





import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


_UpperCAmelCase				       = logging.get_logger(__name__)  # pylint: disable=invalid-name


_UpperCAmelCase				       = 'lfs-multipart-upload'



class 							_UpperCamelCase    (		lowerCAmelCase_   ):

       @staticmethod
       def lowercase       (						_SCREAMING_SNAKE_CASE:       ArgumentParser						)				->       Optional[int]:




              """simple docstring"""


              UpperCamelCase_      				=     parser.add_parser(
                  "lfs-enable-largefiles"    ,      help=(
                      "Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB."
                  )    ,      )
              enable_parser.add_argument("path"    ,      type=_SCREAMING_SNAKE_CASE    ,      help="Local path to repository you want to configure."						)
              enable_parser.set_defaults(func=lambda _SCREAMING_SNAKE_CASE						: LfsEnableCommand(_SCREAMING_SNAKE_CASE						)						)

              UpperCamelCase_      				=     parser.add_parser(
                  _SCREAMING_SNAKE_CASE    ,      help=(
                      "Deprecated: use `huggingface-cli` instead. "
                      "Command will get called by git-lfs, do not call it directly."
                  )    ,      )
              upload_parser.set_defaults(func=lambda _SCREAMING_SNAKE_CASE						: LfsUploadCommand(_SCREAMING_SNAKE_CASE						)						)



class 							_UpperCamelCase    :

       def __init__(						self:       Any    ,      _SCREAMING_SNAKE_CASE:       Any						)				->       List[str]:




              """simple docstring"""


              UpperCamelCase_      				=     args



       def lowercase       (						self:       List[Any]						)				->       Union[str, Any]:




              """simple docstring"""


              warnings.warn(
                  "Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."						)
              UpperCamelCase_      				=     os.path.abspath(self.args.path						)
              if not os.path.isdir(_SCREAMING_SNAKE_CASE						):
                     print("This does not look like a valid git repo."						)
                     exit(1						)
              subprocess.run(
                  "git config lfs.customtransfer.multipart.path transformers-cli".split()    ,      check=_SCREAMING_SNAKE_CASE    ,      cwd=_SCREAMING_SNAKE_CASE						)
              subprocess.run(
                  f'''git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}'''.split()    ,      check=_SCREAMING_SNAKE_CASE    ,      cwd=_SCREAMING_SNAKE_CASE    ,      )
              print("Local repo set up for largefiles"						)




def        lowerCAmelCase_       (							UpperCamelCase_     )   ->						Optional[Any]:
       UpperCamelCase_      				=     json.dumps(UpperCamelCase_     ) + "\n"
       sys.stdout.write(UpperCamelCase_     )
       sys.stdout.flush()




def        lowerCAmelCase_       (							)   ->						Optional[Dict]:
       UpperCamelCase_      				=     json.loads(sys.stdin.readline().strip()     )

       if "terminate" in (msg.get("type"     ), msg.get("event"     )):
              # terminate message received
              return None

       if msg.get("event"     ) not in ("download", "upload"):
              logger.critical("Received unexpected message"     )
              sys.exit(1     )

       return msg



class 							_UpperCamelCase    (		lowerCAmelCase_   ):

       def __init__(						self:       Tuple    ,      _SCREAMING_SNAKE_CASE:       str    ,      _SCREAMING_SNAKE_CASE:       int    ,      _SCREAMING_SNAKE_CASE:       int						)				->       str:




              """simple docstring"""


              UpperCamelCase_      				=     filepath
              UpperCamelCase_      				=     seek_from
              UpperCamelCase_      				=     read_limit
              UpperCamelCase_      				=     0

       def __enter__(						self:       Dict						)				->       Tuple:




              """simple docstring"""


              UpperCamelCase_      				=     open(self.filepath    ,      "rb"						)
              self.f.seek(self.seek_from						)
              return self

       def __len__(						self:       List[Any]						)				->       Optional[int]:




              """simple docstring"""


              UpperCamelCase_      				=     os.fstat(self.f.fileno()						).st_size
              return min(self.read_limit    ,      total_length - self.seek_from						)

       def lowercase       (						self:       Union[str, Any]    ,      _SCREAMING_SNAKE_CASE:       Optional[int]=-1						)				->       Union[str, Any]:




              """simple docstring"""


              if self.n_seen >= self.read_limit:
                     return b""
              UpperCamelCase_      				=     self.read_limit - self.n_seen
              UpperCamelCase_      				=     self.f.read(remaining_amount if n < 0 else min(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)						)
              self.n_seen += len(_SCREAMING_SNAKE_CASE						)
              return data

       def __iter__(						self:       Optional[int]						)				->       Union[str, Any]:




              """simple docstring"""


              yield self.read(n=4 * 1024 * 1024						)



       def __exit__(						self:       Dict    ,      *_SCREAMING_SNAKE_CASE:       str						)				->       str:




              """simple docstring"""


              self.f.close()



class 							_UpperCamelCase    :

       def __init__(						self:       List[Any]    ,      _SCREAMING_SNAKE_CASE:       List[str]						)				->       Optional[int]:




              """simple docstring"""


              UpperCamelCase_      				=     args



       def lowercase       (						self:       Optional[int]						)				->       Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_      				=     json.loads(sys.stdin.readline().strip()						)
              if not (init_msg.get("event"						) == "init" and init_msg.get("operation"						) == "upload"):
                     write_msg({"error": {"code": 32, "message": "Wrong lfs init operation"}}						)
                     sys.exit(1						)

              # The transfer process should use the information it needs from the
              # initiation structure, and also perform any one-off setup tasks it
              # needs to do. It should then respond on stdout with a simple empty
              # confirmation structure, as follows:
              write_msg({}						)

              # After the initiation exchange, git-lfs will send any number of
              # transfer requests to the stdin of the transfer process, in a serial sequence.
              while True:
                     UpperCamelCase_      				=     read_msg()
                     if msg is None:
                            # When all transfers have been processed, git-lfs will send
                            # a terminate event to the stdin of the transfer process.
                            # On receiving this message the transfer process should
                            # clean up and terminate. No response is expected.
                            sys.exit(0						)

                     UpperCamelCase_      				=     msg["oid"]
                     UpperCamelCase_      				=     msg["path"]
                     UpperCamelCase_      				=     msg["action"]["href"]
                     UpperCamelCase_      				=     msg["action"]["header"]
                     UpperCamelCase_      				=     int(header.pop("chunk_size"						)						)
                     UpperCamelCase_      				=     list(header.values()						)

                     UpperCamelCase_      				=     []
                     for i, presigned_url in enumerate(_SCREAMING_SNAKE_CASE						):
                            with FileSlice(_SCREAMING_SNAKE_CASE    ,      seek_from=i * chunk_size    ,      read_limit=_SCREAMING_SNAKE_CASE						) as data:
                                   UpperCamelCase_      				=     requests.put(_SCREAMING_SNAKE_CASE    ,      data=_SCREAMING_SNAKE_CASE						)
                                   r.raise_for_status()
                                   parts.append(
                                       {
                                           "etag": r.headers.get("etag"						),
                                           "partNumber": i + 1,
                                       }						)
                                   # In order to support progress reporting while data is uploading / downloading,
                                   # the transfer process should post messages to stdout
                                   write_msg(
                                       {
                                           "event": "progress",
                                           "oid": oid,
                                           "bytesSoFar": (i + 1) * chunk_size,
                                           "bytesSinceLast": chunk_size,
                                       }						)
                                   # Not precise but that's ok.

                     UpperCamelCase_      				=     requests.post(
                         _SCREAMING_SNAKE_CASE    ,      json={
                             "oid": oid,
                             "parts": parts,
                         }    ,      )
                     r.raise_for_status()

                     write_msg({"event": "complete", "oid": oid}						)








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

              UpperCamelCase_      				=     LoRAAttnProcessor(hidden_size=UpperCamelCase_    ,						cross_attention_dim=UpperCamelCase_     )
              UpperCamelCase_      				=     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   ):
       _UpperCamelCase :    Optional[Any]   			=      UNetaDConditionModel
       _UpperCamelCase :    List[Any]   			=      '''sample'''

       @property
       def lowercase       (						self:       Dict						)				->       Tuple:




              """simple docstring"""


              UpperCamelCase_      				=     4
              UpperCamelCase_      				=     4
              UpperCamelCase_      				=     4
              UpperCamelCase_      				=     (32, 32)

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

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

       @property
       def lowercase       (						self:       Tuple						)				->       str:




              """simple docstring"""


              return (4, 4, 32, 32)

       @property
       def lowercase       (						self:       List[str]						)				->       List[str]:




              """simple docstring"""


              return (4, 4, 32, 32)

       def lowercase       (						self:       Optional[Any]						)				->       Optional[Any]:




              """simple docstring"""


              UpperCamelCase_      				=     {
                  "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,
              }
              UpperCamelCase_      				=     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:       str						)				->       Optional[Any]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()
              UpperCamelCase_      				=     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 lowercase       (						self:       List[str]						)				->       List[str]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     32

              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              model.eval()

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)

                     if isinstance(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						):
                            UpperCamelCase_      				=     output.sample

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

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




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()
              UpperCamelCase_      				=     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						)

                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)
                     if isinstance(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						):
                            UpperCamelCase_      				=     first.sample

                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)
                     if isinstance(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						):
                            UpperCamelCase_      				=     second.sample

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

       def lowercase       (						self:       int						)				->       List[str]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              model.eval()

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

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

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

       def lowercase       (						self:       Optional[Any]						)				->       List[Any]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              UpperCamelCase_      				=     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():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.0}						).sample
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.5}						).sample
                     UpperCamelCase_      				=     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 lowercase       (						self:       Any						)				->       int:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     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						)
                     UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
                     new_model.to(_SCREAMING_SNAKE_CASE						)
                     new_model.load_attn_procs(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     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 lowercase       (						self:       List[str]						)				->       Optional[int]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     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						)
                     UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
                     new_model.to(_SCREAMING_SNAKE_CASE						)
                     new_model.load_attn_procs(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     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 lowercase       (						self:       int						)				->       Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              UpperCamelCase_      				=     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						)
                     UpperCamelCase_      				=     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 lowercase       (						self:       Any						)				->       List[Any]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              UpperCamelCase_      				=     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						)
                     UpperCamelCase_      				=     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 lowercase       (						self:       Optional[Any]						)				->       Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)

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

              model.set_attn_processor(AttnProcessor()						)

              with torch.no_grad():
                     UpperCamelCase_      				=     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 lowercase       (						self:       Union[str, Any]						)				->       Dict:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     4

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)

              # default
              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

                     model.enable_xformers_memory_efficient_attention()
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

                     model.disable_xformers_memory_efficient_attention()
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

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



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




              """simple docstring"""


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

              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              model.eval()

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)[0]

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

