






"""simple docstring"""




from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


UpperCamelCase											=     logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
class  lowercase_     (_UpperCAmelCase  ):
      A__						:		torch.FloatTensor







class  lowercase_     (_UpperCAmelCase,     _UpperCAmelCase  ):
      A__						:		Union[str, Any]							      =					1






      @register_to_config
      def __init__(     self  ,  a_ = 4_0  ,  a_ = 0.002  ,  a_ = 80.0  ,  a_ = 0.5  ,  a_ = 1.0  ,  a_ = 7.0  ,  a_ = True  ,  )						->List[Any]:







        '''simple docstring'''


        _a					      =						sigma_max

        _a					      =						np.linspace(0  ,  1  ,  a_						)
        _a					      =						self._convert_to_karras(a_						)
        _a					      =						self.sigma_to_t(a_						)

        # setable values
        _a					      =						None
        _a					      =						torch.from_numpy(a_						)
        _a					      =						torch.from_numpy(a_						)
        _a					      =						False
        _a					      =						False






      def       lowerCamelCase__     (     self  ,  a_  ,  a_=None						)						->int:







        '''simple docstring'''


        if schedule_timesteps is None:
          _a					      =						self.timesteps

        _a					      =						(schedule_timesteps == timestep).nonzero()
        return indices.item()






      def       lowerCamelCase__     (     self  ,  a_  ,  a_						)						->torch.FloatTensor:







        '''simple docstring'''


        if isinstance(a_  ,  torch.Tensor						):
          _a					      =						timestep.to(self.timesteps.device						)
        _a					      =						self.index_for_timestep(a_						)
        _a					      =						self.sigmas[step_idx]

        _a					      =						sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

        _a					      =						True
        return sample






      def       lowerCamelCase__     (     self  ,  a_						)						->str:







        '''simple docstring'''


        if not isinstance(a_  ,  np.ndarray						):
          _a					      =						np.array(a_  ,  dtype=np.floataa						)

        _a					      =						1_0_0_0 * 0.25 * np.log(sigmas + 1E-44						)

        return timesteps






      def       lowerCamelCase__     (     self  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  )						->Optional[Any]:







        '''simple docstring'''


        if num_inference_steps is None and timesteps is None:
          raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."						)

        if num_inference_steps is not None and timesteps is not None:
          raise ValueError("Can only pass one of `num_inference_steps` or `timesteps`."						)

        # Follow DDPMScheduler custom timesteps logic
        if timesteps is not None:
          for i in range(1  ,  len(a_						)						):
            if timesteps[i] >= timesteps[i - 1]:
              raise ValueError("`timesteps` must be in descending order."						)

          if timesteps[0] >= self.config.num_train_timesteps:
            raise ValueError(
                f'''`timesteps` must start before `self.config.train_timesteps`:'''
                f''' {self.config.num_train_timesteps}.'''						)

          _a					      =						np.array(a_  ,  dtype=np.intaa						)
          _a					      =						True
        else:
          if num_inference_steps > self.config.num_train_timesteps:
            raise ValueError(
                f'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
                f''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
                f''' maximal {self.config.num_train_timesteps} timesteps.'''						)

          _a					      =						num_inference_steps

          _a					      =						self.config.num_train_timesteps // self.num_inference_steps
          _a					      =						(np.arange(0  ,  a_						) * step_ratio).round()[::-1].copy().astype(np.intaa						)
          _a					      =						False

        # Map timesteps to Karras sigmas directly for multistep sampling
        # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
        _a					      =						self.config.num_train_timesteps
        _a					      =						timesteps[::-1].copy()
        _a					      =						ramp / (num_train_timesteps - 1)
        _a					      =						self._convert_to_karras(a_						)
        _a					      =						self.sigma_to_t(a_						)

        _a					      =						np.concatenate([sigmas, [self.sigma_min]]						).astype(np.floataa						)
        _a					      =						torch.from_numpy(a_						).to(device=a_						)

        if str(a_						).startswith("mps"						):
          # mps does not support float64
          _a					      =						torch.from_numpy(a_						).to(a_  ,  dtype=torch.floataa						)
        else:
          _a					      =						torch.from_numpy(a_						).to(device=a_						)






      def       lowerCamelCase__     (     self  ,  a_						)						->int:







        '''simple docstring'''


        _a					      =						self.config.sigma_min
        _a					      =						self.config.sigma_max

        _a					      =						self.config.rho
        _a					      =						sigma_min ** (1 / rho)
        _a					      =						sigma_max ** (1 / rho)
        _a					      =						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas






      def       lowerCamelCase__     (     self  ,  a_						)						->str:







        '''simple docstring'''


        _a					      =						self.config.sigma_data

        _a					      =						sigma_data**2 / (sigma**2 + sigma_data**2)
        _a					      =						sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
        return c_skip, c_out






      def       lowerCamelCase__     (     self  ,  a_						)						->int:







        '''simple docstring'''


        _a					      =						self.config.sigma_min
        _a					      =						self.config.sigma_data

        _a					      =						sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
        _a					      =						(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
        return c_skip, c_out






      def       lowerCamelCase__     (     self  ,  a_  ,  a_  ,  a_  ,  a_ = None  ,  a_ = True  ,  )						->Union[CMStochasticIterativeSchedulerOutput, Tuple]:







        '''simple docstring'''


        if (
            isinstance(a_  ,  a_						)
            or isinstance(a_  ,  torch.IntTensor						)
            or isinstance(a_  ,  torch.LongTensor						)
        ):
          raise ValueError(
              (
                  "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                  f''' `{self.__class__}.step()` is not supported. Make sure to pass'''
                  " one of the `scheduler.timesteps` as a timestep."
              )  ,  )

        if not self.is_scale_input_called:
          logger.warning(
              "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
              "See `StableDiffusionPipeline` for a usage example."						)

        if isinstance(a_  ,  torch.Tensor						):
          _a					      =						timestep.to(self.timesteps.device						)

        _a					      =						self.config.sigma_min
        _a					      =						self.config.sigma_max

        _a					      =						self.index_for_timestep(a_						)

        # sigma_next corresponds to next_t in original implementation
        _a					      =						self.sigmas[step_index]
        if step_index + 1 < self.config.num_train_timesteps:
          _a					      =						self.sigmas[step_index + 1]
        else:
          # Set sigma_next to sigma_min
          _a					      =						self.sigmas[-1]

        # Get scalings for boundary conditions
        _a      ,						_a					      =						self.get_scalings_for_boundary_condition(a_						)

        # 1. Denoise model output using boundary conditions
        _a					      =						c_out * model_output + c_skip * sample
        if self.config.clip_denoised:
          _a					      =						denoised.clamp(-1  ,  1						)

        # 2. Sample z ~ N(0, s_noise^2 * I)
        # Noise is not used for onestep sampling.
        if len(self.timesteps						) > 1:
          _a					      =						randn_tensor(
              model_output.shape  ,  dtype=model_output.dtype  ,  device=model_output.device  ,  generator=a_						)
        else:
          _a					      =						torch.zeros_like(a_						)
        _a					      =						noise * self.config.s_noise

        _a					      =						sigma_next.clamp(min=a_  ,  max=a_						)

        # 3. Return noisy sample
        # tau = sigma_hat, eps = sigma_min
        _a					      =						denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

        if not return_dict:
          return (prev_sample,)

        return CMStochasticIterativeSchedulerOutput(prev_sample=a_						)






      def       lowerCamelCase__     (     self  ,  a_  ,  a_  ,  a_  ,  )						->torch.FloatTensor:







        '''simple docstring'''


        _a					      =						self.sigmas.to(device=original_samples.device  ,  dtype=original_samples.dtype						)
        if original_samples.device.type == "mps" and torch.is_floating_point(a_						):
          # mps does not support float64
          _a					      =						self.timesteps.to(original_samples.device  ,  dtype=torch.floataa						)
          _a					      =						timesteps.to(original_samples.device  ,  dtype=torch.floataa						)
        else:
          _a					      =						self.timesteps.to(original_samples.device						)
          _a					      =						timesteps.to(original_samples.device						)

        _a					      =						[(schedule_timesteps == t).nonzero().item() for t in timesteps]

        _a					      =						sigmas[step_indices].flatten()
        while len(sigma.shape						) < len(original_samples.shape						):
          _a					      =						sigma.unsqueeze(-1						)

        _a					      =						original_samples + noise * sigma
        return noisy_samples






      def __len__(     self						)						->Optional[int]:







        '''simple docstring'''


        return self.config.num_train_timesteps










"""simple docstring"""




import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 							lowerCAmelCase			(   UpperCamelCase_:				Dict  ,			UpperCamelCase_:				Dict  ,			UpperCamelCase_:				Any       )							->				Tuple:







  '''simple docstring'''







  _a					      =						{"small": (512, 8, 24), "medium": (1024, 8, 36), "large": (1536, 16, 48)}
  _a      ,						_a      ,						_a					      =						MODELS[model_size]  # set model hyperparameters
  _a					      =						ImageGPTConfig(n_embd=UpperCamelCase_  ,			n_layer=UpperCamelCase_  ,			n_head=UpperCamelCase_       )
  _a					      =						ImageGPTForCausalLM(UpperCamelCase_       )

  # Load weights from numpy
  load_tf_weights_in_imagegpt(UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_       )

  # Save pytorch-model
  _a					      =						pytorch_dump_folder_path + "/" + WEIGHTS_NAME
  _a					      =						pytorch_dump_folder_path + "/" + CONFIG_NAME
  print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''       )
  torch.save(model.state_dict()  ,			UpperCamelCase_       )
  print(f'''Save configuration file to {pytorch_config_dump_path}'''       )
  with open(UpperCamelCase_  ,			"w"  ,			encoding="utf-8"       ) as f:
    f.write(config.to_json_string()       )


if __name__ == "__main__":
     UpperCamelCase											=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--imagegpt_checkpoint_path""",
         default=None,
         type=str,
         required=True,
         help="""Path to the TensorFlow checkpoint path.""",
     )
     parser.add_argument(
         """--model_size""",
         default=None,
         type=str,
         required=True,
         help="""Size of the model (can be either 'small', 'medium' or 'large').""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     UpperCamelCase											=     parser.parse_args()
     convert_imagegpt_checkpoint_to_pytorch(
         args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
     )


