



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


__snake_case				:		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        lowerCamelCase__			(							nn.Module):




		'''simple docstring'''



		def __init__(self		,__lowerCamelCase )     ->			Optional[Any]:

						"""simple docstring"""






						super().__init__()
						lowerCAmelCase__     :				List[Any]												=     torchvision.models.resnetaaa(pretrained=__lowerCamelCase )
						lowerCAmelCase__     :				List[Any]												=     list(model.children() )[:-2]
						lowerCAmelCase__     :				Any												=     nn.Sequential(*__lowerCamelCase )
						lowerCAmelCase__     :				int												=     nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )



		def 		lowerCAmelCase__      (self		,__lowerCamelCase )     ->			Tuple:

						"""simple docstring"""






						lowerCAmelCase__     :				Dict												=     self.pool(self.model(__lowerCamelCase ) )
						lowerCAmelCase__     :				Optional[int]												=     torch.flatten(__lowerCamelCase		,start_dim=2 )
						lowerCAmelCase__     :				Dict												=     out.transpose(1		,2 ).contiguous()
						return out  # BxNx2048



class        lowerCamelCase__			(							__snake_case):




		'''simple docstring'''



		def __init__(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase )     ->			str:

						"""simple docstring"""






						lowerCAmelCase__     :				Any												=     [json.loads(__lowerCamelCase ) for l in open(__lowerCamelCase )]
						lowerCAmelCase__     :				Optional[int]												=     os.path.dirname(__lowerCamelCase )
						lowerCAmelCase__     :				Optional[int]												=     tokenizer
						lowerCAmelCase__     :				List[str]												=     labels
						lowerCAmelCase__     :				Dict												=     len(__lowerCamelCase )
						lowerCAmelCase__     :				Dict												=     max_seq_length

						lowerCAmelCase__     :				List[Any]												=     transforms



		def __len__(self )     ->			str:

						"""simple docstring"""






						return len(self.data )



		def __getitem__(self		,__lowerCamelCase )     ->			Tuple:

						"""simple docstring"""






						lowerCAmelCase__     :				List[Any]												=     torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']		,add_special_tokens=__lowerCamelCase ) )
						lowerCAmelCase__     :				List[str]												=     sentence[0], sentence[1:-1], sentence[-1]
						lowerCAmelCase__     :				str												=     sentence[: self.max_seq_length]

						lowerCAmelCase__     :				Dict												=     torch.zeros(self.n_classes )
						lowerCAmelCase__     :				Tuple												=     1

						lowerCAmelCase__     :				List[Any]												=     Image.open(os.path.join(self.data_dir		,self.data[index]['''img'''] ) ).convert('''RGB''' )
						lowerCAmelCase__     :				Optional[int]												=     self.transforms(__lowerCamelCase )

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



		def 		lowerCAmelCase__      (self )     ->			Dict:

						"""simple docstring"""






						lowerCAmelCase__     :				str												=     Counter()
						for row in self.data:
										label_freqs.update(row['''label'''] )
						return label_freqs






def 	lowerCAmelCase__		(    lowerCamelCase_		:		List[Any]):





				'''simple docstring'''

				lowerCAmelCase__     :				str												=     [len(row['''sentence''']) for row in batch]
				lowerCAmelCase__     :				int												=     len(SCREAMING_SNAKE_CASE_), max(SCREAMING_SNAKE_CASE_)

				lowerCAmelCase__     :				Any												=     torch.zeros(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,dtype=torch.long)
				lowerCAmelCase__     :				str												=     torch.zeros(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,dtype=torch.long)

				for i_batch, (input_row, length) in enumerate(zip(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_)):
								lowerCAmelCase__     :				Any												=     input_row['sentence']
								lowerCAmelCase__     :				List[str]												=     1

				lowerCAmelCase__     :				Tuple												=     torch.stack([row['''image'''] for row in batch])
				lowerCAmelCase__     :				List[str]												=     torch.stack([row['''label'''] for row in batch])
				lowerCAmelCase__     :				List[Any]												=     torch.stack([row['''image_start_token'''] for row in batch])
				lowerCAmelCase__     :				List[str]												=     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__		(    ):





				'''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 	lowerCAmelCase__		(    ):





				'''simple docstring'''

				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]       ,),
				    ])




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_			  = logging.get_logger(__name__)

snake_case_			  = {
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       Any														=     """distilbert"""
    __lowerCamelCase						:       List[Any]														=     {
        """hidden_size""": """dim""",
        """num_attention_heads""": """n_heads""",
        """num_hidden_layers""": """n_layers""",
    }





    def __init__(      self						,   a=3_0522						,   a=512						,   a=False						,   a=6						,   a=12						,   a=768						,   a=4 * 768						,   a=0.1						,   a=0.1						,   a="gelu"						,   a=0.02						,   a=0.1						,   a=0.2						,   a=0						,   **a						,   ):
         lowercase__    :   int      =       vocab_size
         lowercase__    :   int      =       max_position_embeddings
         lowercase__    :   List[str]      =       sinusoidal_pos_embds
         lowercase__    :   Tuple      =       n_layers
         lowercase__    :   Optional[int]      =       n_heads
         lowercase__    :   List[Any]      =       dim
         lowercase__    :   Any      =       hidden_dim
         lowercase__    :   Union[str, Any]      =       dropout
         lowercase__    :   Optional[int]      =       attention_dropout
         lowercase__    :   List[Any]      =       activation
         lowercase__    :   Union[str, Any]      =       initializer_range
         lowercase__    :   Dict      =       qa_dropout
         lowercase__    :   Tuple      =       seq_classif_dropout
         super().__init__(**a						,   pad_token_id=a)



class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    @property
    def       snake_case_				(      self):
         if self.task == "multiple-choice":
              lowercase__    :   List[str]      =       {0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
              lowercase__    :   Tuple      =       {0: 'batch', 1: 'sequence'}
         return OrderedDict(
             [
                 ('input_ids', dynamic_axis),
                 ('attention_mask', dynamic_axis),
             ])

