


"""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							:			List[str]						=			{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    __A   (nn.Module):





							'''simple docstring'''

							def __init__( self							:					Optional[Any]						,				UpperCAmelCase_							:					List[str]				)    ->str:



														"""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 						lowerCAmelCase				( self							:					Optional[Any]						,				UpperCAmelCase_							:					List[str]				)    ->List[str]:



														"""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    __A   (snake_case__):





							'''simple docstring'''

							def __init__( self							:					Union[str, Any]						,				UpperCAmelCase_							:					int						,				UpperCAmelCase_							:					str						,				UpperCAmelCase_							:					Any						,				UpperCAmelCase_							:					Tuple						,				UpperCAmelCase_							:					Tuple				)    ->int:



														"""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							:					Tuple				)    ->Optional[int]:



														"""simple docstring"""



														return len(self.data				)



							def __getitem__( self							:					Union[str, Any]						,				UpperCAmelCase_							:					Optional[int]				)    ->Any:



														"""simple docstring"""



														snake_case_							=						torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]						,				add_special_tokens=UpperCAmelCase_				)				)
														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 						lowerCAmelCase				( self							:					str				)    ->int:



														"""simple docstring"""



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







def   _a (					_SCREAMING_SNAKE_CASE )			-> Dict:
							snake_case_							=						[len(row["""sentence"""] ) for row in batch]
							snake_case_							=						len(_lowercase ), max(_lowercase )

							snake_case_							=						torch.zeros(_lowercase ,       _lowercase ,       dtype=torch.long )
							snake_case_							=						torch.zeros(_lowercase ,       _lowercase ,       dtype=torch.long )

							for i_batch, (input_row, length) in enumerate(zip(_lowercase ,       _lowercase ) ):
														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   _a (					)			-> Tuple:
							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 (					)			-> List[str]:
							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] ,       ),
							    ] )








'''simple docstring'''




from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[Any]:
							return getitem, k



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			List[str]:
							return setitem, k, v



def       __lowerCamelCase  (					_lowercase		)      ->			int:
							return delitem, k



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				*_lowercase		)      ->			Optional[Any]:
							try:
														return fun(_lowercase	,				*_lowercase		), None
							except Exception as e:
														return None, e


a   :			List[str]					  =    (
    _set("""key_a""", """val_a"""),
    _set("""key_b""", """val_b"""),
)

a   :			List[Any]					  =    [
    _set("""key_a""", """val_a"""),
    _set("""key_a""", """val_b"""),
]

a   :			int					  =    [
    _set("""key_a""", """val_a"""),
    _set("""key_b""", """val_b"""),
    _del("""key_a"""),
    _del("""key_b"""),
    _set("""key_a""", """val_a"""),
    _del("""key_a"""),
]

a   :			List[Any]					  =    [
    _get("""key_a"""),
    _del("""key_a"""),
    _set("""key_a""", """val_a"""),
    _del("""key_a"""),
    _del("""key_a"""),
    _get("""key_a"""),
]

a   :			Tuple					  =    [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

a   :			Optional[Any]					  =    [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set("""key_a""", """val_b"""),
]



@pytest.mark.parametrize(
    """operations"""	,				(
        pytest.param(_add_items	,				id="""add items"""		),
        pytest.param(_overwrite_items	,				id="""overwrite items"""		),
        pytest.param(_delete_items	,				id="""delete items"""		),
        pytest.param(_access_absent_items	,				id="""access absent items"""		),
        pytest.param(_add_with_resize_up	,				id="""add with resize up"""		),
        pytest.param(_add_with_resize_down	,				id="""add with resize down"""		),
    )	,				)
def       __lowerCamelCase  (					_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		List[str]							     =		HashMap(initial_block_size=4		)
							UpperCAmelCase     :		Dict							     =		{}
							for _, (fun, *args) in enumerate(_lowercase		):
														UpperCAmelCase     ,     UpperCAmelCase     :		Union[str, Any]							     =		_run_operation(_lowercase	,				_lowercase	,				*_lowercase		)
														UpperCAmelCase     ,     UpperCAmelCase     :		Any							     =		_run_operation(_lowercase	,				_lowercase	,				*_lowercase		)
														assert my_res == py_res
														assert str(_lowercase		) == str(_lowercase		)
														assert set(_lowercase		) == set(_lowercase		)
														assert len(_lowercase		) == len(_lowercase		)
														assert set(my.items()		) == set(py.items()		)



def       __lowerCamelCase  (					)      ->			List[Any]:
							def is_public(_lowercase		) -> bool:
														return not name.startswith("""_"""		)

							UpperCAmelCase     :		int							     =		{name for name in dir({}		) if is_public(_lowercase		)}
							UpperCAmelCase     :		Any							     =		{name for name in dir(HashMap()		) if is_public(_lowercase		)}

							assert dict_public_names > hash_public_names

