


import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()

class   A__							(						unittest.TestCase					):
      UpperCAmelCase     =  "hf-internal-testing/tiny-stable-diffusion-pipe"
      UpperCAmelCase     =  1
      UpperCAmelCase     =  77
      UpperCAmelCase     =  32
      UpperCAmelCase     =  4
      UpperCAmelCase     =  UpperCAmelCase     =  64
      UpperCAmelCase     =  torch.manual_seed(0					)




      def 							__UpperCamelCase			(    self   : Optional[int]			,      _a   : List[Any]=0.99_99					)							->       Tuple:


          """simple docstring"""





          _SCREAMING_SNAKE_CASE     					=UNetaDConditionModel.from_pretrained(self.model_id			,      subfolder='''unet'''					)
          _SCREAMING_SNAKE_CASE     					=unet.to(_a					)
          _SCREAMING_SNAKE_CASE     					=EMAModel(unet.parameters()			,      decay=_a			,      model_cls=_a			,      model_config=unet.config					)
          return unet, ema_unet




      def 							__UpperCamelCase			(    self   : Dict					)							->       Union[str, Any]:


          """simple docstring"""





          _SCREAMING_SNAKE_CASE     					=torch.randn(
              self.batch_size			,      self.num_in_channels			,      self.latent_height			,      self.latent_width			,      generator=self.generator					).to(_a					)
          _SCREAMING_SNAKE_CASE     					=torch.randint(0			,      1000			,      size=(self.batch_size,)			,      generator=self.generator					).to(_a					)
          _SCREAMING_SNAKE_CASE     					=torch.randn(
              self.batch_size			,      self.prompt_length			,      self.text_encoder_hidden_dim			,      generator=self.generator					).to(_a					)
          return noisy_latents, timesteps, encoder_hidden_states




      def 							__UpperCamelCase			(    self   : Union[str, Any]			,      _a   : Any					)							->       Tuple:


          """simple docstring"""





          _SCREAMING_SNAKE_CASE     					={}
          for k, param in unet.state_dict().items():
              _SCREAMING_SNAKE_CASE     					=torch.randn_like(_a					) + (param * torch.randn_like(_a					))
              updated_state_dict.update({k: updated_param}					)
          unet.load_state_dict(_a					)
          return unet




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


          """simple docstring"""





          _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.get_models()
          # Take the first (hypothetical) EMA step.
          ema_unet.step(unet.parameters()					)
          assert ema_unet.optimization_step == 1

          # Take two more.
          for _ in range(2					):
              ema_unet.step(unet.parameters()					)
          assert ema_unet.optimization_step == 3




      def 							__UpperCamelCase			(    self   : Dict					)							->       Any:


          """simple docstring"""





          _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.get_models()
          # Since the `unet` is not being updated (i.e., backprop'd)
          # there won't be any difference between the `params` of `unet`
          # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
          ema_unet.step(unet.parameters()					)
          _SCREAMING_SNAKE_CASE     					=list(unet.parameters()					)
          for s_param, param in zip(ema_unet.shadow_params			,      _a					):
              assert torch.allclose(_a			,      _a					)

          # The above holds true even if we call `ema.step()` multiple times since
          # `unet` params are still not being updated.
          for _ in range(4					):
              ema_unet.step(unet.parameters()					)
          for s_param, param in zip(ema_unet.shadow_params			,      _a					):
              assert torch.allclose(_a			,      _a					)




      def 							__UpperCamelCase			(    self   : str					)							->       int:


          """simple docstring"""





          _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.get_models()
          # Here we simulate the parameter updates for `unet`. Since there might
          # be some parameters which are initialized to zero we take extra care to
          # initialize their values to something non-zero before the multiplication.
          _SCREAMING_SNAKE_CASE     					=self.simulate_backprop(_a					)

          # Take the EMA step.
          ema_unet.step(unet_pseudo_updated_step_one.parameters()					)

          # Now the EMA'd parameters won't be equal to the original model parameters.
          _SCREAMING_SNAKE_CASE     					=list(unet_pseudo_updated_step_one.parameters()					)
          for s_param, param in zip(ema_unet.shadow_params			,      _a					):
              assert ~torch.allclose(_a			,      _a					)

          # Ensure this is the case when we take multiple EMA steps.
          for _ in range(4					):
              ema_unet.step(unet.parameters()					)
          for s_param, param in zip(ema_unet.shadow_params			,      _a					):
              assert ~torch.allclose(_a			,      _a					)




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


          """simple docstring"""





          _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.get_models()

          # First backprop + EMA
          _SCREAMING_SNAKE_CASE     					=self.simulate_backprop(_a					)
          ema_unet.step(unet_step_one.parameters()					)
          _SCREAMING_SNAKE_CASE     					=ema_unet.shadow_params

          # Second backprop + EMA
          _SCREAMING_SNAKE_CASE     					=self.simulate_backprop(_a					)
          ema_unet.step(unet_step_two.parameters()					)
          _SCREAMING_SNAKE_CASE     					=ema_unet.shadow_params

          for step_one, step_two in zip(_a			,      _a					):
              assert ~torch.allclose(_a			,      _a					)




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


          """simple docstring"""





          _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.get_models(decay=0.0					)
          _SCREAMING_SNAKE_CASE     					=self.simulate_backprop(_a					)
          ema_unet.step(unet_step_one.parameters()					)
          _SCREAMING_SNAKE_CASE     					=ema_unet.shadow_params

          _SCREAMING_SNAKE_CASE     					=self.simulate_backprop(_a					)
          ema_unet.step(unet_step_two.parameters()					)
          _SCREAMING_SNAKE_CASE     					=ema_unet.shadow_params

          for step_one, step_two in zip(_a			,      _a					):
              assert torch.allclose(_a			,      _a					)




      @skip_mps
      def 							__UpperCamelCase			(    self   : Union[str, Any]					)							->       Dict:


          """simple docstring"""





          _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.get_models()
          _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.get_dummy_inputs()

          with tempfile.TemporaryDirectory() as tmpdir:
              ema_unet.save_pretrained(_a					)
              _SCREAMING_SNAKE_CASE     					=UNetaDConditionModel.from_pretrained(_a			,      model_cls=_a					)
              _SCREAMING_SNAKE_CASE     					=loaded_unet.to(unet.device					)

          # Since no EMA step has been performed the outputs should match.
          _SCREAMING_SNAKE_CASE     					=unet(_a			,      _a			,      _a					).sample
          _SCREAMING_SNAKE_CASE     					=loaded_unet(_a			,      _a			,      _a					).sample

          assert torch.allclose(_a			,      _a			,      atol=1E-4					)



import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel







def   lowerCamelCase(				a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=pt_weights[f"{prefix}.in_proj_weight"].chunk(3		,dim=0)
				_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=pt_weights[f"{prefix}.in_proj_bias"].chunk(3		,dim=0)

				_SCREAMING_SNAKE_CASE     					=pt_weights[f"{prefix}.out_proj.weight"]
				_SCREAMING_SNAKE_CASE     					=pt_weights[f"{prefix}.out_proj.bias"]

				_SCREAMING_SNAKE_CASE     					=q_proj
				_SCREAMING_SNAKE_CASE     					=q_proj_bias

				_SCREAMING_SNAKE_CASE     					=k_proj
				_SCREAMING_SNAKE_CASE     					=k_proj_bias

				_SCREAMING_SNAKE_CASE     					=v_proj
				_SCREAMING_SNAKE_CASE     					=v_proj_bias

				_SCREAMING_SNAKE_CASE     					=out_proj_weights
				_SCREAMING_SNAKE_CASE     					=out_proj_bias







def   lowerCamelCase(				a__		,a__		,a__):
				copy_linear(hf_mlp.fca		,a__		,f"{prefix}.c_fc")
				copy_linear(hf_mlp.fca		,a__		,f"{prefix}.c_proj")







def   lowerCamelCase(				a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE     					=pt_weights[f"{prefix}.weight"].data
				_SCREAMING_SNAKE_CASE     					=pt_weights[f"{prefix}.bias"].data







def   lowerCamelCase(				a__		,a__		,a__):
				# copy layer norms
				copy_linear(hf_layer.layer_norma		,a__		,f"{prefix}.ln_1")
				copy_linear(hf_layer.layer_norma		,a__		,f"{prefix}.ln_2")

				# copy MLP
				copy_mlp(hf_layer.mlp		,a__		,f"{prefix}.mlp")

				# copy attn
				copy_attn_layer(hf_layer.self_attn		,a__		,f"{prefix}.attn")







def   lowerCamelCase(				a__		,a__		,a__):
				for layer_id, hf_layer in enumerate(a__):
								copy_layer(a__		,a__		,f"{prefix}.{layer_id}")







def   lowerCamelCase(				a__		,a__):
				# copy projection
				_SCREAMING_SNAKE_CASE     					=pt_weights['''text_projection'''].data.T

				# copy text encoder
				for name, param in hf_model.text_model.named_parameters():
								_SCREAMING_SNAKE_CASE     					=pt_weights[f"bert.{name}"].data







def   lowerCamelCase(				a__		,a__):
				# copy projection
				_SCREAMING_SNAKE_CASE     					=pt_weights['''visual.proj'''].data.T

				# copy layer norms
				copy_linear(hf_model.vision_model.pre_layrnorm		,a__		,'''visual.ln_pre''')
				copy_linear(hf_model.vision_model.post_layernorm		,a__		,'''visual.ln_post''')

				# copy embeddings
				_SCREAMING_SNAKE_CASE     					=pt_weights['''visual.conv1.weight'''].data
				_SCREAMING_SNAKE_CASE     					=pt_weights['''visual.class_embedding'''].data
				_SCREAMING_SNAKE_CASE     					=pt_weights['''visual.positional_embedding'''].data

				# copy encoder
				copy_layers(hf_model.vision_model.encoder.layers		,a__		,'''visual.transformer.resblocks''')







@torch.no_grad()
def   lowerCamelCase(				a__		,a__		,a__=None):
				assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
				_SCREAMING_SNAKE_CASE     					=ChineseCLIPConfig.from_pretrained(a__)

				_SCREAMING_SNAKE_CASE     					=ChineseCLIPModel(a__).eval()

				_SCREAMING_SNAKE_CASE     					=torch.load(a__		,map_location='''cpu''')['''state_dict''']
				_SCREAMING_SNAKE_CASE     					={(name[7:] if name.startswith('''module.''') else name): value for name, value in pt_weights.items()}

				copy_text_model_and_projection(a__		,a__)
				copy_vision_model_and_projection(a__		,a__)
				_SCREAMING_SNAKE_CASE     					=pt_weights['''logit_scale'''].data

				hf_model.save_pretrained(a__)


if __name__ == "__main__":
							snake_case_  :     Tuple							  =							argparse.ArgumentParser()
							parser.add_argument(
							    '''--pytorch_dump_folder_path''',
							    default=None,
							    type=str,
							    help='''Path to the output folder storing converted hf PyTorch model.''',
							)
							parser.add_argument(
							    '''--checkpoint_path''', default=None, type=str, help='''Path to original github format ChineseCLIP checkpoint.'''
							)
							parser.add_argument(
							    '''--config_path''', default=None, required=True, type=str, help='''Path to hf config.json of model to convert.'''
							)
							snake_case_  :     List[str]							  =							parser.parse_args()

							convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
							print('''The conversion is finished!''')