



'''simple docstring'''


from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     return {key.lstrip("-"       ): value for key, value in zip(unknown_args[::2]       ,	unknown_args[1::2]       )}






def  UpperCAmelCase_       (		):


     '''simple docstring'''







     a_       		=ArgumentParser(
         "HuggingFace Datasets CLI tool"       ,	usage="datasets-cli <command> [<args>]"       ,	allow_abbrev=lowercase__       )
     a_       		=parser.add_subparsers(help="datasets-cli command helpers"       )
     set_verbosity_info()

     # Register commands
     ConvertCommand.register_subcommand(lowercase__       )
     EnvironmentCommand.register_subcommand(lowercase__       )
     TestCommand.register_subcommand(lowercase__       )
     RunBeamCommand.register_subcommand(lowercase__       )
     DummyDataCommand.register_subcommand(lowercase__       )

     # Parse args
     a_		,						a_       		=parser.parse_known_args()
     if not hasattr(lowercase__       ,	"func"       ):
          parser.print_help()
          exit(1       )
     a_       		=parse_unknown_args(lowercase__       )

     # Run
     a_       		=args.func(lowercase__       ,	**lowercase__       )
     service.run()


if __name__ == "__main__":
  main()







'''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 	UpperCAmelCase	:


 '''simple docstring'''




 @property
 def       lowercase_  (   self) ->      Any:



      """simple docstring"""







      return self.get_dummy_input()




 @property
 def       lowercase_  (   self) ->      List[str]:



      """simple docstring"""







      if self.block_type == "down":
           return (4, 3_2, 1_6, 1_6)
      elif self.block_type == "mid":
           return (4, 3_2, 3_2, 3_2)
      elif self.block_type == "up":
           return (4, 3_2, 6_4, 6_4)

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




 def       lowercase_  (   self							,  lowerCAmelCase_=True							,  lowerCAmelCase_=False							,  lowerCAmelCase_=False							,  lowerCAmelCase_=False							,  ) ->      Dict:



      """simple docstring"""







      a_       		=4
      a_       		=3_2
      a_       		=(3_2, 3_2)

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

      if include_temb:
           a_       		=1_2_8
           a_       		=randn_tensor((batch_size, temb_channels)							,  generator=lowerCAmelCase_							,  device=lowerCAmelCase_)

      if include_res_hidden_states_tuple:
           a_       		=torch.manual_seed(1)
           a_       		=(randn_tensor(lowerCAmelCase_							,  generator=lowerCAmelCase_							,  device=lowerCAmelCase_),)

      if include_encoder_hidden_states:
           a_       		=floats_tensor((batch_size, 3_2, 3_2)).to(lowerCAmelCase_)

      if include_skip_sample:
           a_       		=randn_tensor(((batch_size, 3) + sizes)							,  generator=lowerCAmelCase_							,  device=lowerCAmelCase_)

      return dummy_input




 def       lowercase_  (   self) ->      Optional[int]:



      """simple docstring"""







      a_       		={
          "in_channels": 3_2,
          "out_channels": 3_2,
          "temb_channels": 1_2_8,
      }
      if self.block_type == "up":
           a_       		=3_2

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

      a_       		=self.dummy_input
      return init_dict, inputs_dict




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Dict:



      """simple docstring"""







      a_		,						a_       		=self.prepare_init_args_and_inputs_for_common()
      a_       		=self.block_class(**lowerCAmelCase_)
      unet_block.to(lowerCAmelCase_)
      unet_block.eval()

      with torch.no_grad():
           a_       		=unet_block(**lowerCAmelCase_)

      if isinstance(lowerCAmelCase_							,  lowerCAmelCase_):
           a_       		=output[0]

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

      a_       		=output[0, -1, -3:, -3:]
      a_       		=torch.tensor(lowerCAmelCase_).to(lowerCAmelCase_)
      assert torch_all_close(output_slice.flatten()							,  lowerCAmelCase_							,  atol=5e-3)




 @unittest.skipIf(torch_device == "mps"							,  "Training is not supported in mps")
 def       lowercase_  (   self) ->      Optional[Any]:



      """simple docstring"""







      a_		,						a_       		=self.prepare_init_args_and_inputs_for_common()
      a_       		=self.block_class(**lowerCAmelCase_)
      model.to(lowerCAmelCase_)
      model.train()
      a_       		=model(**lowerCAmelCase_)

      if isinstance(lowerCAmelCase_							,  lowerCAmelCase_):
           a_       		=output[0]

      a_       		=torch.device(lowerCAmelCase_)
      a_       		=randn_tensor(output.shape							,  device=lowerCAmelCase_)
      a_       		=torch.nn.functional.mse_loss(lowerCAmelCase_							,  lowerCAmelCase_)
      loss.backward()


