





"""simple docstring"""






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A       	=     logging.get_logger(__name__)




class  __lowercase	(	lowercase__							):







					'''simple docstring'''




					__lowerCAmelCase     =     ['''pixel_values''']







					def __init__(  self		,	_UpperCAmelCase = True		,	_UpperCAmelCase = None		,	_UpperCAmelCase = PILImageResampling.BILINEAR		,	_UpperCAmelCase = True		,	_UpperCAmelCase = None		,	_UpperCAmelCase = True		,	_UpperCAmelCase = 1 / 255		,	_UpperCAmelCase = True		,	_UpperCAmelCase = None		,	_UpperCAmelCase = None		,	**_UpperCAmelCase		,	):
									super().__init__(**SCREAMING_SNAKE_CASE__   )
									__a       :  str					=     size if size is not None else {'shortest_edge': 256}
									__a       :  Any					=     get_size_dict(SCREAMING_SNAKE_CASE__		,	default_to_square=SCREAMING_SNAKE_CASE__   )
									__a       :  Dict					=     crop_size if crop_size is not None else {'height': 224, 'width': 224}
									__a       :  Optional[int]					=     get_size_dict(SCREAMING_SNAKE_CASE__   )
									__a       :  List[str]					=     do_resize
									__a       :  Dict					=     size
									__a       :  Optional[Any]					=     resample
									__a       :  Optional[int]					=     do_center_crop
									__a       :  Dict					=     crop_size
									__a       :  int					=     do_rescale
									__a       :  int					=     rescale_factor
									__a       :  Tuple					=     do_normalize
									__a       :  int					=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									__a       :  Tuple					=     image_std if image_std is not None else IMAGENET_STANDARD_STD







					def        _lowerCamelCase						(  self		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase = PILImageResampling.BICUBIC		,	_UpperCAmelCase = None		,	**_UpperCAmelCase		,	):
									__a       :  List[Any]					=     get_size_dict(SCREAMING_SNAKE_CASE__		,	default_to_square=SCREAMING_SNAKE_CASE__   )
									if "shortest_edge" not in size:
													raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""   )
									__a       :  Tuple					=     get_resize_output_image_size(SCREAMING_SNAKE_CASE__		,	size=size['''shortest_edge''']		,	default_to_square=SCREAMING_SNAKE_CASE__   )
									return resize(SCREAMING_SNAKE_CASE__		,	size=SCREAMING_SNAKE_CASE__		,	resample=SCREAMING_SNAKE_CASE__		,	data_format=SCREAMING_SNAKE_CASE__		,	**SCREAMING_SNAKE_CASE__   )







					def        _lowerCamelCase						(  self		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase = None		,	**_UpperCAmelCase		,	):
									__a       :  str					=     get_size_dict(SCREAMING_SNAKE_CASE__   )
									return center_crop(SCREAMING_SNAKE_CASE__		,	size=(size['''height'''], size['''width'''])		,	data_format=SCREAMING_SNAKE_CASE__		,	**SCREAMING_SNAKE_CASE__   )







					def        _lowerCamelCase						(  self		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase = None		,	**_UpperCAmelCase   ):
									return rescale(SCREAMING_SNAKE_CASE__		,	scale=SCREAMING_SNAKE_CASE__		,	data_format=SCREAMING_SNAKE_CASE__		,	**SCREAMING_SNAKE_CASE__   )







					def        _lowerCamelCase						(  self		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase = None		,	**_UpperCAmelCase		,	):
									return normalize(SCREAMING_SNAKE_CASE__		,	mean=SCREAMING_SNAKE_CASE__		,	std=SCREAMING_SNAKE_CASE__		,	data_format=SCREAMING_SNAKE_CASE__		,	**SCREAMING_SNAKE_CASE__   )







					def        _lowerCamelCase						(  self		,	_UpperCAmelCase		,	_UpperCAmelCase = None		,	_UpperCAmelCase = None		,	_UpperCAmelCase = None		,	_UpperCAmelCase = None		,	_UpperCAmelCase = None		,	_UpperCAmelCase = None		,	_UpperCAmelCase = None		,	_UpperCAmelCase = None		,	_UpperCAmelCase = None		,	_UpperCAmelCase = None		,	_UpperCAmelCase = None		,	_UpperCAmelCase = ChannelDimension.FIRST		,	**_UpperCAmelCase		,	):
									__a       :  List[str]					=     do_resize if do_resize is not None else self.do_resize
									__a       :  Dict					=     size if size is not None else self.size
									__a       :  Dict					=     get_size_dict(SCREAMING_SNAKE_CASE__		,	default_to_square=SCREAMING_SNAKE_CASE__   )
									__a       :  List[Any]					=     resample if resample is not None else self.resample
									__a       :  List[Any]					=     do_center_crop if do_center_crop is not None else self.do_center_crop
									__a       :  int					=     crop_size if crop_size is not None else self.crop_size
									__a       :  Optional[int]					=     get_size_dict(SCREAMING_SNAKE_CASE__   )
									__a       :  Dict					=     do_rescale if do_rescale is not None else self.do_rescale
									__a       :  Dict					=     rescale_factor if rescale_factor is not None else self.rescale_factor
									__a       :  Any					=     do_normalize if do_normalize is not None else self.do_normalize
									__a       :  str					=     image_mean if image_mean is not None else self.image_mean
									__a       :  Dict					=     image_std if image_std is not None else self.image_std

									__a       :  Optional[int]					=     make_list_of_images(SCREAMING_SNAKE_CASE__   )

									if not valid_images(SCREAMING_SNAKE_CASE__   ):
													raise ValueError(
													    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
													    '''torch.Tensor, tf.Tensor or jax.ndarray.'''   )

									if do_resize and size is None:
													raise ValueError('''Size must be specified if do_resize is True.'''   )

									if do_center_crop and crop_size is None:
													raise ValueError('''Crop size must be specified if do_center_crop is True.'''   )

									if do_rescale and rescale_factor is None:
													raise ValueError('''Rescale factor must be specified if do_rescale is True.'''   )

									if do_normalize and (image_mean is None or image_std is None):
													raise ValueError('''Image mean and std must be specified if do_normalize is True.'''   )

									# All transformations expect numpy arrays.
									__a       :  Any					=     [to_numpy_array(SCREAMING_SNAKE_CASE__   ) for image in images]

									if do_resize:
													__a       :  str					=     [self.resize(image=SCREAMING_SNAKE_CASE__		,	size=SCREAMING_SNAKE_CASE__		,	resample=SCREAMING_SNAKE_CASE__   ) for image in images]

									if do_center_crop:
													__a       :  int					=     [self.center_crop(image=SCREAMING_SNAKE_CASE__		,	size=SCREAMING_SNAKE_CASE__   ) for image in images]

									if do_rescale:
													__a       :  Optional[Any]					=     [self.rescale(image=SCREAMING_SNAKE_CASE__		,	scale=SCREAMING_SNAKE_CASE__   ) for image in images]

									if do_normalize:
													__a       :  List[Any]					=     [self.normalize(image=SCREAMING_SNAKE_CASE__		,	mean=SCREAMING_SNAKE_CASE__		,	std=SCREAMING_SNAKE_CASE__   ) for image in images]

									__a       :  Dict					=     [to_channel_dimension_format(SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__   ) for image in images]

									__a       :  Tuple					=     {'pixel_values': images}
									return BatchFeature(data=SCREAMING_SNAKE_CASE__		,	tensor_type=SCREAMING_SNAKE_CASE__   )



import math

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







def        SCREAMING_SNAKE_CASE_						(			__A :			jnp.ndarray  ,    __A :			int  ,    __A :			float = 1  ,    __A :			float = 1  ,    __A :			float = 1.0e4  ,    __A :			bool = False  ,    __A :			float = 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"""
       a_					:		int							=  float(embedding_dim // 2				)
       a_					:		str							=  math.log(max_timescale / min_timescale				) / (num_timescales - freq_shift)
       a_					:		Optional[int]							=  min_timescale * jnp.exp(jnp.arange(__A  ,    dtype=jnp.floataa				) * -log_timescale_increment				)
       a_					:		Optional[int]							=  jnp.expand_dims(__A  ,    1				) * jnp.expand_dims(__A  ,    0				)

       # scale embeddings
       a_					:		str							=  scale * emb

       if flip_sin_to_cos:
              a_					:		str							=  jnp.concatenate([jnp.cos(__A				), jnp.sin(__A				)]  ,    axis=1				)
       else:
              a_					:		Any							=  jnp.concatenate([jnp.sin(__A				), jnp.cos(__A				)]  ,    axis=1				)
       a_					:		Optional[int]							=  jnp.reshape(__A  ,    [jnp.shape(__A				)[0], embedding_dim]				)
       return signal
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int        =						32
      snake_case__				:     jnp.dtype        =						jnp.floataa
      @nn.compact
      def __call__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       str     )		->			List[str]:
             a_					:		Optional[Any]							=  nn.Dense(self.time_embed_dim     ,							dtype=self.dtype     ,							name='linear_1'     )(SCREAMING_SNAKE_CASE__     )
             a_					:		Tuple							=  nn.silu(SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  nn.Dense(self.time_embed_dim     ,							dtype=self.dtype     ,							name='linear_2'     )(SCREAMING_SNAKE_CASE__     )
             return temb
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int        =						32
      snake_case__				:     bool        =						False
      snake_case__				:     float        =						1
      @nn.compact
      def __call__(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			Tuple:
             return get_sinusoidal_embeddings(
                 SCREAMING_SNAKE_CASE__     ,							embedding_dim=self.dim     ,							flip_sin_to_cos=self.flip_sin_to_cos     ,							freq_shift=self.freq_shift     )


