






'''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
  if is_torch_available():
    import torch
  if is_tf_available():
    import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


a_ 		=       logging.get_logger(__name__)

a_ 		=       {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a_ 		=       {
    '''vocab_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''Salesforce/codegen-350M-mono''': (
            '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'''
        ),
    },
}

a_ 		=       {
    '''Salesforce/codegen-350M-mono''': 2_0_4_8,
}






class    __SCREAMING_SNAKE_CASE					(	__snake_case ):
 snake_case_     							=							VOCAB_FILES_NAMES
 snake_case_     							=							PRETRAINED_VOCAB_FILES_MAP
 snake_case_     							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 snake_case_     							=							["""input_ids""", """attention_mask"""]
 snake_case_     							=							CodeGenTokenizer




 def __init__(					self		:							Dict    ,					__lowercase		:							Any=None    ,					__lowercase		:							str=None    ,					__lowercase		:							Dict=None    ,					__lowercase		:							Union[str, Any]="<|endoftext|>"    ,					__lowercase		:							List[str]="<|endoftext|>"    ,					__lowercase		:							List[Any]="<|endoftext|>"    ,					__lowercase		:							Optional[Any]=False    ,					**__lowercase		:							int    ,					)		->  List[Any]:
       super().__init__(
           __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					tokenizer_file=__SCREAMING_SNAKE_CASE    ,					unk_token=__SCREAMING_SNAKE_CASE    ,					bos_token=__SCREAMING_SNAKE_CASE    ,					eos_token=__SCREAMING_SNAKE_CASE    ,					add_prefix_space=__SCREAMING_SNAKE_CASE    ,					**__SCREAMING_SNAKE_CASE    ,					)

       if kwargs.pop('''add_bos_token'''    ,					__SCREAMING_SNAKE_CASE					):
             SCREAMING_SNAKE_CASE__   : Any													=kwargs.pop('''name_or_path'''    ,					''''''					)
             raise ValueError(
                 '''Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'''
                 '''Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'''
                 F"`CodeGenTokenizer.from_pretrained(\'{model_id}\')`\nor\n"
                 F"`AutoTokenizer.from_pretrained(\'{model_id}\', use_fast=False)`\n"
                 '''This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'''
                 ''' so that the fast tokenizer works correctly.'''					)

       SCREAMING_SNAKE_CASE__   : str													=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
       if pre_tok_state.get('''add_prefix_space'''    ,					__SCREAMING_SNAKE_CASE					) != add_prefix_space:
             SCREAMING_SNAKE_CASE__   : Tuple													=getattr(__SCREAMING_SNAKE_CASE    ,					pre_tok_state.pop('''type'''					)					)
             SCREAMING_SNAKE_CASE__   : Dict													=add_prefix_space
             SCREAMING_SNAKE_CASE__   : Optional[int]													=pre_tok_class(**__SCREAMING_SNAKE_CASE					)

       SCREAMING_SNAKE_CASE__   : Tuple													=add_prefix_space




 def    __magic_name__		(					self		:							Union[str, Any]    ,					*__lowercase		:							Tuple    ,					**__lowercase		:							Tuple					)		->  BatchEncoding:
       SCREAMING_SNAKE_CASE__   : Tuple													=kwargs.get('''is_split_into_words'''    ,					__SCREAMING_SNAKE_CASE					)
       assert self.add_prefix_space or not is_split_into_words, (
           F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
           "to use it with pretokenized inputs."
       )

       return super()._batch_encode_plus(*__SCREAMING_SNAKE_CASE    ,					**__SCREAMING_SNAKE_CASE					)




 def    __magic_name__		(					self		:							List[Any]    ,					*__lowercase		:							Any    ,					**__lowercase		:							int					)		->  BatchEncoding:
       SCREAMING_SNAKE_CASE__   : Optional[int]													=kwargs.get('''is_split_into_words'''    ,					__SCREAMING_SNAKE_CASE					)

       assert self.add_prefix_space or not is_split_into_words, (
           F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
           "to use it with pretokenized inputs."
       )

       return super()._encode_plus(*__SCREAMING_SNAKE_CASE    ,					**__SCREAMING_SNAKE_CASE					)




 def    __magic_name__		(					self		:							Optional[int]    ,					__lowercase		:							str    ,					__lowercase		:							Optional[str] = None					)		->  Tuple[str]:
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=self._tokenizer.model.save(__SCREAMING_SNAKE_CASE    ,					name=__SCREAMING_SNAKE_CASE					)
       return tuple(__SCREAMING_SNAKE_CASE					)




 def    __magic_name__		(					self		:							Optional[Any]    ,					__lowercase		:							Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]    ,					__lowercase		:							bool = False    ,					__lowercase		:							bool = None    ,					__lowercase		:							Optional[List[str]] = None    ,					**__lowercase		:							Tuple    ,					)		->  str:
       SCREAMING_SNAKE_CASE__   : int													=super().decode(
           token_ids=__SCREAMING_SNAKE_CASE    ,					skip_special_tokens=__SCREAMING_SNAKE_CASE    ,					clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE    ,					**__SCREAMING_SNAKE_CASE    ,					)

       if truncate_before_pattern is not None and len(__SCREAMING_SNAKE_CASE					) > 0:
             SCREAMING_SNAKE_CASE__   : Tuple													=self.truncate(__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					)

       return decoded_text




 def    __magic_name__		(					self		:							Union[str, Any]    ,					__lowercase		:							Optional[Any]    ,					__lowercase		:							Optional[Any]					)		->  Optional[Any]:
       def find_re(__lowercase		:							Optional[Any]    ,					__lowercase		:							Optional[Any]    ,					__lowercase		:							Any					):
             SCREAMING_SNAKE_CASE__   : Optional[int]													=pattern.search(__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					)
             return m.start() if m else -1

       SCREAMING_SNAKE_CASE__   : List[Any]													=[re.compile(__SCREAMING_SNAKE_CASE    ,					re.MULTILINE					) for pattern in truncate_before_pattern]

       SCREAMING_SNAKE_CASE__   : int													=list(re.finditer('''^print'''    ,					__SCREAMING_SNAKE_CASE    ,					re.MULTILINE					)					)

       if len(__SCREAMING_SNAKE_CASE					) > 1:
             SCREAMING_SNAKE_CASE__   : Any													=completion[: prints[1].start()]

       SCREAMING_SNAKE_CASE__   : int													=list(re.finditer('''^def'''    ,					__SCREAMING_SNAKE_CASE    ,					re.MULTILINE					)					)

       if len(__SCREAMING_SNAKE_CASE					) > 1:
             SCREAMING_SNAKE_CASE__   : List[Any]													=completion[: defs[1].start()]

       SCREAMING_SNAKE_CASE__   : str													=0

       SCREAMING_SNAKE_CASE__   : List[Any]													=[
           pos for pos in [find_re(__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					) for terminal in terminals] if pos != -1
       ]

       if len(__SCREAMING_SNAKE_CASE					) > 0:
             return completion[: min(__SCREAMING_SNAKE_CASE					)]
       else:
             return completion







"""simple docstring"""





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					:				List[Any]					    =						{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      a						(					nn.Module		):






    def __init__(       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							Tuple					)     ->		List[Any]:
      super().__init__()
      lowerCamelCase_         =						torchvision.models.resnetaaa(pretrained=__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						list(model.children()					)[:-2]
      lowerCamelCase_         =						nn.Sequential(*__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]					)






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		Any:
      # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
      lowerCamelCase_         =						self.pool(self.model(__SCREAMING_SNAKE_CASE					)					)
      lowerCamelCase_         =						torch.flatten(__SCREAMING_SNAKE_CASE       ,						start_dim=2					)
      lowerCamelCase_         =						out.transpose(1       ,						2					).contiguous()
      return out  # BxNx2048






class      a						(					__snake_case		):






    def __init__(       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							List[Any]					)     ->		Optional[Any]:
      lowerCamelCase_         =						[json.loads(__SCREAMING_SNAKE_CASE					) for l in open(__SCREAMING_SNAKE_CASE					)]
      lowerCamelCase_         =						os.path.dirname(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						tokenizer
      lowerCamelCase_         =						labels
      lowerCamelCase_         =						len(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						max_seq_length

      lowerCamelCase_         =						transforms






    def __len__(       self     :							Any					)     ->		Any:
      return len(self.data					)






    def __getitem__(       self     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							List[str]					)     ->		List[str]:
      lowerCamelCase_         =						torch.LongTensor(self.tokenizer.encode(self.data[index]['text']       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)					)
      lowerCamelCase_     ,	lowerCamelCase_     ,	lowerCamelCase_         =						sentence[0], sentence[1:-1], sentence[-1]
      lowerCamelCase_         =						sentence[: self.max_seq_length]

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

      lowerCamelCase_         =						Image.open(os.path.join(self.data_dir       ,						self.data[index]['img']					)					).convert('RGB'					)
      lowerCamelCase_         =						self.transforms(__SCREAMING_SNAKE_CASE					)

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






    def    UpperCamelCase       (       self     :							Dict					)     ->		Dict:
      lowerCamelCase_         =						Counter()
      for row in self.data:
        label_freqs.update(row['label']					)
      return label_freqs







def      lowerCamelCase__       (							_lowerCamelCase       :		Union[str, Any] )      ->				str:
  lowerCamelCase_         =						[len(row['sentence'] ) for row in batch]
  lowerCamelCase_     ,	lowerCamelCase_         =						len(_lowerCamelCase ), max(_lowerCamelCase )

  lowerCamelCase_         =						torch.zeros(_lowerCamelCase		,				_lowerCamelCase		,				dtype=torch.long )
  lowerCamelCase_         =						torch.zeros(_lowerCamelCase		,				_lowerCamelCase		,				dtype=torch.long )

  for i_batch, (input_row, length) in enumerate(zip(_lowerCamelCase		,				_lowerCamelCase ) ):
    lowerCamelCase_         =						input_row['sentence']
    lowerCamelCase_         =						1

  lowerCamelCase_         =						torch.stack([row['image'] for row in batch] )
  lowerCamelCase_         =						torch.stack([row['label'] for row in batch] )
  lowerCamelCase_         =						torch.stack([row['image_start_token'] for row in batch] )
  lowerCamelCase_         =						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      lowerCamelCase__       (							)      ->				List[str]:
  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      lowerCamelCase__       (							)      ->				Union[str, Any]:
  return transforms.Compose(
      [
          transforms.Resize(256 ),
          transforms.CenterCrop(224 ),
          transforms.ToTensor(),
          transforms.Normalize(
              mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]		,				std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]		,				),
      ] )




