






"""simple docstring"""
import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class      UpperCamelCase_			(__A	):
	__magic_name__       =							['''image_processor''', '''tokenizer''']
	__magic_name__       =							'''FlavaImageProcessor'''
	__magic_name__       =							('''BertTokenizer''', '''BertTokenizerFast''')





	def __init__( self   :					Optional[int]      ,      lowerCAmelCase_   :					Tuple=None      ,      lowerCAmelCase_   :					List[str]=None      ,      **lowerCAmelCase_   :					Union[str, Any]		)		->     List[Any]:
				UpperCAmelCase_	:					Any     	=   None
				if "feature_extractor" in kwargs:
							warnings.warn(
							    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
							    " instead."      ,      lowerCAmelCase_      ,      )
							UpperCAmelCase_	:					Tuple     	=   kwargs.pop("feature_extractor"		)

				UpperCAmelCase_	:					Optional[Any]     	=   image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
							raise ValueError("You need to specify an `image_processor`."		)
				if tokenizer is None:
							raise ValueError("You need to specify a `tokenizer`."		)

				super().__init__(lowerCAmelCase_      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   self.image_processor





	def __call__( self   :					List[Any]      ,      lowerCAmelCase_   :					Optional[ImageInput] = None      ,      lowerCAmelCase_   :					Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					Union[bool, str, PaddingStrategy] = False      ,      lowerCAmelCase_   :					Union[bool, str, TruncationStrategy] = False      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					Optional[Union[str, TensorType]] = None      ,      **lowerCAmelCase_   :					Optional[int]      ,      )		->     List[Any]:

				if text is None and images is None:
							raise ValueError("You have to specify either text or images. Both cannot be none."		)

				if text is not None:
							UpperCAmelCase_	:					Union[str, Any]     	=   self.tokenizer(
							    text=lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_      ,      padding=lowerCAmelCase_      ,      truncation=lowerCAmelCase_      ,      max_length=lowerCAmelCase_      ,      stride=lowerCAmelCase_      ,      pad_to_multiple_of=lowerCAmelCase_      ,      return_token_type_ids=lowerCAmelCase_      ,      return_attention_mask=lowerCAmelCase_      ,      return_overflowing_tokens=lowerCAmelCase_      ,      return_special_tokens_mask=lowerCAmelCase_      ,      return_offsets_mapping=lowerCAmelCase_      ,      return_length=lowerCAmelCase_      ,      verbose=lowerCAmelCase_      ,      return_tensors=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )
				if images is not None:
							UpperCAmelCase_	:					Optional[Any]     	=   self.image_processor(
							    lowerCAmelCase_      ,      return_image_mask=lowerCAmelCase_      ,      return_codebook_pixels=lowerCAmelCase_      ,      return_tensors=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )

				if text is not None and images is not None:
							encoding.update(lowerCAmelCase_		)
							return encoding
				elif text is not None:
							return encoding
				else:
							return BatchEncoding(data=dict(**lowerCAmelCase_		)      ,      tensor_type=lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      *lowerCAmelCase_   :					int      ,      **lowerCAmelCase_   :					List[str]		)		->     Any:
				return self.tokenizer.batch_decode(*lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      *lowerCAmelCase_   :					List[str]      ,      **lowerCAmelCase_   :					Optional[int]		)		->     List[Any]:
				return self.tokenizer.decode(*lowerCAmelCase_      ,      **lowerCAmelCase_		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     int:
				UpperCAmelCase_	:					List[Any]     	=   self.tokenizer.model_input_names
				UpperCAmelCase_	:					Any     	=   self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Optional[Any]:
				warnings.warn(
				    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,      lowerCAmelCase_      ,      )
				return self.image_processor_class





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     int:
				warnings.warn(
				    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,      lowerCAmelCase_      ,      )
				return self.image_processor






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_							:					Optional[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 							__SCREAMING_SNAKE_CASE     (   a__    :   str=True       )					->				List[Any]:
	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=_lowercase	)	)
class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Optional[int]     					=				None
	_lowercase		:   str     					=				None




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     Optional[int]					,      __A						:     Optional[Any]							):
		with TemporaryDirectory() as tmp_dir:
			__A :			List[Any]		    =			dataset_module_factory(__A					,      cache_dir=__A							)

			__A :			Tuple		    =			import_main_class(dataset_module.module_path					,      dataset=__A							)

			__A :			DatasetBuilder		    =			builder_cls(
			    cache_dir=__A					,      config_name=__A					,      hash=dataset_module.hash					,      )

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






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







@pytest.mark.integration
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]       )					->				List[str]:
	__A :			Tuple		    =			dataset_module_factory("""wikipedia"""			,cache_dir=a__       )
	__A :			str		    =			import_main_class(dataset_module.module_path			,dataset=a__       )
	__A :			DatasetBuilder		    =			builder_cls(
	    cache_dir=a__			,config_name="""20220301.frr"""			,hash=dataset_module.hash			,)
	__A :			Optional[int]		    =			builder_instance.as_streaming_dataset()
	assert ds
	assert isinstance(a__			,a__       )
	assert "train" in ds
	assert isinstance(ds["""train"""]			,a__       )
	assert next(iter(ds["""train"""]       )       )





