




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()

SCREAMING_SNAKE_CASE__	:				Union[str, Any]  =       logging.get_logger(__name__)

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

              lowerCamelCase       :				List[str]      			= LoRAAttnProcessor(hidden_size=_SCREAMING_SNAKE_CASE			,cross_attention_dim=_SCREAMING_SNAKE_CASE   )
              lowerCamelCase       :				Union[str, Any]      			= 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__     (lowerCAmelCase__   ,    lowerCAmelCase__   ,    unittest.TestCase					):

    '''simple docstring'''



    lowerCamelCase_				:     Union[str, Any]     					=				UNetaDConditionModel
    lowerCamelCase_				:     Optional[int]     					=				"""sample"""







    @property
    def    _lowercase					(   self					)					->				Dict:
           lowerCamelCase       :				str      			= 4
           lowerCamelCase       :				Dict      			= 4
           lowerCamelCase       :				List[Any]      			= 4
           lowerCamelCase       :				List[Any]      			= (32, 32)

           lowerCamelCase       :				Union[str, Any]      			= floats_tensor((batch_size, num_channels, num_frames) + sizes					).to(UpperCamelCase__					)
           lowerCamelCase       :				str      			= torch.tensor([10]					).to(UpperCamelCase__					)
           lowerCamelCase       :				int      			= floats_tensor((batch_size, 4, 32)					).to(UpperCamelCase__					)

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







    @property
    def    _lowercase					(   self					)					->				Optional[int]:
           return (4, 4, 32, 32)







    @property
    def    _lowercase					(   self					)					->				Any:
           return (4, 4, 32, 32)







    def    _lowercase					(   self					)					->				int:
           lowerCamelCase       :				int      			= {
               "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,
           }
           lowerCamelCase       :				Dict      			= 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    _lowercase					(   self					)					->				Union[str, Any]:
           lowerCamelCase       :				Optional[Any]      			= self.prepare_init_args_and_inputs_for_common()
           lowerCamelCase       :				str      			= 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    _lowercase					(   self					)					->				Optional[int]:
           lowerCamelCase       :				Optional[Any]      			= self.prepare_init_args_and_inputs_for_common()

           lowerCamelCase       :				Tuple      			= 32

           lowerCamelCase       :				Optional[int]      			= self.model_class(**UpperCamelCase__					)
           model.to(UpperCamelCase__					)
           model.eval()

           with torch.no_grad():
                  lowerCamelCase       :				Union[str, Any]      			= model(**UpperCamelCase__					)

                  if isinstance(UpperCamelCase__					,  UpperCamelCase__					):
                         lowerCamelCase       :				Union[str, Any]      			= output.sample

           self.assertIsNotNone(UpperCamelCase__					)
           lowerCamelCase       :				Optional[Any]      			= inputs_dict["sample"].shape
           self.assertEqual(output.shape					,  UpperCamelCase__					,  "Input and output shapes do not match"					)







    def    _lowercase					(   self					)					->				List[str]:
           lowerCamelCase       :				str      			= self.prepare_init_args_and_inputs_for_common()
           lowerCamelCase       :				Tuple      			= 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					)

                  lowerCamelCase       :				Optional[Any]      			= model(**UpperCamelCase__					)
                  if isinstance(UpperCamelCase__					,  UpperCamelCase__					):
                         lowerCamelCase       :				str      			= first.sample

                  lowerCamelCase       :				Tuple      			= model(**UpperCamelCase__					)
                  if isinstance(UpperCamelCase__					,  UpperCamelCase__					):
                         lowerCamelCase       :				Optional[Any]      			= second.sample

           lowerCamelCase       :				List[str]      			= first.cpu().numpy()
           lowerCamelCase       :				Optional[Any]      			= second.cpu().numpy()
           lowerCamelCase       :				Tuple      			= out_a[~np.isnan(UpperCamelCase__					)]
           lowerCamelCase       :				str      			= out_a[~np.isnan(UpperCamelCase__					)]
           lowerCamelCase       :				Optional[Any]      			= np.amax(np.abs(out_a - out_a					)					)
           self.assertLessEqual(UpperCamelCase__					,  1e-5					)







    def    _lowercase					(   self					)					->				int:
           lowerCamelCase       :				str      			= self.prepare_init_args_and_inputs_for_common()

           lowerCamelCase       :				Union[str, Any]      			= 8

           lowerCamelCase       :				Union[str, Any]      			= self.model_class(**UpperCamelCase__					)
           model.to(UpperCamelCase__					)
           model.eval()

           model.set_attention_slice("auto"					)
           with torch.no_grad():
                  lowerCamelCase       :				List[Any]      			= model(**UpperCamelCase__					)
           assert output is not None

           model.set_attention_slice("max"					)
           with torch.no_grad():
                  lowerCamelCase       :				Optional[Any]      			= model(**UpperCamelCase__					)
           assert output is not None

           model.set_attention_slice(2					)
           with torch.no_grad():
                  lowerCamelCase       :				List[Any]      			= model(**UpperCamelCase__					)
           assert output is not None







    def    _lowercase					(   self					)					->				str:
           lowerCamelCase       :				Tuple      			= self.prepare_init_args_and_inputs_for_common()

           lowerCamelCase       :				List[str]      			= 8

           lowerCamelCase       :				Any      			= self.model_class(**UpperCamelCase__					)
           model.to(UpperCamelCase__					)

           with torch.no_grad():
                  lowerCamelCase       :				int      			= model(**UpperCamelCase__					).sample

           lowerCamelCase       :				str      			= 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():
                  lowerCamelCase       :				Union[str, Any]      			= model(**UpperCamelCase__					,  cross_attention_kwargs={"scale": 0.0}					).sample
                  lowerCamelCase       :				Tuple      			= model(**UpperCamelCase__					,  cross_attention_kwargs={"scale": 0.5}					).sample
                  lowerCamelCase       :				Optional[int]      			= 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    _lowercase					(   self					)					->				Any:
           lowerCamelCase       :				Optional[int]      			= self.prepare_init_args_and_inputs_for_common()

           lowerCamelCase       :				List[Any]      			= 8

           torch.manual_seed(0					)
           lowerCamelCase       :				Tuple      			= self.model_class(**UpperCamelCase__					)
           model.to(UpperCamelCase__					)

           with torch.no_grad():
                  lowerCamelCase       :				Union[str, Any]      			= model(**UpperCamelCase__					).sample

           lowerCamelCase       :				Dict      			= create_lora_layers(UpperCamelCase__					)
           model.set_attn_processor(UpperCamelCase__					)

           with torch.no_grad():
                  lowerCamelCase       :				Union[str, Any]      			= 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					)
                  lowerCamelCase       :				Optional[Any]      			= self.model_class(**UpperCamelCase__					)
                  new_model.to(UpperCamelCase__					)
                  new_model.load_attn_procs(UpperCamelCase__					)

           with torch.no_grad():
                  lowerCamelCase       :				str      			= 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    _lowercase					(   self					)					->				List[str]:
           lowerCamelCase       :				int      			= self.prepare_init_args_and_inputs_for_common()

           lowerCamelCase       :				Optional[Any]      			= 8

           torch.manual_seed(0					)
           lowerCamelCase       :				str      			= self.model_class(**UpperCamelCase__					)
           model.to(UpperCamelCase__					)

           with torch.no_grad():
                  lowerCamelCase       :				Optional[Any]      			= model(**UpperCamelCase__					).sample

           lowerCamelCase       :				Any      			= create_lora_layers(UpperCamelCase__					)
           model.set_attn_processor(UpperCamelCase__					)

           with torch.no_grad():
                  lowerCamelCase       :				int      			= 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					)
                  lowerCamelCase       :				List[Any]      			= self.model_class(**UpperCamelCase__					)
                  new_model.to(UpperCamelCase__					)
                  new_model.load_attn_procs(UpperCamelCase__					)

           with torch.no_grad():
                  lowerCamelCase       :				List[Any]      			= 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    _lowercase					(   self					)					->				Any:
           # enable deterministic behavior for gradient checkpointing
           lowerCamelCase       :				str      			= self.prepare_init_args_and_inputs_for_common()

           lowerCamelCase       :				Dict      			= 8

           torch.manual_seed(0					)
           lowerCamelCase       :				Optional[Any]      			= self.model_class(**UpperCamelCase__					)
           model.to(UpperCamelCase__					)

           lowerCamelCase       :				Dict      			= 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					)
                  lowerCamelCase       :				List[str]      			= self.model_class(**UpperCamelCase__					)
                  new_model.to(UpperCamelCase__					)
                  new_model.load_attn_procs(UpperCamelCase__					,  weight_name="pytorch_lora_weights.bin"					)







    def    _lowercase					(   self					)					->				int:
           lowerCamelCase       :				Union[str, Any]      			= self.prepare_init_args_and_inputs_for_common()

           lowerCamelCase       :				Dict      			= 8

           torch.manual_seed(0					)
           lowerCamelCase       :				List[Any]      			= self.model_class(**UpperCamelCase__					)
           model.to(UpperCamelCase__					)

           lowerCamelCase       :				str      			= 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					)
                  lowerCamelCase       :				List[str]      			= 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    _lowercase					(   self					)					->				int:
           lowerCamelCase       :				List[Any]      			= self.prepare_init_args_and_inputs_for_common()

           lowerCamelCase       :				Optional[int]      			= 8

           torch.manual_seed(0					)
           lowerCamelCase       :				Union[str, Any]      			= self.model_class(**UpperCamelCase__					)
           model.to(UpperCamelCase__					)

           with torch.no_grad():
                  lowerCamelCase       :				Any      			= model(**UpperCamelCase__					).sample

           lowerCamelCase       :				int      			= create_lora_layers(UpperCamelCase__					)
           model.set_attn_processor(UpperCamelCase__					)

           with torch.no_grad():
                  lowerCamelCase       :				Optional[int]      			= model(**UpperCamelCase__					,  cross_attention_kwargs={"scale": 0.0}					).sample

           model.set_attn_processor(AttnProcessor()					)

           with torch.no_grad():
                  lowerCamelCase       :				Optional[int]      			= 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    _lowercase					(   self					)					->				List[Any]:
           # enable deterministic behavior for gradient checkpointing
           lowerCamelCase       :				Dict      			= self.prepare_init_args_and_inputs_for_common()

           lowerCamelCase       :				List[Any]      			= 4

           torch.manual_seed(0					)
           lowerCamelCase       :				Any      			= self.model_class(**UpperCamelCase__					)
           model.to(UpperCamelCase__					)
           lowerCamelCase       :				str      			= create_lora_layers(UpperCamelCase__					)
           model.set_attn_processor(UpperCamelCase__					)

           # default
           with torch.no_grad():
                  lowerCamelCase       :				int      			= model(**UpperCamelCase__					).sample

                  model.enable_xformers_memory_efficient_attention()
                  lowerCamelCase       :				Any      			= model(**UpperCamelCase__					).sample

                  model.disable_xformers_memory_efficient_attention()
                  lowerCamelCase       :				Any      			= model(**UpperCamelCase__					).sample

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







    def    _lowercase					(   self					)					->				Union[str, Any]:
           lowerCamelCase       :				Dict      			= self.prepare_init_args_and_inputs_for_common()
           lowerCamelCase       :				List[Any]      			= 32

           lowerCamelCase       :				int      			= self.model_class(**UpperCamelCase__					)
           model.to(UpperCamelCase__					)
           model.eval()

           with torch.no_grad():
                  lowerCamelCase       :				Union[str, Any]      			= model(**UpperCamelCase__					)[0]

           model.enable_forward_chunking()
           with torch.no_grad():
                  lowerCamelCase       :				List[Any]      			= 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.












import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

def 					A				(				*_SCREAMING_SNAKE_CASE   )     ->			Union[str, Any]:
       lowerCamelCase       :				List[str]      			= torch.cuda.device_count()
       print(f'''Device count: {num_processes}'''   )
       with patch_environment(
           world_size=_SCREAMING_SNAKE_CASE			,master_addr="127.0.01"			,master_port="29500"			,mixed_precision=args[1].mixed_precision   ):
              lowerCamelCase       :				Union[str, Any]      			= PrepareForLaunch(_SCREAMING_SNAKE_CASE			,distributed_type="MULTI_GPU"   )
              torch.multiprocessing.start_processes(_SCREAMING_SNAKE_CASE			,args=_SCREAMING_SNAKE_CASE			,nprocs=_SCREAMING_SNAKE_CASE			,start_method="spawn"   )


if __name__ == "__main__":
       # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
       # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

       # on-demand GPU
       # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
       SCREAMING_SNAKE_CASE__	:				Tuple  =       rh.cluster(name='rh-cluster', instance_type='V100:4', provider='cheapest', use_spot=False)  # multi GPU
       gpu.up_if_not()

       # on-prem GPU
       # gpu = rh.cluster(
       #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
       #       )

       # Set up remote function
       SCREAMING_SNAKE_CASE__	:				int  =       [
           'pip:./',
           'transformers',
           'datasets',
           'evaluate',
           'tqdm',
           'scipy',
           'scikit-learn',
           'tensorboard',
           'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
       ]
       SCREAMING_SNAKE_CASE__	:				Dict  =       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

       # Define train args/config, run train function
       SCREAMING_SNAKE_CASE__	:				int  =       argparse.Namespace(cpu=False, mixed_precision='fp16')
       SCREAMING_SNAKE_CASE__	:				int  =       {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       launch_train_gpu(config, train_args, stream_logs=True)

       # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
       # gpu.install_packages(reqs)
       # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])






