




'''simple docstring'''







import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

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


if is_torch_available():
      import torch


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


lowercase      :   Any      =       logging.get_logger(__name__)
lowercase      :   Union[str, Any]      =       {"""vocab_file""": """spiece.model"""}

lowercase      :   int      =       {
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

lowercase      :   Dict      =       {
    """AI-Sweden/gpt-sw3-126m""": 2048,
    """AI-Sweden/gpt-sw3-350m""": 2048,
    """AI-Sweden/gpt-sw3-1.6b""": 2048,
    """AI-Sweden/gpt-sw3-6.7b""": 2048,
    """AI-Sweden/gpt-sw3-20b""": 2048,
}







class 					_a (__A	):





     '''simple docstring'''



     lowerCAmelCase_  :     Optional[Any]          =					VOCAB_FILES_NAMES
     lowerCAmelCase_  :     str          =					PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase_  :     int          =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase_  :     List[Any]          =					["""input_ids""", """attention_mask"""]







     def __init__(				self							,__a							,__a=False							,__a=False							,__a=False							,__a=None							,__a=None							,__a=None							,__a=None							,__a = None							,**__a							,)      ->				Any:
          snake_case			:      int        =			{} if sp_model_kwargs is None else sp_model_kwargs

          snake_case			:      Dict        =			kwargs.get("""name_or_path"""							)
          if name_or_path is None:
               logger.warning(
                   """name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"""
                   """ you are testing the model, this can safely be ignored"""							)
               snake_case			:      Any        =			"""None"""

          # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
          snake_case			:      int        =			"""<|endoftext|>""" if eos_token is None else eos_token
          snake_case			:      Dict        =			"""<unk>""" if unk_token is None else unk_token
          if "gpt-sw3-7b" in name_or_path:
               snake_case			:      str        =			unk_token if pad_token is None else pad_token
               snake_case			:      Union[str, Any]        =			eos_token if bos_token is None else bos_token
          else:
               snake_case			:      Dict        =			"""<pad>""" if pad_token is None else pad_token
               snake_case			:      Optional[Any]        =			"""<s>""" if bos_token is None else bos_token

          super().__init__(
              do_lower_case=__A							,remove_space=__A							,keep_accents=__A							,bos_token=__A							,eos_token=__A							,unk_token=__A							,pad_token=__A							,sp_model_kwargs=self.sp_model_kwargs							,**__A							,)

          snake_case			:      str        =			do_lower_case
          snake_case			:      Any        =			remove_space
          snake_case			:      int        =			keep_accents
          snake_case			:      int        =			vocab_file

          snake_case			:      List[str]        =			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
          self.sp_model.Load(__A							)

          # Used for whitespace normalization in input texts
          # fmt : off
          snake_case			:      int        =			{""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
          # fmt : on

          # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
          snake_case			:      Optional[Any]        =			re.compile(
              F'''[{"".join(map(__A							,list(range(0							,9							)							) + list(range(11							,32							)							) + list(range(127							,160							)							) + [160, 173, 8_203]							)							)}]'''							)







     def __getstate__(				self							)      ->				Any:
          snake_case			:      Optional[Any]        =			self.__dict__.copy()
          snake_case			:      Tuple        =			None
          return state







     def __setstate__(				self							,__a							)      ->				str:
          snake_case			:      str        =			d

          # for backward compatibility
          if not hasattr(self							,"""sp_model_kwargs"""							):
               snake_case			:      Union[str, Any]        =			{}

          snake_case			:      Any        =			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
          self.sp_model.Load(self.vocab_file							)







     @property
     # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
     def 			snake_case_      (				self							)      ->				Tuple:
          return len(self.sp_model							)







     def 			snake_case_      (				self							,__a							)      ->				int:
          snake_case			:      Optional[Any]        =			self.non_printing_characters_re.sub(""""""							,__A							)

          # Normalize whitespaces
          snake_case			:      Tuple        =			"""""".join([char if char not in self.whitespaces else """ """ for char in text]							)

          # NFC Unicode normalization
          snake_case			:      str        =			unicodedata.normalize("""NFC"""							,__A							)
          return text







     def 			snake_case_      (				self							,__a							,**__a							)      ->				Dict:
          snake_case			:      Optional[Any]        =			self.preprocess_text(__A							)
          return self.sp_model.encode(__A							,out_type=__A							)







     def 			snake_case_      (				self							,__a							)      ->				Dict:
          return self.sp_model.PieceToId(__A							)







     def 			snake_case_      (				self							,__a							)      ->				Any:
          return self.sp_model.IdToPiece(__A							)







     @staticmethod
     def 			snake_case_      (				__a							)      ->				List[str]:
          return out_string







     def 			snake_case_      (				self							,__a							)      ->				Optional[int]:
          snake_case			:      int        =			[]
          snake_case			:      Optional[Any]        =			""""""
          snake_case			:      Dict        =			False
          for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                    # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                    if not prev_is_special:
                         out_string += " "

                    out_string += self.sp_model.decode(__A							) + token
                    snake_case			:      Any        =			True
                    snake_case			:      List[str]        =			[]
               else:
                    current_sub_tokens.append(__A							)
                    snake_case			:      Any        =			False
          out_string += self.sp_model.decode(__A							)

          return out_string







     def 			snake_case_      (				self							)      ->				Dict:
          snake_case			:      Optional[int]        =			{self.convert_ids_to_tokens(__A							): i for i in range(self.vocab_size							)}
          vocab.update(self.added_tokens_encoder							)
          return vocab







     def 			snake_case_      (				self							,__a							,__a = None							)      ->				Optional[int]:
          if not os.path.isdir(__A							):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
               return
          snake_case			:      Dict        =			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							) and os.path.isfile(self.vocab_file							):
               copyfile(self.vocab_file							,__A							)
          elif not os.path.isfile(self.vocab_file							):
               with open(__A							,"""wb"""							) as fi:
                    snake_case			:      Any        =			self.sp_model.serialized_model_proto()
                    fi.write(__A							)

          return (out_vocab_file,)







     def 			snake_case_      (				self							,__a							,__a = False							)      ->				Union[str, Any]:

          if isinstance(__A							,__A							):
               snake_case			:      Optional[int]        =			self.preprocess_text(__A							)
               snake_case			:      int        =			self.sp_model.encode(__A							)
          else:
               snake_case			:      Union[str, Any]        =			[self.preprocess_text(__A							) for t in text]
               snake_case			:      List[str]        =			self.sp_model.encode(__A							)

          if return_tensors is True or return_tensors == "pt":
               snake_case			:      List[str]        =			torch.tensor(__A							)

          return token_ids







     def 			snake_case_      (				self							,__a							)      ->				List[Any]:
          return self.sp_model.decode(__A							)







     def 			snake_case_      (				self							,__a							)      ->				List[Any]:
          snake_case			:      Tuple        =			[F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
          snake_case			:      Tuple        =			(
              F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(__A							) + F'''{self.bos_token}Bot:'''
          )
          return self.encode(text=__A							)







import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


SCREAMING_SNAKE_CASE							    =							{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class     __UpperCAmelCase							(       nn.Module							):


     """simple docstring"""


     def __init__(	self  ,       __A    ):
        super().__init__()
        __a					=  torchvision.models.resnetaaa(pretrained=__A    )
        __a					=  list(model.children()    )[:-2]
        __a					=  nn.Sequential(*__A    )
        __a					=  nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]    )


     def 	snake_case_  (	self  ,       __A    ):
        # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
        __a					=  self.pool(self.model(__A    )    )
        __a					=  torch.flatten(__A  ,       start_dim=2    )
        __a					=  out.transpose(1  ,       2    ).contiguous()
        return out  # BxNx2048







class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A    ):
        __a					=  [json.loads(__A    ) for l in open(__A    )]
        __a					=  os.path.dirname(__A    )
        __a					=  tokenizer
        __a					=  labels
        __a					=  len(__A    )
        __a					=  max_seq_length

        __a					=  transforms


     def __len__(	self    ):
        return len(self.data    )


     def __getitem__(	self  ,       __A    ):
        __a					=  torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]  ,       add_special_tokens=__A    )    )
        __a	,    __a	,    __a					=  sentence[0], sentence[1:-1], sentence[-1]
        __a					=  sentence[: self.max_seq_length]

        __a					=  torch.zeros(self.n_classes    )
        __a					=  1

        __a					=  Image.open(os.path.join(self.data_dir  ,       self.data[index]["""img"""]    )    ).convert("""RGB"""    )
        __a					=  self.transforms(__A    )

        return {
            "image_start_token": start_token,
            "image_end_token": end_token,
            "sentence": sentence,
            "image": image,
            "label": label,
        }


     def 	snake_case_  (	self    ):
        __a					=  Counter()
        for row in self.data:
           label_freqs.update(row["""label"""]    )
        return label_freqs







def  a   (lowerCAmelCase__   ):
   __a					=  [len(row["""sentence"""]   ) for row in batch]
   __a	,    __a					=  len(lowerCAmelCase__   ), max(lowerCAmelCase__   )

   __a					=  torch.zeros(lowerCAmelCase__		,				lowerCAmelCase__		,				dtype=torch.long   )
   __a					=  torch.zeros(lowerCAmelCase__		,				lowerCAmelCase__		,				dtype=torch.long   )

   for i_batch, (input_row, length) in enumerate(zip(lowerCAmelCase__		,				lowerCAmelCase__   )   ):
      __a					=  input_row["""sentence"""]
      __a					=  1

   __a					=  torch.stack([row["""image"""] for row in batch]   )
   __a					=  torch.stack([row["""label"""] for row in batch]   )
   __a					=  torch.stack([row["""image_start_token"""] for row in batch]   )
   __a					=  torch.stack([row["""image_end_token"""] for row in batch]   )

   return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor




def  a   ():
   return [
       "Crime",
       "Drama",
       "Thriller",
       "Action",
       "Comedy",
       "Romance",
       "Documentary",
       "Short",
       "Mystery",
       "History",
       "Family",
       "Adventure",
       "Fantasy",
       "Sci-Fi",
       "Western",
       "Horror",
       "Sport",
       "War",
       "Music",
       "Musical",
       "Animation",
       "Biography",
       "Film-Noir",
   ]




def  a   ():
   return transforms.Compose(
       [
           transforms.Resize(256   ),
           transforms.CenterCrop(224   ),
           transforms.ToTensor(),
           transforms.Normalize(
               mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7]		,				std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9]		,				),
       ]   )



