


'''simple docstring'''







import sys






def       lowerCAmelCase__       (				lowerCamelCase     :			int  ,lowerCamelCase     :			list[int]   ):
       if not numbers:
              return 0
       if target == 0 and target in numbers:
              return 0
       if not isinstance(lowerCamelCase  ,(list, tuple)   ) or not all(
           isinstance(lowerCamelCase  ,lowerCamelCase   ) for number in numbers   ):
              raise ValueError('numbers must be an iterable of integers'   )

       _A     :	int      =					0
       _A     :	Optional[Any]      =					sys.maxsize

       while right < len(lowerCamelCase   ):
              curr_sum += numbers[right]
              while curr_sum >= target and left <= right:
                     _A     :	Dict      =					min(lowerCamelCase  ,right - left + 1   )
                     curr_sum -= numbers[left]
                     left += 1
              right += 1

       return 0 if min_len == sys.maxsize else min_len










'''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      :     Optional[int]										= logging.get_logger(__name__)






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

              _A     :	int      =					LoRAAttnProcessor(hidden_size=lowerCamelCase  ,cross_attention_dim=lowerCamelCase   )
              _A     :	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  __lowerCamelCase (       a_				,					a_				,					unittest.TestCase       ):
    """simple docstring"""
    a													=    UNetaDConditionModel
    a													=    "sample"


    @property
    def    A    (  self			:							Tuple):
           _A     :	List[Any]      =					4
           _A     :	Tuple      =					4
           _A     :	int      =					4
           _A     :	Dict      =					(32, 32)

           _A     :	Any      =					floats_tensor((batch_size, num_channels, num_frames) + sizes).to(SCREAMING_SNAKE_CASE)
           _A     :	Optional[Any]      =					torch.tensor([10]).to(SCREAMING_SNAKE_CASE)
           _A     :	Optional[Any]      =					floats_tensor((batch_size, 4, 32)).to(SCREAMING_SNAKE_CASE)

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


    @property
    def    A    (  self			:							Tuple):
           return (4, 4, 32, 32)


    @property
    def    A    (  self			:							str):
           return (4, 4, 32, 32)


    def    A    (  self			:							List[Any]):
           _A     :	List[str]      =					{
               '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,
           }
           _A     :	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    A    (  self			:							Tuple):
           _A  , _A     :	Dict      =					self.prepare_init_args_and_inputs_for_common()
           _A     :	Any      =					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    A    (  self			:							str):
           _A  , _A     :	Union[str, Any]      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Union[str, Any]      =					32

           _A     :	int      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()

           with torch.no_grad():
                  _A     :	List[str]      =					model(**SCREAMING_SNAKE_CASE)

                  if isinstance(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                         _A     :	int      =					output.sample

           self.assertIsNotNone(SCREAMING_SNAKE_CASE)
           _A     :	Optional[int]      =					inputs_dict['sample'].shape
           self.assertEqual(output.shape      ,							SCREAMING_SNAKE_CASE      ,							'Input and output shapes do not match')


    def    A    (  self			:							Dict):
           _A  , _A     :	List[str]      =					self.prepare_init_args_and_inputs_for_common()
           _A     :	Optional[int]      =					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)

                  _A     :	str      =					model(**SCREAMING_SNAKE_CASE)
                  if isinstance(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                         _A     :	Optional[int]      =					first.sample

                  _A     :	Union[str, Any]      =					model(**SCREAMING_SNAKE_CASE)
                  if isinstance(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                         _A     :	Dict      =					second.sample

           _A     :	Dict      =					first.cpu().numpy()
           _A     :	Dict      =					second.cpu().numpy()
           _A     :	Optional[int]      =					out_a[~np.isnan(SCREAMING_SNAKE_CASE)]
           _A     :	Dict      =					out_a[~np.isnan(SCREAMING_SNAKE_CASE)]
           _A     :	List[Any]      =					np.amax(np.abs(out_a - out_a))
           self.assertLessEqual(SCREAMING_SNAKE_CASE      ,							1e-5)


    def    A    (  self			:							Optional[int]):
           _A  , _A     :	Union[str, Any]      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Optional[int]      =					8

           _A     :	Optional[Any]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()

           model.set_attention_slice('auto')
           with torch.no_grad():
                  _A     :	Any      =					model(**SCREAMING_SNAKE_CASE)
           assert output is not None

           model.set_attention_slice('max')
           with torch.no_grad():
                  _A     :	Optional[Any]      =					model(**SCREAMING_SNAKE_CASE)
           assert output is not None

           model.set_attention_slice(2)
           with torch.no_grad():
                  _A     :	Dict      =					model(**SCREAMING_SNAKE_CASE)
           assert output is not None


    def    A    (  self			:							List[Any]):
           _A  , _A     :	Tuple      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Dict      =					8

           _A     :	Any      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Tuple      =					model(**SCREAMING_SNAKE_CASE).sample

           _A     :	List[str]      =					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():
                  _A     :	Union[str, Any]      =					model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.0}).sample
                  _A     :	Dict      =					model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.5}).sample
                  _A     :	Optional[int]      =					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    A    (  self			:							str):
           _A  , _A     :	Optional[Any]      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Tuple      =					8

           torch.manual_seed(0)
           _A     :	List[str]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Dict      =					model(**SCREAMING_SNAKE_CASE).sample

           _A     :	Optional[int]      =					create_lora_layers(SCREAMING_SNAKE_CASE)
           model.set_attn_processor(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Optional[int]      =					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)
                  _A     :	Dict      =					self.model_class(**SCREAMING_SNAKE_CASE)
                  new_model.to(SCREAMING_SNAKE_CASE)
                  new_model.load_attn_procs(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	List[Any]      =					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    A    (  self			:							Union[str, Any]):
           _A  , _A     :	Any      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Any      =					8

           torch.manual_seed(0)
           _A     :	Optional[int]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	str      =					model(**SCREAMING_SNAKE_CASE).sample

           _A     :	Any      =					create_lora_layers(SCREAMING_SNAKE_CASE)
           model.set_attn_processor(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Tuple      =					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)
                  _A     :	Optional[int]      =					self.model_class(**SCREAMING_SNAKE_CASE)
                  new_model.to(SCREAMING_SNAKE_CASE)
                  new_model.load_attn_procs(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Optional[int]      =					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    A    (  self			:							Optional[int]):
           # enable deterministic behavior for gradient checkpointing
           _A  , _A     :	List[Any]      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	int      =					8

           torch.manual_seed(0)
           _A     :	List[str]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           _A     :	List[Any]      =					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)
                  _A     :	Dict      =					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    A    (  self			:							List[Any]):
           _A  , _A     :	Dict      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Optional[Any]      =					8

           torch.manual_seed(0)
           _A     :	Optional[int]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           _A     :	Dict      =					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)
                  _A     :	Union[str, Any]      =					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    A    (  self			:							Optional[int]):
           _A  , _A     :	str      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	str      =					8

           torch.manual_seed(0)
           _A     :	Optional[int]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Optional[int]      =					model(**SCREAMING_SNAKE_CASE).sample

           _A     :	Optional[int]      =					create_lora_layers(SCREAMING_SNAKE_CASE)
           model.set_attn_processor(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	str      =					model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.0}).sample

           model.set_attn_processor(AttnProcessor())

           with torch.no_grad():
                  _A     :	List[Any]      =					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    A    (  self			:							List[str]):
           # enable deterministic behavior for gradient checkpointing
           _A  , _A     :	List[str]      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	int      =					4

           torch.manual_seed(0)
           _A     :	str      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           _A     :	str      =					create_lora_layers(SCREAMING_SNAKE_CASE)
           model.set_attn_processor(SCREAMING_SNAKE_CASE)

           # default
           with torch.no_grad():
                  _A     :	str      =					model(**SCREAMING_SNAKE_CASE).sample

                  model.enable_xformers_memory_efficient_attention()
                  _A     :	Tuple      =					model(**SCREAMING_SNAKE_CASE).sample

                  model.disable_xformers_memory_efficient_attention()
                  _A     :	int      =					model(**SCREAMING_SNAKE_CASE).sample

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


    def    A    (  self			:							Optional[Any]):
           _A  , _A     :	Dict      =					self.prepare_init_args_and_inputs_for_common()
           _A     :	List[Any]      =					32

           _A     :	Any      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()

           with torch.no_grad():
                  _A     :	Any      =					model(**SCREAMING_SNAKE_CASE)[0]

           model.enable_forward_chunking()
           with torch.no_grad():
                  _A     :	Dict      =					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.






