





'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


__lowerCamelCase : List[Any]											=			logging.get_logger(__name__)

__lowerCamelCase : List[Any]											=			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__lowerCamelCase : List[Any]											=			[
    "small",
    "small-base",
    "medium",
    "medium-base",
    "intermediate",
    "intermediate-base",
    "large",
    "large-base",
    "xlarge",
    "xlarge-base",
]

__lowerCamelCase : Any											=			{
    "vocab_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt",
        "funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt",
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt",
        "funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt",
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json",
        "funnel-transformer/small-base": (
            "https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json",
        "funnel-transformer/large-base": (
            "https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json"
        ),
    },
}
__lowerCamelCase : Any											=			{f"funnel-transformer/{name}": 512 for name in _model_names}
__lowerCamelCase : Any											=			{f"funnel-transformer/{name}": {"do_lower_case": True} for name in _model_names}

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	Tuple 			=		VOCAB_FILES_NAMES
		UpperCAmelCase					:	str 			=		PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase					:	Optional[int] 			=		PRETRAINED_INIT_CONFIGURATION
		UpperCAmelCase					:	Any 			=		FunnelTokenizer
		UpperCAmelCase					:	Any 			=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase					:	int 			=		2




		def __init__(self      :	str							,			A__      :	List[Any]=None							,			A__      :	Optional[int]=None							,			A__      :	List[str]=True							,			A__      :	int="<unk>"							,			A__      :	Optional[Any]="<sep>"							,			A__      :	Tuple="<pad>"							,			A__      :	Any="<cls>"							,			A__      :	Dict="<mask>"							,			A__      :	str="<s>"							,			A__      :	List[Any]="</s>"							,			A__      :	Dict=True							,			A__      :	Tuple=True							,			A__      :	Any=None							,			A__      :	List[Any]="##"							,			**A__      :	Tuple							,			)					-> Dict:
						super().__init__(
						    A__							,			tokenizer_file=A__							,			do_lower_case=A__							,			unk_token=A__							,			sep_token=A__							,			pad_token=A__							,			cls_token=A__							,			mask_token=A__							,			bos_token=A__							,			eos_token=A__							,			clean_text=A__							,			tokenize_chinese_chars=A__							,			strip_accents=A__							,			wordpieces_prefix=A__							,			**A__							,			)

						lowercase         = json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
						if (
						    normalizer_state.get("lowercase"							,			A__				) != do_lower_case
						    or normalizer_state.get("strip_accents"							,			A__				) != strip_accents
						    or normalizer_state.get("handle_chinese_chars"							,			A__				) != tokenize_chinese_chars
						):
										lowercase         = getattr(A__							,			normalizer_state.pop("type"				)				)
										lowercase         = do_lower_case
										lowercase         = strip_accents
										lowercase         = tokenize_chinese_chars
										lowercase         = normalizer_class(**A__				)

						lowercase         = do_lower_case




		def 				UpperCAmelCase__   (self      :	Dict							,			A__      :	List[str]							,			A__      :	Union[str, Any]=None				)					-> Tuple:
						lowercase         = [self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
										output += token_ids_a + [self.sep_token_id]

						return output




		def 				UpperCAmelCase__   (self      :	Optional[Any]							,			A__      :	List[int]							,			A__      :	Optional[List[int]] = None				)					-> List[int]:
						lowercase         = [self.sep_token_id]
						lowercase         = [self.cls_token_id]
						if token_ids_a is None:
										return len(cls				) * [self.cls_token_type_id] + len(token_ids_a + sep				) * [0]
						return len(cls				) * [self.cls_token_type_id] + len(token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]





		def 				UpperCAmelCase__   (self      :	Dict							,			A__      :	str							,			A__      :	Optional[str] = None				)					-> Tuple[str]:
						lowercase         = self._tokenizer.model.save(A__							,			name=A__				)
						return tuple(A__				)











'''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


__lowerCamelCase : 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       UpperCAmelCase (						nn.Module  ):




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





		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	Optional[Any]				)					-> Optional[int]:
						# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
						lowercase         = self.pool(self.model(A__				)				)
						lowercase         = torch.flatten(A__							,			start_dim=2				)
						lowercase         = out.transpose(1							,			2				).contiguous()
						return out  # BxNx2048




class       UpperCAmelCase (						_lowercase  ):




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

						lowercase         = transforms




		def __len__(self      :	List[str]				)					-> Dict:
						return len(self.data				)




		def __getitem__(self      :	int							,			A__      :	Union[str, Any]				)					-> List[str]:
						lowercase         = torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]							,			add_special_tokens=A__				)				)
						lowercase   ,							lowercase   ,							lowercase         = sentence[0], sentence[1:-1], sentence[-1]
						lowercase         = sentence[: self.max_seq_length]

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

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

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





		def 				UpperCAmelCase__   (self      :	str				)					-> str:
						lowercase         = Counter()
						for row in self.data:
										label_freqs.update(row["label"]				)
						return label_freqs







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = [len(row["sentence"]							) for row in batch]
				lowercase   ,							lowercase         = len(lowerCAmelCase_							), max(lowerCAmelCase_							)

				lowercase         = torch.zeros(lowerCAmelCase_		,							lowerCAmelCase_		,							dtype=torch.long							)
				lowercase         = torch.zeros(lowerCAmelCase_		,							lowerCAmelCase_		,							dtype=torch.long							)

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

				lowercase         = torch.stack([row["image"] for row in batch]							)
				lowercase         = torch.stack([row["label"] for row in batch]							)
				lowercase         = torch.stack([row["image_start_token"] for row in batch]							)
				lowercase         = 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 							UpperCAmelCase_    (	):

				"""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 							UpperCAmelCase_    (	):

				"""simple docstring"""






				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]		,							),
				    ]							)




