




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     SCREAMING_SNAKE_CASE__		:
     @property
     def        A__      (						self	:		Optional[int]						):



       """simple docstring"""
       return self.get_dummy_input()
     @property
     def        A__      (						self	:		Union[str, Any]						):



       """simple docstring"""
       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        A__      (						self	:		Any       ,						__lowerCamelCase	:		Optional[int]=True       ,						__lowerCamelCase	:		int=False       ,						__lowerCamelCase	:		str=False       ,						__lowerCamelCase	:		List[str]=False       ,						):



       """simple docstring"""
       lowerCAmelCase__										=      4
       lowerCAmelCase__										=      32
       lowerCAmelCase__										=      (32, 32)

       lowerCAmelCase__										=      torch.manual_seed(0						)
       lowerCAmelCase__										=      torch.device(__lowerCamelCase						)
       lowerCAmelCase__										=      (batch_size, num_channels) + sizes
       lowerCAmelCase__										=      randn_tensor(__lowerCamelCase       ,						generator=__lowerCamelCase       ,						device=__lowerCamelCase						)
       lowerCAmelCase__										=      {'''hidden_states''': hidden_states}

       if include_temb:
         lowerCAmelCase__										=      1_28
         lowerCAmelCase__										=      randn_tensor((batch_size, temb_channels)       ,						generator=__lowerCamelCase       ,						device=__lowerCamelCase						)

       if include_res_hidden_states_tuple:
         lowerCAmelCase__										=      torch.manual_seed(1						)
         lowerCAmelCase__										=      (randn_tensor(__lowerCamelCase       ,						generator=__lowerCamelCase       ,						device=__lowerCamelCase						),)

       if include_encoder_hidden_states:
         lowerCAmelCase__										=      floats_tensor((batch_size, 32, 32)						).to(__lowerCamelCase						)

       if include_skip_sample:
         lowerCAmelCase__										=      randn_tensor(((batch_size, 3) + sizes)       ,						generator=__lowerCamelCase       ,						device=__lowerCamelCase						)

       return dummy_input
     def        A__      (						self	:		List[str]						):



       """simple docstring"""
       lowerCAmelCase__										=      {
           '''in_channels''': 32,
           '''out_channels''': 32,
           '''temb_channels''': 1_28,
       }
       if self.block_type == "up":
         lowerCAmelCase__										=      32

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

       lowerCAmelCase__										=      self.dummy_input
       return init_dict, inputs_dict
     def        A__      (						self	:		Dict       ,						__lowerCamelCase	:		Dict						):



       """simple docstring"""
       lowerCAmelCase__				,      lowerCAmelCase__										=      self.prepare_init_args_and_inputs_for_common()
       lowerCAmelCase__										=      self.block_class(**__lowerCamelCase						)
       unet_block.to(__lowerCamelCase						)
       unet_block.eval()

       with torch.no_grad():
         lowerCAmelCase__										=      unet_block(**__lowerCamelCase						)

       if isinstance(__lowerCamelCase       ,						__lowerCamelCase						):
         lowerCAmelCase__										=      output[0]

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

       lowerCAmelCase__										=      output[0, -1, -3:, -3:]
       lowerCAmelCase__										=      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        A__      (						self	:		int						):



       """simple docstring"""
       lowerCAmelCase__				,      lowerCAmelCase__										=      self.prepare_init_args_and_inputs_for_common()
       lowerCAmelCase__										=      self.block_class(**__lowerCamelCase						)
       model.to(__lowerCamelCase						)
       model.train()
       lowerCAmelCase__										=      model(**__lowerCamelCase						)

       if isinstance(__lowerCamelCase       ,						__lowerCamelCase						):
         lowerCAmelCase__										=      output[0]

       lowerCAmelCase__										=      torch.device(__lowerCamelCase						)
       lowerCAmelCase__										=      randn_tensor(output.shape       ,						device=__lowerCamelCase						)
       lowerCAmelCase__										=      torch.nn.functional.mse_loss(__lowerCamelCase       ,						__lowerCamelCase						)
       loss.backward()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__magic_name__  :  Optional[int]					=       {
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  int					=       [
          """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BioGptForCausalLM""",
          """BioGptForTokenClassification""",
          """BioGptForSequenceClassification""",
          """BioGptModel""",
          """BioGptPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
      from .tokenization_biogpt import BioGptTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_biogpt import (
                BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BioGptForCausalLM,
                BioGptForSequenceClassification,
                BioGptForTokenClassification,
                BioGptModel,
                BioGptPreTrainedModel,
            )


else:
      import sys

      __magic_name__  :  int					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

