"""simple docstring"""
import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline





def    lowerCAmelCase__    (			_UpperCamelCase       :				str ,			_UpperCamelCase       :				Optional[Any] ,			_UpperCamelCase       :				Optional[Any] ,			_UpperCamelCase       :				Optional[int] ,			_UpperCamelCase       :				int					)					->				Optional[int]:





						"""simple docstring"""






						snake_case								=			StableDiffusionPipeline.from_pretrained(_UpperCamelCase ,			torch_dtype=torch.floataa					)

						# load LoRA weight from .safetensors
						snake_case								=			load_file(_UpperCamelCase					)

						snake_case								=			[]

						# directly update weight in diffusers model
						for key in state_dict:
												# it is suggested to print out the key, it usually will be something like below
												# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

												# as we have set the alpha beforehand, so just skip
												if ".alpha" in key or key in visited:
																		continue

												if "text" in key:
																		snake_case								=			key.split('.'					)[0].split(LORA_PREFIX_TEXT_ENCODER + '_'					)[-1].split('_'					)
																		snake_case								=			pipeline.text_encoder
												else:
																		snake_case								=			key.split('.'					)[0].split(LORA_PREFIX_UNET + '_'					)[-1].split('_'					)
																		snake_case								=			pipeline.unet

												# find the target layer
												snake_case								=			layer_infos.pop(0					)
												while len(_UpperCamelCase					) > -1:
																		try:
																								snake_case								=			curr_layer.__getattr__(_UpperCamelCase					)
																								if len(_UpperCamelCase					) > 0:
																														snake_case								=			layer_infos.pop(0					)
																								elif len(_UpperCamelCase					) == 0:
																														break
																		except Exception:
																								if len(_UpperCamelCase					) > 0:
																														temp_name += "_" + layer_infos.pop(0					)
																								else:
																														snake_case								=			layer_infos.pop(0					)

												snake_case								=			[]
												if "lora_down" in key:
																		pair_keys.append(key.replace('lora_down' ,			'lora_up'					)					)
																		pair_keys.append(_UpperCamelCase					)
												else:
																		pair_keys.append(_UpperCamelCase					)
																		pair_keys.append(key.replace('lora_up' ,			'lora_down'					)					)

												# update weight
												if len(state_dict[pair_keys[0]].shape					) == 4:
																		snake_case								=			state_dict[pair_keys[0]].squeeze(3					).squeeze(2					).to(torch.floataa					)
																		snake_case								=			state_dict[pair_keys[1]].squeeze(3					).squeeze(2					).to(torch.floataa					)
																		curr_layer.weight.data += alpha * torch.mm(_UpperCamelCase ,			_UpperCamelCase					).unsqueeze(2					).unsqueeze(3					)
												else:
																		snake_case								=			state_dict[pair_keys[0]].to(torch.floataa					)
																		snake_case								=			state_dict[pair_keys[1]].to(torch.floataa					)
																		curr_layer.weight.data += alpha * torch.mm(_UpperCamelCase ,			_UpperCamelCase					)

												# update visited list
												for item in pair_keys:
																		visited.append(_UpperCamelCase					)

						return pipeline


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      						=  argparse.ArgumentParser()

					parser.add_argument(
					    "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
					)
					parser.add_argument(
					    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
					)
					parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
					parser.add_argument(
					    "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
					)
					parser.add_argument(
					    "--lora_prefix_text_encoder",
					    default="lora_te",
					    type=str,
					    help="The prefix of text encoder weight in safetensors",
					)
					parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
					parser.add_argument(
					    "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
					)
					parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")

					SCREAMING_SNAKE_CASE__      						=  parser.parse_args()

					SCREAMING_SNAKE_CASE__      						=  args.base_model_path
					SCREAMING_SNAKE_CASE__      						=  args.checkpoint_path
					SCREAMING_SNAKE_CASE__      						=  args.dump_path
					SCREAMING_SNAKE_CASE__      						=  args.lora_prefix_unet
					SCREAMING_SNAKE_CASE__      						=  args.lora_prefix_text_encoder
					SCREAMING_SNAKE_CASE__      						=  args.alpha

					SCREAMING_SNAKE_CASE__      						=  convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

					SCREAMING_SNAKE_CASE__      						=  pipe.to(args.device)
					pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



"""simple docstring"""
import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				@require_torch
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

										snake_case								=			'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

										snake_case								=			'\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")\nsocket.socket = offline_socket\n        '

										# Force fetching the files so that we can use the cache
										snake_case								=			'hf-internal-testing/tiny-random-bert'
										BertConfig.from_pretrained(lowerCAmelCase    )
										BertModel.from_pretrained(lowerCAmelCase    )
										BertTokenizer.from_pretrained(lowerCAmelCase    )
										pipeline(task='fill-mask'		,    model=lowerCAmelCase    )

										# baseline - just load from_pretrained with normal network
										snake_case								=			[sys.executable, '-c', '\n'.join([load, run, mock]    )]

										# should succeed
										snake_case								=			self.get_env()
										# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
										snake_case								=			'1'
										snake_case								=			subprocess.run(lowerCAmelCase		,    env=lowerCAmelCase		,    check=lowerCAmelCase		,    capture_output=lowerCAmelCase    )
										self.assertEqual(result.returncode		,    0		,    result.stderr    )
										self.assertIn('success'		,    result.stdout.decode()    )

				@require_torch
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

										snake_case								=			'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

										snake_case								=			'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")\nsocket.socket = offline_socket\n        '

										# Force fetching the files so that we can use the cache
										snake_case								=			'hf-internal-testing/tiny-random-bert'
										BertConfig.from_pretrained(lowerCAmelCase    )
										BertModel.from_pretrained(lowerCAmelCase    )
										BertTokenizer.from_pretrained(lowerCAmelCase    )
										pipeline(task='fill-mask'		,    model=lowerCAmelCase    )

										# baseline - just load from_pretrained with normal network
										snake_case								=			[sys.executable, '-c', '\n'.join([load, run, mock]    )]

										# should succeed
										snake_case								=			self.get_env()
										snake_case								=			subprocess.run(lowerCAmelCase		,    env=lowerCAmelCase		,    check=lowerCAmelCase		,    capture_output=lowerCAmelCase    )
										self.assertEqual(result.returncode		,    0		,    result.stderr    )
										self.assertIn('success'		,    result.stdout.decode()    )

				@require_torch
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			'\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        '

										snake_case								=			'\nmname = "hf-internal-testing/tiny-random-bert-sharded"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint("success")\n        '

										snake_case								=			'\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")\nsocket.socket = offline_socket\n        '

										# baseline - just load from_pretrained with normal network
										snake_case								=			[sys.executable, '-c', '\n'.join([load, run]    )]

										# should succeed
										snake_case								=			self.get_env()
										snake_case								=			subprocess.run(lowerCAmelCase		,    env=lowerCAmelCase		,    check=lowerCAmelCase		,    capture_output=lowerCAmelCase    )
										self.assertEqual(result.returncode		,    0		,    result.stderr    )
										self.assertIn('success'		,    result.stdout.decode()    )

										# next emulate no network
										snake_case								=			[sys.executable, '-c', '\n'.join([load, mock, run]    )]

										# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
										# env["TRANSFORMERS_OFFLINE"] = "0"
										# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
										# self.assertEqual(result.returncode, 1, result.stderr)

										# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
										snake_case								=			'1'
										snake_case								=			subprocess.run(lowerCAmelCase		,    env=lowerCAmelCase		,    check=lowerCAmelCase		,    capture_output=lowerCAmelCase    )
										self.assertEqual(result.returncode		,    0		,    result.stderr    )
										self.assertIn('success'		,    result.stdout.decode()    )

				@require_torch
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			'\nfrom transformers import pipeline\n        '
										snake_case								=			'\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n        '

										snake_case								=			'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n        '
										snake_case								=			self.get_env()
										snake_case								=			'1'
										snake_case								=			[sys.executable, '-c', '\n'.join([load, mock, run]    )]
										snake_case								=			subprocess.run(lowerCAmelCase		,    env=lowerCAmelCase		,    check=lowerCAmelCase		,    capture_output=lowerCAmelCase    )
										self.assertEqual(result.returncode		,    1		,    result.stderr    )
										self.assertIn(
										    'You cannot infer task automatically within `pipeline` when using offline mode'		,    result.stderr.decode().replace('\n'		,    ''    )		,    )

				@require_torch
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			'\nfrom transformers import AutoModel\n        '
										snake_case								=			'\nmname = "hf-internal-testing/test_dynamic_model"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint("success")\n        '

										# baseline - just load from_pretrained with normal network
										snake_case								=			[sys.executable, '-c', '\n'.join([load, run]    )]

										# should succeed
										snake_case								=			self.get_env()
										snake_case								=			subprocess.run(lowerCAmelCase		,    env=lowerCAmelCase		,    check=lowerCAmelCase		,    capture_output=lowerCAmelCase    )
										self.assertEqual(result.returncode		,    0		,    result.stderr    )
										self.assertIn('success'		,    result.stdout.decode()    )

										# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
										snake_case								=			'1'
										snake_case								=			subprocess.run(lowerCAmelCase		,    env=lowerCAmelCase		,    check=lowerCAmelCase		,    capture_output=lowerCAmelCase    )
										self.assertEqual(result.returncode		,    0		,    result.stderr    )
										self.assertIn('success'		,    result.stdout.decode()    )


