


"""simple docstring"""






import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


UpperCAmelCase       :	List[Any]  		=						[
    {"dataset": "wikipedia", "config_name": "20220301.de"},
    {"dataset": "wikipedia", "config_name": "20220301.en"},
    {"dataset": "wikipedia", "config_name": "20220301.fr"},
    {"dataset": "wikipedia", "config_name": "20220301.frr"},
    {"dataset": "wikipedia", "config_name": "20220301.it"},
    {"dataset": "wikipedia", "config_name": "20220301.simple"},
    {"dataset": "snli", "config_name": "plain_text"},
    {"dataset": "eli5", "config_name": "LFQA_reddit"},
    {"dataset": "wiki40b", "config_name": "en"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.compressed"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.no_index"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.multiset.no_index"},
    {"dataset": "natural_questions", "config_name": "default"},
]
def 	lowerCamelCase			(	_UpperCamelCase						: Tuple=True				)						->			Any:

							'''simple docstring'''



							if with_config:
														return [
														    {
														        "testcase_name": d["dataset"] + "/" + d["config_name"],
														        "dataset": d["dataset"],
														        "config_name": d["config_name"],
														    }
														    for d in DATASETS_ON_HF_GCP
														]
							else:
														return [
														    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
														]





@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__snake_case							)							)
class 	lowerCamelCase__  (	__snake_case							):







					"""simple docstring"""


					__a      			=				None
					__a      			=				None






					def lowerCamelCase__				(			self       :					str					,			UpperCamelCase       :					List[Any]					,			UpperCamelCase       :					str    ):
												'''simple docstring'''






												with TemporaryDirectory() as tmp_dir:
																			__UpperCAmelCase  :		Optional[Any] 				=   dataset_module_factory(a__					,			cache_dir=a__    )

																			__UpperCAmelCase  :		Dict 				=   import_main_class(dataset_module.module_path					,			dataset=a__    )

																			__UpperCAmelCase  :		int 				=   builder_cls(
																			    cache_dir=a__					,			config_name=a__					,			hash=dataset_module.hash					,			)

																			__UpperCAmelCase  :		Optional[Any] 				=   """/""".join(
																			    [
																			        HF_GCP_BASE_URL,
																			        builder_instance._relative_data_dir(with_hash=a__    ).replace(os.sep					,			"""/"""    ),
																			        config.DATASET_INFO_FILENAME,
																			    ]    )
																			__UpperCAmelCase  :		List[Any] 				=   cached_path(a__					,			cache_dir=a__    )
																			self.assertTrue(os.path.exists(a__    )    )






@pytest.mark.integration
def 	lowerCamelCase			(	_UpperCamelCase						: int				)						->			str:

							'''simple docstring'''



							__UpperCAmelCase  :		Tuple 				=   tmp_path_factory.mktemp("""test_hf_gcp"""				) / """test_wikipedia_simple"""
							__UpperCAmelCase  :		Dict 				=   dataset_module_factory("""wikipedia"""			,     cache_dir=UpperCamelCase__				)
							__UpperCAmelCase  :		int 				=   import_main_class(dataset_module.module_path				)
							__UpperCAmelCase  :		List[str] 				=   builder_cls(
							    cache_dir=UpperCamelCase__			,     config_name="""20220301.frr"""			,     hash=dataset_module.hash			,     )
							# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
							__UpperCAmelCase  :		Optional[Any] 				=   None
							builder_instance.download_and_prepare()
							__UpperCAmelCase  :		str 				=   builder_instance.as_dataset()
							assert ds
@pytest.mark.integration
def 	lowerCamelCase			(	_UpperCamelCase						: str				)						->			Dict:

							'''simple docstring'''



							__UpperCAmelCase  :		int 				=   dataset_module_factory("""wikipedia"""			,     cache_dir=UpperCamelCase__				)
							__UpperCAmelCase  :		Optional[int] 				=   import_main_class(dataset_module.module_path			,     dataset=UpperCamelCase__				)
							__UpperCAmelCase  :		int 				=   builder_cls(
							    cache_dir=UpperCamelCase__			,     config_name="""20220301.frr"""			,     hash=dataset_module.hash			,     )
							__UpperCAmelCase  :		Union[str, Any] 				=   builder_instance.as_streaming_dataset()
							assert ds
							assert isinstance(UpperCamelCase__			,     UpperCamelCase__				)
							assert "train" in ds
							assert isinstance(ds["""train"""]			,     UpperCamelCase__				)
							assert next(iter(ds["""train"""]				)				)




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


_lowercase							:   Union[str, Any]						=						{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					, a__ )						->					Dict:
					super().__init__()
					A             =					torchvision.models.resnetaaa(pretrained=a__ )
					A             =					list(model.children() )[:-2]
					A             =					nn.Sequential(*a__ )
					A             =					nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )

				def   _UpperCAmelCase       (    self					, a__ )						->					List[str]:
					# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
					A             =					self.pool(self.model(a__ ) )
					A             =					torch.flatten(a__					, start_dim=2 )
					A             =					out.transpose(1					, 2 ).contiguous()
					return out  # BxNx2048






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def __init__(    self					, a__					, a__					, a__					, a__					, a__ )						->					Union[str, Any]:
					A             =					[json.loads(a__ ) for l in open(a__ )]
					A             =					os.path.dirname(a__ )
					A             =					tokenizer
					A             =					labels
					A             =					len(a__ )
					A             =					max_seq_length

					A             =					transforms

				def __len__(    self )						->					Tuple:
					return len(self.data )

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

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

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

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

				def   _UpperCAmelCase       (    self )						->					Union[str, Any]:
					A             =					Counter()
					for row in self.data:
						label_freqs.update(row["""label"""] )
					return label_freqs





def 	_lowerCAmelCase					( UpperCamelCase__:  int    )			-> List[str]:





	"""simple docstring"""







	A             =					[len(row["""sentence"""]    ) for row in batch]
	A       ,      A             =					len(UpperCamelCase__    ), max(UpperCamelCase__    )

	A             =					torch.zeros(UpperCamelCase__				,       UpperCamelCase__				,       dtype=torch.long    )
	A             =					torch.zeros(UpperCamelCase__				,       UpperCamelCase__				,       dtype=torch.long    )

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

	A             =					torch.stack([row["""image"""] for row in batch]    )
	A             =					torch.stack([row["""label"""] for row in batch]    )
	A             =					torch.stack([row["""image_start_token"""] for row in batch]    )
	A             =					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					( )			-> Union[str, Any]:





	"""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					( )			-> Optional[Any]:





	"""simple docstring"""







	return transforms.Compose(
	    [
	        transforms.Resize(2_56    ),
	        transforms.CenterCrop(2_24    ),
	        transforms.ToTensor(),
	        transforms.Normalize(
	            mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]				,       std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]				,       ),
	    ]    )



