


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


_lowerCAmelCase:						Dict   				=       logging.get_logger(__name__)

_lowerCAmelCase:						Tuple   				=       {
    '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       lowercase_					(lowercase__   ):
				snake_case	    ='glpn'





				def __init__(				self       ,  lowercase_=3       ,  lowercase_=4       ,  lowercase_=[2, 2, 2, 2]       ,  lowercase_=[8, 4, 2, 1]       ,  lowercase_=[32, 64, 160, 256]       ,  lowercase_=[7, 3, 3, 3]       ,  lowercase_=[4, 2, 2, 2]       ,  lowercase_=[1, 2, 5, 8]       ,  lowercase_=[4, 4, 4, 4]       ,  lowercase_="gelu"       ,  lowercase_=0.0       ,  lowercase_=0.0       ,  lowercase_=0.02       ,  lowercase_=0.1       ,  lowercase_=1e-6       ,  lowercase_=64       ,  lowercase_=10       ,  lowercase_=-1       ,  **lowercase_       ,  )  -> int:
											super().__init__(**lowercase_)

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



'''simple docstring'''



from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import (
						    ImageTextPipelineOutput,
						    UniDiffuserPipeline,
						)
else:
						from .modeling_text_decoder import UniDiffuserTextDecoder
						from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
						from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
