





"""simple docstring"""





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        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			["image_processor", "tokenizer"]
						UpperCAmelCase_ =			"BlipImageProcessor"
						UpperCAmelCase_ =			("BertTokenizer", "BertTokenizerFast")


						def __init__(self   ,							__a   ,							__a		) ->   int:
								UpperCamelCase   =	False
								super().__init__(__a   ,							__a		)
								UpperCamelCase   =	self.image_processor


						def __call__(self   ,							__a = None   ,							__a = None   ,							__a = True   ,							__a = False   ,							__a = None   ,							__a = None   ,							__a = 0   ,							__a = None   ,							__a = None   ,							__a = False   ,							__a = False   ,							__a = False   ,							__a = False   ,							__a = False   ,							__a = True   ,							__a = None   ,							**__a   ,							) ->   BatchEncoding:
								if images is None and text is None:
										raise ValueError("You have to specify either images or text."		)

								# Get only text
								if images is None:
										UpperCamelCase   =	self.tokenizer
										UpperCamelCase   =	self.tokenizer(
										    text=__a   ,							add_special_tokens=__a   ,							padding=__a   ,							truncation=__a   ,							max_length=__a   ,							stride=__a   ,							pad_to_multiple_of=__a   ,							return_attention_mask=__a   ,							return_overflowing_tokens=__a   ,							return_special_tokens_mask=__a   ,							return_offsets_mapping=__a   ,							return_token_type_ids=__a   ,							return_length=__a   ,							verbose=__a   ,							return_tensors=__a   ,							**__a   ,							)
										return text_encoding

								# add pixel_values
								UpperCamelCase   =	self.image_processor(__a   ,							return_tensors=__a		)

								if text is not None:
										UpperCamelCase   =	self.tokenizer(
										    text=__a   ,							add_special_tokens=__a   ,							padding=__a   ,							truncation=__a   ,							max_length=__a   ,							stride=__a   ,							pad_to_multiple_of=__a   ,							return_attention_mask=__a   ,							return_overflowing_tokens=__a   ,							return_special_tokens_mask=__a   ,							return_offsets_mapping=__a   ,							return_token_type_ids=__a   ,							return_length=__a   ,							verbose=__a   ,							return_tensors=__a   ,							**__a   ,							)
								else:
										UpperCamelCase   =	None

								if text_encoding is not None:
										encoding_image_processor.update(__a		)

								return encoding_image_processor


						def     snake_case_   (self   ,							*__a   ,							**__a		) ->   List[Any]:
								return self.tokenizer.batch_decode(*__a   ,							**__a		)


						def     snake_case_   (self   ,							*__a   ,							**__a		) ->   List[Any]:
								return self.tokenizer.decode(*__a   ,							**__a		)




						@property
						def     snake_case_   (self		) ->   Dict:
								UpperCamelCase   =	self.tokenizer.model_input_names
								UpperCamelCase   =	self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)













"""simple docstring"""





import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows


def       a__					(					_SCREAMING_SNAKE_CASE    ):  # picklable for multiprocessing




		"""simple docstring"""


		return i + 1


@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def       a__					(					):




		"""simple docstring"""


		with parallel_backend("spark"    ):
				assert ParallelBackendConfig.backend_name == "spark"

		UpperCamelCase   =	[1, 2, 3]
		with pytest.raises(_SCREAMING_SNAKE_CASE    ):
				with parallel_backend("unsupported backend"    ):
						map_nested(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	num_proc=2    )

		with pytest.raises(_SCREAMING_SNAKE_CASE    ):
				with parallel_backend("unsupported backend"    ):
						map_nested(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	num_proc=-1    )






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc"  ,	[2, -1]    )
def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[1, 2]
		UpperCamelCase   =	{"a": 1, "b": 2}
		UpperCamelCase   =	{"a": [1, 2], "b": [3, 4]}
		UpperCamelCase   =	{"a": {"1": 1}, "b": 2}
		UpperCamelCase   =	{"a": 1, "b": 2, "c": 3, "d": 4}
		UpperCamelCase   =	[2, 3]
		UpperCamelCase   =	{"a": 2, "b": 3}
		UpperCamelCase   =	{"a": [2, 3], "b": [4, 5]}
		UpperCamelCase   =	{"a": {"1": 2}, "b": 3}
		UpperCamelCase   =	{"a": 2, "b": 3, "c": 4, "d": 5}

		with parallel_backend("spark"    ):
				assert map_nested(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	num_proc=_SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
				assert map_nested(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	num_proc=_SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
				assert map_nested(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	num_proc=_SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
				assert map_nested(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	num_proc=_SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
				assert map_nested(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	num_proc=_SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa






