




"""simple docstring"""
import math

import flax.linen as nn
import jax.numpy as jnp





def 						UpperCAmelCase				(			A__:    jnp.ndarray							,				A__:    int							,				A__:    float = 1							,				A__:    float = 1							,				A__:    float = 1.0E4							,				A__:    bool = False							,				A__:    float = 1.0							,				)  ->     jnp.ndarray:
			assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
			assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
			__lowerCamelCase					:	str	       =				float(embedding_dim // 2     )
			__lowerCamelCase					:	Any	       =				math.log(max_timescale / min_timescale     ) / (num_timescales - freq_shift)
			__lowerCamelCase					:	Dict	       =				min_timescale * jnp.exp(jnp.arange(A__							,				dtype=jnp.floataa     ) * -log_timescale_increment     )
			__lowerCamelCase					:	Any	       =				jnp.expand_dims(A__							,				1     ) * jnp.expand_dims(A__							,				0     )

			# scale embeddings
			__lowerCamelCase					:	Any	       =				scale * emb

			if flip_sin_to_cos:
						__lowerCamelCase					:	Tuple	       =				jnp.concatenate([jnp.cos(A__     ), jnp.sin(A__     )]							,				axis=1     )
			else:
						__lowerCamelCase					:	int	       =				jnp.concatenate([jnp.sin(A__     ), jnp.cos(A__     )]							,				axis=1     )
			__lowerCamelCase					:	Any	       =				jnp.reshape(A__							,				[jnp.shape(A__     )[0], embedding_dim]     )
			return signal

class __lowercase(      nn.Module				):




					'''simple docstring'''


					__a						:						int     		= 32
					__a						:						jnp.dtype     		= jnp.floataa
					@nn.compact
					def __call__(    self						,	__a     ):
								__lowerCamelCase					:	List[str]	       =				nn.Dense(self.time_embed_dim						,	dtype=self.dtype						,	name='linear_1'     )(__a     )
								__lowerCamelCase					:	Tuple	       =				nn.silu(__a     )
								__lowerCamelCase					:	Optional[Any]	       =				nn.Dense(self.time_embed_dim						,	dtype=self.dtype						,	name='linear_2'     )(__a     )
								return temb



class __lowercase(      nn.Module				):




					'''simple docstring'''


					__a						:						int     		= 32
					__a						:						bool     		= False
					__a						:						float     		= 1
					@nn.compact
					def __call__(    self						,	__a     ):
								return get_sinusoidal_embeddings(
								    __a						,	embedding_dim=self.dim						,	flip_sin_to_cos=self.flip_sin_to_cos						,	freq_shift=self.freq_shift     )









"""simple docstring"""
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:
					a_				:    List[str]										=     None

a_				:    Any										=     logging.get_logger(__name__)

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

a_				:    int										=     {
    '''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'''
        ),
    },
}

a_				:    Any										=     {
    '''moussaKam/mbarthez''': 10_24,
    '''moussaKam/barthez''': 10_24,
    '''moussaKam/barthez-orangesum-title''': 10_24,
}

a_				:    Union[str, Any]										=     '''▁'''

class __lowercase(      lowercase__				):




					'''simple docstring'''


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

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

								__lowerCamelCase					:	Tuple	       =				vocab_file
								__lowerCamelCase					:	List[str]	       =				False if not self.vocab_file else True
					def     snake_case_				(    self						,	__a						,	__a = None     ):

								if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								__lowerCamelCase					:	Optional[Any]	       =				[self.cls_token_id]
								__lowerCamelCase					:	Dict	       =				[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep
					def     snake_case_				(    self						,	__a						,	__a = None     ):
								__lowerCamelCase					:	Optional[int]	       =				[self.sep_token_id]
								__lowerCamelCase					:	int	       =				[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						,	__a						,	__a = 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(__a     ):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
											return
								__lowerCamelCase					:	List[Any]	       =				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,)



