"""simple docstring"""



import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
   import jax.numpy as jnp






class 				snake_case__     (   snake_case_   ):


 def __get__(				self  ,						lowerCamelCase  ,						lowerCamelCase=None		):
        # See docs.python.org/3/howto/descriptor.html#properties
        if obj is None:
               return self
        if self.fget is None:
               raise AttributeError("unreadable attribute"		)
        __a     				=  "__cached_" + self.fget.__name__
        __a     				=  getattr(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)
        if cached is None:
               __a     				=  self.fget(lowerCamelCase		)
               setattr(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)
        return cached



def 					_lowerCamelCase(					a							):
       __a     				=  val.lower()
       if val in {"y", "yes", "t", "true", "on", "1"}:
              return 1
       if val in {"n", "no", "f", "false", "off", "0"}:
              return 0
       raise ValueError(F"invalid truth value {val!r}"							)



def 					_lowerCamelCase(					a							):
       if is_torch_fx_proxy(a							):
              return True
       if is_torch_available():
              import torch

              if isinstance(a ,  torch.Tensor							):
                     return True
       if is_tf_available():
              import tensorflow as tf

              if isinstance(a ,  tf.Tensor							):
                     return True

       if is_flax_available():
              import jax.numpy as jnp
              from jax.core import Tracer

              if isinstance(a ,  (jnp.ndarray, Tracer)							):
                     return True

       return isinstance(a ,  np.ndarray							)



def 					_lowerCamelCase(					a							):
       return isinstance(a ,  np.ndarray							)



def 					_lowerCamelCase(					a							):
       return _is_numpy(a							)



def 					_lowerCamelCase(					a							):
       import torch

       return isinstance(a ,  torch.Tensor							)



def 					_lowerCamelCase(					a							):
       return False if not is_torch_available() else _is_torch(a							)



def 					_lowerCamelCase(					a							):
       import torch

       return isinstance(a ,  torch.device							)



def 					_lowerCamelCase(					a							):
       return False if not is_torch_available() else _is_torch_device(a							)



def 					_lowerCamelCase(					a							):
       import torch

       if isinstance(a ,  a							):
              if hasattr(a ,  a							):
                     __a     				=  getattr(a ,  a							)
              else:
                     return False
       return isinstance(a ,  torch.dtype							)



def 					_lowerCamelCase(					a							):
       return False if not is_torch_available() else _is_torch_dtype(a							)



def 					_lowerCamelCase(					a							):
       import tensorflow as tf

       return isinstance(a ,  tf.Tensor							)



def 					_lowerCamelCase(					a							):
       return False if not is_tf_available() else _is_tensorflow(a							)



def 					_lowerCamelCase(					a							):
       import tensorflow as tf

       # the `is_symbolic_tensor` predicate is only available starting with TF 2.14
       if hasattr(a ,  "is_symbolic_tensor"							):
              return tf.is_symbolic_tensor(a							)
       return type(a							) == tf.Tensor



def 					_lowerCamelCase(					a							):
       return False if not is_tf_available() else _is_tf_symbolic_tensor(a							)



def 					_lowerCamelCase(					a							):
       import jax.numpy as jnp  # noqa: F811

       return isinstance(a ,  jnp.ndarray							)



def 					_lowerCamelCase(					a							):
       return False if not is_flax_available() else _is_jax(a							)



def 					_lowerCamelCase(					a							):
       if isinstance(a ,  (dict, UserDict)							):
              return {k: to_py_obj(a							) for k, v in obj.items()}
       elif isinstance(a ,  (list, tuple)							):
              return [to_py_obj(a							) for o in obj]
       elif is_tf_tensor(a							):
              return obj.numpy().tolist()
       elif is_torch_tensor(a							):
              return obj.detach().cpu().tolist()
       elif is_jax_tensor(a							):
              return np.asarray(a							).tolist()
       elif isinstance(a ,  (np.ndarray, np.number)							):  # tolist also works on 0d np arrays
              return obj.tolist()
       else:
              return obj



def 					_lowerCamelCase(					a							):
       if isinstance(a ,  (dict, UserDict)							):
              return {k: to_numpy(a							) for k, v in obj.items()}
       elif isinstance(a ,  (list, tuple)							):
              return np.array(a							)
       elif is_tf_tensor(a							):
              return obj.numpy()
       elif is_torch_tensor(a							):
              return obj.detach().cpu().numpy()
       elif is_jax_tensor(a							):
              return np.asarray(a							)
       else:
              return obj






class 				snake_case__     (   snake_case_   ):


 def 		a__ (				self		):
        __a     				=  fields(self		)

        # Safety and consistency checks
        if not len(lowerCamelCase		):
               raise ValueError(F"{self.__class__.__name__} has no fields."		)
        if not all(field.default is None for field in class_fields[1:]		):
               raise ValueError(F"{self.__class__.__name__} should not have more than one required field."		)

        __a     				=  getattr(self  ,						class_fields[0].name		)
        __a     				=  all(getattr(self  ,						field.name		) is None for field in class_fields[1:]		)

        if other_fields_are_none and not is_tensor(lowerCamelCase		):
               if isinstance(lowerCamelCase  ,						lowerCamelCase		):
                      __a     				=  first_field.items()
                      __a     				=  True
               else:
                      try:
                             __a     				=  iter(lowerCamelCase		)
                             __a     				=  True
                      except TypeError:
                             __a     				=  False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
               if first_field_iterator:
                      for idx, element in enumerate(lowerCamelCase		):
                             if (
                                 not isinstance(lowerCamelCase  ,						(list, tuple)		)
                                 or not len(lowerCamelCase		) == 2
                                 or not isinstance(element[0]  ,						lowerCamelCase		)
                             ):
                                    if idx == 0:
                                           # If we do not have an iterator of key/values, set it as attribute
                                           __a     				=  first_field
                                    else:
                                           # If we have a mixed iterator, raise an error
                                           raise ValueError(
                                               F"Cannot set key/value for {element}. It needs to be a tuple (key, value)."		)
                                    break
                             setattr(self  ,						element[0]  ,						element[1]		)
                             if element[1] is not None:
                                    __a     				=  element[1]
               elif first_field is not None:
                      __a     				=  first_field
        else:
               for field in class_fields:
                      __a     				=  getattr(self  ,						field.name		)
                      if v is not None:
                             __a     				=  v


 def __delitem__(				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        raise Exception(F"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."		)


 def 		a__ (				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        raise Exception(F"You cannot use ``setdefault`` on a {self.__class__.__name__} instance."		)


 def 		a__ (				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        raise Exception(F"You cannot use ``pop`` on a {self.__class__.__name__} instance."		)


 def 		a__ (				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        raise Exception(F"You cannot use ``update`` on a {self.__class__.__name__} instance."		)


 def __getitem__(				self  ,						lowerCamelCase		):
        if isinstance(lowerCamelCase  ,						lowerCamelCase		):
               __a     				=  dict(self.items()		)
               return inner_dict[k]
        else:
               return self.to_tuple()[k]


 def __setattr__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        if name in self.keys() and value is not None:
               # Don't call self.__setitem__ to avoid recursion errors
               super().__setitem__(lowerCamelCase  ,						lowerCamelCase		)
        super().__setattr__(lowerCamelCase  ,						lowerCamelCase		)


 def __setitem__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        # Will raise a KeyException if needed
        super().__setitem__(lowerCamelCase  ,						lowerCamelCase		)
        # Don't call self.__setattr__ to avoid recursion errors
        super().__setattr__(lowerCamelCase  ,						lowerCamelCase		)






 def 		a__ (				self		):
        return tuple(self[k] for k in self.keys()		)






class 				snake_case__     (   snake_case_,		snake_case_   ):


 @classmethod
 def 		a__ (				cls  ,						lowerCamelCase		):
        raise ValueError(
            F"{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()		)}"		)






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Union[str, Any]           =		"""longest"""
 _snake_case   :			Dict           =		"""max_length"""
 _snake_case   :			Dict           =		"""do_not_pad"""






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Union[str, Any]           =		"""pt"""
 _snake_case   :			Union[str, Any]           =		"""tf"""
 _snake_case   :			List[str]           =		"""np"""
 _snake_case   :			Dict           =		"""jax"""






class 				snake_case__     :


 def __init__(				self  ,						lowerCamelCase		):
        __a     				=  context_managers
        __a     				=  ExitStack()


 def __enter__(				self		):
        for context_manager in self.context_managers:
               self.stack.enter_context(lowerCamelCase		)






 def __exit__(				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        self.stack.__exit__(*lowerCamelCase  ,						**lowerCamelCase		)



def 					_lowerCamelCase(					a							):
       __a     				=  infer_framework(a							)
       if framework == "tf":
              __a     				=  inspect.signature(model_class.call							)  # TensorFlow models
       elif framework == "pt":
              __a     				=  inspect.signature(model_class.forward							)  # PyTorch models
       else:
              __a     				=  inspect.signature(model_class.__call__							)  # Flax models

       for p in signature.parameters:
              if p == "return_loss" and signature.parameters[p].default is True:
                     return True

       return False



def 					_lowerCamelCase(					a							):
       __a     				=  model_class.__name__
       __a     				=  infer_framework(a							)
       if framework == "tf":
              __a     				=  inspect.signature(model_class.call							)  # TensorFlow models
       elif framework == "pt":
              __a     				=  inspect.signature(model_class.forward							)  # PyTorch models
       else:
              __a     				=  inspect.signature(model_class.__call__							)  # Flax models

       if "QuestionAnswering" in model_name:
              return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
       else:
              return [p for p in signature.parameters if "label" in p]



def 					_lowerCamelCase(					a ,  a = "" ,  a = "."							):

       def _flatten_dict(a ,  a="" ,  a="."							):
              for k, v in d.items():
                     __a     				=  str(a							) + delimiter + str(a							) if parent_key else k
                     if v and isinstance(a ,  a							):
                            yield from flatten_dict(a ,  a ,  delimiter=a							).items()
                     else:
                            yield key, v

       return dict(_flatten_dict(a ,  a ,  a							)							)



@contextmanager
def 					_lowerCamelCase(					a ,  a = False							):
       if use_temp_dir:
              with tempfile.TemporaryDirectory() as tmp_dir:
                     yield tmp_dir
       else:
              yield working_dir



def 					_lowerCamelCase(					a ,  a=None							):
       if is_numpy_array(a							):
              return np.transpose(a ,  axes=a							)
       elif is_torch_tensor(a							):
              return array.T if axes is None else array.permute(*a							)
       elif is_tf_tensor(a							):
              import tensorflow as tf

              return tf.transpose(a ,  perm=a							)
       elif is_jax_tensor(a							):
              return jnp.transpose(a ,  axes=a							)
       else:
              raise ValueError(F"Type not supported for transpose: {type(a							)}."							)



def 					_lowerCamelCase(					a ,  a							):
       if is_numpy_array(a							):
              return np.reshape(a ,  a							)
       elif is_torch_tensor(a							):
              return array.reshape(*a							)
       elif is_tf_tensor(a							):
              import tensorflow as tf

              return tf.reshape(a ,  a							)
       elif is_jax_tensor(a							):
              return jnp.reshape(a ,  a							)
       else:
              raise ValueError(F"Type not supported for reshape: {type(a							)}."							)



def 					_lowerCamelCase(					a ,  a=None							):
       if is_numpy_array(a							):
              return np.squeeze(a ,  axis=a							)
       elif is_torch_tensor(a							):
              return array.squeeze() if axis is None else array.squeeze(dim=a							)
       elif is_tf_tensor(a							):
              import tensorflow as tf

              return tf.squeeze(a ,  axis=a							)
       elif is_jax_tensor(a							):
              return jnp.squeeze(a ,  axis=a							)
       else:
              raise ValueError(F"Type not supported for squeeze: {type(a							)}."							)



def 					_lowerCamelCase(					a ,  a							):
       if is_numpy_array(a							):
              return np.expand_dims(a ,  a							)
       elif is_torch_tensor(a							):
              return array.unsqueeze(dim=a							)
       elif is_tf_tensor(a							):
              import tensorflow as tf

              return tf.expand_dims(a ,  axis=a							)
       elif is_jax_tensor(a							):
              return jnp.expand_dims(a ,  axis=a							)
       else:
              raise ValueError(F"Type not supported for expand_dims: {type(a							)}."							)



def 					_lowerCamelCase(					a							):
       if is_numpy_array(a							):
              return np.size(a							)
       elif is_torch_tensor(a							):
              return array.numel()
       elif is_tf_tensor(a							):
              import tensorflow as tf

              return tf.size(a							)
       elif is_jax_tensor(a							):
              return array.size
       else:
              raise ValueError(F"Type not supported for expand_dims: {type(a							)}."							)



def 					_lowerCamelCase(					a ,  a							):
       for key, value in auto_map.items():
              if isinstance(a ,  (tuple, list)							):
                     __a     				=  [F"{repo_id}--{v}" if (v is not None and "--" not in v) else v for v in value]
              elif value is not None and "--" not in value:
                     __a     				=  F"{repo_id}--{value}"

       return auto_map




def 					_lowerCamelCase(					a							):
       for base_class in inspect.getmro(a							):
              __a     				=  base_class.__module__
              __a     				=  base_class.__name__
              if module.startswith("tensorflow"							) or module.startswith("keras"							) or name == "TFPreTrainedModel":
                     return "tf"
              elif module.startswith("torch"							) or name == "PreTrainedModel":
                     return "pt"
              elif module.startswith("flax"							) or module.startswith("jax"							) or name == "FlaxPreTrainedModel":
                     return "flax"
       else:
              raise TypeError(F"Could not infer framework from class {model_class}."							)

"""simple docstring"""



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 				snake_case__     (   snake_case_   ):
 _snake_case   :			UNetaDModel
 _snake_case   :			KarrasVeScheduler


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        super().__init__()
        self.register_modules(unet=lowerCamelCase  ,						scheduler=lowerCamelCase		)






 @torch.no_grad()
 def __call__(				self  ,						lowerCamelCase = 1  ,						lowerCamelCase = 50  ,						lowerCamelCase = None  ,						lowerCamelCase = "pil"  ,						lowerCamelCase = True  ,						**lowerCamelCase  ,						):
        __a     				=  self.unet.config.sample_size
        __a     				=  (batch_size, 3, img_size, img_size)

        __a     				=  self.unet

        # sample x_0 ~ N(0, sigma_0^2 * I)
        __a     				=  randn_tensor(lowerCamelCase  ,						generator=lowerCamelCase  ,						device=self.device		) * self.scheduler.init_noise_sigma

        self.scheduler.set_timesteps(lowerCamelCase		)

        for t in self.progress_bar(self.scheduler.timesteps		):
               # here sigma_t == t_i from the paper
               __a     				=  self.scheduler.schedule[t]
               __a     				=  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
               __a				, __a     				=  self.scheduler.add_noise_to_input(lowerCamelCase  ,						lowerCamelCase  ,						generator=lowerCamelCase		)

               # 3. Predict the noise residual given the noise magnitude `sigma_hat`
               # The model inputs and output are adjusted by following eq. (213) in [1].
               __a     				=  (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
               __a     				=  self.scheduler.step(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)

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

        __a     				=  (sample / 2 + 0.5).clamp(0  ,						1		)
        __a     				=  sample.cpu().permute(0  ,						2  ,						3  ,						1		).numpy()
        if output_type == "pil":
               __a     				=  self.numpy_to_pil(lowerCamelCase		)

        if not return_dict:
               return (image,)

        return ImagePipelineOutput(images=lowerCamelCase		)
