



"""simple docstring"""




import warnings

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





class  snake_case     (							__SCREAMING_SNAKE_CASE   ):
							UpperCAmelCase__						     =      ['''image_processor''', '''tokenizer''']
							UpperCAmelCase__						     =      '''CLIPImageProcessor'''
							UpperCAmelCase__						     =      ('''CLIPTokenizer''', '''CLIPTokenizerFast''')

							def __init__(self       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=None       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.'''       ,       __snake_case       ,       )
											SCREAMING_SNAKE_CASE_						 = kwargs.pop('''feature_extractor'''    )

									SCREAMING_SNAKE_CASE_						 = 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__(__snake_case       ,       __snake_case    )

							def __call__(self       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=None       ,       **SCREAMING_SNAKE_CASE_    ):

									"""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:
											SCREAMING_SNAKE_CASE_						 = self.tokenizer(__snake_case       ,       return_tensors=__snake_case       ,       **__snake_case    )

									if images is not None:
											SCREAMING_SNAKE_CASE_						 = self.image_processor(__snake_case       ,       return_tensors=__snake_case       ,       **__snake_case    )

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

							def _lowercase					(self       ,       *SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									return self.tokenizer.batch_decode(*__snake_case       ,       **__snake_case    )

							def _lowercase					(self       ,       *SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									return self.tokenizer.decode(*__snake_case       ,       **__snake_case    )

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.tokenizer.model_input_names
									SCREAMING_SNAKE_CASE_						 = self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									warnings.warn(
									    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,       __snake_case       ,       )
									return self.image_processor_class







							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									warnings.warn(
									    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''       ,       __snake_case       ,       )
									return self.image_processor

"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__							=				logging.get_logger(__name__)

lowerCAmelCase__							=				{
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      '''glpn'''

							def __init__(self       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=4       ,       SCREAMING_SNAKE_CASE_=[2, 2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[8, 4, 2, 1]       ,       SCREAMING_SNAKE_CASE_=[32, 64, 1_60, 2_56]       ,       SCREAMING_SNAKE_CASE_=[7, 3, 3, 3]       ,       SCREAMING_SNAKE_CASE_=[4, 2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[1, 2, 5, 8]       ,       SCREAMING_SNAKE_CASE_=[4, 4, 4, 4]       ,       SCREAMING_SNAKE_CASE_="gelu"       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.02       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=1e-6       ,       SCREAMING_SNAKE_CASE_=64       ,       SCREAMING_SNAKE_CASE_=10       ,       SCREAMING_SNAKE_CASE_=-1       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									super().__init__(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = num_channels
									SCREAMING_SNAKE_CASE_						 = num_encoder_blocks
									SCREAMING_SNAKE_CASE_						 = depths
									SCREAMING_SNAKE_CASE_						 = sr_ratios
									SCREAMING_SNAKE_CASE_						 = hidden_sizes
									SCREAMING_SNAKE_CASE_						 = patch_sizes
									SCREAMING_SNAKE_CASE_						 = strides
									SCREAMING_SNAKE_CASE_						 = mlp_ratios
									SCREAMING_SNAKE_CASE_						 = num_attention_heads
									SCREAMING_SNAKE_CASE_						 = hidden_act
									SCREAMING_SNAKE_CASE_						 = hidden_dropout_prob
									SCREAMING_SNAKE_CASE_						 = attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_						 = initializer_range
									SCREAMING_SNAKE_CASE_						 = drop_path_rate
									SCREAMING_SNAKE_CASE_						 = layer_norm_eps
									SCREAMING_SNAKE_CASE_						 = decoder_hidden_size
									SCREAMING_SNAKE_CASE_						 = max_depth
									SCREAMING_SNAKE_CASE_						 = head_in_index