



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:	Tuple      							=					logging.get_logger(__name__)






def      _lowercase(				__a							:	Any						,    __a							:	str = True    ):
							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=_A						,    cross_attention_dim=_A    )
														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 			lowercase_				(_UpperCAmelCase			,				_UpperCAmelCase			,				unittest.TestCase      ):
				snake_case  						=UNetaDConditionModel
				snake_case  						='sample'





				@property
				def 	__UpperCamelCase						(							self)     ->   Union[str, Any]:
											a__     						=4
											a__     						=4
											a__     						=4
											a__     						=(32, 32)

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

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





				@property
				def 	__UpperCamelCase						(							self)     ->   Tuple:
											return (4, 4, 32, 32)





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





				def 	__UpperCamelCase						(							self)     ->   Dict:
											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 	__UpperCamelCase						(							self)     ->   Optional[int]:
											a__						,    a__     						=self.prepare_init_args_and_inputs_for_common()
											a__     						=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)     ->   Optional[int]:
											a__						,    a__     						=self.prepare_init_args_and_inputs_for_common()

											a__     						=32

											a__     						=self.model_class(**lowercase_)
											model.to(lowercase_)
											model.eval()

											with torch.no_grad():
																		a__     						=model(**lowercase_)

																		if isinstance(lowercase_					,     lowercase_):
																									a__     						=output.sample

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





				def 	__UpperCamelCase						(							self)     ->   Optional[Any]:
											a__						,    a__     						=self.prepare_init_args_and_inputs_for_common()
											a__     						=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)

																		a__     						=model(**lowercase_)
																		if isinstance(lowercase_					,     lowercase_):
																									a__     						=first.sample

																		a__     						=model(**lowercase_)
																		if isinstance(lowercase_					,     lowercase_):
																									a__     						=second.sample

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





				def 	__UpperCamelCase						(							self)     ->   List[str]:
											a__						,    a__     						=self.prepare_init_args_and_inputs_for_common()

											a__     						=8

											a__     						=self.model_class(**lowercase_)
											model.to(lowercase_)
											model.eval()

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

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

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





				def 	__UpperCamelCase						(							self)     ->   str:
											a__						,    a__     						=self.prepare_init_args_and_inputs_for_common()

											a__     						=8

											a__     						=self.model_class(**lowercase_)
											model.to(lowercase_)

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

											a__     						=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():
																		a__     						=model(**lowercase_					,     cross_attention_kwargs={'scale': 0.0}).sample
																		a__     						=model(**lowercase_					,     cross_attention_kwargs={'scale': 0.5}).sample
																		a__     						=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)     ->   Optional[int]:
											a__						,    a__     						=self.prepare_init_args_and_inputs_for_common()

											a__     						=8

											torch.manual_seed(0)
											a__     						=self.model_class(**lowercase_)
											model.to(lowercase_)

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

											a__     						=create_lora_layers(lowercase_)
											model.set_attn_processor(lowercase_)

											with torch.no_grad():
																		a__     						=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)
																		a__     						=self.model_class(**lowercase_)
																		new_model.to(lowercase_)
																		new_model.load_attn_procs(lowercase_)

											with torch.no_grad():
																		a__     						=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)     ->   Dict:
											a__						,    a__     						=self.prepare_init_args_and_inputs_for_common()

											a__     						=8

											torch.manual_seed(0)
											a__     						=self.model_class(**lowercase_)
											model.to(lowercase_)

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

											a__     						=create_lora_layers(lowercase_)
											model.set_attn_processor(lowercase_)

											with torch.no_grad():
																		a__     						=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)
																		a__     						=self.model_class(**lowercase_)
																		new_model.to(lowercase_)
																		new_model.load_attn_procs(lowercase_)

											with torch.no_grad():
																		a__     						=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)     ->   Optional[Any]:
											a__						,    a__     						=self.prepare_init_args_and_inputs_for_common()

											a__     						=8

											torch.manual_seed(0)
											a__     						=self.model_class(**lowercase_)
											model.to(lowercase_)

											a__     						=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)
																		a__     						=self.model_class(**lowercase_)
																		new_model.to(lowercase_)
																		new_model.load_attn_procs(lowercase_					,     weight_name='pytorch_lora_weights.bin')





				def 	__UpperCamelCase						(							self)     ->   List[str]:
											a__						,    a__     						=self.prepare_init_args_and_inputs_for_common()

											a__     						=8

											torch.manual_seed(0)
											a__     						=self.model_class(**lowercase_)
											model.to(lowercase_)

											a__     						=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)
																		a__     						=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)     ->   Optional[int]:
											a__						,    a__     						=self.prepare_init_args_and_inputs_for_common()

											a__     						=8

											torch.manual_seed(0)
											a__     						=self.model_class(**lowercase_)
											model.to(lowercase_)

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

											a__     						=create_lora_layers(lowercase_)
											model.set_attn_processor(lowercase_)

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

											model.set_attn_processor(AttnProcessor())

											with torch.no_grad():
																		a__     						=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)     ->   Union[str, Any]:
											a__						,    a__     						=self.prepare_init_args_and_inputs_for_common()

											a__     						=4

											torch.manual_seed(0)
											a__     						=self.model_class(**lowercase_)
											model.to(lowercase_)
											a__     						=create_lora_layers(lowercase_)
											model.set_attn_processor(lowercase_)

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

																		model.enable_xformers_memory_efficient_attention()
																		a__     						=model(**lowercase_).sample

																		model.disable_xformers_memory_efficient_attention()
																		a__     						=model(**lowercase_).sample

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





				def 	__UpperCamelCase						(							self)     ->   Union[str, Any]:
											a__						,    a__     						=self.prepare_init_args_and_inputs_for_common()
											a__     						=32

											a__     						=self.model_class(**lowercase_)
											model.to(lowercase_)
											model.eval()

											with torch.no_grad():
																		a__     						=model(**lowercase_)[0]

											model.enable_forward_chunking()
											with torch.no_grad():
																		a__     						=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.





_lowerCAmelCase:	List[str]      							=					[0.33, 0.44, 0.55, 0.44, 0.33]
_lowerCAmelCase:	Union[str, Any]      							=					3
_lowerCAmelCase:	Dict      							=					5

# Buffer Space (with Parameters Space)
_lowerCAmelCase:	list[float]      							=					[]
_lowerCAmelCase:	list[float]      							=					[]

# Machine Time
_lowerCAmelCase:	Optional[Any]      							=					0






def      _lowercase(				__a							:	Optional[Any]    ):
							global buffer_space, params_space, machine_time, K, m, t

							# Choosing Dynamical Systems (All)
							for key, value in enumerate(__a    ):
														# Evolution Parameter
														a__     						=float(seed / value    )

														# Control Theory: Orbit Change
														a__     						=(buffer_space[(key + 1) % m] + e) % 1

														# Control Theory: Trajectory Change
														a__     						=(params_space[key] + e) % 1 + 3

														# Modification (Transition Function) - Jumps
														a__     						=round(float(r * value * (1 - value)    )						,    10    )
														a__     						=r  # Saving to Parameters Space

							# Logistic Map
							assert max(__a    ) < 1
							assert max(__a    ) < 4

							# Machine Time
							machine_time += 1






def      _lowercase(				):
							global buffer_space, params_space, machine_time, K, m, t

							# PRNG (Xorshift by George Marsaglia)
							def xorshift(__a							:	str						,    __a							:	Dict    ):
														x ^= y >> 13
														y ^= x << 17
														x ^= y >> 5
														return x

							# Choosing Dynamical Systems (Increment)
							a__     						=machine_time % m

							# Evolution (Time Length)
							for _ in range(0						,    __a    ):
														# Variables (Position + Parameters)
														a__     						=params_space[key]
														a__     						=buffer_space[key]

														# Modification (Transition Function) - Flow
														a__     						=round(float(r * value * (1 - value)    )						,    10    )
														a__     						=(machine_time * 0.01 + r * 1.01) % 1 + 3

							# Choosing Chaotic Data
							a__     						=int(buffer_space[(key + 2) % m] * (10**10)    )
							a__     						=int(buffer_space[(key - 2) % m] * (10**10)    )

							# Machine Time
							machine_time += 1

							return xorshift(__a						,    __a    ) % 0Xffff_ffff






def      _lowercase(				):
							global buffer_space, params_space, machine_time, K, m, t

							a__     						=K
							a__     						=[0] * m
							a__     						=0


if __name__ == "__main__":
	# Initialization
	reset()

	# Pushing Data (Input)
	import random

	_lowerCAmelCase:	int      							=					random.sample(range(0xff_fff_fff), 100)
	for chunk in message:
		push(chunk)

	# for controlling
	_lowerCAmelCase:	List[Any]      							=					''

	# Pulling Data (Output)
	while inp in ("e", "E"):
		print(F"""{format(pull(), '#04x')}""")
		print(buffer_space)
		print(params_space)
		_lowerCAmelCase:	str      							=					input('(e)exit? ').strip()

