
'''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


_lowercase			:  List[str]   							=	{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 			lowerCAmelCase__       (  nn.Module					):






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											super().__init__()
											lowercase_     :					Union[str, Any]													=	torchvision.models.resnetaaa(pretrained=__SCREAMING_SNAKE_CASE		)
											lowercase_     :					str													=	list(model.children()		)[:-2]
											lowercase_     :					List[Any]													=	nn.Sequential(*__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Union[str, Any]													=	nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Any													=	self.pool(self.model(__SCREAMING_SNAKE_CASE		)		)
											lowercase_     :					Optional[int]													=	torch.flatten(__SCREAMING_SNAKE_CASE    ,							start_dim=2		)
											lowercase_     :					Dict													=	out.transpose(1    ,							2		).contiguous()
											return out  # BxNx2048







class 			lowerCAmelCase__       (  lowerCamelCase_					):






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Tuple													=	[json.loads(__SCREAMING_SNAKE_CASE		) for l in open(__SCREAMING_SNAKE_CASE		)]
											lowercase_     :					Dict													=	os.path.dirname(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Tuple													=	tokenizer
											lowercase_     :					Dict													=	labels
											lowercase_     :					Union[str, Any]													=	len(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					str													=	max_seq_length

											lowercase_     :					Tuple													=	transforms






					def __len__(       self		):


											"""simple docstring"""





											return len(self.data		)






					def __getitem__(       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']    ,							add_special_tokens=__SCREAMING_SNAKE_CASE		)		)
											lowercase_						,			lowercase_						,			lowercase_     :					Tuple													=	sentence[0], sentence[1:-1], sentence[-1]
											lowercase_     :					Any													=	sentence[: self.max_seq_length]

											lowercase_     :					List[str]													=	torch.zeros(self.n_classes		)
											lowercase_     :					Union[str, Any]													=	1

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

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






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	Counter()
											for row in self.data:
																	label_freqs.update(row['''label''']		)
											return label_freqs







def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Union[str, Any]							):



						"""simple docstring"""





						lowercase_     :					Union[str, Any]													=	[len(row['''sentence''']							) for row in batch]
						lowercase_						,			lowercase_     :					List[Any]													=	len(__SCREAMING_SNAKE_CASE							), max(__SCREAMING_SNAKE_CASE							)

						lowercase_     :					Union[str, Any]													=	torch.zeros(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				dtype=torch.long							)
						lowercase_     :					int													=	torch.zeros(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				dtype=torch.long							)

						for i_batch, (input_row, length) in enumerate(zip(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)							):
												lowercase_     :					Optional[Any]													=	input_row['''sentence''']
												lowercase_     :					List[str]													=	1

						lowercase_     :					str													=	torch.stack([row['''image'''] for row in batch]							)
						lowercase_     :					Dict													=	torch.stack([row['''label'''] for row in batch]							)
						lowercase_     :					str													=	torch.stack([row['''image_start_token'''] for row in batch]							)
						lowercase_     :					Optional[Any]													=	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  snake_case_		(    ):



						"""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  snake_case_		(    ):



						"""simple docstring"""





						return transforms.Compose(
						    [
						        transforms.Resize(256							),
						        transforms.CenterCrop(224							),
						        transforms.ToTensor(),
						        transforms.Normalize(
						            mean=[0.46777044, 0.44531429, 0.40661017]				,				std=[0.12221994, 0.12145835, 0.14380469]				,				),
						    ]							)





'''simple docstring'''







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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowercase			:  Optional[Any]   							=	logging.get_logger(__name__)

_lowercase			:  str   							=	"▁"

_lowercase			:  Optional[int]   							=	{"vocab_file": "sentencepiece.bpe.model"}

_lowercase			:  Dict   							=	{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

_lowercase			:  Optional[Any]   							=	{
    "facebook/xglm-564M": 2_0_4_8,
}






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					VOCAB_FILES_NAMES
					lowerCAmelCase_					       =					PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_					       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_					       =					['''input_ids''', '''attention_mask''']






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE="<s>"    ,							__SCREAMING_SNAKE_CASE="</s>"    ,							__SCREAMING_SNAKE_CASE="</s>"    ,							__SCREAMING_SNAKE_CASE="<s>"    ,							__SCREAMING_SNAKE_CASE="<unk>"    ,							__SCREAMING_SNAKE_CASE="<pad>"    ,							__SCREAMING_SNAKE_CASE = None    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											lowercase_     :					Any													=	{} if sp_model_kwargs is None else sp_model_kwargs

											# Compatibility with the original tokenizer
											lowercase_     :					Optional[Any]													=	7
											lowercase_     :					List[Any]													=	[F'''<madeupword{i}>''' for i in range(self.num_madeup_words		)]

											lowercase_     :					Tuple													=	kwargs.get('''additional_special_tokens'''    ,							[]		)
											kwargs["additional_special_tokens"] += [
											    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
											]

											super().__init__(
											    bos_token=__SCREAMING_SNAKE_CASE    ,							eos_token=__SCREAMING_SNAKE_CASE    ,							unk_token=__SCREAMING_SNAKE_CASE    ,							sep_token=__SCREAMING_SNAKE_CASE    ,							cls_token=__SCREAMING_SNAKE_CASE    ,							pad_token=__SCREAMING_SNAKE_CASE    ,							sp_model_kwargs=self.sp_model_kwargs    ,							**__SCREAMING_SNAKE_CASE    ,							)

											lowercase_     :					int													=	spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(str(__SCREAMING_SNAKE_CASE		)		)
											lowercase_     :					Dict													=	vocab_file

											# Original fairseq vocab and spm vocab must be "aligned":
											# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
											# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
											# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
											# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

											# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
											lowercase_     :					List[Any]													=	1

											# Mimic fairseq token-to-id alignment for the first 4 token
											lowercase_     :					Optional[Any]													=	{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

											lowercase_     :					Dict													=	len(self.sp_model		)
											lowercase_     :					int													=	{F'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words		)}
											self.fairseq_tokens_to_ids.update(__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	{v: k for k, v in self.fairseq_tokens_to_ids.items()}






					def __getstate__(       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	self.__dict__.copy()
											lowercase_     :					Optional[Any]													=	None
											lowercase_     :					List[Any]													=	self.sp_model.serialized_model_proto()
											return state






					def __setstate__(       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	d

											# for backward compatibility
											if not hasattr(self    ,							'''sp_model_kwargs'''		):
																	lowercase_     :					Optional[Any]													=	{}

											lowercase_     :					int													=	spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											if token_ids_a is None:
																	return [self.sep_token_id] + token_ids_a
											lowercase_     :					Optional[Any]													=	[self.sep_token_id]
											return sep + token_ids_a + sep + sep + token_ids_a






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = False		):


											"""simple docstring"""





											if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=__SCREAMING_SNAKE_CASE    ,							token_ids_a=__SCREAMING_SNAKE_CASE    ,							already_has_special_tokens=__SCREAMING_SNAKE_CASE		)

											if token_ids_a is None:
																	return [1] + ([0] * len(__SCREAMING_SNAKE_CASE		))
											return [1] + ([0] * len(__SCREAMING_SNAKE_CASE		)) + [1, 1] + ([0] * len(__SCREAMING_SNAKE_CASE		))






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	[self.sep_token_id]

											if token_ids_a is None:
																	return len(sep + token_ids_a		) * [0]
											return len(sep + token_ids_a + sep + sep + token_ids_a		) * [0]






					@property
					def      _snake_case   (       self		):


											"""simple docstring"""





											return len(self.sp_model		) + self.fairseq_offset + self.num_madeup_words






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Any													=	{self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE		): i for i in range(self.vocab_size		)}
											vocab.update(self.added_tokens_encoder		)
											return vocab






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return self.sp_model.encode(__SCREAMING_SNAKE_CASE    ,							out_type=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
											lowercase_     :					str													=	self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE		)

											# Need to return unknown token if the SP model returned 0
											return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
											return self.sp_model.IdToPiece(index - self.fairseq_offset		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Dict													=	''''''.join(__SCREAMING_SNAKE_CASE		).replace(__SCREAMING_SNAKE_CASE    ,							''' '''		).strip()
											return out_string






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											if not os.path.isdir(__SCREAMING_SNAKE_CASE		):
																	logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
																	return
											lowercase_     :					str													=	os.path.join(
											    __SCREAMING_SNAKE_CASE    ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

											if os.path.abspath(self.vocab_file		) != os.path.abspath(__SCREAMING_SNAKE_CASE		) and os.path.isfile(self.vocab_file		):
																	copyfile(self.vocab_file    ,							__SCREAMING_SNAKE_CASE		)
											elif not os.path.isfile(self.vocab_file		):
																	with open(__SCREAMING_SNAKE_CASE    ,							'''wb'''		) as fi:
																							lowercase_     :					Optional[int]													=	self.sp_model.serialized_model_proto()
																							fi.write(__SCREAMING_SNAKE_CASE		)

											return (out_vocab_file,)



