


"""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()

UpperCAmelCase   =	logging.get_logger(__name__)

def 							_snake_case			(      __snake_case       :			Union[str, Any]	,   __snake_case       :			bool = True      ):




   """simple docstring"""







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

      _lowerCamelCase  :				Tuple      =		LoRAAttnProcessor(hidden_size=__snake_case	,   cross_attention_dim=__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   lowercase__							(				A_   ,A_   ,unittest.TestCase							):
   __UpperCAmelCase       					=				UNetaDConditionModel
   __UpperCAmelCase       					=				'''sample'''





   @property
   def 		UpperCamelCase_     (       self)     ->     Optional[int]:
      _lowerCamelCase  :				Optional[Any]      =		4
      _lowerCamelCase  :				Dict      =		4
      _lowerCamelCase  :				Union[str, Any]      =		4
      _lowerCamelCase  :				List[Any]      =		(32, 32)

      _lowerCamelCase  :				Optional[int]      =		floats_tensor((batch_size, num_channels, num_frames) + sizes).to(SCREAMING_SNAKE_CASE)
      _lowerCamelCase  :				List[str]      =		torch.tensor([10]).to(SCREAMING_SNAKE_CASE)
      _lowerCamelCase  :				Dict      =		floats_tensor((batch_size, 4, 32)).to(SCREAMING_SNAKE_CASE)

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





   @property
   def 		UpperCamelCase_     (       self)     ->     List[str]:
      return (4, 4, 32, 32)





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





   def 		UpperCamelCase_     (       self)     ->     List[str]:
      _lowerCamelCase  :				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,
      }
      _lowerCamelCase  :				List[Any]      =		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[Any]:
      _lowerCamelCase  :				Tuple      =		self.prepare_init_args_and_inputs_for_common()
      _lowerCamelCase  :				List[str]      =		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 		UpperCamelCase_     (       self)     ->     Optional[int]:
      _lowerCamelCase  :				Optional[Any]      =		self.prepare_init_args_and_inputs_for_common()

      _lowerCamelCase  :				str      =		32

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

      with torch.no_grad():
         _lowerCamelCase  :				int      =		model(**SCREAMING_SNAKE_CASE)

         if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
            _lowerCamelCase  :				Tuple      =		output.sample

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





   def 		UpperCamelCase_     (       self)     ->     Tuple:
      _lowerCamelCase  :				Optional[Any]      =		self.prepare_init_args_and_inputs_for_common()
      _lowerCamelCase  :				Any      =		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)

         _lowerCamelCase  :				str      =		model(**SCREAMING_SNAKE_CASE)
         if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
            _lowerCamelCase  :				List[str]      =		first.sample

         _lowerCamelCase  :				int      =		model(**SCREAMING_SNAKE_CASE)
         if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
            _lowerCamelCase  :				int      =		second.sample

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





   def 		UpperCamelCase_     (       self)     ->     int:
      _lowerCamelCase  :				List[Any]      =		self.prepare_init_args_and_inputs_for_common()

      _lowerCamelCase  :				str      =		8

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

      model.set_attention_slice("""auto""")
      with torch.no_grad():
         _lowerCamelCase  :				str      =		model(**SCREAMING_SNAKE_CASE)
      assert output is not None

      model.set_attention_slice("""max""")
      with torch.no_grad():
         _lowerCamelCase  :				Any      =		model(**SCREAMING_SNAKE_CASE)
      assert output is not None

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





   def 		UpperCamelCase_     (       self)     ->     int:
      _lowerCamelCase  :				Optional[Any]      =		self.prepare_init_args_and_inputs_for_common()

      _lowerCamelCase  :				Optional[int]      =		8

      _lowerCamelCase  :				List[Any]      =		self.model_class(**SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)

      with torch.no_grad():
         _lowerCamelCase  :				List[Any]      =		model(**SCREAMING_SNAKE_CASE).sample

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

      _lowerCamelCase  :				Optional[Any]      =		8

      torch.manual_seed(0)
      _lowerCamelCase  :				Tuple      =		self.model_class(**SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)

      with torch.no_grad():
         _lowerCamelCase  :				Optional[Any]      =		model(**SCREAMING_SNAKE_CASE).sample

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

      with torch.no_grad():
         _lowerCamelCase  :				Dict      =		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)
         _lowerCamelCase  :				Union[str, Any]      =		self.model_class(**SCREAMING_SNAKE_CASE)
         new_model.to(SCREAMING_SNAKE_CASE)
         new_model.load_attn_procs(SCREAMING_SNAKE_CASE)

      with torch.no_grad():
         _lowerCamelCase  :				List[str]      =		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 		UpperCamelCase_     (       self)     ->     List[str]:
      _lowerCamelCase  :				List[str]      =		self.prepare_init_args_and_inputs_for_common()

      _lowerCamelCase  :				List[Any]      =		8

      torch.manual_seed(0)
      _lowerCamelCase  :				Any      =		self.model_class(**SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)

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

      _lowerCamelCase  :				Dict      =		create_lora_layers(SCREAMING_SNAKE_CASE)
      model.set_attn_processor(SCREAMING_SNAKE_CASE)

      with torch.no_grad():
         _lowerCamelCase  :				List[str]      =		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)
         _lowerCamelCase  :				Union[str, Any]      =		self.model_class(**SCREAMING_SNAKE_CASE)
         new_model.to(SCREAMING_SNAKE_CASE)
         new_model.load_attn_procs(SCREAMING_SNAKE_CASE)

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

      _lowerCamelCase  :				str      =		8

      torch.manual_seed(0)
      _lowerCamelCase  :				Dict      =		self.model_class(**SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)

      _lowerCamelCase  :				Optional[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)
         _lowerCamelCase  :				List[Any]      =		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 		UpperCamelCase_     (       self)     ->     Tuple:
      _lowerCamelCase  :				Dict      =		self.prepare_init_args_and_inputs_for_common()

      _lowerCamelCase  :				Optional[Any]      =		8

      torch.manual_seed(0)
      _lowerCamelCase  :				str      =		self.model_class(**SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)

      _lowerCamelCase  :				Tuple      =		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)
         _lowerCamelCase  :				Optional[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 		UpperCamelCase_     (       self)     ->     Dict:
      _lowerCamelCase  :				List[str]      =		self.prepare_init_args_and_inputs_for_common()

      _lowerCamelCase  :				Tuple      =		8

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

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

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

      with torch.no_grad():
         _lowerCamelCase  :				Dict      =		model(**SCREAMING_SNAKE_CASE			,      cross_attention_kwargs={"""scale""": 0.0}).sample

      model.set_attn_processor(AttnProcessor())

      with torch.no_grad():
         _lowerCamelCase  :				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 		UpperCamelCase_     (       self)     ->     str:
      # enable deterministic behavior for gradient checkpointing
      _lowerCamelCase  :				Optional[Any]      =		self.prepare_init_args_and_inputs_for_common()

      _lowerCamelCase  :				Any      =		4

      torch.manual_seed(0)
      _lowerCamelCase  :				List[str]      =		self.model_class(**SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      _lowerCamelCase  :				List[Any]      =		create_lora_layers(SCREAMING_SNAKE_CASE)
      model.set_attn_processor(SCREAMING_SNAKE_CASE)

      # default
      with torch.no_grad():
         _lowerCamelCase  :				Tuple      =		model(**SCREAMING_SNAKE_CASE).sample

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

         model.disable_xformers_memory_efficient_attention()
         _lowerCamelCase  :				Union[str, Any]      =		model(**SCREAMING_SNAKE_CASE).sample

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





   def 		UpperCamelCase_     (       self)     ->     List[Any]:
      _lowerCamelCase  :				Any      =		self.prepare_init_args_and_inputs_for_common()
      _lowerCamelCase  :				Tuple      =		32

      _lowerCamelCase  :				Tuple      =		self.model_class(**SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()

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

      model.enable_forward_chunking()
      with torch.no_grad():
         _lowerCamelCase  :				int      =		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.





"""simple docstring"""
import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class   lowercase__							(				utils.Parser							):
   __UpperCAmelCase       					=				"halfcheetah-medium-expert-v2"
   __UpperCAmelCase       					=				"config.offline"







def 							_snake_case			(      __snake_case       :			Any	,   __snake_case       :			Optional[Any]	,   __snake_case       :			Optional[int]	,   __snake_case       :			List[str]	,   __snake_case       :			Union[str, Any]      ):




   """simple docstring"""







   _lowerCamelCase      ,						_lowerCamelCase  :				int      =		utils.load_model(__snake_case	,   __snake_case	,   __snake_case	,   epoch=__snake_case	,   device=__snake_case      )
   _lowerCamelCase  :				List[str]      =		TrajectoryTransformerModel(gpt.config      )

   trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()      )
   _lowerCamelCase  :				str      =		gpt.pos_emb
   trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()      )
   trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()      )
   trajectory_transformer.head.load_state_dict(gpt.head.state_dict()      )

   for i, block in enumerate(gpt.blocks      ):
      trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()      )
      trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()      )
      trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()      )

      trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()      )
      trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()      )
      trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()      )
      trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()      )

   torch.save(trajectory_transformer.state_dict()	,   """pytorch_model.bin"""      )


if __name__ == "__main__":
 UpperCAmelCase   =	Parser().parse_args("""plan""")
 convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
     args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
 )

