



'''simple docstring'''





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      Tuple				=      (DDPMScheduler,)




  def 							UpperCAmelCase_	(    self  ,**__UpperCAmelCase      )    ->		Tuple:
      lowerCAmelCase__ :  Dict							      =					{
          """num_train_timesteps""": 1000,
          """beta_start""": 0.0_0_0_1,
          """beta_end""": 0.0_2,
          """beta_schedule""": """linear""",
          """variance_type""": """fixed_small""",
          """clip_sample""": True,
      }

      config.update(**__UpperCAmelCase      )
      return config




  def 							UpperCAmelCase_	(    self      )    ->		str:
      for timesteps in [1, 5, 100, 1000]:
          self.check_over_configs(num_train_timesteps=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]  ,[0.0_0_2, 0.0_2, 0.2, 2]      ):
          self.check_over_configs(beta_start=__UpperCAmelCase  ,beta_end=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		int:
      for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		int:
      for variance in ["fixed_small", "fixed_large", "other"]:
          self.check_over_configs(variance_type=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      self.check_over_configs(thresholding=__UpperCAmelCase      )
      for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(
                  thresholding=__UpperCAmelCase  ,prediction_type=__UpperCAmelCase  ,sample_max_value=__UpperCAmelCase  ,)




  def 							UpperCAmelCase_	(    self      )    ->		int:
      for prediction_type in ["epsilon", "sample", "v_prediction"]:
          self.check_over_configs(prediction_type=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      for t in [0, 500, 999]:
          self.check_over_forward(time_step=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      lowerCAmelCase__ :  List[Any]							      =					self.scheduler_classes[0]
      lowerCAmelCase__ :  Optional[Any]							      =					self.get_scheduler_config()
      lowerCAmelCase__ :  Dict							      =					scheduler_class(**__UpperCAmelCase      )

      assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 0.0      )      ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.0_0_9_7_9      )      ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.0_2      )      ) < 1E-5




  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  List[str]							      =					self.scheduler_classes[0]
      lowerCAmelCase__ :  int							      =					self.get_scheduler_config()
      lowerCAmelCase__ :  List[Any]							      =					scheduler_class(**__UpperCAmelCase      )

      lowerCAmelCase__ :  Union[str, Any]							      =					len(__UpperCAmelCase      )

      lowerCAmelCase__ :  List[Any]							      =					self.dummy_model()
      lowerCAmelCase__ :  str							      =					self.dummy_sample_deter
      lowerCAmelCase__ :  List[str]							      =					torch.manual_seed(0      )

      for t in reversed(range(__UpperCAmelCase      )      ):
          # 1. predict noise residual
          lowerCAmelCase__ :  int							      =					model(__UpperCAmelCase  ,__UpperCAmelCase      )

          # 2. predict previous mean of sample x_t-1
          lowerCAmelCase__ :  Union[str, Any]							      =					scheduler.step(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,generator=__UpperCAmelCase      ).prev_sample

          # if t > 0:
          #     noise = self.dummy_sample_deter
          #     variance = scheduler.get_variance(t) ** (0.5) * noise
          #
          # sample = pred_prev_sample + variance
          lowerCAmelCase__ :  List[Any]							      =					pred_prev_sample

      lowerCAmelCase__ :  List[Any]							      =					torch.sum(torch.abs(__UpperCAmelCase      )      )
      lowerCAmelCase__ :  List[Any]							      =					torch.mean(torch.abs(__UpperCAmelCase      )      )

      assert abs(result_sum.item() - 2_5_8.9_6_0_6      ) < 1E-2
      assert abs(result_mean.item() - 0.3_3_7_2      ) < 1E-3




  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      lowerCAmelCase__ :  int							      =					self.scheduler_classes[0]
      lowerCAmelCase__ :  List[str]							      =					self.get_scheduler_config(prediction_type="""v_prediction"""      )
      lowerCAmelCase__ :  List[str]							      =					scheduler_class(**__UpperCAmelCase      )

      lowerCAmelCase__ :  List[str]							      =					len(__UpperCAmelCase      )

      lowerCAmelCase__ :  List[Any]							      =					self.dummy_model()
      lowerCAmelCase__ :  Tuple							      =					self.dummy_sample_deter
      lowerCAmelCase__ :  Union[str, Any]							      =					torch.manual_seed(0      )

      for t in reversed(range(__UpperCAmelCase      )      ):
          # 1. predict noise residual
          lowerCAmelCase__ :  List[Any]							      =					model(__UpperCAmelCase  ,__UpperCAmelCase      )

          # 2. predict previous mean of sample x_t-1
          lowerCAmelCase__ :  List[str]							      =					scheduler.step(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,generator=__UpperCAmelCase      ).prev_sample

          # if t > 0:
          #     noise = self.dummy_sample_deter
          #     variance = scheduler.get_variance(t) ** (0.5) * noise
          #
          # sample = pred_prev_sample + variance
          lowerCAmelCase__ :  str							      =					pred_prev_sample

      lowerCAmelCase__ :  List[Any]							      =					torch.sum(torch.abs(__UpperCAmelCase      )      )
      lowerCAmelCase__ :  Any							      =					torch.mean(torch.abs(__UpperCAmelCase      )      )

      assert abs(result_sum.item() - 2_0_2.0_2_9_6      ) < 1E-2
      assert abs(result_mean.item() - 0.2_6_3_1      ) < 1E-3




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      lowerCAmelCase__ :  Optional[int]							      =					self.scheduler_classes[0]
      lowerCAmelCase__ :  List[Any]							      =					self.get_scheduler_config()
      lowerCAmelCase__ :  Optional[Any]							      =					scheduler_class(**__UpperCAmelCase      )

      lowerCAmelCase__ :  str							      =					[100, 87, 50, 1, 0]

      scheduler.set_timesteps(timesteps=__UpperCAmelCase      )

      lowerCAmelCase__ :  str							      =					scheduler.timesteps

      for i, timestep in enumerate(__UpperCAmelCase      ):
          if i == len(__UpperCAmelCase      ) - 1:
              lowerCAmelCase__ :  Union[str, Any]							      =					-1
          else:
              lowerCAmelCase__ :  List[str]							      =					timesteps[i + 1]

          lowerCAmelCase__ :  str							      =					scheduler.previous_timestep(__UpperCAmelCase      )
          lowerCAmelCase__ :  Dict							      =					prev_t.item()

          self.assertEqual(__UpperCAmelCase  ,__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      lowerCAmelCase__ :  Optional[int]							      =					self.scheduler_classes[0]
      lowerCAmelCase__ :  Optional[int]							      =					self.get_scheduler_config()
      lowerCAmelCase__ :  Any							      =					scheduler_class(**__UpperCAmelCase      )

      lowerCAmelCase__ :  Tuple							      =					[100, 87, 50, 51, 0]

      with self.assertRaises(__UpperCAmelCase  ,msg="""`custom_timesteps` must be in descending order."""      ):
          scheduler.set_timesteps(timesteps=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  Dict							      =					self.scheduler_classes[0]
      lowerCAmelCase__ :  List[str]							      =					self.get_scheduler_config()
      lowerCAmelCase__ :  Tuple							      =					scheduler_class(**__UpperCAmelCase      )

      lowerCAmelCase__ :  List[Any]							      =					[100, 87, 50, 1, 0]
      lowerCAmelCase__ :  Union[str, Any]							      =					len(__UpperCAmelCase      )

      with self.assertRaises(__UpperCAmelCase  ,msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""      ):
          scheduler.set_timesteps(num_inference_steps=__UpperCAmelCase  ,timesteps=__UpperCAmelCase      )






  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      lowerCAmelCase__ :  List[Any]							      =					self.scheduler_classes[0]
      lowerCAmelCase__ :  int							      =					self.get_scheduler_config()
      lowerCAmelCase__ :  Tuple							      =					scheduler_class(**__UpperCAmelCase      )

      lowerCAmelCase__ :  List[str]							      =					[scheduler.config.num_train_timesteps]

      with self.assertRaises(
          __UpperCAmelCase  ,msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""  ,):
          scheduler.set_timesteps(timesteps=__UpperCAmelCase      )







'''simple docstring'''





from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


_lowerCAmelCase      						=	logging.get_logger(__name__)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    try:
        with open(UpperCamelCase							,    """rb"""			) as flax_state_f:
            lowerCAmelCase__ :  Union[str, Any]							      =					from_bytes(UpperCamelCase							,    flax_state_f.read()			)
    except UnpicklingError as e:
        try:
            with open(UpperCamelCase			) as f:
                if f.read().startswith("""version"""			):
                    raise OSError(
                        """You seem to have cloned a repository without having git-lfs installed. Please"""
                        """ install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
                        """ folder you cloned."""			)
                else:
                    raise ValueError from e
        except (UnicodeDecodeError, ValueError):
            raise EnvironmentError(f"""Unable to convert {model_file} to Flax deserializable object. """			)

    return load_flax_weights_in_pytorch_model(UpperCamelCase							,    UpperCamelCase			)




def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            """Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
            """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
            """ instructions."""			)
        raise

    # check if we have bf16 weights
    lowerCAmelCase__ :  str							      =					flatten_dict(jax.tree_util.tree_map(lambda UpperCamelCase			: x.dtype == jnp.bfloataa							,    UpperCamelCase			)			).values()
    if any(UpperCamelCase			):
        # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

        # and bf16 is not fully supported in PT yet.
        logger.warning(
            """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
            """before loading those in PyTorch model."""			)
        lowerCAmelCase__ :  Dict							      =					jax.tree_util.tree_map(
            lambda UpperCamelCase			: params.astype(np.floataa			) if params.dtype == jnp.bfloataa else params							,    UpperCamelCase			)

    lowerCAmelCase__ :  Any							      =					""""""

    lowerCAmelCase__ :  Any							      =					flatten_dict(UpperCamelCase							,    sep="""."""			)
    lowerCAmelCase__ :  Optional[int]							      =					pt_model.state_dict()

    # keep track of unexpected & missing keys
    lowerCAmelCase__ :  Optional[Any]							      =					[]
    lowerCAmelCase__ :  int							      =					set(pt_model_dict.keys()			)

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        lowerCAmelCase__ :  Union[str, Any]							      =					flax_key_tuple.split("""."""			)

        if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
            lowerCAmelCase__ :  Optional[int]							      =					flax_key_tuple_array[:-1] + ["""weight"""]
            lowerCAmelCase__ :  Any							      =					jnp.transpose(UpperCamelCase							,    (3, 2, 0, 1)			)
        elif flax_key_tuple_array[-1] == "kernel":
            lowerCAmelCase__ :  str							      =					flax_key_tuple_array[:-1] + ["""weight"""]
            lowerCAmelCase__ :  Any							      =					flax_tensor.T
        elif flax_key_tuple_array[-1] == "scale":
            lowerCAmelCase__ :  int							      =					flax_key_tuple_array[:-1] + ["""weight"""]

        if "time_embedding" not in flax_key_tuple_array:
            for i, flax_key_tuple_string in enumerate(UpperCamelCase			):
                lowerCAmelCase__ :  List[str]							      =					(
                    flax_key_tuple_string.replace("""_0"""							,    """.0"""			)
                    .replace("""_1"""							,    """.1"""			)
                    .replace("""_2"""							,    """.2"""			)
                    .replace("""_3"""							,    """.3"""			)
                    .replace("""_4"""							,    """.4"""			)
                    .replace("""_5"""							,    """.5"""			)
                    .replace("""_6"""							,    """.6"""			)
                    .replace("""_7"""							,    """.7"""			)
                    .replace("""_8"""							,    """.8"""			)
                    .replace("""_9"""							,    """.9"""			)
                )

        lowerCAmelCase__ :  Union[str, Any]							      =					""".""".join(UpperCamelCase			)

        if flax_key in pt_model_dict:
            if flax_tensor.shape != pt_model_dict[flax_key].shape:
                raise ValueError(
                    f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
                    f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""			)
            else:
                # add weight to pytorch dict
                lowerCAmelCase__ :  int							      =					np.asarray(UpperCamelCase			) if not isinstance(UpperCamelCase							,    np.ndarray			) else flax_tensor
                lowerCAmelCase__ :  int							      =					torch.from_numpy(UpperCamelCase			)
                # remove from missing keys
                missing_keys.remove(UpperCamelCase			)
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(UpperCamelCase			)

    pt_model.load_state_dict(UpperCamelCase			)

    # re-transform missing_keys to list
    lowerCAmelCase__ :  Optional[int]							      =					list(UpperCamelCase			)

    if len(UpperCamelCase			) > 0:
        logger.warning(
            """Some weights of the Flax model were not used when initializing the PyTorch model"""
            f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
            f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
            """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
            f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
            """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
            """ FlaxBertForSequenceClassification model)."""			)
    if len(UpperCamelCase			) > 0:
        logger.warning(
            f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
            f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
            """ use it for predictions and inference."""			)

    return pt_model


