


"""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      lowerCamelCase__   (     A				):







					"""simple docstring"""


					__a	       =							42






class      lowerCamelCase__   (     A     ,    A				):







					"""simple docstring"""


					__a	       =							True






					@register_to_config
					def __init__(       self    : Union[str, Any] ,       UpperCamelCase    : int = 3 ,       UpperCamelCase    : int = 3 ,       UpperCamelCase    : Tuple[str] = ("DownEncoderBlock2D",) ,       UpperCamelCase    : Tuple[str] = ("UpDecoderBlock2D",) ,       UpperCamelCase    : Tuple[int] = (64,) ,       UpperCamelCase    : int = 1 ,       UpperCamelCase    : str = "silu" ,       UpperCamelCase    : int = 4 ,       UpperCamelCase    : int = 32 ,       UpperCamelCase    : int = 32 ,       UpperCamelCase    : float = 0.18215 ,       ):
												'''simple docstring'''






												super().__init__()

												# pass init params to Encoder
												__UpperCAmelCase   :						str      							=					Encoder(
												    in_channels=UpperCamelCase ,       out_channels=UpperCamelCase ,       down_block_types=UpperCamelCase ,       block_out_channels=UpperCamelCase ,       layers_per_block=UpperCamelCase ,       act_fn=UpperCamelCase ,       norm_num_groups=UpperCamelCase ,       double_z=UpperCamelCase ,       )

												# pass init params to Decoder
												__UpperCAmelCase   :						Optional[int]      							=					Decoder(
												    in_channels=UpperCamelCase ,       out_channels=UpperCamelCase ,       up_block_types=UpperCamelCase ,       block_out_channels=UpperCamelCase ,       layers_per_block=UpperCamelCase ,       norm_num_groups=UpperCamelCase ,       act_fn=UpperCamelCase ,       )

												__UpperCAmelCase   :						int      							=					nn.Convad(2 * latent_channels ,       2 * latent_channels ,       1  )
												__UpperCAmelCase   :						Union[str, Any]      							=					nn.Convad(UpperCamelCase ,       UpperCamelCase ,       1  )

												__UpperCAmelCase   :						int      							=					False
												__UpperCAmelCase   :						List[Any]      							=					False

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






					def lowerCamelCase__							(       self    : Optional[Any] ,       UpperCamelCase    : List[Any] ,       UpperCamelCase    : Any=False  ):
												'''simple docstring'''






												if isinstance(UpperCamelCase ,       (Encoder, Decoder)  ):
																			__UpperCAmelCase   :						Tuple      							=					value






					def lowerCamelCase__							(       self    : int ,       UpperCamelCase    : bool = True  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Dict      							=					use_tiling






					def lowerCamelCase__							(       self    : Union[str, Any]  ):
												'''simple docstring'''






												self.enable_tiling(UpperCamelCase  )






					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					True






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					False






					@property
					# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
					def lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					{}

												def fn_recursive_add_processors(UpperCamelCase    : str ,       UpperCamelCase    : torch.nn.Module ,       UpperCamelCase    : Dict[str, AttentionProcessor]  ):
																			if hasattr(UpperCamelCase ,       """set_processor"""  ):
																										__UpperCAmelCase   :						List[Any]      							=					module.processor

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

																			return processors

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

												return processors






					def lowerCamelCase__							(       self    : Union[str, Any] ,       UpperCamelCase    : Union[AttentionProcessor, Dict[str, AttentionProcessor]]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					len(self.attn_processors.keys()  )

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

												def fn_recursive_attn_processor(UpperCamelCase    : str ,       UpperCamelCase    : torch.nn.Module ,       UpperCamelCase    : List[Any]  ):
																			if hasattr(UpperCamelCase ,       """set_processor"""  ):
																										if not isinstance(UpperCamelCase ,       UpperCamelCase  ):
																																	module.set_processor(UpperCamelCase  )
																										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}''' ,       UpperCamelCase ,       UpperCamelCase  )

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






					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												self.set_attn_processor(AttnProcessor()  )






					@apply_forward_hook
					def lowerCamelCase__							(       self    : Optional[Any] ,       UpperCamelCase    : torch.FloatTensor ,       UpperCamelCase    : bool = 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(UpperCamelCase ,       return_dict=UpperCamelCase  )

												if self.use_slicing and x.shape[0] > 1:
																			__UpperCAmelCase   :						List[str]      							=					[self.encoder(UpperCamelCase  ) for x_slice in x.split(1  )]
																			__UpperCAmelCase   :						Tuple      							=					torch.cat(UpperCamelCase  )
												else:
																			__UpperCAmelCase   :						Any      							=					self.encoder(UpperCamelCase  )

												__UpperCAmelCase   :						int      							=					self.quant_conv(UpperCamelCase  )
												__UpperCAmelCase   :						List[Any]      							=					DiagonalGaussianDistribution(UpperCamelCase  )

												if not return_dict:
																			return (posterior,)

												return AutoencoderKLOutput(latent_dist=UpperCamelCase  )






					def lowerCamelCase__							(       self    : Union[str, Any] ,       UpperCamelCase    : torch.FloatTensor ,       UpperCamelCase    : bool = 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(UpperCamelCase ,       return_dict=UpperCamelCase  )

												__UpperCAmelCase   :						List[Any]      							=					self.post_quant_conv(UpperCamelCase  )
												__UpperCAmelCase   :						Optional[int]      							=					self.decoder(UpperCamelCase  )

												if not return_dict:
																			return (dec,)

												return DecoderOutput(sample=UpperCamelCase  )






					@apply_forward_hook
					def lowerCamelCase__							(       self    : List[Any] ,       UpperCamelCase    : torch.FloatTensor ,       UpperCamelCase    : bool = True  ):
												'''simple docstring'''






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

												if not return_dict:
																			return (decoded,)

												return DecoderOutput(sample=UpperCamelCase  )






					def lowerCamelCase__							(       self    : Any ,       UpperCamelCase    : str ,       UpperCamelCase    : Optional[int] ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					min(a.shape[2] ,       b.shape[2] ,       UpperCamelCase  )
												for y in range(UpperCamelCase  ):
																			__UpperCAmelCase   :						int      							=					a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
												return b






					def lowerCamelCase__							(       self    : Optional[int] ,       UpperCamelCase    : List[str] ,       UpperCamelCase    : List[str] ,       UpperCamelCase    : Optional[int]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					min(a.shape[3] ,       b.shape[3] ,       UpperCamelCase  )
												for x in range(UpperCamelCase  ):
																			__UpperCAmelCase   :						List[str]      							=					a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
												return b






					def lowerCamelCase__							(       self    : Optional[Any] ,       UpperCamelCase    : torch.FloatTensor ,       UpperCamelCase    : bool = True  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)  )
												__UpperCAmelCase   :						int      							=					int(self.tile_latent_min_size * self.tile_overlap_factor  )
												__UpperCAmelCase   :						List[str]      							=					self.tile_latent_min_size - blend_extent

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

												__UpperCAmelCase   :						List[Any]      							=					torch.cat(UpperCamelCase ,       dim=2  )
												__UpperCAmelCase   :						List[Any]      							=					DiagonalGaussianDistribution(UpperCamelCase  )

												if not return_dict:
																			return (posterior,)

												return AutoencoderKLOutput(latent_dist=UpperCamelCase  )






					def lowerCamelCase__							(       self    : List[str] ,       UpperCamelCase    : torch.FloatTensor ,       UpperCamelCase    : bool = True  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[Any]      							=					int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)  )
												__UpperCAmelCase   :						Optional[Any]      							=					int(self.tile_sample_min_size * self.tile_overlap_factor  )
												__UpperCAmelCase   :						List[Any]      							=					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.
												__UpperCAmelCase   :						int      							=					[]
												for i in range(0 ,       z.shape[2] ,       UpperCamelCase  ):
																			__UpperCAmelCase   :						Tuple      							=					[]
																			for j in range(0 ,       z.shape[3] ,       UpperCamelCase  ):
																										__UpperCAmelCase   :						Union[str, Any]      							=					z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																										__UpperCAmelCase   :						int      							=					self.post_quant_conv(UpperCamelCase  )
																										__UpperCAmelCase   :						List[Any]      							=					self.decoder(UpperCamelCase  )
																										row.append(UpperCamelCase  )
																			rows.append(UpperCamelCase  )
												__UpperCAmelCase   :						Any      							=					[]
												for i, row in enumerate(UpperCamelCase  ):
																			__UpperCAmelCase   :						List[str]      							=					[]
																			for j, tile in enumerate(UpperCamelCase  ):
																										# blend the above tile and the left tile
																										# to the current tile and add the current tile to the result row
																										if i > 0:
																																	__UpperCAmelCase   :						Any      							=					self.blend_v(rows[i - 1][j] ,       UpperCamelCase ,       UpperCamelCase  )
																										if j > 0:
																																	__UpperCAmelCase   :						Tuple      							=					self.blend_h(row[j - 1] ,       UpperCamelCase ,       UpperCamelCase  )
																										result_row.append(tile[:, :, :row_limit, :row_limit]  )
																			result_rows.append(torch.cat(UpperCamelCase ,       dim=3  )  )

												__UpperCAmelCase   :						Optional[int]      							=					torch.cat(UpperCamelCase ,       dim=2  )
												if not return_dict:
																			return (dec,)

												return DecoderOutput(sample=UpperCamelCase  )






					def lowerCamelCase__							(       self    : List[str] ,       UpperCamelCase    : torch.FloatTensor ,       UpperCamelCase    : bool = False ,       UpperCamelCase    : bool = True ,       UpperCamelCase    : Optional[torch.Generator] = None ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[int]      							=					sample
												__UpperCAmelCase   :						Tuple      							=					self.encode(UpperCamelCase  ).latent_dist
												if sample_posterior:
																			__UpperCAmelCase   :						Dict      							=					posterior.sample(generator=UpperCamelCase  )
												else:
																			__UpperCAmelCase   :						Union[str, Any]      							=					posterior.mode()
												__UpperCAmelCase   :						Any      							=					self.decode(UpperCamelCase  ).sample

												if not return_dict:
																			return (dec,)

												return DecoderOutput(sample=UpperCamelCase  )




"""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:
							UpperCAmelCase    :     Union[str, Any]   						=		None

UpperCAmelCase    :     Union[str, Any]   						=		logging.get_logger(__name__)

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

UpperCAmelCase    :     List[str]   						=		{
    '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    :     Tuple   						=		{
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

UpperCAmelCase    :     str   						=		'▁'





class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					__a	       =							VOCAB_FILES_NAMES
					__a	       =							PRETRAINED_VOCAB_FILES_MAP
					__a	       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__a	       =							["""input_ids""", """attention_mask"""]
					__a	       =							BarthezTokenizer






					def __init__(       self    : Optional[int] ,       UpperCamelCase    : List[str]=None ,       UpperCamelCase    : List[str]=None ,       UpperCamelCase    : Union[str, Any]="<s>" ,       UpperCamelCase    : Any="</s>" ,       UpperCamelCase    : Tuple="</s>" ,       UpperCamelCase    : Tuple="<s>" ,       UpperCamelCase    : int="<unk>" ,       UpperCamelCase    : List[str]="<pad>" ,       UpperCamelCase    : int="<mask>" ,       **UpperCamelCase    : Any ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					AddedToken(UpperCamelCase ,       lstrip=UpperCamelCase ,       rstrip=UpperCamelCase  ) if isinstance(UpperCamelCase ,       UpperCamelCase  ) else mask_token

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

												__UpperCAmelCase   :						List[Any]      							=					vocab_file
												__UpperCAmelCase   :						Tuple      							=					False if not self.vocab_file else True






					def lowerCamelCase__							(       self    : Any ,       UpperCamelCase    : List[int] ,       UpperCamelCase    : Optional[List[int]] = None  ):
												'''simple docstring'''






												if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												__UpperCAmelCase   :						List[Any]      							=					[self.cls_token_id]
												__UpperCAmelCase   :						Tuple      							=					[self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep






					def lowerCamelCase__							(       self    : Union[str, Any] ,       UpperCamelCase    : List[int] ,       UpperCamelCase    : Optional[List[int]] = None  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					[self.sep_token_id]
												__UpperCAmelCase   :						Union[str, 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 lowerCamelCase__							(       self    : Tuple ,       UpperCamelCase    : str ,       UpperCamelCase    : Optional[str] = None  ):
												'''simple docstring'''






												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
												__UpperCAmelCase   :						List[Any]      							=					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,)
