


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


UpperCAmelCase   :    Tuple     =		{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        __lowercase       (							nn.Module  ):



		"""simple docstring"""

		def __init__( self		,      A	)  -> List[Any]:







					'''simple docstring'''







					super().__init__()
					lowerCamelCase    		=  torchvision.models.resnetaaa(pretrained=A	)
					lowerCamelCase    		=  list(model.children()	)[:-2]
					lowerCamelCase    		=  nn.Sequential(*A	)
					lowerCamelCase    		=  nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]	)







		def 					__A   ( self		,      A	)  -> Tuple:







					'''simple docstring'''







					lowerCamelCase    		=  self.pool(self.model(A	)	)
					lowerCamelCase    		=  torch.flatten(A		,      start_dim=2	)
					lowerCamelCase    		=  out.transpose(1		,      2	).contiguous()
					return out  # BxNx2048







class        __lowercase       (							a_  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A		,      A		,      A		,      A	)  -> List[Any]:







					'''simple docstring'''







					lowerCamelCase    		=  [json.loads(A	) for l in open(A	)]
					lowerCamelCase    		=  os.path.dirname(A	)
					lowerCamelCase    		=  tokenizer
					lowerCamelCase    		=  labels
					lowerCamelCase    		=  len(A	)
					lowerCamelCase    		=  max_seq_length

					lowerCamelCase    		=  transforms







		def __len__( self	)  -> int:







					'''simple docstring'''







					return len(self.data	)







		def __getitem__( self		,      A	)  -> Any:







					'''simple docstring'''







					lowerCamelCase    		=  torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]		,      add_special_tokens=A	)	)
					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(A	)

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







		def 					__A   ( self	)  -> Any:







					'''simple docstring'''







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






def 	__lowerCamelCase						(							lowerCamelCase__		:						Any      ):





			'''simple docstring'''




			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						(							):





			'''simple docstring'''




			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						(							):





			'''simple docstring'''




			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]  ,							),
			    ]      )





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_pegasus import PegasusTokenizer
else:
		UpperCAmelCase   :    Union[str, Any]     =		None


UpperCAmelCase   :    Any     =		logging.get_logger(__name__)


UpperCAmelCase   :    Optional[Any]     =		"▁"

UpperCAmelCase   :    str     =		{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase   :    Dict     =		{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

UpperCAmelCase   :    List[Any]     =		{
    "google/pegasus-xsum": 5_12,
}







class        __lowercase       (							a_  ):



		"""simple docstring"""

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







		def __init__( self		,      A=None		,      A=None		,      A="<pad>"		,      A="</s>"		,      A="<unk>"		,      A="<mask_2>"		,      A="<mask_1>"		,      A=None		,      A=1_03		,      **A		,      )  -> Any:







					'''simple docstring'''







					lowerCamelCase    		=  offset

					if additional_special_tokens is not None:
								if not isinstance(A		,      A	):
											raise TypeError(
											    F'additional_special_tokens should be of type {type(A	)}, but is'
											    F' {type(A	)}'	)

								lowerCamelCase    		=  (
								    ([mask_token_sent] + additional_special_tokens)
								    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
								    else additional_special_tokens
								)
								# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
								additional_special_tokens_extended += [
								    F'<unk_{i}>' for i in range(len(A	)		,      self.offset - 1	)
								]

								if len(set(A	)	) != len(A	):
											raise ValueError(
											    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
											    F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'	)
								lowerCamelCase    		=  additional_special_tokens_extended
					else:
								lowerCamelCase    		=  [mask_token_sent] if mask_token_sent is not None else []
								additional_special_tokens += [F'<unk_{i}>' for i in range(2		,      self.offset	)]

					super().__init__(
					    A		,      tokenizer_file=A		,      pad_token=A		,      eos_token=A		,      unk_token=A		,      mask_token=A		,      mask_token_sent=A		,      offset=A		,      additional_special_tokens=A		,      **A		,      )
					lowerCamelCase    		=  vocab_file
					lowerCamelCase    		=  False if not self.vocab_file else True







		def 					__A   ( self		,      A	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  set(self.all_special_ids	)  # call it once instead of inside list comp
					all_special_ids.remove(self.unk_token_id	)  # <unk> is only sometimes special

					if all_special_ids != set(range(len(self.additional_special_tokens	) + 3	)	):
								raise ValueError(
								    """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
								    F' {len(self.additional_special_tokens	)} additional_special_tokens, but got {all_special_ids}'	)

					return [1 if x in all_special_ids else 0 for x in seq]







		def 					__A   ( self		,      A		,      A = None		,      A = False	)  -> List[int]:







					'''simple docstring'''







					if already_has_special_tokens:
								return self._special_token_mask(A	)
					elif token_ids_a is None:
								return self._special_token_mask(A	) + [1]
					else:
								return self._special_token_mask(token_ids_a + token_ids_a	) + [1]







		def 					__A   ( self		,      A		,      A=None	)  -> List[int]:







					'''simple docstring'''







					if token_ids_a is None:
								return token_ids_a + [self.eos_token_id]
					# We don't expect to process pairs, but leave the pair logic for API consistency
					return token_ids_a + token_ids_a + [self.eos_token_id]







		def 					__A   ( self		,      A		,      A = None	)  -> Tuple[str]:







					'''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(A	):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
								return
					lowerCamelCase    		=  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	):
								copyfile(self.vocab_file		,      A	)

					return (out_vocab_file,)

