




from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				42
    lowerCAmelCase        =				42





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Dict:
         super().__init__()
         self.register_modules(unet=_SCREAMING_SNAKE_CASE       ,scheduler=_SCREAMING_SNAKE_CASE  )





    @torch.no_grad()
    def __call__(				self       ,_SCREAMING_SNAKE_CASE = 1       ,_SCREAMING_SNAKE_CASE = 50       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = "pil"       ,_SCREAMING_SNAKE_CASE = True       ,**_SCREAMING_SNAKE_CASE       ,)			->		Union[Tuple, ImagePipelineOutput]:
         UpperCAmelCase_      :							List[str]       =      self.unet.config.sample_size
         UpperCAmelCase_      :							Optional[int]       =      (batch_size, 3, img_size, img_size)

         UpperCAmelCase_      :							Union[str, Any]       =      self.unet

         # sample x_0 ~ N(0, sigma_0^2 * I)
         UpperCAmelCase_      :							str       =      randn_tensor(_SCREAMING_SNAKE_CASE       ,generator=_SCREAMING_SNAKE_CASE       ,device=self.device  ) * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )

         for t in self.progress_bar(self.scheduler.timesteps  ):
              # here sigma_t == t_i from the paper
              UpperCAmelCase_      :							Any       =      self.scheduler.schedule[t]
              UpperCAmelCase_      :							Optional[Any]       =      self.scheduler.schedule[t - 1] if t > 0 else 0

              # 1. Select temporarily increased noise level sigma_hat
              # 2. Add new noise to move from sample_i to sample_hat
              UpperCAmelCase_,   UpperCAmelCase_      :							Any       =      self.scheduler.add_noise_to_input(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,generator=_SCREAMING_SNAKE_CASE  )

              # 3. Predict the noise residual given the noise magnitude `sigma_hat`
              # The model inputs and output are adjusted by following eq. (213) in [1].
              UpperCAmelCase_      :							Optional[Any]       =      (sigma_hat / 2) * model((sample_hat + 1) / 2       ,sigma_hat / 2  ).sample

              # 4. Evaluate dx/dt at sigma_hat
              # 5. Take Euler step from sigma to sigma_prev
              UpperCAmelCase_      :							List[str]       =      self.scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

              if sigma_prev != 0:
                   # 6. Apply 2nd order correction
                   # The model inputs and output are adjusted by following eq. (213) in [1].
                   UpperCAmelCase_      :							Union[str, Any]       =      (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2       ,sigma_prev / 2  ).sample
                   UpperCAmelCase_      :							Optional[Any]       =      self.scheduler.step_correct(
                       _SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,step_output.prev_sample       ,step_output['''derivative''']       ,)
              UpperCAmelCase_      :							Any       =      step_output.prev_sample

         UpperCAmelCase_      :							Dict       =      (sample / 2 + 0.5).clamp(0       ,1  )
         UpperCAmelCase_      :							Optional[Any]       =      sample.cpu().permute(0       ,2       ,3       ,1  ).numpy()
         if output_type == "pil":
              UpperCAmelCase_      :							Tuple       =      self.numpy_to_pil(_SCREAMING_SNAKE_CASE  )

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE  )





import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__a          =			logging.get_logger(__name__)







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							str       =      r'''\w+[.]\d+'''
     UpperCAmelCase_      :							int       =      re.findall(_lowercase			,						_lowercase				)
     for pat in pats:
          UpperCAmelCase_      :							List[Any]       =      key.replace(_lowercase			,						'''_'''.join(pat.split('''.'''				)				)				)
     return key







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Dict       =      pt_tuple_key[:-1] + ('''scale''',)
     if (
         any('''norm''' in str_ for str_ in pt_tuple_key				)
         and (pt_tuple_key[-1] == "bias")
         and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
         and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
     ):
          UpperCAmelCase_      :							List[Any]       =      pt_tuple_key[:-1] + ('''scale''',)
          return renamed_pt_tuple_key, pt_tensor
     elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
          UpperCAmelCase_      :							Any       =      pt_tuple_key[:-1] + ('''scale''',)
          return renamed_pt_tuple_key, pt_tensor

     # embedding
     if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
          UpperCAmelCase_      :							Tuple       =      pt_tuple_key[:-1] + ('''embedding''',)
          return renamed_pt_tuple_key, pt_tensor

     # conv layer
     UpperCAmelCase_      :							List[Any]       =      pt_tuple_key[:-1] + ('''kernel''',)
     if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
          UpperCAmelCase_      :							List[Any]       =      pt_tensor.transpose(2			,						3			,						1			,						0				)
          return renamed_pt_tuple_key, pt_tensor

     # linear layer
     UpperCAmelCase_      :							Dict       =      pt_tuple_key[:-1] + ('''kernel''',)
     if pt_tuple_key[-1] == "weight":
          UpperCAmelCase_      :							Tuple       =      pt_tensor.T
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm weight
     UpperCAmelCase_      :							Any       =      pt_tuple_key[:-1] + ('''weight''',)
     if pt_tuple_key[-1] == "gamma":
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm bias
     UpperCAmelCase_      :							Tuple       =      pt_tuple_key[:-1] + ('''bias''',)
     if pt_tuple_key[-1] == "beta":
          return renamed_pt_tuple_key, pt_tensor

     return pt_tuple_key, pt_tensor







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase=42				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      {k: v.numpy() for k, v in pt_state_dict.items()}

     # Step 2: Since the model is stateless, get random Flax params
     UpperCAmelCase_      :							str       =      flax_model.init_weights(PRNGKey(_lowercase				)				)

     UpperCAmelCase_      :							List[Any]       =      flatten_dict(_lowercase				)
     UpperCAmelCase_      :							int       =      {}

     # Need to change some parameters name to match Flax names
     for pt_key, pt_tensor in pt_state_dict.items():
          UpperCAmelCase_      :							Optional[int]       =      rename_key(_lowercase				)
          UpperCAmelCase_      :							List[str]       =      tuple(renamed_pt_key.split('''.'''				)				)

          # Correctly rename weight parameters
          UpperCAmelCase_,   UpperCAmelCase_      :							Any       =      rename_key_and_reshape_tensor(_lowercase			,						_lowercase			,						_lowercase				)

          if flax_key in random_flax_state_dict:
               if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                    raise ValueError(
                        f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
                        f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''				)

        # also add unexpected weight so that warning is thrown
          UpperCAmelCase_      :							int       =      jnp.asarray(_lowercase				)

     return unflatten_dict(_lowercase				)