import warnings

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


class       lowerCamelCase_	(  snake_case_					):






    '''simple docstring'''






    a__      :		Any         = ["""image_processor""", """tokenizer"""]
    a__      :		Optional[int]         = """ChineseCLIPImageProcessor"""
    a__      :		int         = ("""BertTokenizer""", """BertTokenizerFast""")







    def __init__(      self					,					__lowercase=None					,					__lowercase=None					,					**__lowercase)			->      int:
        __UpperCamelCase	:List[str]         =						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	:Optional[Any]         =						kwargs.pop('''feature_extractor''')

        __UpperCamelCase	:Dict         =						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	:List[Any]         =						self.image_processor







    def __call__(      self					,					__lowercase=None					,					__lowercase=None					,					__lowercase=None					,					**__lowercase)			->      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	:Optional[int]         =						self.tokenizer(A_					,					return_tensors=A_					,					**A_)

        if images is not None:
            __UpperCamelCase	:Union[str, Any]         =						self.image_processor(A_					,					return_tensors=A_					,					**A_)

        if text is not None and images is not None:
            __UpperCamelCase	:Optional[Any]         =						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					,					*__lowercase					,					**__lowercase)			->      Optional[Any]:
        return self.tokenizer.batch_decode(*A_					,					**A_)







    def      UpperCamelCase__  (      self					,					*__lowercase					,					**__lowercase)			->      Tuple:
        return self.tokenizer.decode(*A_					,					**A_)







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







    @property
    def      UpperCamelCase__  (      self)			->      List[str]:
        warnings.warn(
            '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''					,					A_					,					)
        return self.image_processor_class







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase					:    Union[str, Any]           = {
    'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
    'processing_git': ['GitProcessor'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    List[Any]           = [
						    'GIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'GitForCausalLM',
						    'GitModel',
						    'GitPreTrainedModel',
						    'GitVisionModel',
						]

if TYPE_CHECKING:
						from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
						from .processing_git import GitProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_git import (
												    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GitForCausalLM,
												    GitModel,
												    GitPreTrainedModel,
												    GitVisionModel,
												)

else:
						import sys

						lowerCAmelCase					:    Optional[Any]           = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




