






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__       =     {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    :    List[Any]			,		UpperCamelCase__    :    Optional[Any]):






											'''simple docstring'''







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



				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    Any):






											'''simple docstring'''







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


class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				def __init__(      self    :    List[Any]			,		UpperCamelCase__    :    Tuple			,		UpperCamelCase__    :    List[str]			,		UpperCamelCase__    :    Optional[Any]			,		UpperCamelCase__    :    Any			,		UpperCamelCase__    :    Union[str, Any]):






											'''simple docstring'''







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

											snake_case__	  =       transforms



				def __len__(      self    :    Optional[Any]):






											'''simple docstring'''







											return len(self.data)



				def __getitem__(      self    :    Tuple			,		UpperCamelCase__    :    Union[str, Any]):






											'''simple docstring'''







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

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

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

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



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







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


def _UpperCAmelCase   (	a						:	List[str] ):
							snake_case__	  =       [len(row["""sentence"""] ) for row in batch]
							snake_case__  , snake_case__	  =       len(a ), max(a )

							snake_case__	  =       torch.zeros(a  ,					a  ,					dtype=torch.long )
							snake_case__	  =       torch.zeros(a  ,					a  ,					dtype=torch.long )

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

							snake_case__	  =       torch.stack([row["""image"""] for row in batch] )
							snake_case__	  =       torch.stack([row["""label"""] for row in batch] )
							snake_case__	  =       torch.stack([row["""image_start_token"""] for row in batch] )
							snake_case__	  =       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   (	):
							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   (	):
							return transforms.Compose(
							    [
							        transforms.Resize(256 ),
							        transforms.CenterCrop(224 ),
							        transforms.ToTensor(),
							        transforms.Normalize(
							            mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]  ,					std=[0.1222_1994, 0.1214_5835, 0.1438_0469]  ,					),
							    ] )








import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout

def _UpperCAmelCase   (	a						:	List[Any] ):
							snake_case__	  =       FileLock(str(tmpdir / """foo.lock""" ) )
							snake_case__	  =       FileLock(str(tmpdir / """foo.lock""" ) )
							snake_case__	  =       0.01
							with locka.acquire():
														with pytest.raises(a ):
																					snake_case__	  =       time.time()
																					locka.acquire(a )
																					assert time.time() - _start > timeout



def _UpperCAmelCase   (	a						:	str ):
							snake_case__	  =       """a""" * 1000 + """.lock"""
							snake_case__	  =       FileLock(str(tmpdir / filename ) )
							assert locka._lock_file.endswith(""".lock""" )
							assert not locka._lock_file.endswith(a )
							assert len(os.path.basename(locka._lock_file ) ) <= 255
							snake_case__	  =       FileLock(tmpdir / filename )
							with locka.acquire():
														with pytest.raises(a ):
																					locka.acquire(0 )
