

import math

import flax.linen as nn
import jax.numpy as jnp
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase = 1,					_UpperCAmelCase = 1,					_UpperCAmelCase = 1.0e4,					_UpperCAmelCase = False,					_UpperCAmelCase = 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"
    lowerCAmelCase :	Any				=					float(embedding_dim // 2 )
    lowerCAmelCase :	Union[str, Any]				=					math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
    lowerCAmelCase :	List[str]				=					min_timescale * jnp.exp(jnp.arange(a__,					dtype=jnp.floataa ) * -log_timescale_increment )
    lowerCAmelCase :	Any				=					jnp.expand_dims(a__,					1 ) * jnp.expand_dims(a__,					0 )

    # scale embeddings
    lowerCAmelCase :	List[Any]				=					scale * emb

    if flip_sin_to_cos:
        lowerCAmelCase :	Optional[Any]				=					jnp.concatenate([jnp.cos(a__ ), jnp.sin(a__ )],					axis=1 )
    else:
        lowerCAmelCase :	Tuple				=					jnp.concatenate([jnp.sin(a__ ), jnp.cos(a__ )],					axis=1 )
    lowerCAmelCase :	List[Any]				=					jnp.reshape(a__,					[jnp.shape(a__ )[0], embedding_dim] )
    return signal






class       __A (							nn.Module       ):
    lowerCAmelCase_							:						Optional[Any]						 =							32
    lowerCAmelCase_							:						Tuple						 =							jnp.floataa






    @nn.compact
    def __call__(		self	:			Optional[Any]			, UpperCAmelCase_	:			Tuple			):
        lowerCAmelCase :	int				=					nn.Dense(self.time_embed_dim			, dtype=self.dtype			, name='linear_1'			)(_snake_case			)
        lowerCAmelCase :	str				=					nn.silu(_snake_case			)
        lowerCAmelCase :	List[Any]				=					nn.Dense(self.time_embed_dim			, dtype=self.dtype			, name='linear_2'			)(_snake_case			)
        return temb






class       __A (							nn.Module       ):
    lowerCAmelCase_							:						Any						 =							32
    lowerCAmelCase_							:						List[str]						 =							False
    lowerCAmelCase_							:						Optional[Any]						 =							1






    @nn.compact
    def __call__(		self	:			Optional[Any]			, UpperCAmelCase_	:			int			):
        return get_sinusoidal_embeddings(
            _snake_case			, embedding_dim=self.dim			, flip_sin_to_cos=self.flip_sin_to_cos			, freq_shift=self.freq_shift			)







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 			__A( a			):
				snake_case_						   =    ['''image_processor''', '''tokenizer''']
				snake_case_						   =    '''ChineseCLIPImageProcessor'''
				snake_case_						   =    ('''BertTokenizer''', '''BertTokenizerFast''')




				def __init__(     self		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Tuple:


								'''simple docstring'''




								__a										=    None
								if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''		,     _snake_case		,     )
												__a										=    kwargs.pop('''feature_extractor'''				)

								__a										=    image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
												raise ValueError('''You need to specify an `image_processor`.'''				)
								if tokenizer is None:
												raise ValueError('''You need to specify a `tokenizer`.'''				)

								super().__init__(_snake_case		,     _snake_case				)
								__a										=    self.image_processor




				def __call__(     self		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								if text is None and images is None:
												raise ValueError('''You have to specify either text or images. Both cannot be none.'''				)

								if text is not None:
												__a										=    self.tokenizer(_snake_case		,     return_tensors=_snake_case		,     **_snake_case				)

								if images is not None:
												__a										=    self.image_processor(_snake_case		,     return_tensors=_snake_case		,     **_snake_case				)

								if text is not None and images is not None:
												__a										=    image_features.pixel_values
												return encoding
								elif text is not None:
												return encoding
								else:
												return BatchEncoding(data=dict(**_snake_case				)		,     tensor_type=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      str:


								'''simple docstring'''




								return self.tokenizer.batch_decode(*_snake_case		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      Dict:


								'''simple docstring'''




								return self.tokenizer.decode(*_snake_case		,     **_snake_case				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    self.tokenizer.model_input_names
								__a										=    self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''		,     _snake_case		,     )
								return self.image_processor_class