






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


A	:		int    =       {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 							A   (	nn.Module							):
					'''simple docstring'''






					def __init__(self   :						Any				, _UpperCAmelCase   :						Union[str, Any]		)						->       str:




							"""simple docstring"""


							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   lowerCamelCase__					(self   :						Union[str, Any]				, _UpperCAmelCase   :						int		)						->       List[str]:




							"""simple docstring"""


							lowercase__					      =		self.pool(self.model(_a		)		)
							lowercase__					      =		torch.flatten(_a				, start_dim=2		)
							lowercase__					      =		out.transpose(1				, 2		).contiguous()
							return out  # BxNx2048






class 							A   (	__SCREAMING_SNAKE_CASE							):
					'''simple docstring'''






					def __init__(self   :						Dict				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						Optional[int]				, _UpperCAmelCase   :						List[str]				, _UpperCAmelCase   :						Any				, _UpperCAmelCase   :						Dict		)						->       Union[str, Any]:




							"""simple docstring"""


							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   :						int		)						->       Optional[int]:




							"""simple docstring"""


							return len(self.data		)

					def __getitem__(self   :						Dict				, _UpperCAmelCase   :						str		)						->       Union[str, Any]:




							"""simple docstring"""


							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   lowerCamelCase__					(self   :						Dict		)						->       List[Any]:




							"""simple docstring"""


							lowercase__					      =		Counter()
							for row in self.data:
									label_freqs.update(row["""label"""]		)
							return label_freqs






def 			UpperCamelCase (     __magic_name__					:							List[str]					)       ->  int:
		"""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 (     )       ->  int:
		"""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 (     )       ->  str:
		"""simple docstring"""




		return transforms.Compose(
		    [
		        transforms.Resize(256					),
		        transforms.CenterCrop(224					),
		        transforms.ToTensor(),
		        transforms.Normalize(
		            mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7]   ,   std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9]   ,   ),
		    ]					)





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
	import numpy as np
	import tensorflow as tf

	from transformers import TFXLMRobertaModel



@require_tf
@require_sentencepiece
@require_tokenizers
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					@slow
					def   lowerCamelCase__					(self   :						Any		)						->       Union[str, Any]:




							"""simple docstring"""


							lowercase__					      =		TFXLMRobertaModel.from_pretrained("""jplu/tf-xlm-roberta-base"""		)

							lowercase__					      =		{
							    """input_ids""": tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]]				, dtype=tf.intaa		),  # "My dog is cute"
							    """attention_mask""": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]				, dtype=tf.intaa		),
							}

							lowercase__					      =		model(_UpperCAmelCase		)["""last_hidden_state"""]
							lowercase__					      =		tf.TensorShape((1, 6, 768)		)
							self.assertEqual(output.shape				, _UpperCAmelCase		)
							# compare the actual values for a slice.
							lowercase__					      =		tf.convert_to_tensor(
							    [
							        [
							            [0.0_681_762, 0.10_894_451, 0.06_772_504],
							            [-0.06_423_668, 0.02_366_615, 0.04_329_344],
							            [-0.06_057_295, 0.09_974_135, -0.00_070_584],
							        ]
							    ]				, dtype=tf.floataa				, )

							self.assertTrue(np.allclose(output[:, :3, :3].numpy()				, expected_slice.numpy()				, atol=1E-4		)		)

