




import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


UpperCamelCase__ :   Dict						=							logging.getLogger(__name__)







class 				lowerCAmelCase_       (					lowerCamelCase_							):






				def __init__(			self      ,snake_case__=-1 ):
							# in NER datasets, the last column is usually reserved for NER label
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      label_idx






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ ):
							if isinstance(snake_case__      ,snake_case__ ):
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      mode.value
							SCREAMING_SNAKE_CASE_   : int					  =      os.path.join(snake_case__      ,F'{mode}.txt' )
							SCREAMING_SNAKE_CASE_   : Dict					  =      1
							SCREAMING_SNAKE_CASE_   : Any					  =      []
							with open(snake_case__      ,encoding='utf-8' ) as f:
										SCREAMING_SNAKE_CASE_   : Any					  =      []
										SCREAMING_SNAKE_CASE_   : Tuple					  =      []
										for line in f:
													if line.startswith('-DOCSTART-' ) or line == "" or line == "\n":
																if words:
																			examples.append(InputExample(guid=F'{mode}-{guid_index}'      ,words=snake_case__      ,labels=snake_case__ ) )
																			guid_index += 1
																			SCREAMING_SNAKE_CASE_   : int					  =      []
																			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      []
													else:
																SCREAMING_SNAKE_CASE_   : Dict					  =      line.split(' ' )
																words.append(splits[0] )
																if len(snake_case__ ) > 1:
																			labels.append(splits[self.label_idx].replace('\n'      ,'' ) )
																else:
																			# Examples could have no label for mode = "test"
																			labels.append('O' )
										if words:
													examples.append(InputExample(guid=F'{mode}-{guid_index}'      ,words=snake_case__      ,labels=snake_case__ ) )
							return examples






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      0
							for line in test_input_reader:
										if line.startswith('-DOCSTART-' ) or line == "" or line == "\n":
													writer.write(snake_case__ )
													if not preds_list[example_id]:
																example_id += 1
										elif preds_list[example_id]:
													SCREAMING_SNAKE_CASE_   : int					  =      line.split()[0] + ' ' + preds_list[example_id].pop(0 ) + '\n'
													writer.write(snake_case__ )
										else:
													logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.'      ,line.split()[0] )






				def 		snake_case	(			self      ,snake_case__ ):
							if path:
										with open(snake_case__      ,'r' ) as f:
													SCREAMING_SNAKE_CASE_   : Dict					  =      f.read().splitlines()
										if "O" not in labels:
													SCREAMING_SNAKE_CASE_   : int					  =      ['O'] + labels
										return labels
							else:
										return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]







class 				lowerCAmelCase_       (					lowerCamelCase_							):






				def __init__(			self ):
							# in CONLL2003 dataset chunk column is second-to-last
							super().__init__(label_idx=-2 )






				def 		snake_case	(			self      ,snake_case__ ):
							if path:
										with open(snake_case__      ,'r' ) as f:
													SCREAMING_SNAKE_CASE_   : str					  =      f.read().splitlines()
										if "O" not in labels:
													SCREAMING_SNAKE_CASE_   : int					  =      ['O'] + labels
										return labels
							else:
										return [
										    "O",
										    "B-ADVP",
										    "B-INTJ",
										    "B-LST",
										    "B-PRT",
										    "B-NP",
										    "B-SBAR",
										    "B-VP",
										    "B-ADJP",
										    "B-CONJP",
										    "B-PP",
										    "I-ADVP",
										    "I-INTJ",
										    "I-LST",
										    "I-PRT",
										    "I-NP",
										    "I-SBAR",
										    "I-VP",
										    "I-ADJP",
										    "I-CONJP",
										    "I-PP",
										]







class 				lowerCAmelCase_       (					lowerCamelCase_							):






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ ):
							if isinstance(snake_case__      ,snake_case__ ):
										SCREAMING_SNAKE_CASE_   : List[str]					  =      mode.value
							SCREAMING_SNAKE_CASE_   : str					  =      os.path.join(snake_case__      ,F'{mode}.txt' )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      1
							SCREAMING_SNAKE_CASE_   : List[str]					  =      []

							with open(snake_case__      ,encoding='utf-8' ) as f:
										for sentence in parse_incr(snake_case__ ):
													SCREAMING_SNAKE_CASE_   : Tuple					  =      []
													SCREAMING_SNAKE_CASE_   : List[str]					  =      []
													for token in sentence:
																words.append(token['form'] )
																labels.append(token['upos'] )
													assert len(snake_case__ ) == len(snake_case__ )
													if words:
																examples.append(InputExample(guid=F'{mode}-{guid_index}'      ,words=snake_case__      ,labels=snake_case__ ) )
																guid_index += 1
							return examples






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : int					  =      0
							for sentence in parse_incr(snake_case__ ):
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      preds_list[example_id]
										SCREAMING_SNAKE_CASE_   : Tuple					  =      ''
										for token in sentence:
													out += F'{token["form"]} ({token["upos"]}|{s_p.pop(0 )}) '
										out += "\n"
										writer.write(snake_case__ )
										example_id += 1






				def 		snake_case	(			self      ,snake_case__ ):
							if path:
										with open(snake_case__      ,'r' ) as f:
													return f.read().splitlines()
							else:
										return [
										    "ADJ",
										    "ADP",
										    "ADV",
										    "AUX",
										    "CCONJ",
										    "DET",
										    "INTJ",
										    "NOUN",
										    "NUM",
										    "PART",
										    "PRON",
										    "PROPN",
										    "PUNCT",
										    "SCONJ",
										    "SYM",
										    "VERB",
										    "X",
										]








'''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


SCREAMING_SNAKE_CASE__          =  {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 					snake_case			(nn.Module ):



       def __init__(					self  ,UpperCAmelCase_   )	->				Dict:
           super().__init__()
           lowercase__									=    torchvision.models.resnetaaa(pretrained=UpperCAmelCase_   )
           lowercase__									=    list(model.children()   )[:-2]
           lowercase__									=    nn.Sequential(*UpperCAmelCase_   )
           lowercase__									=    nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )







       def 				_a   (					self  ,UpperCAmelCase_   )	->				str:
           # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
           lowercase__									=    self.pool(self.model(UpperCAmelCase_   )   )
           lowercase__									=    torch.flatten(UpperCAmelCase_  ,start_dim=2   )
           lowercase__									=    out.transpose(1  ,2   ).contiguous()
           return out  # BxNx2048





class 					snake_case			(UpperCamelCase ):



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				Dict:
           lowercase__									=    [json.loads(UpperCAmelCase_   ) for l in open(UpperCAmelCase_   )]
           lowercase__									=    os.path.dirname(UpperCAmelCase_   )
           lowercase__									=    tokenizer
           lowercase__									=    labels
           lowercase__									=    len(UpperCAmelCase_   )
           lowercase__									=    max_seq_length

           lowercase__									=    transforms



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



       def __getitem__(					self  ,UpperCAmelCase_   )	->				Optional[int]:
           lowercase__									=    torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]  ,add_special_tokens=UpperCAmelCase_   )   )
           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(UpperCAmelCase_   )

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







       def 				_a   (					self   )	->				str:
           lowercase__									=    Counter()
           for row in self.data:
               label_freqs.update(row["label"]   )
           return label_freqs





def 							lowerCamelCase	(					_snake_case	:    Optional[int]							):





    '''simple docstring'''


    lowercase__									=    [len(row["sentence"]							) for row in batch]
    lowercase__							,							lowercase__									=    len(_snake_case							), max(_snake_case							)

    lowercase__									=    torch.zeros(_snake_case  ,_snake_case  ,dtype=torch.long							)
    lowercase__									=    torch.zeros(_snake_case  ,_snake_case  ,dtype=torch.long							)

    for i_batch, (input_row, length) in enumerate(zip(_snake_case  ,_snake_case							)							):
        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 							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=[0.46_777_044, 0.44_531_429, 0.40_661_017]  ,std=[0.12_221_994, 0.12_145_835, 0.14_380_469]  ,),
        ]							)




