




'''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				: str					      =			logging.get_logger(__name__)
def 							snake_case_    (  a__						:      List[Any]       ,a__						:      bool = True     ):



    """simple docstring"""




    __lowercase     							=		{}
    for name in model.attn_processors.keys():
        __lowercase     							=		name.endswith("""attn2.processor"""     ) and not (
            name.startswith("""transformer_in"""     ) or """temp_attentions""" in name.split("""."""     )
        )
        __lowercase     							=		model.config.cross_attention_dim if has_cross_attention else None
        if name.startswith("""mid_block"""     ):
            __lowercase     							=		model.config.block_out_channels[-1]
        elif name.startswith("""up_blocks"""     ):
            __lowercase     							=		int(name[len("""up_blocks."""     )]     )
            __lowercase     							=		list(reversed(model.config.block_out_channels     )     )[block_id]
        elif name.startswith("""down_blocks"""     ):
            __lowercase     							=		int(name[len("""down_blocks."""     )]     )
            __lowercase     							=		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
            __lowercase     							=		8 * model.config.attention_head_dim

        __lowercase     							=		LoRAAttnProcessor(hidden_size=a__       ,cross_attention_dim=a__     )
        __lowercase     							=		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 			SCREAMING_SNAKE_CASE(							__A  , __A  , unittest.TestCase	):
       snake_case_					:	str       =   UNetaDConditionModel
       snake_case_					:	Union[str, Any]       =   """sample"""

       @property
       def        snake_case__     (       self	)       ->   Tuple:

           """simple docstring"""



           __lowercase     							=		4
           __lowercase     							=		4
           __lowercase     							=		4
           __lowercase     							=		(32, 32)

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

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

       @property
       def        snake_case__     (       self	)       ->   Dict:

           """simple docstring"""



           return (4, 4, 32, 32)

       @property
       def        snake_case__     (       self	)       ->   str:

           """simple docstring"""



           return (4, 4, 32, 32)

       def        snake_case__     (       self	)       ->   Dict:

           """simple docstring"""



           __lowercase     							=		{
               """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,
           }
           __lowercase     							=		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        snake_case__     (       self	)       ->   Tuple:

           """simple docstring"""



           __lowercase							,__lowercase     							=		self.prepare_init_args_and_inputs_for_common()
           __lowercase     							=		self.model_class(**lowerCamelCase__	)

           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        snake_case__     (       self	)       ->   List[Any]:

           """simple docstring"""



           __lowercase							,__lowercase     							=		self.prepare_init_args_and_inputs_for_common()

           __lowercase     							=		32

           __lowercase     							=		self.model_class(**lowerCamelCase__	)
           model.to(lowerCamelCase__	)
           model.eval()

           with torch.no_grad():
               __lowercase     							=		model(**lowerCamelCase__	)

               if isinstance(lowerCamelCase__   ,						lowerCamelCase__	):
                   __lowercase     							=		output.sample

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

       def        snake_case__     (       self	)       ->   int:

           """simple docstring"""



           __lowercase							,__lowercase     							=		self.prepare_init_args_and_inputs_for_common()
           __lowercase     							=		self.model_class(**lowerCamelCase__	)
           model.to(lowerCamelCase__	)
           model.eval()

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

               __lowercase     							=		model(**lowerCamelCase__	)
               if isinstance(lowerCamelCase__   ,						lowerCamelCase__	):
                   __lowercase     							=		first.sample

               __lowercase     							=		model(**lowerCamelCase__	)
               if isinstance(lowerCamelCase__   ,						lowerCamelCase__	):
                   __lowercase     							=		second.sample

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

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

           """simple docstring"""



           __lowercase							,__lowercase     							=		self.prepare_init_args_and_inputs_for_common()

           __lowercase     							=		8

           __lowercase     							=		self.model_class(**lowerCamelCase__	)
           model.to(lowerCamelCase__	)
           model.eval()

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

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

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

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

           """simple docstring"""



           __lowercase							,__lowercase     							=		self.prepare_init_args_and_inputs_for_common()

           __lowercase     							=		8

           __lowercase     							=		self.model_class(**lowerCamelCase__	)
           model.to(lowerCamelCase__	)

           with torch.no_grad():
               __lowercase     							=		model(**lowerCamelCase__	).sample

           __lowercase     							=		create_lora_layers(lowerCamelCase__	)

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

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

           with torch.no_grad():
               __lowercase     							=		model(**lowerCamelCase__   ,						cross_attention_kwargs={"""scale""": 0.0}	).sample
               __lowercase     							=		model(**lowerCamelCase__   ,						cross_attention_kwargs={"""scale""": 0.5}	).sample
               __lowercase     							=		model(**lowerCamelCase__   ,						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        snake_case__     (       self	)       ->   int:

           """simple docstring"""



           __lowercase							,__lowercase     							=		self.prepare_init_args_and_inputs_for_common()

           __lowercase     							=		8

           torch.manual_seed(0	)
           __lowercase     							=		self.model_class(**lowerCamelCase__	)
           model.to(lowerCamelCase__	)

           with torch.no_grad():
               __lowercase     							=		model(**lowerCamelCase__	).sample

           __lowercase     							=		create_lora_layers(lowerCamelCase__	)
           model.set_attn_processor(lowerCamelCase__	)

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

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

           with torch.no_grad():
               __lowercase     							=		new_model(**lowerCamelCase__   ,						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        snake_case__     (       self	)       ->   str:

           """simple docstring"""



           __lowercase							,__lowercase     							=		self.prepare_init_args_and_inputs_for_common()

           __lowercase     							=		8

           torch.manual_seed(0	)
           __lowercase     							=		self.model_class(**lowerCamelCase__	)
           model.to(lowerCamelCase__	)

           with torch.no_grad():
               __lowercase     							=		model(**lowerCamelCase__	).sample

           __lowercase     							=		create_lora_layers(lowerCamelCase__	)
           model.set_attn_processor(lowerCamelCase__	)

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

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

           with torch.no_grad():
               __lowercase     							=		new_model(**lowerCamelCase__   ,						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        snake_case__     (       self	)       ->   Tuple:

           """simple docstring"""



           __lowercase							,__lowercase     							=		self.prepare_init_args_and_inputs_for_common()

           __lowercase     							=		8

           torch.manual_seed(0	)
           __lowercase     							=		self.model_class(**lowerCamelCase__	)
           model.to(lowerCamelCase__	)

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

       def        snake_case__     (       self	)       ->   Dict:

           """simple docstring"""



           __lowercase							,__lowercase     							=		self.prepare_init_args_and_inputs_for_common()

           __lowercase     							=		8

           torch.manual_seed(0	)
           __lowercase     							=		self.model_class(**lowerCamelCase__	)
           model.to(lowerCamelCase__	)

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

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

           """simple docstring"""



           __lowercase							,__lowercase     							=		self.prepare_init_args_and_inputs_for_common()

           __lowercase     							=		8

           torch.manual_seed(0	)
           __lowercase     							=		self.model_class(**lowerCamelCase__	)
           model.to(lowerCamelCase__	)

           with torch.no_grad():
               __lowercase     							=		model(**lowerCamelCase__	).sample

           __lowercase     							=		create_lora_layers(lowerCamelCase__	)
           model.set_attn_processor(lowerCamelCase__	)

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

           model.set_attn_processor(AttnProcessor()	)

           with torch.no_grad():
               __lowercase     							=		model(**lowerCamelCase__	).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        snake_case__     (       self	)       ->   str:

           """simple docstring"""



           __lowercase							,__lowercase     							=		self.prepare_init_args_and_inputs_for_common()

           __lowercase     							=		4

           torch.manual_seed(0	)
           __lowercase     							=		self.model_class(**lowerCamelCase__	)
           model.to(lowerCamelCase__	)
           __lowercase     							=		create_lora_layers(lowerCamelCase__	)
           model.set_attn_processor(lowerCamelCase__	)

           # default
           with torch.no_grad():
               __lowercase     							=		model(**lowerCamelCase__	).sample

               model.enable_xformers_memory_efficient_attention()
               __lowercase     							=		model(**lowerCamelCase__	).sample

               model.disable_xformers_memory_efficient_attention()
               __lowercase     							=		model(**lowerCamelCase__	).sample

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




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

           """simple docstring"""



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

           __lowercase     							=		self.model_class(**lowerCamelCase__	)
           model.to(lowerCamelCase__	)
           model.eval()

           with torch.no_grad():
               __lowercase     							=		model(**lowerCamelCase__	)[0]

           model.enable_forward_chunking()
           with torch.no_grad():
               __lowercase     							=		model(**lowerCamelCase__	)[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'''
from math import gcd
def 							snake_case_    (  a__						:      int = 1_20_00     ):



    """simple docstring"""




    __lowercase     							=		0
    for d in range(max_d + 1     ):
        for n in range(d // 3 + 1       ,(d + 1) // 2     ):
            if gcd(a__       ,a__     ) == 1:
                fractions_number += 1
    return fractions_number


if __name__ == "__main__":
  print(F"""{solution() = }""")




