'''simple docstring'''




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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowercase        =    logging.get_logger(__name__)

__lowercase        =    {'''vocab_file''': '''sentencepiece.bpe.model'''}

__lowercase        =    {
    '''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'''
        ),
    },
}

__lowercase        =    {
    '''moussaKam/mbarthez''': 1_0_2_4,
    '''moussaKam/barthez''': 1_0_2_4,
    '''moussaKam/barthez-orangesum-title''': 1_0_2_4,
}

__lowercase        =    '''▁'''






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Dict						=			VOCAB_FILES_NAMES
			UpperCAmelCase_     :	int						=			PRETRAINED_VOCAB_FILES_MAP
			UpperCAmelCase_     :	Tuple						=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCAmelCase_     :	Optional[Any]						=			['''input_ids''', '''attention_mask''']



			def __init__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase="<s>"			,       __lowerCAmelCase="</s>"			,       __lowerCAmelCase="</s>"			,       __lowerCAmelCase="<s>"			,       __lowerCAmelCase="<unk>"			,       __lowerCAmelCase="<pad>"			,       __lowerCAmelCase="<mask>"			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    AddedToken(__lowerCAmelCase			,       lstrip=__lowerCAmelCase			,       rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase			,       __lowerCAmelCase) else mask_token

					lowerCAmelCase     =    {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=__lowerCAmelCase			,       eos_token=__lowerCAmelCase			,       unk_token=__lowerCAmelCase			,       sep_token=__lowerCAmelCase			,       cls_token=__lowerCAmelCase			,       pad_token=__lowerCAmelCase			,       mask_token=__lowerCAmelCase			,       sp_model_kwargs=self.sp_model_kwargs			,       **__lowerCAmelCase			,       )

					lowerCAmelCase     =    vocab_file
					lowerCAmelCase     =    spm.SentencePieceProcessor(**self.sp_model_kwargs)
					self.sp_model.Load(str(__lowerCAmelCase))

					lowerCAmelCase     =    {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

					lowerCAmelCase     =    len(self.sp_model) - 1
					lowerCAmelCase     =    {v: k for k, v in self.fairseq_tokens_to_ids.items()}



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None):




					"""simple docstring"""


					if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					lowerCAmelCase     =    [self.cls_token_id]
					lowerCAmelCase     =    [self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = False):




					"""simple docstring"""


					if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=__lowerCAmelCase			,       token_ids_a=__lowerCAmelCase			,       already_has_special_tokens=__lowerCAmelCase)

					if token_ids_a is None:
							return [1] + ([0] * len(__lowerCAmelCase)) + [1]
					return [1] + ([0] * len(__lowerCAmelCase)) + [1, 1] + ([0] * len(__lowerCAmelCase)) + [1]



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None):




					"""simple docstring"""


					lowerCAmelCase     =    [self.sep_token_id]
					lowerCAmelCase     =    [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]



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return len(self.sp_model)



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    {self.convert_ids_to_tokens(__lowerCAmelCase): i for i in range(self.vocab_size)}
					vocab.update(self.added_tokens_encoder)
					return vocab



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					return self.sp_model.encode(__lowerCAmelCase			,       out_type=__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
					lowerCAmelCase     =    self.sp_model.PieceToId(__lowerCAmelCase)

					return spm_id if spm_id else self.unk_token_id



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					if index in self.fairseq_ids_to_tokens:
							return self.fairseq_ids_to_tokens[index]
					return self.sp_model.IdToPiece(__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    []
					lowerCAmelCase     =    """"""
					lowerCAmelCase     =    False
					for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
									if not prev_is_special:
											out_string += " "
									out_string += self.sp_model.decode(__lowerCAmelCase) + token
									lowerCAmelCase     =    True
									lowerCAmelCase     =    []
							else:
									current_sub_tokens.append(__lowerCAmelCase)
									lowerCAmelCase     =    False
					out_string += self.sp_model.decode(__lowerCAmelCase)
					return out_string.strip()



			def __getstate__(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.__dict__.copy()
					lowerCAmelCase     =    None
					return state



			def __setstate__(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    d

					# for backward compatibility
					if not hasattr(self			,       """sp_model_kwargs"""):
							lowerCAmelCase     =    {}

					lowerCAmelCase     =    spm.SentencePieceProcessor(**self.sp_model_kwargs)
					self.sp_model.Load(self.vocab_file)







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None):




					"""simple docstring"""


					if not os.path.isdir(__lowerCAmelCase):
							logger.error(f"Vocabulary path ({save_directory}) should be a directory")
							return
					lowerCAmelCase     =    os.path.join(
					    __lowerCAmelCase			,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

					if os.path.abspath(self.vocab_file) != os.path.abspath(__lowerCAmelCase) and os.path.isfile(self.vocab_file):
							copyfile(self.vocab_file			,       __lowerCAmelCase)
					elif not os.path.isfile(self.vocab_file):
							with open(__lowerCAmelCase			,       """wb""") as fi:
									lowerCAmelCase     =    self.sp_model.serialized_model_proto()
									fi.write(__lowerCAmelCase)

					return (out_vocab_file,)





'''simple docstring'''




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

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder






@dataclass
class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	"DiagonalGaussianDistribution"







class 							a__(   lowerCAmelCase__					,		lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[Any]						=			True



			@register_to_config
			def __init__(  self			,       __lowerCAmelCase = 3			,       __lowerCAmelCase = 3			,       __lowerCAmelCase = ("DownEncoderBlock2D",)			,       __lowerCAmelCase = ("UpDecoderBlock2D",)			,       __lowerCAmelCase = (64,)			,       __lowerCAmelCase = 1			,       __lowerCAmelCase = "silu"			,       __lowerCAmelCase = 4			,       __lowerCAmelCase = 32			,       __lowerCAmelCase = 32			,       __lowerCAmelCase = 0.18215			,       ):




					"""simple docstring"""


					super().__init__()

					# pass init params to Encoder
					lowerCAmelCase     =    Encoder(
					    in_channels=__lowerCAmelCase			,       out_channels=__lowerCAmelCase			,       down_block_types=__lowerCAmelCase			,       block_out_channels=__lowerCAmelCase			,       layers_per_block=__lowerCAmelCase			,       act_fn=__lowerCAmelCase			,       norm_num_groups=__lowerCAmelCase			,       double_z=__lowerCAmelCase			,       )

					# pass init params to Decoder
					lowerCAmelCase     =    Decoder(
					    in_channels=__lowerCAmelCase			,       out_channels=__lowerCAmelCase			,       up_block_types=__lowerCAmelCase			,       block_out_channels=__lowerCAmelCase			,       layers_per_block=__lowerCAmelCase			,       norm_num_groups=__lowerCAmelCase			,       act_fn=__lowerCAmelCase			,       )

					lowerCAmelCase     =    nn.Convad(2 * latent_channels			,       2 * latent_channels			,       1)
					lowerCAmelCase     =    nn.Convad(__lowerCAmelCase			,       __lowerCAmelCase			,       1)

					lowerCAmelCase     =    False
					lowerCAmelCase     =    False

					# only relevant if vae tiling is enabled
					lowerCAmelCase     =    self.config.sample_size
					lowerCAmelCase     =    (
					    self.config.sample_size[0]
					    if isinstance(self.config.sample_size			,       (list, tuple))
					    else self.config.sample_size
					)
					lowerCAmelCase     =    int(sample_size / (2 ** (len(self.config.block_out_channels) - 1)))
					lowerCAmelCase     =    0.25



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase=False):




					"""simple docstring"""


					if isinstance(__lowerCAmelCase			,       (Encoder, Decoder)):
							lowerCAmelCase     =    value



			def 				a_			(  self			,       __lowerCAmelCase = True):




					"""simple docstring"""


					lowerCAmelCase     =    use_tiling



			def 				a_			(  self):




					"""simple docstring"""


					self.enable_tiling(__lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    True



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    False



			@property
			# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    {}

					def fn_recursive_add_processors(__lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):
							if hasattr(__lowerCAmelCase			,       """set_processor"""):
									lowerCAmelCase     =    module.processor

							for sub_name, child in module.named_children():
									fn_recursive_add_processors(f"{name}.{sub_name}"			,       __lowerCAmelCase			,       __lowerCAmelCase)

							return processors

					for name, module in self.named_children():
							fn_recursive_add_processors(__lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase)

					return processors



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    len(self.attn_processors.keys())

					if isinstance(__lowerCAmelCase			,       __lowerCAmelCase) and len(__lowerCAmelCase) != count:
							raise ValueError(
							    f"A dict of processors was passed, but the number of processors {len(__lowerCAmelCase)} does not match the"
							    f" number of attention layers: {count}. Please make sure to pass {count} processor classes.")

					def fn_recursive_attn_processor(__lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):
							if hasattr(__lowerCAmelCase			,       """set_processor"""):
									if not isinstance(__lowerCAmelCase			,       __lowerCAmelCase):
											module.set_processor(__lowerCAmelCase)
									else:
											module.set_processor(processor.pop(f"{name}.processor"))

							for sub_name, child in module.named_children():
									fn_recursive_attn_processor(f"{name}.{sub_name}"			,       __lowerCAmelCase			,       __lowerCAmelCase)

					for name, module in self.named_children():
							fn_recursive_attn_processor(__lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					self.set_attn_processor(AttnProcessor())



			@apply_forward_hook
			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = True):




					"""simple docstring"""


					if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
							return self.tiled_encode(__lowerCAmelCase			,       return_dict=__lowerCAmelCase)

					if self.use_slicing and x.shape[0] > 1:
							lowerCAmelCase     =    [self.encoder(__lowerCAmelCase) for x_slice in x.split(1)]
							lowerCAmelCase     =    torch.cat(__lowerCAmelCase)
					else:
							lowerCAmelCase     =    self.encoder(__lowerCAmelCase)

					lowerCAmelCase     =    self.quant_conv(__lowerCAmelCase)
					lowerCAmelCase     =    DiagonalGaussianDistribution(__lowerCAmelCase)

					if not return_dict:
							return (posterior,)

					return AutoencoderKLOutput(latent_dist=__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = True):




					"""simple docstring"""


					if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
							return self.tiled_decode(__lowerCAmelCase			,       return_dict=__lowerCAmelCase)

					lowerCAmelCase     =    self.post_quant_conv(__lowerCAmelCase)
					lowerCAmelCase     =    self.decoder(__lowerCAmelCase)

					if not return_dict:
							return (dec,)

					return DecoderOutput(sample=__lowerCAmelCase)



			@apply_forward_hook
			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = True):




					"""simple docstring"""


					if self.use_slicing and z.shape[0] > 1:
							lowerCAmelCase     =    [self._decode(__lowerCAmelCase).sample for z_slice in z.split(1)]
							lowerCAmelCase     =    torch.cat(__lowerCAmelCase)
					else:
							lowerCAmelCase     =    self._decode(__lowerCAmelCase).sample

					if not return_dict:
							return (decoded,)

					return DecoderOutput(sample=__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    min(a.shape[2]			,       b.shape[2]			,       __lowerCAmelCase)
					for y in range(__lowerCAmelCase):
							lowerCAmelCase     =    a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
					return b



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    min(a.shape[3]			,       b.shape[3]			,       __lowerCAmelCase)
					for x in range(__lowerCAmelCase):
							lowerCAmelCase     =    a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
					return b



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = True):




					"""simple docstring"""


					lowerCAmelCase     =    int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))
					lowerCAmelCase     =    int(self.tile_latent_min_size * self.tile_overlap_factor)
					lowerCAmelCase     =    self.tile_latent_min_size - blend_extent

					# Split the image into 512x512 tiles and encode them separately.
					lowerCAmelCase     =    []
					for i in range(0			,       x.shape[2]			,       __lowerCAmelCase):
							lowerCAmelCase     =    []
							for j in range(0			,       x.shape[3]			,       __lowerCAmelCase):
									lowerCAmelCase     =    x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
									lowerCAmelCase     =    self.encoder(__lowerCAmelCase)
									lowerCAmelCase     =    self.quant_conv(__lowerCAmelCase)
									row.append(__lowerCAmelCase)
							rows.append(__lowerCAmelCase)
					lowerCAmelCase     =    []
					for i, row in enumerate(__lowerCAmelCase):
							lowerCAmelCase     =    []
							for j, tile in enumerate(__lowerCAmelCase):
									# blend the above tile and the left tile
									# to the current tile and add the current tile to the result row
									if i > 0:
											lowerCAmelCase     =    self.blend_v(rows[i - 1][j]			,       __lowerCAmelCase			,       __lowerCAmelCase)
									if j > 0:
											lowerCAmelCase     =    self.blend_h(row[j - 1]			,       __lowerCAmelCase			,       __lowerCAmelCase)
									result_row.append(tile[:, :, :row_limit, :row_limit])
							result_rows.append(torch.cat(__lowerCAmelCase			,       dim=3))

					lowerCAmelCase     =    torch.cat(__lowerCAmelCase			,       dim=2)
					lowerCAmelCase     =    DiagonalGaussianDistribution(__lowerCAmelCase)

					if not return_dict:
							return (posterior,)

					return AutoencoderKLOutput(latent_dist=__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = True):




					"""simple docstring"""


					lowerCAmelCase     =    int(self.tile_latent_min_size * (1 - self.tile_overlap_factor))
					lowerCAmelCase     =    int(self.tile_sample_min_size * self.tile_overlap_factor)
					lowerCAmelCase     =    self.tile_sample_min_size - blend_extent

					# Split z into overlapping 64x64 tiles and decode them separately.
					# The tiles have an overlap to avoid seams between tiles.
					lowerCAmelCase     =    []
					for i in range(0			,       z.shape[2]			,       __lowerCAmelCase):
							lowerCAmelCase     =    []
							for j in range(0			,       z.shape[3]			,       __lowerCAmelCase):
									lowerCAmelCase     =    z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
									lowerCAmelCase     =    self.post_quant_conv(__lowerCAmelCase)
									lowerCAmelCase     =    self.decoder(__lowerCAmelCase)
									row.append(__lowerCAmelCase)
							rows.append(__lowerCAmelCase)
					lowerCAmelCase     =    []
					for i, row in enumerate(__lowerCAmelCase):
							lowerCAmelCase     =    []
							for j, tile in enumerate(__lowerCAmelCase):
									# blend the above tile and the left tile
									# to the current tile and add the current tile to the result row
									if i > 0:
											lowerCAmelCase     =    self.blend_v(rows[i - 1][j]			,       __lowerCAmelCase			,       __lowerCAmelCase)
									if j > 0:
											lowerCAmelCase     =    self.blend_h(row[j - 1]			,       __lowerCAmelCase			,       __lowerCAmelCase)
									result_row.append(tile[:, :, :row_limit, :row_limit])
							result_rows.append(torch.cat(__lowerCAmelCase			,       dim=3))

					lowerCAmelCase     =    torch.cat(__lowerCAmelCase			,       dim=2)
					if not return_dict:
							return (dec,)

					return DecoderOutput(sample=__lowerCAmelCase)







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = False			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    sample
					lowerCAmelCase     =    self.encode(__lowerCAmelCase).latent_dist
					if sample_posterior:
							lowerCAmelCase     =    posterior.sample(generator=__lowerCAmelCase)
					else:
							lowerCAmelCase     =    posterior.mode()
					lowerCAmelCase     =    self.decode(__lowerCAmelCase).sample

					if not return_dict:
							return (dec,)

					return DecoderOutput(sample=__lowerCAmelCase)




