


# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
 import torch






class _lowerCamelCase(		TensorFormatter[Mapping, """torch.Tensor""", Mapping]  ):
       def __init__(   self, lowerCamelCase=None, **lowerCamelCase)			->  Tuple:




           """simple docstring"""

           super().__init__(features=lowerCamelCase)
           _lowercase			:				List[str] 						=						torch_tensor_kwargs
           import torch  # noqa import torch at initialization
       def      UpperCamelCase	(   self, lowerCamelCase)			->  str:




           """simple docstring"""

           import torch

           if isinstance(lowerCamelCase, lowerCamelCase) and column:
               if all(
                   isinstance(lowerCamelCase, torch.Tensor) and x.shape == column[0].shape and x.dtype == column[0].dtype
                   for x in column):
                   return torch.stack(lowerCamelCase)
           return column
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Any:




           """simple docstring"""

           import torch

           if isinstance(lowerCamelCase, (str, bytes, type(lowerCamelCase))):
               return value
           elif isinstance(lowerCamelCase, (np.character, np.ndarray)) and np.issubdtype(value.dtype, np.character):
               return value.tolist()

           _lowercase			:				Optional[int] 						=						{}

           if isinstance(lowerCamelCase, (np.number, np.ndarray)) and np.issubdtype(value.dtype, np.integer):
               _lowercase			:				List[str] 						=						{'dtype': torch.intaa}
           elif isinstance(lowerCamelCase, (np.number, np.ndarray)) and np.issubdtype(value.dtype, np.floating):
               _lowercase			:				Any 						=						{'dtype': torch.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
               import PIL.Image

               if isinstance(lowerCamelCase, PIL.Image.Image):
                   _lowercase			:				List[Any] 						=						np.asarray(lowerCamelCase)
           return torch.tensor(lowerCamelCase, **{**default_dtype, **self.torch_tensor_kwargs})
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Any:




           """simple docstring"""

           import torch

           # support for torch, tf, jax etc.
           if hasattr(lowerCamelCase, '__array__') and not isinstance(lowerCamelCase, torch.Tensor):
               _lowercase			:				Any 						=						data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(lowerCamelCase, np.ndarray):
               if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                   return self._consolidate([self.recursive_tensorize(lowerCamelCase) for substruct in data_struct])
           elif isinstance(lowerCamelCase, (list, tuple)):
               return self._consolidate([self.recursive_tensorize(lowerCamelCase) for substruct in data_struct])
           return self._tensorize(lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Optional[int]:




           """simple docstring"""

           return map_nested(self._recursive_tensorize, lowerCamelCase, map_list=lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Mapping:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						self.numpy_arrow_extractor().extract_row(lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						self.python_features_decoder.decode_row(lowerCamelCase)
           return self.recursive_tensorize(lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  "torch.Tensor":




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						self.numpy_arrow_extractor().extract_column(lowerCamelCase)
           _lowercase			:				int 						=						self.python_features_decoder.decode_column(lowerCamelCase, pa_table.column_names[0])
           _lowercase			:				Union[str, Any] 						=						self.recursive_tensorize(lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						self._consolidate(lowerCamelCase)
           return column




       def      UpperCamelCase	(   self, lowerCamelCase)			->  Mapping:




           """simple docstring"""

           _lowercase			:				str 						=						self.numpy_arrow_extractor().extract_batch(lowerCamelCase)
           _lowercase			:				List[Any] 						=						self.python_features_decoder.decode_batch(lowerCamelCase)
           _lowercase			:				Any 						=						self.recursive_tensorize(lowerCamelCase)
           for column_name in batch:
               _lowercase			:				Tuple 						=						self._consolidate(batch[column_name])
           return batch






import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE			: str				   =					"scheduler_config.json"






class _lowerCamelCase(		_a  ):
       lowercase_					:      Any       					=     1
       lowercase_					:      Dict       					=     2
       lowercase_					:      Union[str, Any]       					=     3
       lowercase_					:      Tuple       					=     4
       lowercase_					:      Optional[Any]       					=     5






@dataclass
class _lowerCamelCase(		_a  ):
       lowercase_					:      jnp.ndarray






class _lowerCamelCase:
       lowercase_					:      Union[str, Any]       					=     SCHEDULER_CONFIG_NAME
       lowercase_					:      str       					=     ["""dtype"""]
       lowercase_					:      Dict       					=     []
       lowercase_					:      int       					=     True
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase=False, **lowerCamelCase, )			->  Optional[int]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Optional[int] 						=						cls.load_config(
               pretrained_model_name_or_path=lowerCamelCase, subfolder=lowerCamelCase, return_unused_kwargs=lowerCamelCase, **lowerCamelCase, )
           _lowercase						,							_lowercase			:				Tuple 						=						cls.from_config(lowerCamelCase, return_unused_kwargs=lowerCamelCase, **lowerCamelCase)

           if hasattr(lowerCamelCase, 'create_state') and getattr(lowerCamelCase, 'has_state', lowerCamelCase):
               _lowercase			:				List[Any] 						=						scheduler.create_state()

           if return_unused_kwargs:
               return scheduler, state, unused_kwargs

           return scheduler, state
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = False, **lowerCamelCase)			->  Any:




           """simple docstring"""

           self.save_config(save_directory=lowerCamelCase, push_to_hub=lowerCamelCase, **lowerCamelCase)
       @property
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           return self._get_compatibles()




       @classmethod
       def      UpperCamelCase	(   cls)			->  Any:




           """simple docstring"""

           _lowercase			:				Any 						=						list(set([cls.__name__] + cls._compatibles))
           _lowercase			:				Dict 						=						importlib.import_module(__name__.split('.')[0])
           _lowercase			:				Any 						=						[
               getattr(lowerCamelCase, lowerCamelCase) for c in compatible_classes_str if hasattr(lowerCamelCase, lowerCamelCase)
           ]
           return compatible_classes



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		jnp.ndarray:
    assert len(lowerCamelCase_					) >= x.ndim
    return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase_					) - x.ndim)					)				,					lowerCamelCase_					)



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=0.9_99				,					lowerCamelCase_=jnp.floataa					)       ->		jnp.ndarray:

    def alpha_bar(lowerCamelCase_					):
        return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

    _lowercase			:				List[Any] 						=						[]
    for i in range(lowerCamelCase_					):
        _lowercase			:				Any 						=						i / num_diffusion_timesteps
        _lowercase			:				Any 						=						(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar(lowerCamelCase_					) / alpha_bar(lowerCamelCase_					)				,					lowerCamelCase_					)					)
    return jnp.array(lowerCamelCase_				,					dtype=lowerCamelCase_					)






@flax.struct.dataclass
class _lowerCamelCase:
       lowercase_					:      jnp.ndarray
       lowercase_					:      jnp.ndarray
       lowercase_					:      jnp.ndarray
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase)			->  str:




           """simple docstring"""

           _lowercase			:				int 						=						scheduler.config

           if config.trained_betas is not None:
               _lowercase			:				str 						=						jnp.asarray(config.trained_betas, dtype=scheduler.dtype)
           elif config.beta_schedule == "linear":
               _lowercase			:				List[Any] 						=						jnp.linspace(config.beta_start, config.beta_end, config.num_train_timesteps, dtype=scheduler.dtype)
           elif config.beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _lowercase			:				Dict 						=						(
                   jnp.linspace(
                       config.beta_start**0.5, config.beta_end**0.5, config.num_train_timesteps, dtype=scheduler.dtype)
                   ** 2
               )
           elif config.beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _lowercase			:				Optional[int] 						=						betas_for_alpha_bar(config.num_train_timesteps, dtype=scheduler.dtype)
           else:
               raise NotImplementedError(
                   F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''')

           _lowercase			:				List[str] 						=						1.0 - betas

           _lowercase			:				Union[str, Any] 						=						jnp.cumprod(lowerCamelCase, axis=0)

           return cls(
               alphas=lowerCamelCase, betas=lowerCamelCase, alphas_cumprod=lowerCamelCase, )



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		int:
    _lowercase			:				str 						=						state.alphas_cumprod

    _lowercase			:				str 						=						alphas_cumprod[timesteps] ** 0.5
    _lowercase			:				Optional[Any] 						=						sqrt_alpha_prod.flatten()
    _lowercase			:				Tuple 						=						broadcast_to_shape_from_left(lowerCamelCase_				,					original_samples.shape					)

    _lowercase			:				Union[str, Any] 						=						(1 - alphas_cumprod[timesteps]) ** 0.5
    _lowercase			:				Optional[Any] 						=						sqrt_one_minus_alpha_prod.flatten()
    _lowercase			:				int 						=						broadcast_to_shape_from_left(lowerCamelCase_				,					original_samples.shape					)

    return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		List[str]:
    _lowercase						,							_lowercase			:				Optional[int] 						=						get_sqrt_alpha_prod(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				str 						=						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    return noisy_samples



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Tuple:
    _lowercase						,							_lowercase			:				Tuple 						=						get_sqrt_alpha_prod(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				List[Any] 						=						sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
    return velocity


