


'''simple docstring'''

import math

import flax.linen as nn
import jax.numpy as jnp

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A = 1    ,					A = 1    ,					A = 1.0E4    ,					A = False    ,					A = 1.0    ,					)			->			jnp.ndarray:






       """simple docstring"""







       assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
       assert embedding_dim % 2 == 0, f"Embedding dimension {embedding_dim} should be even"
       lowercase__      =					float(embedding_dim // 2			)
       lowercase__      =					math.log(max_timescale / min_timescale			) / (num_timescales - freq_shift)
       lowercase__      =					min_timescale * jnp.exp(jnp.arange(A    ,					dtype=jnp.floataa			) * -log_timescale_increment			)
       lowercase__      =					jnp.expand_dims(A    ,					1			) * jnp.expand_dims(A    ,					0			)

       # scale embeddings
       lowercase__      =					scale * emb

       if flip_sin_to_cos:
              lowercase__      =					jnp.concatenate([jnp.cos(A			), jnp.sin(A			)]    ,					axis=1			)
       else:
              lowercase__      =					jnp.concatenate([jnp.sin(A			), jnp.cos(A			)]    ,					axis=1			)
       lowercase__      =					jnp.reshape(A    ,					[jnp.shape(A			)[0], embedding_dim]			)
       return signal
class     __lowerCAmelCase		(nn.Module	):





      '''simple docstring'''






      lowerCAmelCase__   :	int       =				32
      lowerCAmelCase__   :	jnp.dtype       =				jnp.floataa



      @nn.compact
      def __call__(self       :       Union[str, Any]  ,		UpperCamelCase       :       int					):

             '''simple docstring'''







             lowercase__      =					nn.Dense(self.time_embed_dim  ,		dtype=self.dtype  ,		name='''linear_1'''					)(UpperCamelCase					)
             lowercase__      =					nn.silu(UpperCamelCase					)
             lowercase__      =					nn.Dense(self.time_embed_dim  ,		dtype=self.dtype  ,		name='''linear_2'''					)(UpperCamelCase					)
             return temb






class     __lowerCAmelCase		(nn.Module	):





      '''simple docstring'''






      lowerCAmelCase__   :	int       =				32
      lowerCAmelCase__   :	bool       =				False
      lowerCAmelCase__   :	float       =				1



      @nn.compact
      def __call__(self       :       Any  ,		UpperCamelCase       :       int					):

             '''simple docstring'''







             return get_sinusoidal_embeddings(
                 UpperCamelCase  ,		embedding_dim=self.dim  ,		flip_sin_to_cos=self.flip_sin_to_cos  ,		freq_shift=self.freq_shift					)








'''simple docstring'''

# 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]	):





      '''simple docstring'''






      def __init__(self       :       Optional[Any]  ,		UpperCamelCase       :       Tuple=None  ,		**UpperCamelCase       :       List[str]					):

             '''simple docstring'''







             super().__init__(features=UpperCamelCase					)
             lowercase__      =					torch_tensor_kwargs
             import torch  # noqa import torch at initialization



      def  UpperCamelCase__						(self       :       List[str]  ,		UpperCamelCase       :       Union[str, Any]					):

             '''simple docstring'''







             import torch

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



      def  UpperCamelCase__						(self       :       List[Any]  ,		UpperCamelCase       :       Optional[Any]					):

             '''simple docstring'''







             import torch

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

             lowercase__      =					{}

             if isinstance(UpperCamelCase  ,		(np.number, np.ndarray)					) and np.issubdtype(value.dtype  ,		np.integer					):
                    lowercase__      =					{'''dtype''': torch.intaa}
             elif isinstance(UpperCamelCase  ,		(np.number, np.ndarray)					) and np.issubdtype(value.dtype  ,		np.floating					):
                    lowercase__      =					{'''dtype''': torch.floataa}
             elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                    import PIL.Image

                    if isinstance(UpperCamelCase  ,		PIL.Image.Image					):
                           lowercase__      =					np.asarray(UpperCamelCase					)
             return torch.tensor(UpperCamelCase  ,		**{**default_dtype, **self.torch_tensor_kwargs}					)



      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       Dict					):

             '''simple docstring'''







             import torch

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



      def  UpperCamelCase__						(self       :       List[str]  ,		UpperCamelCase       :       dict					):

             '''simple docstring'''







             return map_nested(self._recursive_tensorize  ,		UpperCamelCase  ,		map_list=UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Dict  ,		UpperCamelCase       :       pa.Table					):

             '''simple docstring'''







             lowercase__      =					self.numpy_arrow_extractor().extract_row(UpperCamelCase					)
             lowercase__      =					self.python_features_decoder.decode_row(UpperCamelCase					)
             return self.recursive_tensorize(UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[Any]  ,		UpperCamelCase       :       pa.Table					):

             '''simple docstring'''







             lowercase__      =					self.numpy_arrow_extractor().extract_column(UpperCamelCase					)
             lowercase__      =					self.python_features_decoder.decode_column(UpperCamelCase  ,		pa_table.column_names[0]					)
             lowercase__      =					self.recursive_tensorize(UpperCamelCase					)
             lowercase__      =					self._consolidate(UpperCamelCase					)
             return column






      def  UpperCamelCase__						(self       :       Tuple  ,		UpperCamelCase       :       pa.Table					):

             '''simple docstring'''







             lowercase__      =					self.numpy_arrow_extractor().extract_batch(UpperCamelCase					)
             lowercase__      =					self.python_features_decoder.decode_batch(UpperCamelCase					)
             lowercase__      =					self.recursive_tensorize(UpperCamelCase					)
             for column_name in batch:
                    lowercase__      =					self._consolidate(batch[column_name]					)
             return batch




