





'''simple docstring'''

import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
			from transformers.models.bert.modeling_flax_bert import (
			    FlaxBertForMaskedLM,
			    FlaxBertForMultipleChoice,
			    FlaxBertForNextSentencePrediction,
			    FlaxBertForPreTraining,
			    FlaxBertForQuestionAnswering,
			    FlaxBertForSequenceClassification,
			    FlaxBertForTokenClassification,
			    FlaxBertModel,
			)

class       UpperCAmelCase (						unittest.TestCase  ):




		def __init__(self      :	List[Any]							,			A__      :	int							,			A__      :	Tuple=1_3							,			A__      :	List[str]=7							,			A__      :	List[str]=True							,			A__      :	List[Any]=True							,			A__      :	List[str]=True							,			A__      :	List[str]=True							,			A__      :	Any=9_9							,			A__      :	int=3_2							,			A__      :	Dict=5							,			A__      :	Any=4							,			A__      :	Union[str, Any]=3_7							,			A__      :	str="gelu"							,			A__      :	str=0.1							,			A__      :	Any=0.1							,			A__      :	Tuple=5_1_2							,			A__      :	Union[str, Any]=1_6							,			A__      :	Union[str, Any]=2							,			A__      :	Any=0.0_2							,			A__      :	Optional[int]=4							,			)					-> Optional[int]:
						lowercase         = parent
						lowercase         = batch_size
						lowercase         = seq_length
						lowercase         = is_training
						lowercase         = use_attention_mask
						lowercase         = use_token_type_ids
						lowercase         = use_labels
						lowercase         = vocab_size
						lowercase         = hidden_size
						lowercase         = num_hidden_layers
						lowercase         = num_attention_heads
						lowercase         = intermediate_size
						lowercase         = hidden_act
						lowercase         = hidden_dropout_prob
						lowercase         = attention_probs_dropout_prob
						lowercase         = max_position_embeddings
						lowercase         = type_vocab_size
						lowercase         = type_sequence_label_size
						lowercase         = initializer_range
						lowercase         = num_choices




		def 				UpperCAmelCase__   (self      :	int				)					-> Union[str, Any]:
						lowercase         = ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size				)

						lowercase         = None
						if self.use_attention_mask:
										lowercase         = random_attention_mask([self.batch_size, self.seq_length]				)

						lowercase         = None
						if self.use_token_type_ids:
										lowercase         = ids_tensor([self.batch_size, self.seq_length]							,			self.type_vocab_size				)

						lowercase         = BertConfig(
						    vocab_size=self.vocab_size							,			hidden_size=self.hidden_size							,			num_hidden_layers=self.num_hidden_layers							,			num_attention_heads=self.num_attention_heads							,			intermediate_size=self.intermediate_size							,			hidden_act=self.hidden_act							,			hidden_dropout_prob=self.hidden_dropout_prob							,			attention_probs_dropout_prob=self.attention_probs_dropout_prob							,			max_position_embeddings=self.max_position_embeddings							,			type_vocab_size=self.type_vocab_size							,			is_decoder=A__							,			initializer_range=self.initializer_range							,			)

						return config, input_ids, token_type_ids, attention_mask




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> List[str]:
						lowercase         = self.prepare_config_and_inputs()
						lowercase   ,							lowercase   ,							lowercase   ,							lowercase         = config_and_inputs
						lowercase         = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
						return config, inputs_dict





		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Dict:
						lowercase         = self.prepare_config_and_inputs()
						lowercase   ,							lowercase   ,							lowercase   ,							lowercase         = config_and_inputs

						lowercase         = True
						lowercase         = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
						lowercase         = ids_tensor([self.batch_size, self.seq_length]							,			vocab_size=2				)

						return (
						    config,
						    input_ids,
						    attention_mask,
						    encoder_hidden_states,
						    encoder_attention_mask,
						)




@require_flax
class       UpperCAmelCase (						_lowercase			,       unittest.TestCase  ):
		UpperCAmelCase					:	Dict 			=		True

		UpperCAmelCase					:	Tuple 			=		(
		    (
		        FlaxBertModel,
		        FlaxBertForPreTraining,
		        FlaxBertForMaskedLM,
		        FlaxBertForMultipleChoice,
		        FlaxBertForQuestionAnswering,
		        FlaxBertForNextSentencePrediction,
		        FlaxBertForSequenceClassification,
		        FlaxBertForTokenClassification,
		        FlaxBertForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)




		def 				UpperCAmelCase__   (self      :	str				)					-> Optional[Any]:
						lowercase         = FlaxBertModelTester(self				)





		@slow
		def 				UpperCAmelCase__   (self      :	Dict				)					-> Optional[Any]:
						# Only check this for base model, not necessary for all model classes.
						# This will also help speed-up tests.
						lowercase         = FlaxBertModel.from_pretrained("bert-base-cased"				)
						lowercase         = model(np.ones((1, 1)				)				)
						self.assertIsNotNone(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]		,							),
				    ]							)




