





'''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


_a   :					Tuple           =       {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,__SCREAMING_SNAKE_CASE     ):



					'''simple docstring'''

					super().__init__()
					__lowerCAmelCase   		=							torchvision.models.resnetaaa(pretrained=__SCREAMING_SNAKE_CASE     )
					__lowerCAmelCase   		=							list(model.children()     )[:-2]
					__lowerCAmelCase   		=							nn.Sequential(*__SCREAMING_SNAKE_CASE     )
					__lowerCAmelCase   		=							nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]     )







		def     lowerCamelCase__     (    self,__SCREAMING_SNAKE_CASE     ):



					'''simple docstring'''

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





class        _UpperCAmelCase				(		lowerCAmelCase_  ):
		def __init__(    self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE     ):



					'''simple docstring'''

					__lowerCAmelCase   		=							[json.loads(__SCREAMING_SNAKE_CASE     ) for l in open(__SCREAMING_SNAKE_CASE     )]
					__lowerCAmelCase   		=							os.path.dirname(__SCREAMING_SNAKE_CASE     )
					__lowerCAmelCase   		=							tokenizer
					__lowerCAmelCase   		=							labels
					__lowerCAmelCase   		=							len(__SCREAMING_SNAKE_CASE     )
					__lowerCAmelCase   		=							max_seq_length

					__lowerCAmelCase   		=							transforms
		def __len__(    self     ):



					'''simple docstring'''

					return len(self.data     )
		def __getitem__(    self,__SCREAMING_SNAKE_CASE     ):



					'''simple docstring'''

					__lowerCAmelCase   		=							torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""],add_special_tokens=__SCREAMING_SNAKE_CASE     )     )
					__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase   		=							sentence[0], sentence[1:-1], sentence[-1]
					__lowerCAmelCase   		=							sentence[: self.max_seq_length]

					__lowerCAmelCase   		=							torch.zeros(self.n_classes     )
					__lowerCAmelCase   		=							1

					__lowerCAmelCase   		=							Image.open(os.path.join(self.data_dir,self.data[index]["""img"""]     )     ).convert("""RGB"""     )
					__lowerCAmelCase   		=							self.transforms(__SCREAMING_SNAKE_CASE     )

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







		def     lowerCamelCase__     (    self     ):



					'''simple docstring'''

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





def   _lowerCAmelCase		(		lowercase      )   ->   List[Any]:
			__lowerCAmelCase   		=							[len(row["""sentence"""]      ) for row in batch]
			__lowerCAmelCase		,			__lowerCAmelCase   		=							len(UpperCamelCase_      ), max(UpperCamelCase_      )

			__lowerCAmelCase   		=							torch.zeros(UpperCamelCase_   ,      UpperCamelCase_   ,      dtype=torch.long      )
			__lowerCAmelCase   		=							torch.zeros(UpperCamelCase_   ,      UpperCamelCase_   ,      dtype=torch.long      )

			for i_batch, (input_row, length) in enumerate(zip(UpperCamelCase_   ,      UpperCamelCase_      )      ):
						__lowerCAmelCase   		=							input_row["""sentence"""]
						__lowerCAmelCase   		=							1

			__lowerCAmelCase   		=							torch.stack([row["""image"""] for row in batch]      )
			__lowerCAmelCase   		=							torch.stack([row["""label"""] for row in batch]      )
			__lowerCAmelCase   		=							torch.stack([row["""image_start_token"""] for row in batch]      )
			__lowerCAmelCase   		=							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   _lowerCAmelCase		(		)   ->   int:
			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   _lowerCAmelCase		(		)   ->   Optional[Any]:
			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]   ,      ),
			    ]      )





'''simple docstring'''
import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
							import jax

							from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
							from transformers.models.bert.modeling_flax_bert import FlaxBertModel
							from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel






@require_flax
class 					__lowerCAmelCase   (   unittest.TestCase						):

						"""simple docstring"""






						@slow
						def 				_UpperCAmelCase       (  self							:      Dict	):
													for model_name in ["bert-base-cased", "bert-large-uncased"]:
																				with self.subTest(lowerCAmelCase	):
																											A_			    =		AutoConfig.from_pretrained(lowerCAmelCase	)
																											self.assertIsNotNone(lowerCAmelCase	)
																											self.assertIsInstance(lowerCAmelCase ,  lowerCAmelCase	)

																											A_			    =		FlaxAutoModel.from_pretrained(lowerCAmelCase	)
																											self.assertIsNotNone(lowerCAmelCase	)
																											self.assertIsInstance(lowerCAmelCase ,  lowerCAmelCase	)






						@slow
						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													for model_name in ["roberta-base", "roberta-large"]:
																				with self.subTest(lowerCAmelCase	):
																											A_			    =		AutoConfig.from_pretrained(lowerCAmelCase	)
																											self.assertIsNotNone(lowerCAmelCase	)
																											self.assertIsInstance(lowerCAmelCase ,  lowerCAmelCase	)

																											A_			    =		FlaxAutoModel.from_pretrained(lowerCAmelCase	)
																											self.assertIsNotNone(lowerCAmelCase	)
																											self.assertIsInstance(lowerCAmelCase ,  lowerCAmelCase	)






						@slow
						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													for model_name in ["bert-base-cased", "bert-large-uncased"]:
																				A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase	)
																				A_			    =		FlaxBertModel.from_pretrained(lowerCAmelCase	)
																				A_			    =		tokenizer("Do you support jax jitted function?" ,  return_tensors=TensorType.JAX	)

																				@jax.jit
																				def eval(**lowerCAmelCase							:      Tuple	):
																											return model(**lowerCAmelCase	)

																				eval(**lowerCAmelCase	).block_until_ready()






						@slow
						def 				_UpperCAmelCase       (  self							:      str	):
													for model_name in ["roberta-base", "roberta-large"]:
																				A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase	)
																				A_			    =		FlaxRobertaModel.from_pretrained(lowerCAmelCase	)
																				A_			    =		tokenizer("Do you support jax jitted function?" ,  return_tensors=TensorType.JAX	)

																				@jax.jit
																				def eval(**lowerCAmelCase							:      Any	):
																											return model(**lowerCAmelCase	)

																				eval(**lowerCAmelCase	).block_until_ready()






						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													with self.assertRaisesRegex(
													    lowerCAmelCase ,  "bert-base is not a local folder and is not a valid model identifier"	):
																				A_			    =		FlaxAutoModel.from_pretrained("bert-base"	)






						def 				_UpperCAmelCase       (  self							:      Any	):
													with self.assertRaisesRegex(
													    lowerCAmelCase ,  r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"	):
																				A_			    =		FlaxAutoModel.from_pretrained(lowerCAmelCase ,  revision="aaaaaa"	)






						def 				_UpperCAmelCase       (  self							:      Dict	):
													with self.assertRaisesRegex(
													    lowerCAmelCase ,  "hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack" ,  ):
																				A_			    =		FlaxAutoModel.from_pretrained("hf-internal-testing/config-no-model"	)






						def 				_UpperCAmelCase       (  self							:      Any	):
													with self.assertRaisesRegex(lowerCAmelCase ,  "Use `from_pt=True` to load this model"	):
																				A_			    =		FlaxAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only"	)





