





'''simple docstring'''

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


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

__lowerCamelCase : Optional[int]											=			{
    "google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	Optional[int] 			=		'''umt5'''
		UpperCAmelCase					:	Any 			=		['''past_key_values''']




		def __init__(self      :	Any							,			A__      :	Dict=2_5_0_1_1_2							,			A__      :	Tuple=5_1_2							,			A__      :	Tuple=6_4							,			A__      :	str=1_0_2_4							,			A__      :	str=8							,			A__      :	int=None							,			A__      :	Any=6							,			A__      :	List[Any]=3_2							,			A__      :	Optional[Any]=1_2_8							,			A__      :	Optional[int]=0.1							,			A__      :	List[str]=1e-6							,			A__      :	Optional[int]=1.0							,			A__      :	Union[str, Any]="gated-gelu"							,			A__      :	str=True							,			A__      :	Optional[int]=True							,			A__      :	List[Any]="T5Tokenizer"							,			A__      :	Optional[int]=True							,			A__      :	Optional[Any]=0							,			A__      :	List[Any]=1							,			A__      :	Union[str, Any]=0							,			**A__      :	List[str]							,			)					-> Dict:
						super().__init__(
						    is_encoder_decoder=A__							,			tokenizer_class=A__							,			tie_word_embeddings=A__							,			pad_token_id=A__							,			eos_token_id=A__							,			decoder_start_token_id=A__							,			**A__							,			)
						lowercase         = vocab_size
						lowercase         = d_model
						lowercase         = d_kv
						lowercase         = d_ff
						lowercase         = num_layers
						lowercase         = (
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						lowercase         = num_heads
						lowercase         = relative_attention_num_buckets
						lowercase         = relative_attention_max_distance
						lowercase         = dropout_rate
						lowercase         = layer_norm_epsilon
						lowercase         = initializer_factor
						lowercase         = feed_forward_proj
						lowercase         = use_cache

						lowercase         = self.feed_forward_proj.split("-"				)
						lowercase         = act_info[-1]
						lowercase         = act_info[0] == "gated"

						if len(A__				) > 1 and act_info[0] != "gated" or len(A__				) > 2:
										raise ValueError(
										    f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
										    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
										    "'gated-gelu' or 'relu'"				)

						if feed_forward_proj == "gated-gelu":
										lowercase         = "gelu_new"




		@property
		def 				UpperCAmelCase__   (self      :	Dict				)					-> Union[str, Any]:
						return self.d_model




		@property
		def 				UpperCAmelCase__   (self      :	Tuple				)					-> Tuple:
						return self.num_heads





		@property
		def 				UpperCAmelCase__   (self      :	int				)					-> Union[str, Any]:
						return self.num_layers




class       UpperCAmelCase (						_lowercase  ):




		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
		def 				UpperCAmelCase__   (self      :	int				)					-> Mapping[str, Mapping[int, str]]:
						lowercase         = {
						    "input_ids": {0: "batch", 1: "encoder_sequence"},
						    "attention_mask": {0: "batch", 1: "encoder_sequence"},
						}
						if self.use_past:
										lowercase         = "past_encoder_sequence + sequence"
										lowercase         = {0: "batch"}
										lowercase         = {0: "batch", 1: "past_decoder_sequence + sequence"}
						else:
										lowercase         = {0: "batch", 1: "decoder_sequence"}
										lowercase         = {0: "batch", 1: "decoder_sequence"}

						if self.use_past:
										self.fill_with_past_key_values_(A__							,			direction="inputs"				)

						return common_inputs




		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
		def 				UpperCAmelCase__   (self      :	int				)					-> int:
						return 1_3





		@property
		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> float:
						return 5e-4











'''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]		,							),
				    ]							)




