



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import MobileNetVaImageProcessor


class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						def __init__(  self    :				Any       ,	snake_case    :				Any       ,	snake_case    :				str=7       ,	snake_case    :				List[str]=3       ,	snake_case    :				Union[str, Any]=18       ,	snake_case    :				Optional[Any]=30       ,	snake_case    :				Optional[int]=400       ,	snake_case    :				Optional[int]=True       ,	snake_case    :				List[Any]=None       ,	snake_case    :				Union[str, Any]=True       ,	snake_case    :				Union[str, Any]=None       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Any						=				size if size is not None else {'shortest_edge': 20}
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				crop_size if crop_size is not None else {'height': 18, 'width': 18}
											SCREAMING_SNAKE_CASE							:     List[Any]						=				parent
											SCREAMING_SNAKE_CASE							:     int						=				batch_size
											SCREAMING_SNAKE_CASE							:     List[Any]						=				num_channels
											SCREAMING_SNAKE_CASE							:     Any						=				image_size
											SCREAMING_SNAKE_CASE							:     int						=				min_resolution
											SCREAMING_SNAKE_CASE							:     Any						=				max_resolution
											SCREAMING_SNAKE_CASE							:     Tuple						=				do_resize
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				size
											SCREAMING_SNAKE_CASE							:     List[str]						=				do_center_crop
											SCREAMING_SNAKE_CASE							:     List[Any]						=				crop_size







						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											}


@require_torch
@require_vision
class 						lowercase    (							SCREAMING_SNAKE_CASE_      ,    unittest.TestCase):
						'''simple docstring'''


						UpperCAmelCase			:		Optional[Any]		=		MobileNetVaImageProcessor if is_vision_available() else None







						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				MobileNetVaImageProcessingTester(self	)







						@property
						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											return self.image_processor_tester.prepare_image_processor_dict()







						def       lowerCamelCase_				(  self    :				Union[str, Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				self.image_processing_class(**self.image_processor_dict	)
											self.assertTrue(hasattr(snake_case       ,	'do_resize'	)	)
											self.assertTrue(hasattr(snake_case       ,	'size'	)	)
											self.assertTrue(hasattr(snake_case       ,	'do_center_crop'	)	)
											self.assertTrue(hasattr(snake_case       ,	'crop_size'	)	)







						def       lowerCamelCase_				(  self    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				self.image_processing_class.from_dict(self.image_processor_dict	)
											self.assertEqual(image_processor.size       ,	{'shortest_edge': 20}	)
											self.assertEqual(image_processor.crop_size       ,	{'height': 18, 'width': 18}	)

											SCREAMING_SNAKE_CASE							:     Dict						=				self.image_processing_class.from_dict(self.image_processor_dict       ,	size=42       ,	crop_size=84	)
											self.assertEqual(image_processor.size       ,	{'shortest_edge': 42}	)
											self.assertEqual(image_processor.crop_size       ,	{'height': 84, 'width': 84}	)







						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											pass







						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     int						=				self.image_processing_class(**self.image_processor_dict	)
											# create random PIL images
											SCREAMING_SNAKE_CASE							:     List[str]						=				prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=snake_case	)
											for image in image_inputs:
																self.assertIsInstance(snake_case       ,	Image.Image	)

											# Test not batched input
											SCREAMING_SNAKE_CASE							:     str						=				image_processing(image_inputs[0]       ,	return_tensors='pt'	).pixel_values
											self.assertEqual(
											    encoded_images.shape       ,	(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )       ,	)

											# Test batched
											SCREAMING_SNAKE_CASE							:     int						=				image_processing(snake_case       ,	return_tensors='pt'	).pixel_values
											self.assertEqual(
											    encoded_images.shape       ,	(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )       ,	)







						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				self.image_processing_class(**self.image_processor_dict	)
											# create random numpy tensors
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=snake_case       ,	numpify=snake_case	)
											for image in image_inputs:
																self.assertIsInstance(snake_case       ,	np.ndarray	)

											# Test not batched input
											SCREAMING_SNAKE_CASE							:     int						=				image_processing(image_inputs[0]       ,	return_tensors='pt'	).pixel_values
											self.assertEqual(
											    encoded_images.shape       ,	(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )       ,	)

											# Test batched
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				image_processing(snake_case       ,	return_tensors='pt'	).pixel_values
											self.assertEqual(
											    encoded_images.shape       ,	(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )       ,	)







						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     int						=				self.image_processing_class(**self.image_processor_dict	)
											# create random PyTorch tensors
											SCREAMING_SNAKE_CASE							:     Tuple						=				prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=snake_case       ,	torchify=snake_case	)
											for image in image_inputs:
																self.assertIsInstance(snake_case       ,	torch.Tensor	)

											# Test not batched input
											SCREAMING_SNAKE_CASE							:     List[Any]						=				image_processing(image_inputs[0]       ,	return_tensors='pt'	).pixel_values
											self.assertEqual(
											    encoded_images.shape       ,	(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )       ,	)

											# Test batched
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				image_processing(snake_case       ,	return_tensors='pt'	).pixel_values
											self.assertEqual(
											    encoded_images.shape       ,	(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )       ,	)




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]      ,	),
					    ]   )