


'''simple docstring'''







lowerCAmelCase			:   Optional[int] 		=  {
    """+""": lambda x, y: x + y,
    """-""": lambda x, y: x - y,
    """*""": lambda x, y: x * y,
    """/""": lambda x, y: x / y,
}






def 	lowercase (_A    ):







       """simple docstring"""







       return c.isdigit()






def 	lowercase (_A    ):







       """simple docstring"""







       _lowerCAmelCase			:							Union[str, Any]      	=				[]

       # iterate over the string in reverse order
       for c in expression.split()[::-1]:
              # push operand to stack
              if is_operand(UpperCamelCase_    ):
                     stack.append(int(UpperCamelCase_    )    )

              else:
                     # pop values from stack can calculate the result
                     # push the result onto the stack again
                     _lowerCAmelCase			:							Optional[int]      	=				stack.pop()
                     _lowerCAmelCase			:							int      	=				stack.pop()
                     stack.append(calc[c](UpperCamelCase_      ,  UpperCamelCase_    )    )

       return stack.pop()


# Driver code
if __name__ == "__main__":
       lowerCAmelCase			:   str 		=  """+ 9 * 2 6"""
       print(evaluate(test_expression))

       lowerCAmelCase			:   int 		=  """/ * 10 2 + 4 1 """
       print(evaluate(test_expression))








import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

_UpperCAmelCase				       = logging.get_logger(__name__)




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_ = True     )   ->						Any:
       UpperCamelCase_      				=     {}
       for name in model.attn_processors.keys():
              UpperCamelCase_      				=     name.endswith("attn2.processor"     ) and not (
                  name.startswith("transformer_in"     ) or "temp_attentions" in name.split("."     )
              )
              UpperCamelCase_      				=     model.config.cross_attention_dim if has_cross_attention else None
              if name.startswith("mid_block"     ):
                     UpperCamelCase_      				=     model.config.block_out_channels[-1]
              elif name.startswith("up_blocks"     ):
                     UpperCamelCase_      				=     int(name[len("up_blocks."     )]     )
                     UpperCamelCase_      				=     list(reversed(model.config.block_out_channels     )     )[block_id]
              elif name.startswith("down_blocks"     ):
                     UpperCamelCase_      				=     int(name[len("down_blocks."     )]     )
                     UpperCamelCase_      				=     model.config.block_out_channels[block_id]
              elif name.startswith("transformer_in"     ):
                     # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
                     UpperCamelCase_      				=     8 * model.config.attention_head_dim

              UpperCamelCase_      				=     LoRAAttnProcessor(hidden_size=UpperCamelCase_    ,						cross_attention_dim=UpperCamelCase_     )
              UpperCamelCase_      				=     lora_attn_procs[name].to(model.device     )

              if mock_weights:
                     # add 1 to weights to mock trained weights
                     with torch.no_grad():
                            lora_attn_procs[name].to_q_lora.up.weight += 1
                            lora_attn_procs[name].to_k_lora.up.weight += 1
                            lora_attn_procs[name].to_v_lora.up.weight += 1
                            lora_attn_procs[name].to_out_lora.up.weight += 1

       return lora_attn_procs







@skip_mps
class 							_UpperCamelCase    (		lowerCAmelCase_     ,  lowerCAmelCase_     ,  unittest.TestCase   ):
       _UpperCamelCase :    Optional[Any]   			=      UNetaDConditionModel
       _UpperCamelCase :    List[Any]   			=      '''sample'''

       @property
       def lowercase       (						self:       Dict						)				->       Tuple:




              """simple docstring"""


              UpperCamelCase_      				=     4
              UpperCamelCase_      				=     4
              UpperCamelCase_      				=     4
              UpperCamelCase_      				=     (32, 32)

              UpperCamelCase_      				=     floats_tensor((batch_size, num_channels, num_frames) + sizes						).to(_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     torch.tensor([10]						).to(_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     floats_tensor((batch_size, 4, 32)						).to(_SCREAMING_SNAKE_CASE						)

              return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}

       @property
       def lowercase       (						self:       Tuple						)				->       str:




              """simple docstring"""


              return (4, 4, 32, 32)

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




              """simple docstring"""


              return (4, 4, 32, 32)

       def lowercase       (						self:       Optional[Any]						)				->       Optional[Any]:




              """simple docstring"""


              UpperCamelCase_      				=     {
                  "block_out_channels": (32, 64),
                  "down_block_types": (
                      "CrossAttnDownBlock3D",
                      "DownBlock3D",
                  ),
                  "up_block_types": ("UpBlock3D", "CrossAttnUpBlock3D"),
                  "cross_attention_dim": 32,
                  "attention_head_dim": 8,
                  "out_channels": 4,
                  "in_channels": 4,
                  "layers_per_block": 1,
                  "sample_size": 32,
              }
              UpperCamelCase_      				=     self.dummy_input
              return init_dict, inputs_dict

       @unittest.skipIf(
           torch_device != "cuda" or not is_xformers_available()    ,      reason="XFormers attention is only available with CUDA and `xformers` installed"    ,      )
       def lowercase       (						self:       str						)				->       Optional[Any]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)

              model.enable_xformers_memory_efficient_attention()

              assert (
                  model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
                  == "XFormersAttnProcessor"
              ), "xformers is not enabled"

       def lowercase       (						self:       List[str]						)				->       List[str]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     32

              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              model.eval()

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)

                     if isinstance(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						):
                            UpperCamelCase_      				=     output.sample

              self.assertIsNotNone(_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     inputs_dict["sample"].shape
              self.assertEqual(output.shape    ,      _SCREAMING_SNAKE_CASE    ,      "Input and output shapes do not match"						)

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




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              model.eval()

              with torch.no_grad():
                     # Warmup pass when using mps (see #372)
                     if torch_device == "mps" and isinstance(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						):
                            model(**self.dummy_input						)

                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)
                     if isinstance(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						):
                            UpperCamelCase_      				=     first.sample

                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)
                     if isinstance(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						):
                            UpperCamelCase_      				=     second.sample

              UpperCamelCase_      				=     first.cpu().numpy()
              UpperCamelCase_      				=     second.cpu().numpy()
              UpperCamelCase_      				=     out_a[~np.isnan(_SCREAMING_SNAKE_CASE						)]
              UpperCamelCase_      				=     out_a[~np.isnan(_SCREAMING_SNAKE_CASE						)]
              UpperCamelCase_      				=     np.amax(np.abs(out_a - out_a						)						)
              self.assertLessEqual(_SCREAMING_SNAKE_CASE    ,      1e-5						)

       def lowercase       (						self:       int						)				->       List[str]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              model.eval()

              model.set_attention_slice("auto"						)
              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)
              assert output is not None

              model.set_attention_slice("max"						)
              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)
              assert output is not None

              model.set_attention_slice(2						)
              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)
              assert output is not None

       def lowercase       (						self:       Optional[Any]						)				->       List[Any]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)

              # make sure we can set a list of attention processors
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              # test that attn processors can be set to itself
              model.set_attn_processor(model.attn_processors						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.0}						).sample
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.5}						).sample
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.5}						).sample

              assert (samplea - samplea).abs().max() < 3e-3
              assert (samplea - samplea).abs().max() < 3e-3

              # sample 2 and sample 3 should be different
              assert (samplea - samplea).abs().max() > 3e-3

       def lowercase       (						self:       Any						)				->       int:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.5}						).sample

              with tempfile.TemporaryDirectory() as tmpdirname:
                     model.save_attn_procs(_SCREAMING_SNAKE_CASE						)
                     self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE    ,      "pytorch_lora_weights.bin"						)						)						)
                     torch.manual_seed(0						)
                     UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
                     new_model.to(_SCREAMING_SNAKE_CASE						)
                     new_model.load_attn_procs(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     new_model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.5}						).sample

              assert (sample - new_sample).abs().max() < 1e-3

              # LoRA and no LoRA should NOT be the same
              assert (sample - old_sample).abs().max() > 1e-4

       def lowercase       (						self:       List[str]						)				->       Optional[int]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.5}						).sample

              with tempfile.TemporaryDirectory() as tmpdirname:
                     model.save_attn_procs(_SCREAMING_SNAKE_CASE    ,      safe_serialization=_SCREAMING_SNAKE_CASE						)
                     self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE    ,      "pytorch_lora_weights.safetensors"						)						)						)
                     torch.manual_seed(0						)
                     UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
                     new_model.to(_SCREAMING_SNAKE_CASE						)
                     new_model.load_attn_procs(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     new_model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.5}						).sample

              assert (sample - new_sample).abs().max() < 3e-3

              # LoRA and no LoRA should NOT be the same
              assert (sample - old_sample).abs().max() > 1e-4

       def lowercase       (						self:       int						)				->       Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE    ,      mock_weights=_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)
              # Saving as torch, properly reloads with directly filename
              with tempfile.TemporaryDirectory() as tmpdirname:
                     model.save_attn_procs(_SCREAMING_SNAKE_CASE						)
                     self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE    ,      "pytorch_lora_weights.bin"						)						)						)
                     torch.manual_seed(0						)
                     UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
                     new_model.to(_SCREAMING_SNAKE_CASE						)
                     new_model.load_attn_procs(_SCREAMING_SNAKE_CASE    ,      weight_name="pytorch_lora_weights.bin"						)

       def lowercase       (						self:       Any						)				->       List[Any]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE    ,      mock_weights=_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)
              # Saving as torch, properly reloads with directly filename
              with tempfile.TemporaryDirectory() as tmpdirname:
                     model.save_attn_procs(_SCREAMING_SNAKE_CASE						)
                     self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE    ,      "pytorch_lora_weights.bin"						)						)						)
                     torch.manual_seed(0						)
                     UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
                     new_model.to(_SCREAMING_SNAKE_CASE						)
                     with self.assertRaises(_SCREAMING_SNAKE_CASE						) as e:
                            new_model.load_attn_procs(_SCREAMING_SNAKE_CASE    ,      use_safetensors=_SCREAMING_SNAKE_CASE						)
                     self.assertIn("Error no file named pytorch_lora_weights.safetensors"    ,      str(e.exception						)						)

       def lowercase       (						self:       Optional[Any]						)				->       Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.0}						).sample

              model.set_attn_processor(AttnProcessor()						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              assert (sample - new_sample).abs().max() < 1e-4
              assert (sample - old_sample).abs().max() < 3e-3

       @unittest.skipIf(
           torch_device != "cuda" or not is_xformers_available()    ,      reason="XFormers attention is only available with CUDA and `xformers` installed"    ,      )
       def lowercase       (						self:       Union[str, Any]						)				->       Dict:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     4

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)

              # default
              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

                     model.enable_xformers_memory_efficient_attention()
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

                     model.disable_xformers_memory_efficient_attention()
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              assert (sample - on_sample).abs().max() < 1e-4
              assert (sample - off_sample).abs().max() < 1e-4



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




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()
              UpperCamelCase_      				=     32

              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              model.eval()

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)[0]

              model.enable_forward_chunking()
              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)[0]

              self.assertEqual(output.shape    ,      output_a.shape    ,      "Shape doesn't match"						)
              assert np.abs(output.cpu() - output_a.cpu()						).max() < 1e-2


# (todo: sayakpaul) implement SLOW tests.

