





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

__A							   =							[
    # (stable-diffusion, HF Diffusers)
    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
    ("input_blocks.0.0.weight", "conv_in.weight"),
    ("input_blocks.0.0.bias", "conv_in.bias"),
    ("out.0.weight", "conv_norm_out.weight"),
    ("out.0.bias", "conv_norm_out.bias"),
    ("out.2.weight", "conv_out.weight"),
    ("out.2.bias", "conv_out.bias"),
]

__A							   =							[
    # (stable-diffusion, HF Diffusers)
    ("in_layers.0", "norm1"),
    ("in_layers.2", "conv1"),
    ("out_layers.0", "norm2"),
    ("out_layers.3", "conv2"),
    ("emb_layers.1", "time_emb_proj"),
    ("skip_connection", "conv_shortcut"),
]

__A							   =							[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
     # loop over downblocks/upblocks

     for j in range(2):
          # loop over resnets/attentions for downblocks
          __A							   =							f'down_blocks.{i}.resnets.{j}.'
          __A							   =							f'input_blocks.{3*i + j + 1}.0.'
          unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

          if i < 3:
               # no attention layers in down_blocks.3
               __A							   =							f'down_blocks.{i}.attentions.{j}.'
               __A							   =							f'input_blocks.{3*i + j + 1}.1.'
               unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

     for j in range(3):
          # loop over resnets/attentions for upblocks
          __A							   =							f'up_blocks.{i}.resnets.{j}.'
          __A							   =							f'output_blocks.{3*i + j}.0.'
          unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

          if i > 0:
               # no attention layers in up_blocks.0
               __A							   =							f'up_blocks.{i}.attentions.{j}.'
               __A							   =							f'output_blocks.{3*i + j}.1.'
               unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

     if i < 3:
          # no downsample in down_blocks.3
          __A							   =							f'down_blocks.{i}.downsamplers.0.conv.'
          __A							   =							f'input_blocks.{3*(i+1)}.0.op.'
          unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

          # no upsample in up_blocks.3
          __A							   =							f'up_blocks.{i}.upsamplers.0.'
          __A							   =							f'output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'
          unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

__A							   =							"mid_block.attentions.0."
__A							   =							"middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
     __A							   =							f'mid_block.resnets.{j}.'
     __A							   =							f'middle_block.{2*j}.'
     unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))





def 	lowerCAmelCase_					(			__a						)	->						Tuple:







  """simple docstring"""





  lowerCamelCase__: Any         ={k: k for k in unet_state_dict.keys()}
  for sd_name, hf_name in unet_conversion_map:
    lowerCamelCase__: List[str]         =sd_name
  for k, v in mapping.items():
    if "resnets" in k:
      for sd_part, hf_part in unet_conversion_map_resnet:
        lowerCamelCase__: int         =v.replace(__a      ,       __a						)
      lowerCamelCase__: List[str]         =v
  for k, v in mapping.items():
    for sd_part, hf_part in unet_conversion_map_layer:
      lowerCamelCase__: Dict         =v.replace(__a      ,       __a						)
    lowerCamelCase__: List[str]         =v
  lowerCamelCase__: List[str]         ={v: unet_state_dict[k] for k, v in mapping.items()}
  return new_state_dict


# ================#
# VAE Conversion #
# ================#

__A							   =							[
    # (stable-diffusion, HF Diffusers)
    ("nin_shortcut", "conv_shortcut"),
    ("norm_out", "conv_norm_out"),
    ("mid.attn_1.", "mid_block.attentions.0."),
]

for i in range(4):
     # down_blocks have two resnets
     for j in range(2):
          __A							   =							f'encoder.down_blocks.{i}.resnets.{j}.'
          __A							   =							f'encoder.down.{i}.block.{j}.'
          vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

     if i < 3:
          __A							   =							f'down_blocks.{i}.downsamplers.0.'
          __A							   =							f'down.{i}.downsample.'
          vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

          __A							   =							f'up_blocks.{i}.upsamplers.0.'
          __A							   =							f'up.{3-i}.upsample.'
          vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

     # up_blocks have three resnets
     # also, up blocks in hf are numbered in reverse from sd
     for j in range(3):
          __A							   =							f'decoder.up_blocks.{i}.resnets.{j}.'
          __A							   =							f'decoder.up.{3-i}.block.{j}.'
          vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
     __A							   =							f'mid_block.resnets.{i}.'
     __A							   =							f'mid.block_{i+1}.'
     vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


__A							   =							[
    # (stable-diffusion, HF Diffusers)
    ("norm.", "group_norm."),
    ("q.", "query."),
    ("k.", "key."),
    ("v.", "value."),
    ("proj_out.", "proj_attn."),
]





def 	lowerCAmelCase_					(			__a						)	->						int:







  """simple docstring"""





  return w.reshape(*w.shape      ,       1      ,       1						)





def 	lowerCAmelCase_					(			__a						)	->						Any:







  """simple docstring"""





  lowerCamelCase__: Optional[Any]         ={k: k for k in vae_state_dict.keys()}
  for k, v in mapping.items():
    for sd_part, hf_part in vae_conversion_map:
      lowerCamelCase__: Dict         =v.replace(__a      ,       __a						)
    lowerCamelCase__: Dict         =v
  for k, v in mapping.items():
    if "attentions" in k:
      for sd_part, hf_part in vae_conversion_map_attn:
        lowerCamelCase__: Optional[int]         =v.replace(__a      ,       __a						)
      lowerCamelCase__: Dict         =v
  lowerCamelCase__: Any         ={v: vae_state_dict[k] for k, v in mapping.items()}
  lowerCamelCase__: List[Any]         =["q", "k", "v", "proj_out"]
  for k, v in new_state_dict.items():
    for weight_name in weights_to_convert:
      if F"""mid.attn_1.{weight_name}.weight""" in k:
        print(F"""Reshaping {k} for SD format"""						)
        lowerCamelCase__: Any         =reshape_weight_for_sd(__a						)
  return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


__A							   =							[
    # (stable-diffusion, HF Diffusers)
    ("resblocks.", "text_model.encoder.layers."),
    ("ln_1", "layer_norm1"),
    ("ln_2", "layer_norm2"),
    (".c_fc.", ".fc1."),
    (".c_proj.", ".fc2."),
    (".attn", ".self_attn"),
    ("ln_final.", "transformer.text_model.final_layer_norm."),
    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
]
__A							   =							{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
__A							   =							re.compile("|".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
__A							   =							{"q": 0, "k": 1, "v": 2}





def 	lowerCAmelCase_					(			__a						)	->						Dict:







  """simple docstring"""





  lowerCamelCase__: List[str]         ={}
  lowerCamelCase__: Tuple         ={}
  lowerCamelCase__: List[str]         ={}
  for k, v in text_enc_dict.items():
    if (
        k.endswith(".self_attn.q_proj.weight"						)
        or k.endswith(".self_attn.k_proj.weight"						)
        or k.endswith(".self_attn.v_proj.weight"						)
    ):
      lowerCamelCase__: int         =k[: -len(".q_proj.weight"						)]
      lowerCamelCase__: str         =k[-len("q_proj.weight"						)]
      if k_pre not in capture_qkv_weight:
        lowerCamelCase__: Union[str, Any]         =[None, None, None]
      lowerCamelCase__: Optional[Any]         =v
      continue

    if (
        k.endswith(".self_attn.q_proj.bias"						)
        or k.endswith(".self_attn.k_proj.bias"						)
        or k.endswith(".self_attn.v_proj.bias"						)
    ):
      lowerCamelCase__: Dict         =k[: -len(".q_proj.bias"						)]
      lowerCamelCase__: Tuple         =k[-len("q_proj.bias"						)]
      if k_pre not in capture_qkv_bias:
        lowerCamelCase__: int         =[None, None, None]
      lowerCamelCase__: str         =v
      continue

    lowerCamelCase__: str         =textenc_pattern.sub(lambda __a						: protected[re.escape(m.group(0						)						)]      ,       __a						)
    lowerCamelCase__: Union[str, Any]         =v

  for k_pre, tensors in capture_qkv_weight.items():
    if None in tensors:
      raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"						)
    lowerCamelCase__: int         =textenc_pattern.sub(lambda __a						: protected[re.escape(m.group(0						)						)]      ,       __a						)
    lowerCamelCase__: Any         =torch.cat(__a						)

  for k_pre, tensors in capture_qkv_bias.items():
    if None in tensors:
      raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"						)
    lowerCamelCase__: Optional[Any]         =textenc_pattern.sub(lambda __a						: protected[re.escape(m.group(0						)						)]      ,       __a						)
    lowerCamelCase__: str         =torch.cat(__a						)

  return new_state_dict





def 	lowerCAmelCase_					(			__a						)	->						str:







  """simple docstring"""





  return text_enc_dict


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

     parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
     parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
     parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
     parser.add_argument(
         "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt."
     )

     __A							   =							parser.parse_args()

     assert args.model_path is not None, "Must provide a model path!"

     assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

     # Path for safetensors
     __A							   =							osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
     __A							   =							osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
     __A							   =							osp.join(args.model_path, "text_encoder", "model.safetensors")

     # Load models from safetensors if it exists, if it doesn't pytorch
     if osp.exists(unet_path):
          __A							   =							load_file(unet_path, device="cpu")
     else:
          __A							   =							osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
          __A							   =							torch.load(unet_path, map_location="cpu")

     if osp.exists(vae_path):
          __A							   =							load_file(vae_path, device="cpu")
     else:
          __A							   =							osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
          __A							   =							torch.load(vae_path, map_location="cpu")

     if osp.exists(text_enc_path):
          __A							   =							load_file(text_enc_path, device="cpu")
     else:
          __A							   =							osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
          __A							   =							torch.load(text_enc_path, map_location="cpu")

     # Convert the UNet model
     __A							   =							convert_unet_state_dict(unet_state_dict)
     __A							   =							{"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}

     # Convert the VAE model
     __A							   =							convert_vae_state_dict(vae_state_dict)
     __A							   =							{"first_stage_model." + k: v for k, v in vae_state_dict.items()}

     # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
     __A							   =							"text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict

     if is_vaa_model:
          # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
          __A							   =							{"transformer." + k: v for k, v in text_enc_dict.items()}
          __A							   =							convert_text_enc_state_dict_vaa(text_enc_dict)
          __A							   =							{"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
     else:
          __A							   =							convert_text_enc_state_dict(text_enc_dict)
          __A							   =							{"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

     # Put together new checkpoint
     __A							   =							{**unet_state_dict, **vae_state_dict, **text_enc_dict}
     if args.half:
          __A							   =							{k: v.half() for k, v in state_dict.items()}

     if args.use_safetensors:
          save_file(state_dict, args.checkpoint_path)
     else:
          __A							   =							{"state_dict": state_dict}
          torch.save(state_dict, args.checkpoint_path)


"""simple docstring"""






import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

__lowerCAmelCase       :			List[str]   	=[
    """cross_validation.py""",
    """gradient_accumulation.py""",
    """local_sgd.py""",
    """multi_process_metrics.py""",
    """memory.py""",
    """automatic_gradient_accumulation.py""",
    """fsdp_with_peak_mem_tracking.py""",
    """deepspeed_with_config_support.py""",
    """megatron_lm_gpt_pretraining.py""",
]





class 					_A		(			unittest.TestCase      ):

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None		):







            """simple docstring"""



            lowercase								=      None
            lowercase								=      os.path.abspath(os.path.join("""examples"""						,			"""by_feature"""		)		)
            lowercase								=      os.path.abspath("""examples"""		)
            for item in os.listdir(__lowerCAmelCase		):
                  if item not in EXCLUDE_EXAMPLES:
                        lowercase								=      os.path.join(__lowerCAmelCase						,			__lowerCAmelCase		)
                        if os.path.isfile(__lowerCAmelCase		) and ".py" in item_path:
                              with self.subTest(
                                  tested_script=__lowerCAmelCase						,			feature_script=__lowerCAmelCase						,			tested_section="""main()""" if parser_only else """training_function()"""						,			):
                                    lowercase								=      compare_against_test(
                                        os.path.join(__lowerCAmelCase						,			__lowerCAmelCase		)						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)
                                    lowercase								=      """\n""".join(__lowerCAmelCase		)
                                    if special_strings is not None:
                                          for string in special_strings:
                                                lowercase								=      diff.replace(__lowerCAmelCase						,			""""""		)
                                    self.assertEqual(__lowerCAmelCase						,			""""""		)

      def 							A__			(		self		):







            """simple docstring"""



            self.one_complete_example("""complete_nlp_example.py"""						,			__lowerCAmelCase		)
            self.one_complete_example("""complete_nlp_example.py"""						,			__lowerCAmelCase		)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      os.path.abspath(os.path.join("""examples"""						,			"""cv_example.py"""		)		)
            lowercase								=      [
                """ """ * 16 + """{\n\n""",
                """ """ * 20 + """\"accuracy\": eval_metric[\"accuracy\"],\n\n""",
                """ """ * 20 + """\"f1\": eval_metric[\"f1\"],\n\n""",
                """ """ * 20 + """\"train_loss\": total_loss.item() / len(train_dataloader),\n\n""",
                """ """ * 20 + """\"epoch\": epoch,\n\n""",
                """ """ * 16 + """},\n\n""",
                """ """ * 16 + """step=epoch,\n""",
                """ """ * 12,
                """ """ * 8 + """for step, batch in enumerate(active_dataloader):\n""",
            ]
            self.one_complete_example("""complete_cv_example.py"""						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)
            self.one_complete_example("""complete_cv_example.py"""						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)






@mock.patch.dict(os.environ    ,       {'TESTING_MOCKED_DATALOADERS': '1'}      )
class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Any				    =      False

      @classmethod
      def 							A__			(		cls		):







            """simple docstring"""



            super().setUpClass()
            lowercase								=      tempfile.mkdtemp()
            lowercase								=      os.path.join(cls._tmpdir						,			"""default_config.yml"""		)

            write_basic_config(save_location=cls.configPath		)
            lowercase								=      ["""accelerate""", """launch""", """--config_file""", cls.configPath]

      @classmethod
      def 							A__			(		cls		):







            """simple docstring"""



            super().tearDownClass()
            shutil.rmtree(cls._tmpdir		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '.split()
            run_command(self._launch_args + testargs		)
            self.assertTrue(os.path.exists(os.path.join(self.tmpdir						,			"""epoch_0"""		)		)		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
            lowercase								=      run_command(self._launch_args + testargs		)
            self.assertTrue(os.path.exists(os.path.join(self.tmpdir						,			"""step_2"""		)		)		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir						,			"epoch_0"		)}\n        '.split()
            lowercase								=      run_command(self._launch_args + testargs						,			return_stdout=__lowerCAmelCase		)
            self.assertNotIn("""epoch 0:"""						,			__lowerCAmelCase		)
            self.assertIn("""epoch 1:"""						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir						,			"step_2"		)}\n        '.split()
            lowercase								=      run_command(self._launch_args + testargs						,			return_stdout=__lowerCAmelCase		)
            if torch.cuda.is_available():
                  lowercase								=      torch.cuda.device_count()
            else:
                  lowercase								=      1
            if num_processes > 1:
                  self.assertNotIn("""epoch 0:"""						,			__lowerCAmelCase		)
                  self.assertIn("""epoch 1:"""						,			__lowerCAmelCase		)
            else:
                  self.assertIn("""epoch 0:"""						,			__lowerCAmelCase		)
                  self.assertIn("""epoch 1:"""						,			__lowerCAmelCase		)

      @slow
      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      """
        examples/by_feature/cross_validation.py
        --num_folds 2
        """.split()
            with mock.patch.dict(os.environ						,			{"""TESTING_MOCKED_DATALOADERS""": """0"""}		):
                  lowercase								=      run_command(self._launch_args + testargs						,			return_stdout=__lowerCAmelCase		)
                  lowercase								=      re.findall("""({.+})"""						,			__lowerCAmelCase		)
                  lowercase								=      [r for r in results if """accuracy""" in r][-1]
                  lowercase								=      ast.literal_eval(__lowerCAmelCase		)
                  self.assertGreaterEqual(results["""accuracy"""]						,			0.7_5		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      ["""examples/by_feature/multi_process_metrics.py"""]
            run_command(self._launch_args + testargs		)

      @require_trackers
      @mock.patch.dict(os.environ						,			{"""WANDB_MODE""": """offline"""}		)
      def 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdir:
                  lowercase								=      f'\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '.split()
                  run_command(self._launch_args + testargs		)
                  self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase						,			"""tracking"""		)		)		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      ["""examples/by_feature/gradient_accumulation.py"""]
            run_command(self._launch_args + testargs		)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      ["""examples/by_feature/local_sgd.py"""]
            run_command(self._launch_args + testargs		)



