




# 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 				lowerCAmelCase_       :
				__a     :       CommonSchedulerState

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






				@classmethod
				def 		snake_case	(			cls      ,snake_case__      ,snake_case__      ,snake_case__ ):
							return cls(common=snake_case__      ,init_noise_sigma=snake_case__      ,timesteps=snake_case__ )







@dataclass
class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       DDPMSchedulerState







class 				lowerCAmelCase_       (					lowerCamelCase_						,     lowerCamelCase_							):
				__a     :       Tuple						=     [e.name for e in FlaxKarrasDiffusionSchedulers]

				__a     :       jnp.dtype






				@property
				def 		snake_case	(			self ):
							return True






				@register_to_config
				def __init__(			self      ,snake_case__ = 1000      ,snake_case__ = 0.0001      ,snake_case__ = 0.02      ,snake_case__ = "linear"      ,snake_case__ = None      ,snake_case__ = "fixed_small"      ,snake_case__ = True      ,snake_case__ = "epsilon"      ,snake_case__ = jnp.floataa      ,):
							SCREAMING_SNAKE_CASE_   : Any					  =      dtype






				def 		snake_case	(			self      ,snake_case__ = None ):
							if common is None:
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      CommonSchedulerState.create(self )

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

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      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 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__ = None ):
							return sample






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__ = () ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      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
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      (jnp.arange(0      ,snake_case__ ) * step_ratio).round()[::-1]

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






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__=None      ,snake_case__=None ):
							SCREAMING_SNAKE_CASE_   : List[str]					  =      state.common.alphas_cumprod[t]
							SCREAMING_SNAKE_CASE_   : int					  =      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
							SCREAMING_SNAKE_CASE_   : Dict					  =      (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

							if variance_type is None:
										SCREAMING_SNAKE_CASE_   : int					  =      self.config.variance_type

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

							return variance






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__ = None      ,snake_case__ = True      ,):
							SCREAMING_SNAKE_CASE_   : int					  =      timestep

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

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

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

							# 6. Add noise
							def random_variance():
										SCREAMING_SNAKE_CASE_   : int					  =      jax.random.split(snake_case__      ,num=1 )
										SCREAMING_SNAKE_CASE_   : List[str]					  =      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

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      jnp.where(t > 0      ,random_variance()      ,jnp.zeros(model_output.shape      ,dtype=self.dtype ) )

							SCREAMING_SNAKE_CASE_   : str					  =      pred_prev_sample + variance

							if not return_dict:
										return (pred_prev_sample, state)

							return FlaxDDPMSchedulerOutput(prev_sample=snake_case__      ,state=snake_case__ )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,):
							return add_noise_common(state.common      ,snake_case__      ,snake_case__      ,snake_case__ )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,):
							return get_velocity_common(state.common      ,snake_case__      ,snake_case__      ,snake_case__ )






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









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

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


if is_sentencepiece_available():
						from .tokenization_barthez import BarthezTokenizer
else:
						UpperCamelCase__ :   Dict						=							None

UpperCamelCase__ :   Tuple						=							logging.get_logger(__name__)

UpperCamelCase__ :   Union[str, Any]						=							{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase__ :   Dict						=							{
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCamelCase__ :   Dict						=							{
    '''moussaKam/mbarthez''': 10_24,
    '''moussaKam/barthez''': 10_24,
    '''moussaKam/barthez-orangesum-title''': 10_24,
}

UpperCamelCase__ :   Any						=							'''▁'''







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       List[str]						=     VOCAB_FILES_NAMES
				__a     :       int						=     PRETRAINED_VOCAB_FILES_MAP
				__a     :       Optional[int]						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a     :       List[Any]						=     ["input_ids", "attention_mask"]
				__a     :       Dict						=     BarthezTokenizer






				def __init__(			self      ,snake_case__=None      ,snake_case__=None      ,snake_case__="<s>"      ,snake_case__="</s>"      ,snake_case__="</s>"      ,snake_case__="<s>"      ,snake_case__="<unk>"      ,snake_case__="<pad>"      ,snake_case__="<mask>"      ,**snake_case__      ,):
							# Mask token behave like a normal word, i.e. include the space before it
							SCREAMING_SNAKE_CASE_   : str					  =      AddedToken(snake_case__      ,lstrip=snake_case__      ,rstrip=snake_case__ ) if isinstance(snake_case__      ,snake_case__ ) else mask_token

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

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      vocab_file
							SCREAMING_SNAKE_CASE_   : int					  =      False if not self.vocab_file else True






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):

							if token_ids_a is None:
										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							SCREAMING_SNAKE_CASE_   : List[str]					  =      [self.cls_token_id]
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      [self.sep_token_id]
							SCREAMING_SNAKE_CASE_   : Any					  =      [self.cls_token_id]

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






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = 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
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      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,)



