




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()

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

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

            SCREAMING_SNAKE_CASE_					=    LoRAAttnProcessor(hidden_size=__UpperCAmelCase    ,    cross_attention_dim=__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_					=    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  lowerCamelCase_							(   a__ ,     a__ ,     unittest.TestCase      ):


 '''simple docstring'''







 lowercase_       					=					UNetaDConditionModel
 lowercase_       					=					"sample"



 @property
 def    lowerCAmelCase_						(							self						:	Union[str, Any]		):
       SCREAMING_SNAKE_CASE_					=    4
       SCREAMING_SNAKE_CASE_					=    4
       SCREAMING_SNAKE_CASE_					=    4
       SCREAMING_SNAKE_CASE_					=    (32, 32)

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

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



 @property
 def    lowerCAmelCase_						(							self						:	Optional[Any]		):
       return (4, 4, 32, 32)



 @property
 def    lowerCAmelCase_						(							self						:	Dict		):
       return (4, 4, 32, 32)



 def    lowerCAmelCase_						(							self						:	List[str]		):
       SCREAMING_SNAKE_CASE_					=    {
           "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,
       }
       SCREAMING_SNAKE_CASE_					=    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    lowerCAmelCase_						(							self						:	Any		):
       SCREAMING_SNAKE_CASE_					=    self.prepare_init_args_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_					=    self.model_class(**__A		)

       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    lowerCAmelCase_						(							self						:	Optional[int]		):
       SCREAMING_SNAKE_CASE_					=    self.prepare_init_args_and_inputs_for_common()

       SCREAMING_SNAKE_CASE_					=    32

       SCREAMING_SNAKE_CASE_					=    self.model_class(**__A		)
       model.to(__A		)
       model.eval()

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_					=    model(**__A		)

             if isinstance(__A       ,       __A		):
                   SCREAMING_SNAKE_CASE_					=    output.sample

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



 def    lowerCAmelCase_						(							self						:	List[Any]		):
       SCREAMING_SNAKE_CASE_					=    self.prepare_init_args_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_					=    self.model_class(**__A		)
       model.to(__A		)
       model.eval()

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

             SCREAMING_SNAKE_CASE_					=    model(**__A		)
             if isinstance(__A       ,       __A		):
                   SCREAMING_SNAKE_CASE_					=    first.sample

             SCREAMING_SNAKE_CASE_					=    model(**__A		)
             if isinstance(__A       ,       __A		):
                   SCREAMING_SNAKE_CASE_					=    second.sample

       SCREAMING_SNAKE_CASE_					=    first.cpu().numpy()
       SCREAMING_SNAKE_CASE_					=    second.cpu().numpy()
       SCREAMING_SNAKE_CASE_					=    out_a[~np.isnan(__A		)]
       SCREAMING_SNAKE_CASE_					=    out_a[~np.isnan(__A		)]
       SCREAMING_SNAKE_CASE_					=    np.amax(np.abs(out_a - out_a		)		)
       self.assertLessEqual(__A       ,       1E-5		)



 def    lowerCAmelCase_						(							self						:	List[Any]		):
       SCREAMING_SNAKE_CASE_					=    self.prepare_init_args_and_inputs_for_common()

       SCREAMING_SNAKE_CASE_					=    8

       SCREAMING_SNAKE_CASE_					=    self.model_class(**__A		)
       model.to(__A		)
       model.eval()

       model.set_attention_slice('auto'		)
       with torch.no_grad():
             SCREAMING_SNAKE_CASE_					=    model(**__A		)
       assert output is not None

       model.set_attention_slice('max'		)
       with torch.no_grad():
             SCREAMING_SNAKE_CASE_					=    model(**__A		)
       assert output is not None

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



 def    lowerCAmelCase_						(							self						:	Union[str, Any]		):
       SCREAMING_SNAKE_CASE_					=    self.prepare_init_args_and_inputs_for_common()

       SCREAMING_SNAKE_CASE_					=    8

       SCREAMING_SNAKE_CASE_					=    self.model_class(**__A		)
       model.to(__A		)

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_					=    model(**__A		).sample

       SCREAMING_SNAKE_CASE_					=    create_lora_layers(__A		)

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

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

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_					=    model(**__A       ,       cross_attention_kwargs={'scale': 0.0}		).sample
             SCREAMING_SNAKE_CASE_					=    model(**__A       ,       cross_attention_kwargs={'scale': 0.5}		).sample
             SCREAMING_SNAKE_CASE_					=    model(**__A       ,       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    lowerCAmelCase_						(							self						:	Optional[int]		):
       SCREAMING_SNAKE_CASE_					=    self.prepare_init_args_and_inputs_for_common()

       SCREAMING_SNAKE_CASE_					=    8

       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    self.model_class(**__A		)
       model.to(__A		)

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_					=    model(**__A		).sample

       SCREAMING_SNAKE_CASE_					=    create_lora_layers(__A		)
       model.set_attn_processor(__A		)

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_					=    model(**__A       ,       cross_attention_kwargs={'scale': 0.5}		).sample

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

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_					=    new_model(**__A       ,       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    lowerCAmelCase_						(							self						:	List[Any]		):
       SCREAMING_SNAKE_CASE_					=    self.prepare_init_args_and_inputs_for_common()

       SCREAMING_SNAKE_CASE_					=    8

       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    self.model_class(**__A		)
       model.to(__A		)

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_					=    model(**__A		).sample

       SCREAMING_SNAKE_CASE_					=    create_lora_layers(__A		)
       model.set_attn_processor(__A		)

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_					=    model(**__A       ,       cross_attention_kwargs={'scale': 0.5}		).sample

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

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_					=    new_model(**__A       ,       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    lowerCAmelCase_						(							self						:	List[Any]		):
       SCREAMING_SNAKE_CASE_					=    self.prepare_init_args_and_inputs_for_common()

       SCREAMING_SNAKE_CASE_					=    8

       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    self.model_class(**__A		)
       model.to(__A		)

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



 def    lowerCAmelCase_						(							self						:	List[Any]		):
       SCREAMING_SNAKE_CASE_					=    self.prepare_init_args_and_inputs_for_common()

       SCREAMING_SNAKE_CASE_					=    8

       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    self.model_class(**__A		)
       model.to(__A		)

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



 def    lowerCAmelCase_						(							self						:	List[str]		):
       SCREAMING_SNAKE_CASE_					=    self.prepare_init_args_and_inputs_for_common()

       SCREAMING_SNAKE_CASE_					=    8

       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    self.model_class(**__A		)
       model.to(__A		)

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_					=    model(**__A		).sample

       SCREAMING_SNAKE_CASE_					=    create_lora_layers(__A		)
       model.set_attn_processor(__A		)

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_					=    model(**__A       ,       cross_attention_kwargs={'scale': 0.0}		).sample

       model.set_attn_processor(AttnProcessor()		)

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_					=    model(**__A		).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    lowerCAmelCase_						(							self						:	List[Any]		):
       SCREAMING_SNAKE_CASE_					=    self.prepare_init_args_and_inputs_for_common()

       SCREAMING_SNAKE_CASE_					=    4

       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    self.model_class(**__A		)
       model.to(__A		)
       SCREAMING_SNAKE_CASE_					=    create_lora_layers(__A		)
       model.set_attn_processor(__A		)

       # default
       with torch.no_grad():
             SCREAMING_SNAKE_CASE_					=    model(**__A		).sample

             model.enable_xformers_memory_efficient_attention()
             SCREAMING_SNAKE_CASE_					=    model(**__A		).sample

             model.disable_xformers_memory_efficient_attention()
             SCREAMING_SNAKE_CASE_					=    model(**__A		).sample

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




 def    lowerCAmelCase_						(							self						:	Optional[Any]		):
       SCREAMING_SNAKE_CASE_					=    self.prepare_init_args_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_					=    32

       SCREAMING_SNAKE_CASE_					=    self.model_class(**__A		)
       model.to(__A		)
       model.eval()

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_					=    model(**__A		)[0]

       model.enable_forward_chunking()
       with torch.no_grad():
             SCREAMING_SNAKE_CASE_					=    model(**__A		)[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.





from __future__ import annotations

def       UpperCAmelCase_							(   __UpperCAmelCase		:			list[int]							)      ->				list[int]:
      SCREAMING_SNAKE_CASE_					=    [0] * len(__UpperCAmelCase							)
      for i in range(1    ,    len(__UpperCAmelCase							)							):
            SCREAMING_SNAKE_CASE_					=    duration_times[i - 1] + waiting_times[i - 1]
      return waiting_times

def       UpperCAmelCase_							(   __UpperCAmelCase		:			list[int]    ,    __UpperCAmelCase		:			list[int]							)      ->				list[int]:
      return [
          duration_time + waiting_times[i]
          for i, duration_time in enumerate(__UpperCAmelCase							)
      ]

def       UpperCAmelCase_							(   __UpperCAmelCase		:			list[int]							)      ->				float:
      return sum(__UpperCAmelCase							) / len(__UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			list[int]							)      ->				float:
      return sum(__UpperCAmelCase							) / len(__UpperCAmelCase							)


if __name__ == "__main__":
   # process id's
   lowerCamelCase__				:     Union[str, Any]			       =							[1, 2, 3]

   # ensure that we actually have processes
   if len(processes) == 0:
      print('Zero amount of processes')
      raise SystemExit(0)

   # duration time of all processes
   lowerCamelCase__				:     Optional[Any]			       =							[19, 8, 9]

   # ensure we can match each id to a duration time
   if len(duration_times) != len(processes):
      print('Unable to match all id\'s with their duration time')
      raise SystemExit(0)

   # get the waiting times and the turnaround times
   lowerCamelCase__				:     Dict			       =							calculate_waiting_times(duration_times)
   lowerCamelCase__				:     int			       =							calculate_turnaround_times(duration_times, waiting_times)

   # get the average times
   lowerCamelCase__				:     Any			       =							calculate_average_waiting_time(waiting_times)
   lowerCamelCase__				:     Tuple			       =							calculate_average_turnaround_time(turnaround_times)

   # print all the results
   print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
   for i, process in enumerate(processes):
      print(
          f'''{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t'''
          f'''{turnaround_times[i]}'''
      )
   print(f'''Average waiting time = {average_waiting_time}''')
   print(f'''Average turn around time = {average_turnaround_time}''')