





"""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


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





@dataclass
class 			snake_case		(   __UpperCAmelCase   ):
      lowerCamelCase__						= 42







class 			snake_case		(   __UpperCAmelCase ,     __UpperCAmelCase   ):
      lowerCamelCase__						= 1

      @register_to_config
      def __init__(			self   :Tuple						,							_lowerCamelCase   :int = 4_0						,							_lowerCamelCase   :float = 0.0_0_2						,							_lowerCamelCase   :float = 8_0.0						,							_lowerCamelCase   :float = 0.5						,							_lowerCamelCase   :float = 1.0						,							_lowerCamelCase   :float = 7.0						,							_lowerCamelCase   :bool = True						,							):
       # standard deviation of the initial noise distribution
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       sigma_max

       __SCREAMING_SNAKE_CASE :			str												=       np.linspace(0						,							1						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       self._convert_to_karras(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       self.sigma_to_t(_lowerCamelCase						)

       # setable values
       __SCREAMING_SNAKE_CASE :			Dict												=       None
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       torch.from_numpy(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       torch.from_numpy(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       False
       __SCREAMING_SNAKE_CASE :			str												=       False

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :List[str]=None						):
       if schedule_timesteps is None:
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       self.timesteps

       __SCREAMING_SNAKE_CASE :			int												=       (schedule_timesteps == timestep).nonzero()
       return indices.item()

      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						,							_lowerCamelCase   :torch.FloatTensor						,							_lowerCamelCase   :Union[float, torch.FloatTensor]						):
       # Get sigma corresponding to timestep
       if isinstance(_lowerCamelCase						,							torch.Tensor						):
        __SCREAMING_SNAKE_CASE :			Any												=       timestep.to(self.timesteps.device						)
       __SCREAMING_SNAKE_CASE :			int												=       self.index_for_timestep(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Any												=       self.sigmas[step_idx]

       __SCREAMING_SNAKE_CASE :			int												=       sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       True
       return sample

      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						,							_lowerCamelCase   :Union[float, np.ndarray]						):
       if not isinstance(_lowerCamelCase						,							np.ndarray						):
        __SCREAMING_SNAKE_CASE :			Any												=       np.array(_lowerCamelCase						,							dtype=np.floataa						)

       __SCREAMING_SNAKE_CASE :			List[str]												=       1_0_0_0 * 0.2_5 * np.log(sigmas + 1e-44						)

       return timesteps

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						,							_lowerCamelCase   :Optional[int] = None						,							_lowerCamelCase   :Union[str, torch.device] = None						,							_lowerCamelCase   :Optional[List[int]] = None						,							):
       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(_lowerCamelCase						)						):
         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}.'''						)

        __SCREAMING_SNAKE_CASE :			Dict												=       np.array(_lowerCamelCase						,							dtype=np.intaa						)
        __SCREAMING_SNAKE_CASE :			Any												=       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.'''						)

        __SCREAMING_SNAKE_CASE :			Optional[int]												=       num_inference_steps

        __SCREAMING_SNAKE_CASE :			Any												=       self.config.num_train_timesteps // self.num_inference_steps
        __SCREAMING_SNAKE_CASE :			List[Any]												=       (np.arange(0						,							_lowerCamelCase						) * step_ratio).round()[::-1].copy().astype(np.intaa						)
        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       False

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

       __SCREAMING_SNAKE_CASE :			List[str]												=       np.concatenate([sigmas, [self.sigma_min]]						).astype(np.floataa						)
       __SCREAMING_SNAKE_CASE :			Any												=       torch.from_numpy(_lowerCamelCase						).to(device=_lowerCamelCase						)

       if str(_lowerCamelCase						).startswith('''mps'''						):
        # mps does not support float64
        __SCREAMING_SNAKE_CASE :			List[str]												=       torch.from_numpy(_lowerCamelCase						).to(_lowerCamelCase						,							dtype=torch.floataa						)
       else:
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       torch.from_numpy(_lowerCamelCase						).to(device=_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						,							_lowerCamelCase   :Any						):
       __SCREAMING_SNAKE_CASE :			float												=       self.config.sigma_min
       __SCREAMING_SNAKE_CASE :			float												=       self.config.sigma_max

       __SCREAMING_SNAKE_CASE :			str												=       self.config.rho
       __SCREAMING_SNAKE_CASE :			Tuple												=       sigma_min ** (1 / rho)
       __SCREAMING_SNAKE_CASE :			int												=       sigma_max ** (1 / rho)
       __SCREAMING_SNAKE_CASE :			Tuple												=       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						,							_lowerCamelCase   :List[Any]						):
       __SCREAMING_SNAKE_CASE :			str												=       self.config.sigma_data

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       sigma_data**2 / (sigma**2 + sigma_data**2)
       __SCREAMING_SNAKE_CASE :			int												=       sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						,							_lowerCamelCase   :Optional[Any]						):
       __SCREAMING_SNAKE_CASE :			str												=       self.config.sigma_min
       __SCREAMING_SNAKE_CASE :			Any												=       self.config.sigma_data

       __SCREAMING_SNAKE_CASE :			Tuple												=       sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
       __SCREAMING_SNAKE_CASE :			List[str]												=       (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out

      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						,							_lowerCamelCase   :torch.FloatTensor						,							_lowerCamelCase   :Union[float, torch.FloatTensor]						,							_lowerCamelCase   :torch.FloatTensor						,							_lowerCamelCase   :Optional[torch.Generator] = None						,							_lowerCamelCase   :bool = True						,							):

       if (
           isinstance(_lowerCamelCase						,							_lowerCamelCase						)
           or isinstance(_lowerCamelCase						,							torch.IntTensor						)
           or isinstance(_lowerCamelCase						,							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(_lowerCamelCase						,							torch.Tensor						):
        __SCREAMING_SNAKE_CASE :			Any												=       timestep.to(self.timesteps.device						)

       __SCREAMING_SNAKE_CASE :			Dict												=       self.config.sigma_min
       __SCREAMING_SNAKE_CASE :			List[Any]												=       self.config.sigma_max

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       self.index_for_timestep(_lowerCamelCase						)

       # sigma_next corresponds to next_t in original implementation
       __SCREAMING_SNAKE_CASE :			List[Any]												=       self.sigmas[step_index]
       if step_index + 1 < self.config.num_train_timesteps:
        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       self.sigmas[step_index + 1]
       else:
        # Set sigma_next to sigma_min
        __SCREAMING_SNAKE_CASE :			Any												=       self.sigmas[-1]

       # Get scalings for boundary conditions
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Optional[int]												=       self.get_scalings_for_boundary_condition(_lowerCamelCase						)

       # 1. Denoise model output using boundary conditions
       __SCREAMING_SNAKE_CASE :			List[str]												=       c_out * model_output + c_skip * sample
       if self.config.clip_denoised:
        __SCREAMING_SNAKE_CASE :			Optional[Any]												=       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:
        __SCREAMING_SNAKE_CASE :			List[str]												=       randn_tensor(
            model_output.shape						,							dtype=model_output.dtype						,							device=model_output.device						,							generator=_lowerCamelCase						)
       else:
        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       torch.zeros_like(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       noise * self.config.s_noise

       __SCREAMING_SNAKE_CASE :			List[Any]												=       sigma_next.clamp(min=_lowerCamelCase						,							max=_lowerCamelCase						)

       # 3. Return noisy sample
       # tau = sigma_hat, eps = sigma_min
       __SCREAMING_SNAKE_CASE :			List[Any]												=       denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

       if not return_dict:
        return (prev_sample,)

       return CMStochasticIterativeSchedulerOutput(prev_sample=_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						,							_lowerCamelCase   :torch.FloatTensor						,							_lowerCamelCase   :torch.FloatTensor						,							_lowerCamelCase   :torch.FloatTensor						,							):
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       __SCREAMING_SNAKE_CASE :			List[Any]												=       self.sigmas.to(device=original_samples.device						,							dtype=original_samples.dtype						)
       if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCamelCase						):
        # mps does not support float64
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       self.timesteps.to(original_samples.device						,							dtype=torch.floataa						)
        __SCREAMING_SNAKE_CASE :			List[str]												=       timesteps.to(original_samples.device						,							dtype=torch.floataa						)
       else:
        __SCREAMING_SNAKE_CASE :			List[Any]												=       self.timesteps.to(original_samples.device						)
        __SCREAMING_SNAKE_CASE :			List[str]												=       timesteps.to(original_samples.device						)

       __SCREAMING_SNAKE_CASE :			List[str]												=       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

       __SCREAMING_SNAKE_CASE :			Dict												=       sigmas[step_indices].flatten()
       while len(sigma.shape						) < len(original_samples.shape						):
        __SCREAMING_SNAKE_CASE :			List[str]												=       sigma.unsqueeze(-1						)

       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       original_samples + noise * sigma
       return noisy_samples



      def __len__(			self   :Optional[Any]						):
       return self.config.num_train_timesteps








"""simple docstring"""





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()




def 		lowerCAmelCase_	(  lowercase_   :							List[str] ,      lowercase_   :							List[str] ,      lowercase_   :							Dict		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Tuple												=       TaConfig.from_json_file(lowercase_		)
 print(F'''Building PyTorch model from configuration: {config}'''		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       TaForConditionalGeneration(lowercase_		)

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(lowercase_ ,      lowercase_ ,      lowercase_		)

 # Save pytorch-model
 print(F'''Save PyTorch model to {pytorch_dump_path}'''		)
 model.save_pretrained(lowercase_		)


if __name__ == "__main__":
    _lowerCamelCase       =	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    _lowerCamelCase       =	parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

