
"""simple docstring"""





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()

_a									=				logging.get_logger(__name__)







def       __a	(						__lowerCamelCase,						__lowerCamelCase = True    ):
   UpperCAmelCase_   :   Tuple     	=       {}
   for name in model.attn_processors.keys():
      UpperCAmelCase_   :   Optional[Any]     	=       name.endswith("attn2.processor"    ) and not (
          name.startswith("transformer_in"    ) or "temp_attentions" in name.split("."    )
      )
      UpperCAmelCase_   :   List[Any]     	=       model.config.cross_attention_dim if has_cross_attention else None
      if name.startswith("mid_block"    ):
         UpperCAmelCase_   :   int     	=       model.config.block_out_channels[-1]
      elif name.startswith("up_blocks"    ):
         UpperCAmelCase_   :   int     	=       int(name[len("up_blocks."    )]    )
         UpperCAmelCase_   :   Optional[int]     	=       list(reversed(model.config.block_out_channels    )    )[block_id]
      elif name.startswith("down_blocks"    ):
         UpperCAmelCase_   :   str     	=       int(name[len("down_blocks."    )]    )
         UpperCAmelCase_   :   Tuple     	=       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_   :   Optional[Any]     	=       8 * model.config.attention_head_dim

      UpperCAmelCase_   :   Dict     	=       LoRAAttnProcessor(hidden_size=__lowerCamelCase,						cross_attention_dim=__lowerCamelCase    )
      UpperCAmelCase_   :   Tuple     	=       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 A_      (lowercase__							,lowercase__							,unittest.TestCase ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Tuple    	=						UNetaDConditionModel
  SCREAMING_SNAKE_CASE__     :		Optional[int]    	=						"""sample"""







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       4
     UpperCAmelCase_   :   Dict     	=       4
     UpperCAmelCase_   :   int     	=       4
     UpperCAmelCase_   :   Union[str, Any]     	=       (32, 32)

     UpperCAmelCase_   :   Any     	=       floats_tensor((batch_size, num_channels, num_frames) + sizes    ).to(lowercase_    )
     UpperCAmelCase_   :   int     	=       torch.tensor([10]    ).to(lowercase_    )
     UpperCAmelCase_   :   List[str]     	=       floats_tensor((batch_size, 4, 32)    ).to(lowercase_    )

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







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return (4, 4, 32, 32)







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return (4, 4, 32, 32)







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       {
         "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_   :   int     	=       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 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[int]     	=       self.prepare_init_args_and_inputs_for_common()
     UpperCAmelCase_   :   List[str]     	=       self.model_class(**lowercase_    )

     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 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   List[Any]     	=       self.prepare_init_args_and_inputs_for_common()

     UpperCAmelCase_   :   Tuple     	=       32

     UpperCAmelCase_   :   List[str]     	=       self.model_class(**lowercase_    )
     model.to(lowercase_    )
     model.eval()

     with torch.no_grad():
        UpperCAmelCase_   :   List[Any]     	=       model(**lowercase_    )

        if isinstance(lowercase_							,  lowercase_    ):
           UpperCAmelCase_   :   List[str]     	=       output.sample

     self.assertIsNotNone(lowercase_    )
     UpperCAmelCase_   :   Tuple     	=       inputs_dict["sample"].shape
     self.assertEqual(output.shape							,  lowercase_							,  "Input and output shapes do not match"    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   List[Any]     	=       self.prepare_init_args_and_inputs_for_common()
     UpperCAmelCase_   :   List[Any]     	=       self.model_class(**lowercase_    )
     model.to(lowercase_    )
     model.eval()

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

        UpperCAmelCase_   :   Union[str, Any]     	=       model(**lowercase_    )
        if isinstance(lowercase_							,  lowercase_    ):
           UpperCAmelCase_   :   Tuple     	=       first.sample

        UpperCAmelCase_   :   List[str]     	=       model(**lowercase_    )
        if isinstance(lowercase_							,  lowercase_    ):
           UpperCAmelCase_   :   Dict     	=       second.sample

     UpperCAmelCase_   :   List[str]     	=       first.cpu().numpy()
     UpperCAmelCase_   :   Optional[int]     	=       second.cpu().numpy()
     UpperCAmelCase_   :   int     	=       out_a[~np.isnan(lowercase_    )]
     UpperCAmelCase_   :   List[str]     	=       out_a[~np.isnan(lowercase_    )]
     UpperCAmelCase_   :   Optional[int]     	=       np.amax(np.abs(out_a - out_a    )    )
     self.assertLessEqual(lowercase_							,  1E-5    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   Tuple     	=       self.prepare_init_args_and_inputs_for_common()

     UpperCAmelCase_   :   Tuple     	=       8

     UpperCAmelCase_   :   Optional[Any]     	=       self.model_class(**lowercase_    )
     model.to(lowercase_    )
     model.eval()

     model.set_attention_slice("auto"    )
     with torch.no_grad():
        UpperCAmelCase_   :   List[Any]     	=       model(**lowercase_    )
     assert output is not None

     model.set_attention_slice("max"    )
     with torch.no_grad():
        UpperCAmelCase_   :   List[Any]     	=       model(**lowercase_    )
     assert output is not None

     model.set_attention_slice(2    )
     with torch.no_grad():
        UpperCAmelCase_   :   Union[str, Any]     	=       model(**lowercase_    )
     assert output is not None







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[int]     	=       self.prepare_init_args_and_inputs_for_common()

     UpperCAmelCase_   :   Optional[int]     	=       8

     UpperCAmelCase_   :   str     	=       self.model_class(**lowercase_    )
     model.to(lowercase_    )

     with torch.no_grad():
        UpperCAmelCase_   :   Tuple     	=       model(**lowercase_    ).sample

     UpperCAmelCase_   :   int     	=       create_lora_layers(lowercase_    )

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

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

     with torch.no_grad():
        UpperCAmelCase_   :   List[Any]     	=       model(**lowercase_							,  cross_attention_kwargs={"scale": 0.0}    ).sample
        UpperCAmelCase_   :   str     	=       model(**lowercase_							,  cross_attention_kwargs={"scale": 0.5}    ).sample
        UpperCAmelCase_   :   List[Any]     	=       model(**lowercase_							,  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 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[Any]     	=       self.prepare_init_args_and_inputs_for_common()

     UpperCAmelCase_   :   str     	=       8

     torch.manual_seed(0    )
     UpperCAmelCase_   :   int     	=       self.model_class(**lowercase_    )
     model.to(lowercase_    )

     with torch.no_grad():
        UpperCAmelCase_   :   Any     	=       model(**lowercase_    ).sample

     UpperCAmelCase_   :   Optional[int]     	=       create_lora_layers(lowercase_    )
     model.set_attn_processor(lowercase_    )

     with torch.no_grad():
        UpperCAmelCase_   :   Tuple     	=       model(**lowercase_							,  cross_attention_kwargs={"scale": 0.5}    ).sample

     with tempfile.TemporaryDirectory() as tmpdirname:
        model.save_attn_procs(lowercase_    )
        self.assertTrue(os.path.isfile(os.path.join(lowercase_							,  "pytorch_lora_weights.bin"    )    )    )
        torch.manual_seed(0    )
        UpperCAmelCase_   :   List[Any]     	=       self.model_class(**lowercase_    )
        new_model.to(lowercase_    )
        new_model.load_attn_procs(lowercase_    )

     with torch.no_grad():
        UpperCAmelCase_   :   Union[str, Any]     	=       new_model(**lowercase_							,  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 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   List[Any]     	=       self.prepare_init_args_and_inputs_for_common()

     UpperCAmelCase_   :   List[str]     	=       8

     torch.manual_seed(0    )
     UpperCAmelCase_   :   int     	=       self.model_class(**lowercase_    )
     model.to(lowercase_    )

     with torch.no_grad():
        UpperCAmelCase_   :   Any     	=       model(**lowercase_    ).sample

     UpperCAmelCase_   :   Tuple     	=       create_lora_layers(lowercase_    )
     model.set_attn_processor(lowercase_    )

     with torch.no_grad():
        UpperCAmelCase_   :   Tuple     	=       model(**lowercase_							,  cross_attention_kwargs={"scale": 0.5}    ).sample

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

     with torch.no_grad():
        UpperCAmelCase_   :   Optional[Any]     	=       new_model(**lowercase_							,  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 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     # enable deterministic behavior for gradient checkpointing
     UpperCAmelCase_	,  UpperCAmelCase_   :   List[Any]     	=       self.prepare_init_args_and_inputs_for_common()

     UpperCAmelCase_   :   Optional[int]     	=       8

     torch.manual_seed(0    )
     UpperCAmelCase_   :   Dict     	=       self.model_class(**lowercase_    )
     model.to(lowercase_    )

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







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   List[Any]     	=       self.prepare_init_args_and_inputs_for_common()

     UpperCAmelCase_   :   str     	=       8

     torch.manual_seed(0    )
     UpperCAmelCase_   :   List[Any]     	=       self.model_class(**lowercase_    )
     model.to(lowercase_    )

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







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   List[Any]     	=       self.prepare_init_args_and_inputs_for_common()

     UpperCAmelCase_   :   List[Any]     	=       8

     torch.manual_seed(0    )
     UpperCAmelCase_   :   str     	=       self.model_class(**lowercase_    )
     model.to(lowercase_    )

     with torch.no_grad():
        UpperCAmelCase_   :   str     	=       model(**lowercase_    ).sample

     UpperCAmelCase_   :   Any     	=       create_lora_layers(lowercase_    )
     model.set_attn_processor(lowercase_    )

     with torch.no_grad():
        UpperCAmelCase_   :   str     	=       model(**lowercase_							,  cross_attention_kwargs={"scale": 0.0}    ).sample

     model.set_attn_processor(AttnProcessor()    )

     with torch.no_grad():
        UpperCAmelCase_   :   Optional[Any]     	=       model(**lowercase_    ).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 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     # enable deterministic behavior for gradient checkpointing
     UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[int]     	=       self.prepare_init_args_and_inputs_for_common()

     UpperCAmelCase_   :   List[str]     	=       4

     torch.manual_seed(0    )
     UpperCAmelCase_   :   Any     	=       self.model_class(**lowercase_    )
     model.to(lowercase_    )
     UpperCAmelCase_   :   int     	=       create_lora_layers(lowercase_    )
     model.set_attn_processor(lowercase_    )

     # default
     with torch.no_grad():
        UpperCAmelCase_   :   Dict     	=       model(**lowercase_    ).sample

        model.enable_xformers_memory_efficient_attention()
        UpperCAmelCase_   :   Tuple     	=       model(**lowercase_    ).sample

        model.disable_xformers_memory_efficient_attention()
        UpperCAmelCase_   :   Tuple     	=       model(**lowercase_    ).sample

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







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[Any]     	=       self.prepare_init_args_and_inputs_for_common()
     UpperCAmelCase_   :   Optional[int]     	=       32

     UpperCAmelCase_   :   str     	=       self.model_class(**lowercase_    )
     model.to(lowercase_    )
     model.eval()

     with torch.no_grad():
        UpperCAmelCase_   :   Optional[Any]     	=       model(**lowercase_    )[0]

     model.enable_forward_chunking()
     with torch.no_grad():
        UpperCAmelCase_   :   Optional[Any]     	=       model(**lowercase_    )[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.



"""simple docstring"""


def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				sorted(A     ,					key=lambda A   : value[2]     ,					reverse=A   )

     # Create a list of size equal to the maximum deadline
     # and initialize it with -1
     lowercase__								=				max(A     ,					key=lambda A   : value[1]   )[1]
     lowercase__								=				[-1] * max_deadline

     # Finding the maximum profit and the count of jobs
     lowercase__								=				0
     lowercase__								=				0
     for job in jobs:
          # Find a free time slot for this job
          # (Note that we start from the last possible slot)
          for i in range(job[1] - 1     ,					-1     ,					-1   ):
               if time_slots[i] == -1:
                    lowercase__								=				job[0]
                    count += 1
                    max_profit += job[2]
                    break
     return [count, max_profit]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




