


'''simple docstring'''






# 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 		a__				:





							'''simple docstring'''






							lowercase__			:   Optional[jnp.ndarray]							=      None
							lowercase__			:   Optional[jnp.ndarray]							=      None
							lowercase__			:   Optional[jnp.ndarray]							=      None







							@classmethod
							def   __SCREAMING_SNAKE_CASE							(		cls   )       ->							Tuple:
														return cls()




@dataclass
class 		a__				(							_A  ):





							'''simple docstring'''






							lowercase__			:   ScoreSdeVeSchedulerState
							lowercase__			:   jnp.ndarray
							lowercase__			:   Optional[jnp.ndarray]							=      None




class 		a__				(							_A				,				_A  ):





							'''simple docstring'''






							@property
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Optional[Any]:
														return True







							@register_to_config
							def __init__(		self					,    lowerCamelCase_ = 20_00					,    lowerCamelCase_ = 0.15					,    lowerCamelCase_ = 0.01					,    lowerCamelCase_ = 1_348.0					,    lowerCamelCase_ = 1e-5					,    lowerCamelCase_ = 1					,    )       ->							Tuple:
														pass







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Optional[int]:
														lowerCAmelCase__          =	ScoreSdeVeSchedulerState.create()
														return self.set_sigmas(
														    snake_case_					,    self.config.num_train_timesteps					,    self.config.sigma_min					,    self.config.sigma_max					,    self.config.sampling_eps					,    )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_ = ()					,    lowerCamelCase_ = None   )       ->							List[Any]:
														lowerCAmelCase__          =	sampling_eps if sampling_eps is not None else self.config.sampling_eps

														lowerCAmelCase__          =	jnp.linspace(1					,    snake_case_					,    snake_case_   )
														return state.replace(timesteps=snake_case_   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_ = None					,    lowerCamelCase_ = None					,    lowerCamelCase_ = None					,    )       ->							Any:
														lowerCAmelCase__          =	sigma_min if sigma_min is not None else self.config.sigma_min
														lowerCAmelCase__          =	sigma_max if sigma_max is not None else self.config.sigma_max
														lowerCAmelCase__          =	sampling_eps if sampling_eps is not None else self.config.sampling_eps
														if state.timesteps is None:
																					lowerCAmelCase__          =	self.set_timesteps(snake_case_					,    snake_case_					,    snake_case_   )

														lowerCAmelCase__          =	jnp.exp(jnp.linspace(jnp.log(snake_case_   )					,    jnp.log(snake_case_   )					,    snake_case_   )   )
														lowerCAmelCase__          =	jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]   )

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







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							int:
														return jnp.where(timesteps == 0					,    jnp.zeros_like(snake_case_   )					,    state.discrete_sigmas[timesteps - 1]   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_ = True					,    )       ->							List[Any]:
														if state.timesteps is None:
																					raise ValueError(
																					    '''`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''   )

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

														lowerCAmelCase__          =	state.discrete_sigmas[timesteps]
														lowerCAmelCase__          =	self.get_adjacent_sigma(snake_case_					,    snake_case_					,    snake_case_   )
														lowerCAmelCase__          =	jnp.zeros_like(snake_case_   )
														lowerCAmelCase__          =	(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
														lowerCAmelCase__          =	diffusion.flatten()
														lowerCAmelCase__          =	broadcast_to_shape_from_left(snake_case_					,    sample.shape   )
														lowerCAmelCase__          =	drift - diffusion**2 * model_output

														#  equation 6: sample noise for the diffusion term of
														lowerCAmelCase__          =	random.split(snake_case_					,    num=1   )
														lowerCAmelCase__          =	random.normal(key=snake_case_					,    shape=sample.shape   )
														lowerCAmelCase__          =	sample - drift  # subtract because `dt` is a small negative timestep
														# TODO is the variable diffusion the correct scaling term for the noise?
														lowerCAmelCase__          =	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=snake_case_					,    prev_sample_mean=snake_case_					,    state=snake_case_   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_ = True					,    )       ->							Union[str, Any]:
														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
														lowerCAmelCase__          =	random.split(snake_case_					,    num=1   )
														lowerCAmelCase__          =	random.normal(key=snake_case_					,    shape=sample.shape   )

														# compute step size from the model_output, the noise, and the snr
														lowerCAmelCase__          =	jnp.linalg.norm(snake_case_   )
														lowerCAmelCase__          =	jnp.linalg.norm(snake_case_   )
														lowerCAmelCase__          =	(self.config.snr * noise_norm / grad_norm) ** 2 * 2
														lowerCAmelCase__          =	step_size * jnp.ones(sample.shape[0]   )

														# compute corrected sample: model_output term and noise term
														lowerCAmelCase__          =	step_size.flatten()
														lowerCAmelCase__          =	broadcast_to_shape_from_left(snake_case_					,    sample.shape   )
														lowerCAmelCase__          =	sample + step_size * model_output
														lowerCAmelCase__          =	prev_sample_mean + ((step_size * 2) ** 0.5) * noise

														if not return_dict:
																					return (prev_sample, state)

														return FlaxSdeVeOutput(prev_sample=snake_case_					,    state=snake_case_   )







							def __len__(		self   )       ->							Tuple:
														return self.config.num_train_timesteps

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

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


if is_sentencepiece_available():
							from .tokenization_xglm import XGLMTokenizer
else:
							lowerCamelCase_  :       Optional[Any]					      =     None


lowerCamelCase_  :       str					      =     logging.get_logger(__name__)

lowerCamelCase_  :       List[str]					      =     {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase_  :       List[str]					      =     {
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase_  :       Dict					      =     {
    """facebook/xglm-564M""": 20_48,
}

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			__UpperCamelCase					:  Optional[Any]    					=       VOCAB_FILES_NAMES
			__UpperCamelCase					:  int    					=       PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase					:  List[str]    					=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase					:  Dict    					=       ["""input_ids""", """attention_mask"""]
			__UpperCamelCase					:  Union[str, Any]    					=       XGLMTokenizer

			def __init__(				self       :				Optional[Any]  ,		snake_case_       :				Optional[Any]=None  ,		snake_case_       :				Any=None  ,		snake_case_       :				Optional[Any]="<s>"  ,		snake_case_       :				Union[str, Any]="</s>"  ,		snake_case_       :				Optional[Any]="</s>"  ,		snake_case_       :				Dict="<s>"  ,		snake_case_       :				int="<unk>"  ,		snake_case_       :				Dict="<pad>"  ,		**snake_case_       :				List[str]  ,		):
							# Compatibility with the original tokenizer
							UpperCamelCase_:							List[Any]		=  7
							UpperCamelCase_:							int		=  [f'''<madeupword{i}>''' for i in range(self.num_madeup_words					)]

							UpperCamelCase_:							Any		=  kwargs.get("""additional_special_tokens"""  ,		[]					)
							kwargs["additional_special_tokens"] += [
							    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    snake_case_  ,		tokenizer_file=snake_case_  ,		bos_token=snake_case_  ,		eos_token=snake_case_  ,		sep_token=snake_case_  ,		cls_token=snake_case_  ,		unk_token=snake_case_  ,		pad_token=snake_case_  ,		**snake_case_  ,		)

							UpperCamelCase_:							Dict		=  vocab_file
							UpperCamelCase_:							Union[str, Any]		=  False if not self.vocab_file else True

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

							if token_ids_a is None:
											return [self.sep_token_id] + token_ids_a
							UpperCamelCase_:							Optional[Any]		=  [self.sep_token_id]
							return sep + token_ids_a + sep + sep + token_ids_a

			def 					lowerCAmelCase__		(				self       :				Optional[Any]  ,		snake_case_       :				List[int]  ,		snake_case_       :				Optional[List[int]] = None					):
							UpperCamelCase_:							Any		=  [self.sep_token_id]

							if token_ids_a is None:
											return len(sep + token_ids_a					) * [0]
							return len(sep + token_ids_a + sep + sep + token_ids_a					) * [0]




			def 					lowerCAmelCase__		(				self       :				Optional[Any]  ,		snake_case_       :				str  ,		snake_case_       :				Optional[str] = None					):
							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(snake_case_					):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''					)
											return
							UpperCamelCase_:							Optional[int]		=  os.path.join(
							    snake_case_  ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

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

							return (out_vocab_file,)






