





'''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()

_lowerCamelCase					:						Dict					     =							logging.get_logger(__name__)




def  __a			(  UpperCAmelCase							,      UpperCAmelCase = True     )   ->Dict:



	"""simple docstring"""







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

		A					=   LoRAAttnProcessor(hidden_size=_SCREAMING_SNAKE_CASE							,      cross_attention_dim=_SCREAMING_SNAKE_CASE     )
		A					=   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	(							snake_case__		,       snake_case__		,       unittest.TestCase    ):







		'''simple docstring'''







		__lowerCAmelCase								=     UNetaDConditionModel
		__lowerCAmelCase								=     """sample"""







		@property
		def 							A  (self    :   Dict   ):
			A					=   4
			A					=   4
			A					=   4
			A					=   (32, 32)

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

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







		@property
		def 							A  (self    :   Union[str, Any]   ):
			return (4, 4, 32, 32)







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







		def 							A  (self    :   Union[str, Any]   ):
			A					=   {
			    """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					=   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    :   List[str]   ):
			A	,     A					=   self.prepare_init_args_and_inputs_for_common()
			A					=   self.model_class(**UpperCAmelCase_   )

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

			A					=   32

			A					=   self.model_class(**UpperCAmelCase_   )
			model.to(UpperCAmelCase_   )
			model.eval()

			with torch.no_grad():
				A					=   model(**UpperCAmelCase_   )

				if isinstance(UpperCAmelCase_   ,   UpperCAmelCase_   ):
					A					=   output.sample

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







		def 							A  (self    :   List[str]   ):
			A	,     A					=   self.prepare_init_args_and_inputs_for_common()
			A					=   self.model_class(**UpperCAmelCase_   )
			model.to(UpperCAmelCase_   )
			model.eval()

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

				A					=   model(**UpperCAmelCase_   )
				if isinstance(UpperCAmelCase_   ,   UpperCAmelCase_   ):
					A					=   first.sample

				A					=   model(**UpperCAmelCase_   )
				if isinstance(UpperCAmelCase_   ,   UpperCAmelCase_   ):
					A					=   second.sample

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







		def 							A  (self    :   str   ):
			A	,     A					=   self.prepare_init_args_and_inputs_for_common()

			A					=   8

			A					=   self.model_class(**UpperCAmelCase_   )
			model.to(UpperCAmelCase_   )
			model.eval()

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

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

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







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

			A					=   8

			A					=   self.model_class(**UpperCAmelCase_   )
			model.to(UpperCAmelCase_   )

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

			A					=   create_lora_layers(UpperCAmelCase_   )

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

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

			with torch.no_grad():
				A					=   model(**UpperCAmelCase_   ,   cross_attention_kwargs={"""scale""": 0.0}   ).sample
				A					=   model(**UpperCAmelCase_   ,   cross_attention_kwargs={"""scale""": 0.5}   ).sample
				A					=   model(**UpperCAmelCase_   ,   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    :   Tuple   ):
			A	,     A					=   self.prepare_init_args_and_inputs_for_common()

			A					=   8

			torch.manual_seed(0   )
			A					=   self.model_class(**UpperCAmelCase_   )
			model.to(UpperCAmelCase_   )

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

			A					=   create_lora_layers(UpperCAmelCase_   )
			model.set_attn_processor(UpperCAmelCase_   )

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

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

			with torch.no_grad():
				A					=   new_model(**UpperCAmelCase_   ,   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    :   Tuple   ):
			A	,     A					=   self.prepare_init_args_and_inputs_for_common()

			A					=   8

			torch.manual_seed(0   )
			A					=   self.model_class(**UpperCAmelCase_   )
			model.to(UpperCAmelCase_   )

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

			A					=   create_lora_layers(UpperCAmelCase_   )
			model.set_attn_processor(UpperCAmelCase_   )

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

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

			with torch.no_grad():
				A					=   new_model(**UpperCAmelCase_   ,   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]   ):
			A	,     A					=   self.prepare_init_args_and_inputs_for_common()

			A					=   8

			torch.manual_seed(0   )
			A					=   self.model_class(**UpperCAmelCase_   )
			model.to(UpperCAmelCase_   )

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







		def 							A  (self    :   Dict   ):
			A	,     A					=   self.prepare_init_args_and_inputs_for_common()

			A					=   8

			torch.manual_seed(0   )
			A					=   self.model_class(**UpperCAmelCase_   )
			model.to(UpperCAmelCase_   )

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







		def 							A  (self    :   Optional[int]   ):
			A	,     A					=   self.prepare_init_args_and_inputs_for_common()

			A					=   8

			torch.manual_seed(0   )
			A					=   self.model_class(**UpperCAmelCase_   )
			model.to(UpperCAmelCase_   )

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

			A					=   create_lora_layers(UpperCAmelCase_   )
			model.set_attn_processor(UpperCAmelCase_   )

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

			model.set_attn_processor(AttnProcessor()   )

			with torch.no_grad():
				A					=   model(**UpperCAmelCase_   ).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    :   Tuple   ):
			A	,     A					=   self.prepare_init_args_and_inputs_for_common()

			A					=   4

			torch.manual_seed(0   )
			A					=   self.model_class(**UpperCAmelCase_   )
			model.to(UpperCAmelCase_   )
			A					=   create_lora_layers(UpperCAmelCase_   )
			model.set_attn_processor(UpperCAmelCase_   )

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

				model.enable_xformers_memory_efficient_attention()
				A					=   model(**UpperCAmelCase_   ).sample

				model.disable_xformers_memory_efficient_attention()
				A					=   model(**UpperCAmelCase_   ).sample

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







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

			A					=   self.model_class(**UpperCAmelCase_   )
			model.to(UpperCAmelCase_   )
			model.eval()

			with torch.no_grad():
				A					=   model(**UpperCAmelCase_   )[0]

			model.enable_forward_chunking()
			with torch.no_grad():
				A					=   model(**UpperCAmelCase_   )[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"""
import doctest

import projectq
from projectq.ops import H, Measure







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					int:
							snake_case_     							=		quantum_engine.allocate_qubit()
							H | qubit
							Measure | qubit
							return int(_SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
				doctest.testmod()

				# initialises a new quantum backend
				__SCREAMING_SNAKE_CASE				:     Dict		=     projectq.MainEngine()

				# Generate a list of 10 random numbers
				__SCREAMING_SNAKE_CASE				:     Optional[Any]		=     [get_random_number(quantum_engine) for _ in range(10)]

				# Flushes the quantum engine from memory
				quantum_engine.flush()

				print('Random numbers', random_numbers_list)



