

'''simple docstring'''




import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

_lowerCAmelCase         =						[
    # (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"),
]

_lowerCAmelCase         =						[
    # (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"),
]

_lowerCAmelCase         =						[]
# 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
            _lowerCAmelCase         =						F'''down_blocks.{i}.resnets.{j}.'''
            _lowerCAmelCase         =						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
                  _lowerCAmelCase         =						F'''down_blocks.{i}.attentions.{j}.'''
                  _lowerCAmelCase         =						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
            _lowerCAmelCase         =						F'''up_blocks.{i}.resnets.{j}.'''
            _lowerCAmelCase         =						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
                  _lowerCAmelCase         =						F'''up_blocks.{i}.attentions.{j}.'''
                  _lowerCAmelCase         =						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
            _lowerCAmelCase         =						F'''down_blocks.{i}.downsamplers.0.conv.'''
            _lowerCAmelCase         =						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
            _lowerCAmelCase         =						F'''up_blocks.{i}.upsamplers.0.'''
            _lowerCAmelCase         =						F'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
            unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

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

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




def 						UpperCamelCase	(			a			)				->     Dict:






     '''simple docstring'''
     __magic_name__						       =    {k: k for k in unet_state_dict.keys()}
     for sd_name, hf_name in unet_conversion_map:
          __magic_name__						       =    sd_name
     for k, v in mapping.items():
          if "resnets" in k:
               for sd_part, hf_part in unet_conversion_map_resnet:
                    __magic_name__						       =    v.replace(a		,						a			)
               __magic_name__						       =    v
     for k, v in mapping.items():
          for sd_part, hf_part in unet_conversion_map_layer:
               __magic_name__						       =    v.replace(a		,						a			)
          __magic_name__						       =    v
     __magic_name__						       =    {v: unet_state_dict[k] for k, v in mapping.items()}
     return new_state_dict


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

_lowerCAmelCase         =						[
    # (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):
            _lowerCAmelCase         =						F'''encoder.down_blocks.{i}.resnets.{j}.'''
            _lowerCAmelCase         =						F'''encoder.down.{i}.block.{j}.'''
            vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

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

            _lowerCAmelCase         =						F'''up_blocks.{i}.upsamplers.0.'''
            _lowerCAmelCase         =						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):
            _lowerCAmelCase         =						F'''decoder.up_blocks.{i}.resnets.{j}.'''
            _lowerCAmelCase         =						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):
      _lowerCAmelCase         =						F'''mid_block.resnets.{i}.'''
      _lowerCAmelCase         =						F'''mid.block_{i+1}.'''
      vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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




def 						UpperCamelCase	(			a			)				->     Dict:






     '''simple docstring'''
     return w.reshape(*w.shape		,						1		,						1			)




def 						UpperCamelCase	(			a			)				->     Tuple:






     '''simple docstring'''
     __magic_name__						       =    {k: k for k in vae_state_dict.keys()}
     for k, v in mapping.items():
          for sd_part, hf_part in vae_conversion_map:
               __magic_name__						       =    v.replace(a		,						a			)
          __magic_name__						       =    v
     for k, v in mapping.items():
          if "attentions" in k:
               for sd_part, hf_part in vae_conversion_map_attn:
                    __magic_name__						       =    v.replace(a		,						a			)
               __magic_name__						       =    v
     __magic_name__						       =    {v: vae_state_dict[k] for k, v in mapping.items()}
     __magic_name__						       =    ['''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'''			)
                    __magic_name__						       =    reshape_weight_for_sd(a			)
     return new_state_dict


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


_lowerCAmelCase         =						[
    # (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"),
]
_lowerCAmelCase         =						{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
_lowerCAmelCase         =						re.compile("|".join(protected.keys()))

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




def 						UpperCamelCase	(			a			)				->     Tuple:






     '''simple docstring'''
     __magic_name__						       =    {}
     __magic_name__						       =    {}
     __magic_name__						       =    {}
     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'''			)
          ):
               __magic_name__						       =    k[: -len('''.q_proj.weight'''			)]
               __magic_name__						       =    k[-len('''q_proj.weight'''			)]
               if k_pre not in capture_qkv_weight:
                    __magic_name__						       =    [None, None, None]
               __magic_name__						       =    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'''			)
          ):
               __magic_name__						       =    k[: -len('''.q_proj.bias'''			)]
               __magic_name__						       =    k[-len('''q_proj.bias'''			)]
               if k_pre not in capture_qkv_bias:
                    __magic_name__						       =    [None, None, None]
               __magic_name__						       =    v
               continue

          __magic_name__						       =    textenc_pattern.sub(lambda a			: protected[re.escape(m.group(0			)			)]		,						a			)
          __magic_name__						       =    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'''			)
          __magic_name__						       =    textenc_pattern.sub(lambda a			: protected[re.escape(m.group(0			)			)]		,						a			)
          __magic_name__						       =    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'''			)
          __magic_name__						       =    textenc_pattern.sub(lambda a			: protected[re.escape(m.group(0			)			)]		,						a			)
          __magic_name__						       =    torch.cat(a			)

     return new_state_dict




def 						UpperCamelCase	(			a			)				->     int:






     '''simple docstring'''
     return text_enc_dict


if __name__ == "__main__":
      _lowerCAmelCase         =						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."
      )

      _lowerCAmelCase         =						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
      _lowerCAmelCase         =						osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
      _lowerCAmelCase         =						osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
      _lowerCAmelCase         =						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):
            _lowerCAmelCase         =						load_file(unet_path, device="cpu")
      else:
            _lowerCAmelCase         =						osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
            _lowerCAmelCase         =						torch.load(unet_path, map_location="cpu")

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

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

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

      # Convert the VAE model
      _lowerCAmelCase         =						convert_vae_state_dict(vae_state_dict)
      _lowerCAmelCase         =						{"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
      _lowerCAmelCase         =						"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
            _lowerCAmelCase         =						{"transformer." + k: v for k, v in text_enc_dict.items()}
            _lowerCAmelCase         =						convert_text_enc_state_dict_vaa(text_enc_dict)
            _lowerCAmelCase         =						{"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
      else:
            _lowerCAmelCase         =						convert_text_enc_state_dict(text_enc_dict)
            _lowerCAmelCase         =						{"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

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

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




'''simple docstring'''




import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


_lowerCAmelCase         =						get_tests_dir("fixtures")
_lowerCAmelCase         =						get_tests_dir("fixtures/dummy_feature_extractor_config.json")
_lowerCAmelCase         =						get_tests_dir("fixtures/dummy-config.json")






class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    0



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h'''      )
      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )
      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      with tempfile.TemporaryDirectory() as tmpdirname:
           __magic_name__						       =    WavaVecaConfig()

           # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      ).to_dict()

           config_dict.pop('''feature_extractor_type'''      )
           __magic_name__						       =    WavaVecaFeatureExtractor(**a__      )

           # save in new folder
           model_config.save_pretrained(a__      )
           config.save_pretrained(a__      )

           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )

           # make sure private variable is not incorrectly saved
           __magic_name__						       =    json.loads(config.to_json_string()      )
           self.assertTrue('''_processor_class''' not in dict_as_saved      )

      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )
      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   str      ):
      with self.assertRaisesRegex(
          a__			,				'''bert-base is not a local folder and is not a valid model identifier'''      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained('''bert-base'''      )



 def 			snake_case__  (   self		:   str      ):
      with self.assertRaisesRegex(
          a__			,				r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__			,				revision='''aaaaaa'''      )



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      with self.assertRaisesRegex(
          a__			,				'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''			,				):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model'''      )



 def 			snake_case__  (   self		:   Dict      ):
      # If remote code is not set, we will time out when asking whether to load the model.
      with self.assertRaises(a__      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''      )
      # If remote code is disabled, we can't load this config.
      with self.assertRaises(a__      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=a__      )

      __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
          '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=a__      )
      self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )

      # Test feature extractor can be reloaded.
      with tempfile.TemporaryDirectory() as tmp_dir:
           feature_extractor.save_pretrained(a__      )
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__			,				trust_remote_code=a__      )
      self.assertEqual(reloaded_feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )



 def 			snake_case__  (   self		:   int      ):
      try:
           AutoConfig.register('''custom'''			,				a__      )
           AutoFeatureExtractor.register(a__			,				a__      )
           # Trying to register something existing in the Transformers library will raise an error
           with self.assertRaises(a__      ):
                AutoFeatureExtractor.register(a__			,				a__      )

           # Now that the config is registered, it can be used as any other config with the auto-API
           __magic_name__						       =    CustomFeatureExtractor.from_pretrained(a__      )
           with tempfile.TemporaryDirectory() as tmp_dir:
                feature_extractor.save_pretrained(a__      )
                __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )
                self.assertIsInstance(a__			,				a__      )

      finally:
           if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
           if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]



 def 			snake_case__  (   self		:   int      ):






      class   _SCREAMING_SNAKE_CASE   (   __a     ):
       __SCREAMING_SNAKE_CASE					:Optional[int]      	=					True

      try:
           AutoConfig.register('''custom'''			,				a__      )
           AutoFeatureExtractor.register(a__			,				a__      )
           # If remote code is not set, the default is to use local
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''      )
           self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )
           self.assertTrue(feature_extractor.is_local      )

           # If remote code is disabled, we load the local one.
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=a__      )
           self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )
           self.assertTrue(feature_extractor.is_local      )

           # If remote is enabled, we load from the Hub
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=a__      )
           self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )
           self.assertTrue(not hasattr(a__			,				'''is_local'''      )      )

      finally:
           if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
           if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]






