



"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
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 							lowerCamelCase    :






					'''simple docstring'''





					_A						:     CommonSchedulerState
					_A						:     jnp.ndarray

					# setable values
					_A						:     jnp.ndarray
					_A						:     jnp.ndarray
					_A						:     Optional[int]		=	None






					@classmethod
					def   lowerCAmelCase_							(			cls:				str		,	snake_case:				CommonSchedulerState		,	snake_case:				jnp.ndarray		,	snake_case:				jnp.ndarray		,	snake_case:				jnp.ndarray		,	)  ->      str:
											return cls(
											    common=snake_case		,	final_alpha_cumprod=snake_case		,	init_noise_sigma=snake_case		,	timesteps=snake_case		,	)

@dataclass
class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					_A						:     DDIMSchedulerState

class 							lowerCamelCase    (     _lowerCAmelCase       ,		_lowerCAmelCase							):






					'''simple docstring'''





					_A						:     Any		=	[e.name for e in FlaxKarrasDiffusionSchedulers]

					_A						:     jnp.dtype






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






					@register_to_config
					def __init__(			self:				Dict		,	snake_case:				int = 1_000		,	snake_case:				float = 0.0_0_0_1		,	snake_case:				float = 0.0_2		,	snake_case:				str = "linear"		,	snake_case:				Optional[jnp.ndarray] = None		,	snake_case:				bool = True		,	snake_case:				int = 0		,	snake_case:				str = "epsilon"		,	snake_case:				jnp.dtype = jnp.floataa		,	)  ->      int:
											snake_case_					:int		=     dtype






					def   lowerCAmelCase_							(			self:				Union[str, Any]		,	snake_case:				Optional[CommonSchedulerState] = None		)  ->      DDIMSchedulerState:
											if common is None:
																	snake_case_					:str		=     CommonSchedulerState.create(self		)

											# At every step in ddim, we are looking into the previous alphas_cumprod
											# For the final step, there is no previous alphas_cumprod because we are already at 0
											# `set_alpha_to_one` decides whether we set this parameter simply to one or
											# whether we use the final alpha of the "non-previous" one.
											snake_case_					:Union[str, Any]		=     (
											    jnp.array(1.0		,	dtype=self.dtype		) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
											)

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

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

											return DDIMSchedulerState.create(
											    common=snake_case		,	final_alpha_cumprod=snake_case		,	init_noise_sigma=snake_case		,	timesteps=snake_case		,	)






					def   lowerCAmelCase_							(			self:				int		,	snake_case:				DDIMSchedulerState		,	snake_case:				jnp.ndarray		,	snake_case:				Optional[int] = None		)  ->      jnp.ndarray:
											return sample






					def   lowerCAmelCase_							(			self:				Dict		,	snake_case:				DDIMSchedulerState		,	snake_case:				int		,	snake_case:				Tuple = ()		)  ->      DDIMSchedulerState:
											snake_case_					:str		=     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_					:List[str]		=     (jnp.arange(0		,	snake_case		) * step_ratio).round()[::-1] + self.config.steps_offset

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






					def   lowerCAmelCase_							(			self:				Optional[Any]		,	snake_case:				DDIMSchedulerState		,	snake_case:				Union[str, Any]		,	snake_case:				int		)  ->      str:
											snake_case_					:List[Any]		=     state.common.alphas_cumprod[timestep]
											snake_case_					:Union[str, Any]		=     jnp.where(
											    prev_timestep >= 0		,	state.common.alphas_cumprod[prev_timestep]		,	state.final_alpha_cumprod		)
											snake_case_					:Dict		=     1 - alpha_prod_t
											snake_case_					:List[str]		=     1 - alpha_prod_t_prev

											snake_case_					:List[Any]		=     (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

											return variance






					def   lowerCAmelCase_							(			self:				Union[str, Any]		,	snake_case:				DDIMSchedulerState		,	snake_case:				jnp.ndarray		,	snake_case:				int		,	snake_case:				jnp.ndarray		,	snake_case:				float = 0.0		,	snake_case:				bool = True		,	)  ->      Union[FlaxDDIMSchedulerOutput, Tuple]:
											if state.num_inference_steps is None:
																	raise ValueError(
																	    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""		)

											# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
											# Ideally, read DDIM paper in-detail understanding

											# Notation (<variable name> -> <name in paper>
											# - pred_noise_t -> e_theta(x_t, t)
											# - pred_original_sample -> f_theta(x_t, t) or x_0
											# - std_dev_t -> sigma_t
											# - eta -> η
											# - pred_sample_direction -> "direction pointing to x_t"
											# - pred_prev_sample -> "x_t-1"

											# 1. get previous step value (=t-1)
											snake_case_					:Any		=     timestep - self.config.num_train_timesteps // state.num_inference_steps

											snake_case_					:Union[str, Any]		=     state.common.alphas_cumprod
											snake_case_					:int		=     state.final_alpha_cumprod

											# 2. compute alphas, betas
											snake_case_					:int		=     alphas_cumprod[timestep]
											snake_case_					:Optional[int]		=     jnp.where(prev_timestep >= 0		,	alphas_cumprod[prev_timestep]		,	snake_case		)

											snake_case_					:List[Any]		=     1 - alpha_prod_t

											# 3. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											if self.config.prediction_type == "epsilon":
																	snake_case_					:str		=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																	snake_case_					:Any		=     model_output
											elif self.config.prediction_type == "sample":
																	snake_case_					:Optional[Any]		=     model_output
																	snake_case_					:List[Any]		=     (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
											elif self.config.prediction_type == "v_prediction":
																	snake_case_					:str		=     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																	snake_case_					:Union[str, Any]		=     (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
											else:
																	raise ValueError(
																	    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
																	    """ `v_prediction`"""		)

											# 4. compute variance: "sigma_t(η)" -> see formula (16)
											# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
											snake_case_					:Optional[int]		=     self._get_variance(snake_case		,	snake_case		,	snake_case		)
											snake_case_					:Union[str, Any]		=     eta * variance ** 0.5

											# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											snake_case_					:Any		=     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

											# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											snake_case_					:Optional[int]		=     alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

											if not return_dict:
																	return (prev_sample, state)

											return FlaxDDIMSchedulerOutput(prev_sample=snake_case		,	state=snake_case		)






					def   lowerCAmelCase_							(			self:				Dict		,	snake_case:				DDIMSchedulerState		,	snake_case:				jnp.ndarray		,	snake_case:				jnp.ndarray		,	snake_case:				jnp.ndarray		,	)  ->      jnp.ndarray:
											return add_noise_common(state.common		,	snake_case		,	snake_case		,	snake_case		)






					def   lowerCAmelCase_							(			self:				int		,	snake_case:				DDIMSchedulerState		,	snake_case:				jnp.ndarray		,	snake_case:				jnp.ndarray		,	snake_case:				jnp.ndarray		,	)  ->      jnp.ndarray:
											return get_velocity_common(state.common		,	snake_case		,	snake_case		,	snake_case		)






					def __len__(			self:				List[Any]		)  ->      List[Any]:
											return self.config.num_train_timesteps







"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_mbartaa import MBartaaTokenizer
else:
		_snake_case	:						Dict					=					None


_snake_case	:						int					=					logging.get_logger(__name__)

_snake_case	:						Optional[Any]					=					{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_snake_case	:						str					=					{
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'
        ),
    },
}

_snake_case	:						Union[str, Any]					=					{
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
_snake_case	:						int					=					['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				VOCAB_FILES_NAMES
					UpperCamelCase						=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCamelCase						=				PRETRAINED_VOCAB_FILES_MAP
					UpperCamelCase						=				['''input_ids''', '''attention_mask''']
					UpperCamelCase						=				MBartaaTokenizer

					UpperCamelCase						=				[]
					UpperCamelCase						=				[]


					def __init__(   self     :Any	,			__UpperCamelCase     :str=None	,			__UpperCamelCase     :Any=None	,			__UpperCamelCase     :int=None	,			__UpperCamelCase     :Union[str, Any]=None	,			__UpperCamelCase     :Optional[Any]="</s>"	,			__UpperCamelCase     :Tuple="</s>"	,			__UpperCamelCase     :Any="<s>"	,			__UpperCamelCase     :Optional[int]="<unk>"	,			__UpperCamelCase     :str="<pad>"	,			__UpperCamelCase     :int="<mask>"	,			**__UpperCamelCase     :List[str]	,			):
												# Mask token behave like a normal word, i.e. include the space before it
												A						=    AddedToken(__UpperCamelCase	,			lstrip=__UpperCamelCase	,			rstrip=__UpperCamelCase						) if isinstance(__UpperCamelCase	,			__UpperCamelCase						) else mask_token

												A						=    kwargs.get("additional_special_tokens"	,			[]						)
												kwargs["additional_special_tokens"] += [
												    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
												]

												super().__init__(
												    __UpperCamelCase	,			src_lang=__UpperCamelCase	,			tgt_lang=__UpperCamelCase	,			tokenizer_file=__UpperCamelCase	,			eos_token=__UpperCamelCase	,			sep_token=__UpperCamelCase	,			cls_token=__UpperCamelCase	,			unk_token=__UpperCamelCase	,			pad_token=__UpperCamelCase	,			mask_token=__UpperCamelCase	,			**__UpperCamelCase	,			)

												A						=    vocab_file
												A						=    False if not self.vocab_file else True

												A						=    {
												    lang_code: self.convert_tokens_to_ids(__UpperCamelCase						) for lang_code in FAIRSEQ_LANGUAGE_CODES
												}

												A						=    src_lang if src_lang is not None else "en_XX"
												A						=    tgt_lang
												A						=    self.lang_code_to_id[self._src_lang]
												self.set_src_lang_special_tokens(self._src_lang						)


					@property
					def       lowerCamelCase			(   self     :int						):
												return self._src_lang


					@src_lang.setter
					def       lowerCamelCase			(   self     :Optional[int]	,			__UpperCamelCase     :str						):
												A						=    new_src_lang
												self.set_src_lang_special_tokens(self._src_lang						)


					def       lowerCamelCase			(   self     :Any	,			__UpperCamelCase     :List[int]	,			__UpperCamelCase     :Optional[List[int]] = None						):
												if token_ids_a is None:
																			return self.prefix_tokens + token_ids_a + self.suffix_tokens
												# We don't expect to process pairs, but leave the pair logic for API consistency
												return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


					def       lowerCamelCase			(   self     :Optional[Any]	,			__UpperCamelCase     :List[str]	,			__UpperCamelCase     :str = "en_XX"	,			__UpperCamelCase     :Optional[List[str]] = None	,			__UpperCamelCase     :str = "ro_RO"	,			**__UpperCamelCase     :Tuple	,			):
												A						=    src_lang
												A						=    tgt_lang
												return super().prepare_seqaseq_batch(__UpperCamelCase	,			__UpperCamelCase	,			**__UpperCamelCase						)


					def       lowerCamelCase			(   self     :str						):
												return self.set_src_lang_special_tokens(self.src_lang						)


					def       lowerCamelCase			(   self     :Optional[Any]						):
												return self.set_tgt_lang_special_tokens(self.tgt_lang						)


					def       lowerCamelCase			(   self     :Any	,			__UpperCamelCase     :str						):
												A						=    self.convert_tokens_to_ids(__UpperCamelCase						)
												A						=    [self.cur_lang_code_id]
												A						=    [self.eos_token_id]

												A						=    self.convert_ids_to_tokens(self.prefix_tokens						)
												A						=    self.convert_ids_to_tokens(self.suffix_tokens						)

												A						=    processors.TemplateProcessing(
												    single=prefix_tokens_str + ["$A"] + suffix_tokens_str	,			pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str	,			special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str	,			self.prefix_tokens + self.suffix_tokens						)						)	,			)


					def       lowerCamelCase			(   self     :Optional[Any]	,			__UpperCamelCase     :str						):
												A						=    self.convert_tokens_to_ids(__UpperCamelCase						)
												A						=    [self.cur_lang_code_id]
												A						=    [self.eos_token_id]

												A						=    self.convert_ids_to_tokens(self.prefix_tokens						)
												A						=    self.convert_ids_to_tokens(self.suffix_tokens						)

												A						=    processors.TemplateProcessing(
												    single=prefix_tokens_str + ["$A"] + suffix_tokens_str	,			pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str	,			special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str	,			self.prefix_tokens + self.suffix_tokens						)						)	,			)


					def       lowerCamelCase			(   self     :Optional[Any]	,			__UpperCamelCase     :Any	,			__UpperCamelCase     :str	,			__UpperCamelCase     :Optional[str]	,			__UpperCamelCase     :Optional[str]	,			**__UpperCamelCase     :Union[str, Any]						):
												if src_lang is None or tgt_lang is None:
																			raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"						)
												A						=    src_lang
												A						=    self(__UpperCamelCase	,			add_special_tokens=__UpperCamelCase	,			return_tensors=__UpperCamelCase	,			**__UpperCamelCase						)
												A						=    self.convert_tokens_to_ids(__UpperCamelCase						)
												A						=    tgt_lang_id
												return inputs


					def       lowerCamelCase			(   self     :Tuple	,			__UpperCamelCase     :str	,			__UpperCamelCase     :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(__UpperCamelCase						):
																			logger.error(f"Vocabulary path ({save_directory}) should be a directory"						)
																			return
												A						=    os.path.join(
												    __UpperCamelCase	,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

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

												return (out_vocab_file,)


