

"""simple docstring"""
import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel




def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   str:
      UpperCAmelCase__				,		UpperCAmelCase__				,		UpperCAmelCase__ :       Optional[int]						=						pt_weights[F"""{prefix}.in_proj_weight"""].chunk(3 ,  dim=0						)
      UpperCAmelCase__				,		UpperCAmelCase__				,		UpperCAmelCase__ :       Optional[Any]						=						pt_weights[F"""{prefix}.in_proj_bias"""].chunk(3 ,  dim=0						)

      UpperCAmelCase__ :       str						=						pt_weights[F"""{prefix}.out_proj.weight"""]
      UpperCAmelCase__ :       Optional[Any]						=						pt_weights[F"""{prefix}.out_proj.bias"""]

      UpperCAmelCase__ :       Union[str, Any]						=						q_proj
      UpperCAmelCase__ :       List[str]						=						q_proj_bias

      UpperCAmelCase__ :       List[str]						=						k_proj
      UpperCAmelCase__ :       Dict						=						k_proj_bias

      UpperCAmelCase__ :       int						=						v_proj
      UpperCAmelCase__ :       str						=						v_proj_bias

      UpperCAmelCase__ :       str						=						out_proj_weights
      UpperCAmelCase__ :       str						=						out_proj_bias




def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   str:
      copy_linear(hf_mlp.fca ,  lowerCAmelCase ,  F"""{prefix}.c_fc"""						)
      copy_linear(hf_mlp.fca ,  lowerCAmelCase ,  F"""{prefix}.c_proj"""						)




def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Optional[Any]:
      UpperCAmelCase__ :       Optional[Any]						=						pt_weights[F"""{prefix}.weight"""].data
      UpperCAmelCase__ :       Union[str, Any]						=						pt_weights[F"""{prefix}.bias"""].data




def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Union[str, Any]:
      # copy layer norms
      copy_linear(hf_layer.layer_norma ,  lowerCAmelCase ,  F"""{prefix}.ln_1"""						)
      copy_linear(hf_layer.layer_norma ,  lowerCAmelCase ,  F"""{prefix}.ln_2"""						)

      # copy MLP
      copy_mlp(hf_layer.mlp ,  lowerCAmelCase ,  F"""{prefix}.mlp"""						)

      # copy attn
      copy_attn_layer(hf_layer.self_attn ,  lowerCAmelCase ,  F"""{prefix}.attn"""						)




def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Dict:
      for layer_id, hf_layer in enumerate(lowerCAmelCase						):
            copy_layer(lowerCAmelCase ,  lowerCAmelCase ,  F"""{prefix}.{layer_id}"""						)




def 				a__	(	lowerCAmelCase ,  lowerCAmelCase						)  ->   Union[str, Any]:
      # copy projection
      UpperCAmelCase__ :       Tuple						=						pt_weights["""text_projection"""].data.T

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




def 				a__	(	lowerCAmelCase ,  lowerCAmelCase						)  ->   Dict:
      # copy projection
      UpperCAmelCase__ :       str						=						pt_weights["""visual.proj"""].data.T

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

      # copy embeddings
      UpperCAmelCase__ :       Union[str, Any]						=						pt_weights["""visual.conv1.weight"""].data
      UpperCAmelCase__ :       Optional[int]						=						pt_weights["""visual.class_embedding"""].data
      UpperCAmelCase__ :       List[str]						=						pt_weights["""visual.positional_embedding"""].data

      # copy encoder
      copy_layers(hf_model.vision_model.encoder.layers ,  lowerCAmelCase ,  """visual.transformer.resblocks"""						)




@torch.no_grad()
def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase=None						)  ->   Optional[Any]:
      assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
      UpperCAmelCase__ :       Dict						=						ChineseCLIPConfig.from_pretrained(lowerCAmelCase						)

      UpperCAmelCase__ :       int						=						ChineseCLIPModel(lowerCAmelCase						).eval()

      UpperCAmelCase__ :       Union[str, Any]						=						torch.load(lowerCAmelCase ,  map_location="""cpu"""						)["""state_dict"""]
      UpperCAmelCase__ :       Optional[Any]						=						{(name[7:] if name.startswith("""module."""						) else name): value for name, value in pt_weights.items()}

      copy_text_model_and_projection(lowerCAmelCase ,  lowerCAmelCase						)
      copy_vision_model_and_projection(lowerCAmelCase ,  lowerCAmelCase						)
      UpperCAmelCase__ :       Tuple						=						pt_weights["""logit_scale"""].data

      hf_model.save_pretrained(lowerCAmelCase						)


if __name__ == "__main__":
    _A						=     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."""
    )
    _A						=     parser.parse_args()

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




"""simple docstring"""
import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 			lowerCamelCase  (					unittest.TestCase      ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE							=     (ScoreSdeVeScheduler,)
  SCREAMING_SNAKE_CASE							=     ()



  @property
  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       List[Any]						=						4
        UpperCAmelCase__ :       Tuple						=						3
        UpperCAmelCase__ :       Optional[Any]						=						8
        UpperCAmelCase__ :       Dict						=						8

        UpperCAmelCase__ :       List[Any]						=						torch.rand((batch_size, num_channels, height, width)  )

        return sample



  @property
  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Tuple						=						4
        UpperCAmelCase__ :       Optional[Any]						=						3
        UpperCAmelCase__ :       List[str]						=						8
        UpperCAmelCase__ :       Dict						=						8

        UpperCAmelCase__ :       List[str]						=						batch_size * num_channels * height * width
        UpperCAmelCase__ :       Any						=						torch.arange(_lowerCamelCase  )
        UpperCAmelCase__ :       Any						=						sample.reshape(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
        UpperCAmelCase__ :       Dict						=						sample / num_elems
        UpperCAmelCase__ :       Optional[int]						=						sample.permute(3       ,					0       ,					1       ,					2  )

        return sample



  def    _a			(self  ):


        """simple docstring"""

        def model(_lowerCamelCase       ,					_lowerCamelCase       ,					*_lowerCamelCase  ):
              return sample * t / (t + 1)

        return model



  def    _a			(self       ,					**_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       List[str]						=						{
            """num_train_timesteps""": 2000,
            """snr""": 0.15,
            """sigma_min""": 0.01,
            """sigma_max""": 1348,
            """sampling_eps""": 1e-5,
        }

        config.update(**_lowerCamelCase  )
        return config



  def    _a			(self       ,					_lowerCamelCase=0       ,					**_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       str						=						dict(self.forward_default_kwargs  )

        for scheduler_class in self.scheduler_classes:
              UpperCAmelCase__ :       int						=						self.dummy_sample
              UpperCAmelCase__ :       Tuple						=						0.1 * sample

              UpperCAmelCase__ :       int						=						self.get_scheduler_config(**_lowerCamelCase  )
              UpperCAmelCase__ :       Dict						=						scheduler_class(**_lowerCamelCase  )

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(_lowerCamelCase  )
                    UpperCAmelCase__ :       Union[str, Any]						=						scheduler_class.from_pretrained(_lowerCamelCase  )

              UpperCAmelCase__ :       Tuple						=						scheduler.step_pred(
                  _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
              UpperCAmelCase__ :       Optional[int]						=						new_scheduler.step_pred(
                  _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample

              assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

              UpperCAmelCase__ :       Optional[int]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
              UpperCAmelCase__ :       Union[str, Any]						=						new_scheduler.step_correct(
                  _lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample

              assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"



  def    _a			(self       ,					_lowerCamelCase=0       ,					**_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[int]						=						dict(self.forward_default_kwargs  )
        kwargs.update(_lowerCamelCase  )

        for scheduler_class in self.scheduler_classes:
              UpperCAmelCase__ :       List[str]						=						self.dummy_sample
              UpperCAmelCase__ :       List[str]						=						0.1 * sample

              UpperCAmelCase__ :       Optional[Any]						=						self.get_scheduler_config()
              UpperCAmelCase__ :       Optional[Any]						=						scheduler_class(**_lowerCamelCase  )

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(_lowerCamelCase  )
                    UpperCAmelCase__ :       Union[str, Any]						=						scheduler_class.from_pretrained(_lowerCamelCase  )

              UpperCAmelCase__ :       Optional[int]						=						scheduler.step_pred(
                  _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
              UpperCAmelCase__ :       Any						=						new_scheduler.step_pred(
                  _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample

              assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

              UpperCAmelCase__ :       Optional[Any]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
              UpperCAmelCase__ :       Dict						=						new_scheduler.step_correct(
                  _lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample

              assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"



  def    _a			(self  ):


        """simple docstring"""

        for timesteps in [10, 100, 1000]:
              self.check_over_configs(num_train_timesteps=_lowerCamelCase  )



  def    _a			(self  ):


        """simple docstring"""

        for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]       ,					[1, 100, 1000]  ):
              self.check_over_configs(sigma_min=_lowerCamelCase       ,					sigma_max=_lowerCamelCase  )



  def    _a			(self  ):


        """simple docstring"""

        for t in [0.1, 0.5, 0.75]:
              self.check_over_forward(time_step=_lowerCamelCase  )



  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[Any]						=						dict(self.forward_default_kwargs  )

        UpperCAmelCase__ :       int						=						self.scheduler_classes[0]
        UpperCAmelCase__ :       int						=						self.get_scheduler_config()
        UpperCAmelCase__ :       Dict						=						scheduler_class(**_lowerCamelCase  )

        UpperCAmelCase__ :       List[str]						=						3

        UpperCAmelCase__ :       List[Any]						=						self.dummy_model()
        UpperCAmelCase__ :       int						=						self.dummy_sample_deter

        scheduler.set_sigmas(_lowerCamelCase  )
        scheduler.set_timesteps(_lowerCamelCase  )
        UpperCAmelCase__ :       Tuple						=						torch.manual_seed(0  )

        for i, t in enumerate(scheduler.timesteps  ):
              UpperCAmelCase__ :       Tuple						=						scheduler.sigmas[i]

              for _ in range(scheduler.config.correct_steps  ):
                    with torch.no_grad():
                          UpperCAmelCase__ :       Optional[int]						=						model(_lowerCamelCase       ,					_lowerCamelCase  )
                    UpperCAmelCase__ :       List[str]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=_lowerCamelCase       ,					**_lowerCamelCase  ).prev_sample

              with torch.no_grad():
                    UpperCAmelCase__ :       Tuple						=						model(_lowerCamelCase       ,					_lowerCamelCase  )

              UpperCAmelCase__ :       str						=						scheduler.step_pred(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=_lowerCamelCase       ,					**_lowerCamelCase  )
              UpperCAmelCase__				,		UpperCAmelCase__ :       List[str]						=						output.prev_sample, output.prev_sample_mean

        UpperCAmelCase__ :       int						=						torch.sum(torch.abs(_lowerCamelCase  )  )
        UpperCAmelCase__ :       str						=						torch.mean(torch.abs(_lowerCamelCase  )  )

        assert np.isclose(result_sum.item()       ,					14_372_758_528.0  )
        assert np.isclose(result_mean.item()       ,					18_714_530.0  )




  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[int]						=						dict(self.forward_default_kwargs  )

        UpperCAmelCase__ :       Union[str, Any]						=						kwargs.pop("""num_inference_steps"""       ,					_lowerCamelCase  )

        for scheduler_class in self.scheduler_classes:
              UpperCAmelCase__ :       Dict						=						self.get_scheduler_config()
              UpperCAmelCase__ :       List[Any]						=						scheduler_class(**_lowerCamelCase  )

              UpperCAmelCase__ :       str						=						self.dummy_sample
              UpperCAmelCase__ :       Tuple						=						0.1 * sample

              if num_inference_steps is not None and hasattr(_lowerCamelCase       ,					"""set_timesteps"""  ):
                    scheduler.set_timesteps(_lowerCamelCase  )
              elif num_inference_steps is not None and not hasattr(_lowerCamelCase       ,					"""set_timesteps"""  ):
                    UpperCAmelCase__ :       Dict						=						num_inference_steps

              UpperCAmelCase__ :       str						=						scheduler.step_pred(_lowerCamelCase       ,					0       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
              UpperCAmelCase__ :       int						=						scheduler.step_pred(_lowerCamelCase       ,					1       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample

              self.assertEqual(output_a.shape       ,					sample.shape  )
              self.assertEqual(output_a.shape       ,					output_a.shape  )

