


"""simple docstring"""






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

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block





@dataclass
class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					__a	       =							42






class      lowerCamelCase__   (     A     ,    A				):







					"""simple docstring"""


					@register_to_config
					def __init__(       self    : Any ,       UpperCamelCase    : int = 65_536 ,       UpperCamelCase    : Optional[int] = None ,       UpperCamelCase    : int = 2 ,       UpperCamelCase    : int = 2 ,       UpperCamelCase    : int = 0 ,       UpperCamelCase    : str = "fourier" ,       UpperCamelCase    : bool = True ,       UpperCamelCase    : bool = False ,       UpperCamelCase    : float = 0.0 ,       UpperCamelCase    : Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") ,       UpperCamelCase    : Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") ,       UpperCamelCase    : Tuple[str] = "UNetMidBlock1D" ,       UpperCamelCase    : str = None ,       UpperCamelCase    : Tuple[int] = (32, 32, 64) ,       UpperCamelCase    : str = None ,       UpperCamelCase    : int = 8 ,       UpperCamelCase    : int = 1 ,       UpperCamelCase    : bool = False ,       ):
												'''simple docstring'''






												super().__init__()
												__UpperCAmelCase   :						List[Any]      							=					sample_size

												# time
												if time_embedding_type == "fourier":
																			__UpperCAmelCase   :						Union[str, Any]      							=					GaussianFourierProjection(
																			    embedding_size=8 ,       set_W_to_weight=UpperCamelCase ,       log=UpperCamelCase ,       flip_sin_to_cos=UpperCamelCase  )
																			__UpperCAmelCase   :						List[str]      							=					2 * block_out_channels[0]
												elif time_embedding_type == "positional":
																			__UpperCAmelCase   :						Optional[Any]      							=					Timesteps(
																			    block_out_channels[0] ,       flip_sin_to_cos=UpperCamelCase ,       downscale_freq_shift=UpperCamelCase  )
																			__UpperCAmelCase   :						List[Any]      							=					block_out_channels[0]

												if use_timestep_embedding:
																			__UpperCAmelCase   :						Union[str, Any]      							=					block_out_channels[0] * 4
																			__UpperCAmelCase   :						List[Any]      							=					TimestepEmbedding(
																			    in_channels=UpperCamelCase ,       time_embed_dim=UpperCamelCase ,       act_fn=UpperCamelCase ,       out_dim=block_out_channels[0] ,       )

												__UpperCAmelCase   :						int      							=					nn.ModuleList([]  )
												__UpperCAmelCase   :						Tuple      							=					None
												__UpperCAmelCase   :						int      							=					nn.ModuleList([]  )
												__UpperCAmelCase   :						Optional[int]      							=					None

												# down
												__UpperCAmelCase   :						Dict      							=					in_channels
												for i, down_block_type in enumerate(UpperCamelCase  ):
																			__UpperCAmelCase   :						Dict      							=					output_channel
																			__UpperCAmelCase   :						int      							=					block_out_channels[i]

																			if i == 0:
																										input_channel += extra_in_channels

																			__UpperCAmelCase   :						Dict      							=					i == len(UpperCamelCase  ) - 1

																			__UpperCAmelCase   :						int      							=					get_down_block(
																			    UpperCamelCase ,       num_layers=UpperCamelCase ,       in_channels=UpperCamelCase ,       out_channels=UpperCamelCase ,       temb_channels=block_out_channels[0] ,       add_downsample=not is_final_block or downsample_each_block ,       )
																			self.down_blocks.append(UpperCamelCase  )

												# mid
												__UpperCAmelCase   :						Tuple      							=					get_mid_block(
												    UpperCamelCase ,       in_channels=block_out_channels[-1] ,       mid_channels=block_out_channels[-1] ,       out_channels=block_out_channels[-1] ,       embed_dim=block_out_channels[0] ,       num_layers=UpperCamelCase ,       add_downsample=UpperCamelCase ,       )

												# up
												__UpperCAmelCase   :						Tuple      							=					list(reversed(UpperCamelCase  )  )
												__UpperCAmelCase   :						Optional[Any]      							=					reversed_block_out_channels[0]
												if out_block_type is None:
																			__UpperCAmelCase   :						Optional[int]      							=					out_channels
												else:
																			__UpperCAmelCase   :						str      							=					block_out_channels[0]

												for i, up_block_type in enumerate(UpperCamelCase  ):
																			__UpperCAmelCase   :						Tuple      							=					output_channel
																			__UpperCAmelCase   :						Dict      							=					(
																			    reversed_block_out_channels[i + 1] if i < len(UpperCamelCase  ) - 1 else final_upsample_channels
																			)

																			__UpperCAmelCase   :						Optional[int]      							=					i == len(UpperCamelCase  ) - 1

																			__UpperCAmelCase   :						Optional[Any]      							=					get_up_block(
																			    UpperCamelCase ,       num_layers=UpperCamelCase ,       in_channels=UpperCamelCase ,       out_channels=UpperCamelCase ,       temb_channels=block_out_channels[0] ,       add_upsample=not is_final_block ,       )
																			self.up_blocks.append(UpperCamelCase  )
																			__UpperCAmelCase   :						str      							=					output_channel

												# out
												__UpperCAmelCase   :						int      							=					norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,       32  )
												__UpperCAmelCase   :						Optional[Any]      							=					get_out_block(
												    out_block_type=UpperCamelCase ,       num_groups_out=UpperCamelCase ,       embed_dim=block_out_channels[0] ,       out_channels=UpperCamelCase ,       act_fn=UpperCamelCase ,       fc_dim=block_out_channels[-1] // 4 ,       )






					def lowerCamelCase__							(       self    : Dict ,       UpperCamelCase    : torch.FloatTensor ,       UpperCamelCase    : Union[torch.Tensor, float, int] ,       UpperCamelCase    : bool = True ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					timestep
												if not torch.is_tensor(UpperCamelCase  ):
																			__UpperCAmelCase   :						Union[str, Any]      							=					torch.tensor([timesteps] ,       dtype=torch.long ,       device=sample.device  )
												elif torch.is_tensor(UpperCamelCase  ) and len(timesteps.shape  ) == 0:
																			__UpperCAmelCase   :						Optional[Any]      							=					timesteps[None].to(sample.device  )

												__UpperCAmelCase   :						Union[str, Any]      							=					self.time_proj(UpperCamelCase  )
												if self.config.use_timestep_embedding:
																			__UpperCAmelCase   :						str      							=					self.time_mlp(UpperCamelCase  )
												else:
																			__UpperCAmelCase   :						Tuple      							=					timestep_embed[..., None]
																			__UpperCAmelCase   :						Optional[int]      							=					timestep_embed.repeat([1, 1, sample.shape[2]]  ).to(sample.dtype  )
																			__UpperCAmelCase   :						Optional[Any]      							=					timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])  )

												# 2. down
												__UpperCAmelCase   :						List[str]      							=					()
												for downsample_block in self.down_blocks:
																			__UpperCAmelCase							,__UpperCAmelCase   :						Optional[Any]      							=					downsample_block(hidden_states=UpperCamelCase ,       temb=UpperCamelCase  )
																			down_block_res_samples += res_samples

												# 3. mid
												if self.mid_block:
																			__UpperCAmelCase   :						Optional[Any]      							=					self.mid_block(UpperCamelCase ,       UpperCamelCase  )

												# 4. up
												for i, upsample_block in enumerate(self.up_blocks  ):
																			__UpperCAmelCase   :						str      							=					down_block_res_samples[-1:]
																			__UpperCAmelCase   :						List[Any]      							=					down_block_res_samples[:-1]
																			__UpperCAmelCase   :						Tuple      							=					upsample_block(UpperCamelCase ,       res_hidden_states_tuple=UpperCamelCase ,       temb=UpperCamelCase  )

												# 5. post-process
												if self.out_block:
																			__UpperCAmelCase   :						Any      							=					self.out_block(UpperCamelCase ,       UpperCamelCase  )

												if not return_dict:
																			return (sample,)

												return UNetaDOutput(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,)
