'''simple docstring'''





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


SCREAMING_SNAKE_CASE_:					List[Any]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					Union[str, Any]  ={
    """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 	__A				(			lowerCamelCase__     ):
    a__	:						str	       =  'glpn'



    def __init__(self   :   Tuple       ,      __a   :   Tuple=3       ,      __a   :   List[Any]=4       ,      __a   :   Optional[int]=[2, 2, 2, 2]       ,      __a   :   str=[8, 4, 2, 1]       ,      __a   :   Union[str, Any]=[32, 64, 160, 256]       ,      __a   :   List[Any]=[7, 3, 3, 3]       ,      __a   :   str=[4, 2, 2, 2]       ,      __a   :   str=[1, 2, 5, 8]       ,      __a   :   Optional[Any]=[4, 4, 4, 4]       ,      __a   :   Any="gelu"       ,      __a   :   Dict=0.0       ,      __a   :   List[str]=0.0       ,      __a   :   str=0.02       ,      __a   :   int=0.1       ,      __a   :   List[Any]=1E-6       ,      __a   :   Optional[int]=64       ,      __a   :   Optional[int]=10       ,      __a   :   Tuple=-1       ,      **__a   :   List[str]       ,      ):
         super().__init__(**__a			)

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





'''simple docstring'''





import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch




@require_torch
class 	__A				:



    @property
    def  _lowercase						(self   :   int			):
         return self.get_dummy_input()



    @property
    def  _lowercase						(self   :   Any			):
         if self.block_type == "down":
              return (4, 32, 16, 16)
         elif self.block_type == "mid":
              return (4, 32, 32, 32)
         elif self.block_type == "up":
              return (4, 32, 64, 64)

         raise ValueError(f"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""			)



    def  _lowercase						(self   :   Tuple       ,      __a   :   List[str]=True       ,      __a   :   Any=False       ,      __a   :   List[Any]=False       ,      __a   :   Any=False       ,      ):
         UpperCAmelCase_	   =       4
         UpperCAmelCase_	   =       32
         UpperCAmelCase_	   =       (32, 32)

         UpperCAmelCase_	   =       torch.manual_seed(0			)
         UpperCAmelCase_	   =       torch.device(__a			)
         UpperCAmelCase_	   =       (batch_size, num_channels) + sizes
         UpperCAmelCase_	   =       randn_tensor(__a       ,      generator=__a       ,      device=__a			)
         UpperCAmelCase_	   =       {"hidden_states": hidden_states}

         if include_temb:
              UpperCAmelCase_	   =       128
              UpperCAmelCase_	   =       randn_tensor((batch_size, temb_channels)       ,      generator=__a       ,      device=__a			)

         if include_res_hidden_states_tuple:
              UpperCAmelCase_	   =       torch.manual_seed(1			)
              UpperCAmelCase_	   =       (randn_tensor(__a       ,      generator=__a       ,      device=__a			),)

         if include_encoder_hidden_states:
              UpperCAmelCase_	   =       floats_tensor((batch_size, 32, 32)			).to(__a			)

         if include_skip_sample:
              UpperCAmelCase_	   =       randn_tensor(((batch_size, 3) + sizes)       ,      generator=__a       ,      device=__a			)

         return dummy_input



    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_	   =       {
             "in_channels": 32,
             "out_channels": 32,
             "temb_channels": 128,
         }
         if self.block_type == "up":
              UpperCAmelCase_	   =       32

         if self.block_type == "mid":
              init_dict.pop("out_channels"			)

         UpperCAmelCase_	   =       self.dummy_input
         return init_dict, inputs_dict



    def  _lowercase						(self   :   Tuple       ,      __a   :   Any			):
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.prepare_init_args_and_inputs_for_common()
         UpperCAmelCase_	   =       self.block_class(**__a			)
         unet_block.to(__a			)
         unet_block.eval()

         with torch.no_grad():
              UpperCAmelCase_	   =       unet_block(**__a			)

         if isinstance(__a       ,      __a			):
              UpperCAmelCase_	   =       output[0]

         self.assertEqual(output.shape       ,      self.output_shape			)

         UpperCAmelCase_	   =       output[0, -1, -3:, -3:]
         UpperCAmelCase_	   =       torch.tensor(__a			).to(__a			)
         assert torch_all_close(output_slice.flatten()       ,      __a       ,      atol=5E-3			)






    @unittest.skipIf(torch_device == "mps"       ,      "Training is not supported in mps"			)
    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.prepare_init_args_and_inputs_for_common()
         UpperCAmelCase_	   =       self.block_class(**__a			)
         model.to(__a			)
         model.train()
         UpperCAmelCase_	   =       model(**__a			)

         if isinstance(__a       ,      __a			):
              UpperCAmelCase_	   =       output[0]

         UpperCAmelCase_	   =       torch.device(__a			)
         UpperCAmelCase_	   =       randn_tensor(output.shape       ,      device=__a			)
         UpperCAmelCase_	   =       torch.nn.functional.mse_loss(__a       ,      __a			)
         loss.backward()




