




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


SCREAMING_SNAKE_CASE__			: 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 						UpperCamelCase__      (nn.Module	):

    '''simple docstring'''



    def __init__(       self	,    UpperCamelCase__	)	-> List[str]:
           super().__init__()
           lowerCamelCase  : Optional[Any]     					=   torchvision.models.resnetaaa(pretrained=UpperCamelCase__	)
           lowerCamelCase  : Optional[Any]     					=   list(model.children()	)[:-2]
           lowerCamelCase  : List[str]     					=   nn.Sequential(*UpperCamelCase__	)
           lowerCamelCase  : Optional[int]     					=   nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]	)







    def        _lowercase	(       self	,    UpperCamelCase__	)	-> List[Any]:
           lowerCamelCase  : Optional[int]     					=   self.pool(self.model(UpperCamelCase__	)	)
           lowerCamelCase  : Any     					=   torch.flatten(UpperCamelCase__	,    start_dim=2	)
           lowerCamelCase  : Union[str, Any]     					=   out.transpose(1	,    2	).contiguous()
           return out  # BxNx2048




class 						UpperCamelCase__      (SCREAMING_SNAKE_CASE_	):

    '''simple docstring'''



    def __init__(       self	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	)	-> int:
           lowerCamelCase  : Optional[Any]     					=   [json.loads(UpperCamelCase__	) for l in open(UpperCamelCase__	)]
           lowerCamelCase  : Optional[Any]     					=   os.path.dirname(UpperCamelCase__	)
           lowerCamelCase  : Optional[int]     					=   tokenizer
           lowerCamelCase  : Dict     					=   labels
           lowerCamelCase  : Optional[Any]     					=   len(UpperCamelCase__	)
           lowerCamelCase  : int     					=   max_seq_length

           lowerCamelCase  : List[Any]     					=   transforms







    def __len__(       self	)	-> Optional[int]:
           return len(self.data	)







    def __getitem__(       self	,    UpperCamelCase__	)	-> List[Any]:
           lowerCamelCase  : Optional[int]     					=   torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]	,    add_special_tokens=UpperCamelCase__	)	)
           lowerCamelCase  : Optional[Any]     					=   sentence[0], sentence[1:-1], sentence[-1]
           lowerCamelCase  : str     					=   sentence[: self.max_seq_length]

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

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

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







    def        _lowercase	(       self	)	-> List[Any]:
           lowerCamelCase  : int     					=   Counter()
           for row in self.data:
                  label_freqs.update(row["label"]	)
           return label_freqs




def 	A ( _SCREAMING_SNAKE_CASE       )      ->					Union[str, Any]:
       lowerCamelCase  : str     					=   [len(row["sentence"]       ) for row in batch]
       lowerCamelCase  : Dict     					=   len(__lowerCAmelCase       ), max(__lowerCAmelCase       )

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

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

       lowerCamelCase  : List[str]     					=   torch.stack([row["image"] for row in batch]       )
       lowerCamelCase  : Union[str, Any]     					=   torch.stack([row["label"] for row in batch]       )
       lowerCamelCase  : Tuple     					=   torch.stack([row["image_start_token"] for row in batch]       )
       lowerCamelCase  : 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=[0.46777044, 0.44531429, 0.40661017]					,std=[0.12221994, 0.12145835, 0.14380469]					,),
           ]       )











import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
			import jax.numpy as jnp

			from transformers.models.distilbert.modeling_flax_distilbert import (
			    FlaxDistilBertForMaskedLM,
			    FlaxDistilBertForMultipleChoice,
			    FlaxDistilBertForQuestionAnswering,
			    FlaxDistilBertForSequenceClassification,
			    FlaxDistilBertForTokenClassification,
			    FlaxDistilBertModel,
			)


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


						def __init__(  self    :				int       ,	snake_case    :				Optional[Any]       ,	snake_case    :				Optional[Any]=13       ,	snake_case    :				str=7       ,	snake_case    :				Optional[Any]=True       ,	snake_case    :				int=True       ,	snake_case    :				str=True       ,	snake_case    :				List[str]=True       ,	snake_case    :				Optional[Any]=99       ,	snake_case    :				Optional[int]=32       ,	snake_case    :				Optional[int]=5       ,	snake_case    :				Optional[Any]=4       ,	snake_case    :				Optional[Any]=37       ,	snake_case    :				str="gelu"       ,	snake_case    :				List[str]=0.1       ,	snake_case    :				List[str]=0.1       ,	snake_case    :				int=512       ,	snake_case    :				Union[str, Any]=16       ,	snake_case    :				Optional[Any]=2       ,	snake_case    :				int=0.02       ,	snake_case    :				str=4       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				parent
											SCREAMING_SNAKE_CASE							:     str						=				batch_size
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				seq_length
											SCREAMING_SNAKE_CASE							:     int						=				is_training
											SCREAMING_SNAKE_CASE							:     List[str]						=				use_attention_mask
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				use_token_type_ids
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				use_labels
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				vocab_size
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				hidden_size
											SCREAMING_SNAKE_CASE							:     Any						=				num_hidden_layers
											SCREAMING_SNAKE_CASE							:     Tuple						=				num_attention_heads
											SCREAMING_SNAKE_CASE							:     Any						=				intermediate_size
											SCREAMING_SNAKE_CASE							:     Tuple						=				hidden_act
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				hidden_dropout_prob
											SCREAMING_SNAKE_CASE							:     str						=				attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE							:     Tuple						=				max_position_embeddings
											SCREAMING_SNAKE_CASE							:     Any						=				type_vocab_size
											SCREAMING_SNAKE_CASE							:     List[Any]						=				type_sequence_label_size
											SCREAMING_SNAKE_CASE							:     Tuple						=				initializer_range
											SCREAMING_SNAKE_CASE							:     List[str]						=				num_choices







						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size	)

											SCREAMING_SNAKE_CASE							:     Optional[int]						=				None
											if self.use_attention_mask:
																SCREAMING_SNAKE_CASE							:     Any						=				random_attention_mask([self.batch_size, self.seq_length]	)

											SCREAMING_SNAKE_CASE							:     Tuple						=				DistilBertConfig(
											    vocab_size=self.vocab_size       ,	dim=self.hidden_size       ,	n_layers=self.num_hidden_layers       ,	n_heads=self.num_attention_heads       ,	hidden_dim=self.intermediate_size       ,	hidden_act=self.hidden_act       ,	dropout=self.hidden_dropout_prob       ,	attention_dropout=self.attention_probs_dropout_prob       ,	max_position_embeddings=self.max_position_embeddings       ,	initializer_range=self.initializer_range       ,	tie_weights_=snake_case       ,	)

											return config, input_ids, attention_mask







						def       lowerCamelCase_				(  self    :				int	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				self.prepare_config_and_inputs()
											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Optional[int]						=				config_and_inputs
											SCREAMING_SNAKE_CASE							:     int						=				{'input_ids': input_ids, 'attention_mask': attention_mask}
											return config, inputs_dict


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


						UpperCAmelCase			:		int		=		(
						    (
						        FlaxDistilBertModel,
						        FlaxDistilBertForMaskedLM,
						        FlaxDistilBertForMultipleChoice,
						        FlaxDistilBertForQuestionAnswering,
						        FlaxDistilBertForSequenceClassification,
						        FlaxDistilBertForTokenClassification,
						        FlaxDistilBertForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)







						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     str						=				FlaxDistilBertModelTester(self	)







						@slow
						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											for model_class_name in self.all_model_classes:
																SCREAMING_SNAKE_CASE							:     Dict						=				model_class_name.from_pretrained('distilbert-base-uncased'	)
																SCREAMING_SNAKE_CASE							:     Dict						=				model(np.ones((1, 1)	)	)
																self.assertIsNotNone(snake_case	)


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


						@slow
						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				FlaxDistilBertModel.from_pretrained('distilbert-base-uncased'	)
											SCREAMING_SNAKE_CASE							:     List[str]						=				np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]	)
											SCREAMING_SNAKE_CASE							:     Dict						=				np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
											SCREAMING_SNAKE_CASE							:     str						=				model(snake_case       ,	attention_mask=snake_case	)[0]
											SCREAMING_SNAKE_CASE							:     int						=				(1, 11, 768)
											self.assertEqual(output.shape       ,	snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]	)

											self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]       ,	snake_case       ,	atol=1E-4	)	)