code: 
  """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
  
  
  _A						=     logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  @dataclass
  class 			lowerCamelCase  (					lowerCamelCase__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     4_2
  
  
  
  
  class 			lowerCamelCase  (					lowerCamelCase__				,				lowerCamelCase__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     1
  
  
  
    @register_to_config
    def __init__(self       ,					_lowerCamelCase = 40       ,					_lowerCamelCase = 0.002       ,					_lowerCamelCase = 80.0       ,					_lowerCamelCase = 0.5       ,					_lowerCamelCase = 1.0       ,					_lowerCamelCase = 7.0       ,					_lowerCamelCase = True       ,					):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						sigma_max
  
          UpperCAmelCase__ :       Tuple						=						np.linspace(0       ,					1       ,					SCREAMING_SNAKE_CASE_  )
          UpperCAmelCase__ :       Tuple						=						self._convert_to_karras(SCREAMING_SNAKE_CASE_  )
          UpperCAmelCase__ :       Tuple						=						self.sigma_to_t(SCREAMING_SNAKE_CASE_  )
  
          # setable values
          UpperCAmelCase__ :       Optional[Any]						=						None
          UpperCAmelCase__ :       Any						=						torch.from_numpy(SCREAMING_SNAKE_CASE_  )
          UpperCAmelCase__ :       int						=						torch.from_numpy(SCREAMING_SNAKE_CASE_  )
          UpperCAmelCase__ :       Any						=						False
          UpperCAmelCase__ :       Optional[Any]						=						False
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase=None  ):
  
  
          """simple docstring"""
  
          if schedule_timesteps is None:
                UpperCAmelCase__ :       Any						=						self.timesteps
  
          UpperCAmelCase__ :       Any						=						(schedule_timesteps == timestep).nonzero()
          return indices.item()
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          if isinstance(SCREAMING_SNAKE_CASE_       ,					torch.Tensor  ):
                UpperCAmelCase__ :       List[str]						=						timestep.to(self.timesteps.device  )
          UpperCAmelCase__ :       Union[str, Any]						=						self.index_for_timestep(SCREAMING_SNAKE_CASE_  )
          UpperCAmelCase__ :       Optional[int]						=						self.sigmas[step_idx]
  
          UpperCAmelCase__ :       str						=						sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)
  
          UpperCAmelCase__ :       Tuple						=						True
          return sample
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          if not isinstance(SCREAMING_SNAKE_CASE_       ,					np.ndarray  ):
                UpperCAmelCase__ :       Dict						=						np.array(SCREAMING_SNAKE_CASE_       ,					dtype=np.floataa  )
  
          UpperCAmelCase__ :       Dict						=						1000 * 0.25 * np.log(sigmas + 1e-44  )
  
          return timesteps
  
  
  
    def    _a			(self       ,					_lowerCamelCase = None       ,					_lowerCamelCase = None       ,					_lowerCamelCase = None       ,					):
  
  
          """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(SCREAMING_SNAKE_CASE_  )  ):
                      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}."""  )
  
                UpperCAmelCase__ :       Any						=						np.array(SCREAMING_SNAKE_CASE_       ,					dtype=np.intaa  )
                UpperCAmelCase__ :       List[str]						=						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."""  )
  
                UpperCAmelCase__ :       Union[str, Any]						=						num_inference_steps
  
                UpperCAmelCase__ :       Union[str, Any]						=						self.config.num_train_timesteps // self.num_inference_steps
                UpperCAmelCase__ :       Any						=						(np.arange(0       ,					SCREAMING_SNAKE_CASE_  ) * step_ratio).round()[::-1].copy().astype(np.intaa  )
                UpperCAmelCase__ :       str						=						False
  
          # Map timesteps to Karras sigmas directly for multistep sampling
          # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
          UpperCAmelCase__ :       Optional[int]						=						self.config.num_train_timesteps
          UpperCAmelCase__ :       Optional[int]						=						timesteps[::-1].copy()
          UpperCAmelCase__ :       Any						=						ramp / (num_train_timesteps - 1)
          UpperCAmelCase__ :       int						=						self._convert_to_karras(SCREAMING_SNAKE_CASE_  )
          UpperCAmelCase__ :       List[str]						=						self.sigma_to_t(SCREAMING_SNAKE_CASE_  )
  
          UpperCAmelCase__ :       Optional[Any]						=						np.concatenate([sigmas, [self.sigma_min]]  ).astype(np.floataa  )
          UpperCAmelCase__ :       int						=						torch.from_numpy(SCREAMING_SNAKE_CASE_  ).to(device=SCREAMING_SNAKE_CASE_  )
  
          if str(SCREAMING_SNAKE_CASE_  ).startswith("""mps"""  ):
                # mps does not support float64
                UpperCAmelCase__ :       Dict						=						torch.from_numpy(SCREAMING_SNAKE_CASE_  ).to(SCREAMING_SNAKE_CASE_       ,					dtype=torch.floataa  )
          else:
                UpperCAmelCase__ :       List[Any]						=						torch.from_numpy(SCREAMING_SNAKE_CASE_  ).to(device=SCREAMING_SNAKE_CASE_  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Tuple						=						self.config.sigma_min
          UpperCAmelCase__ :       Union[str, Any]						=						self.config.sigma_max
  
          UpperCAmelCase__ :       Optional[Any]						=						self.config.rho
          UpperCAmelCase__ :       Dict						=						sigma_min ** (1 / rho)
          UpperCAmelCase__ :       Any						=						sigma_max ** (1 / rho)
          UpperCAmelCase__ :       List[Any]						=						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
          return sigmas
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						self.config.sigma_data
  
          UpperCAmelCase__ :       Optional[int]						=						sigma_data**2 / (sigma**2 + sigma_data**2)
          UpperCAmelCase__ :       Tuple						=						sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
          return c_skip, c_out
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						self.config.sigma_min
          UpperCAmelCase__ :       List[str]						=						self.config.sigma_data
  
          UpperCAmelCase__ :       Optional[int]						=						sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
          UpperCAmelCase__ :       Union[str, Any]						=						(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
          return c_skip, c_out
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase = None       ,					_lowerCamelCase = True       ,					):
  
  
          """simple docstring"""
  
          if (
              isinstance(SCREAMING_SNAKE_CASE_       ,					SCREAMING_SNAKE_CASE_  )
              or isinstance(SCREAMING_SNAKE_CASE_       ,					torch.IntTensor  )
              or isinstance(SCREAMING_SNAKE_CASE_       ,					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(SCREAMING_SNAKE_CASE_       ,					torch.Tensor  ):
                UpperCAmelCase__ :       Union[str, Any]						=						timestep.to(self.timesteps.device  )
  
          UpperCAmelCase__ :       List[str]						=						self.config.sigma_min
          UpperCAmelCase__ :       List[str]						=						self.config.sigma_max
  
          UpperCAmelCase__ :       Union[str, Any]						=						self.index_for_timestep(SCREAMING_SNAKE_CASE_  )
  
          # sigma_next corresponds to next_t in original implementation
          UpperCAmelCase__ :       Optional[Any]						=						self.sigmas[step_index]
          if step_index + 1 < self.config.num_train_timesteps:
                UpperCAmelCase__ :       int						=						self.sigmas[step_index + 1]
          else:
                # Set sigma_next to sigma_min
                UpperCAmelCase__ :       int						=						self.sigmas[-1]
  
          # Get scalings for boundary conditions
          UpperCAmelCase__				,		UpperCAmelCase__ :       Optional[int]						=						self.get_scalings_for_boundary_condition(SCREAMING_SNAKE_CASE_  )
  
          # 1. Denoise model output using boundary conditions
          UpperCAmelCase__ :       Optional[int]						=						c_out * model_output + c_skip * sample
          if self.config.clip_denoised:
                UpperCAmelCase__ :       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:
                UpperCAmelCase__ :       List[Any]						=						randn_tensor(
                    model_output.shape       ,					dtype=model_output.dtype       ,					device=model_output.device       ,					generator=SCREAMING_SNAKE_CASE_  )
          else:
                UpperCAmelCase__ :       Tuple						=						torch.zeros_like(SCREAMING_SNAKE_CASE_  )
          UpperCAmelCase__ :       Dict						=						noise * self.config.s_noise
  
          UpperCAmelCase__ :       List[Any]						=						sigma_next.clamp(min=SCREAMING_SNAKE_CASE_       ,					max=SCREAMING_SNAKE_CASE_  )
  
          # 3. Return noisy sample
          # tau = sigma_hat, eps = sigma_min
          UpperCAmelCase__ :       Any						=						denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5
  
          if not return_dict:
                return (prev_sample,)
  
          return CMStochasticIterativeSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       int						=						self.sigmas.to(device=original_samples.device       ,					dtype=original_samples.dtype  )
          if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_  ):
                # mps does not support float64
                UpperCAmelCase__ :       Optional[Any]						=						self.timesteps.to(original_samples.device       ,					dtype=torch.floataa  )
                UpperCAmelCase__ :       Tuple						=						timesteps.to(original_samples.device       ,					dtype=torch.floataa  )
          else:
                UpperCAmelCase__ :       Tuple						=						self.timesteps.to(original_samples.device  )
                UpperCAmelCase__ :       int						=						timesteps.to(original_samples.device  )
  
          UpperCAmelCase__ :       List[str]						=						[(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
          UpperCAmelCase__ :       Union[str, Any]						=						sigmas[step_indices].flatten()
          while len(sigma.shape  ) < len(original_samples.shape  ):
                UpperCAmelCase__ :       str						=						sigma.unsqueeze(-1  )
  
          UpperCAmelCase__ :       Optional[Any]						=						original_samples + noise * sigma
          return noisy_samples
  
  
  
  
    def __len__(self  ):
  
  
          """simple docstring"""
  
          return self.config.num_train_timesteps
  
  
style_context: 
  
  """simple docstring"""
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Tuple:
        UpperCAmelCase__ :       int						=						TaConfig.from_pretrained(lowerCAmelCase						)
        UpperCAmelCase__ :       Optional[int]						=						FlaxTaForConditionalGeneration(config=lowerCAmelCase						)
        UpperCAmelCase__ :       int						=						checkpoints.load_tax_checkpoint(lowerCAmelCase						)
  
        UpperCAmelCase__ :       Optional[Any]						=						"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]
  
        # Encoder
        for layer_index in range(config.num_layers						):
              UpperCAmelCase__ :       Union[str, Any]						=						F"""layers_{str(lowerCAmelCase						)}"""
  
              # Self-Attention
              UpperCAmelCase__ :       Dict						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
              UpperCAmelCase__ :       Optional[Any]						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
              UpperCAmelCase__ :       Dict						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]
  
              if split_mlp_wi:
                    UpperCAmelCase__ :       int						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
                    UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
              else:
                    UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
              UpperCAmelCase__ :       Tuple						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
              # Assigning
              UpperCAmelCase__ :       str						=						tax_attention_key
              UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_out
              UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_query
              UpperCAmelCase__ :       List[Any]						=						tax_attention_value
  
              UpperCAmelCase__ :       Any						=						tax_attention_layer_norm
  
              if split_mlp_wi:
                    UpperCAmelCase__ :       Any						=						tax_mlp_wi_a
                    UpperCAmelCase__ :       List[Any]						=						tax_mlp_wi_a
              else:
                    UpperCAmelCase__ :       Union[str, Any]						=						tax_mlp_wi
  
              UpperCAmelCase__ :       int						=						tax_mlp_wo
              UpperCAmelCase__ :       List[Any]						=						tax_mlp_layer_norm
  
        # Only for layer 0:
        UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
        UpperCAmelCase__ :       str						=						tax_encoder_rel_embedding
  
        # Assigning
        UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
        UpperCAmelCase__ :       Optional[Any]						=						tax_encoder_norm
  
        # Decoder
        for layer_index in range(config.num_decoder_layers						):
              UpperCAmelCase__ :       str						=						F"""layers_{str(lowerCAmelCase						)}"""
  
              # Self-Attention
              UpperCAmelCase__ :       List[Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
              UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
              UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
                  """scale"""
              ]
  
              # Encoder-Decoder-Attention
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
                  """kernel"""
              ]
              UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
                  """kernel"""
              ]
              UpperCAmelCase__ :       Optional[Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
                  """kernel"""
              ]
              UpperCAmelCase__ :       Dict						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
                  """kernel"""
              ]
  
              # Layer Normalization
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]
  
              # MLP
              if split_mlp_wi:
                    UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
                    UpperCAmelCase__ :       int						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
              else:
                    UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
              UpperCAmelCase__ :       Optional[int]						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
              # Assigning
              UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_key
              UpperCAmelCase__ :       str						=						tax_attention_out
              UpperCAmelCase__ :       Tuple						=						tax_attention_query
              UpperCAmelCase__ :       int						=						tax_attention_value
  
              UpperCAmelCase__ :       List[Any]						=						tax_pre_attention_layer_norm
  
              UpperCAmelCase__ :       Union[str, Any]						=						tax_enc_dec_attention_key
              UpperCAmelCase__ :       Optional[int]						=						tax_enc_dec_attention_out
              UpperCAmelCase__ :       List[Any]						=						tax_enc_dec_attention_query
              UpperCAmelCase__ :       Any						=						tax_enc_dec_attention_value
  
              UpperCAmelCase__ :       List[Any]						=						tax_cross_layer_norm
  
              if split_mlp_wi:
                    UpperCAmelCase__ :       Tuple						=						tax_mlp_wi_a
                    UpperCAmelCase__ :       Dict						=						tax_mlp_wi_a
              else:
                    UpperCAmelCase__ :       str						=						tax_mlp_wi
  
              UpperCAmelCase__ :       Tuple						=						tax_mlp_wo
  
              UpperCAmelCase__ :       Any						=						txa_mlp_layer_norm
  
        # Decoder Normalization
        UpperCAmelCase__ :       int						=						tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
        UpperCAmelCase__ :       Union[str, Any]						=						txa_decoder_norm
  
        # Only for layer 0:
        UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
        UpperCAmelCase__ :       Optional[int]						=						tax_decoder_rel_embedding
  
        # Token Embeddings
        UpperCAmelCase__ :       Tuple						=						tax_model["""target"""]["""token_embedder"""]["""embedding"""]
        UpperCAmelCase__ :       int						=						txa_token_embeddings
  
        # LM Head (only in v1.1 checkpoints)
        if "logits_dense" in tax_model["target"]["decoder"]:
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]
  
        flax_model.save_pretrained(lowerCAmelCase						)
        print("""T5X Model was sucessfully converted!"""						)
  
  
  if __name__ == "__main__":
      _A						=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the TX5 checkpoint."""
      )
      parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of T5 model.""")
      parser.add_argument(
          """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
      )
      _A						=     parser.parse_args()
      convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
  
  