
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Optional[int]			  =       ["image_processor", "tokenizer"]
		__lowercase : List[Any]			  =       "ChineseCLIPImageProcessor"
		__lowercase : int			  =       ("BertTokenizer", "BertTokenizerFast")



		def __init__(				self       ,   A_=None       ,   A_=None       ,   **A_ )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  None
				if "feature_extractor" in kwargs:
						warnings.warn(
						    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
						    ' instead.'       ,   A_       ,   )
						UpperCamelCase						=  kwargs.pop('feature_extractor' )

				UpperCamelCase						=  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__(A_       ,   A_ )
				UpperCamelCase						=  self.image_processor



		def __call__(				self       ,   A_=None       ,   A_=None       ,   A_=None       ,   **A_ )						->      Dict:
				"""simple docstring"""

				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						=  self.tokenizer(A_       ,   return_tensors=A_       ,   **A_ )

				if images is not None:
						UpperCamelCase						=  self.image_processor(A_       ,   return_tensors=A_       ,   **A_ )

				if text is not None and images is not None:
						UpperCamelCase						=  image_features.pixel_values
						return encoding
				elif text is not None:
						return encoding
				else:
						return BatchEncoding(data=dict(**A_ )       ,   tensor_type=A_ )



		def __UpperCamelCase				(				self       ,   *A_       ,   **A_ )						->      str:
				"""simple docstring"""
				return self.tokenizer.batch_decode(*A_       ,   **A_ )



		def __UpperCamelCase				(				self       ,   *A_       ,   **A_ )						->      str:
				"""simple docstring"""
				return self.tokenizer.decode(*A_       ,   **A_ )



		@property
		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  self.tokenizer.model_input_names
				UpperCamelCase						=  self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )



		@property
		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				warnings.warn(
				    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       ,   A_       ,   )
				return self.image_processor_class


import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset


@pytest.mark.parametrize('dataset_size'					, [None, 400 * 2**20, 600 * 2**20]				)
@pytest.mark.parametrize('input_in_memory_max_size'					, ['default', 0, 100 * 2**20, 900 * 2**20]				)
def    A (      lowercase					, lowercase					, lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

		if input_in_memory_max_size != "default":
				monkeypatch.setattr(datasets.config					, 'IN_MEMORY_MAX_SIZE'					, lowercase				)
		UpperCamelCase						=  datasets.config.IN_MEMORY_MAX_SIZE
		if input_in_memory_max_size == "default":
				assert in_memory_max_size == 0
		else:
				assert in_memory_max_size == input_in_memory_max_size
		if dataset_size and in_memory_max_size:
				UpperCamelCase						=  dataset_size < in_memory_max_size
		else:
				UpperCamelCase						=  False
		UpperCamelCase						=  is_small_dataset(lowercase				)
		assert result == expected
