






"""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:
			lowerCamelCase_					=      None

lowerCamelCase_					=      logging.get_logger(__name__)

lowerCamelCase_					=      {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase_					=      {
    '''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'''
        ),
    },
}

lowerCamelCase_					=      {
    '''moussaKam/mbarthez''': 1024,
    '''moussaKam/barthez''': 1024,
    '''moussaKam/barthez-orangesum-title''': 1024,
}

lowerCamelCase_					=      '''▁'''







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				VOCAB_FILES_NAMES
	__magic_name__	      =				PRETRAINED_VOCAB_FILES_MAP
	__magic_name__	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__	      =				['''input_ids''', '''attention_mask''']
	__magic_name__	      =				BarthezTokenizer





	def __init__(      self					: List[Any]	,	lowerCAmelCase_					: Optional[Any]=None	,	lowerCAmelCase_					: int=None	,	lowerCAmelCase_					: str="<s>"	,	lowerCAmelCase_					: Any="</s>"	,	lowerCAmelCase_					: str="</s>"	,	lowerCAmelCase_					: Any="<s>"	,	lowerCAmelCase_					: Any="<unk>"	,	lowerCAmelCase_					: Any="<pad>"	,	lowerCAmelCase_					: int="<mask>"	,	**lowerCAmelCase_					: Optional[int]	,	)						->			str:
				# Mask token behave like a normal word, i.e. include the space before it
				UpperCAmelCase_     : str			     =					AddedToken(lowerCAmelCase_	,	lstrip=lowerCAmelCase_	,	rstrip=lowerCAmelCase_						) if isinstance(lowerCAmelCase_	,	lowerCAmelCase_						) else mask_token

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

				UpperCAmelCase_     : Any			     =					vocab_file
				UpperCAmelCase_     : int			     =					False if not self.vocab_file else True





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	lowerCAmelCase_					: List[int]	,	lowerCAmelCase_					: Optional[List[int]] = None						)						->			List[int]:

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





	def      _SCREAMING_SNAKE_CASE      (      self					: Any	,	lowerCAmelCase_					: List[int]	,	lowerCAmelCase_					: Optional[List[int]] = None						)						->			List[int]:
				UpperCAmelCase_     : Union[str, Any]			     =					[self.sep_token_id]
				UpperCAmelCase_     : Tuple			     =					[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      _SCREAMING_SNAKE_CASE      (      self					: str	,	lowerCAmelCase_					: str	,	lowerCAmelCase_					: Optional[str] = None						)						->			Tuple[str]:
				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(lowerCAmelCase_						):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
							return
				UpperCAmelCase_     : Optional[Any]			     =					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_						):
							copyfile(self.vocab_file	,	lowerCAmelCase_						)

				return (out_vocab_file,)











"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


lowerCamelCase_					=      logging.get_logger(__name__)  # pylint: disable=invalid-name







class 			UpperCamelCase_       (__A     ):





	def __init__(      self					: Optional[int]	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: Dict=768						)						->			List[Any]:
				super().__init__(lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					proj_size

				UpperCAmelCase_     : Optional[Any]			     =					CLIPVisionModel(lowerCAmelCase_						)
				UpperCAmelCase_     : Tuple			     =					PaintByExampleMapper(lowerCAmelCase_						)
				UpperCAmelCase_     : str			     =					nn.LayerNorm(config.hidden_size						)
				UpperCAmelCase_     : List[Any]			     =					nn.Linear(config.hidden_size	,	self.proj_size						)

				# uncondition for scaling
				UpperCAmelCase_     : Optional[int]			     =					nn.Parameter(torch.randn((1, 1, self.proj_size)						)						)





	def      _SCREAMING_SNAKE_CASE      (      self					: int	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Dict=False						)						->			Union[str, Any]:
				UpperCAmelCase_     : Optional[int]			     =					self.model(pixel_values=lowerCAmelCase_						)
				UpperCAmelCase_     : Optional[Any]			     =					clip_output.pooler_output
				UpperCAmelCase_     : List[Any]			     =					self.mapper(latent_states[:, None]						)
				UpperCAmelCase_     : List[str]			     =					self.final_layer_norm(lowerCAmelCase_						)
				UpperCAmelCase_     : Union[str, Any]			     =					self.proj_out(lowerCAmelCase_						)
				if return_uncond_vector:
							return latent_states, self.uncond_vector

				return latent_states







class 			UpperCamelCase_       (nn.Module     ):





	def __init__(      self					: Dict	,	lowerCAmelCase_					: Union[str, Any]						)						->			Tuple:
				super().__init__()
				UpperCAmelCase_     : List[Any]			     =					(config.num_hidden_layers + 1) // 5
				UpperCAmelCase_     : Optional[Any]			     =					config.hidden_size
				UpperCAmelCase_     : List[str]			     =					1
				UpperCAmelCase_     : Union[str, Any]			     =					nn.ModuleList(
				    [
				        BasicTransformerBlock(lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_	,	activation_fn="gelu"	,	attention_bias=lowerCAmelCase_						)
				        for _ in range(lowerCAmelCase_						)
				    ]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: List[str]						)						->			str:
				for block in self.blocks:
							UpperCAmelCase_     : int			     =					block(lowerCAmelCase_						)

				return hidden_states



