



from typing import Mapping

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


_lowerCamelCase    :		Tuple		      =    logging.get_logger(__name__)

_lowerCamelCase    :		Union[str, Any]		      =    {
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						UpperCAmelCase			:		Any		=		't5'
						UpperCAmelCase			:		str		=		['past_key_values']
						UpperCAmelCase			:		Tuple		=		{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}







						def __init__(  self    :				Optional[int]       ,	snake_case    :				List[str]=32128       ,	snake_case    :				List[Any]=512       ,	snake_case    :				Optional[int]=64       ,	snake_case    :				Optional[Any]=2048       ,	snake_case    :				Optional[int]=6       ,	snake_case    :				Union[str, Any]=None       ,	snake_case    :				str=8       ,	snake_case    :				List[Any]=32       ,	snake_case    :				str=128       ,	snake_case    :				Optional[int]=0.1       ,	snake_case    :				Optional[Any]=1E-6       ,	snake_case    :				Optional[int]=1.0       ,	snake_case    :				Union[str, Any]="relu"       ,	snake_case    :				Dict=True       ,	snake_case    :				Any=True       ,	snake_case    :				Any=0       ,	snake_case    :				Optional[Any]=1       ,	**snake_case    :				Dict       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				vocab_size
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				d_model
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				d_kv
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				d_ff
											SCREAMING_SNAKE_CASE							:     Tuple						=				num_layers
											SCREAMING_SNAKE_CASE							:     Tuple						=				(
											    num_decoder_layers if num_decoder_layers is not None else self.num_layers
											)  # default = symmetry
											SCREAMING_SNAKE_CASE							:     Tuple						=				num_heads
											SCREAMING_SNAKE_CASE							:     List[Any]						=				relative_attention_num_buckets
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				relative_attention_max_distance
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				dropout_rate
											SCREAMING_SNAKE_CASE							:     str						=				layer_norm_epsilon
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				initializer_factor
											SCREAMING_SNAKE_CASE							:     str						=				feed_forward_proj
											SCREAMING_SNAKE_CASE							:     List[Any]						=				use_cache

											SCREAMING_SNAKE_CASE							:     List[Any]						=				self.feed_forward_proj.split('-'	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				act_info[-1]
											SCREAMING_SNAKE_CASE							:     Dict						=				act_info[0] == 'gated'

											if len(snake_case	) > 1 and act_info[0] != "gated" or len(snake_case	) > 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\''	)

											# for backwards compatibility
											if feed_forward_proj == "gated-gelu":
																SCREAMING_SNAKE_CASE							:     Optional[Any]						=				'gelu_new'

											super().__init__(
											    pad_token_id=snake_case       ,	eos_token_id=snake_case       ,	is_encoder_decoder=snake_case       ,	**snake_case       ,	)


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						@property
						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				{
											    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
											    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
											}
											if self.use_past:
																SCREAMING_SNAKE_CASE							:     int						=				'past_encoder_sequence + sequence'
																SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				{0: 'batch'}
																SCREAMING_SNAKE_CASE							:     Dict						=				{0: 'batch', 1: 'past_decoder_sequence + sequence'}
											else:
																SCREAMING_SNAKE_CASE							:     List[Any]						=				{0: 'batch', 1: 'decoder_sequence'}
																SCREAMING_SNAKE_CASE							:     int						=				{0: 'batch', 1: 'decoder_sequence'}

											if self.use_past:
																self.fill_with_past_key_values_(snake_case       ,	direction='inputs'	)

											return common_inputs







						@property
						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											return 13




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    :		Dict		      =    {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    :				Optional[Any]       ,	snake_case    :				int	):



											'''simple docstring'''


											super().__init__()
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				torchvision.models.resnetaaa(pretrained=snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				list(model.children()	)[:-2]
											SCREAMING_SNAKE_CASE							:     List[str]						=				nn.Sequential(*snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]	)







						def       lowerCamelCase_				(  self    :				Any       ,	snake_case    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.pool(self.model(snake_case	)	)
											SCREAMING_SNAKE_CASE							:     Any						=				torch.flatten(snake_case       ,	start_dim=2	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				out.transpose(1       ,	2	).contiguous()
											return out  # BxNx2048


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						def __init__(  self    :				str       ,	snake_case    :				Optional[Any]       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				Optional[int]       ,	snake_case    :				int       ,	snake_case    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				[json.loads(snake_case	) for l in open(snake_case	)]
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				os.path.dirname(snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				tokenizer
											SCREAMING_SNAKE_CASE							:     Dict						=				labels
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				len(snake_case	)
											SCREAMING_SNAKE_CASE							:     int						=				max_seq_length

											SCREAMING_SNAKE_CASE							:     List[Any]						=				transforms







						def __len__(  self    :				str	):



											'''simple docstring'''


											return len(self.data	)







						def __getitem__(  self    :				Dict       ,	snake_case    :				int	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				torch.LongTensor(self.tokenizer.encode(self.data[index]['text']       ,	add_special_tokens=snake_case	)	)
											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Optional[Any]						=				sentence[0], sentence[1:-1], sentence[-1]
											SCREAMING_SNAKE_CASE							:     str						=				sentence[: self.max_seq_length]

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				torch.zeros(self.n_classes	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				1

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

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







						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     int						=				Counter()
											for row in self.data:
																label_freqs.update(row['label']	)
											return label_freqs


def        __a			(   __lowerCAmelCase   )     ->     Union[str, Any]:
					SCREAMING_SNAKE_CASE							:     str						=				[len(row['sentence']   ) for row in batch]
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Dict						=				len(__lowerCAmelCase   ), max(__lowerCAmelCase   )

					SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.zeros(__lowerCAmelCase      ,	__lowerCAmelCase      ,	dtype=torch.long   )
					SCREAMING_SNAKE_CASE							:     List[str]						=				torch.zeros(__lowerCAmelCase      ,	__lowerCAmelCase      ,	dtype=torch.long   )

					for i_batch, (input_row, length) in enumerate(zip(__lowerCAmelCase      ,	__lowerCAmelCase   )   ):
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				input_row['sentence']
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				1

					SCREAMING_SNAKE_CASE							:     List[str]						=				torch.stack([row['image'] for row in batch]   )
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.stack([row['label'] for row in batch]   )
					SCREAMING_SNAKE_CASE							:     Tuple						=				torch.stack([row['image_start_token'] for row in batch]   )
					SCREAMING_SNAKE_CASE							:     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        __a			(   )     ->     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        __a			(   )     ->     Union[str, Any]:
					return transforms.Compose(
					    [
					        transforms.Resize(256   ),
					        transforms.CenterCrop(224   ),
					        transforms.ToTensor(),
					        transforms.Normalize(
					            mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]      ,	std=[0.12_221_994, 0.12_145_835, 0.14_380_469]      ,	),
					    ]   )