size * jnp.ones(sample.shape[0]			)

					# compute corrected sample: model_output term and noise term
					__UpperCAmelCase   :					Optional[Any]													=     step_size.flatten()
					__UpperCAmelCase   :					Tuple													=     broadcast_to_shape_from_left(__A	,	sample.shape			)
					__UpperCAmelCase   :					Tuple													=     sample + step_size * model_output
					__UpperCAmelCase   :					Optional[int]													=     prev_sample_mean + ((step_size * 2) ** 0.5) * noise

					if not return_dict:
							return (prev_sample, state)

					return FlaxSdeVeOutput(prev_sample=__A	,	state=__A			)




			def __len__(       self							:			Optional[Any]			):
					'''simple docstring'''





					return self.config.num_train_timesteps







import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_reformer import ReformerTokenizer
else:
				__A             =None


__A             =logging.get_logger(__name__)


__A             ="▁"

__A             ={"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__A             ={
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

__A             ={
    "google/reformer-crime-and-punishment": 5_2_4_2_8_8,
}

class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	VOCAB_FILES_NAMES
			UpperCamelCase      						=	PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase      						=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase      						=	["""input_ids""", """attention_mask"""]
			UpperCamelCase      						=	ReformerTokenizer




			def __init__(       self							:			Tuple	,	a_							:			Optional[int]=None	,	a_							:			int=None	,	a_							:			Dict="</s>"	,	a_							:			Dict="<unk>"	,	a_							:			List[Any]=[]	,	**a_							:			int	,	):
					'''simple docstring'''





					super().__init__(
					    a_	,	tokenizer_file=a_	,	eos_token=a_	,	unk_token=a_	,	additional_special_tokens=a_	,	**a_	,	)

					__UpperCAmelCase   :					Optional[Any]													=     vocab_file
					__UpperCAmelCase   :					Optional[Any]													=     False if not self.vocab_file else True




			def 			snake_case__					(       self							:			Dict	,	a_							:			str	,	a_							:			Optional[str] = None			):
					'''simple docstring'''





					if not self.can_save_slow_tokenizer:
							raise ValueError(
							    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
							    '''tokenizer.'''			)

					if not os.path.isdir(a_			):
							logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
							return
					__UpperCAmelCase   :					Tuple													=     os.path.join(
					    a_	,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

					if os.path.abspath(self.vocab_file			) != os.path.abspath(a_			):
							copyfile(self.vocab_file	,	a_			)

					return (out_vocab_file,)





